Articles published in IJEER

Volume 10

Load Balancing in Cloud Computing Based on Honey Bee Foraging Behavior and Load Balance Min-Min Scheduling Algorithm

This work proposes novel load-balancing algorithm based on artificial bee colony algorithm and load balancing min-min scheduling algorithm for balancing load in cloud computing network. Simulation here is carried out in clouds to generate comparative results. Improving on various parameters like power consumption, resource utilization, stability of system are some major areas focused on. This work has used algorithm that has the best efficiency of resources, optimal performance, minimal response time, scalability and durability in integrated resource planning.

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Facial Expression Analysis and Estimation Based on Facial Salient Points and Action Unit (AUs)

Humans use their facial expressions as one of the most effective, quick, and natural ways to convey their feelings and intentions to others. In this research, presents the analyses of human facial structure along with its components using Facial Action Units (AUs) and Geometric structures for identifying human facial expressions. The approach considers facial components such as Nose, Mouth, eyes and eye brows for FER. Nostril contours such as left lower tip, right lower tip, and centre tip are considered as salient points of Nose.

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A New Hybrid Approach for Efficient Emotion Recognition using Deep Learning

Facial emotion recognition has been very popular area for researchers in last few decades and it is found to be very challenging and complex task due to large intra-class changes. Existing frameworks for this type of problem depends mostly on techniques like Gabor filters, principle component analysis (PCA), and independent component analysis(ICA) followed by some classification techniques trained by given videos and images. Most of these frameworks works significantly well image database acquired in limited conditions but not perform well with the dynamic images having varying faces and images.

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A Technology Review of Energy Storage Systems, Battery Charging Methods and Market Analysis of EV Based on Electric Drives

The transportation sector is by far the largest oil consumer making it a prime contributor to air pollution. EVs (Electric vehicles) will be beneficial to the environment and will help to alleviate the energy crisis due to their low dependence on oil and negligible emissions. Technology innovation in EVs is of significant interest to researchers, companies, and policy-makers in many countries. EVs integrate various kinds of distinct technologies where some of the important factors in considerations related to EVs are: a wide range of electrical drive configurations; advanced electronics that enable automotive innovations; meeting the challenges of automotive electronics; electrifying transportation in the future.

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IOT Based Smart Health Care System to Monitor Covid-19 Patients

Clinical Surveillance Solutions are the most significant in the concise non-industrial nation people improves requests for caretaking. Coronavirus is as a substitute infectious it is vital to isolation Corona virus people however at the equivalent time clinical analysts need to really take a look at wellness of Corona virus victims additionally. With the helping sort of occurrences it's miles transforming into extreme to safeguard a tune on the wellbeing and prosperity issues of a few isolated people. Underneath the empowered machine plan of a Wi-Fi sensor network in light of IOT development. It is typically utilized for gathering just as moving the special sensors following information in regards to the individuals in medical services communities.

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LoRa enabled Real-time Monitoring of Workers in Building Construction Site

In construction, the real-time monitoring of the worker is necessary for ensuring safety in terms of health and accidents. The technology advancement in the sensors and wireless communication technology has inspired to implement Internet of Things (IoT) real-time monitoring in construction site. With this motivation, in this study we have proposed a system that is powered with long range (LoRa) and IEEE 802.15.4 based Zigbee communication for real-time implementation. Worker health monitoring mote, helmet detection mote, shoe detection mote, and glove detection mote are the primary components of the proposed system

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Identification of Power Leakage and Protection of Over Voltage in Residential Buildings

In many residential buildings the electrical wires of individual houses are laid in the same conduit pipe and some mistakes could be made in identifying similar coloured wires when they are laid in same conduit pipe. Most of the faults are caused by the neutral interconnection in the wiring system. Usually neutral wires are connected to neutral bus within the panel board or switchboard, and are "bonded" to earth ground. In our secondary distribution, tree system of supply is mostly utilized. The voltage of each phase to neutral will be maintained at rated value even during the unbalanced load conditions. If neutral wire connection is poor the voltage at each phase will be different from one another, such an isolated neutral point is called floating neutral and the voltage of the point is always changing.

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Performance Analysis of Feature Extraction Approach: Local Binary Pattern and Principal Component Analysis for Iris Recognition system

In this paper, feature extraction approaches like local binary pattern and principal component analysis assimilation has been offered. For classification, Support Vector Machine has been used. This paper compares the efficiency of two popular feature extraction methods Principal Component Analysis and Local Binary Pattern using two different iris databases CASIA and UBIRIS. The models were tested using 200 iris images. Statistical parameters like F1 score and Accuracy are tested for different threshold values. Our proposed method results with accuracy of 94 and 92%, is obtained for using Local Binary Pattern for CASIA and UBIRIS data set respectively. The Receiver Operating Characteristic Curve has been drawn and Area under Curve is also calculated. The experiment has been extended by varying the dataset sizes. The result shows that LBP achieves better performance with both CASIA and UBIRIS databases compared to PCA.

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IoT-Based Sensor Shoes System for Gait Correction

This study aimed at determining the walking styles through pressure sensor. It uses a three-axis accelerometer and a gyro sensor to identify pigeon-toed walking and splay-footed walking. The system can monitor gait using the sensor data stored in the PC and smartphone applications transferred via Bluetooth. This can be visually confirmed through color changes in accordance with the sensor value with the neo-pixels. Research developments can analyze the type of gait as compared to the value for the case of incorrect gait based on normal pace with the acceleration value through the sensor. Through the experiment, the recognition rate capable of distinguishing in toeing gait was 56.25%, and out toeing gait was 81.25%. The system of this paper continuously collects gait data to enable monitoring whenever and wherever users want.

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Optimizing QoS-Based Clustering Using a Multi-Hop with Single Cluster Communication for Efficient Packet Routing

Modern day communication systems have gained a revolutionized growth in long-distance wireless data transmission. High speed packet transfer impacts quality requirements. Critical factors that ruin service quality (Qos) are calculated by the primary factors involving power efficiency, packet delivery ratio, and overall transmission and reception delay. A well-developed routing protocol with unique attributes should be deployed to give improved QoS. The drawback of single path routing in delivering a packet at traffic is challenging since it does not have an alternative path in case of path failure. This problem can be targeted by a properly structured protocol with a multipath mechanism. In this article, Multi-hop with single cluster (SCMC) protocol is designed to increase the overall system efficiency by improving bandwidth, packet delivery ratio (PDR), reducing communication delay, and quality improvement.

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Blockchain Technology to Handle Security and Privacy for IoT Systems: Analytical Review

With a large number of mobile terminals accessing IoT for information exchange and communication, security issues such as identity authentication, data transmission, and device failure are becoming more and more serious. Most of the traditional security technologies are based on centralized systems, and due to the limitation of IoT topology, traditional security technologies can only be applied to specific industries. Blockchain technology has the features of decentralization, data encryption, and tamper-proof, which are especially suitable for application in complex heterogeneous networks. This paper discusses for the first time the use of the block chain in many fields, providing an opportunity to address IoT security issues. Second, it discussed the IoT acceptance on various domains and the privacy issues IoT faces on limited resources.

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Object Detection and Recognition in Real Time Using Deep Learning for Visually Impaired People

This paper aims at combining object detection at real time and recognition with suitable deep learning methods in order to detect and recognize objects position as well as the names of multiple objects detected by the camera using an object detector algorithm. This is to aid the visually impaired user without the help of any other person. The image and video processing algorithms were designed to take real-time inputs from the camera, Deep Neural Networks were used to predict the objects and uses Google’s famous Text-To-Speech (GTTS) API module for the anticipated voice output precisely detecting and recognizing the category or class of objects and locations contained. Our best result shows that the system recognizes 91 categories of outdoor objects and produces the output in speech i.e. in an audio format even when a reduced amount of spectral information from the data is available.

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Cyber Defense in the Age of Artificial Intelligence and Machine Learning for Financial Fraud Detection Application

Cyber security comes with a combination of various security policies, AI techniques, network technologies that work together to protect various computing resources like computing networks, intelligent programs, and sensitive data from attacks. Nowadays, the shift to digital freedom had led to opened many new challenges for financial services. Cybercriminals have found the ability to leverage e- currency exchanges and other financial transactions to perform their fraudulent activities. The unregulated channel makes it essential for banks and financial institutions to deploy advanced AI & ML (DL) techniques to fight cybercrime. This can be implemented by deploying AI & ML (DL) techniques. Customers are experiencing an increase in the fraud-hit rate in financial banking operations. It is difficult to defend against dynamic cyber-attacks using conventional non- dynamic algorithms.

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Enhanced Visual Analytics Technique for Content-Based Medical Image Retrieval

Content-based image retrieval (CBIR) is a method for searching that finds related images in a medical database. Furthermore, a clinical adaptation of CBIR is hampered in part by a contextual gap that is the disparity among the person characterization of the picture and the framework characterization of the image. This technique makes it tough for the user to validate the fetched images that are similar to the query image in addition to that it only fetches the images of top-ranked and ignores the low-ranking ones. Visual Analytics for Medical Image Retrieval is a novel procedure for medicinal CBIR proposed in this research (VAMIR).

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Caries Detection from Dental Images using Novel Maximum Directional Pattern (MDP) and Deep Learning

Various machine learning technologies and artificial intelligence techniques were applied on different applications of dentistry. Caries detection in orthodontics is a very much needed process. Computer-aided diagnosis (CAD) method is used to detect caries in dental radiographs. The feature extraction and classification are involved in the process of caries detection in dental images. In the 2D images the geometric feature extraction methods are applied and the features are extracted and then applied to machine learning algorithms for classification. Different feature extraction techniques can also be combined and then the fused features can be used for classification. Different classifiers support vector machine (SVM), deep learning, decision tree classifier (DT), Naïve Bayes (NB) classifier, k-nearest neighbor classifier (KNN) and random forest (RF) classifier can be used for the classification process.

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Intelligent Solutions for Manipulating Purchasing Decisions of Customers Using Internet of Things during Covid-19 Pandemic

It is a well-known fact that consumers may gain significant benefits from the effective use of IoT in pandemic and post-pandemic settings. Security vulnerabilities can be seen in the ever-increasing Internet of Things (IoT) ecosystem from cloud to edge, which is crucial to note in this particular circumstance. Most merchants, even luxury stores, have failed to implement robust IoT cyber security procedures. Therefore, the researchers sought to put forth secondary research methodologies to bring forward efficient scrutiny regarding this particular issue to properly comprehend the influence of IoT in various devices, including a smartwatch, power displaying metre, brilliant weight showing gadgets and many more.

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Effective Cyber Security Using IoT to Prevent E-Threats and Hacking During Covid-19

This research work is conducted to make the analysis of digital technology is one of the most admired and effective technologies that has been applied in the global context for faster data management. Starting from business management to connectivity, everywhere the application of IoT and digital technology is undeniable. Besides the advancement of the data management, cyber security is also important to prevent the data stealing or accessing from the unauthorized data. In this context the IoT security technology focusing on the safeguarding the IoT devices connected with internet. Different technologies are taken under the consideration for developing the IoT based cyber security such as Device authentication, Secure on boarding, data encryption and creation of the bootstrap server. All of these technologies are effective to its ground for protecting the digital data.

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Effect of Machine Learning Techniques for Efficient Classification of EMG Patterns in Gait Disorders

Gait disorder is very common in neurodegenerative diseases and differentiating among the same kinematic design is a very challenging task. The muscle activity is responsible for the creation of kinematic patterns. Hence, one optimal way to monitor this issue is to analyse the muscle pattern to identify the gait disorders. In this paper, we will investigate the possibility of identifying GAIT disorders using EMG patterns with the help of various machine learning algorithms. Twenty-five normal persons (13 male and 12 females, age around 28 years of age) and 21 persons having GAIT disorders (11 male and 10 females, age around 67 years of age). Four different machine learning algorithms have been used to identify EMG patterns to recognize healthy and unhealthy persons.

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Image Steganography Technique based on Singular Value Decomposition and Discrete Wavelet Transform

Steganography is a technique of hiding information in digital media. In recent years plenty of work has been done in this domain, and the work can be compared on various parameters such as high robustness and large capacity to achieve a goal. This paper proposed the method of steganography in digital media using Singular Value Decomposition (SVD) and Discrete Wavelet Transform (DWT). The DWT is a frequency-domain technique comprising DWT which comparatively offers better robustness and high PSNR value of stego image over other techniques. The proposed method works well for information hiding against AWGN (additive white Gaussian noise) attack and fulfills the objective to achieve high robustness and high PSNR.

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On the Connection of Matroids and Greedy Algorithms

Matroids are the combinatorial structure and Greedy algorithmic methods always produces optimal solutions for these mathematical models. A greedy method always selects the option that looks best at each step of process of finding optimal solution. In other words, it selects a choice which is optimal choice locally in such a strategy that this locally chosen option may direct to a solution that will be globally optimal. It is true that while selecting locally optimal solution at each stage, Greedy algorithms may not always yield optimal solutions [1-2], but if we can transform an unknown problem into matroid structure, then there must be a greedy algorithm that will always lead optimal solution for that unknown problem. The range of solutions provided by Greedy is large as compared to the applicability of the Matroid structure.

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Optimization of Software Quality Attributes using Evolutionary Algorithm

Software quality is a multidimensional concept. Single attribute can’t define the overall quality of the software. Software developer aims to develop software that possesses maximum software quality which depends upon various software quality attributes such as understand ability, flexibility, reusability, effectiveness, extendibility, functionality, and many more. All these software quality attributes are linked with each other and conflicting in nature. Further, these quality attributes depend upon the design properties of the software. During the designing phase of software, developers must optimize the design properties to develop good software quality. To obtain the appropriate value optimization is done. This paper implemented two multi-objective evolutionary algorithms (NSGA-2 and MOEA/D) to optimize software design properties to enhance software quality.

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Free Hold Price Predictor Using Machine Learning

People who want to buy a new home tend to save more on their budgets and market strategies. The current system includes real estate calculations without the necessary forecasts for future market trends and inflation. The housing market is one of the most competitive in terms of pricing and the same has varied greatly in terms of many factors. Asset pricing is an important factor in decision- making for both buyers and investors in supporting budget allocation, acquisition strategies and deciding on the best plans as a result, it is one of the most important areas in which machine learning ideas can be used to maximize and accurately anticipate prices. As a result, in this paper, we present the different significant factors that we employ to accurately anticipate property values.

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A Cost-Effective and Scalable Processing of Heavy Workload with AWS Batch

Recent technological advancements in the IT field have pushed many products and technologies into the cloud. In the present scenario, the cloud service providers mainly focus on the delivery of IT services and technologies rather than throughput. In this research paper, we used a scalable cost-effective approach to configure AWS Batch with AWS Fargate and CloudFormation and implemented it in order to handle a heavy workload. The AWS service configuration procedure, GitHub repository, and Docker desktop applications have been clearly described in this work.

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A New Fail-Stop Group Signature over Elliptic Curves Secure against Computationally Unbounded Adversary

If an adversary has unlimited computational power, then signer needs security against forgery. Fail Stop signature solves it. If the motive of the signature is to hide the identity of the signer who makes signature on behalf of the whole group then solution is Group signature. We combine these two features and propose “A new Fail Stop Group Signature scheme (FSGSS) over elliptic curves”. Security of our proposed FSGSS is based on “Elliptic curve discrete logarithm problem” (ECDLP). Use of elliptic curve makes our proposed FSGSS feasible to less bandwidth environment, Block chains etc. Due to security settings over elliptic curves, efficiency of proposed scheme increases in terms of computational complexity.

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Application of Chaotic Increasing Linear Inertia Weight and Diversity Improved Particle Swarm Optimization to Predict Accurate Software Cost Estimation

Nowadays usage of software products is increases exponential in different areas in society, accordingly, the development of software products as well increases by the software organizations, but they are unable to focus to predict effective techniques for planning resources, reliable design, and estimation of time, budget, and high quality at the preliminary phase of the development of the product lifecycle. Consequently, it delivered improper software products. Hence, a customer loses the money, time, and not belief on the company as well as effort of teamwork will be lost. We need an efficient and effective accurate effort estimation procedure.

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Compression of Medical Images Using Wavelet Transform and Metaheuristic Algorithm for Telemedicine Applications

Medical image compression becomes necessary to efficiently handle huge number of medical images for storage and transmission purposes. Wavelet transform is one of the popular techniques widely used for medical image compression. However, these methods have some limitations like discontinuity which occurs when reducing image size employing thresholding method. To overcome this, optimization method is considered with the available compression methods. In this paper, a method is proposed for efficient compression of medical images based on integer wavelet transform and modified grasshopper optimization algorithm. Medical images are pre-processed using hybrid median filter to discard noise and then decomposed using integer wavelet transform.

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Performance Analysis of MIMO System Using Fish Swarm Optimization Algorithm

During the signal identification process, massive multiple-input multiple-output (MIMO) systems must manage a high quantity of matrix inversion operations. To prevent exact matrix inversion in huge MIMO systems, several strategies have been presented, which can be loosely classified into similarity measures and evolutionary computation. In the existing Neumann series expansion and Newton methods, the initial value will be taken as zero as a result wherein the closure speed will be slowed and the prediction of the channel state information is not done properly. In this paper, fish swarm optimization algorithm is proposed in which initial values are chosen optimally for ensuring the faster and accurate signal detection with reduced complexity. The optimal values are chosen between 0 to 1 value and the initial arbitrary values are chosen based on number of input signals.

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FPGA Implementation of High-Performance s-box Model and Bit-level Masking for AES Cryptosystem

The inadequacies inherent in the existing cryptosystem have driven the development of exploit the benefits of cipher key characteristics and associated key generation tasks in cryptosystems for high-performance security systems. In this paper, cipher key-related issues that exists in conventional symmetric AES crypto system is considered as predominant issues and also discussed other problems such as lack of throughput rate, reliability and unified key management problems are considered and solved using appropriate hierarchical transformation measures. The inner stage pipelining is introduced over composite field based s-box transformation models to reduce the path delay. In addition to that, this work also includes some bit level masking technique for AES.

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A Survey on Various Machine Learning Techniques for an Efficient Brain Tumor Detection from MRI Images

On account of the uncontrolled and quick growth of cells, Brain Tumor (BT) occurs. It may bring about death if not treated at an early phase. Brain Tumor Detection (BTD) has turned out to be a propitious research field in the current decennia. Precise segmentation along with classification sustains to be a difficult task in spite of several important efforts and propitious results in this field. The main complexity of BTD emerges from the change in tumor location, shape, along with size. Providing detailed literature on BTD via Magnetic Resonance Imaging (MRI) utilizing Machine Learning (ML) methods to aid the researchers is the goal of this review. Diverse datasets are mentioned which are utilized most often in the surveyed articles as a prime source of Brain Disease (BD) data.

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Knowledge Management using IoT-Blockchain Technology: State of the Art

This research investigated the database of journal articles related to knowledge management and blockchain technology. Knowledge is the most important production factor in competition. The purpose of knowledge management is to improve the ability of knowledge to create value. Blockchain is a shared distributed database, and blockchain technology, as one of the most popular computer technologies in recent years, has received great attention from all over the world. Purpose/Significance: This article aims to review and comment on the domestic and foreign literature on the use of blockchain technology for knowledge management, outline the current research status in this field, and predict its future research directions.

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Security and Privacy Challenges using IoT-Blockchain Technology in a Smart City: Critical Analysis

A smart city is a comprehensive concept created by multiple digital industries. Smart city is a new generation of information technologies such as the Internet of Things, cloud computing, big data, and geospatial information to promote smart new ideas for urban planning, construction, management and services, power city operation and administrative management, industrial development, and public services in various fields. It is a modern high-end urban development form. A smart city is to establish a city center system by connecting terminals, applying information technology and network, and ultimately promoting the efficiency improvement and economic structure optimization in various fields.

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A Compact High-Gain Microstrip Patch Antenna with Improved Bandwidth for 5G Applications

This paper presents a High Gain, enhanced Bandwidth Patch antenna for 5G operations. The dual-band is achieved using an inset-fed feeding technique for the microstrip patch antenna, which operates at the 28/38GHz millimeter-wave band. The high gain of the patch is achieved by inserting two rectangular slots on the radiating element of the patch. The designed antenna Bandwidth is improved by incorporating three steps at the edge of the rectangular patch. The substrate used for the format is Rogers RT Duroid 5880, with a thickness of 0.508mm, loss tangent of 0.0009, and a relative permittivity constant of 2.2. Ansys HFSS software is used for the simulation.

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FPGA Design of Real Time Hardware for Face Detection

This paper proposes the hardware architecture of face detection FPGA hardware system using the AdaBoost algorithm. The proposed structure of face detection hardware system is possible to work in 30 frames per second and in real time. And the AdaBoost algorithm is adopted to learn and generate the characteristics of the face data by MATLAB, and finally detected the face using this data. This paper describes the face detection hardware structure composed of image scaler, integral image extraction, face comparing, memory interface, data grouper and detected result display. The proposed circuit is so designed to process one point in one cycle that the proposed design can process full HD (1920x1080) image at 70MHz, which is approximate 2316087 x 30 cycle.

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Analysis of Interior Permanent Magnet Synchronous Motor according to Winding Method

In this paper, the hairpin method is applied to an Electric Vehicle (EV) driving motor with a stator winding designed with a round copper wire. The hairpin method is a method to secure a high space factor by using round copper wire instead of round copper wire for the stator winding. The applicable model is a 300kW Interior Permanent Magnet Synchronous Motor (IPMSM), and the cooling method is water cooling. The current density has a proportional relationship with the thermal characteristics, and in the case of a round copper wire, a method of lowering the current density by using the stator winding as a stranded wire is used.

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1-Dimensional Convolutional Neural Network Based Blood Pressure Estimation with Photo plethysmography Signals and Semi-Classical Signal Analysis

In this paper, we propose a 1-Dimensional Convolutional Neural Network (1D-CNN) based Blood Pressure (BP) estimation using Photo plethysmography (PPG) signals and their features obtained through Semi-classical Signal Analysis (SCSA). The procedure of the proposed BP estimation technique is as follows. First, PPG signals are divided into each beat. Then, 9 features are obtained through SCSA for the divided beats. In addition, 5 biometric data are used. The Biometrics data include Heart Rate (HR), age, sex, height, and weight. The total 14 features are used for training and validating the 1D-CNN BP estimation model. After testing three types of 1D-CNNs, the model with the most optimal performance is selected. The selected model structure consists of three convolutional layers and one fully connected layer.

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A Study on Edge Board for Blur Image Judgment and Cluster Image Behavior Analysis: AI-Based Image Processing System Research

The purpose of this study is to solve the problem that the control center cannot cope with the situation properly due to the difficulty of analyzing the behavior in the case of cluster images or the occurrence of unclear images due to weather conditions and fine dust. Edge board development is necessary for cases in which the image sharpness check and overlap image check are inaccurate. In addition, evaluation techniques such as PSNR (; Peak Signal-to-Noise Ratio) and SSIM (; Structural Similarity Index) are used for the corresponding images to evaluate the degree of image improvement of the model with a validation dataset for each fixed image. After evaluating the model's inference speed in terms of FPS (; Frame Per Second), verification is performed for each stored model for each training, and the improvement rate of the image is calculated to evaluate which model is the most optimal for each weather condition.

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Fan-Shaped Flooding in Wireless Sensor Networks

In a wireless sensor network, data flows in two main directions. There are flooding that transfers data from the sink node to the entire node and routing that transfers data sensed by each sensor node to the sink node. Transferring data from the sink node of the wireless sensor network to the entire sensor node is called flooding. In an energy-constrained environment, a more efficient method has been developed, because the most basic flooding technique contains a lot of data redundancy. In this paper, the combination of the distance-based approach and the neighboring node information method is proposed as a more energy-efficient method. Flood data can be transmitted by adjusting the angle of the transmission line within the transmission radius to the shape of a fan and limiting the distance within the communication radius.

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Design of an Integrated Cryptographic SoC Architecture for Resource-Constrained Devices

One of the active research areas in recent years that has seen researchers from numerous related fields converging and sharing ideas and developing feasible solutions is the area of hardware security. The hardware security discipline deals with the protection from vulnerabilities by way of physical devices such as hardware firewalls or hardware security modules rather than installed software programs. These hardware security modules use physical security measures, logical security controls, and strong encryption to protect sensitive data that is in transit, in use, or stored from unauthorized interferences.

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128-Bit LEA Block Encryption Architecture to Improve the Security of IoT Systems with Limited Resources and Area

The LEA block encryption algorithm is an architecture suitable for IoT systems with limited resources and space. It was developed by the National Security Technology Research Institute in 2013 and established as an international standard for cryptography by the International Electrotechnical Commission in 2019, drawing much attention from developers. In this paper, the 128-bit LEA block encryption algorithm was light weighted and implemented in a hardware environment. All modules share and reuse registers and are designed and implemented in a bottom area through the resource sharing function.

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Analysis of Score Level Fusion of Biometric Features

Biometric systems have gained acceptance in a variety of industries in recent years, and they continue to improve security features for access control systems. Numerous types of monotonic biometric systems have been developed. On the other hand, these systems can only provide low- to mid-level security features. As a result, combining two or more collinear biometrics are necessitated for significantly greater functionality. In this paper, a multimodal biometric technology for iris, face, and fingerprint assimilation has offered. Here, an effective matching approach based on Principal Component Analysis that employs three biometric modes to solve this challenge: iris, face, and fingerprint.

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Energy Management System and Enhancement of Power Quality with Grid Integrated Micro-Grid using Fuzzy Logic Controller

A modern hybrid model is introduced, which is a combination of PV, Wind turbine, converter components to improve Microgrid (MG) operation, to improve system dependability, effective efficiency, which are fundamental qualities. In view of renewable energy Maximum Power Point Tracking (MPPT) is frequently applied to improve PV efficiency in which randomness, flexibility of solar energy because of changes in temperature. To achieve MPPT P&O rule, Incremental conductance (IC) methods are implemented in this manuscript. The design, execution of EMS with Fuzzy Logic Controller (FLC) for AC/DC microgrid is implemented. Apart from designing of EMS the power quality of MG is improved. It proposes analysis, control of storage devices.

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An Advanced and Efficient Cluster Key Management Scheme for Agriculture Precision IoT Based Systems

Things that connect to other devices & systems via Internet or communication networks are called IoT. It can also be said as a network of wireless sensors connected to a cloud and controlled by embedded devices. Considering the large framework of IoT, it becomes a little difficult to maintain security at each sensor node especially with limited information regarding hardware and deployment capabilities. Therefore, management of keys has become a point of concern peculiarly taking account of node capturing attack. This paper proposes an advanced cluster key management scheme for agriculture precision which involves EBS constructor and Chinese remainder theorem together.

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A Novel Algorithm to Secure Data in New Generation Health Care System from Cyber Attacks Using IoT

The rise of digital technology has essentially enhanced the overall communication and data management system, facilitating essential medical care services. Considering this aspect, the healthcare system successfully managed patient requirements through online services and facilitated patient experience. However, the lack of adequate data security and increased digital activities during Covid-19 made the healthcare system a soft target for hackers to gain unauthorized access and steal crucial and sensitive information. Countries such as the UK and the US recently received such challenges, highlighting the need for effective data maintenance. IoT emerged as one of the critical solutions for data management systems in terms of addressing data security which certainly can enhance overall data collection, storage, maintenance, prediction of potential data security breaches and taking appropriate measurements.

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Application of Internet of Things (IoT) and Artificial Intelligence in Unmanned Aerial Vehicles

In the current era, to upgrade the facilities and features of UAVs, the implementation of IoT and AI is mandatory. It helps the drone to provide accurate data after analysing a particular situation. Moreover, it also helps to access the drone from any device with the help of an android app. The application of AI and IoT has enhanced the popularity of drones worldwide. This study has analysed the application of IoT and AI in UAVs to make them more efficient. This research has evaluated IoT and AI's positive and negative impacts on UAVs. Moreover, it has determined solutions to mitigate them effectively.

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PMSG Wind Turbine Based Current Fed Three Phase Inverter with Model Predictive Control

Design of an improved Permanent Magnet Synchronous Generator (PMSG) wind turbine power based Current Fed Inverter (CFI) using Model Predictive Controller (MPC) is proposed in this paper. Optimum torque control is proposed in wind energy conversion system, MPC is used to adjust the dynamic response time based on the application need. This model deals with torque control strategy of PMSG in the machine side controller. Impact of normal mode of operation by the copper losses and torque ripples are minimized by maximizing the average torque. Synthetization of adequate stator phase current are obtained naturally. Uncertainty of the steady state errors of the plant and parameter error are rectified in the system model. The designed CFI with MPC was implemented using medium range wind turbine in MATLAB /Simulink.

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Multiport Converter for CubeSat

Maximizing solar energy harvesting and miniaturizing DC-to-DC converters will be a difficult task for the CubeSat that operates in low earth orbit (LEO), where size and weight are limited. To maximize solar energy collection, the electrical power system (EPS) architectures use numerous separate DC–DC converters, which will be problem for miniaturization of whole model because several inductors will be used in each converter. The key purpose of this article is to demonstrate a topology of multiport converter that requires only one inductor and a small number of components, reducing the overall system size.

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Performance Analysis of Fault Tolerant Operation of PMSM using Direct Torque Control and Fuzzy Logic Control

Electromagnets have traditionally been used in all drives. Because they take up space, the size of the machine grows in tandem with increased torque and it’s rating thereby lowering its energy efficiency. If the rotor winding is replaced with permanent magnets, the motor will reverse. The recent improvement of magnetic materials resulted in a reduction in motor size and more effective use of redial space. Permanent Magnet Synchronous Motors (PMSM) have a high-power factor, are extremely durable, and require almost no maintenance. Such motors can be designed with power ranging from a few watts to a few kilowatts for applications ranging from fans to alternators including electric vehicles. This need reliable and safe operation of drives which would be fault tolerant.

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A Comparison of Machine Learning and Deep Learning Models for Predicting Household Food Security Status

ML and DL algorithms are becoming more popular to predict household food security status, which can be used by the governments and policymakers of the country to provide a food supply for the needy in case of emergency. ML models, namely: k-Nearest Neighbor (kNN), Logistic Regression (LR), Support Vector Machine (SVM), Naive Bayes (NB), Multi-Layer Perceptron (MLP) and DL models, namely: Artificial Neural Network (ANN) and Convolutional Neural network (CNN) are investigated to predict household food security status in Household Income, Consumption and Expenditure (HICE) survey data of Ethiopia.

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A Novel Medical Image Segmentation Model with Domain Generalization Approach

In deep learning-based computing vision for image processing, image segmentation is a prominent issue. There is promising generalisation performance in the medical image segmentation sector for approaches using domain generalisation (DG). Single domain generalisation (SDG) is a more difficult problem than conventional generalisation (DG), which requires numerous source domains to be accessible during network training, as opposed to conventional generalisation (DG). Color medical images may be incorrectly segmented because of the augmentation of the full image in order to increase model generalisation capacity. An arbitrary illumination SDG model for improving generalisation power for colour image segmentation approach for medical images through synthesizing random radiance charts is presented as a first solution to this challenge. Color medical images may be decomposed into reflectivity and illumination maps using retinex-based neural networks (ID-Nets).

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Investigation analysis of open circuit and short circuit fault on cascaded H-bridged multilevel inverter using artificial neural network approach

Cascaded H-bridge multilevel inverters are becoming increasingly used in applications such as distribution systems, electrical traction systems, high voltage direct conversion systems, and many others. Despite the fact that multilevel inverters contain a large number of control switches, detecting a malfunction takes a significant amount of time. In the fault switch configurations diode included for freewheeling operation during open-fault condition. During short circuit fault conditions are carried out by the fuse, which can reveal the freewheeling current direction. The fault category can be identified independently and also failure of power switches harmed by the functioning and reliability of cascaded H-bridge multilevel inverters.

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Performance Analysis of Heat Exchanger System Using Deep Learning Controller

Conventional PID controllers have utilised in most of the process industries. Despite being the most used controller, the traditional PID controller suffers from several disadvantages. Due to rapid development in the field of the process control system, various controllers have been developed that try to overcome the limitations of the PID controller. In this paper, a heat exchanger system has been simulated, and the generated data has been used to train a deep learning-based controller using Backpropagation. The obtained results are compared with the conventional controller on several metrics, including time response, performance indices, frequency response etc.

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Implementation of Elliptical Curve Cryptography Based Diffie-Hellman Key Exchange Mechanism in Contiki Operating System for Internet of Things

Wireless Sensor Networks have gradually upgraded to Internet of Things (IoT) of embedded devices wherein the constrained devices have been connected directly onto the Internet. This transformation has not only facilitated the expansion in connectivity and accessibility of the sensor network but has also enabled one sensor network to interact with other through Internet. Security of IoT devices has been researched extensively. The challenge to transform the complex cryptographic algorithms into lighter and faster has kept researchers on their toes. Contiki-OS is one of the purest implementations of 6LoWPAN and IEEE 802.15.4. That makes Contiki-OS lightest and therefore preferred OS for implementation on ultra-low power sensor nodes. Elliptical Cryptography has proved to be the choice of most of the security researchers for constrained devices.

