Articles published in IJEER


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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