Articles published in IJEER


Articles in press are peer reviewed, accepted articles to be published in this publication. When the final article is assigned to volumes 10, Issue 3 of the publication, the article in press version will be removed and the final version will appear in the associated published volumes/issues of the publication.

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

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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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

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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