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Design and Leakage Power Optimization of 6T Static Random Access Memory Cell Using Cadence Virtuoso

Reduction of Leakage power at nano meter regime has become a challenging factor for VLSI designers. This is owing to the need for low-power, battery-powered portable pads, high-end gadgets and various communication devices. Memories are made up of Static RAM and Dynamic RAM. SRAM has had a tremendous impact on the global VLSI industry and is preferred over DRAM because of its low read and write access time. This research study proposes a new method has been proposed of 6T Static Random Access Memory cell to decrease the leakage current at various technologies. Three source biasing methods are used to minimize the 6T SRAM cell leakage power. The three methods are NMOS diode clamping, PMOS diode clamping and NMOS-PMOS diode clamping at 45 nm and 90 nm technology nodes.

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Short Term Load Forecasting of Residential and Commercial Consumers of Karnataka Electricity Board using CFNN

Electricity use and its access are correlated in the economic development of any country. Economically, electricity cannot be stored, and for stability of an electrical network a balance between generation and consumption is necessary. Electricity demand depends on various factors like temperature, everyday activities, time of day, days of the week days/Holidays. These parameters have led to price volatility and huge spikes in electricity prices. The research work proposes a short term Load prediction Model for LT2 (residential consumers), LT3 (Commercial Consumers) of Karnataka State Electricity Board using Cascaded Feed Forward Neural Network (CFNN). MATLAB software is utilized to design and test the forecasting model for predicting the power consumption.

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Analysis of Price and Incentive Based Demand Response programs on Unit Commitment using Particle Swarm Optimization

The increase in power demand by various infrastructural development activities and industrial automations in recent years have made a vital effect with respect to the load demand. To effectively manage the load demand, several Load Management (LM) techniques has been adopted in all energy policy decisions. In the de-regulated power system, the Demand Side Management (DSM) owing to its advantages at economic environments are regarded as remarkable choice and has been extended to incorporate Demand Response Programs (DRPs) in the load management techniques. In this paper, a responsive load economic model is developed. This model is based on the two factors such as price elasticity of demand and welfare function of customers. A Demand Response (DR) based Unit Commitment (DRUC) problem is studied to execute the economic analysis of DRPs.

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Indexed Steep Descent Fish Optimization with Modified Certificateless Signcryption for Secured IoT Healthcare Data Transmission

IoMT is a healthcare strategy and utilization connected with online computer networks for IoT. During data communication from machine to machine, Security is one of essential barriers. In order to improve security, Jaccardized Czekanowski Indexive, Steepest Descent Fish Optimization Based Kupyna Schmidt-Samoa Certificateless Signcryption (JCISDFO-KSSCS) is introduced. JCISDFO-KSSCS is used for enhancing authentication and secure Data Transmission. JCISDFO-KSSCS comprises two major processes, namely authentication, and secured data transmission. The discussed results indicate that proposed JCISDFO-KSSCS increases the performance results than the conventional approaches.

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Detection and Severity Identification of Covid-19 in Chest X-ray Images Using Deep Learning

COVID-19 pandemic is causing a significant flare-up, seriously affecting the wellbeing and life of many individuals all around the world. One of the significant stages in battling COVID-19 is the capacity to recognize the tainted patients early and put them under exceptional consideration. In the proposed model we used deep learning-based exception Net under transfer learning paradigm. We trained the proposed model using chest-X rays collected from the open-source dataset (COVID -19 Dataset) using K10 cross-validation. We further calculated the severity in the covid classified images by the model using radiologist ground truth. We achieved an accuracy of 96.1% in the classification, and we are able to calculate the severity of the COVID -19 within the range of 75-100 % risk. Our proposed model successfully classified the COVID chest x-rays with severity measure.

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Behavioral Dynamics of High Impedance Fault Under Different Line Parameters

Predominantly the impedance faults can be classified as Low Impedance Faults (LIF) and High Impedance Faults (HIF). The LIF can be detected and protected by a conventional protective device. However, if High impedance fault occurs in the system, it is difficult to detect because of the low magnitude current. The overcurrent relay, which smartly detects the Low impedance faults, fails to detect the High impedance faults. This research work is organized as two components. In the first component some literatures regrading high impedance fault have been reviewed. In the second component classical modified several Emmanuel arc model is taken as the test system. Feeder number four is taken as the candidate feeder for testing the high impedance faults. The simulation is done through MATLAB and the results are obtained. From the results certain investigations are proposed.

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Performance Analysis of Quantum Classifier on Benchmarking Datasets

Quantum machine learning (QML) is an evolving field which is capable of surpassing the classical machine learning in solving classification and clustering problems. The enormous growth in data size started creating barrier for classical machine learning techniques. QML stand out as a best solution to handle big and complex data. In this paper quantum support vector machine (QSVM) based models for the classification of three benchmarking datasets namely, Iris species, Pumpkin seed and Raisin has been constructed. These QSVM based classification models are implemented on real-time superconducting quantum computers/simulators. The performance of these classification models is evaluated in the context of execution time and accuracy and compared with the classical support vector machine (SVM) based models.

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Performance Analysis of 9T SRAM using 180nm, 90nm, 65nm, 32nm, 14nm CMOS Technologies

The growing markets for low-power electronic devices energized by battery have created the need for smaller power-efficient chips to prevent frequent charging of the source. Nowadays the market capitalization of low-power appliances is expected to grow from USD 4.9 billion by 2022 to USD 7.9 billion by 2027 as per global forecast to 2027 published by markets. The main factor leading to growth of low power electronics market includes demand of energy saving components, miniaturization, and entry of IoT (Internet of Things) devices. In addition, increased investment by automotive OEM (Original Equipment Manufacturer) and governments to promote the adoption of electric vehicles is expected to create more market opportunities.

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A Classy Memory Management System (CyM2S) using an Isolated Dynamic Two-Level Memory Allocation (ID2LMA) Algorithm for the Real Time Embedded Systems

Due to an increased scalability, flexibility, and reduced cost complexity, the dynamic memory allocation models are highly preferred for the real-time embedded systems. For this purpose, the different types of dynamic models have been developed in the conventional works, which are highly focused on allocating the memory blocks with increased searching capability. However, it faced some of the problems and issues related to the factors of complex operations, high time consumption, memory overhead, and reduced speed of processing. Thus, this research work objects to design an advanced and intelligent dynamic memory allocation mechanism for the real-time embedded systems.

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A High-Performance Infrastructure for Remote Sensing Data Applications Using HPC Paradigms

Individuals and businesses are currently involved in the administration of remote sensing data that was previously handled only by government agencies. There is a lot more information in remote sensing data than go through the eye, and retrieving it is time-consuming and computationally expensive. Clusters, distributed networks, and specialized hardware devices are essential to speeding up remote sensing data extraction calculations. HPC advances in remote sensing applications are examined in this research. High-performance computing (HPC) concepts for instance FPGAs and GPUs as well as large-scale and heterogeneous computer networks are examined (GPUs). Using HPC paradigms, remote sensing applications are examined in these sections.

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Cross Layer Based Dynamic Traffic Scheduling Algorithm for Wireless Multimedia Sensor Network

The data traffic volume is generally huge in multimedia networks since it comprises multimodal sensor nodes also communication takes place with variable capacity during video transmission. The data should be processed in a collision free mode. Therefore, the packets should be scheduled and prioritized dynamically. Dynamic traffic scheduling and optimal routing protocol with cross layer design is proposed here to select the energy efficient nodes and to transmit the scheduled data effectively. At first, the optimal routes are discovered by selecting the best prime nodes then the packets are dynamically scheduled on the basis of severity of data traffic.

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Automatic Framework for Vegetable Classification using Transfer-Learning

Globally, fresh vegetables are a crucial part of our lives and they provide most of the vitamins, minerals, and proteins, in short, every nutrition that a growing body need. They vary in colors like; red, green, and yellow but as our ancestors say that green vegetables are a must for every age. To identify the fresh vegetable that makes our body healthy and notion positive the proposed automatic multi-class vegetable classifier is used. In this paper, a framework based on a deep learning approach has been proposed for multi-class vegetable classification from scratch. The accuracy of the proposed model is further increased using the transfer-learning concept (DenseNet201). The whole process is divided into four modules; data collection and pre-processing, data splitting, CNN model training, and testing, and performance improvement using a pre-trained DenseNet201 network.

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Synthetic Transformer using Operational Transconductance Amplifier (OTA) and Voltage Differencing Current Conveyor (VDCC)

This paper presents a new realization of synthetic transformer using off the shelf active blocks. This proposed transformer is designed using operational transconductance amplifier (OTA), voltage differencing current conveyor (VDCC), resistor and capacitor. Use of VDCC helps to utilizes benefits of both voltage differencing unit and current conveyor. The working of proposed circuit is verified through simulations in LTSPICE using TSMC 180nm process characteristics. The proposed circuit offers the feature of adjusting primary and secondary self-inductances and mutual inductance independently. The bias current of the VDCC is used to control the primary and secondary self-inductance and mutual inductance of synthetic transformer.

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Design and Development of Hybrid Vehicle using Four Different Sources of Energy

These days, where there are energy crises and the assets are depleting at a higher rate, there is a necessity for specific innovation that recovers the energy, which gets commonly squandered, and to find new sources of energy. Thus, if there should arise an occurrence of cars one of these valuable innovations are the HYBRID VEHICLES. By the actual name it tends to be surmised that a hybrid vehicle is an extemporization to the conventional gasoline run vehicle joined with the force of an electric engine. In this project, we created a working model of a system that can charge its battery from four different sources. This system can further be replaced with the existing electrical system of hybrid vehicle technology. Hence, improving energy efficiency and leading to even lower emissions than the conventional hybrid vehicle.

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Simulation and Optimization of Ultrasonic Transducer

The ultrasonic transducers have numerous applications in industries, including medical probes for performing ultrasound scans. One of the significant drawbacks of the ultrasonic transducer is the wastage of a large portion of energy, due to high acoustic impedance, while transmitting ultrasonic waves to the target object. The present study is aimed to investigate the material design of the piezo-composite transducer and improve its performance. Different piezo-composite transducers were simulated in the COMSOL environment by varying input parameters, and three key performance indicators (KPI) were calculated. Many constraint-based multivariable optimization algorithms have been used to maximize the KPIs. A set of parameters, such as Sensitivity and Fractional Bandwidth, have been found to increase the performance of piezo-composite transducer model and its overall efficiency. This study is intended to impinge unidirectional property to the transducer which is found to be beneficial in more accurate medical as well as structural reports and cost savings.

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Design and Development of a Solar Electric Delivery Pod

Use of autonomous solar electric vehicles for road delivery purposes in India remains highly unexplored area. The aim of these research paper is to cover major design aspects and develop a prototype solar electric delivery vehicle with autonomous drive for city logistic purposes. The vehicle design is constructed using the locally available raw materials and is aerodynamically tested. CAD modelling on SolidWorks’20 has been used to build a virtual physical model of the vehicle and aerodynamic testing is performed.

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Rectifier Acoustical Cardiac Activity Detection Analysis of ECG Signal

Skilled cardiologists follow a series of steps to recognize the heartbeats of a patient. But it is a very difficult task to tune to particular frequencies for a doctor. So, in this manuscript, it is sorted into two series MIT-BIH data set steps for processing the heartbeat of a person without noise from a respiratory system to save a person from false detection of heart diseases. So, we expect our work is useful for researchers, educators, physicians. If the speed of the heart is faster or slower than it is said to be it is called an abnormality. Sudden cardiac death may also be attained due to false detection of a heartbeat. So, the early detection of this heartbeat is necessary to save the life of the patients. So, the algorithm proposed in this paper is useful in removing unnecessary sounds by surroundings and the overall mortality rate due to heart diseases can be reduced.

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Current Conveyor Transconductance Amplifier (CCTA) based Grounded Memcapacitor Emulator

A new emulator circuit for designing memcapacitor is proposed in this work. The suggested circuit is designed using a current conveyor transconductance amplifier (CCTA), a memristor and a capacitor. Behaviour of the proposed circuit has been examined for a frequency range of 0.6Hz to 6.4Hz with the help of simulations performed in LTSPICE using TSMC 180nm process parameters. It has been observed that the area inside lobes reduces with increase in frequency. In comparison to other emulators reported in literature, the suggested circuit uses fewer passive components and does not require analog multipliers, thus making it simple to design. The correctness and efficacy of the proposed design are verified using transient analysis, non-volatility analysis, and pinched hysteresis loops.

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Identifying and Mitigating the Barriers for Vehicle-to-Grid Adoption in India

This study gauges the current scenario of EVs in India as a precursor to the adoption of V2G on a large scale. It outlines the barriers to complete EV adoption under three challenge categories. It discusses the motivation for the use of vehicle-to-grid by describing the technology in detail and discussing an overview of how it works. Lastly, the study outlines how popular optimization techniques have been employed to solve individual optimization and scheduling tasks to optimize power, cost, and emissions for V2G.

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Comparative Study of NCM and NCA Electrode Material for Capacity-Fade Using 1-D Modeling

Today, Lithium-ion (Li-ion) batteries are one of the most emerging power sources for almost all modern consumer electronic products. LiNi0.8Co0.15Al0.05O2 (NCA) and LiNi0.3Co0.3Mn0.3O2 (NCM) are projected to be utilized in lithium-ion power batteries as two typical layered nickel-rich ternary cathode materials. Moreover, there is still a need for systematic study from an industrial aspect as to the advantages and drawbacks of these two nickel-rich materials. Hence, a comparative study of NCM and NCA electrode material for capacity-fade has been explored using a 1-D simulated model constructed in the multi-physics software. The capacity of a battery depends on the cell potential, discharge rate, state of charge (SoC), and state of health (SoH). Therefore, the comparison of these parameters and the cycle number of a battery is extremely important. During this comparative study of NCM and NCA electrode material, the capacity fade based on discharge rate, SoC, and SoH over cycle number of a battery has been reported

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Comparative Analysis of Particle Swarm Optimization and Artificial Neural Network Based MPPT with Variable Irradiance and Load

The escalating demands and increasing awareness for the environment, resulted in deployment of Photovoltaic (PV) system as a viable option. PV system are widely installed for numerous applications. However, the challenges in tracking the maximum power with intermittent atmospheric condition and varying load is significant. Maximum Power Point Tracking (MPPT) algorithms are employed and based on their convergence speed, control of external variations and oscillation, the output power efficiency, and other significant factors viz. the algorithm complexity and implementation cost, novel MPPT approach are preferable than the conventional approach. This paper presents an artificial intelligence-based optimization controller for MPPT in a PV system under varying load and irradiance conditions. Comparative analysis of Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) based MPPT is simulated and analysed.

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A Soft Computing Techniques Analysis for Planar Microstrip Antenna for Wireless Communications

The use of neural-network computational modules for radio frequency and microwave modelling and design has lately gained popularity as an uncommon but useful technique for this type of modelling and design. It is possible to train neural networks to study the behaviour of active and passive mechanisms and circuits. In this study, technologists will learn about what neural networks are and how they can be used to model microstrip patch antennas. An artificial neural network is used in this work to investigate in depth several designs and analysis methodologies for microstrip patch antennas. Various network structures are also discussed in this study for wireless communications. Microstrip antenna design has been presented and the use of ANN in microstrip antenna design are also shown in this article.

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Speed Control Analysis of Brushless DC Motor Using PI, PID and Fuzzy-PI Controllers

Brushless DC Motor (BLDC) are relatively new in the industry in comparison to DC motor and induction motor. Conventional controllers like PI, PID are easy to implement but they are not as good as a Hybrid Fuzzy-PI controller for smooth operations. In this paper with the help of MATLAB/SIMULINK, speed response of BLDC motor drive system has been done using PI, PID and Fuzzy-PI controller.

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Solar Power Prediction using LTC Models

Renewable energy production has been increasing at a tremendous rate in the past decades. This increase in production has led to various benefits such as low cost of energy production and making energy production independent of fossil fuels. However, in order to fully reap the benefits of renewable energy and produce energy in an optimum manner, it is essential that we forecast energy production. Historically deep learning-based techniques have been successful in accurately forecasting solar energy production. In this paper we develop an ensemble model that utilizes ordinary differential based neural networks (Liquid Time constant Networks and Recurrent Neural networks) to forecast solar power production 24 hours ahead. Our ensemble is able to achieve superior result with MAPE of 5.70% and an MAE of 1.07 MW.

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Ensemble Deep Convolution Neural Network for Sars-Cov-V2 Detection

The continuing Covid-19 pandemic, caused by the SARS-CoV2 virus, has attracted the eye of researchers and many studies have focussed on controlling it. Covid-19 has affected the daily life, employment, and health of human beings along with socio-economic disruption. Deep Learning (DL) has shown great potential in various medical applications in the past decade and continues to assist in effective medical image analysis. Therefore, it is effectively being utilized to explore its potential in controlling the pandemic. Chest X-Ray (CXR) images were used in studies pertaining to DL for medical image analysis. With the burgeoning of Covid-19 cases by day, it becomes imperative to effectively screen patients whose CXR images show a tendency of Covid-19 infection. Several innovative Convolutional Neural Network (CNN) models have been proposed so far for classifying medical CXR images.

