2023 8th International Conference on Communication and Electronics Systems (ICCES)最新文献_第4页

Design and Performance Comparison of X-Masking Models in DFT Applications DFT应用中x -掩模模型的设计与性能比较

2023 8th International Conference on Communication and Electronics Systems (ICCES) Pub Date : 2023-06-01 DOI: 10.1109/ICCES57224.2023.10192694

Anaswar Ajit, Geethu R S, R. Bhakthavatchalu

{"title":"Design and Performance Comparison of X-Masking Models in DFT Applications","authors":"Anaswar Ajit, Geethu R S, R. Bhakthavatchalu","doi":"10.1109/ICCES57224.2023.10192694","DOIUrl":"https://doi.org/10.1109/ICCES57224.2023.10192694","url":null,"abstract":"VLSI technology advancements are rapidly evolving with the growing demands of human lifestyles. With all these advancements the circuit complexity and speeds are also increasing making the circuits even more difficult to test. From time to time various test schemes are being implemented to increase the quality of tests to bring more reliability to systems. Introduction of BIST was a major change in the testing method used till then. With the increase in complexity the occurrence of unknown values also increase. For the same different techniques are used as the presence of ’X’ values significantly degrade the tests and test results. In this design a two stage X-masking model is implemented that is capable of masking the ’X’ states that occur during the test cycle. Mostly in medical and space related systems require high level of accuracy where tolerance methods can reduce the efficiency of these systems. The design proposed here is an X-Masking model that monitors and masks the unknown values that occur during the test phase. The enhanced design is then compared for its utilisation and power consumption with a basic concept model for the masking. In the design scan chains are monitored for the occurrence of unknown values and they are masked using a two layered masking scheme preventing the propagation of unknown values into the MISR. The design is is simulated using Xilinx Vivado 2020.1 and implemented in Basys-3 FPGA board.","PeriodicalId":442189,"journal":{"name":"2023 8th International Conference on Communication and Electronics Systems (ICCES)","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123168303","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

Unifying Governance, Risk and Controls Framework Using SDLC, CICD and DevOps 使用SDLC、CICD和DevOps统一治理、风险和控制框架

2023 8th International Conference on Communication and Electronics Systems (ICCES) Pub Date : 2023-06-01 DOI: 10.1109/ICCES57224.2023.10192730

Sai Alekhya Ganugapati, S. Prabhu

引用次数: 0

Enhancing Smart Grid Security: Detecting Electricity Theft through Ensemble Deep Learning 增强智能电网安全:通过集成深度学习检测电力盗窃

2023 8th International Conference on Communication and Electronics Systems (ICCES) Pub Date : 2023-06-01 DOI: 10.1109/ICCES57224.2023.10192747

Sowmya C S, Vibin R, Praveen Mannam, Lakkakula Mounika, Subash Ranjan Kabat, J. P. Patra

{"title":"Enhancing Smart Grid Security: Detecting Electricity Theft through Ensemble Deep Learning","authors":"Sowmya C S, Vibin R, Praveen Mannam, Lakkakula Mounika, Subash Ranjan Kabat, J. P. Patra","doi":"10.1109/ICCES57224.2023.10192747","DOIUrl":"https://doi.org/10.1109/ICCES57224.2023.10192747","url":null,"abstract":"Theft of electricity is a major problem that causes financial losses and inconsistent service for paying consumers for power distribution companies all over the world. The safety of the power grid depends on the ability to identify and stop electricity theft. The use of deep learning techniques has shown great promise in recent years, particularly in the areas of computer vision and natural language processing. This study recommends a random forest-based ensemble deep learning method for identifying cases of electricity theft. The proposed ensemble deep learning model leverages the best features of many kinds of deep learning architectures, including stacked Convolutional Neural Networks (CNN)and Long Short-Term Memory (LSTM). Each architecture has its own strengths when it comes to monitoring normal and abnormal electrical use for signs of theft. The final forecast is derived by adding together the predictions of the different models in the random forest ensemble. The ensemble model is trained using a massive dataset of energy usage records and theft information. Information about consumption patterns is extracted using feature engineering methods once the dataset has been preprocessed to get rid of noise and outliers. This preprocessed dataset is used to train the ensemble model, which then optimizes its parameters to reduce prediction errors. We use many measures, including accuracy, precision, recall, and F1-score, to assess the proposed ensemble deep learning model’s performance. Experiments are run against both conventional machine learning methods and standalone deep learning models to prove that the ensemble method is superior. The findings demonstrate that the ensemble model is more accurate and has a greater detection rate, making it suitable for spotting energy theft.","PeriodicalId":442189,"journal":{"name":"2023 8th International Conference on Communication and Electronics Systems (ICCES)","volume":"272 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115902281","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

Blockchain based Authentication for Internet of Things Devices based on Smart Farming 基于区块链的智能农业物联网设备认证

2023 8th International Conference on Communication and Electronics Systems (ICCES) Pub Date : 2023-06-01 DOI: 10.1109/ICCES57224.2023.10192605

Animesh Srivastava, Sant Kumar Maurya, Parveen Kumar Saini

{"title":"Blockchain based Authentication for Internet of Things Devices based on Smart Farming","authors":"Animesh Srivastava, Sant Kumar Maurya, Parveen Kumar Saini","doi":"10.1109/ICCES57224.2023.10192605","DOIUrl":"https://doi.org/10.1109/ICCES57224.2023.10192605","url":null,"abstract":"Farmers and other users who are using smart farming technology can access agricultural data through a unified blockchain-based platform. In this way, the transparency, anonymity, and traceability added by the blocks in the blockchain ensure that the correct data can be used when it is needed or in the future. It is necessary to authenticate with the original owner of the cattle to buy or sell cattle, and in some cases, governments also want to use that data in case of theft or accident. With its inherent characteristics, blockchain offers a promising approach for decentralized authentication in IoT networks. To provide efficient, decentralized mutual authentication and privacy protection for IoT users. This study examines the efficient, secure, and decentralized store of information of blockchain. This study also discusses on the cattle farmers who buy and sell animals, authenticate the owner of the cattle, and ensures scalability, interoperability, cost-effectiveness, and simplicity that help farmers to use blockchain-based authentication for their IoT devices.","PeriodicalId":442189,"journal":{"name":"2023 8th International Conference on Communication and Electronics Systems (ICCES)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124163099","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 Deep Learning Architectures for Spectrum Sensing in Cellular Networks 蜂窝网络中频谱感知的增强深度学习架构

2023 8th International Conference on Communication and Electronics Systems (ICCES) Pub Date : 2023-06-01 DOI: 10.1109/ICCES57224.2023.10192889

M. Mani, K. Vishnuvardhan Reddy, M. Monisha

{"title":"Enhanced Deep Learning Architectures for Spectrum Sensing in Cellular Networks","authors":"M. Mani, K. Vishnuvardhan Reddy, M. Monisha","doi":"10.1109/ICCES57224.2023.10192889","DOIUrl":"https://doi.org/10.1109/ICCES57224.2023.10192889","url":null,"abstract":"The expansion of 5G technologies and the Internet of Things (IoT) increases the demand for spectrum efficiency. In future smart city and Industrial IoT (IIoT) applications, the number of wireless users and IoT devices will be excessive. The effect will be spectrum congestion. Moreover, the existing wireless technology has security flaws and inadequate service quality. Cognitive Radio (CR) technology intends to enhance the functioning of the existing system and meet the growing bandwidth needs of users. Spectrum awareness with identification of various signal patterns, is crucial in a cellular system environment. In this work, two deep neural network architectures are presented to distinguish 5G NR (new Rradio) signals from Long-Term Evolution (LTE) signals. This paper presents AlexNet and SqueezeNet architectures for the classification of NR signal with LTE signal. The analysis is conducted by training the classifiers with three distinct optimizers, including RMSprop (root mean squared propagation), ADAM (adaptive moment estimation) and SGDM (stochastic gradient descent with momentum), In addition, performance study is conducted at three distinct training frequencies to assess the classifiers’ superiority.","PeriodicalId":442189,"journal":{"name":"2023 8th International Conference on Communication and Electronics Systems (ICCES)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116875406","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

A Novel Data Flow Security Protection Algorithm for Digital Media in 6G Environment 一种新的6G环境下数字媒体数据流安全保护算法

2023 8th International Conference on Communication and Electronics Systems (ICCES) Pub Date : 2023-06-01 DOI: 10.1109/ICCES57224.2023.10192719

Linqin Zhang

引用次数: 0

U-slot Loaded Half-Circled Microstrip Patch Antenna Analysis using XGBOOST Machine Learning Algorithm 基于XGBOOST机器学习算法的u槽加载半圆微带贴片天线分析

2023 8th International Conference on Communication and Electronics Systems (ICCES) Pub Date : 2023-06-01 DOI: 10.1109/ICCES57224.2023.10192742

Venkateshwar Reddy Vedipala, Avinash Reddy Radharapu, Arun Kumar Gajula, Sivani Sivani, Akshaya Akshaya

{"title":"U-slot Loaded Half-Circled Microstrip Patch Antenna Analysis using XGBOOST Machine Learning Algorithm","authors":"Venkateshwar Reddy Vedipala, Avinash Reddy Radharapu, Arun Kumar Gajula, Sivani Sivani, Akshaya Akshaya","doi":"10.1109/ICCES57224.2023.10192742","DOIUrl":"https://doi.org/10.1109/ICCES57224.2023.10192742","url":null,"abstract":"Half-circled U-slot loaded antenna is studied using the HFSS and Machine Learning (ML) algorithm. The proposed work is for predicting the resonance frequency of the U-slot loaded antennaby providing the dimensions of the antennas. The proposed antenna is designed for Wi-MAX application with operating frequency of 3.4 GHz. The HFSS tool is being used for designing and analyzing fractal antennasand generating the training data. Parametric analysis of the designed U-slot-loaded half-circled antenna is developed by altering the half-circle radius, length of the U-slot and width. The data set is then given to theXGBoostML algorithm for training the model. The XGBoost contains remarkably high processing speed and contains features like parallelization, cache optimization, and out-of-core computation which makes the perfect algorithm for predicting the resonance frequencies.U-slot loaded half-circled antenna offers a substantial size reduction, a wide impedance bandwidth, and a uniform radiation pattern on all sides.","PeriodicalId":442189,"journal":{"name":"2023 8th International Conference on Communication and Electronics Systems (ICCES)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117338096","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

Ultrathin Wideband Printed Antenna for FR-II NR n257, n258 and n261, for 5G Wireless Applications 超薄宽带印刷天线FR-II NR n257, n258和n261，用于5G无线应用

2023 8th International Conference on Communication and Electronics Systems (ICCES) Pub Date : 2023-06-01 DOI: 10.1109/ICCES57224.2023.10192772

Tej Raj, Ankush Kapoor, Ranjan Mishra

引用次数: 0

Data Security in Healthcare: Enhancing the Safety of Data with CyberSecurity 医疗保健中的数据安全:利用网络安全增强数据安全

2023 8th International Conference on Communication and Electronics Systems (ICCES) Pub Date : 2023-06-01 DOI: 10.1109/ICCES57224.2023.10192596

Mayuri Puri, Saikat Gochhait

{"title":"Data Security in Healthcare: Enhancing the Safety of Data with CyberSecurity","authors":"Mayuri Puri, Saikat Gochhait","doi":"10.1109/ICCES57224.2023.10192596","DOIUrl":"https://doi.org/10.1109/ICCES57224.2023.10192596","url":null,"abstract":"Cyberattacks are used to steal money, data, or intellectual property, but the goal is increasingly to produce overt disruption or political influence. Healthcare is more vulnerable to cyberattacks than other industries due to inherent weaknesses in its security posture. In addition to medical equipment and other systems connected to IT networks, cybersecurity threats and vulnerabilities can pose a threat to the confidentiality, resilience, and veracity of those systems. As a result of the rich supply of valuable data, Healthcare makes a good target for cybercriminals. Additionally, while Cybersecurity is critical for patient safety, it has an unreliable track record. Breach of infrastructure has resulted in millions of health records being stolen, potentially putting patients' lives at risk. This necessitates the integration of Cybersecurity into patient safety. Before these attacks, many security experts struggled to persuade corporate executives of the necessity of cyber security; significantly, a great deal can be gained, in the long run, from risk mitigation, through both cost savings and reputation protection. A holistic solution to prioritizing Cybersecurity in the healthcare business necessitates cultural transformations, enhanced leadership communication, and changes in how practitioners conduct their roles in the clinical setting.","PeriodicalId":442189,"journal":{"name":"2023 8th International Conference on Communication and Electronics Systems (ICCES)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123566521","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

Optimizing Deep Neural Network for Automatic Number Plate Recognition in Challenging Environment 复杂环境下车牌自动识别的深度神经网络优化

2023 8th International Conference on Communication and Electronics Systems (ICCES) Pub Date : 2023-06-01 DOI: 10.1109/ICCES57224.2023.10192870

Jayant Choubey, S.M.Kav itha, Dr R. Subash

{"title":"Optimizing Deep Neural Network for Automatic Number Plate Recognition in Challenging Environment","authors":"Jayant Choubey, S.M.Kav itha, Dr R. Subash","doi":"10.1109/ICCES57224.2023.10192870","DOIUrl":"https://doi.org/10.1109/ICCES57224.2023.10192870","url":null,"abstract":"The study of automatic number plate recognition, a subfield of computer vision and machine learning, has grown in importance. Parking management, toll collection, and traffic monitoring are just a few of the many uses for automatic number plate recognition devices. However, automatic number plate recognition in difficult situations like dim lighting, bad image quality, and occlusions is still a difficult task. In this study, using the TensorFlow and EasyOCR libraries, a novel deep neural network architecture is suggested for automatic number plate recognition in difficult environments. First, examination of the different automatic number plate recognition challenges and how they affect the effectiveness of the current automatic number plate recognition systems. It is then suggested that a deep neural network design should be used to boost the automatic number plate recognition systems’ recognition accuracy in difficult environments by combining convolutional and recurrent layers. Overall, this study suggests new deep neural network architecture for Automatic Number Plate Recognition (automatic number plate recognition) in difficult environments. To increase recognition accuracy, the proposed architecture combines convolutional and recurrent layers, including a kind of recurrent neural network (RNN) dubbed long short-term memory (LSTM). On an openly accessible dataset, the system’s accuracy was 91% and it was developed using the libraries TensorFlow and EasyOCR. The results of this study could be used in a number of industries, including law enforcement, transportation, and parking administration.","PeriodicalId":442189,"journal":{"name":"2023 8th International Conference on Communication and Electronics Systems (ICCES)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122038836","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