International Journal of Electrical and Electronics Research最新文献

{"title":"Design of an Efficient & Secure Steganographic Model using Modified LSB & Encryption Process","authors":"Ekta Yadav, Ajit Singh","doi":"10.37391/10.37391/ijeer.120110","DOIUrl":"https://doi.org/10.37391/10.37391/ijeer.120110","url":null,"abstract":"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. This adaptive strategy optimizes curve types and prime key sets, significantly enhancing data security while minimizing delays and complexities across diverse data sizes. The proposed model achieves an 8.3% reduction in encryption and steganographic process delays, while simultaneously maintaining superior Peak Signal to Noise Ratio (PSNR) and lower Mean Squared Error (MSE) levels compared to existing methods when applied to the same data samples. This highlights its effectiveness in securing dynamic datasets without compromising efficiency.","PeriodicalId":158560,"journal":{"name":"International Journal of Electrical and Electronics Research","volume":"54 31","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139965436","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}

{"title":"Optimizing Current Injection Technique for Enhancing Resistivity Method","authors":"Sifa Nurpadillah, Willy Anugrah Cahyadi, Husneni Mukhtar, Kusnahadi Susanto, Akhmad Fauzi Ikhsan, Agung Ihwan Nurdin","doi":"10.37391/10.37391/ijeer.120115","DOIUrl":"https://doi.org/10.37391/10.37391/ijeer.120115","url":null,"abstract":"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. The embedded TTGO LoRa ESP32 SX1276 facilitates wireless communication over 1.5 km, addressing challenges in remote and internet-limited areas. The model demonstrates reliability, validity, and durability in CRM mode and current injection measurement. Regarding reliability, we determine the relative error of the model by carrying out measurements repeatedly. In lab-scale testing, the average Relative Error in CRM mode is 0.65%, and in earth resistance measurement testing, it is 1.58%. These relative errors are below the 2% maximum error applied in the “Supersting”, a commercial resistivity instrument. The model's validity is defined by comparing the model with the measuring instrument; we have absolute error. In lab scale testing, the average Absolute Error in CRM mode is 3.08%, and in earth resistance measurement testing, it is 3.73%. The model's durability is tested by injecting current for a minute. After one minute of current injection, the power resistor component's temperature is stable at 30°C.","PeriodicalId":158560,"journal":{"name":"International Journal of Electrical and Electronics Research","volume":"56 31","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139965399","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}

{"title":"Compression of Medical images using SPIHT Algorithm for Telemedicine Application","authors":"Jins Sebastian, Deny J, S. N. Kumar","doi":"10.37391/ijeer.120108","DOIUrl":"https://doi.org/10.37391/ijeer.120108","url":null,"abstract":"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. The Haar wavelet transform generates LL, HL, HL and HH sub-bands. In the sorting pass, the coefficients are classified into significant and insignificant. The refinement pass creates the precision bits for the significant coefficients. The main characteristic of the SPIHT algorithm is that it does not use an entropy coder. The reconstructed image in the decoding stage was validated by performance metrics. The SPIHT algorithm generates proficient results, when compared with the classical algorithms like wavelet and embedded zero tree wavelet (EZW) algorithms.","PeriodicalId":158560,"journal":{"name":"International Journal of Electrical and Electronics Research","volume":"57 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139965455","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}

{"title":"Deep Learning based Effective Watermarking Technique for IoT Systems Signal Authentication","authors":"Dr. Manish Korde, Dr. Vinit Gupta, Dr. Aditya Mandloi, Dr. Sachin Puntambekar, Devendra Singh Bais","doi":"10.37391/ijeer.120109","DOIUrl":"https://doi.org/10.37391/ijeer.120109","url":null,"abstract":"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.","PeriodicalId":158560,"journal":{"name":"International Journal of Electrical and Electronics Research","volume":"54 19","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139965242","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}

{"title":"Indian Classical Music Recognition using Deep Convolution Neural Network","authors":"Swati Aswale, Dr. Prabhat Chandra Shrivastava, Dr. Ratnesh Ranjan, Seema Shende","doi":"10.37391/10.37391/ijeer.120112","DOIUrl":"https://doi.org/10.37391/10.37391/ijeer.120112","url":null,"abstract":"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. The lightweight DCNN is used to improve the representation of the raga sound and to provide higher order abstract level features. The overall performance of the raga type is estimated using various performance metrics, including accuracy, precision, recall and F1-score. The proposed DCNN achieves an accuracy, precision, recall, and F1-score of 89.38%, 0.89, 0.89, and 0.89, respectively, for eight raga classifications. The extensive experimentation on eight classical ragas has shown a noteworthy improvement over the traditional state of art.","PeriodicalId":158560,"journal":{"name":"International Journal of Electrical and Electronics Research","volume":"55 38","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139965421","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}

{"title":"Deep-GD: Deep Learning based Automatic Garment Defect Detection and Type Classification","authors":"Dennise Mathew, N. C. Brintha","doi":"10.37391/ijeer.120107","DOIUrl":"https://doi.org/10.37391/ijeer.120107","url":null,"abstract":"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. A squeeze-and-excitation network (SE-net) combined with Random Decision Forest Classifier with Bayesian Optimization (RDF-BO) algorithm is used to detect and classify garment defects. By analyzing the differences among the original and reconstruction images, the unsupervised technique trains to rebuild the fabric pattern. The SE-net is used to identify certain fabric flaws in the garments of the pre-processed images. Then, the defects-related garments are processed using RDF-BO algorithm for classifying the garment defect from these regions. Finally, the proposed Deep-GD model is used for classifying defected fabric into 12 classes such as Defect free, soil stain, oil stain, Double end, Snarls, Miss, Horizontal stripes, Lumpy, Dye spot, Fall out, Hairiness, tiny hole. The proposed Deep-GD model achieves the overall classification accuracy of 97.16%, which is comparatively superior to the existing techniques.","PeriodicalId":158560,"journal":{"name":"International Journal of Electrical and Electronics Research","volume":"56 44","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139965459","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}

{"title":"Optimizing Electric Vehicle Range through Integrating Rooftop Solar on Vehicle","authors":"Gouthami Eragamreddy, S. Naik","doi":"10.37391/10.37391/ijeer.120111","DOIUrl":"https://doi.org/10.37391/10.37391/ijeer.120111","url":null,"abstract":"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. The installation of Rooftop Solar Photovoltaic (RTSPV) improved the range of the vehicle with 18.65%, energy consumption is increased with 15.5%, Cost savings in charging the battery is up to 15.5% and travel distance with full charge is increased up to 19.6%. This analysis is made with the reference drive cycle shown in the paper. While the integration of solar panels significantly extends the vehicle range, the research emphasizes the potential for sustainable charging practices to mitigate grid dependence and reduce emissions.","PeriodicalId":158560,"journal":{"name":"International Journal of Electrical and Electronics Research","volume":"57 35","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139965451","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}

{"title":"Energy Efficient Routing in Wireless Mesh Networks using Multi-Objective Dwarf Mongoose Optimization Algorithm","authors":"Kamadenahalli Narayanaswamy Prema, Mandalur Basavarajappa Ushadevi, Shivalingappa Mallesh Smitha","doi":"10.37391/10.37391/ijeer.120113","DOIUrl":"https://doi.org/10.37391/10.37391/ijeer.120113","url":null,"abstract":"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. The nomadic behaviour of MO-DMOA helps in detecting the optimal routing path with minimized over-exploitation. The proposed MO-DMOA is evaluated with different routing schemes such as Load Balance and Interference Avoid-Partially Overlapped Channels Assignment (LBIA-POCA) framework, and Multi-Objective Dyna Q-based Routing (MODQR). The outcomes obtained through the experimental analysis show that the proposed approach acquires a better throughput of 13.5×105kbps for 22 flows, whereas the existing LBIA-POCA achieves a throughput 60× 103 kbps","PeriodicalId":158560,"journal":{"name":"International Journal of Electrical and Electronics Research","volume":"55 49","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139965417","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}

{"title":"A Route Planning Method using Neural Network and HIL Technology Applied for Cargo Ships","authors":"Huu-Khanh Doan, Anh-Tuan Dinh, Duc-Tuan Hoang","doi":"10.37391/10.37391/ijeer.120116","DOIUrl":"https://doi.org/10.37391/10.37391/ijeer.120116","url":null,"abstract":"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. This predictive model then combines the A* algorithm, weather forecast data, ship parameters, and waypoint coordinates to find the optimal routes for ships before each voyage. The test results show that the proposed method works reliably, helping to improve fuel efficiency and enhance the safety of the ships.","PeriodicalId":158560,"journal":{"name":"International Journal of Electrical and Electronics Research","volume":"56 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139965415","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}

{"title":"SCSO-MHEF: Sand Cat Swarm Optimization based MHEF for Nonlinear LTI-IoT Sensor Data Enhancement","authors":"Anees Fathima Bashir, M. P. F. Queen, Irfan Habib","doi":"10.37391/10.37391/ijeer.120114","DOIUrl":"https://doi.org/10.37391/10.37391/ijeer.120114","url":null,"abstract":"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. In the proposed method the MHEF is optimized using Sand Cat Swarm Optimization to enhance sensor data stability tuned by initial parameters. Simulation experiments were conducted on various and unique scenarios in various orders LTI system with IOT sensor data in order to validate the suggested approach. This method can be used to analyze systems with dynamically changing systems. The proposed SCSO-MHEF technique overall accuracy of 84.5%, 87.3 %, and 99.5 % better than Kalman Filter (KF), EKF and Moving Horizon Filter (MHEF) respectively.","PeriodicalId":158560,"journal":{"name":"International Journal of Electrical and Electronics Research","volume":"54 43","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139965432","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}