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Voltage Differencing Buffered Amplifier (VDBA) Based Grounded Meminductor Emulator

A new meminductor emulator using a capacitor, a memristor and a voltage differencing buffered amplifier (VDBA) is proposed in this paper. This reported realization of meminductor is very simple than proposed in literature as it needs only 1 active block. The proposed emulator has been found suitable for low frequency operations with electrical tunability, and multiplier free topology. The characteristics of the proposed emulator have been verified for a frequency range of 1.8Hz to 4.9Hz using the LTspice simulation tool with 180nm CMOS technology parameters. Pinched hysteresis loops observed in flux versus current plane verifies its meminductive behavior. Moreover, the non-volatility test of the proposed emulator proves its memory behavior. The pinched hysteresis loops obtained through simulations show that the lobe area reduces with increase in frequency

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Analysis of Flying Capacitor Boost Converter

This paper analyzes the working principle of flying capacitor boost converter and its different variants such as synchronous flying capacitor boost converter and n-level flying capacitor boost converter. The circuit diagram and analysis of different waveforms have been provided. Voltage conversion ratio of different converters have been provided. The lower voltage conversion ratio (VCR), higher voltage stress, and low efficiency of the boost converter at higher duty cycle levels are the primary limitations of the device. Magnetic coupling components are employed to boost the VCR, but the rating is reduced as a result. The leakage current stored in the magnetic component causes unwanted voltage spikes to occur in the switches as a result of the leakage current.

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Grounded Meminductor Emulator Using Operational Amplifier-Based Generalized Impedance Converter and Its Application in High Pass Filter

This paper exhibits a grounded meminductor emulator designed using an operational amplifier generalized impedance converter (GIC) and a memristor. One of the resistors of GIC has been judiciously replaced by memristor to convert active inductor circuit into meminductor emulator circuits. For the proposed grounded meminductor emulator, pinched hysteresis loops of up to 5kHz have been produced. The simulation findings were obtained using the LTspice simulation tool. The pinched hysteresis loops are shrinking when the frequency is varied from 100 Hz – 5 kHz. A high pass filter has also been constructed and simulated using the proposed meminductor emulator to validate its performance.

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Multilevel Inverter with Predictive Control for Renewable Energy Smart Grid Applications

In a world where climate changes and power management are becoming increasingly important, research work focuses on renewable energy based smart grid to meet adequate demands of energy. The smart grid is a modernized autonomous power network that can transmit electricity effectively, conserve resources and costs, and increase the local grid's stability. As a result, a smart grid connected multilevel inverter is presented in this work. The inverter is controlled using a model predictive control algorithm with increased levels with the primary goal of controlling the injected power generated by the renewable source, improving the quality of the current waveform, lowering THD, and eliminating the shift phase among the injected current and the grid voltage in effort to match the smart grid network's requirements.

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Performance Analysis of Renewable Integrated UPQC

The enhancement in electric power quality using a single-stage solar PV integrated Unified Power Quality Conditioner (UPQC) has been discussed in this paper. The UPQC is the combination of Distributed static compensator (DSTATCOM) and Dynamic Voltage Restorer (DVR) having the common DC voltage supply link. The DSTATCOM compensates for the load current associated problems like load power factor improvement, even and odd current harmonics elimination etc. Also, it performs the additional work of transferring power from the solar PV system to the load of the distribution system. The DVR compensates the voltage-associated power quality problems like source voltage sag, source voltage swell, and voltage distortion.

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Implementation of AI based Safety and Security System Integration for Smart City

Our Indian government has set a goal of creating 100 smart cities that will use smart technology such as smart grids, smart phones, and various monitoring devices to generate large amount of data. Traditionally, data centres have been in charge of these files. One of the most pressing issues in data centres is resource management. One efficient strategy to address this issue is to use the best method for handling data, and when we're talking about Smart Cities, which will create a big quantity of data, it's becoming increasingly important to manage this massive amount of data.

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Low-Power VLSI Implementation of Novel Hybrid Adaptive Variable-Rate and Recursive Systematic Convolutional Encoder for Resource Constrained Wireless Communication Systems

In the modern wireless communication system, digital technology has tremendous growth, and all the communication channels are slowly moving towards digital form. Wireless communication has to provide the reliable and efficient transfer of information between transmitter and receiver over a wireless channel. The channel coding technique is the best practical approach to delivering reliable communication for the end-users. Many conventional encoder and decoder units are used as error detection and correction codes in the digital communication system to overcome the multiple transient errors. The proposed convolutional encoder consists of both Recursive Systematic Convolutional (RSC) Encoder and Adaptive Variable-Rate Convolutional (AVRC) encoder. Adaptive Variable-Rate Convolutional encoder improves the bit error rate performance and is more suitable for a power-constrained wireless system to transfer the data. Recursive Systematic Convolutional encoder also reduces the bit error rate and improves the throughput by employing the trellis termination strategy.

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Design Analysis of SSD Optimized Speed Controller for BLDC Motor

Brushless Direct Current (BLDC) motor is widely applied for both domestic and industrial applications especially in computer peripheral devices and electric vehicles. This paper introduces BLDC motor design using Social Ski Driver (SSD) optimized speed controller. Better efficiency, high power density, good reliability, less noise & maintenance, and the use of simpler control mechanisms are major benefits of the BLDC motor. Proposed work mainly focuses on torque ripple compensation with speed control at low cost. The use of a small DC link capacitor instead of a bulkier capacitor helps to reduce ripples by using Social-Ski Driver optimized controllers. However, torque reduction with reasonable speed control has not been achieved in existing works. So, the proposed work planned to design an advanced controller with the recent bio-inspired algorithm to control the PWM signal.

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An Intelligent Secure Monitoring Phase in Blockchain Framework for Large Transaction

Blockchain is the key concept for security purposes for digital applications. But, in some cases, the effectiveness of the malicious behavior has degraded the security function of the blockchain. So, to enrich the blockchain process prediction and to neglect the malicious event from the data broadcasting medium is very important. So, the current research article intends to develop an efficient monitoring strategy based on incorporating deep features. Hence, the designed paradigm is termed as Lion-based Convolutional Neural Model (LbCNM) with serpent encryption. Before performing the encryption process, the novel LbCNM parameters have been activated to monitor the data process channel in the blockchain environment. Here, the malicious behaviors were estimated by incorporating the known and unknown user behavior in the Lion fitness model.

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Augmented ASC Network for Photo Voltaic Applications

This work uses a DC-DC converter that employs an Active Switched Capacitor (ASC) to provide high gain that makes it appropriate for the Photo Voltaic (PV) system. The transformer less converter with an ASC network consists of a capacitor and a diode that boosts voltage effectively. The well-liked converter operates effectually on both Continuous Conduction Mode (CCM) and Discontinuous Conduction Modes (DCM). The suggested topology of converter is easy to design, and it renders a less stress on auxiliary diode and capacitors. This preferred converter scheme is validated through MATLAB Simulink and the outcomes are confirmed using hardware prototype.

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Regression Based Predictive Machine Learning Model for Pervasive Data Analysis in Power Systems

The main aim of this paper is to highlight the benefits of Machine Learning in the power system applications. The regression-based machine learning model is used in this paper for predicting the power system analysis and Economic analysis results. In this paper, Predictive ML models for two modified IEEE 14-bus and IEEE-30 bus systems, integrated with renewable energy sources and reactive power compensative devices are proposed and developed with features that include an hour of the day, solar irradiation, wind velocity, dynamic grid price, and system load. An hour-wise input database for the model development is generated from monthly average data and hour-wise daily curves with normally distributed standard deviations. A very significant Validation technique (K Fold cross validation technique) is explained.

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IoT Based Pulse Oximeter for Remote Health Assessment: Design, Challenges and Futuristic Scope

The Internet of Things (IoT) comprises the networking, computing, and storage with analytics technologies that do wonders in every aspect of human life through its applications and turns their life style as smart as possible. The application of IoT in healthcare domain would transform the medical service to be timely accessible and affordable by all people. The cardiovascular diseases (CVD) are marked as one of the most common cause of death around the world. A research study states that CVD targets the public with age limit of 30 - 60 belongs to developing countries like India in an evidential growth. The continuous monitoring of human heart, which is a fist sized strongest muscle through invasive sensors helps in early detection and anticipating necessary treatment on time. This induces a design of IoT enabled pulse rate monitoring system to continuously track the patient at anywhere and better serve them at any time through any device.

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Directional Shape Feature Extraction Using Modified Line Filter Technique for Weed Classification

Precision agriculture is gaining attention as it employs modern technologies and intelligence for automation in agricultural practices. In the area of weed management, automation is advantageous to select the appropriate herbicide and manage the amount used, which consequently reduced the cost and minimizes the environmental impact. Selective spraying using a sprayer boom can be implemented using automatic detection of weed type. This paper presents a weed classification method based on a modified line filter image analysis technique that can effectively detect the morphological differences, mainly directional shape features, between two types of weeds. After the result for binary classification has been verified, a third dataset is introduced which is mixed leaves which consists of an approximately balanced amount of broadleaves and narrow leaves. The weed images were pre-processed using the adaptive histogram method and difference of Gaussian to improve the image contrast and delineate the edges of the weed.

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Digital Hysteresis Control Algorithm for Switched Inductor Quasi Z-Source Inverter with Constant Switching Frequency

In this paper, a digital hysteresis current limit controller is developed for Switched Inductor Quasi Z-Source Inverter (SLQZSI). Traditional methods like hysteresis current fixed limit and adjustable hysteresis current limit techniques changes the hysteresis bandwidth in accordance to modulating frequency and gradient of reference current. The operating shifting frequency of typical approaches oscillates and crosses the intended steady shifting frequency under noise. It leads to undesirable heavy interference between the phases and more power loss. In the planned digital hysteresis current limit technique, the hysteresis current limit is calculated by resolving the optimization problem. In the proposed approach the operating shifting frequency is kept same or inferior to the intended steady shifting frequency even under noise.

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Enhanced Classification of Faults of Photovoltaic Module Through Generative Adversarial Network

The faults occurring in the photo voltaic system has to be detected to make it work efficiently .To detect and classify the faults occurring in the photo voltaic module infrared images, electro luminescent images, photo luminescent images of photo voltaic module is used .Using infrared images around 11 faults of photovoltaic module such as cell ,cell-multi, hot-spot-multi , hot-spot, cracking, diode, diode-multi, vegetation, shadowing, off-line module and soiling faults can be detected. In addition to the original infra-red images (IR) available in the IR dataset, the IR images are generated for each and every category of faults by using generative adversarial networks (GAN’s) to increase the dataset size. 45000 images are generated by GAN’s. Later the images are used to train and test the convolution neural network. The dataset visualization of original and that of GAN generated images are done in 2-dimensional space using uniform manifold approximation and projection.

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Full-duplex QoS Optimization using Enhanced firefly Algorithm

The major goal is to determine how to allocate resources in a full-duplex cloud radio access network. Furthermore, due of the dispersed characteristic of the Portable Broadcasting Antenna that decreases self-interference. A full-duplex communication system enables information to be sent and processed at the same time among terminals. It has a bandwidth efficiency that is double that of a half-duplex data transmission. The goal of the research is to determine the best power allocation for the receiver transmitter whenever the flow of information is at its highest. The Enhanced Firefly Algorithm is used for efficiency. It's an improvement process that operates in the same way that a firefly's fascination to strobe does. The stronger light encourages the less brilliant firefly to come closer. It's an iterative procedure, and also the community of fireflies finally propagates on the strongest one.

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Finite Element Electromagnetic Based Design of Universal Motor for Agro Application

The commutator part of the universal motor has a considerable effect on the machine's performance. The analysis of pole structure of universal motor becomes important to study the various aspects. The parametric analysis has ratings of 1 kW, 16000 rpm of the universal model designed for different iterations of brush angle for agro applications. The objective of the paper is to improve the efficiency of the model while maintaining the rest of the other parameters at the desirable tolerance range. The customization of the model has been introduced for the various pairs of variables. The transient solution is performed for the better accuracy of the performance of the motor with the help of the finite element method. The designed model offers significant improvement in the design with the improved output torque value.

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Detection and Classification of MRI Brain Tumors using S3-DRLSTM Based Deep Learning Model

Developing an automated brain tumor diagnosis system is a highly challenging task in current days, due to the complex structure of nervous system. The Magnetic Resonance Imaging (MRIs) are extensively used by the medical experts for earlier disease identification and diagnosis. In the conventional works, the different types of medical image processing techniques are developed for designing an automated tumor detection system. Still, it remains with the problems of reduced learning rate, complexity in mathematical operations, and high time consumption for training. Therefore, the proposed work intends to implement a novel segmentation-based classification system for developing an automated brain tumor detection system.

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A Deep Fusion Model For Automated Industrial Iot Cyber Attack Detection And Mitigation

The Industrial Internet of Things (IIoT) is a new field of study that connects digital devices and services to physical systems. The IIoT has been utilized to create massive amounts of data from various sensors, and it has run into several problems. The IIoT has been subjected to a variety of hacks, putting its ability to provide enterprises with flawless operations in jeopardy. Businesses suffer financial and reputational losses as a result of such threats, as well as the theft of critical data. As a result, numerous Network Intrusion Detection Systems (NIDSs) have been created to combat and safeguard IIoT systems, but gathering data that can be utilized in the construction of an intelligent NIDS is a tough operation; consequently, identifying current and new assaults poses major issues. In this research work, a novel IIOT attack detection framework and mitigation model is designed by following four major phases “(a) pre-processing, (b) feature extraction, (c) feature selection and (d) attack detection”. Initially, the collected raw data (input) is subjected to pre-processing phase, wherein the data cleaning and data standardization operations take place.

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Auto-Threshold Dynamic Memory Efficient Frequent Pattern Growth for Data Excavation

Discovering patterns from large datasets is inevitable in the modern data driven civilization. Many research works, and business models are depending on this data excavation task. An efficient method for identifying and categorizing different data patterns from an exponentially growing database is required to perform a clear data excavation. A set of fresh processes such as Repeat Pattern Finder, Repeat Pattern Table, Repeat Pattern Threshold Analyzer, and Repeat Pattern Node are conceptualized in this work named as Auto-Threshold Dynamic Memory Efficient Frequent Pattern Growth for Data Excavation (AT-DME-FP).

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Novel Algorithm for Nonlinear Distortion Reduction Based on Clipping and Compressive Sensing in OFDM/OQAM System

Orthogonal Frequency Division Multiplexing with Offset Quadrature Amplitude Modulation (OFDM/OQAM) signal has high peak-to-average power ratio (PAPR) problem. It not only affects the distortion in High Power Amplifier (HPA) but also results in bit error ratio (BER) degradation. In this paper an improved algorithm based on Clipping and Compressed Sensing (CS) is proposed. The transmitter uses clipping to reduce the PAPR and, the receiver uses an improved inverse model, to reduce the nonlinear distortion introduced by HPA and CS cancels the signal distortion introduced by clipping. Simulation results show that the proposed method not only significantly reduces the PAPR of OFDM/OQAM signals, but also effectively improves the BER performance of the system

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Text Transmission Using Visible Light Communication

Recently, WiFi wireless technology was used to send data by using radio signals, this paper will focus on LiFi technology which is an optical wireless networking technology that uses LEDs for the transmission of data using light-emitting diodes. LiFi production models were capable to transmit 150 megabits per second (Mbps). Visible light communication (VLC) is a facile method to overcome the spectrum crisis of radiofrequency. Light Fidelity (Li-Fi) is the wireless data transfer using LED. In this study LEDs have been used to transfer text between two computers using a processing software method, coding the Arduino Mega board by the Arduino software in both sender and receiver is observed. The system has worked better for a white LED than the red LED and IR LED.

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An Optimized Pipeline Based Blind Source Separation Architecture for FPGA Applications

This work proposes an optimized blind source separation (BSS) architecture utilizing accumulator-based radix 4 multipliers incorporating an independent component analysis (ICA) approach. The signal observed in distinct environmental conditions degraded from its original form. ICA-based filtering is a suitable choice for recovering the desired signal components. Field Programmable Gate Array (FPGA) implementation makes the design much more attractive in high-performance. The proposed BSS-ICA architecture consists of three Random Access Memory (RAM) units and a pipeline-based accumulator radix-4 multiplier. In this work, different source signals such as sinusoidal and speech signals are considered for the analysis.

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Cardio Vascular Diseases Detection Using Ultrasonic Image by Retaining Deep Learning Model

Nowadays people are taking more care of their health and lifestyle. At the same time, diseases affected probability also increased even at most one of the deadly diseases is cardiovascular disease. Earlier prediction and diagnosis are the only solution for resolving the issues. To identify deep language models will be used to predict issues efficiently in the earliest stage in the affected location. In this paper, we recommend an Enhanced DCNN model to classify and segment the issue in affected areas using ultrasonic Images. The model has three layers for the primary layer will train the input and passed the hidden layer. The secondary layer will classify the image based on the model and dataset using the convolution layer and finally the affected area presented by the bound box.

