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

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

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

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

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

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

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

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

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Autoadaptive Flame Detection and Classification Using Deep Learning of FastFlameNet CNN

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

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Lime Diseases Detection and Classification Using Spectroscopy and Computer Vision

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

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IoT Based Smart Control of Load for Demand Side Management

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

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Design of an Efficient Face Recognition system using Deep Learning Technique

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

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Revaluating Pretraining in Small Size Training Sample Regime

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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A Review of Methods of Removing Haze from An Image

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

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

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

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

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

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

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

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

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

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Grey Wolf Optimization Based Energy Management Strategy for Hybrid Electrical Vehicles

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

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