International Journal of Electrical and Electronics Research最新文献_第5页

Accuracy Measurement of Hyperspectral Image Classification in Remote Sensing with the Light Spectrum-based Affinity Propagation Clustering-based Segmentation 利用基于光谱的亲和传播聚类分割法测量遥感中的高光谱图像分类精度

International Journal of Electrical and Electronics Research Pub Date : 2024-01-20 DOI: 10.37391/ijeer.120105

A. J. Christilda, R. Manoharan

{"title":"Accuracy Measurement of Hyperspectral Image Classification in Remote Sensing with the Light Spectrum-based Affinity Propagation Clustering-based Segmentation","authors":"A. J. Christilda, R. Manoharan","doi":"10.37391/ijeer.120105","DOIUrl":"https://doi.org/10.37391/ijeer.120105","url":null,"abstract":"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. However, fails to attain the optimal accuracy due to the intricate nature of the images. To tackle these issues, we propose a novel Modified Extreme Learning machine (M-ELM) approach for the credible hyperspectral image classification outcomes with the publicly available Kaggle datasets. Before the classification, the input images are segmented using the Light Spectrum-based modified affinity propagation clustering technique (LSO-MAPC). In the beginning, the images are pre-processed using the non-linear diffusion partial differential equations technique which effectively pre-processed the image spatially. Experiments are effectuated to analyze the performance of the proposed method and compared it with state-of-art works in a quantitative way. The proposed approach ensures a classification accuracy of 96%.","PeriodicalId":158560,"journal":{"name":"International Journal of Electrical and Electronics Research","volume":"139 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140501593","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Parameters Measurement and Secrecy Diversity Analysis for Physical Layer Security in WSNs using Projection Pursuit Gaussian Process Regression 利用投影追求高斯过程回归进行 WSN 物理层安全的参数测量和保密多样性分析

International Journal of Electrical and Electronics Research Pub Date : 2024-01-20 DOI: 10.37391/ijeer.120104

Shruti Sharma

{"title":"Parameters Measurement and Secrecy Diversity Analysis for Physical Layer Security in WSNs using Projection Pursuit Gaussian Process Regression","authors":"Shruti Sharma","doi":"10.37391/ijeer.120104","DOIUrl":"https://doi.org/10.37391/ijeer.120104","url":null,"abstract":"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. In our approach we propose a novel Projection Pursuit Gaussian Process regression (PPGPR) technique to analyze the diverse secrecy of physical layer in wireless sensor networks. The proposed approach is utilized to safeguard the sensitive data with secured physical layer. Simulations are performed and made an analogous study with state-of-art works to ensure the secrecy of the proposed work. The proposed work can use for the secured wireless communication with higher secrecy capacity than the existing approaches. The performance is analyzed with the parameters such as secrecy capacity, secrecy outage probability, packet delivery ratio, secrecy throughput, and energy consumption.","PeriodicalId":158560,"journal":{"name":"International Journal of Electrical and Electronics Research","volume":"59 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140502173","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhanced Recognition of Human Activity using Hybrid Deep Learning Techniques 利用混合深度学习技术增强人类活动识别能力

International Journal of Electrical and Electronics Research Pub Date : 2024-01-20 DOI: 10.37391/ijeer.120106

Abinaya S, Rajasenbagam T, Indira K, Uttej Kumar K, Potti Sai Pavan Guru Jayanth

{"title":"Enhanced Recognition of Human Activity using Hybrid Deep Learning Techniques","authors":"Abinaya S, Rajasenbagam T, Indira K, Uttej Kumar K, Potti Sai Pavan Guru Jayanth","doi":"10.37391/ijeer.120106","DOIUrl":"https://doi.org/10.37391/ijeer.120106","url":null,"abstract":"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. CDAA reimagines motion clusters, merging origin and destination movements while quantifying domain disparities. Expanding our horizons, we incorporate image datasets, leveraging Convolutional Neural Networks (CNNs). The innovative aspects of the proposed hybrid GRU_CNN model, showcasing its superiority in addressing specific challenges in human activity recognition, such as subject and sensor variations. This approach consistently achieves 98.5% accuracy across image, UCI-HAR, and PAMAP2 datasets. It excels in distinguishing activities with similar postures. Our research not only pushes boundaries but also reshapes the landscape of HAR, opening doors to innovative applications in healthcare, fitness tracking, and beyond.","PeriodicalId":158560,"journal":{"name":"International Journal of Electrical and Electronics Research","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140502317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Design and Analysis of Ultra-low Power Voltage Controlled Oscillator in Nanoscale Technologies 纳米技术中超低功率压控振荡器的设计与分析

International Journal of Electrical and Electronics Research Pub Date : 2024-01-15 DOI: 10.37391/ijeer.120103

Priyanka kumari B.S, Dr. Sobhit Saxena

{"title":"Design and Analysis of Ultra-low Power Voltage Controlled Oscillator in Nanoscale Technologies","authors":"Priyanka kumari B.S, Dr. Sobhit Saxena","doi":"10.37391/ijeer.120103","DOIUrl":"https://doi.org/10.37391/ijeer.120103","url":null,"abstract":"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. Therefore, low power techniques along with the technology scaling is the best way of low power designs. In this article, discussed various low power schemes. The ring oscillator designs are carried out in various nano meter scaled technologies such as 180nm, 90nm,65nm and 45nm. A 5-stage ring oscillator is implemented in each technology along with low power schemes, simulated in Cadence virtuoso, and noted power, delay, and area. Observed that the proposed ring oscillator with sleepy keeper technique generated a stable frequency of oscillations in the range of 1GHz-2GHz. A control voltage of 1.8V to 0.4V is applied and targeted the power less than 30mW and delay in 0.25p sec.","PeriodicalId":158560,"journal":{"name":"International Journal of Electrical and Electronics Research","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140508229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Artificial Neural Network based FACTS in a Contingency Situation 紧急情况下基于人工神经网络的 FACTS

International Journal of Electrical and Electronics Research Pub Date : 2024-01-15 DOI: 10.37391/ijeer.120102

Rachakonda Raghavendra, Dr. N.C. Kotaiah, Dr. K. Radha Rani

引用次数: 0

Fuzzy Logic Controller Based Charging and Discharging Control for Battery in EV Applications 基于模糊逻辑控制器的电动汽车电池充放电控制

International Journal of Electrical and Electronics Research Pub Date : 2024-01-15 DOI: 10.37391/ijeer.120101

Azra Zaineb, M. Vijayasanthi, Nagaraju Mandadi

{"title":"Fuzzy Logic Controller Based Charging and Discharging Control for Battery in EV Applications","authors":"Azra Zaineb, M. Vijayasanthi, Nagaraju Mandadi","doi":"10.37391/ijeer.120101","DOIUrl":"https://doi.org/10.37391/ijeer.120101","url":null,"abstract":"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. Due to the lack of mathematical calculations, a fuzzy controller is also simpler to construct, has less additional sensing components, and fewer deep discharging and overcharging protections, making it more efficient in terms of speed and complexity. The effectiveness of the suggested charging-discharging controller system is demonstrated through numerical simulations employing load demand and generation. Performance of the suggested controller is evaluated under simulated load conditions. The model's ability to regulate battery charging and discharging is confirmed by the trial's successful conclusion. The output shows that the battery's state of charge (SOC) never goes above the 20% to 80% safe range for that specific type. A new fuzzy model and an operational real-time system for regulating battery charging and discharging are the major results of this research.","PeriodicalId":158560,"journal":{"name":"International Journal of Electrical and Electronics Research","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140508552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Frequency Stability of Multi-source Power System using Whale Optimization Algorithm 利用鲸鱼优化算法实现多源电力系统的频率稳定

International Journal of Electrical and Electronics Research Pub Date : 2023-12-28 DOI: 10.37391/ijeer.110446

Rajkishore Swain, Umesh Chandra Mishra

{"title":"Frequency Stability of Multi-source Power System using Whale Optimization Algorithm","authors":"Rajkishore Swain, Umesh Chandra Mishra","doi":"10.37391/ijeer.110446","DOIUrl":"https://doi.org/10.37391/ijeer.110446","url":null,"abstract":"The Whale Optimization Algorithm (WOA), an evolutionary computing approach, is presented in this study and is used to auto regulated frequency of many composed power systems including thermoelectric power station, hydroelectric, and gas power plants. The purpose of this process follows the concept of a hunting mechanism of fish through water bubbles. The WOA is first applied to a single region with a multi-source power system for optimal gain adjustment of proportional integral controllers (PID). This approach is then applied to two areas, each having six generating sources with AC and AC-DC links. The credible achievements of the WOA-based PID controller are compared with previously constructed optimization approaches like Teaching Learning Based Optimization (TLBO), Differential evolution (DE) and Optimal Controller (OC), which is demonstrated in terms of frequency error, settling time and damping ratio. The performance indices of the purposed controller are analyzed through different objective functions like integral square error (ISE), integral time absolute error (ITAE), integral absolute error (IAE) and integral time squared error (ITSE). By using step load agitation, the simulation results indicate that the suggested technique is computationally stable.","PeriodicalId":158560,"journal":{"name":"International Journal of Electrical and Electronics Research","volume":"18 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139148392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep Learning for Enhanced Marine Vision: Object Detection in Underwater Environments 深度学习增强海洋视觉：水下环境中的物体检测

International Journal of Electrical and Electronics Research Pub Date : 2023-12-26 DOI: 10.37391/ijeer.110443

Radhwan Adnan Dakhil, A. R. Khayeat

{"title":"Deep Learning for Enhanced Marine Vision: Object Detection in Underwater Environments","authors":"Radhwan Adnan Dakhil, A. R. Khayeat","doi":"10.37391/ijeer.110443","DOIUrl":"https://doi.org/10.37391/ijeer.110443","url":null,"abstract":"This study leverages the Semantic Segmentation of Underwater Imagery (SUIM) dataset, encompassing over 1,500 meticulously annotated images that delineate eight distinct object categories. These categories encompass a diverse array, ranging from vertebrate fish and invertebrate reefs to aquatic vegetation, wreckage, human divers, robots, and the seafloor. The use of this dataset involves a methodical synthesis of data through extensive oceanic expeditions and collaborative experiments, featuring both human participants and robots. The research extends its scope to evaluating cutting-edge semantic segmentation techniques, employing established metrics to gauge their performance comprehensively. Additionally, we introduce a fully convolutional encoder-decoder model designed with a dual purpose: delivering competitive performance and computational efficiency. Notably, this model boasts a remarkable accuracy of 88%, underscoring its proficiency in underwater image segmentation. Furthermore, this model's integration within the autonomy pipeline of visually-guided underwater robots presents its tangible applicability. Its rapid end-to-end inference capability addresses the exigencies of real-time decision-making, vital for autonomous systems. This study elucidates the model's practical benefits across diverse applications like visual serving, saliency prediction, and intricate scene comprehension. Crucially, the utilization of the Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) elevates image quality, enriching the foundation upon which our model's success rests. This research establishes a solid groundwork for future exploration in underwater robot vision by presenting the model and the benchmark dataset.","PeriodicalId":158560,"journal":{"name":"International Journal of Electrical and Electronics Research","volume":"17 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139156235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Development of Wireless Smart Current Sensor for Power Monitoring System 为电力监控系统开发无线智能电流传感器

International Journal of Electrical and Electronics Research Pub Date : 2023-12-26 DOI: 10.37391/ijeer.110444

Abdullah Fawzi Shafeeq, M. Mahammad, Firas Fadhil Salih

{"title":"Development of Wireless Smart Current Sensor for Power Monitoring System","authors":"Abdullah Fawzi Shafeeq, M. Mahammad, Firas Fadhil Salih","doi":"10.37391/ijeer.110444","DOIUrl":"https://doi.org/10.37391/ijeer.110444","url":null,"abstract":"The traditional distribution network must be replaced by a smart grid, durable, and dependable, due to the rising demand for power by the customer. The smart grid will need a smart monitoring system based on the smart current sensor at all buildings to determine the power consumption with its costs. This paper proposed a wireless monitoring system for measuring the three-phase currents in the building by using three HW-666 current sensors with Arduino and ESP32 microcontrollers to transmit the data according to the Internet of Things (IoT) technique to the Telegram app on the mobile phone. The power consumption is determined by assuming the constant voltages and power factor at the building that is tested with this monitoring system. The results are scheduled on the personal computer (PC) by using the serial monitor of the Arduino software and also by receiving the message every 20 minutes on the telegram app furthermore, real-time data can be obtained immediately upon receiving transmitted commands from the Telegram application. In addition, this monitoring system is designed to cut off power to the building and sound an alarm if abnormal currents are detected. The proposed monitoring system is given the same results when a comparison with the ammeter with errors not exceeding 10%.","PeriodicalId":158560,"journal":{"name":"International Journal of Electrical and Electronics Research","volume":"85 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139155741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Vertebra Segmentation Based Vertebral Compression Fracture Determination from Reconstructed Spine X-Ray Images 基于椎体分割的椎体压缩骨折诊断法

International Journal of Electrical and Electronics Research Pub Date : 2023-12-26 DOI: 10.37391/ijeer.110445

Srinivasa Rao Gadu, Chandra Sekhar Potala

{"title":"Vertebra Segmentation Based Vertebral Compression Fracture Determination from Reconstructed Spine X-Ray Images","authors":"Srinivasa Rao Gadu, Chandra Sekhar Potala","doi":"10.37391/ijeer.110445","DOIUrl":"https://doi.org/10.37391/ijeer.110445","url":null,"abstract":"The vertebral compression fracture represents the vertebral body deformity appeared over lateral spine imageries. In order to evaluate the vertebral compression fracture (VCF), the vertebral compression ratio (VCR) has to be accurately measured. In most of the existing vertebral segmentation approaches, degraded accuracy, increased possibilities of error and time complexity are found to be the major drawbacks. Hence to conquer these issues and to enhance the overall segmentation performance, rapid automated vertebral segmentation approach is proposed for evaluating the VCR. Initially the reconstructed spine X-ray images are collected and directed over the Hybrid UDA Net architecture from this model, the features are extracted using encoder section of U-net architecture through the adoption of channel attention layer (CaL) and hybrid attention dilated Quantum convolutional layer (HaDQcL). The segmental outcomes are accomplished through the decoder section of U-Net. Based on the extracted features given as the input, exact segmentation of spinal images is attained using Twin attention mechanism called Gated-decoder attention module (GDAM). Through GDAM, the segmented spine X-ray images are obtained with effective results through the fusion of spatial and channel features in decoder attention module. The losses in the neural network are optimized using Amended pelican optimization algorithm (APoA). The diverse stages of VCF are finally analysed through VCR evaluation. The overall accuracy of 98.41%, F1 score of 96.75% and specificity of 99% is obtained by the proposed model whereas the performance is analysed using PYTHON. On comparison of proposed and existing models, the proposed model through segmentation and VCF diagnosis are highly superior.","PeriodicalId":158560,"journal":{"name":"International Journal of Electrical and Electronics Research","volume":"104 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139154874","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0