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A Novel Optimization based Energy Efficient and Secured Routing Scheme using SRFIS-CWOSRR for Wireless Sensor Networks

Ensuring the reliable and energy efficient data routing in Wireless Sensor Networks (WSNs) is still remains one of the challenging and demanding tasks due to its dynamic architecture. For this purpose, the different types of routing methodologies and security schemes have been developed in the conventional works. However, it faced the problems related to increased network overhead, high cost consumption, reduced Quality of Service (QoS), and inefficient bandwidth utilization. The main contribution of this work is to implement an optimization based secured routing methodology for establishing an energy efficient data communication in WSNs. For clustering the nodes, the parameters such as residual energy, trust score, and mobility have been considered, which also helps to simplify the networking operations.

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Designing and Implementation of Failure-Aware Based Approach for Task Scheduling in Grid Computing

Grid computing makes large-scale computations easier to handle. In heterogeneous systems like grid computing, failure is inevitable. Because of the volume and diversity of the resources, scheduling algorithm is among the most difficult challenges to overcome in grid computing. To reduce the make-span of the job to be executed a thorough understanding of scheduling in grid is important. Say there are two computing nodes that aren't being used right now. The scheduler may choose the node that has higher computing strength (for example, higher CPU speed, higher free memory), even though this node may also have high potential of failure. High potential of failure refers to the possibility of the failure occurring at execution time, resulting in the decrease of system performance.

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Energy-Resourceful Routing by Fuzzy Based Secured CH Clustering for Smart Dust

Smart Dust Network (SDN) consists of no-infrastructure, sovereign network, smart dust nodes are associated with wireless paths in multihop fashion. No-infrastructure and mobility atmosphere contains complexity to establish an innovative secure routing approach for MWSN. The major problem in MWSN is in routing because of its scarce resource accessibility and mobility in nature. Energy-resourceful routing is indispensable since each smart dust node is containing constrained battery energy. Power breakdown of a particular smart dust node splits network design. So MWSN routing utilizes offered battery power in successful manner to amplify network life. Fuzzy Based Secured CH Clustered (FSCC) approach identifies trustworthy and loop-open path among smart dust nodes by deciding a finest cluster-head.

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Experimental Study of Multistage Constant Current Charging with Temperature Awareness Control Method

Temperature and charging time are critical parameters during charging period of a battery as temperature rise affects battery life. In a particular charging method, setting high current minimizes charging time but raises temperature. In this study attention is given to multistage constant current charging approach to shorten charging time while maintaining battery temperature below preset range. Battery charging characteristics of various methods are studied, and their performance is compared. The proposed multistage charging method is compared with constant current constant voltage and traditional multistage charging method.

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Autoadaptive Flame Detection and Classification Using Deep Learning of FastFlameNet CNN

Image processing technologies in the domain of pattern recognition have many successful researches and implementations. In that sequence, earlier detection of fire from the video footage of the surveillance cameras is an interesting and promising technique that serves mankind and nature as well. The traditional and existing methods of fire detection in the video frames are advantageous in industry-based applications. But whereas these techniques are applied to detect forest fire in a wider area, they have their limitations of inadequate output due to interferences caused by the sunlight and other natural attributes. To improve the detection efficiency using optical flow algorithms and to estimate the direction of the flame, a novel flame detection technique from the video frames using Optimal flow algorithm and the estimation of the fire flow direction using the Deep learning CNN FastFlameNet algorithm is explained in detail in this article.

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Lime Diseases Detection and Classification Using Spectroscopy and Computer Vision

In the agricultural industry, plant diseases and pests pose the greatest risks. Lime is rich 10 source of vitamin C which works as an immunity booster in human body. Because of the late and manually diseases detection in lime causes a vast loss in crop production worldwide. The most common diseases are found in limes are lime canker, lemon scab, brown rot, sooty mould and Armillaria. In this paper we used imaging and non-imaging (spectral based sensing) methods with the combination of machine learning technique to detect the lime canker and sooty mould diseases. Image acquirement, pre-processing, segmentation and classification are all steps in the imaging methodology, which is then followed by feature extraction.

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IoT Based Smart Control of Load for Demand Side Management

One of the most significant gifts that science has bestowed upon humanity is electricity. It has also assimilated into contemporary life, and it is impossible to imagine existence without it. In our daily lives, electricity serves a variety of purposes. Based on World Bank report, the quantity of lost earnings due to electrical outages is projected at 5.47%. Especially in India, these losses are even more. Energy meter in India doesn't provide two-way communication. However, domestic consumers and farmers cannot control their loads remotely. Another challenge in today’s system is electricity consumers are unaware of their electricity consumption patterns and tariff accounting process. Every time a consumer cannot go outside of the house and check their reading in energy meter. To overcome these challenges, IoT technology has been used.

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Design of an Efficient Face Recognition system using Deep Learning Technique

Greater reliance on smart and portable electronic devices demands engineers to provide solutions with better performance and minimized demerits. Face Recognition involves the method of associating and confirming the faces. It is fit for distinguishing, following, recognizing, or checking human appearances from a picture or video caught utilizing an advanced camera. Feature extraction is the most significant stage for the achievement of the face recognition framework. The different ways of implementing this project depends on the programming language or algorithms used such as MATLAB, OpenCV, visual basics C#, Viola-Jones algorithm and many more while the core functioning remains the same.

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Revaluating Pretraining in Small Size Training Sample Regime

Deep neural network (DNN) based models are highly acclaimed in medical image classification. The existing DNN architectures are claimed to be at the forefront of image classification. These models require very large datasets to classify the images with a high level of accuracy. However, fail to perform when trained on datasets of small size. Low accuracy and overfitting are the problems observed when medical datasets of small sizes are used to train a classifier using deep learning models such as Convolutional Neural Networks (CNN). These existing methods and models either always overfit when training on these small datasets or will result in classification accuracy which tends towards randomness.

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Optimization of Harmonics in Novel Multilevel Inverter using Black Wolf and Whale Optimization algorithms

High-quality electrical energy is the most needed thing for standard living. We use power electronics converters for the conversion of different forms of electrical energy and we use them for producing quality power output. We use semiconductor devices as switches in the process of conversion of DC-DC, AC-DC, AC-AC, and DC-AC according to the requirement of the system. In this paper, an attempt is made to analyze the quality of output power from a multilevel inverter which is used in the conversion of DC supply to AC output voltage. Production of quality power by optimizing the multilevel inverter switching using Whale Optimization Algorithm helps the proposed inverter topology to perform well. The suggested topology and the optimization technique will help in harvesting multiple renewable energy sources with improved quality of power.

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Cycling of Induced Magnets (CIM) – Principle: A New Discovery

Cycling of Induced Magnets (CIM) within the interruption of repulsion is a new discovered phenomenon that utilizes the inherent induction and repulsion properties of magnetic materials. The cyclic motion of magnetic conductors, the effect of CIM, is utilized to facilitate the prime mover action for generation of electrical energy as per Faraday’s law. This CIM may leads to the innovation and development of new technology in the area of electrical power generation. In this paper the foundation stage, which can be referred as ‘Zero Base’ stage of the new discovered principle of CIM, is stated and detailed cause effect and orientation prospects for the justification of the principle is discussed. The application of the outcome of CIM for electrical power generation possibility is also presented.

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Controlling of Cascaded Voltage Source Two Level Inverter Based Grid Connected PV System by Using SVPWM and Quadratic Boost Converter

In this paper, two cascaded voltage source grid-connected inverters (VSI) with Quadratic boost converter (QBC) has been described and simulated as more attractive for a grid to interface with the PV system. The simulation is carried out by using the open loop control method to synchronize the grid with the photovoltaic system and these two inverters are controlled with the Sinusoidal Pulse Width Modulation (SPWM) approach and SV Pulse Width Modulation (SVPWM) skill technique for dynamic behavior. These two inverters individually operate as two-level inverters and after cascading with the transformer will get the three-level output voltage and it is interfaced to a three-phase ac grid.

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An Efficient Solution of Phase Interferometry Ambiguity using Zero-Crossing Technique

Direction finding systems applying phase interferometer of long baseline gives high accuracy of the angle of arrival measurements; however, they are suffering from phase ambiguity and phase error due to antenna spacing greater than half wavelength of the intercepted signals. In this paper, the simple two-antenna interferometer system has been adopted with the zero-crossing technique used to solve the phase measurement ambiguities in the processing unit. The zeros-crossing of both channels (lead and lag) were extracted using electronic circuitry. A count gate was formed to count the zeros throughout the phase difference between the two channels. The ambiguity factor was taken to be half of the even count which will be added to the output of the phase comparator to estimate the total phase difference.

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Co-Simulation of Three Phase Induction Motor Controlled by Three Level Inverter

The main objective of this research is to build and control a three-phase induction motor using the ANSYS Simplorer and Maxwell2D program to obtain a high-quality sinusoidal voltage profile with little distortion and therefore low harmonics. A control circuit has been proposed using diodes to control the motor speed by v/f method. This method depends on the principle of changing the source voltage in addition to the frequency in a fixed ratio to obtain the best working conditions and the best characteristics of the motor with the least possible losses. The modeling results show the effectiveness of the proposed circuit in controlling the motor speed effectively and enhancing the motor performance by reducing losses when using traditional methods of controlling the motor speed. The effectiveness of the control system is verified by analyzing the results using the ANSYS program.

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Design and Simulation of Modified Type-2 Fuzzy Logic Controller for Power System

This Article exhibits the structure of a Modified Type-2 (MT2) Fuzzy Logic (FL) Controller (MT2FLC), direction line programming of development, also performance optimization for different power systems. The implementation of the MT2FLC for control of a power system. New participation capacities were considered in adjusting a domain for an Interval Type-2 (IT2) Fuzzy Logic (FL) System (IT2FLS). Another structure in graphic user interface (GUI) mimicked four controllers: an optimal PID controller, FLC, a Type-1(TIFLC), an Interval Type-2 ((IT2FLC), and the MIT2FLC.

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Performance Evaluation of a Reduction Vibration in Robotic Arm Controller by Tuning Gain

In this paper, the challenges that a designer has while attempting to create a variable structure controller for a robotic arm controller that exhibits vibration rattling are examined. This challenge is made more difficult by a number of characteristics, including oscillation, a limited frequency range, and amplitude. The outcomes of this research make it very evident that these challenges must be selected. The majority of the time, this is because the gain setting on the controller was left at an inappropriately high level. A solution that has been referred to as a Modified Variable Structure Controller (MVSC) has been suggested for this issue.

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A Review of Methods of Removing Haze from An Image

A literature review aids in comprehending and gaining further information about a certain area of a subject. The presence of haze, fog, smoke, rain, and other harsh weather conditions affects outdoor photos. Images taken in unnatural weather have weak contrast and poor colors. This may make detecting objects in the produced hazy pictures difficult. In computer vision, scenes and images taken in a foggy atmosphere suffer from blurring. This work covers a study of many remove haze algorithms for eliminating haze collected in real-world weather scenarios in order to recover haze-free images rapidly and with improved quality. The contrast, viewing range, and color accuracy have been enhanced.

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The Effect of Tropospheric Scintillation on Microwave Frequencies for GSM System in The Iraqi Atmosphere

Several papers have been published recently on the effects of scintillation on microwave propagation in standard atmospheres. Most of them have analyzed theoretically the influence of various parameters on the propagation, but barely a few researchers were able to extract the results from the model relying on microwave links in a nonstandard atmosphere. A method is proposed to predict the tropospheric scintillation on the space path of Earth for both standard and nonstandard atmospheres using the frequency range (20-38) GHz which is used in the Global System for Mobile (GSM). This method can be applied to the different atmospheric conditions in different regions.

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IoT-Deep Learning Based Face Mask Detection System for Entrance and Exit Door

During the pandemic, it has been seen that the global population follows the guidelines issued by the health organization regarding wearing face masks, but some people do not take care of this and do not use masks. The objective of the proposed system, Wollega University Face Mask Detection System (WUFMDS), is to restrict people who are not wearing a mask on the door side by identifying the face mask from the face or open the door if the incoming person is wearing the mask. This system is based on the Internet of Things (IoT) and a Deep Learning algorithm called Convolutional Neural Network (CNN). For this purpose, images with and without masks were collected as samples from the university. The CNN algorithm is used to detect the mask and classify it as with or without masks.

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Power Coordination based Efficient Resource Allocation for Device-to-Device Communication in 5G Networks

Device to device communication for mobile networks establishes connections between parameters of mobile devices. As the number of D2D connections and resources are increasing, optimization of power allocation and spectrum feasibility is required. Most of the proposed algorithm schemes for resource allocation support slow-moving D2D terminals in a cellular network, therefore causing huge amount of signaling loss and reducing the efficiency of the cellular network. In energy and spectrum efficiency for the wireless network to meet the power requirement in D2D communication for better resource allocation in upcoming 5G technology is required. The proposed approach outplays the older power distribution approach using MATLAB simulation.

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Performance Evaluation of Fuzzy One Cycle Control Based Custom Power Device for Harmonic Mitigation

Custom power devices (CPDs) provide better harmonic minimization when they are connected in parallel with the distribution network. Power switches have a hard impact on harmonic production in distribution networks, which leads to aging effects. Techniques used to control CPD’s provide full switching in various ways. A pulse width modulation (PWM) scheme requires a reference frame transformation that tracks source and load currents to produce a control signal. The voltage de-coupler is installed in the power device's current controllers to minimize fast current harmonics and remove complexity. One-cycle control (OCC) operates in dual boost converter mode and requires only source currents to produce a control signal. Minimum distortions are obtained by the output voltage feedback compensator.

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Grey Wolf Optimization Based Energy Management Strategy for Hybrid Electrical Vehicles

Electric vehicles (EVs) are seen as a necessary component of transportation's future growth. However, the performance of batteries related to power density and energy density restricts the adoption of electric vehicles. To make the transition from a conventional car to a pure electric vehicle (PEV), a Hybrid Electric Vehicle's (HEV) Energy Management System (EMS) is crucial. The HEVs are often powered with hybrid electrical sources, therefore it is important to select the optimal power source to improve the HEV performance, minimize the fuel cost and minimize hydrocarbon and nitrogen oxides emission. This paper presents the Grey Wolf Optimization (GWO) algorithm for the control of the power sources in the HEVs based on power requirement and economy. The proposed GWO-based EMS provides optimized switching of the power sources and economical and pollution free control of HEV.

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Output Power Prediction of Solar Photovoltaic Panel Using Machine Learning Approach

Solar power-based photovoltaic energy conversion could be considered one of the best sustainable sources of electric power generation. Thus, the prediction of the output power of the photovoltaic panel becomes necessary for its efficient utilization. The main aim of this paper is to predict the output power of solar photovoltaic panels using different machine learning algorithms based on the various input parameters such as ambient temperature, solar radiation, panel surface temperature, relative humidity and time of the day. Three different machine learning algorithms namely, multiple regression, support vector machine regression and gaussian regression were considered, for the prediction of output power, and compared on the basis of results obtained by different machine learning algorithms.

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Pressure Sensing Using a Two-Dimensional Photonic Crystal Sensor

The noticing qualities such as the level of Sensitivity and Quality factor (Q-factor) are evaluated deliberately. A two-dimensional photonic crystal (2DPC) centered stress sensing unit is modeled and designed. The 2DPC centered pressure sensing unit is designed using holes in slab configuration. The L3 defect is created by customizing the spans of three silicon poles and is introduced between two waveguides. It has been revealed that the sensor’s wavelength is transferred linearly to a larger wavelength area, which improves the sensor’s performance.

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Oral Tumor Segmentation and Detection using Clustering and Morphological Process

Oral tumor is one of the most widely recognized tumors growing globally, continuously promoting a high mortality rate. Because early detection and treatment remain the most effective interventions in improving oral cancer outcomes, developing complementary vision-based technologies that can reveal potential evil high-quality oral diseases (OPMDs), which carry the risk of developing cancer, represent significant opportunities for the oral screening process. This paper proposes a morphological algorithm to preserve edge details and prominent features in dental radiographs. This technique, in the early stage identifies the oral tumor detection using clustering and morphological processing.

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Modeling and Simulation of an Optical Sensor for Cancer Cell Detection

In this article, modeling of ring resonator for the identification of the various cancer cells has been proposed. The model exhibits high Q factor and high selectivity for sensing various cancer cells. The parameters of the design are optimized for sensing cancer cells in the sample based on its distinct refractive index. The proposed device has been modeled by the FDTD simulation method which confirms satisfactorily distinct resonant wavelengths for various cancer cells. The device has explored the feasibility of label-free cancer cell sensing with improved characteristics.

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Identification of Unhealthy Leaves in Paddy by using Computer Vision based Deep Learning Model

India is one of the leading productions of Paddy. Compared to previous year Gross Domestic Product (GDP) Export rate of Paddy in the year 2021 has increased to around 33%. Paddy is the Major food production crop in India. Every crop is prone to many diseases throughout their lifespan. The disease can affect the crop at any stage of their growing phase. Early detection of disease is the only solution to reduce the damage. Early detection may reduce the damage caused and increase the quality as well as quantity of Production. Major disease which causes more damage in paddy production is Rice Blast, Brown Spot, Sheath Blight, Sheath Rot and False Smut. Early detection of these diseases can reduce the damage and increase the production value.

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Efficient Brain Tumour Segmentation Using Fuzzy Level Set Method and Intensity Normalization

This paper is developed to implement a fuzzy set technique with intensity normalization intended for the identification of location and tumor shape from an MRI image. Normally, the tumor can be an uncontrolled growth of tissue in any portion of the body. Here, different kinds of cancers have various conditions with the treatments. Hence, brain tumor segmentation is an essential topic in medical applications. The fuzzy level set technique is utilized to segment the tumor from the brain MRI images. Additionally, intensity normalization is utilized to enhance image quality. The proposed technique is implemented in MATLAB and the exhibitions are evaluated by performance scores and implementation scales of quality ratings.

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Performance Analysis of Various Fin Patterns of Hybrid Tunnel FET

High speed and low power dissipation devices are expected from future generation technology of Nano-electronic devices. Tunnel field effect transistor (TFET) technology is unique to the prominent devices in low power applications. To minimize leakage currents, the tunnel switching technology of TFETs is superior to conventional MOS FETs. The gate coverage area of different fin shape hybrid tunnel field-effect transistors is more impacted on electric characteristics of drive current, leakage current and subthreshold slope. In this paper design various fin patterns of hybrid TFET devices and shows on better performance as compared with other fin shape hybrid tunnel FET.

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Modelling and Performance Analysis of CuPc and C60 Based Bilayer Organic Photodetector

An optoelectronic device model for organic photodetector based on bilayer structure has been presented. Drift-diffusion and optical-generation model from Synopsys tool have been incorporated and its optoelectronics behavior has been discussed. The model shows an outstanding rectifying behavior under dark condition due to the different work function of the electrodes. Photocurrent density of 6.64 mA/cm2 is found under the illumination of 3 W/cm2. To analyze rectifying behavior of current density-voltage characteristics of the organic photodetector, the curve has been fitted with the Shockley equation.

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Tongue Diagnosis using CNN for Disease Detection

In this modern lifestyle, technologies are helping us to maintain our finances, our household things, shopping, and so on. In our research work, we have proposed an application that would tell you the disease or infection that you may have with the help of the developing technology. In this pandemic period, we have to be safer and more Responsible. We have to avoid visiting public places as much as possible for us and our society. Our main aim is to reduce death rates which are all caused due to finding the disease at its final stage because of hesitation to visit the hospital during this pandemic or because of our carelessness. We can overcome it by checking for diseases or infections frequently using a mobile app.

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SEMG Signals Identification Using DT And LR Classifier by Wavelet-Based Features

In the recent era of technology, biomedical signals have been attracted lots of attention regarding the development of rehabilitation robotic technology. The surface electromyography (SEMG) signals are the fabulous signals utilized in the field of robotics. In this context, SEMG signals have been acquired by twenty-five right-hand dominated healthy human subjects to discriminate the various hand gestures. The placement of SEMG electrodes has been done according to the predefined acupressure point of required hand movements. After the SEMG signal acquisition, pre-processing and noise rejection have been performed. The de-noising and four levels of SEMG signal decomposition have been accomplished by discrete wavelet transform (DWT).

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Detection and Analysis of Bacterial Water using Photonic Crystal Ring-Resonator Based Refractive-Index Sensor on SOI Platform

A refractive-index biosensor is modeled using a photonic crystal ring resonator. The proposed sensor possesses a high selectivity and high quality-factor against different bacterial water samples. The introduction of the circular rim in the ring resonator structure is responsible for a sharp resonance that makes it suitable for detecting bacterial impurities. The sufficiently separated resonant peak for different samples offers a possibility of highly selective label-free bacterial water detection. The proposed biosensor is highly sensitive, real-time, lab-on-chip, and label-free, which is necessary for on-site detection. The proposed sensor is designed using a silicon-on-insulator platform.

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A Novel Approach for Identification of Weeds in Paddy By using Deep Learning Techniques

Weed is an unwanted plant which is grown in agriculture land. The land which is not cultivated will be fully covered by Weeds. Management of weed is the major concern for farmer because the weed will reduce the crop production quantity. There are many methods to control the weeds, one of those methods is manual plucking which is expensive because it takes more time, consumes human work. Second is by applying any chemicals suggested by external experts. This may cause damage to the crop which is cultivated. Identifying weeds in early stage of crop growth and destroying them through proper method is most important for increasing the crop production.

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Fractional-order Diffusion based Image Denoising Model

Edge indicating operators such as gradient, mean curvature, and Gauss curvature-based image noise removal algorithms are incapable of classifying edges, ramps, and flat areas adequately. These operators are often affected by the loss of fine textures. In this paper, these problems are addressed and proposed a new coefficient of diffusion for noise removal. This new coefficient consists of two edge indicating operators, namely fractional-order difference curvature and fractional-order gradient. The fractional-order difference curvature is capable of analyzing flat surfaces, edges, ramps, and tiny textures. The fractional-order gradient can able to distinguish texture regions. The selection of the order is more flexible for the fractional order gradient and fractional-order difference curvature. This will result in effective image denoising.

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An Improved Deep Learning Approach for Prediction of The Chronic Kidney Disease

Kidney function is harmed by chronic kidney disease, leading to renal failure. Machine learning and data mining come in handy to detect kidney disease. Machine learning employs a variety of algorithms to make predictions and classify data. CT scans have been used to detect chronic renal disease. When CT scans are used to diagnose disease in the kidney, cross-infection occurs, and the results are delayed. The authors of the prior study developed a model for categorizing chronic renal illness utilizing multiple classification methods. A unique deep learning model is presented in this study for the early identification and prognosis of Chronic Kidney Disease (CKD).

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Speaker Identification Analysis Based on Long-Term Acoustic Characteristics with Minimal Performance

The identity of the speakers depends on the phonological properties acquired from the speech. The Mel-Frequency Cepstral Coefficients (MFCC) are better researched for derived the acoustic characteristic. This speaker model is based on a small representation and the characteristics of the acoustic features. These are derived from the speaker model and the cartographic representation by the MFCCs. The MFCC is used for independent monitoring of speaker text. There is a problem with the recognition of speakers by small representation, so proposed the Gaussian Mixture Model (GMM), mean super vector core for training. Unknown vector modules are cleared using rarity and experiments based on the TMIT database.

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Predictive Biomarker Grade Transfer Alzheimer's disease and Mild Cognitive Impairment

The disease of Alzheimer’s is a neurodegenerative disease that affects the brain. This participated in the progress to Mild Cognitive Impairment (MCI) in Alzheimer's disease (AD) with effect is not solitary critical in medical observation but also has a considerable perspective to improve medical trials. This learning intends to establish an efficient biomarker for predicting accurately the conversion of AD in MCI to Magnetic Resonance Image (MRI). This learning executed an Event-Related Potential (ERP) study on patient and control collection commencing 32 channel EEG obtained throughout N-back functioning recollection to find an ERP- based biomarker and examined whether or not.

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CNN Classification of Multi-Scale Ensemble OCT for Macular Image Analysis

Computer-Aided Diagnosis (CAD) of retinal pathology is a dynamic medical analysis area. The CAD system in the optical coherence tomography (OCT) is important for the monitoring of ocular diseases because of the heavy utilization of the retinal OCT imaging process. The Multi-Scale Expert Convolution Mixture (MCME) is designed to classify the normal retina. OCT is becoming one of the most popular non-invasive evaluation approaches for retinal eye disease. The amount of OCT is growing and the automation of OCT image analysis is becoming increasingly necessary. The surrogate-aided classification approach is to automatically classify retinal OCT images because of the Convolution Neural Network (CNN).

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Speaker Recognition Assessment in a Continuous System for Speaker Identification

This research article presented and focused on recognizing speakers through multi-speaker speeches. The participation of several speakers includes every conference, talk or discussion. This type of talk has different problems as well as stages of processing. Challenges include the unique impurity of the surroundings, the involvement of speakers, speaker distance, microphone equipment etc. In addition to addressing these hurdles in real time, there are also problems in the treatment of the multi-speaker speech. Identifying speech segments, separating the speaking segments, constructing clusters of similar segments and finally recognizing the speaker using these segments are the common sequential operations in the context of multi-speaker speech recognition. All linked phases of speech recognition processes are discussed with relevant methodologies in this article.

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Non-Volatile Logic Design Considerations for Energy Efficient Tolerant Variation

Systems design for the non-volatile application must work on less energy or power. The spin-transfer torque-magnetic tunnel junction (STT-MTJ) devices added to the flip-flops which are regarded as non-volatile storage devices. Those are addresses to save the energy of that system stated by the nonvolatile logic. The changes during the production of STT-MTJ and CMOS transistors decrease the yield, which leads to overdesign as well as more energy consumption. The total processes of driver circuitry design for the tradeoffs for backup and restore performance. A new method called the novel method is introduced for flawless energy drivers for given results.

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CORDIC Processors: A Comparative Perspective of Radix-8, Radix-4, and Radix-2

Some data streaming applications make use of digital signal processing (DSP). The DSP algorithms include transcendental functions like trigonometry, inverse trigonometry, logarithms, exponentials, and other functions in addition to the basic arithmetic operations of multiplication and division. By modifying a few simple parameters, it is relatively simple to generate a large range of functions, including logarithmic, exponential, and trigonometric ones. Since the CPU needs around n rounds to process n bits of incoming data, the CORDIC radix-2 causes a substantial amount of latency.

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A Miniaturized Dual-Band Modified Rectangular-Shaped Antenna for Wireless Applications

In this article, compact dual-band with rectangular-shaped monopole is presented for wireless applications. Presented antenna built of modified rectangular monopole and two C-shaped strips are attached on the radiating patch. An excellent matching to get the desired dual band operation is found from the back side of the substrate for possible feeding through microstrip. The suggested antenna has extremely small dimensions, measuring just 12 × 15 × 1.59 mm3 while operating at the lower frequency of 2.4 GHz, and it has a bandwidth of 400 MHz (2.1-2.5 GHz) and 6.7 GHz (3.4-10.1 GHz) accordingly with an S11 value that is less than -10 dB. An investigation of the performance capabilities of this dual-band omnidirectional antenna with a variety of geometric parameters has been carried out.

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An Improved Method for Skin Cancer Prediction Using Machine Learning Techniques

Among skin diseases the type that causes cancer are the fatal ones and pose the biggest issues. These issues arise since cancers are just much larger quantities of the same cells that are present around the body, which makes diagnosis very difficult until later stages. Now the onset of artificial intelligence and machine learning techniques, in the field of images, has allowed computers to identify sequences and patterns in images that can never be observed by the naked eye. Hence in order to battle skin cancer in its early stages a system has been proposed to identify and predict skin cancer in its earlier stages.

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Optimized Feature Selection and Image Processing Based Machine Learning Technique for Lung Cancer Detection

The primary contributor to lung cancer is an abnormal proliferation of lung cells. Tobacco usage and smoking cigarettes are the primary contributors to the development of lung cancer. The most common forms of lung cancer fall into two distinct types. Non-small-cell lung cancers and small-cell lung cancers are the two primary subtypes of lung cancer. A computed tomography, or CT, scan is an essential diagnostic technique that may determine the kind of cancer a patient has, its stage, the location of any metastases, and the degree to which it has spread to other organs.

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Detecting Vehicles at Hair Pin Curves using Internet of Things (IOT)

With increase in the number of vehicles especially personal vehicles there is an increase in accidents due to which, every year almost 1.30 million people die due to accidents involving vehicles. There is no effective way to prevent accidents and know the location where the accidents happen to get help easily, especially in hilly areas. In Hilly areas, there are no straight roads for vehicles and sometimes we encounter so many curves, some of which are dangerous that we have no idea if there are any other vehicles coming or not if not maneuvered properly can cause an accident.

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Deep Learning Techniques for Early Detection of Alzheimer’s Disease: A Review

Alzheimer's disease (AD) is the most prevalent kind of dementia illness that can significantly impair a person's capability to carry out everyday tasks. According to findings, AD may be the third provoking reason of mortality among older adults, behind cancer and heart disease. Individuals at risk of acquiring AD must be identified before treatment strategies may be tested. The study's goal is to give a thorough examination of tissue structures using segmented MRI, which will lead to a more accurately labeling of certain brain illnesses. Several complicated segmentation approaches for identify AD have been developed.

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Linear Vector Quantization for the Diagnosis of Ground Bud Necrosis Virus in Tomato

In this varying environment, a correct and appropriate disease diagnosis including early preclusion has never been more significant. Our study on disease identification of groundnut originated by Groundnut Bud Necrosis Virus will cover the way to the effective use of image processing approach in agriculture. The difficulty of capable plant disease protection is very much linked to the problems of sustainable agriculture and climate change. Due to the fast advancement of Artificial Intelligence, the work in this paper is primarily focused on applying Pattern Recognition based techniques.

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Feed Forward Neural Network based Brain Tumor Diagnosis in Magnetic Resonance Images

In the realm of medicine, value, resource use and final care are determined by good technological advancement. However, there are crucial components that must be present for a disease to be diagnosed. The monitoring of illness progression traditionally relies primarily on a subjective human judgment and is neither precise nor timely. One important aspect that utilizes data at various disease progression phases is to maintain routine disease surveillance. The Feed Forward Neural Network based Brain Tumor Diagnosis in Magnetic Resonance Images is provided in this paper as an automatic brain cancer diagnosis and grade classification method. It is highly helpful to have accurate information about the disease in order to classify it and make decisions.

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Performance Investigation of Solar Photovoltaic System for Mobile Communication Tower Power Feeding Application

In emerging nations like India, the use of energy is rising quickly over time. The moment is opportune to rely increasingly on renewable energy sources, such as solar photovoltaic, to satisfy the demand. Mobile communication towers are one of the industries with the highest power consumption rates, and a lot of these towers are situated rather distant from the power grid. This research develops the performance investigation of solar photovoltaic system for mobile communication tower power feeding application. In order to power the mobile tower, a 6 kWP solar photovoltaic system with 250WP polycrystalline solar panels is designed.

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Modified Synchronous Reluctance Motor for Electric Vehicle Applications

This research article explores a comparative investigation on synchronous reluctance motor (SyRM) for electrified transportation system. The SyRM has salient features like absence of magnet, singly excited but it shrinks its application due to high torque ripple aspects. The novelty of the proposed work is the rib and flux barrier of the rotor in SyRM are modified in order to achieve low torque ripple without affecting the average torque of the motor. Analysis in the electromagnetic domain infers to enhance the sustainability and reliability of the transportation system. So, it results in the reduction of torque ripple, leads to minimize the acoustic noise.

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An Adaptive Technique for Underwater Image Enhancement with CNN and Ensemble Classifier

Image Restoration is a significant phase to process images for their enhancement. Underwater photographs are subject to quality issues such as blurry photos, poor contrast, uneven lighting, etc. Image processing is crucial in the processing of these degraded images. This research introduced an ensemble-based classifier based on the bagging approach to enhance UW images. The support vector machine and random forest classifiers serve as the ensemble classifier's main classifiers. Additionally, to complement the feature optimization technique, the proposed ensemble classifier leverages particle swarm optimization. The feature selection method for the classifier is improved by the feature optimization process.

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Retinal Disease Identification Using Anchor-Free Modified Faster Region-Based Convolutional Neural Network for Eye Fundus Image

Major Improvements in diagnostic methods are providing previously insight into the condition of the retina and other conditions outside of ocular disease. Infections of the retinal tissue, as well as delayed or untreated therapy, may result in visual loss. Furthermore, when a large dataset is involved, the diagnosis is prone to inaccuracies. As a consequence, a completely automated model of retinal illness diagnosis is presented to get rid of human input while maintaining high accuracy classification findings. ODALAs (Optimal Deep Assimilation Learning Algorithms) are unable to handle zero errors or covariance or linearity and normalcy. DLTs (Deep Learning Techniques) such as GANs (Generative Adversarial Networks) or CNNs might replace the numerical solution of dynamic systems (Convolution Neural Networks), in order to speed up the runs.

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Hybrid Deep-Generative Adversarial Network Based Intrusion Detection Model for Internet of Things Using Binary Particle Swarm Optimization

The applications of internet of things networks extensively increasing which provide ease of data communication among interconnected smart devices. IoT connected with smart devices diverse in a range of fields associated with smart cities, smart-transportation, smart- industrial, healthcare, hospitality etc. The smart devices lack with computational power, energy and inconsistent topology. Due to these factors these are most vulnerable to security attacks which affect the transmission reliability of data between nodes. An IoT network connects heterogeneous devices together and generates high volume of data. To provide security against intrusion attacks, deep neural network (DNN) techniques are adopted to detect malicious attacks.

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Modeling of ZnO Nanorods on Paper Substrate for Energy Harvesting

Nano devices are used for energy generation, storing, and harvesting. Among other metal oxides, ZnO shows high performance in piezoelectric energy generation. In this work, the analysis of the parameter dependency of the amount of energy generated for a piezoelectric material on a paper flexible substrate is done. The load is applied in the shape of lines and alphabets. It is found that the displacement is directly proportional to the generated energy. The role of surface area in producing energy and the distribution of pressure with respect to material strength are added.

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An Image Enhancement Method Using Nonlinear Function

Image enhancement plays an important role in image processing. This paper proposed an image enhancement method using a nonlinear(exponential) function. Firstly, we use the exponential function to construct the gray transformation equation. Then, depending on the position of each pixel, the gray scale obtained by gray transformation is further adjusted based on the compression factor. In section 3, the comparative experiment shows that the proposed method could achieve good performance.

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Intensive Analysis of Routing Algorithm Detection over Mobile Ad hoc Network Using Machine Learning

As we are in the Digital Era, in our daily life activities we use different types of communication devices like Mobile Phone, Laptops, Smart watches, etc. and In Common these Mobile Devices allows users to access Information and Services through wireless mode. Basically, there are two types of wireless Networks they are Infrastructure and Infrastructure less Networks. This Infrastructure Network is known as Cellular Networks, they are stationary components, and they used to connect the Mobile Nodes within the Network. But this Infrastructure less Network is also known as Mobile Ad hoc Network (MANET). MANET consist of no fixed Routers and unlimited Nodes. They are Dynamic in Nature. They can move at any direction and at any Movement. It forms a temporary Network for Data Transmission Purpose from Source and Destination.

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Remora Optimization Based Sample Weighted Random SVM For Human Gait Authentication

In this paper, we present a novel ESVM-SWRF method for authenticating human using a gait cycle. The different covariates related to walking are analyzed and investigated. The walking speed of people may change due to the individual body structure, gender, and age thereby creating a complex situation. Based on the studies over past decades, different perspectives with cross-speed gait authentication were suggested. The factors influencing the identification of gait are some of the covariate factors namely walking speed, injuries, walking surface, viewpoint, and clothing. Our proposed work uses an effective dataset CASIA-C.

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Describing Function Approach with PID Controller to Reduce Nonlinear Action

The nonlinear effect in the control system is so important and it may have a hard or soft effect on the electrical, mechanical, biological, and many other systems. This paper analyzes the describing function (DF) which is the transfer function of the nonlinear (NL) control systems of many NL elements found such as saturation, and backlash. The effect of the NL on the third-order delayed system is considered. The PID controller is considered the heart of the control system and continuously finds the error between input and output, and formulates the desired signal for the actuator to control the plant. Experimental tanning of PID controller with the saturation NL as a case study with buffer Operation Amplifier (Op-Amp) to maintain the gain and phase shift.

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Skin Cancer Detection and Segmentation Using Convolutional Neural Network Models

Skin cancer is known as one of the killing diseases in humans around the world. In this paper, melanoma skin cancer images are classified and the cancer regions are segmented using Convolutional Neural Networks (CNN). The skin images are data augmented into high number of skin images for obtaining the high classification accuracy. Then, CNN classifier is used to classify the skin image into either melanoma or normal. Finally, morphological segmentation method is used to segment the cancer regions. The simulation results are obtained by applying the proposed methods on ISIC and HAM dataset skin images.

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Performance Evaluation of Low Power Hybrid Combinational Circuits using Memristor

Recently, extending the use of memristor technology from memory to computing has received a lot of attention. Memristor-based logic design is a new concept that aims to make computing systems more efficient. Several logic families have emerged, each with its own set of characteristics. In this paper, CMOS-based hybrid memristor-based combinational circuits are designed. Many computational devices require combinational circuits. All of the proposed designs were analysed for power, latency, and transistor count. Cadence Virtuoso is used for simulation of circuits.

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Task Scheduling on Cloudlet in Mobile Cloud Computing with Load Balancing

The recent growth in the use of mobile devices has contributed to increased computing and storage requirements. Cloud computing has been used over the past decade to cater to computational and storage needs over the internet. However, the use of various mobile applications like Augmented Reality (AR), M2M Communications, V2X Communications, and the Internet of Things (IoT) led to the emergence of mobile cloud computing (MCC). All data from mobile devices is offloaded and computed on the cloud, removing all limitations incorporated with mobile devices.

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Brain Tumor Classification Using Machine Learning and Deep Learning Algorithms

Early identification and diagnosis of brain tumors have been a difficult problem. Many approaches have been proposed using machine learning techniques and a recent study has explored deep learning techniques which are the subset of machine learning. In this analysis, Feature extraction techniques such as GLCM, Haralick, GLDM, and LBP are applied to the Brain tumor dataset to extract different features from MRI images. The features which have been extracted from the MRI brain tumor dataset are trained using classification algorithms such as SVM, Decision Tree, and Random Forest.

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An Advanced Artificial Neural Network Energy Management in Standalone PV Systems

With the ever-increasing prevalent power crisis and pollution of the environment, solar power, has attracted greater attention as a new and clean energy source. It provides an alternative solution for isolated sites with an unavailable grid connection. However, it is not without any drawbacks, mainly its intermittent nature, related primarily owing to its reliance on meteorological variables such as the temperature outside and the amount of sunlight. In effect, the PV systems that produced electrical energy could well display an electricity excess or deficit at the loads level, likely to result in system service discontinuity.

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A Morphological Change in Leaves-Based Image Processing Approach for Detecting Plant Diseases

In recent years, rice production is mostly affected by rice plant leaf diseases due to the unawareness of suitable management strategies. The paddy leaves are regularly impacted by Brown spot and Bacterial blight diseases, which result in creating major loss to the farm owners. The naked-eye observation is used by the farmer to analyse the condition of paddy leaves, but, it takes more time and the accuracy of it is based on the observer. The naked-eye observation is generally difficult and it has a high possibility of human error. To overcome these drawbacks, a fast and suitable recognition system is required.

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Optimal Spot Pricing Evaluation in Restructured Electrical Power System

Electricity markets in both developed and developing countries have been considerably reorganized over the previous two decades. The unbundling of generation and transmission results in restructured electricity market which enhances the competition among the market traders in the regime of open access. Therefore, the transmission pricing methods should be skilled in translating transmission costs into tariffs to allow participation, which leads to profitable effectiveness, allows the grid owner to recover prices, and makes market participants aware of the system's supply defense and consistency maintained.

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Prediction and Classification of CT images for Early Detection of Lung Cancer Using Various Segmentation Models

One of the most serious and deadly diseases in the world is lung cancer. On the other hand, prompt diagnosis, as well as care, could save lives. Probably the most capable imaging method in the medical world, computed tomography (CT) scans are challenging for clinicians to analyze as well as detect cancer. In recent years, there has been an increase in the use of image analysis techniques for the detection of CT scan images matching cancer tissues. Using a Computer-aided detection (CAD) system employing CT scans to aid inside the early lung cancer diagnosis as well as to differentiate among benign/malignant tumors is thus interesting to address.

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A Novel Hexagonal Psuedo framework for Edge Detection Operators on Hexagonal Framework

Edge detection using a gradient-based detector is a gold-standard method for identifying and analyzing different edge points in an image. A hexagonal grid structure is a powerful architecture dominant for intelligent human-computer vision. This structure provides the best angle resolution, good packing density, high sampling efficiency, equidistant pixels, and consistent connectivity. Edge detection application on hexagonal framework provides more accurate and efficient computations. All the real-time hardware devices available capture and display images in rectangular-shaped pixels. So, an alternative approach to mimic hexagonal pixels using software approaches is modeled in this paper.

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Certain Investigation on Improved Cluster Protocol with Trust security for Wireless Sensor Networks

Immense development of Micro Electro Mechanical Systems (MEMS) made an incredible advancement in wireless technology. The Wireless Sensor Network (WSN) has created many opportunities for the development of various applications in the fields of military, research, medical, engineering, etc. In this research article, a novel trust-based energy-aware clustering protocol is proposed. The clustering algorithm concentrates on reducing the time spent on cluster formation, controlling redundant data forwarding, and prolonging the network's lifespan.

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Novel Predictive Control and Monitoring System based on IoT for Evaluating Industrial Safety Measures

In this paper, the Accident Reduction Model (ARM) technique has been used to analyze different critical criteria in various industries. This ARM technique is used to determine the conclusions of the decision-making process. Valid data is obtained in the structure of the IoT with proper and consistent and useful information. The network address utility allows efficient sensor data. The necessary configuration procedure effectively monitors relevant sensor boundary values. Finally, we have ensured that the system will be able to provide dynamic performance in an IoT-based use of low-cost estimates and lower execution time.

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High Gain Converter Design and Implementation for Electric Vehicles

In this paper, the Accident Reduction Model (ARM) technique has been used to analyze different critical criteria in various industries. This ARM technique is used to determine the conclusions of the decision-making process. Valid data is obtained in the structure of the IoT with proper and consistent and useful information. The network address utility allows efficient sensor data. The necessary configuration procedure effectively monitors relevant sensor boundary values. Finally, we have ensured that the system will be able to provide dynamic performance in an IoT-based use of low-cost estimates and lower execution time.

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Performance Evaluation of DCSS using Two Level 1-Bit Hard Decision Strategies over TWDP Fading Channel

The spectrum sensing method's dependability is greatly influenced by two of the most crucial factors, including various fading channels and nearby wireless users. Multipath fading, buried terminals, and shadowing are just a few of the challenges encountered by users of non-cooperative spectrum sensing systems. Cooperative spectrum sensing approach gives a remedy for this issue. With the use of the common receiver, CSS permits the user to detect the spectrum. Additionally, it has been separated into distributed CSS (D-CSS) and centralized CSS (C-CSS). By using particular rules to identify the presence of the licensed user, both concepts are compared to one another in this article. The effectiveness of cluster-based distributed cooperative spectrum sensing over two-wave diffuse power fading channels (TWDP Channel) is also examined in the article.

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DC-DC Converter with Active Switched LC Network for PV System Along with MPPT

To get maximum power from Photo Voltaic (PV) panel, a DC-DC converter is operated with an active switched LC-network and a fuzzy controller, which boosts the output voltage by utilizing Maximum Power Point Tracking (MPPT). In areas where solar energy is abundant, PV systems offer a low-cost source of electricity. Non-Polluting and low maintenance are two advantages of a PV system. There are several factors that can reduce the output of solar energy, including irradiance, temperature, and partial shading in the cells. A DC-DC converter with active switched LC networks can be used to provide constant output voltage.

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Enhanced Diagnostic Methods for Identifying Anomalies in Imaging of Skin Lesions

There are several types of skin diseases, to protect and keep them healthy from these ailments; an effective and efficient diagnosis is required. One of the domains used by medical experts to diagnose severe class of skin disease is medical imaging. It is non-invasive way of diagnosis in which screen of the abnormal region performs first and then the dermatologist examines the subcutaneous structure and forecasts the severity of the lesion. One severe class of lesions is skin cancer, which is categorized as melanoma and non-melanoma. Most of the research has been performed on melanoma as yet and non-melanoma cancer diagnosis is still an untouched area. The cure rate of skin cancer is high, when diagnosed at an earlier stage.

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Design and Development of Unmanned Aerial Vehicle (UAV) Directed Artillery Prototype for Defense Application

Unmanned Aerial Vehicles (UAVs) technology is one of the fastest growing technologies nowadays and is deployed in various fields. UAVs are used in many domains such as surveillance, precision agriculture, mapping and surveying, militaries etc. UAVs can move fast, it can enable the user to look past walls and fences, and it provides a large aerial view of the area where the drone flies. UAVs can also be used to harness information from dangerous places to prevent human casualties. These advantages of UAVs allow us to use Unmanned Aerial Vehicles in many fields. This paper proposes a prototype of an automatic artillery system that can get GPS coordinates of a target location based on the intelligence provided by an Unmanned Aerial Vehicle which spots the location of the target easily.

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A Novel Approach for Dynamic Stable Clustering in VANET Using Deep Learning (LSTM) Model

Clustering in VANETs, which dynamically evolve into wireless networks, is difficult due to the networks' frequent disconnection and fast changing topology. The stability of the cluster head (CH) has a huge impact on the network's robustness and scalability. The overhead is decreased. The stable CH assures that intra- and inter-cluster communication is minimal. Because of these difficulties, the authors seek a CH selection technique based on a weighted combination of four variables: community neighborhood, quirkiness, befit factor, and trust. The stability of CH is influenced by the vehicle's speed, distance, velocity, and change in acceleration. These are considered for in the befit factor. Also, when changing the model, the precise location of the vehicle is critical. Thus, the predicted location is used to evaluate CH stability with the help of the Kalman filter.

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Optimization of Power and Area Using VLSI Implementation of MAC Unit Based on Additive Multiply Module

The development of Digital Signal Processors (DSPs), graphical systems, Field Programmable Gate Arrays (FPGAs)/ Application-Specific Integrated Circuits (ASICs), and multimedia systems all rely heavily on digital circuits. The need for high-precision fixed-point or floating-point multipliers suitable for Very Large-Scale Integration (VLSI) implementation in high-speed DSP applications is developing rapidly. An integral part of any digital system is the multiplier. In digital systems as well as signal processing, the adder and multiplier seem to be the fundamental arithmetic units. Problems arise when using a multiplier in the realms of area, power, complexity, and speed. This paper details a more efficient MAC (Multiply- Accumulate) multiplier that has been tuned for space usage.

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Design and Speed Analysis of Low Power Single and Double Edge Triggered Flip Flop with Pulse Signal Feed-Through Scheme

Flip flop is a fundamental electrical design component. Most electrical designs incorporate memory and their corresponding designs. The consumer electronics or end users need mobility and extended battery backup to enhance design performance. The focus on any parameter in the system is to maximize the performance of the design. Here the task is to reduce the energy use of flip flop. Due to the increased frequency clock delivered to the networks within the design, the edge or level triggered by a flip flop will contribute to power consumption. Due to the short circuit power consu mption between ground and Vdd, the static design of the flip flop will increase power consumption. The flip flop is dynamically designed and implemented, leading to higher leakage power. Dynamic clock implementation helps for short-circuit power avoidance.

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Modelling and Analysis of Non-ideal Two stage Lift Luo Converter with Positive Output

High power applications such as motor drive control requires a higher voltage level of the DC-DC power converter fed from low-level DC input sources. For increasing the output voltage, a gain of the converter is increased using the pump circuit consisting of inductors and capacitors connected in different forms. The effect of adding the energy storage elements includes the parasitic resistances in the converter and affects the performance. This research paper is intended to model and investigate the effects of non-idealities in two stages cascaded lift circuit type Luo converter with positive output voltage. To analyze the non-ideal effects, state-space averaging (SSA) technique is used to model the converter.

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VLSI Implementation of Integrated Massive MIMO Systems (IMMS) for N-point FFT/IFFT Processor

The 5G technologies and OFDM introduce a substantial element of latency in the baseband Massive MIMO system. To declaim the low delay demand of multiple input and multiple outputs, a Fast Fourier Transform (FFT) and also consequent implementation was proposed. The main idea of this proposed system is to utilize the VLSI chip routing technology and reduce computations, processing time, and low latency. This proposed system is to reduce the number of computational complexities in the downlink and reorder the uplink. In OFDM implementation, the chip area of FFTs and IFFTs is occupied by memories, and these memories can be extracted using registers or RAM. An efficient data programming approach for memories and butterflies has been developed using embedded VLSI technology with multiple inputs and outputs (MIMO), known as mass embedded MIMO systems.

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FEA Based Design of Outer Rotor BLDC Motor for Battery Electric Vehicle

Due to enormous advantages, BLDC motors have been identified for the design in various applications. With its good power density, this motor is being used by the automobile industry. The paper aims to design the motor for the battery electric vehicle. The major challenge of the BLDC motor is to reduce torque ripples which appear because of high cogging torque. Torque ripple results in acoustic noise and vibrations in the battery electric vehicle which badly affects the performance of the vehicle. The cogging torque and efficiency are characterized by a parametric technique with varying pole embrace factor and magnetic thickness. The selection of optimum values of rotor pole embrace factor and magnetic thickness has a significant role in the reduction of cogging torque.

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Minimization of Power Loss in Distribution System by Tap Changing Transformer using PSO Algorithm

Energy is a primary requirement for everyone and it is available in different forms in nature, in all forms of energy “Electrical Energy” is the most significant and useful in the daily life of humans. In the last two decades, the usages of electrical & electronic devices are rapidly increased and technology modernizes lifestyles as well as simplified their lives. In this way, the load demand also significantly increased and leads to an imbalance between generated power and load demand. Load uncertainty also increased with the rise of load demand; it leads higher power losses & poor voltage in distribution system (DS). The main objective of this paper is going to discuss the minimization of losses by adjusting tap settings of the distribution transformer with the help of particle swarm optimization (PSO) algorithm.

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Research on Steel Surface Defect Detection Algorithm Based on Improved Deep Learning

With the development of industrial economy, more and more enterprises use machine vision and artificial intelligence to replace manual detection. Therefore, the research of steel surface defect detection based on artificial intelligence is of great significance to promote the rapid development of intelligent factory and intelligent manufacturing system. In this paper, Yolov5 deep learning algorithm is used to build a classification model of steel surface defects to realize the classification and detection of steel surface defects. At the same time, on the basis of Yolov5, combined with the attention mechanism, the backbone network is improved to further improve the classification model of steel surface defects. The experiment shows that the Recall and mAP of improved Yolov5 perform better on the steel surface defect data set.

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Centralized Reactive Power Controller for Grid Stability and Voltage Control

The integration of renewable energy sources like solar and wind energy into the transmission and distribution grid has increased gradually for quenching the increasing demand for alternative sources to fossil fuels. However, due to the intermittent nature of the renewable sources primarily solar and wind, the injection of the renewable power generation into the grid shall also be fluctuating which in turn will impact the voltage profile of the transmission and distribution grid. Also, in case of any major load disconnection or generator tripping in a weak grid, the voltage profile will be severely impacted in a weak grid. The aim is to control the sudden major voltage profile disturbance of a weak grid in case of variation of power injected into the weak grid from solar and wind energy and also due to sudden load tripping or generator tripping in the weak grid by controlling the reactive power in the weak grid.

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Performance Improvement of Induction Motor Controlled by Thyristor Chopper on the Rotor Side

In this paper, the performance characteristics of a variable-speed drive system with a wound rotor induction motor incorporating a 3-phase diode bridge rectifier - thyristor chopper with a modified commutation circuit system on the rotor side were studied. A DC equivalent circuit was used in the analysis of the motor-rectifier-chopper system and suitable equations have been derived for the determination of the system performance. The analytical results obtained are compared with those obtained experimentally to ascertain the validity of the system in practical applications.

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Power Quality Improvement using Solar Fed Multilevel Inverter Based STATCOM

The major goal about suggested technique is to use a PSO-based proportional-integral (PI) controller towards enhance power quality performance about three-phase grid-connected inverter system. An approach like aforementioned tries to stabilize output current & voltage, lower harmonics, & lessen DC link input voltage fluctuation. Through reducing error about voltage regulator & current controller schemes in inverter system, particle swarm optimization (PSO) technique was used towards adjust PI controller settings. When compared to conventional approach, simulation results show certain optimal parameters PI controller created among PSO produces better performance index results.

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Improved Mask R-CNN Segmentation and Bayesian Interactive Adaboost CNN Classification for Breast Cancer Detection on Bach Dataset

Breast cancer is considered as the predominant type of cancer that affects more than ten percentage of the worldwide female population. Though microscopic evaluation remains to be a significant method for diagnosing, time and cost complexity seeks alternative and effective computer aided design for rapid and more accurate detection of the disease. As DL (Deep Learning) possess a significant contribution in accomplishing machine automation, this study intends to resolve existing problems with regard to lack of accuracy by proposing DL based algorithms. The study proposes Improved-Mask R CNN (I-MRCNN) method for segmentation.

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Human Emotion Recognition using Deep Learning with Special Emphasis on Infant’s Face

This paper discusses a deep learning-based image processing method to recognize human emotion from their facial expression with special concentration on infant’s face between one to five years of age. The work has importance because most of the time it becomes necessary to understand need of a child from their facial expression and behavior. This work is still a challenge in the field of Human Facial Emotion Recognition due to confusing facial expression that sometimes found in some of the samples. We have tried to recognize any facial expression into one of the mostly understood human mood namely Angry, Disgust, Fear, Happy, Sad, Surprise and Neutral.

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An Optimized Transfer Learning Based Framework for Brain Tumor Classification

Brain Tumor (BT) categorization is an indispensable task for evaluating Tumors and making an appropriate treatment. Magnetic Resonance Imaging (MRI) modality is commonly used for such an errand due to its unparalleled nature of the imaging and the actuality that it doesn't rely upon ionizing radiations. The pertinence of Deep Learning (DL) in the space of imaging has cleared the way for exceptional advancements in identifying and classifying complex medical conditions, similar to a BT.

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A Comparative Analysis of FinFET Based SRAM Design

FinFETs are widely used as efficient alternatives to the single gate general transistor in technology scaling because of their narrow channel characteristic. The width quantization of the FinFET devices helps to reduce the design flexibility of Static Random Access Memory (SRAM) and tackles the design divergence between stable, write and read operations. SRAM is widely used in many medical applications due to its low power consumption but traditional 6T SRAM has short channel effect problems. Recently, to overcome these problems various 7T, 9T, 12T, and 14T SRAM architectures are designed using FinFET. This article provides a comprehensive survey of various designs of SRAM using FinFET. It offers a comparative analysis of FinFET technology, power consumption, propagation delay, power delay product, read and write margin.

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Strategic Integration of DG and ESS by using Hybrid Multi Objective Optimization with Wind Dissemination in Distribution Network

The importance of distributed generation (DG) has increased recently as a result of the growth in commercial and industrial loads, which has put more pressure on conventional energy sources and utilities. Alternative power generation methods that can handle the massive load without endangering the environment are therefore urgently needed. The installation of Energy Storage Systems (ESSs) may give a substantial opportunity to enhance the aesthetic appeal of the distribution system. DG is a practical substitute for conventional energy sources, which have drawbacks for both the economics and the environment. It goes without saying that there are situations in which a large amount of land and money are required.

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Indirect Vector Control for Five-Phase Asynchronous Generator Using Three Level Rectifier in Wind Energy Systems

Five-phase induction generators in variable-speed wind energy systems are a focus of this study, and a unique method of regulating them utilizing indirect vector control is proposed in wind energy systems. Grid-side regulation is handled by a two-level converter, whereas machine-side control is handled by a five-phase three-level converter. There are five electrically distinct phases in ASG, and each one is 720 apart. More power can be generated in the same machine frame with this configuration than with a normal three-phase induction generator, and the system is also more stable and sturdier. In order to connect ASIG to the grid, voltage source converters (VSCs) must be employed. A mathematical analysis of the suggested control system is performed, and simulation results are generated in the MATLAB/ SIMULINK software package to account for the various ramps in wind speed.

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Remote Fault Identification and Analysis in Electrical Distribution Network Using Artificial Intelligence

This research describes a method for wavelet decomposition and machine learning-based fault site classification in a radial power distribution network. The first statistical observation is produced using wavelet decomposition and wavelet-based detailed coefficients in terms of Kurtosis and Skewness parameters. For this objective, six distinct machine learning methods are deployed. They are evaluated and compared using unknown data sets with varying degrees of unpredictability. One approach has been shown to be the most accurate in locating the location of the problem bus.

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An Approach for Identifying Network Intrusion in an Automated Process Control Computer System

Technology and networks have improved significantly in recent decades, and Internet services are now available in almost every business. It has become increasingly important to develop information security technology to identify the most recent attack as hackers are getting better at stealing information. The most important technology for security is an Intrusion Detection System (IDS) which employs machine learning and deep learning technique to identify network irregularities. To detect an unknown attack, we propose to use a new intrusion detection system using a deep neural network methodology which provides excellent performance to detect intrusion. This research focuses on an automated process control computer system that recognizes, records, analyzes, and correlates threats to online safety.

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A Switchable Filtering Antenna Integrated with U-Shaped Resonators for Bluetooth, WLAN & UWB Applications

In this paper, A Switchable Filtering Antenna Integrated With U-Shaped Resonators for Bluetooth, WLAN & UWB Applications is presented. To achieve the UWB operation, a rectangular defect on the ground plane and square slots are etched on the rectangular patch. The selectivity of frequency is achieved by integrating a Hairpin filter. Three switches are connected at the corners of the filter to achieve frequency re-configurability. A total of eight Boolean combinations are possible by controlling ON and OFF these switches. This filtering antenna acts as a band pass filter and picks distinct frequency bands within the UWB frequency. It can be used for wireless applications such as Long-Distance Radio Communication, Aviation Services, WLAN, C-band satellite communication, Wi-MAX, mobile satellite sub-band, radar communications, and space communications.

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A Multi Stub Polygon Shaped Textile Antenna for Deformation Performance Using Aperiodic DGS

A compact fully flexible antenna with 1.22 as dielectric constant with 0.016 loss tangent is designed for wearable biomedical applications. It has dimensions of 40.5 x 23 x 0.97 mm3. The polygon-shaped patch is modified to achieve ultra-wideband frequencies using partial ground as 16.5mm and a periodic vertical slot of 13x2 mm2 structure in defected ground structure to enhance the wide bandwidth of 2.6-11.1 GHz. Using a transmission line equation and a microstrip line feeding method, the resulting antenna operates at a biomedical frequency in open space and on the body-worn case with multi band i.e. 3.1 and 5GHz. The deformation process works well for the proposed antenna without affecting its actual bandwidth with 20 to 60 mm radii.

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Design of Low Power and Process Control Hybrid Adder with Complemented Carry Structure

A Hybrid logic style is most popular when compared to other logic styles in implementation of full adder circuits. Conventional hybrid adder uses truth table with true form of carry in and carry out. This will result in non-identical outputs of sum and carry for about 75% of the input combinations. Alternate truth table has been proposed to increase the similarity of sum and carry outputs. In this paper, circuit is designed for complemented carry in and complemented carry out of full adder. This novel structure allowed to design 20-T hybrid adder with process control, low power and low power delay product. The proposed adder structure is applicable for ripple carry adder.

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THD Minimization under Low Switching Frequency for Superior Two Level Three Phase Inverter Performance

The total number of switching pulses permitted in one-quarter of the fundamental-cycle of an inverter is restricted due to the restrictions of switching losses in high-power inverter devices. The elimination of major low-order dominating harmonics using SHEPWM (Selective Harmonic Elimination) is suggested in this research paper. The positive or negative voltage transition at the instant where fundamental voltage has the greatest positive slope distinguishes the various types of waveforms. Due to the switching loss limitations, two forms of the pole voltage PWM waveforms i.e., type A, type B, were studied in this research at lower pulse number (P = 5).

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A Novel User-Friendly Application for Foreground Detection with Post-Processing in Surveillance Video Analytics

Detection of the object in the video is a primary task of all video-processing-based applications. It is one of the challenging areas in computer vision. The paper presents a novel MATLAB-based object detection application based on an improved Gaussian Mixture Model. Gaussian Mixture Model with post-processing applied here for segmentation of foreground from background. The application is divided into three modules pre-processing, detection and post-processing. The morphological gradient filter uses here for segmenting the foreground objects from the background.

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Genetic Algorithm Based BER Aware Channel Selection Using Break Point Technique For Next Generation Milli-Meter (mm) Wave Communication Systems

Due to exponential increase in communication speed when shifting from 4th Generation 4G to 5G networks, there is a requirement to redesign equipment to support spectrum ranges from 450 MHz to 52.6 GHz, which makes them operate at very high speeds. In order to maintain good communication performance while operating at this bandwidth, millimeter waves (mmWaves) are used. As communication radius increases, the BER also increases linearly, which limits range of these equipment’s, thereby incurring higher deployment costs. In order to reduce these costs, and design mmWave communication components to work for larger areas, this text proposes a Genetic optimization architecture that uses intelligent channel modelling and selection.

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Miniaturization of Microstrip Patch Antenna for Biomedical Applications

In this study, a novel quad-band antenna for biomedical applications was designed, fabricated and analyzed. Biomedical application defines the use of antenna in detecting cancerous cells and its cure using hyperthermia. In this research paper, a rectangular micro strip patch antenna is modified with the circular and pentagonal shapes of negative media (Metamaterial). Antenna was reduced in size and ameliorated to operate on multiple frequency bands.

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Mitigation of Critical Delay in the Carry Skip Adders Using FinFET 18nm Technology

In this paper optimization of full adder in 3-dimensional (3-D) using Fin Field Effect Transistor (FinFET) with Gate Diffusion Input (GDI) is proposed to optimize critical delay, power. FinFET technology is more suitable for below 10nm technology process. The major aim of this work is to indemnify significant factor in adder structure i.e. critical delay. Pipelining architecture is enforced to accomplish the objective with the aid of FinFET 18nm technology. The structure is optimized to get the minimum delay confinement. Suggested design needs less logic resources. The outcomes are validated using FPGA synthesis methods.

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Electro-Geometrical Analysis of Transversal V-Fold Patch Antenna

This paper investigates an original concept of electro-geometrical analysis of flexible microstrip antenna. The research work is focused on the analysis of the innovative effect of “V”- shape folding on the resonance frequency and input impedance of microstrip patch antenna. The V-folded antenna is assumed to behave as an arm constituted by horizontal and tilted members which are geometrically characterized by the folding angle, a. The folding is characterized by the angle between the horizontal plane and the folded part of the patch antenna printed on a Kapton flexible substrate. The initially flat-configured planar antenna was designed to operate at about 2.45 GHz. The innovative design method of the V-shape folded flexible antenna is explained.

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An Efficient Modified Z Source Grid Tied Inverter System

In this paper presents the controlling the power of using quasi-Z source inverter for renewable source application. The proposed quasi z source is single stage DC to AC converter it delivered buck-boost operation with respect to input. These buck-boost converter is highly suitable compare to the normal dc to dc converter for photovoltaic applications. The proposed single stage converter are reliable and suitable for renewable energy source application. The source impedance quasi network offers high gain compare to the exciting system and reduced the total harmonic level. The quasi-z source followed by single phase H bridge converter uses to convert DC to AC with high reliability output. A proposed system-built Simulink and the hardware made for the open loop control.

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Electromagnetic Band Gap based compact UWB Antenna with Dual Band Notch Response

In Antenna Technology, Electromagnetic Band Gap (EBG) structures are one of the mostly used structures to improve gain of antenna, suppress surface waves and band notching. In UWB frequency spectrum more number of small frequency bands are exists and these bands cause electromagnetic interference, to reject such bands more number of EBG structures are required. Due to limited monopole antenna's ground plane, it is required to have EBG with small in size and single EBG to reject two or more bands. The simulated results of proposed new C-shaped EBG by placing near the feedline of fork-shape Ultra-Wide Band (UWB) antenna to reject two bands namely Lower Wireless Local Area Network(L-WLAN) band range from 5.15 GHz to 5.35GHz and X-band of satellite downlink communications networks (7.25GHz –7.75GHz) within a UWB antenna's spectrum.

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Power Flow Parameter Estimation in Power System Using Machine Learning Techniques Under Varying Load Conditions

In power transmission network state estimation is more complex and the measurements are critical in nature. Estimation of power flow parameters such as voltage magnitude and phasor angle in a power system is challenging when the loads are varying. The objective of the work is to estimate the voltage magnitude and phase angle using machine learning techniques. Some of the Machine learning techniques are decision trees (DT), support vector machines (SVM), ensemble boost (E-Boost), ensemble bags (E-bag), and artificial neural networks (ANN) are proposed in this work. Among these methods, the best machine learning techniques are selected for this study based on performance metrics. Performance metrics are Mean Square Error (MSE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). Neural network produces minimum error when compared to other ML Techniques. Among three Performance Metrics MSE provides minimum error and is used to predict the exact model in this work.

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Novel PMU Model for Dynamic State Disturbance Analysis with Effective Data Handling System

In this paper a hybrid DFT phasor calculation method is presented. This method is used to calculate the fundamental component phasor value of the harmonic signal without any physical filter. With this method the computational time for each phasor value calculation is reduced and the calculated phasor value has the constant magnitude and rotating phase angle. This calculated phasor values are used for the disturbance or fault identification in the power system based on Total Vector Error (TVE). The %TVE-based fault identification is more effective, because the TVE value is calculated with reference phasor value. If any fault/disturbance occurs or frequency changes then %TVE value changes.

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Optimal Design of Microstrip Antenna for UWB Applications using EBG Structure with the Aid of Pigeon Inspired Optimization Technique

Ultra-wideband (UWB) technology has become influential among academic research and industry because of its vast application in the wireless world. However, several drawbacks have in UWB based antenna. To tackle this, EBG (Electromagnetic Band Gap) structures have proposed. Furthermore, the design of EBG structures is very complex due to the uncertain EBG properties dependence upon unit cell parameters. Therefore, to the optimal design of micro strip antenna for applications of UWB, EBG-PIO (pigeon inspired optimization) on the basis of micro strip patch antenna has been proposed to enhance micro strip antenna’s performance in terms of directivity, gain, bandwidth and efficiency.

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