Articles published in Volume 12

Volume 12

Fuzzy Logic Controller Based Charging and Discharging Control for Battery in EV Applications

The present research addresses the fuzzy charging and discharge control method for batteries made with lithium-ion utilized in EV applications. The proposed fuzzy-based solution takes into account available parameter to charge or discharge the store within the safe functioning area. To analyses and control battery performance, a variety of controlling methods have been used, but each has its own set of drawbacks, such as the inability to stop two charging conditions, the difficulty of the controller, the lengthy charge time.

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Artificial Neural Network based FACTS in a Contingency Situation

The two biggest issues facing today's energy management systems are the ongoing monitoring of online voltage stability and the improved loadability of the transmission lines for the current electrical power system. As a result, it is highly difficult and time-consuming to assess online voltage stability under diverse loading conditions. This study describes a practical voltage stability monitoring system that automates online voltage monitoring and alerts the operator before voltage drops by computing line voltage stability indices using an ANN.

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Design and Analysis of Ultra-low Power Voltage Controlled Oscillator in Nanoscale Technologies

In latest wired and wireless communication equipment, VCO (voltage-controlled oscillator) is the major building block and particularly used as the stable high frequency clock generator. VCO performance is measured through frequency range, power supply used, area occupied, power consumption, delay, and phase noise. VCO is the cascaded of odd number of inverter stages in a ring format, hence it is also articulated as a ring oscillator. Today’s portable communication devices are battery operated. Hence, low power and area efficient designs play a key role in battery life enhancement and device size reduction. Device scaling improves the effective silicon area utilization, but it leads to more leakages.

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Parameters Measurement and Secrecy Diversity Analysis for Physical Layer Security in WSNs using Projection Pursuit Gaussian Process Regression

Wireless sensor networks are specialized networks, geographically dispersed monitors that keep track of environmental external factors and conduct the collected information to a centralized opinion. The rapid growth of wireless sensor networks and its connected have pushed the saturation level of the communication. Moreover, the information passed is prone to the attacks and hence researchers have considered these as crucial factors in wireless sensor networks. Physical layer security is the one of the main approaches to ensure the secrecy of wireless sensor networks and has been attained with several encryption and signal processing approach.

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Accuracy Measurement of Hyperspectral Image Classification in Remote Sensing with the Light Spectrum-based Affinity Propagation Clustering-based Segmentation

The area of remote sensing and computer vision includes the challenge of hyperspectral image classification. It entails grouping pixels in hyperspectral pictures into several classes according to their spectral signature. Hyperspectral photographs are helpful for a variety of applications, including vegetation study, mineral mapping, and mapping urban land use, since they include information on an object's reflectance in hundreds of small, contiguous wavelength bands. This task's objective is to correctly identify and categorize several item categories in the image. Many approaches have been stated by several researchers in this field to enhance the accuracy of the segmentation and accuracy.

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Enhanced Recognition of Human Activity using Hybrid Deep Learning Techniques

In the domain of deep learning, Human Activity Recognition (HAR) models stand out, surpassing conventional methods. These cutting-edge models excel in autonomously extracting vital data features and managing complex sensor data. However, the evolving nature of HAR demands costly and frequent retraining due to subjects, sensors, and sampling rate variations. To address this challenge, we introduce Cross-Domain Activities Analysis (CDAA) combined with a clustering-based Gated Recurrent Unit (GRU) model.

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Deep-GD: Deep Learning based Automatic Garment Defect Detection and Type Classification

Garment defect detection has been successfully implemented in the quality quick response system for textile manufacturing automation. Defects in the production of textiles waste a lot of resources and reduce the quality of the finished goods. It is challenging to detect garment defects automatically because of the complexity of images and variety of patterns in textiles. This study presented a novel deep learning-based Garment defect detection framework named as Deep-GD model for sequentially identifying image defects in patterned garments and classify the defect types. Initially, the images are gathered from the HKBU database and bilateral filters are used in the pre-processing of images to remove distortions.

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Compression of Medical images using SPIHT Algorithm for Telemedicine Application

Image compression plays a pivotal role in the medical field for the storage and transfer of DICOM images. This research work focuses on the compression of medical images using Set Partitioning in Hierarchy Trees (SPIHT) algorithm. The CT/MR images are used as input, the images are subjected to filtering by a median filter. The CT images in general are corrupted by Gaussian noise and MR images are corrupted by rician noise. The SPIHT algorithm comprises of following phases; transformation into wavelet domain, refinement pass and sorting pass. The Haar wavelet transform is employed and the wavelet coefficients are subjected to sorting and refinement pass.

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Deep Learning based Effective Watermarking Technique for IoT Systems Signal Authentication

In order to identify cyber-attacks, this research suggests a special watermarking technique for dynamic IoT System signal validation. IoT Systems (IoTSs) can extract a group of randomly generated characteristics from their produced signal and then periodically watermark these attributes into the transmission owing to the proposed efficient watermarking technique. Using dynamic watermarking for IoT signal authentication, a potent deep learning technique is used to detect cyber-attacks. Based on an LSTM structure, the proposed learning system enables IoT devices to extract a set of random features from the signal they release, hence enabling dynamic watermarking of the signal.

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Design of an Efficient & Secure Steganographic Model using Modified LSB & Encryption Process

This paper introduces a novel steganographic model for robust multimodal data security, seamlessly integrating a modified Least Significant Bit (LSB) technique with encryption, making it applicable to diverse data types such as images, audio, video, and text. Overcoming challenges posed by existing complex models and communication delays, our approach employs a modified LSB technique to encode similar sized data samples, followed by dynamic bioinspired elliptic curve cryptography (BECC) utilizing a Mayfly Optimization (MO) Model.

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Optimizing Electric Vehicle Range through Integrating Rooftop Solar on Vehicle

This paper includes the research work to investigate the optimization of electric vehicle (EV) range by integrating rooftop solar panels onto the vehicle. The primary motivation stems from the increasing power demand for EV charging, requiring substantial grid electricity production. The paper explores the installation of rooftop solar panels to augment the EV range with a single full charge, reducing the dependence on the grid. The simulations are conducted using MATLAB modeling, optimizing solar and grid charging schedules based on solar irradiation data. The outcomes showcase a 1.44 kWh battery integration with an EV equipped with a 1 kW BLDC motor, weighing 800 kg, including payload.

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Indian Classical Music Recognition using Deep Convolution Neural Network

A divine approach to communicate feelings about the world occurs through music. There is a huge variety in the language of music. One of the principal variables of Indian social legacy is classical music. Hindustani and Carnatic are the two primary subgenres of Indian classical music. Models have been trained and taught to distinguish between Carnatic and Hindustani songs. This paper presents Indian classical music recognition based on multiple acoustic features (MAF) consisting of various statistical, spectral, and time domain features. The MAF provides the changes in intonation, timbre, prosody and pitch of the musical speech due to different ragas.

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Energy Efficient Routing in Wireless Mesh Networks using Multi-Objective Dwarf Mongoose Optimization Algorithm

Wireless Mesh Networks (WMNs) are part of wireless technologies that are known for their flexibility and extended coverage. Wireless applications have reached their peak in applications related to various fields such as healthcare, image processing, and so on. However, delay and energy efficiency are considered the two aspects that diminish the performance of WMNs. To overcome the aforementioned issues, this research introduces an effective routing method using Multi-Objective Dwarf Mongoose Optimization Algorithm (MO-DMOA). The MO-DMOA performs routing by considering the multiple paths using an enriched population resource.

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SCSO-MHEF: Sand Cat Swarm Optimization based MHEF for Nonlinear LTI-IoT Sensor Data Enhancement

Sensor data is an integral component of internet of things (IoT) and edge computing environments and initiatives. In IoT, almost any entity imaginable can be outfitted with a unique identifier and the capacity to transfer data over a network. The estimate problem was formulated as a min-max problem subject to system dynamics and limitations on states and disturbances within the moving horizon strategy framework. In this paper, a novel Sand Cat Swarm Optimization Based MHEF for Nonlinear LTI IOT Sensor Data Enhancement (SCSO-MHEF) is proposed.

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Optimizing Current Injection Technique for Enhancing Resistivity Method

Geo-electrical resistivity methods are widely used in various fields and have significant applications in scientific and practical research. Despite the widespread use of resistivity methods, current injection is a critical step in the process of resistivity methods, and the quality of current injection significantly impacts the accuracy of the resistivity measurements. One primary challenge is optimizing current injection techniques to enhance resistivity methods. The developed current injector model for the resistivity meter instrument enhances performance by increasing the voltage source to 400 Volts, extending measurement coverage. It provides three injection current options, 0.5A, 0.8A, and 1A, for efficient accumulator use, considering electrode distances and estimating earth resistance using Contact Resistance Measurement (CRM) to estimate the earth resistance. CRM mode ensures proper electrode connection before injection, thus improving measurement efficiency.

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A Route Planning Method using Neural Network and HIL Technology Applied for Cargo Ships

This paper presents the development of a method to find optimal routes for cargo ships with three criteria: fuel consumption, safety, and required time. Unlike most previous works, operational data are used for the studies. In this study, we use data collected from a hardware-in-loop (HIL) simulator, with the plant model being a 3D dynamic model of a bulk carrier designed and programmed from 6 degrees of freedom (6-DOF) equations that can interact with forces and moments from the environmental disturbances. The dataset generated from the HIL simulator with various operating scenarios is used to train an artificial neural network (ANN) model.

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Electronically Tunable Sinusoidal Oscillator Using Only Single Current-Controlled Current Conveyor Trans-Conductance Amplifier

This work presents a wide frequency range sinusoidal oscillator design that relies on only one active component, a current-controlled current conveyor trans-conductance amplifier, and a few passive components. It just employs two grounded and non-floating capacitors and one resistor to complete the procedure. This design has the advantage of allowing the oscillation frequency and condition to be adjusted not only electronically, but also separately, without affecting the values of any passive components.

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Benefit Through Vehicles Passing on Highways in Electrical Power Generation

The turbulent airflow caused by vehicular movement on highways is a source of kinetic energy for wind energy (WE) that can be utilized to power highway lighting and communications. The purpose of the current work is to design, install and measure the extent of benefit from small wind turbines along a Highway (HW) in one of the governorates of Iraq - Dohuk. In this investigation, wind speed measurements are close to a significant HW on the Dohuk-Zakho-Iraq (DZI) Road. The three positional characteristics are examined for the wind turbines' optimal position. These factors are heights above ground level, lateral distances from the road shoulder, and the wind turbines' highway-facing orientation.

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Fault Prognosis of Induction Motor Using Multi Resolution Current Signature Analysis

There are various methods for the condition monitoring and this paper focuses on the multi resolution current signature analysis for fault prediction of induction motors. Variable frequency drives-based induction motors are used widely in industries. Monitoring the health of the motors is of great importance to reduce downtime and increase productivity. The multi resolution coefficients features from current signal are extracted using empirical wavelet transform. The extracted features are fed as input to artificial neural network to do prognosis on the data obtained for finding the condition of the motor.

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Enhancing FPGA Testing Efficiency: A PRBS-Based Approach for DSP Slices and Multipliers

The multiplication operations are pivotal in (Application-Specific Integrated Circuits) ASICs and Digital Signal Processors (DSPs). The integration of Field-Programmable Gate Arrays (FPGAs) into modern embedded systems, efficient Built-in Self-Tests (BISTs), particularly for complex components like DSP slices, is essential. This paper evaluates Pseudo Random Binary Sequence (PRBS) generators and checkers as BIST tools for high-speed data transfers in FPGAs. The design achieves minimal errors and remarkable efficiency with less than 4% logic utilization within available Look-Up Tables (LUTs). The testing of embedded multipliers in modern FPGAs is analyzed, shedding light on their performance.

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Sustainability of precision agriculture as a proposal for the development of autonomous crops using IoT

Agricultural activities have experienced a significant increase due to population growth; hence, the demand for food has risen to the point where prioritizing greater efficiency and quality in crop production within a short period is crucial. This paper addresses the contemporary need to design prototypes focused on optimizing natural resources, specifically in the agricultural sector, where recurring wastage of water, fertilizers, and pesticides is evident. This research proposes a comprehensive prototype incorporating a monitoring and control system managed through the IoT Arduino Cloud platform using an ESP32 development board to improve resource management from the initial germination stages to harvest. The planting phase is based on a 3D printer mechanism with three-dimensional movements controlled.

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Classification of Lung Cancer in Segmented CT Images Using Pre-Trained Deep Learning Models

Many Diagnosis systems have been designed and used for diagnosing different types of cancer. Identification of carcinoma at an earlier stage is more important, and it is made possible due to the use of processing of medical images and deep learning techniques. Lung cancer is seen to develop often to be increased, and Computed Tomography (CT) scan images were utilized in the investigation to locate and classify lung cancer and also to determine the severity of cancer. This work is aimed at employing pre-trained deep neural networks for lung cancer classification. A Gaussian-based approach is used to segment CT scan images. This work exploits a transfer learning-based classification method for the chest CT images acquired from Cancer Image Archive and available in the Kaggle platform.

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Efficient User Association Strategy for Maximizing User Satisfaction and Resource Utilization in Heterogeneous Cloud Radio Access Networks (H-CRANs)

The primary aim of this study is to present a User Association Strategy that can effectively optimize the Heterogeneous Cloud Radio Access Network (H CRAN). The primary objective of this strategy is to enhance customer satisfaction by optimizing the utilization of available network resources, such as transmission power and bandwidth. To achieve this objective, the proposed approach employs a logarithmic barrier function to address inequality constraints and transform the optimization problem into a formulation that facilitates effective convergence towards the optimal solution.

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A Modern Distribution Power Flow Controller With A PID-Fuzzy Approach : Improves The Power Quality

Technological improvements have led to an increase of nonlinear loads, which in turn has a significant impact on the quality of power transmission. It is imperative that the level of energy purity conveyed by a transmission line be elevated. The key factors influencing power transmission are line impedance, sending end voltage, and receiving end voltage. Harmonic currents are made by nonlinear loads, which can cause system resonance, capacitor overloading, less efficiency, and a change in the amount of the voltage. The Distributed Power Flow Controller (DPFC) is a recently developed Flexible AC Transmission System (FACTS) device that utilizes the distributed FACTS (D-FACTS) idea. Unlike the Unified Power Flow Controller (UPFC), which employs a single large-sized three-phase series converter, the DPFC incorporates several small-sized single-phase converters.

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Investigation and Reduction of Harmonic in Grid Connected PV Fed DSTATCOM System

The utilization of a photovoltaic-based distribution static compensator (PV-DSTATCOM) stands out as a prominent solution for addressing energy demand deficits and power quality challenges within contemporary power systems. This article focuses on enhancing the performance of PV-DSTATCOM to facilitate grid integration and elevate power quality standards. In the envisioned system, the power from the photovoltaic array is harnessed through the utilization of the sliding mode control, ensuring the extraction of maximum power. The performance of the PV-DSTATCOM is analyzed by using a Packed U cell 5 inverter.

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Wind Energy Conversion System using Cascading H-Bridge Multilevel Inverter in High Ripple Scenario

This paper presents wind energy conversion system using CHB MLI and phase interleaved boost converter to overcome high voltage and current ripple. Developments in power electronics technology have a direct impact on advances in wind energy conversion systems. WECS output voltage may fluctuate depending on wind speed. For WECS to maintain a constant output voltage, a power converter is required. This paper explains how to configure a phase-interleaved boost converter and voltage controller to maintain a stable intermediate circuit voltage in the system. The proposed cascading H-bridge multilevel inverter (CHB MLI) converts DC/AC using a novel topology.

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Performance Analysis of ANFIS-PID Controller based Speed Regulation and Harmonic Reduction in BLDC Motor Application

This study focuses on assessing the performance of a Proportional-Integral-Derivative (PID) controller integrated with an Adaptive Neuro-Fuzzy Inference System (ANFIS) in the context of speed regulation and harmonic reduction in Brushless DC (BLDC) motor applications. Rising BLDC motor speed elevates Total harmonic distortion (THD) due to non-linearity. THD reduction is vital for efficiency, reliability, and compliance in applications like electric vehicles, HVAC, and industrial automation, ensuring optimal performance and longevity. Through simulation-based design and implementation, the effectiveness of the ANFIS-PID controller is evaluated for achieving precise speed control and reducing harmonic distortions in a virtual environment.

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A Compact Hardware Design and Implementation on FPGA Based Hybrid of AES and Keccak SHA3-512 for Enhancing Data Security

Data security means protecting important information from unauthorised persons. In a security system, cryptography is the most secure method. Cryptography has many kinds, but the Advanced Encryption Standard (AES) is the most secure system. If combined with AES and Secure Hash Algorithm-3-512Bits (SHA3-512), it becomes compact, more secure, and more authenticated for data communications. The proposed methodology is a hybrid cryptography technique that combines AES with the SHA3-512 algorithm. This system becomes a strong, secure system and produces a strong cipher text.

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Performance Evaluation and Dynamic Characteristics of a Self-Excited Induction Generator for Pico Hydro Power Plants

The dynamic performance of an isolated three-phase squirrel cage self-excited induction generator (SEIG) in a Pico Hydro Power Plant (PHPP) is examined in this work. The investigation is carried out with the help of MATLAB/Simulink for mathematical modeling and simulation of the proposed system under various operational situations. The SEIG model, which was created using the steady-state equivalent circuit approach, included the electrical, magnetic, and mechanical components of the SEIG and PHPP. The dynamic behavior of the SEIG is explored under a variety of operating situations.

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Power Transformer Inrush Current Minimization During Energization using ANFIS based Peak Voltage Tracking Approach

Energizing the power transformer at no load causes inrush current flow. The value of this current depends on main three factors, the residual and saturation flux of the transformer core, the rating of the transformer, and the switching instant. Inrush current may decrease the life of the transformer and causes mall function of the protective relays. Many efforts were done for limiting the inrush current using a current limiter or improve the core material to reduce residual flux. Other treating is to control energizing instance. This paper focused on controlling the instant of the transformer energization switch using fuzzy logic inference system. A new technique depends on adaptive seeking the crest of the voltage waveform. By this method there is no need to zero-crossing technique or phase looked loop. At this point, the flux of the core reaches the minimum value. Simulation and laboratory results show the success of this technique in reducing the inrush current. This technique gives the freedom to the operational engineering for energizing the power transformer at any time.

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Improving Intrusion Detection using Satin Bowerbird Optimization with Deep Learning Model for IIoT Environment

Intrusion Detection in the Industrial Internet of Things (IIoT) concentrations on the security and safety of critical structures and industrial developments. IIoT extends IoT principles to industrial environments, but linked sensors and devices can be deployed for monitoring, automation, and control of manufacturing, energy, and other critical systems. Intrusion detection systems (IDS) in IoT drive to monitor network traffic, device behavior, and system anomalies for detecting and responding to security breaches. These IDS solutions exploit a range of systems comprising signature-based detection, anomaly detection, machine learning (ML), and behavioral analysis, for identifying suspicious actions like device tampering, unauthorized access, data exfiltration, and denial-of-service (DoS) attacks. This study presents an Improving Intrusion Detection using Satin Bowerbird Optimization with Deep Learning (IID-SBODL) model for IIoT Environment. The IID-SBODL technique initially preprocesses the input data for compatibility.

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Design of a Deep Learning based Intelligent Receiver for a Wireless Communication System

In communication systems, deep learning techniques can provide better predictions than model-based methods when the hidden features of the problem are prone to deviating substantially from the formulated assumptions. Severe signal impairments due to multipath fading and higher channel noise levels degrade the performance of conventional receivers. To overcome this, a novel intelligent receiver based on a deep learning network is presented, achieving better performance in terms of reduced bit error rate than a standalone conventional receiver.

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Hybrid Data Driven Clock Gating and Data Gating Technique for Better Saving Power in ALU RISC-V

The study proposes a hybrid data driven clock gating and data gating technique which is applied to ALU in RISC-V. By doing so, the proposed low power technique can improve the power saving efficiency. The proposed low power technique is compared with various low power techniques such as latch-free based clock gating, latch-based clock gating, single data driven clock gating, and single data gating. The results show that the proposed low power ALU saves 46.67% power consumption compared to original ALU. The proposed ALU also shows better saving power than the latch-free based clock gating, latch-based clock gating, sdata driven clock gating, and data gating from 10.84% to 22.23%.

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Static Synchronous Compensator (STATCOM) and Static VAR Compensators (SVCs) -based neural network controllers for improving power system grid

The stability of the electrical network is considered a major challenge in the development of energy systems based on various sources. This research provides a comparison of the dynamic performance of FACTS devices such as STATCOM and SVC. These techniques, which are integrated stability devices with a multi-source power system, are used. The neural network technology unit is used to control FACTS devices to enhance the performance of power sources under abnormal and different conditions. Testing is conducted under conditions of three-phase short circuit to ground at bus (3) in the system.

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Integrating PEVs into Smart Home Energy Management: A Vehicle-to-Home Backup Power Solution with Solar power system

This study focuses on leveraging the capabilities of plug-in electric vehicles (PEVs) to serve as an alternative power supply for suburban demands during disruptions, encompassing backup solutions, particularly in emerging or deprived regions. This initiative is part of an overarching strategy to establish household microgrids. Importantly, this utilization of PEVs for backup power is engineered to have no adverse impact on their primary function as electric vehicles. The proposed Vehicle-to-Home (V2H) system integrates seamlessly with solar photovoltaic (PV) charging. This synergy transforms the entire setup into a nano grid, a self-contained energy ecosystem. In a specific capacity, the plug-in electric vehicle (PEV) operates as a household load, utilizing its battery that gets charged either from solar photovoltaic (PV) systems or grid connections.

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SimCoDe-NET: Similarity Detection in Binary Code using Deep Learning Network

Binary code similarity detection is a fundamental task in the field of computer binary security. However, code similarity is crucial today because of the prevalence of issues like plagiarism, code cloning, and recycling in software due to the ongoing increase of software scale. To resolve these issues, a novel SIMilarity detection in binary COde using DEep learning NETwork (SimCoDe-NET) has been proposed. Initially, op-code features are extracted from the input data by using reverse engineering process and the opcode embedding is generated using N-skip gram method. The extracted features are fed into Bi-GRU neural network for classifying the similarity of the binary codes.

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Advancements in Steel Surface Defect Detection: An Enhanced YOLOv5 Algorithm with EfficientNet Integration

Steel surface defect detection is of utmost importance for ensuring product quality, cost reduction, enhanced safety, and heightened customer satisfaction. To address the limitations of traditional steel surface defect detection algorithms, which often yielded singular detection results and suffered from high miss detection rates, we proposed an enhanced Yolov5 steel surface defect detection algorithm. In this approach, this paper employed the EfficientNet network as a replacement for the Yolov5 backbone network. Subsequently, we trained and tested this modified network on a steel surface defect dataset to mitigate the challenges associated with high miss detection rates and underperforming evaluation metrics. Read more

An Evaluation of the Proposed Security Access Control for BYOD Devices with Mobile Device Management (MDM)

Bring Your Own Device (BYOD) at Work is a growing practice that has significantly increased network security vulnerabilities. This development has tremendous implications for both businesses and individuals in every organization. As a result of the extensive spreading of viruses, spyware, and other problematic downloads onto personal devices, the government has been forced to examine its data protection legislation. Dangerous apps are downloaded into personal devices without the user's awareness. As a result, both people and governments may suffer disastrous repercussions. In this research, proposed BYODs are troublesome since they can change policies without consent and expose private information. Read more

Nutrient Deficiency of Paddy Leaf Classification using Hybrid Convolutional Neural Network

For billions of people worldwide, enhancing the quantity and quality of paddy production stands as an essential goal. Rice, being a primary grain consumed in Asia, demands efficient farming techniques to ensure both sufficient yields and high-quality crops. Detecting diseases in rice crops is crucial to prevent financial losses and maintain food quality. Traditional methods in the agricultural industry often fall short in accurately identifying and addressing these issues. However, leveraging artificial intelligence (AI) offers a promising avenue due to its superior accuracy and speed in evaluation. Nutrient deficiencies significantly impact paddy growth, causing issues like insufficient potassium, phosphorus, and nitrogen. Read more

Simulation and Analysis of Optimal Power Injection System Based on Intelligent Controller

Many countries are seeing significant improvements in the fields of building, urban planning, technology, network management, and the need for diverse forms of energy and different generating techniques, as well as the necessity for low and middle distributing voltage in all areas. Depending on the needs of the user, starting needs, capacity, intended usage, waste output, and economic efficiency, many methods are used to generate this energy. To solve the problems brought on by the suggested excessive voltage of the provided system, energy collection devices can be used, and they can be used efficiently with smart grid intelligent control systems. Read more

A Novel Transfer Learning Approach to Improve Breast Cancer Diagnosing on Screening Mammography

Segmentation is a technique for separating an image into discrete areas in order to separate objects of interest from their surroundings. In image analysis, segmentation—which encompasses detection, feature extraction, classification, and treatment—is crucial. In order to plan treatments, segmentation aids doctors in measuring the amount of tissue in the breast. Categorizing the input data into two groups that are mutually exclusive is the aim of a binary classification problem. In this case, the training data is labeled in a binary format based on the problem being solved. Identifying breast lumps accurately in mammography pictures is essential for the purpose of prenatal testing for breast cancer. Read more

An Optimized Fuzzy C-Means with Deep Neural Network for Image Copy-Move Forgery Detection

Copy Move Forgery Detection (CMFD) is one of the significant forgery attacks in which a region of the same image is copied and pasted to develop a forged image. Initially, the input digital images are preprocessed. Here the contrast of input image is enhanced. After preprocessing, Optimized Fuzzy C-means (OFCM) clustering is used to group the images into several clusters. Here the traditional FCM centroid selection is optimized by means of Salp Swarm Algorithm (SSA). The main inspiration of SSA is the swarming behavior of salps when navigating and foraging in oceans. Based on that algorithm, optimal centroid is selected for grouping images. Next, the unique features are extracted from each cluster. Due to the robust performance, the existing approach uses the SIFT-based framework for detecting CMFD. Read more

Design of Enhanced Wide Band Microstrip Patch Antenna Based on Defected Ground Structures (DGS) for Sub-6 GHz Applications

In this paper a comprehensive comparative study of three distinct microstrip patch antenna (MPA) designs, each optimized for the sub-6 GHz applications, is presented. The initial design phase utilized a Rogers RT 5880 substrate with a permittivity (εr1) of 2.2 and a thickness(H1) of 1.42 mm. The proposed model achieved a resonance band ranging from 4.8 to 7 GHz, with a bandwidth of 2.2 GHz and a return loss (S11) of -20 dB. Subsequent enhancements involved integrating a Barium Strontium Titanate (BST) thin film (εr2 = 250, thickness(H2) = 0.005 mm), effectively shifting the operational band to 3.5-5.3 GHz. Read more

Speech Enhancement with Background Noise Suppression in Various Data Corpus Using Bi-LSTM Algorithm

Noise reduction is one of the crucial procedures in today’s teleconferencing scenarios. The signal-to-noise ratio (SNR) is a paramount factor considered for reducing the Bit error rate (BER). Minimizing the BER will result in the increase of SNR which improves the reliability and performance of the communication system. The microphone is the primary audio input device that captures the input signal, as the input signal is carried away it gets interfered with white noise and phase noise. Thus, the output signal is the combination of the input signal and reverberation noise. Our idea is to minimize the interfering noise thus improving the SNR. To achieve this, we develop a real-time speech-enhancing method that utilizes an enhanced recurrent neural network with Bidirectional Long Short Term Memory (Bi-LSTM). Read more

Improved Power Sharing Strategy for Parallel Connected Inverters in Standalone Micro-grid

The primary goal of integrating alternative energy systems such as solar and wind turbines into the power grid using power electronic devices is to meet the growing energy demands. Connecting inverters in parallel effectively enhance power capacity, reliability, and overall system efficiency. However, an uneven power distribution among the inverters is a significant limitation in these parallel connected inverters (PCI). This study focuses on a distributed generation (DG) unit comprising a solar photovoltaic system (SPV) and a battery energy storage system (BESS) connected to voltage source inverters (VSI) 1 and 2. The proposed approach aims to achieve uniform load/power distribution among the inverters with power management, maintaining a constant DC link voltage despite variations in solar irradiation and temperature. Additionally, the strategy targets the reduction of total harmonic distortion (THD) in the load current.

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Optimizing Capacitor Placement in Distribution Systems Under Variable Loading Conditions with Golden Jack Optimization (GJO)

In modern society, the demand for electricity is ever-growing, making the minimization of power losses in distribution systems paramount. One significant aspect contributing to these losses is the strategic placement of capacitors within the distribution network. Efficient capacitor placement not only reduces power losses but also enhances the overall performance and reliability of the system. In today's world, where electricity is indispensable, minimizing power losses in the distribution system holds significant importance. This research introduces the Golden Jack Optimization (GJO) algorithm as a novel approach to address the challenge of capacitor placement in distribution systems. GJO, inspired by the foraging behavior of jackals, exhibits unique characteristics such as adaptability and efficiency in finding optimal solutions this paper proposes an innovative algorithm specifically designed for this purpose.

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Green Power Ev Charging Station Design and Analysis for Electric Vehicles

The primary goal of this research is to design on electric vehicle charging station with less emission in Chennai due to an increase in electric vehicles. The wind and solar are common renewable energy sources which produces green power. These renewable sources can also be implemented with diesel generator and grid connection to run the Electric Vehicle (EV) charging station. This research also focuses on the cost of energy and the total cost of the system for different sources to operate EV charging station. The sources to operate an EV charging station in various period of time to charge the vehicle are analyzed. The sensitivity analysis like derating of solar also done to examine the status of different parameters in entire system with low cost. The design of low-cost system for Electric Vehicle charging station will be a useful implementation to Chennai city for charge various EV vehicles.

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DFIG in Wind Energy Applications with High Order Sliding Mode Observer-based Fault-Tolerant Control Scheme using Sea Gull Optimization

This paper describes a new method for maximizing power extraction from a wind energy conversion system (WECS) by using a doubly fed induction generator (DFIG) that operates below nominal wind speed. To maximize the collected power of a wind turbine (WTG) exposed to actuator failure, a fault-tolerant high-order sliding mode observer (HOSMO) and Seagull Optimization Algorithm with a model predictive controller (MPC) technique is proposed. Evaluate both the real state and the sensor error simultaneously using a higher-order sliding-mode observer. Active fault tolerant controllers are designed to regulate wind turbine rotor speed and power in the presence of actuator defects and uncertainty. With the growing interest in employing wind turbines (WTGs) as the primary generators of electrical energy, fault tolerance has been seen as essential to improving efficiency and reliability.

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Resource Optimization in H-CRN with Supervised Learning Based Spectrum Prediction Technique

Cognitive radio network shows potential means of granting intensifying demand for wireless applications. In this model, an efficient resource optimization scheme with Priority Pricing Technique (PPT) is proposed with supervised learning-based SVM to tackle limited spectrum availability and underutilization in Hybrid-Cognitive Radio Networks (H-CRN). H-CRN works under the principle of detection of PUs states (active/inactive). If spectrum sensing is made in favor of active PUs, then the CSI (Channel State Information) is estimated and works in underlay principle. If it is made in favor of inactive PUs, then the transmission is performed in overlay manner. In the proposed PPT the PUs and SUs with highest channel gain have the highest priority to use the spectral resources. SVM is used as an effective technique of spectrum sensing to provide higher probability of detection of PUs as soon as possible.

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Empowering Smart City IoT Network Intrusion Detection with Advanced Ensemble Learning-based Feature Selection

This study presents an advanced methodology tailored for enhancing the performance of Intrusion Detection Systems (IDS) deployed in Internet of Things (IoT) networks within smart city environments. Through the integration of advanced techniques in data preprocessing, feature selection, and ensemble classification, the proposed approach addresses the unique challenges associated with securing IoT networks in urban settings. Leveraging techniques such as SelectKBest, Recursive Feature Elimination (RFE), and Principal Component Analysis (PCA), combined with the Gradient-Based One Side Sampling (GOSS) technique for model training, the methodology achieves high accuracy, precision, recall, and F1 score across various evaluation scenarios.

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Rate 5/6 TCM Code Having 64 States with 64 QAM for Fading Channel

Trellis Coded Modulation (TCM) is powerful technique employed in digital communication systems to improve the reliability and efficiency of data transmission. TCM combines error control coding and modulation schemes to achieve superior performance in challenging channel conditions. In TCM, information bits are encoded using a trellis encoder, which generates a sequence of encoded symbols. These symbols are then mapped onto a modulation scheme, such as Quadrature Amplitude Modulation (QAM) or Phase Shift Keying (PSK), to create the modulated signal. At the receiver, the received signal is demodulated and decoded using a trellis decoder, which employs maximum likelihood decoding to recover the original information bits. The trellis structure allows for efficient error correction and makes TCM particularly suitable for channels with fading, noise, and other impairments.

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IoT Security Framework Optimized Evaluation for Smart Grid

Modern systems' needs may be satisfied by smart grid technologies. Since we frequently struggle to effectively manage security, the smart grid's capacity is frequently underutilized. Despite the fact that a variety of solutions have been offered for securing the smart grid, the problem still exists that no single solution can entirely protect the environment. We provide a protection architecture for the IoT-connected smart grid. The proposed framework to secure IoT devices for the smart grid includes three complementary approaches. By conducting a rigorous comparative analysis of our proposed solution alongside four existing models, we contribute to the ongoing discourse on bolstering the security infrastructure of the smart grid IoT environment.

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Improved Magnetic Resonance Image Reconstruction using Compressed Sensing and Adaptive Multi Extreme Particle Swarm Optimization Algorithm

One powerful technique that can offer a thorough examination of the body's internal structure is magnetic resonance imaging (MRI). MRI's lengthy acquisition times, however, may restrict its clinical usefulness, particularly in situations where time is of the essence. Compressed sensing (CS) has emerged as a potentially useful method for cutting down on MRI acquisition times; nevertheless, the effectiveness of CS-MRI is dependent on the selection of the sparsity-promoting algorithm and sampling scheme. This research paper presents a novel method based on adaptive multi-extreme particle swarm optimization (AMEPSO) and dual tree complex wavelet transform (DTCWT) for fast image acquisition in magnetic resonance. The method uses AMEPSO in order to maximize the sampling pattern and minimize reconstruction error, while also exploiting the sparsity of MR images in the DTCWT domain to improve directional selectivity and shift invariance.

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Design and Development of Mathematical and Thermal Load Modelling for Induction Heating Systems

Complex systems can be modelled and their performance can be anticipated with the help of current computers and software tools that are applicable in real-world situations. The IH system is capable of being modelled and put through analysis in two distinct domains. The modelling is compatible with the research and applications in their entirety, including the particular control circuits and converter systems under consideration. The use of electrical modelling makes it possible to analyse the features of the IH load and to examine the variations in the parameters in order to determine the impact of load variations. In order to gain an understanding of the load's flow and thermal distribution, thermal modelling is a crucial component of this study.

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A Study of High Gain DC-DC Boost Converters for Renewable Energy Sources

This paper presents a comprehensive investigation into the various topologies of DC-DC boost converters which are designed for optimal integration with RES like photovoltaic (PV) systems. Photovoltaic applications demand efficient energy harvesting and management to maximize the conversion of solar energy into electrical power. The DC-DC topologies include switched coupled inductor, basic coupled inductor, coupled capacitor with coupled inductor with a snubber circuit, active clamp, high step-up and three-winding dual switches are considered for study. Each topology is analyzed in terms of its suitability for PV applications, considering factors such as efficiency, voltage gain, and reliability.

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Improved Mayfly Algorithm for Optimizing Power Flow with Integrated Solar and Wind Energy

Across the globe, the transition towards sustainable energy systems necessitates seamless implementation of Renewable Energy Sources (RES) into traditional power grids. Such RESs include solar and wind power. The current research work intends to overcome the challenges associated with Optimal Power Flow (OPF) problem in power systems in which the traditional operation parameters ought to be optimized for effective and trustworthy integration of the RESs. The current study proposes an innovative nature-inspired approach by enhancing the Mayfly algorithm on the basis of mating behaviour of mayflies. The aim of this approach is to tackle the complexities introduced by dynamic and discontinuous nature of solar and wind power. The improved Mayfly algorithm aims at minimizing power losses, emission, optimize voltage profiles, and ensure reliable integration of solar and wind power.

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Enhancing Performance of Power Allocation for VLC Networks by Non-Orthogonal Multiple Access-MIMO

Visible light communication, or VLC, networks emerged as a viable option for data access, particularly indoors. Their cost-effectiveness, protection to radio frequency (RF) intrusion, and extraordinarily high data speeds make them a desirable option for the upcoming generation of indoor networking technologies. In this paper, we propose the Exponential Gain Ratio Power Allocation (EGRPA), an effective and low-complex power splitting method, to increase the attainable sum amount in Multiple Input Multiple Output (MIMO) VLC downlink networks. Using numerical simulations, we assess the enactment of an indoor 2x2 MIMO VLC downlink model for several users.

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Modelling of IPFC with multifunctional VSC for low-frequency oscillations damping and system stability improvement

The unified power flow controller (UPFC) approach maximizes active power transfer with the least amount of losses by independently controlling both reactive and active power flow. This makes it possible to use individual transmission lines more effectively. The interline power flow controller (IPFC) utilizes the concept of UPFC for economic operation and control, management of multiline transmission systems. In its most basic form, the IPFC consists of many DC to AC converters such as voltage source inverters (VSCs), each of which performs the same purpose as the UPFC: providing series compensation for every line in multiline transmission system. A novel idea for the efficient power flow control management and compensation in multiline transmission system is the IPFC. This research proposed a backup controller for an effective modelling of IPFC in order to reduce low-frequency oscillations using four different damping controller options.

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Exact Computing Multiplier Design using 5-to-3 Counters for Image Processing

This work presents a novel approach to improve the area and energy efficiency of 5:3 counter, a key element used in digital arithmetic. To provide an effective substitute for addition operations, mostly in the partial product reduction stage of larger multipliers, this study suggests a new 5:3 counter. The Input Shuffling Unit (ISU) is employed within the proposed 5:3 counter to minimize gate-level implementation and path delay during partial product reduction in 16-bit and larger multipliers, thereby enhancing area and energy efficiency. Consequently, there are 84% fewer choices of input-output combinations, thereby decreasing the circuit complexity with respect to area and energy usage.

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Reliability Improvement of Grid Connected PV Inverter Considering Monofacial and Bifacial Panels Using Hybrid IGBT

The development of bifacial photovoltaics has led to significant advancements in solar energy. Unlike traditional solar panels, which only generate electricity from the front side, these panels capture the energy from the rear and front surfaces. Bifacial photovoltaics utilize a dual-sided absorption to capture the sunlight that falls on nearby structures and the ground. This technology helps boost their efficiency and makes them an economical and sustainable choice. Furthermore, the increased energy production from the rear side of bifacial panels may lead to higher voltage fluctuations, which affects the thermal stability of PV inverter. Nevertheless, PV inverter is regarded as critical component which affects the reliability performance. Hence in this paper reliability improvement methodology with hybrid IGBT is proposed for the PV inverter.

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Risk Assessment of Radial Distribution Systems using Modified Jelly Fish Search Algorithm to analyse the Performance Indices

One of the essential techniques for figuring out Power Distribution System performance is reliability evaluation. With time, the range of methods for assessing reliability has grown, and the distribution system's evolution has also become more intricate. The likelihood of a network failing grows with time once it begins to function, especially if it is used for an extended period. Reliability indices have been evaluated using different algorithms such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and various modified versions of algorithms. The Jelly Fish Search Algorithm has been used in various power system applications such as to determine the most cost-effective way to dispatch generating units' loads, integrate Distributed Generation (DG) units, track the maximum power of photovoltaic systems, and determine optimal power flow solutions, among other uses.

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A Switched Z-Source and Switched Capacitor Multi-Level Inverter Integrated Low Voltage Renewable Source for Grid Connected Application

Most of the renewable sources generate power at lower voltage levels in the range of 20-50V which cannot be utilized by the loads. Therefore, stacking multiple modules in series increases the voltage level or using conventional boost converter or QZSis helpful. However, due to series stacking and boost converter or QZS there is a great power loss and also have reliability issues.The QZS inverter has very less boosting gain in the range of 2times. Theconventional boost converter or QZSis replaced with SZSC for voltage boosting and inverter operation. The SZSC boosts the voltage 4-5 times to the input voltage level. For further mitigation of harmonics, the conventional 6-switch inverter is replaced with switched capacitor MLI. Multiple renewable sources are at the input which include PV array, battery unit and PMSG wind module.

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Application of Many-Objective Arithmetic Optimization Algorithm and TOPSIS for Optimal Planning of DGS in Distribution Systems

The traditional planning of distribution networks is changing because of the accelerated expansion of distributed generation (DG) technologies in various capacities and forms. However, the improper integration of DGs in current distribution networks can give rise to several technical difficulties despite the advantages provided by distributed generation technologies. This paper presents the optimal DG planning in the distribution system using a Pareto-based many-objective arithmetic optimization algorithm (MOAOA) for optimal DG planning problems in the distribution system. This work focuses on improving four technical metrics related to distribution systems: mitigation of electrical energy not served (EENS), total voltage deviation (TVD) minimization, voltage stability index (VSI) maximization, and energy loss mitigation.

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VSC-STATCOM Performance Under Different Fault Sensing using PSO Tuned Hybrid SMC

In this paper, we investigate the PSO-tuned hybrid SMC performance based VSC-STATCOM under different conditions of fault using hybrid renewable energy sources (HRES). A hybrid renewable energy resource system (HRES) consists of PV, wind power, and batteries. Here the Irradiance is the PV input and the wind energy is Wind Input. The storage of energy is used for battery. The battery is used for changing weather condition or the changing the condition of the environment. Hybrid VSC-STATCOM controller based on SMC to reduce power quality issues like sag, swell, harmonics etc. associated with HRES system mainly due to non-linear load conditions.

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Analysis of Disc Motor with Asymmetrical Conducting Rotor

The homogenous disc rotor construction allows higher rotational speeds and relatively higher power densities. This paper deals with a two-phase induction motor with homogenous disc rotor construction. The field analysis of an axial flux disk motor with a conducting disc rotor is carried out, using a new application of the Maxwell's field equations. A Suggested model is introduced to establish the geometry of motor construction and to enable the derivation of the field system differential equations. A new strategy is applied for the boundary conditions to complete the field solution. This is carried out by determining the complex integration constants.

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Improvement of 5G Core Network Performance using Network Slicing and Deep Reinforcement Learning

Users have increasingly been having more use cases for the network while expecting the best Quality of Service (QoS) and Quality of Experience (QoE). The Fifth Generation of mobile telecommunications technology (5G) network had promised to satisfy most of the expectations and network slicing had been introduced in 5G to be able to satisfy various use cases. However, creating slices in a real-life environment with just the resources required while having optimized QoS has been a challenge. This has necessitated more intelligence to be required in the network and machine learning (ML) has been used recently to add the intelligence and ensure zero-touch automation. This research addresses the open question of creating slices to satisfy various use cases based on their QoS requirements, managing, and orchestrating them optimally with minimal resources while allowing the isolation of services by introducing a Deep reinforcement Machine Learning (DRL) algorithm.

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A Smart Secure model for Detection of DDoS Malicious Traces in Integrated LEO Satellite-Terrestrial Communications

For many researchers, defense against DDoS attacks has always been a major subject of attention. Within the LEO Satellite-Terrestrial (LSTN) network field, distributed denial of service (DDoS) attacks is considered to be one of the most potentially harmful attack techniques. For the facilitation of network protection by the detection of DDoS malicious traces inside a network of satellite devices, machine learning algorithms plays a significant role. This paper uses modern machine learning approaches on a novel benchmark Satellite dataset. The STIN and NSL-KDD datasets has been used to detect network anomalies.

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Forward Node Selection by Evaluating Link Quality Using Fuzzy Logic in WBAN

WBAN technology plays a vital role in human life monitoring and maintaining health remotely without being hospitalized, particularly during pandemic situations. The miniature-sized and heterogeneous sensors involved in WBAN with limited resources face reliability as a key challenge that limits the growth of WBAN technology. Designing an efficient routing protocol helps to achieve reliable data transmission between sensor nodes in WBAN. The proposed Fuzzy logic-based Forward Node Selection chooses the best node to transmit the data by introducing fuzzy logic on routing parameters such as link quality, data rate, node’s residual energy and node-to-node distance. The key advantages of our proposed system are to extend the network lifetime and boost the packet delivery ratio.

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Control for Wind Turbine System using PMSG when Wind Speed Changes

This paper presents the proposed model to control grid-connected wind turbine by permanent magnet synchronous generator (PMSG). With the wind speed changing continuously, the rotor system needs to be able to self-regulate according to wind speed and direction to ensure efficient operation of the turbine. The PMSG was chosen because the magnetic flux is always available thanks to the permanent magnet system glued to the rotor surface. The generator provides power with low rotational speed but high efficiency.

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Impact of Propagation Environment on the Performance of Direction Oriented Forwarding through Minimum Number of Edge Nodes (DOF-MEN) Routing Protocol In Ad Hoc Networks

One sort of wireless ad-hoc network is called MANET (Mobile Ad-hoc Network), which is an autonomous network made up of wireless nodes and routers connected by wireless connections. Transmission of data in effective way is important. The DOF-MEN (Direction Oriented Forwarding through Minimum Number of Edge Nodes) protocol is the protocol which lessens the amount of messages for route discovery. It attempts to choose just single node as the next following hop. The node's address is added by the sender. Therefore, only the chosen node will receive and subsequently transmit the data. This protocol increases the throughput, Packet Delivery Ratio, and cuts down on the Routing Overhead. Several aspects affect the routing protocol's execution accuracy in mobile ad hoc networks (MANET).

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Design and Analysis of Microstrip Sierpinski Fractal Antenna for Wireless application

This paper describes a novel design for a microstrip fractal antenna based on the Sierpinski triangle shape. It is built on a FR4 substrate and operates in the 5.5 GHz frequency range. The proposed antenna is designed and validated using ANSYS Electronic Desktop's High Frequency Structure Simulator (HFSS). The simulated results show good performance in terms of radiation pattern, gain and input impedance. This proposed antenna can be widely used in wireless communication equipment that is progressing towards miniaturization and high frequency.

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Derivation of Generalized Design Formulas for Modified Branch-Line Coupler with the Second Harmonic Suppression Characteristics

We propose a modified branch-line coupler composed of the open stubs and stepped impedance lines. This structure allows the second harmonic suppression and size reduction. We present the steps to transform from a conventional branch-line coupler to the proposed structure using equivalent circuits and derive the generalized design formulas. From the results analysis of the design sample, we demonstrate the validity of the derived formulas. The fabricated branch-line coupler provides more than 30 dB the second harmonic suppression and about 30 % size reduction compared to a conventional branch-line coupler.

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A Review on 5G Antenna: Challenges and Parameter Enhancement Techniques

The need for a high-speed mobile network has increased due to the COVID19 pandemic.5G is the newest and most sophisticated technology designed to handle the demands of the internet. The 5G network ensures connection security and simplifies mobile device connectivity to wireless devices. This paper explains every parameter related to 5G technology that has been covered in various papers. It addresses some of the difficulties that 5G technology faces. The development of Vivaldi, conformal, MIMO antennas satisfies the requirements of the 5G mobile network and presents opportunities to overcome obstacles. In the paper, MIMO antenna is discussed along with various techniques for enhancing its parameters, such as appropriate substrate selection, antenna element placement, and mutual coupling reduction.

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Analysis and optimization of 4G / LTE network pathloss using Particles Swarm Optimization algorithm

This paper aims to optimize the pathloss in 4G/LTE networks obtained by empirical Radio Frequency (RF) propagation models to enhance user access quality. The radio wave propagation models are mainly used to predict the pathloss which are necessary for planning and optimizing wireless communication systems. In this paper, we propose a parametric optimization for loss estimation in a 4G/LTE network leveraging the Particle Swarm Optimization (PSO) algorithm to enhance the performances of this type of networks and decrease their complexity. For this sake, comparison and performance analysis were conducted using different environments such as urban, sub-urban and rural areas. First, we provide an analysis of radio propagation models, namely: Okumura-Hata, Stanford University Interim (SUI) and Ericsson 9999 models that would be used for outdoor propagation in LTE.

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Capacity Optimization of an Isolated Renewable Energy Microgrid Using an Improved Gray Wolf Algorithm

To achieve the goal of allocating the generation capacity of isolated renewable energy system microgrids in a stable, economical, and clean manner, an optimization model considering economic costs, environmental protection, and power supply reliability was established. Compared with the normalization of fixed weight coefficients, a dynamic adaptive parameter method was used in this study to balance the weights of economic, environmental, and stability factors in the objective function. The Levy Flight Strategy, Golden Sine Strategy, and Dynamic Inverse Learning Strategy were embedded to increase algorithm performance for optimization and simulation to address issues such as local optima, slow convergence speed, and lack of diversity commonly associated with traditional Grey Wolf Optimization algorithm.

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An Enhanced Multi-Objective Evolutionary Optimization Algorithm based on Decomposition for Optimal Placement of Distributed Generation and EV Fast Charging Stations in Distribution System

An Enhanced multi-objective evolutionary optimization algorithm based on decomposition (E-MOEA-D) proposed for optimal placement of Distributed Generation (DG) and Electric Vehicle (EV) Fast Charging Station (FCS) in distribution system. The diversity of the evolutionary algorithm improves the convergence and diverse solution in the process of evolutionary optimization. The proposed algorithm is improved using enhanced diversity algorithm, which yield diverse candidate solutions in population. The optimal placement of DGs and FCS are formulated using three objective functions as i) Active power loss ii) Voltage deviation iii) DG cost. The proposed algorithm is simulated on IEEE-33 bus distribution system.

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Design, Fabrication and Performance Analysis of a Compact Unidirectional Quasi-Yagi Antenna for High Gain and High Directivity at 6.2 GHz

Antenna gain, directivity, and radiation efficiency are being enhanced by researchers to satisfy the demands of emerging mobile communication systems. Primarily, the quasi-Yagi antenna satisfies the expanded criterion. This study presents a microstrip quasi-Yagi antenna operating at 6.2 GHz. Enhancements are made to the antenna's gain, directivity, and radiation efficacy. At 6.2 GHz, the antenna was engineered to have a return loss S11 of -36 dB. In addition, from 5.85 to 6.4 GHz, -10 dB return loss was incorporated into its design.

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An IoT based Traffic Control System using Spatio-Temporal Shape Process for Density Estimation

In response to the escalating challenges posed by urban congestion and road accidents, this paper addresses the imperative for advanced traffic control systems in smart cities. However, there is limited research work available in the literature to develop this traffic management system due to unpredictable traffic flow occurring on the road. To overcome this shortcoming in the traffic control system, this paper proposed a novel vehicle density estimation method that considers group of vehicles, availability and applicability of IoT in smart cities provide an efficient medium to handle public safety by using condition-based intensity function that will be a medium to cope with traffic challenges and thus build an intelligent traffic control system.

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Advancing Sleep Stage Classification with EEG Signal Analysis: LSTM Optimization Using Puffer Fish Algorithm and Explainable AI

In this study, we introduce SleepXAI, a Convolutional Neural Network-Conditional Random Field (CNN-CRF) technique for automatic multi-class sleep stage classification from polysomnography data. SleepXAI enhances classification accuracy while ensuring explainability by highlighting crucial signal segments. Leveraging Long Short-Term Memory (LSTM) networks, it effectively categorizes epileptic EEG signals. Continuous Wavelet Transform (CWT) optimizes signal quality by analyzing eigenvalue characteristics and removing noise. Eigenvalues, which are scalar values indicating the scaling effect on eigenvectors during linear transformations, are used to ensure clean and representative EEG signals.

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Innovative Noise Reduction Strategies in Ultrasound Images Using Shearlet Transform and Bayesian Thresholding

Uterine fibroids are prevalent benign tumors affecting women, often diagnosed through imaging modalities such as ultrasound. Ultrasound imaging is a widely used diagnostic modality for uterine fibroid due to its non-invasive nature. However, the images obtained often suffer from speckle noise, which can obscure fine details and complicate accurate diagnosis. Existing methods for removing speckle noise have limitations, including losing texture and edge information and not being able to handle low frequency noises. This paper presents a novel approach for speckle noise reduction by combining Shearlet Transform with Bayesian thresholding. The proposed method aims to achieve superior noise reduction while retaining important image features crucial for accurate diagnosis.

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The potential of rice husks for electrical energy generation in Cambodia

The purpose of this study was to ascertain the electrical potential of rice husk as a viable fuel source for electricity generation in Cambodia. The rice husk potential in Cambodia for each year was determined by analyzing statistical data on rice output from 2000 to 2021. The results indicate a significant 120% improvement in the capacity of rice husk to be transformed into power during a span of 22 years. On average, about 5.4% per year. Annual husk potential was calculated using 2019 statistical data. In 2019, there is a potential of about 1,741 million tons of husks, equivalent to about 864,408 tons of coal, which provides electricity and a potential of about 6,483 GWh and 740,075 MW.

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Error Mitigation in Noma for Underlay CR Networks with Imperfect Successive Interference Cancellation

This study examines the outage probability in a partial relay selection relaying network, an underlay cognitive radio (CR) network, and a non-orthogonal multiple access (NOMA) system. NOMA, which consists of K half-duplex Decode and Forward (DF) relays, is used in the secondary network. These relays are used to enable data transmission to secondary users (SUs) from the secondary base station (SBS). By establishing mathematical formulations, it is feasible to quantify the outage probability that SUs experience while accounting for imperfect successive interference cancellation (i-SIC).

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Enhancing Smart Grid Stability: Data-Driven Predictive Modeling in Distribution Systems

The system's ability to retain the equilibrium state during regular and under disturbance decides the power system stability. The power system stability is highly affected by continuous load variation, voltage variation, frequency variation, power flow variation, topology and the work environment. Hence the stability analysis is made to ensure the acceptable equilibrium state throughout the operation of the power system while meeting the demand. As there has been numerous inclusion of renewable energy sources into the electric network, there occurs challenge to maintain the equilibrium level of this decentralized supply with temporary needs. So to establish this kind of scenario, a Decentralized smart grid control (DSGC) is developed.

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Power Quality Enhancement through Active Power Filters in Radial Distribution System using Pelican Optimizer

In this paper, an application of pelican optimization algorithms (POA) for the enhancement of power quality (PQ) using active power filters (APFs) in radial distribution systems (RDS) is addressed. The harmonics is the main concern of the PQ. Nonlinear loads (NLs) inject the harmonics into the RDS. Here, nonlinear distributed generation (NLDG) is also considered along with NL at two end nodes. By using APFs, the harmonics are minimized to standard limits. Here, APFs are placed with proper size to minimize the harmonics and to improve the PQ. The POA is utilized to optimize the size of APF at proper placement. Inspired by natural processes, the POA has balanced exploration and exploitation characteristics.

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Multi Renewable source system stabilization using ANFIS controller for energy storage module

When a system is operated with multiple renewable sources connected to the same bus, several power quality issues are raised which may damage the devices connected to it. The issues like DC voltage regulation, harmonics in the AC voltages and ripple in the currents of the devices might be a major concern in the system. This compromising power quality can be improved by integrating advanced adaptive controller into the system for stable voltages. For this a multi renewable source system is considered including PMSG wind farm, FC module, PV source and a battery unit energy storage module. The battery unit is a mandatory module which maintains the power exchange and DC link voltage stability. The fuel cell module is a backup unit to the system when the battery unit fails.

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Design and Analysis of 5G Broadband Elliptical Cut Octagon Patch Antenna

In this paper, a 5G Broadband Elliptical Cut Octagon Patch Antenna is designed whose operating frequency band is from 20.82- 22.95GHz, 25.13-28.46GHz. In this antenna, FR4 substrate whose dielectric constant is 4.4 and loss tangent (tan δ) is 0.002 is utilized as substrate. This antenna has a compact size of 15×25×1.6mm3 and has a radiation efficiency of 92.1%. In order to increase the band of frequency of an antenna, two similar elliptical cut octagon patches are added to form a Broadband Antenna. The resultant microstrip patch antenna is a 5G Broadband Elliptical Cut Octagon Patch Antenna.

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An Efficient Load Frequency Control for Multiple Power Systems Using Fuzzy Logic-Proportional Integral Derivative Controller

To ensure that customers receive a steady and dependable supply of electricity, power systems must operate and be under control. One of the main problems encountered during interruptions to the system is irregular electrical power flow through interconnected power areas and frequency aberrations. Therefore, the load frequency control system (LFC) was used to reduce frequency variations and provide a stable power flow in multiple-areas power system. This study presents several techniques for controlling the load frequency in two area power systems employing a combination of fractional order proportional integral derivative (FOPID) and fuzzy logic-proportional integral derivative (FPID) controllers and comparing them to conventional controllers (PID). MATLAB/Simulink is used to simulate the overall system. The error is estimated using the integral of time-weighted squared error (ITSE) goal function.

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Maximum Power Point Tracking Controller of PV System Based on Two Hidden Layer Recurrent Neural Network

Solar energy is one of the most well-known and cutting-edge energy sources in the age of renewable energy. However, because of fluctuating meteorological factors like solar insolation and temperature, the output of a solar photovoltaic system varies greatly. For the effective use of solar energy harvested using solar PV units under different climate factors, the Maximum Power Point Tracking (MPPT) technique is a crucial component that needs to be present. The MPPT system regulates the PV system's output (current and voltage) to give maximal power to the load. Conventional approaches may not efficiently use available electricity and may fail in partial shade conditions. This study describes how to build MPPT for a photovoltaic system utilizing a two-hidden-layer recurrent neural network (THLRNN).

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CGSX Ensemble: An Integrative Machine Learning and Deep Learning Approach for Improved Diabetic Retinopathy Classification

This research proposes an integrated approach for automated diabetic retinopathy (DR) diagnosis, leveraging a combination of machine learning and deep learning techniques to extract features and perform classification tasks effectively. Through preprocessing of retinal images to enhance features and mitigate noise, two distinct methodologies are employed: machine learning feature extraction, targeting texture features like Gray-Level Co-occurrence Matrix (GLCM) and Gray-Level Run Length Matrix (GLRLM), and deep learning feature extraction, utilizing pre-trained convolutional neural networks (CNNs) such as VGG, ResNet, or Inception.

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Design DC/AC Converter for Renewable Energy Sources

Multilevel inverters are vastly used in power systems and “renewable energy sources” (RES) to provide an AC voltage with a low level of harmonic contents. This paper aims to design a 9-level inverter for RES such as photovoltaic (PV), wind turbines, and fuel cells… etc. The proposed inverter is constructed by 12 IGBT switching devices where all of which are powered by 4 DC sources of 81 V without balancing capacitors to make DC voltage 324 V. A Phase disposition (PD), alternative phase opposition disposition (APOD), and POD with a slight phase shift are the methods of modulations that are used to provide a sinusoidal waveform of the output voltage and current.

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A Novel Flying Robot Swarm Formation Technique Based on Adaptive Wireless Communication using MUSIC Algorithm

This paper presents a novel technique to address the challenge of coordinating swarm flying robots in a leader-follower configuration. A combination of the Multi Signal Classification (MUSIC) estimation algorithm, based on a wireless MIMO array antenna, along with onboard robot control are used for precise route tracking of an individual robot. Employing an array antenna reduces energy consumption for followers in passive mode and reduces computational complexity when measuring the angles of leader angle interferences, which depends on the phase difference of the impinging signal on the antenna elements of the array. Additionally, the angles estimation and beamforming processes, utilizing MUSIC algorithm, form an inner loop that furnishes orientation angles in 3D (Azimuth and elevation angles) for both the leader and potential interference sources.

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Deepfake Detection using Integrate-backward-integrate Logic Optimization Algorithm with CNN

The emergence of deepfake technology has spurred the need for robust and adaptive methods to detect manipulated media content. This study explores the integration of the Integrate-backward-integrate (IbI) Logic Optimization Algorithm with Convolutional Neural Networks (CNNs) for enhanced deepfake detection. The proposed approach involves a multi-phase iterative process: the CNN initially trained on a diverse dataset encompassing both real and deepfake images. The CNN serves as the foundation for the IbI-driven optimization. The integration phase employs the trained CNN to forward-integrate images, classifying them as real or deepfake. Subsequently, the IbI Logic Optimization Algorithm engages in the backward phase, utilizing feedback from the CNN's performance to iteratively refine the network's parameters, architecture, and feature extraction capabilities. This iterative optimization process aims to adaptively enhance the CNN's ability to discern subtle nuances between authentic and manipulated visuals.

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A Cascaded H-Bridge Multilevel Inverter with DC Cells Input Fault Tolerance Capability Based on PSC-PWM Control

Multilevel inverters have proven their efficiency in generating better AC voltage outputs. This functional quality is mainly due to the use of multi-source DC inputs. Despite the fact that this type of topology is generally reliable, switching faults caused by the complete loss of a switching component or DC input cell may still occur. Such incident may inflict heavy impacts to the conversion chain resulting in a permanent damage to the switching cells or the connected load. This article presents a dynamic switching control strategy capable of tolerating DC cells open-circuit input faults in basic symmetric Cascaded H-bridge multilevel inverter architectures.

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Enhanced Wireless Communication Optimization with Neural Networks, Proximal Policy Optimization and Edge Computing for Latency and Energy Efficiency

This research proposes a novel approach for efficient resource allocation in wireless communication systems. It combines dynamic neural networks, Proximal Policy Optimization (PPO), and Edge Computing Orchestrator (ECO) for latency-aware and energy-efficient resource allocation. The proposed system integrates multiple components, including a dynamic neural network, PPO, ECO, and a Mobile Edge Computing (MEC) server. The experimental methodology involves utilizing the NS-3 simulation platform to assess latency and energy efficiency in resource allocation within a wireless communication network, incorporating an ECO, MEC server, and dynamic task scheduling algorithms. It demonstrates a holistic and adaptable approach to resource allocation in dynamic environments, showcasing a notable reduction in latency for devices and tasks. Latency values range from 5 to 20 milliseconds, with corresponding resource utilization percentages varying between 80% and 95%.

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Design Wien Bridge Oscillator for VLF to VHF Using Practical Op – Amp

An electronic oscillator is generally a major part of electrical, electronic and communications circuits and systems and it is can be divided into linear and nonlinear families. Wien Bridge is a type of RC phase shift oscillators mostly used for around 1MHz and its design adopts positive feedback technology. In this research, novel look the reasons for the inability to achieve high frequencies was understanding and the ambiguity was removing from the determinants of obtaining a high frequency signal for this type of oscillators, also, new results were obtained with a unique presentation. The output formula for the oscillation resonant frequency was deriving based on the oscillator’s theory.

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A New Electric EEL Foraging Optimization Technique for Multi-Objective PV Unit Allocation in the Context of PHEV Charging Demand

The existing electric distribution system is under tremendous stress due to reasons like power efficacy & voltage profile, load growth, radial structure etc. Additionally, electric load demand due to PHEVs worsens the existing distribution system performance. Planning of DGs in distribution system is one of the potential solutions for improving existing distribution system performance without changing its infrastructure. Therefore, the primary objective of this research is to determine the optimal way to allocate photovoltaic (PV) based distributed generators (DGs) inside radial distribution networks while taking into account the load demands of both conventional and PHEVs. In the study, three key technical metrics of the distribution network are improved via optimal planning of PV units: maximizing the voltage stability index, minimizing total voltage variation, and minimizing energy loss. Mathematically, weighted objective function is formulated for dealing the above-citied technical metrics.

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Advanced Energy Management System for Hybrid AC/DC Microgrids with Electric Vehicles Using Hybridized Solution

The rapid expansion of the automotive sector promising this technology is going forward and deeply ingrained in human society. Without a doubt, the unpredictable and erratic charging demands of these devices would have an impact on the power grid's scheduling and optimal performance, which may be seen as a new issue. This research introduces an efficient energy management system for hybrid renewable energy in AC/DC microgrids, including electric vehicle (EV) renewable microgrids, utilizing sources such as solar and wind energy. These systems offer promising solutions for enhancing security, reliability, and efficiency in power systems, with the added benefit of reducing greenhouse gas emissions. The proposed optimization approach utilizes Honey Badger Algorithm (HBA) Golden Jackal Optimization (GJO) called Advanced HBA (AHBA) for voltage and power control in hybrid AC/DC microgrids with EVs.

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Speed Control of Sensorless Induction motor based on Grey Wolf Optimizer Fractional Order Controller using MRAS based Speed Estimation

Traditional induction motor control methods typically require feedback from sensors like encoders or resolvers to determine the motor's rotor position and speed accurately. The speed control of a sensorless induction motor is critical, so this study provides a novel method that combines the Model Reference Adaptive System (MRAS) for speed estimate with the Fractional Order PID controller for speed control. This controller's parameters are optimized using the Grey Wolf Optimizer Algorithm. After being implemented in the MATLAB/Simulink environment, the suggested approach's performance is compared to that of a standard PI controller. From the findings, it is clear that the proposed method effectively maintaining the specified speed as compared with PI controller. The proposed controller performance is also validated through experimental results.

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A New Quazi Z-Source Seven-Level inverter for Photovoltaic Applications

This work presents a Quazi Z-Source Seven-Level inverter (qZS7LI) for Photovoltaic (PV) applications. The presented topology partakes the benefit of having a lesser switch count (8 switches) compared to current 7-Level qZ source topologies. The presented qZS7LI consists of three quasi-Z-source based impedance network, 02 bidirectional power switches, and one H-bridge inverter. The presented qZS7LI topology is studied by using pulse-width modulation (PWM) technique. The bidirectional switches and one leg of the H-bridge inverter are employed to insert shot through and generate levels, operating at a high frequency.

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A Novel Open-Circuit Fault-Tolerant MMC with Multi-carrier PWM Techniques for Solar PV Applications

Modular multilevel converters (MMC) have attracted much interest from researchers worldwide. It is a desirable solution due to important features such as modularity, low harmonic content at high output voltages, avoiding large capacitors and separate DC sources, easy scaling to any voltage level, and reduced voltage stress on switches, as well as being suitable for high and medium power applications, such as HVDC and motor drives. High-voltage applications require a cascade of hundreds of sub-modules. Depending on the type of sub-module selected for the application, the sub-module of the MMC may contain several switches. Depending on the MMC-based application, the converter typically needs to operate for a long period, two or three years, without interruption. MMCs can experience many electrical problems, including single line-to-ground faults, DC-bus short circuit faults, switch open circuit faults, and short circuits. A malfunction like this can damage the MMC and cause the system voltage to drop.

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Cost-Benefit Evaluation of the Implementation of a Photovoltaic Solar Generation System in the Archipelago of San Andrés, Providencia, and Santa Catalina

The study highlights the importance of photovoltaic solar energy as a viable alternative for the Archipelago of San Andrés, Providencia, and Santa Catalina in response to the current 99% dependence on fossil sources, especially diesel. There is a need to diversify the energy matrix and reduce associated costs, including significant subsidies. The implementation of renewable sources is considered crucial to achieve sustainable development goals and improve energy autonomy, in addition to mitigating environmental impacts. The study proposes a detailed cost-benefit analysis of implementing a solar photovoltaic system, considering the return on investment and the savings in monthly energy payments for different user segments, classified into four clusters: strata 1 and 2, strata 6, commercial, and official.

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MS-CFFS: Multistage Coarse and Fine Feature Selection for Advanced Anomaly Detection in IoT Security Networks

In recent years, the concept of Internet-of-Things (IoT) has increased in popularity, leading to a massive increase in both the number of connected devices and the volume of data they handle. With IoT devices constantly collecting and sharing large quantities of sensitive data, securing this data is of major concern, especially with the increase in network anomalies. A network-based anomaly detection system serves as a crucial safeguard for IoT networks, aiming to identify irregularities in the network entry point by continuously monitoring traffic. However, the research community has contributed more to this field, the security system still faces several challenges with detecting these anomalies, often resulting in a high rate of false alarms and missed detections when it comes to classifying network traffic and computational complexity. Seeing this, we propose a novel method to increase the capabilities of Anomaly Detection in IoT.

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Enhancing Facial Recognition Accuracy through KNN Classification with Principal Component Analysis and Local Binary Pattern

Recent developments in deep learning techniques have led to remarkable progress in facial recognition. As a component of biometric verification, human face recognition has become widely used in a variety of applications, including surveillance systems, home entry access, mobile face unlocking, and network security. Conventional facial recognition techniques are especially useful when dealing with low-resolution photos or difficult lighting situations. The K-nearest neighbor (KNN) classifier has been used in this paper. KNN is a non-parametric, instance-based learning algorithm that is commonly used for classification tasks. Principal Component Analysis (PCA) and local binary pattern (LBP) are used in this study to develop face identification.

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An Intelligent Approach for MPPT Extraction in Hybrid Renewable Energy Sources

A multi-source power system that integrates sustainable energy sources for power generation. MPPT, or Maximum Power Point Tracking, is a method employed to optimise the power generation of sources, such as solar panels or wind turbines. Since the efficiency of these sources can vary due to environmental conditions (like sunlight intensity or wind speed), MPPT algorithms optimize the electrical operational parameters of the modules to guarantee they are functioning at their highest efficiency. In the context of MPPT, fuzzy logic is used to handle the uncertainties and nonlinearities in the behaviour of these sources. It allows for a more adaptive and resilient control strategy, which can be particularly effective in fluctuating environmental conditions. When fuzzy logic is applied to MPPT in a hybrid power system, the goal is to intelligently manage and optimize the power output from various sources.

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Advancements in Mathematical Modelling for Estimation of Lifetime of Wireless Mobile Ad Hoc Networks

This article proposes a comprehensive mathematical model along with reviews on various mathematical models used by the researchers to estimate the lifetime of Mobile Ad-hoc Networks (MANETs). Network lifetime is a crucial quality-of-service (QoS) metric, and researchers have defined it in multiple ways, including the time from network establishment to its failure or the average time until the network dies. Many models utilize the First Order Radio Model (FORM) to calculate energy consumption during data transmission and reception. There are different factors which influence network lifetime which include, energy consumption during data transmission and reception, battery capacity and discharge rate, energy consumption by the microcontroller, transceiver, and sensors in various modes (active, sleep, idle, etc.). The article also discusses existing mathematical models that are based on some of these factors.

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Enhancing Data Security through Hybrid Error Detection:Combining Cyclic Redundancy Check (CRC) and Checksum Techniques

Error detection is a critical aspect of ensuring the accuracy of data transmission in communication systems. In this study, the performance of two error detection techniques has been investigated when combined to achieve a Bit Error Rate of 10^(-5)for single and multiple error detection ability. The two techniques studied were Cyclic Redundancy Check and Checksum with a new combination process. This proposed method showed that when CRC and Checksum were combined, the overall error detection performance significantly improved compared to using either technique alone. Specifically, the combined technique was able to achieve a BER of 10^(-5) for 6 given examples with higher accuracy and lower false positive rates. These findings demonstrate the potential benefits of combining error detection techniques to enhance the reliability of data transmission systems.

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Studying Relative Merits of FOC and DTC for 1-∅ Synchronous Induction Motor Powered by Solar Cell

The paper studies the merits of a 1-∅ induction motor powered by a Trina Solar's module TSM-250PA05.08 that employs the perturbs and observes (P&O) technique, utilizing two types of controllers, FOC and DTC. The merits of the two types of controllers were studied according to the results of the electromagnetic torque (Te), the currents of the main winding Ia and auxiliary winding Ib, and the Speed of a rotor. The results showed that both controllers' electromagnetic torque and speed rotor are very close. The results also showed distortions in the main and auxiliary winding currents when using the DTC controller. In contrast, when utilizing FOC, the results demonstrate smoother waveforms for main and auxiliary winding curves.

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Implementation of Realtime Image Fusion for Biomedical Applications Using ICA And Discrete Wavelet Transform

Image fusion is an extensively used technique in various areas like computer visualization, enhanced diagnostic imaging, radio therapy, automatic object recognition, image analysis, and remote sensing. The main aim of image fusion is to combine several input images into one image containing more information than the individual images. This type of image fusion results in a new image that is easier for computers and humans to see, making it possible for additional image processing operations like object detection, segmentation, and feature extraction. This paper examines the potential application of customized wavelet transform for image fusion.

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Design of a Multi-loop PI Controller for Minimum Phase System Level Regulation in a Quadruple Tank: A Method for Constraint Optimization

A nonlinear optimization based decentralized PI controller for Two Input Two Output (TITO) is presented in this paper. Modelling of Quadruple tank minimum phase system with time delay is introduced here. The basic principles of nonlinear optimization are utilized to design the proposed PI controller in which the overshoot is bounded with constraints on the maximum closed-loop amplitude ratio, maximum closed loop width, gain and angle bounds. Besides, the control algorithm is designed for decoupled systems to reduce the loop interactions. Further, the first order plus dead time (FOPDT) model is derived for each of the decoupled subsystems to design the control law.

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Dynamic Optimization in 5G Network Slices: A Comparative Study of Whale Optimization, Particle Swarm Optimization, and Genetic Algorithm

This study presents a comprehensive framework for optimizing 5G network slices using metaheuristic algorithms, focusing on Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low-Latency Communications (URLLC), and massive Machine Type Communications (mMTC) scenarios. The initial setup involves a MATLAB-based 5G New Radio (NR) Physical Downlink Shared Channel (PDSCH) simulation and OpenAir-Interface (OAI) 5G network testbed, utilizing Ubuntu 22.04 Long Term Support (LTS), MicroStack, Open-Source MANO (OSM), and k3OS to create a versatile testing environment. Key network parameters are identified for optimization, including power control settings, signal-to-noise ratio targets, and resource block allocation, to address the unique requirements of different 5G use cases. Metaheuristic algorithms, specifically the Whale Optimization Algorithm (WOA), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA), are employed to optimize these parameters.

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Multi-Source Data Integration for Navigation in GPS-Denied Autonomous Driving Environments

Autonomous driving is making rapid advances, and the future of driverless cars is close to fruition. The biggest hurdle for autonomous driving currently is the reliability and dependability of navigation systems. Navigation systems are predominantly based on GPS signals and despite it being highly available there are scenarios where GPS is either not present or unavailable such as in tunnels, indoor environments, and urban areas with high signal interference. This paper proposes an adaptive decision-making algorithm that leverages multi source data source integration for navigation in GPS-denied environments. The algorithm enables seamless switching between the different data sources such as LTE or 5G for autonomous driving systems to maintain accurate navigation even when GPS signals are unavailable.

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Advanced Artificial Neural Network for Steering and Braking Control of Autonomous Electric Vehicle

Sensors are necessary for an autonomous electric vehicle (AEV) system to identify its environment and take appropriate action, such avoiding obstacles and crashes. Despite their limitations about color, light, and non-metallic items, cameras, radar, and lidar are widely employed to detect objects surrounding a vehicle. Ultrasonic sensors are weather and light-resistant. Thus, the goal of this work was to create object detectors by combining multiple long-range ultrasonic sensors into a multi-sensor circuit. The Arduino processor incorporates an artificial neural network that uses the advanced artificial neural network as a novel approach to control the sensors. There are two steps to this method: offline training and implementation test. The most ideal neural network weights are found offline using the adaptive back propagation algorithm, and the best fixed weight is then embedded into the neural network software on Arduino for implementation test.

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Real Power Losses Reduced by Network Reconfiguration the Distribution Systems using Modified BAT Algorithm

This research paper is proposed to achieving the minimum power losses in all the branches, minimum number of switching operations, maximizing the power flow through the placing the DG sources, minimizing the voltage deviations with satisfying all the constraints using the modified BAT algorithm. The effect of the offered method is tested on standard systems like IEEE 33, 69 buses and Indian standard 62 bus distribution systems. The mBAT effect is estimated with the recent algorithm including Shuffled Frog, Stud krill, Dingo, Grey Wolf, and Antlion algorithms. MATLAB results are proved that the total power active power losses and branch voltages and number of switches, capacity of DG sources and cost of the DG sources are drastically reduced. The results are compared with many techniques are tabulated.

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Field Analysis and Equivalent Circuit Parameters of Linear Induction Motor with Eddy Current Secondary, Taking End Effect into Consideration

The field analysis of a single-sided linear induction motor (LIM) taking end effect into consideration is carried out. A suitable model is used to establish the two components of secondary current density and the air gap field intensity, these components, beside the force density are carried out through the three regions model. The drive force acting on a conducting secondary sheet is calculated and plotted as functions of the speed, taking the length of secondary ends as parameter. The motor speed can be controlled by changing the displaced angle φ of the electric loading wave in the second stator phase.

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Creating a Logic Divider Based on BCD and Utilizing the Vedic Direct Flag Method

Reversible logic has potential for a variety of applications demanding low energy usage since it prevents information loss and energy waste. The purpose of this work is to design a new Vedic divider circuit with reversible gates. Efficiency in quantum and ASIC parameters is demonstrated by the Reversible Direct Flag Vedic Division Method (RDFVDM), which has been devised. Block-level reversible gates are used in the RDFVDM to provide benefits including lower quantum costs and less trash outputs. The performance of Cadence EDA Tool is validated by simulation trials. Based on a comparative examination utilizing current methodologies, RDFVDM performs better than comparable designs.

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Implementation of Massive Multiple-Input Multiple-Output (MIMO) 5G Communication System using Modified Least-Mean-Square (LMS) Adaptive Filters Algorithm

The massive MIMO systems are the more popular field in the present era for the 5G wireless communication system. The MIMO system is a demanding research topic for the last four decades. This topic is under implementation and observation from the last few years. These systems have many advantages and many research sub-areas but this paper investigates the modified model of the massive MIMO receiver system. The traditional receiver system model of massive MIMO system reduces the channel noise using a linear filter in the receive combiner bank (RCB) but the proposed model removes the channel noise before the RCB using an adaptive filter bank (AFB). The AFB is the combination of LMS adaptive filters.

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Experimental implementation of Speed Stabilizer Based Field Oriented Control of Brushless DC Motor for Scooter Applications

An electric scooter, as one type of Lightweight vehicles technology, is a motorized vehicle designed for short-distance travel and recreational purposes. It is powered by an electric motor and typically has a rechargeable battery that provides sufficient power to operate the vehicle. Electric scooters are similar to traditional scooters but are much quieter, eco-friendly, and more energy-efficient. They are commonly used as an alternative mode of transportation for commuting, sightseeing, and recreational activities. This work presents an experimental implementation of speed stabilizer of electric scooter. In fact, a constant speed function might be required in a specific case in the operation of the scooter. A Field Oriented Control (FOC) method was chosen to control the speed of 3-phase Brushless DC motor of the scooter using a PIC16F873A Microcontroller through a Driver circuit.

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Economic Load Dispatch of Thermal-Solar-Wind System using Modified Grey Wolf Optimization Technique

The growing demand for electrical energy, coupled with the uneven distribution of natural resources, necessitates the integration of power plants. Coordinating the operation of interconnected generating units is crucial to meet the fluctuating load demand efficiently. This research focuses on the Economic Load Dispatch (ELD) problem in hybrid power systems that incorporate solar thermal and wind energy. Renewable energy resources, such as wind and solar thermal energy, depend on atmospheric conditions like wind speed, solar radiation, and temperature. This study addresses the ELD problem using a Modified Grey Wolf Optimization (MGWO) approach to obtain the most optimal solution for generator fuel costs. The Grey Wolf Optimization (GWO) approach, inspired by natural processes, is utilized but may exhibit both exploratory and exploitative behavior.

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Deep Learning based DWT- Bi-LSTM Classifier for Enhanced Cardiovascular Arrhythmia Classification

Nowadays heart diseases and their diagnosis have emerged as a prominent subject in health care systems, given that the heart performs a crucial role in the human body. Several computational techniques have been explored for the recognition and classification of cardiac diseases using Electrocardiogram (ECG) signals. Deep Learning (DL) is a present focus in healthcare solicitations, particularly in the classification of heartbeats in ECG signals. Many studies have utilized dissimilar DL models, including RNN (Recurrent Neural Networks), GRU (Gated Recurrent Unit), and CNN (Convolutional Neural Networks), to classify heartbeats using the MIT-BIH arrhythmia dataset.

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A Classical Approach for MPPT Extraction in Hybrid Energy Systems

A novel approach for Maximum Power Point Tracking (MPPT) extraction using the Hill Climbing method in hybrid solar and wind energy systems. MPPT is essential for optimizing the energy harvesting efficiency of sustainable energy sources, the integration of multiple sources poses unique challenges. The proposed Hill Climbing algorithm is applied to both solar photovoltaic (PV) panels and wind turbines, enabling efficient tracking of the Maximum Power Points (MPPs) under varying environmental circumstances. This article investigates the performance of the Hill Climbing MPPT method through simulation and experimental validation in a hybrid energy system.

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An Enhanced Dynamic Collector Voltage and Current Clamping Method for Semiconductor in Electric Vehicles

The electric drive and the batteries are the two primary parts of an electric vehicle (EV). In order to increase the availability and dependability of the semiconductors used in traction converters, this research focuses on a new approach of semiconductor protection. The IGBT overshoot in voltage during a short circuit situation was successfully reduced by a newly created active voltage and current clamping circuit. This innovative method restricts IGBT’s collector-emitter voltage during the turn-off event. As soon as the collector-emitter voltage of the IGBT crosses a predetermined threshold, the IGBT is partially turned on.

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Improvement of Solar Farm Performance based on Photovoltaic Modules Selection

The emissions of greenhouse gases from conventional power plants are currently a significant cause for worry. In China, about 75% of total domestic energy is dependent on coal-fire power, which emits 50% of total SO2 and has a significant impact on the human respiratory system. Therefore, solar power plants are a viable option that can mitigate this problem. Furthermore, the efficiency of solar modules exhibits a progressive upward trend, while their price per watt experiences a corresponding decline, making it a promising source for future energy. This article examines the performance and effectiveness of several photovoltaic (PV) modules in designing solar plants on a certain land area measuring 10000 m2 (100 m * 100 m). The PV plant performance was evaluated by comparing occupation ratio (OR), PV power capacity, net energy production, performance ratio (PR) via PVsyst software, and lastly financial analysis.

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Millimeter Wave Massive MIMO systems using Hybrid Beamforming

The goal of next-generation wireless systems is to support many users by achieving higher data rates and reduced latency. Multiple input and multiple output systems (MIMO) are utilized in order to achieve high data rates. Multiple antennas are employed by Massive MIMO systems in both the transmitter along with the receiver. A signal processing method known as beamforming is used on several transmitting and receiving stations in order to deliver and receive multiple messages at once. To increase spectral efficiency, hybrid beamforming using a uniform rectangular antenna array is used in this research work. The results of hybrid beamforming using different numbers of antennas are compared with those of fully digital beamforming.

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An Efficient Image Encryption Scheme for Medical Image Security

In the contemporary landscape of digital healthcare, the confidentiality and integrity of medical images have become paramount concerns, necessitating the development of robust security measures. This research endeavors to address these concerns by proposing an innovative image encryption scheme tailored specifically for enhancing medical image security. The proposed scheme integrates a sophisticated blend of symmetric and asymmetric encryption techniques, complemented by a novel key management system, to fortify the protection of medical image data against unauthorized access and malicious tampering. The proposed DNA-based encryption algorithm leverages the unique properties of DNA encoding to securely scramble image data, providing an added layer of protection. By utilizing DNA sequences in the encryption and decryption processes, the scheme achieves a high level of data confusion and diffusion, significantly enhancing security.

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Improving Robustness and Dynamic Performance of Sensor less Vector-Controlled IM Drives with ANFIS-Enhanced MRAS

The Model Reference Adaptive System (MRAS) enables effective speed control of sensorless Induction Motor (IM) drives at zero and very low speeds. This study aims to enhance the resilience and dynamic performance of MRAS by integrating an Adaptive Neuro-Fuzzy Inference System (ANFIS) controller into sensorless vector-controlled IM drives. To address issues related to parameter uncertainties, load variations, and disturbances, the combination of MRAS and ANFIS is investigated. The ANFIS controller improves the dynamic performance by adapting its parameters based on the error between estimated and measured rotor speeds. This allows for better tracking of the reference speed and smoother drive operation.

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Conversion of An Existing 3 – Phase BLDC Hub motor to A 6 – Phase BLDC Hub Motor and their Performance Analysis for EV Application

In this research work a six–phase BLDC Hub motor designed, developed by using an existing 3–phase BLDC Hub motor material. This conversion orients towards design modifications in the stator winding layout, the number of phases, the number of hall-sensors, placement of hall-sensors and stator winding commutation. Also, a new controller is designed to commutate the six-phase stator winding. The rotor is kept the same without any modification in geometry and number of magnets. The existing 3–phase BLDC Hub motor, which is used in two – wheel EV application, has 48 slots and 52 magnets and a concentrated double layer winding layout is observed in it. This paper presents a novel direct approach for the conversion of the existing 3–phase BLDC Hub motor to a six–phase BLDC Hub motor. Using this proposed approach, a six-phase winding layout is designed and developed for the existing 48 slots stator of the BLDC Hub motor.

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A Comparative Investigation of Hybrid MPPT Methods for Enhancing Solar Power Generation in Renewable Energy Systems

Photovoltaic (PV) systems are among the types of renewable energy that are frequently employed. Since the characteristics of the solar cell depend on the amount of insolation and temperature, it is necessary to use MPPT “Maximum Power Point Tracking” to move the operating voltage close to the maximum power point under changing weather conditions. This article aims to design a photovoltaic energy system based on boost converter control to obtain maximum power using a hybrid algorithm based on artificial neurons (ANN). Additionally included is a proportional-integral (PI) controller, which improves the performance of the ANN-MPPT controller; this method is quick and precise for tracking the maximum power point (MPP) in the face of variations in temperature and solar radiation.

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Optimal Reactive Power Dispatch Using Artificial Gorilla Troops Optimizer Considering Voltage Stability

The power system has been expanded to supply and fulfil the consumers’ requirements for reliability, affordability, and power quality. Power loss reduction and voltage stability enhancement are important points and have been considered interesting subjects for researchers and utilities. Furthermore, reactive power plays an important role in power system stability, security, and voltage improvement, and it is known as reactive power dispatch (RPD). In this paper, a newly developed meta-heuristic optimization technique that inspired the gorilla troop’s social intelligence in nature is applied. It is named Artificial Gorilla Troop Optimization (GTO). In addition, GTO is utilized to solve the optimal reactive power dispatch (ORPD) problem, whose real active power and voltage deviation reduction are the objective functions of this study. Generator voltage, transformer tap-changers, and reactive power compensators are the controlled variables that are optimized for achieving the minimum real power loss and bus voltage deviation.

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Effects of Number of Filters and Frequency Cutoff in Continuous Interleaved Sampling and Frequency Amplitude Modulation Encoding Schemes in Cochlear Implant

Cochlear implants are devices designed to transform sound into electrical signals perceived by the brain, making them vital prostheses for deaf individuals. This study examines two schemes used in cochlear implants, namely Continuous Interleaved Sampling (CIS) and Frequency Amplitude Modulation Encoding (FAME), to compare their performance while varying the number of bandpass filters and cutoff frequencies used. Both schemes were simulated using 8 and 5 bandpass filters, and cutoff frequencies of 2000 Hz and 200 Hz. Results show that the CIS scheme can maintain signal intelligibility despite the loss of some frequency components when the number of bandpass filters is lowered.

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Comparative Analysis and Performance Evaluation of Dual Active Bridge Converter Using Different Modulation Techniques

The development in the renewable energy systems and the necessity of the enhanced grid that includes the conventional energy sources and the renewable energy sources and storage systems have realized the importance of power electronics conversion systems. These interfaces are crucial for enhancing the efficiency as well as the control in bi-directional power flow. The use of HEVs as a means of preserving the future supply of fossil fuels, as well as the need for improving the efficiency of the power electronics interfaces required for efficient power management between the two energy sources of the vehicle, is discussed. Furthermore, the improvements in the Uninterruptible Power Supply (UPS) systems and regenerative power systems also require sophisticated power electronic conversion systems.

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Experimental analysis of an Interleaved Boost Converter for Electric Vehicle Applications

With its potential to improve fuel efficiency and contribute to a more sustainable energy future, electric cars will be an integral part of the transportation system of the future. For the time being, this industry is using traditional boost converters. A proposal for an interleaved boost converter for EVs is made in this article. When contrasted with the Classic boost converter, the suggested one produces higher-quality results. In this proposed work, we use a two-phase boost converter to lower the output waveform's ripple current, which is often rather significant in boost converters. It is suggested to use an Interleaved Boost Converter with MPPT to maintain a steady DC output voltage from PV systems. Electric vehicle propulsion performance and system current ripple are both enhanced by the suggested integrated circuit.

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Comparative Study of Photovoltaic Thermal Performance with Water and Aloe Vera Heat Extracting Fluids

Crystalline solar panels are widely used in households and for public road lighting. Mono-crystalline panels are well-known for their higher efficiency and long service life. However, their efficiency decreases as the module temperature increases under consistent solar radiation conditions. To enhance module power generation and efficiency, effective temperature reduction techniques are necessary. This study investigates the use of water and aloe vera fluid as cooling agents for a mono-crystalline photovoltaic thermal (PVT) system. The system was designed with a circulating mass flow rate of 0.016 kg/s or 1 LPM (liter per minute) and tested under the climate conditions of Phnom Penh city, Cambodia.

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Performance Analysis of Hybrid Relay Selection in Cooperative NOMA Systems

Cooperative NOMA is a method to improve the system performance, this could be done by enabling a user to relay the signal to other users or a dedicated relay. Several dedicated hybrid relays are distributed to forward the source’s signal to the destinations to further enhance the system and serve the users at the cell edge. However, operating several relays simultaneously reduces the spectral efficiency because they need orthogonal channels. Thus, hybrid relay selection is proposed and investigated as a cooperative technique to improve NOMA performance.

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Improved PID based Adaptive Controllers for Denoising Biomedical Signals

Biomedical signal processing is one of the most popular research domains. Very fine features in biomedical signals carry important information regarding patient’s health. So, it is necessary to have noise free biomedical signals for the correct diagnosis. The major trouble for biomedical equipment is Power Line Interference (PLI) which impairs the signals. An adaptive filter can be one of the possible solutions for the removal of non-stationary noise, but maintaining the system stability along with a high convergence rate is a critical issue. The adaptive algorithm works on the principle of minimization of error for optimized coefficients updating while PID controller attempts to minimize the error over time by adjusting the control variables.

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Stabilizing Voltage and Managing Power Loss in Medium Voltage Distribution Systems Through Strategic Maneuvers

Repairs and maintenance on the electrical power system often require power outages. To ensure safety and security during maintenance, limiting the extent of these outages is essential. This study aims to analyze the impact of network maneuvers on voltage drop and power loss in feeders in Medan, Indonesia. The research involves simulating the feeder circuit under various maneuver scenarios using ETAP 19.0.1 software to analyze voltage drop and power loss in the 20 kV distribution network. The end voltage on the backup feeder decreased from 20.31 kV before maneuvering to 20.10 kV after maneuvering.

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A Novel Hybrid FSO-mm Wave System for Enhanced Mobile Network Capacity and Reliability

The ever-growing demand for high-bandwidth communication in mobile networks necessitates innovative solutions that provide exceptional capacity and reliability. This paper introduces a novel dual hop hybrid system model that leverages the complementary strengths of free space optics (FSO) and millimeter-wave (mm W) communication, while incorporating a direct mm W link for added robustness. This paper analyzes the comprehensive performance of the proposed hybrid system using a single threshold selection combining. We consider a scenario where the channel state for the FSO link adheres to a Log-Normal distribution under conditions of weak turbulence. For the millimeter wave link, we assume it follows a Nakagami-m distribution, which encompasses a broad range of commonly encountered radio frequency (RF) fading distributions.

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PSbBO-Net: A Hybrid Particle Swarm and Bayesian Optimization-based DenseNet for Lung Cancer Detection using Histopathological and CT Images

Lung cancer remains a substantial global fatality; early detection is imperative for successful intervention and treatment. Deep learning (DL) models have shown promise in predicting lung cancer from medical images, but optimizing their parameters remains a challenging task. To improve prediction capability, this study introduces an approach by merging Particle Swarm Optimization and Bayesian Optimization (PSbBO) to optimize deep learning parameters. PSO provides an effective way for exploring the hyperparameter space, while Bayesian optimization provides a probabilistic framework for the effective evaluation and refining of a DL network.

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Comparative Analysis of Congestion Management Methods with Integrated Renewable Energy Generation

Inclusion of renewable energy in power grid at micro and macro level has imposed numerous challenges in the recent years. Occurrence and managing congestion in the power transmission line due to unpredictable and stochastic nature of Renewable Energy Source (RES) integration has become a challenging task to the grid operators. Transmission lines operate at bottlenecks during a congestion episode adding to the extra congestion cost and risk in grid stability which becomes burden to the generation as well as end users. Different methodologies are applied to detect and manage the congestion to eliminate the congestion cost factor and maintain grid stability.

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Brain Tumor Detection using Improved Binomial Thresholding Segmentation and Sparse Bayesian Extreme Learning Machine Classification

People are dying these days from numerous deadliest diseases. One such illness is brain tumour, in which the unusual cells within the tumour quickly begin to damage the brain's healthy cells. Owing to this rapid growth, a person may pass away before the disease receives a correct diagnosis. Early disease detection is essential for any disease to help save the patient by providing them with better care. In a similar vein, a patient's life depends on early brain tumour detection. Brain tumour detection is an extremely challenging procedure that we would like to simplify in order to save time. The proposed model facilitates the quicker and more accurate identification of abnormal brain cells, leading to the early detection of brain tumours. In this work, an improved binomial thresholding-based segmentation (IBTBS) is introduced for segmentation purpose. From this segmented image, information theoretic based, wavelet transform (WT) based, and wavelet scattering transform (WST) based features are extracted.

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Optimal Control Strategy for Power Management Control of an Independent Photovoltaic, Wind Turbine, Battery System with Diesel Generator

The need for a greater supply of energy from sustainable sources is growing because of increasing energy prices, concerns about nuclear power, climate change, and power grid disruptions. This research offers a method for the balance of power management of a combination of multi-source DC and AC supplier systems that enables sources of clean energy based on an independent grid to function economically and with the highest levels of system predictability and stability possible. The DC microgrid's hybrid generation source consists of a diesel power source, wind, photovoltaic (PV) power, and a battery bank. The energy system can fulfill the load demand for electricity at any moment by connecting various renewable sources. It can function both off and on the grid. The microgrid may occasionally not be able to provide sufficient electricity, while every green energy source's electricity contribution is based on how its supply varies and how much power is needed to meet demand. As a result, a diesel generator is required as additional backup power, particularly while operating off-grid.

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State Estimation of Radial Distribution Systems Based on Multiple Legendre Neural Networks

The conventional weighted least square (WLS) method is the most effective technique used in the state estimation of high voltage transmission system. Unfortunately, the application of WLS in radial distribution network encounter difficulties due to the inherent characteristics of these systems, such as the low measurement redundancy and high r/x ratio of the distribution systems. Given the structure of bulky systems that require a bulky number of measurements, the use of artificial neural networks is considered an effective alternative to estimate these values using a lesser number of measurements than conventional techniques. Due to state estimation based on ANN technique, the time-consuming gain matrix manipulation and pseudo measurements required in the conventional WLS method are no longer necessary.

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Exploiting PV System Performance: A Combined Approach Using MPPT, IoT, Cleaning, Cooling, and Neural Networks

This paper investigates the effects of three foremost methodologies—Maximum Power Point Tracking (MPPT), Internet of Things (IoT)-driven cleaning and cooling, and Neural Network Training (NNT)—on improving the efficacy of solar photovoltaic (PV) systems. Solar photovoltaic systems have considerable complications in sustaining maximum performance due to environmental conditions such as dust collection, temperature variations, and an insufficient energy management. A new control method is presented to challenge these difficulties, including MPPT, IoT-based cleaning and cooling, and NNT for the real-time optimization of PV systems. The simulation findings indicate a substantial increase in power production when both technologies are used together.

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An Optimal DC Microgrid for Hybrid Consumer Loads and Electric Vehicle Integration

This paper investigates the effects of three foremost methodologies—Maximum Power Point Tracking (MPPT), Internet of Things (IoT)-driven cleaning and cooling, and Neural Network Training (NNT)—on improving the efficacy of solar photovoltaic (PV) systems. Solar photovoltaic systems have considerable complications in sustaining maximum performance due to environmental conditions such as dust collection, temperature variations, and an insufficient energy management. A new control method is presented to challenge these difficulties, including MPPT, IoT-based cleaning and cooling, and NNT for the real-time optimization of PV systems. The simulation findings indicate a substantial increase in power production when both technologies are used together.

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Effective Energy Management System in Microgrid Employing Model Predictive Controller

The primary focus of this study is to develop an energy management system that regulates the energy transfers between the hybrid microgrid system and the loads connected to it, and the grid via MATLAB/Simulink so as to model the flow of energy. The secondary aim is to make recommendations aimed at the charging and discharging of what is referred to as the hybrid energy storage system (HESS). The results indicate that the proposed algorithm successfully carried out the required task of bridging the HESS charging to discharging ratio in relation to the different operating conditions as well as power management between the microgrid and the network. In this application, a stronger charging power might be employed on the HESS. It has been seen that the HESS is more likely to complete charging within a short time than the greater charging power.

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Fast Charging System of Electric Vehicle Using Optimized Isolated Multi-Port DC-DC Converter based on Modified Coati Optimization Algorithm

Once clean, renewable energy sources are used to charge the batteries in electric vehicles (EVs), the vehicles can produce zero gas emissions, greatly improving the environment. EVs and other distributed energy storage devices can be used in a smart microgrid to deliver energy to the loads throughout highest times, reducing the impact of load shading and improving the quality of the electricity. To achieve these goals of energy balance between EVs, the grid, and renewable energy sources, an isolated hybrid multiport converter is required. This paper develops an optimized isolated multi-port DC-DC converter for controlling power flow in multiple directions in an EV. This converter contains a dc-dc unidirectional converter, a bidirectional dc-dc converter, a triple active bridge (TAB), and a multi-port dual active bridge converter.

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Optimizing Real-Time Scheduling for Post Islanding Energy Management Using African Vulture Optimization Algorithm on Hybrid Microgrids Environment

Microgrids (MG) are small-scale energy systems that use distributed energy storage and sources. Hybrid microgrids are transforming energy management by incorporating various energy resources like wind, solar, and battery storage. Effective scheduling of this resource is vital to minimize the costs and maximize energy autonomy. Advanced scheduling algorithm optimizes the operation of hybrid microgrids, which dynamically adjusts the energy consumption and generation to satisfy the demand while ensuring power balancing. This scheduling strategy has been instrumental in improving the sustainability and resilience of MGS, which paves the way for an environmentally friendly and more reliable energy future. They can operate on islanded or grid-connected modes. The optimization of hybrid MG scheduling is paramount in the field of post-island management to ensure effective energy sustainability and distribution. Using metaheuristic approaches like simulated annealing or genetic algorithms allows the finetuning of scheduling parameters to increase energy utilization while reducing environmental impact and costs.

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EEG-Based Early Detection of Autism Using Convolutional Neural Networks

This Timely intervention and improved management of autism spectrum disorder (ASD) are contingent upon early detection. In this paper a novel method for early autism identification using EEG signals and convolutional neural networks (CNNs) is proposed. Preprocessing, wavelet transform, Discrete Cosine Transform (DCT) , energy and entropy function feature extraction, and CNN classifier classification are some of the phases in the suggested approach. EEG signals are first pre-processed to get rid of artifacts and noise. Then, to extract pertinent characteristics from the EEG signals, wavelet transform and DCT are used. For feature extraction, energy and entropy calculations are used to identify unique patterns suggestive of ASD. After then, a CNN classifier receives these features and divides them into two categories: Autism identified or normal identified.

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Optimized Energy Management for PV Hybrid Power Systems with DC Bus Voltage Control

The growing popularity of direct current (DC) power sources, energy storage systems, and DC loads has recently shifted the focus away from alternating current (AC) microgrids and towards DC-only systems. However, smart and energy-efficient building integration and effective microgrid administration are prerequisites. Direct current microgrids, which include solar modules as their principal power source, an energy storage device (battery), and an essential DC load, may have their energy consumption managed with the help of our study. Within the microgrid (MG) architecture, the DC-DC boost converter enables the PV module to operate in many modes, one of which is Maximum Power Point Tracking (MPPT). In order to link the battery and supercapacitor to the DC bus, the system also makes use of a DC-DC bidirectional converter.

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An Optimized Stationary Wavelet Fusion Technique for Image de-hazing

Nowadays, the amount of smoke and dust in the air is increasing significantly due to industrialization. The smoke and dust particles accumulate in the relatively dry air and cause haze in the surrounding area, impairs visibility. This haze also affects photography, which reduces the images' quality and looks unnatural. The hazy atmosphere affects even pictures taken with a cell phone in everyday life. There are many methods to remove this haze content from the image, but they have not yielded great results. The long-time and short-time shots constantly differed while attempting to eliminate atmospheric haze from the images. To solve this problem, a fusion rule was proposed to fuse the luminance and dark channel prior (DCP) methods. The transmission estimated with the DCP method contributes mainly to the foreground regions, while the luminance model deals with the celestial regions.

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Load modeling of electric bus charging station from data obtained through Phasor Measurement Units

While the increased adoption of electric vehicles (EVs) is a promising alternative to reduce CO2 emissions, it creates new challenges for the power grid due to increased energy demand and power quality (PQ) issues. These impacts vary depending on several factors such as the level of EV adoption, charging technology, network voltage level, charging patterns, charging station location, battery condition, and driving habits. Analyzing these impacts and developing solutions, such as characterizing the demand curve for charging stations and understanding EV charging patterns, is crucial to ensure a sustainable transition to an electrified EV future. A study using the ZIP load model that represents voltage dependence by combining constant impedance (“Z”), constant current (“I”), constant power (“P”) components, and phasor measurement units (PMUs) demonstrates the effectiveness of EV demand characterization. The importance of this aspect for grid stability and charging management is highlighted.

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Implementation of PI Controller of Hybrid DC Microgrid Energy Management

This paper presents energy management in DC Microgrid. Microgrids are a growing power generating source in remote areas than the utility grid. It can be operated as a standalone & grid-connected to serve the entities. This paper concentrates on the DC Microgrid and manages the energy within the system when the grid suffers from an outage of service, and unbalanced load conditions in the grid. During grid conditions, the buses connected to it should synchronize with the grid. The voltage in the buses is same, that is verified in this paper work through controllers to the converter are also discussed.

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Vehicle-to-Grid Power Transfer Method for Electric Vehicles using off-board charger

This article explores a power transfer technique from vehicle to grid (V2G) via the construction of an off-board charger for electric cars (EVs). The charger accommodates several charging modes, such as grid-to-vehicle (G2V), vehicle-to-vehicle (V2V), and vehicle-to-grid (V2G), facilitating efficient and adaptable energy management. In G2V mode, the charger utilizes grid power to recharge electric vehicle batteries, whilst V2V mode enables direct energy transfer between electric vehicles, circumventing the grid. The novel integration of G2V and V2V modes enables the concurrent use of grid electricity and energy from other electric vehicles, therefore diminishing grid reliance and enhancing power efficiency. The system has a three-phase pulse width modulation (PWM) rectifier that sustains a constant DC link voltage and attains a unity power factor on the grid side, therefore adhering to the IEEE 519 standard for total harmonic distortion (THD).

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Designing RNS-based FIR filter with optimal area, delay, and power via the use of swift adders and swift multipliers

Based on the Residue Number System (RNS), Finite Impulse Response filters have gained prominence in digital signal processing due to their efficiency in handling complex computations. This work presents a comprehensive analysis on optimizing area, delay, and power in the FIR filter by exploring different adders and multipliers. Three prominent adders, namely Ripple Carry Adder, Kogge Stone Adder, and Proposed Adder, are evaluated for their impact on area and delay. The choice of adder influences the overall performance of the FIR filter, and a careful selection is made based on the trade-offs between area and delay. Furthermore, various multipliers, including Booth, Baugh-Wooley, Braun, and Array, are compared in terms of their efficiency in power consumption. Multipliers contribute significantly to the overall power consumption, and the analysis involves selecting the most suitable multiplier for achieving the desired power optimization.

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Application of Walrus Optimizer for Power Quality Improvement in Radial Distribution System

To increase power quality (PQ) in radial distribution systems (RDS) by utilizing active power filters (APFs), this research discusses the application of walrus optimization algorithms (WaOA). The main problem with the PQ is harmonics. The harmonics are added to the RDS by nonlinear loads (NLs). In this instance, together with NL at two end nodes, nonlinear distributed generation (NLDG) is additionally considered. APFs are used to decrease the harmonics to specified limits. In this instance, APFs are positioned correctly to reduce harmonics and improve PQ. WaOA is utilized to maximize the APF's size at the ideal bus location. The WaOA is inspired by natural processes and contains features that are well-balanced for both exploration and exploitation. Within limitations on inequality, optimization seeks to minimize APF's current. On the IEEE-69 bus RDS, a simulation is run to assess the WaOA's performance.

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An Optimal Load Frequency Control in a Two-Area Power System Using a Fractional Order Proportional- Integral-Derivative-Based Zebra Optimization Algorithm

Load frequency control systems are crucial for maintaining the stability and reliability of power grids. They help ensure that the power supply matches the demand, preventing fluctuations in grid frequency. However, Load frequency control systems have inherent limitations, such as the potential for instability and oscillation if not properly controlled. In this work, A fractional order proportional integral derivative controller is proposed to address this issue, which exhibits strong capabilities in managing parameter uncertainties, rejecting disturbances, and handling non-linear systems controllers. The novel approach in this search is the application of the zebra optimization algorithm to fine-tune controller parameters, a technique not previously used in load frequency control systems. In this Study the two-area power system with a reheat turbine is used a test case for fractional order proportional integral derivative controller, and the system is simulated using MATLAB/SIMULINK. The objective function integral time of square error is used that acts as a bridge of communication between the system's behavior and the control strategy. The performance of this controller is evaluated under disturbance 0.04.

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Minimization of Harmonic in Stator current for Grid Connected Five Phase Squirrel Cage Induction Generator with Predictive Torque Control Technique

Wind energy systems have become a highly practical kind of renewable energy, which requires the development of more advanced control mechanisms to enhance efficiency and dependability. Five-phase machines provide several advantages compared to standard three-phase systems in this particular situation. These advantages include reduced torque variation, improved ability to handle faults, and increased capacity to handle power. The Direct Torque Control technology has attracted considerable interest for controlling five-phase squirrel cage induction generators employed in wind energy conversion systems. The main goal of DTC is to achieve efficient energy extraction from the wind by regulating torque and flux without the need for complex transformations and decoupling mechanisms, as required in field-oriented control. DTC is known for its simplicity and fast response, enabling high dynamic performance. This work introduces the notion of predictive torque control as a sophisticated control method for five-phase asynchronous generators in wind energy systems.

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MRI brain tumor classification using HOG features selected via impurity-based importances measure

MRI is considered the primary method for confirming the diagnosis of brain tumors and choosing the appropriate treatment. Automating the process of detecting brain tumors in MRI images using deep models has become a popular trend in the scientific research community. However, deep neural networks require a large volume of data to avoid overfitting, which is not ideally available. This is where handcrafted features come in handy. In this paper, we present an efficient approach for brain tumor classification that can outperform deep CNN models. In the proposed system, the histogram of oriented gradients algorithm is used to extract feature descriptors from brain MRI images.

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Comparison of SoC in Ni-MH and Lithium-Ion Battery for E-Vehicle

The current energy side of the battery is indicating in a percentage level is called state of charge (SOC). Nickel-metal hydride and lithium-ion batteries are a type of rechargeable battery. The chemical response at the positive electrode in nickel-metal hydride (NiMH) batteries is like that in nickel-cadmium (NiCd) batteries, as both use nickel oxide hydroxide (NiOOH). However, while NiCd cells use cadmium, NiMH batteries feature a hydrogen-absorbing alloy in their negative electrodes. NiMH batteries provide two to three times the capacity and a significantly higher energy density compared to NiCd batteries of the same size. The research was mainly focused on the aspect of SoC of Ni-MH and Lion batteries with operation of an electric vehicle with total weights of 600 kg was investigated using mat lab Simulink.

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Optimizing Solar PV Array Reconfiguration for Maximum Power Extraction Using Hippopotamus Algorithm under Partial Shading

This research paper presents an innovative approach to maximizing power extraction from solar photovoltaic (PV) arrays under partial shading conditions by employing the Hippopotamus Optimization Algorithm (HOA). Partial shading is a common issue that significantly reduces the efficiency of PV systems by creating multiple local maxima on the P-V curve, thereby challenging conventional Maximum Power Point Tracking (MPPT) methods. To address this, we propose an adaptive reconfiguration strategy for the PV array, optimized using HOA, which successfully moderates the impacts of shading and enhances overall energy yield. The Hippopotamus Optimization Algorithm, inspired by the foraging behavior of hippopotamuses, is utilized for its robust global search capabilities and fast convergence. The algorithm dynamically adjusts the arrangement of the PV module to locate and maintain operation at the global maximum power point.

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Design and Optimization of a Half-Circular Ultra-Wideband Patch Antenna Using Genetic Algorithm

This research introduces an optimization design of an ultra-wideband (UWB) half-circular planar antenna using genetic algorithm. The optimization process is conducted using an Application Programming Interface (API) links two softwares; MATLAB environment and ANSYS HFSS software. The UWB antenna design includes a semi-circular patch element, the UWB behavior is obtained using truncated ground plane incorporating a rectangular slot cut out of the ground. Genetic algorithm is exploited to optimize the length of partial ground plane and the size and position of the rectangular slot. The overall size of the antenna is 28×29×1.6 mm3.

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Wide Bandwidth Tri-Band MIMO Antenna Design for 5G Communication

Wireless communication research is currently focused on 5G. On an FR4 substrate, a building-like structure with two slot-based planar multiple input multiple output (MIMO) antenna has been designed. 4.3 is the dielectric constant, and the substrate thickness is 1.6mm. The proposed antenna is designed and simulated for 5G applications using CST microwave studio and HFSS at 24.25GHz, 29.25GHz, and 32.40GHz. According to the simulated results, the VSWR is lower than 3:1 and the return loss at both ports is -43.27dB, -60.55dB, and –42.61dB at 29.25GHz, 34.20GHz, and 34.075GHz resonating frequencies respectively. Isolation between both ports is better. At 24.25GHz, 29.25GHz, and 32.40GHz the proposed design achieves a gain of 5.1dBi, 5.5dBi, and 3.5dBi respectively.

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Power Quality Improvement of PV fed Grid Connected System using ANN Controlled Shunt Active Power Filter

Harmonics are common in integrated power systems, especially with the increasing use of nonlinear loads (NLL), such as those found in photo voltaic (PV) systems connected to the grid. Traditional LC filters Shunt active power filters (SAPF) have been developed to effectively correct harmonics and improve power quality performance. This study presents a three-phase voltage-fed SAPF implementation to mitigate harmonics using an artificial neural network (ANN) controller. The SAPF control system focuses on generating reference source currents to counterbalance the harmonic effects caused by NLL. The model's effectiveness is validated using experimental data gathered from a nonlinear load through MATLAB/Simulink simulations.

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System Performance Evaluation of Indoor Visible Light Communication

The objective of the paper is to exhibit the significance of the LiFi-based data transmission using light. LiFi is deployed in numerous applications such as security, augmented reality, intelligent transport system etc as typical indoor localization is essential and is being done with mobile robots. This research article proposes a short range, indoor design, light fidelity model and discusses the simulations conducted for the Lifi model and the model is analyzed for various aspects that uses different LED light sources with Line Of Sight (LOS) and without Non Light Of Sight (NLOS), different room sizes, different modulation formats, and simulation is also performed with and without noise models. The LiFi proposed model is designed to transfer data wirelessly with a data rate of 10 Gbps.

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Hybrid Learning Approach for Automated Identification and Categorization of Cardiovascular Disorders

Cardio Vascular Diseases (CVDs) pose an important global health challenge, contributing substantially to mortality rates worldwide. Electrocardiography (ECG) is a necessary diagnostic tool in the detection of CVDs. Manual analysis by medical experts, for ECG interpretation, is laborious and subject to interobserver variability. To overcome these limitations, automated categorization technique has gained prominence, enabling efficient CVDs classification. The major focus of this work is to utilize deep learning (DL) approach for the identification of CVDs using ECG signals. The presented work incorporates two hybrid models: one-dimensional convolutional neural network (1D-CNN) with Recurrent Hopfield Neural Network (1DCNN-RHNN) and with Residual Network (1D-CNN-ResNet), to obtain important features from raw data and categorize them into different groups that correlate to CVD situation.

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Symbol Interference Cancellation in MIMO-GFDM Using Singular Value Decomposition Technique for 5G

The Physical layer (PHY) is implemented with the new Generalized Frequency Division Multiplexing (GFDM) scheme to increase the throughput of wireless systems in the present communication era. The GFDM system is combined with Multi-Input and Multi Output antenna system (MIMO GFDM) to achieve higher channel capacity and low out-of-coverage probability. Therefore, the MIMO-GFDM system is suffering from internal Inter-Carrier-Interference (ICI) and external Inter-Symbol-Interference (ISI) at the receiver. This makes the receiver design more complex and challenging.

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Performance Analysis of Various Nonlinear Elements in Frequency Multiplying Circuits for Wireless Applications

This paper presents the performance comparison of various nonlinear elements in frequency multiplying circuits for low power wireless applications. An analysis and design are derived. Passive frequency multipliers take advantage of the nonlinear behavior of passive circuit components. Passive frequency multipliers take advantage of the nonlinearity of passive circuit components. In this category, Schottky diodes and varactor diodes are used for frequency multiplication. We analyzed passive devices like resistive diodes (Schottky-barrier diodes) and nonlinear capacitors (varactors). The performance between frequency doubling System using Schottky diode and frequency doubling System using varactor diode is compared.

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Highly Directive and High Gain Multiple Beam Reconfigurable Antenna for Base Stations

A directional pattern reconfigurable array with high gain is proposed in this paper in which each antenna element is an array of driven and parasitic arc dipoles. The elements can be selectively excited using RF switches and power dividers to produce high gain patterns in desired single or multiple directions. By providing optimum spacing to array elements via stacking, gain can be further improved by exciting multiple elements simultaneously. The array resonates at 5.8 GHz, which is an ISM frequency. The directivity and realized gain of the unit element are 12 dB and 10.2 dB respectively.

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Design and Development of an Enhanced Modular Multilevel Converter Using Advanced Predictive Control for Motor Drive Systems

This paper presents the design and implementation of a Modular Multilevel Converter (MMC) integrated with a motor drive system. The MMC topology offers numerous advantages such as improved voltage waveform quality, reduced harmonics, and enhanced reliability, making it an attractive choice for high-power motor drive applications. The proposed system architecture is modular in nature, facilitating scalability and flexibility to meet varying power requirements. Design considerations including sub module configuration, control strategies, and modulation techniques are discussed in detail.

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Leveraging Hybrid Precoding for Enhanced Terahertz Communication System

Terahertz (THz) transmission is a promising strategy for future 6G networks, offering ultra-wide bandwidth. Effective channel modeling and precoding techniques are essential for achieving required coverage and addressing significant path loss in THz communications. In this paper, we comprehensively examine the major THz precoding algorithms for future 6G networks, focusing on their significant challenges and prospects. We discriminate between millimeter-wave and THz channels and uncover issues with THz precoding, such as distance-dependent direction loss, beam split impact, and excessive power usage.

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Impact of Extreme Weather Parameters on Optimum Sizing of Solar Photovoltaic-Battery Energy Storage Systems: A Case Study

The performance of an off-grid solar Photovoltaic (PV) system with Battery Energy Storage (BES) depends on the system's location. Incorporating climatic variables such as solar irradiance, ambient and cell temperatures into the modelling of PV systems helps predict the system's appropriate behaviour. The paper discusses the impact of variations in seasonal irradiance, temperature, and system location on the optimum sizing of a standalone solar PV-BES system for minimum total annual cost using Improved Particle Swarm Optimization. Three locations: Pune (Maharashtra), Ladakh (Jammu and Kashmir), India, and Rafsanjan (Iran) with extreme weather conditions (winter and summer) are identified to analyze the effect for optimum sizing of PV-battery energy systems. In addition, the optimum system sizing is analyzed for lead acid and lithium-ion batteries for both seasons.

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MCBIR: Deep Learning based Framework for Efficient Content Based Image Retrieval System of Medical Images

Content-Based Image Retrieval (CBIR) in computer vision applications, enables retrieval of images reflecting user intent. Traditionally CBIR is based on image processing techniques. With the emergence of Artificial Intelligence (AI), it is now possible to realize CBIR using learning-based approaches. Particularly deep learning techniques such as Convolutional Neural Network (CNN) are efficient for image analysis. In this paper, we proposed a framework known Medical Content Based Image Retrieval System (MCBIRS), which exploits pre-trained CNN variants for retrieving medical images based on image input. The framework has an offline phase for extracting visual features from training data and an online phase for processing given user queries. The descriptors obtained by CNN variants in the offline phase are persisted in a database. These are later used in the online phase to compute the distance between persisted descriptors and input image descriptor. A set of closely matching images are returned against the query image based on similarity.

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