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4th International Conference on Smart electronics and Communication, ICOSEC 2023

作者： Muthu, A. Essaki Diraviam, A. Justin Niranjana, R. Saravanan, K. Sundararajan, S. Krishnan, R. Santhana Scad College of Engineering and Technology Department of Computer Science and Engineering Cheranmahadevi India Sri Krishna College of Engineering and Technology Department of Electronics and Communication Engineering Coimbatore India College of Engineering Guindy Anna University Department of Computer Science and Engineering Chennai India Scad College of Engineering and Technology Department of Mechanical Engineering Cheranmahadevi India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India

ISBN: (纸本)9798350300888

Modern banking relies heavily on Automated Teller Machines (ATMs), although card-based transactions have historically faced security issues. This study proposes Near-Field Communication (NFC) Card Emulation and fingerprint technology as secure alternatives for ATM cash transactions. NFC Card Emulation enables secure data exchanges within a short distance, ideal for handling sensitive information. To augment security during authentication, fingerprint technology is proposed as an additional layer of protection. By employing fingerprint sensors, user identification can be established without reliance on physical cards or NFC tags. This integration ensures a heightened level of security during the authentication process, surpassing the current card-based approach employed in ATMs. The system eliminates the need for physical cards, enhancing security and mitigating risks such as ATM tampering and magnetic strip damage. A high level of security is guaranteed during ATM transactions as authentication becomes more effective and secure. This novel strategy strengthens ATM security and provides users with a seamless and safe banking experience by incorporating NFC Card Emulation and fingerprint technologies. © 2023 IEEE.

关键词： Authentication

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Integration of Gradient Descent Optimized Polynomial Regression with Neural Networks in Remaining Useful Life of Lithium ion Batteries 1

Integration of Gradient Descent Optimized Polynomial Regress...

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1st International Conference on Innovations in Communications, Electrical and computer engineering, ICICEC 2024

作者： Keren Persis, P. Kondekar, Vipul H. Sankaran, D. Suresh Kumar, K. Saveetha School of Engineering Simats Department of Coding Linguistics Chennai India Walchand Institute of Technology Department Of Electronics and Telecommunication Engineering Solapur India St. Joseph's College of Engineering Department Of Electronics and Instrumentation Engineering Chennai India Chennai Institute of Technology Department Of Computer and Science Engineering Chennai India

ISBN: (纸本)9798350376517

This research explores both Batch Gradient Descent and Stochastic Gradient Descent to optimize polynomial regression models at degrees 2 and 3, assessing the utility of this method in estimating the Remaining Useful Life (RUL) of lithium-ion batteries. After conducting an investigation, it was determined that the degree 2 polynomial regression optimized by Batch Gradient Descent had the maximum accuracy. As a result, it was chosen to be combined with Fully Convolutional Networks (FCN) in order to improve RUL prediction. Using Polynomial Feature Transformation and degree 2 polynomial regression, the 6,000 record dataset produces an R-squared (R) score of 0.9550 and a Mean Squared Error (MSE) of *** the other hand, an MSE of 0.0741, an Explained Variance Score of 0.9847, and a R2 of 0.9847 are obtained by the combination model of Batch Gradient Descent polynomial regression and FCN using a simple average. In comparison, the merging of the two models yields an impressive improvement in the accuracy of RUL forecasts for lithium-ion batteries, with an MSE decrease of around 66.1 percent from 0.2177 to 0.0741, and an improvement in R2 of nearly 3.1 percent. © 2024 IEEE.

关键词： Convolutional neural networks

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A new symbol timing and carrier frequency offset estimation algorithm for OTFS for B5G 2

A new symbol timing and carrier frequency offset estimation ...

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2nd IEEE International Conference on Emerging Research in Computational Science, ICERCS 2024

作者： Thangavel, Gunasekaran Rajan, Sheeba Subbulekshmi, S. Anthonisamy, Arun Ganapathi, Rohini Velankanni, Gowri University of Technology and Applied Sciences Department of Electronics and Communication Engineering Muscat Oman SRM Institute of Science and Technology School of Computing Tiruchirapalli India Prathyusha Engineering College Department of Electronics and Communication Engineering Chennai India Panimalar Engineering College Department of Computer Science and Business System Chennai India St. