IJEER Vol no. 12, Issue 1

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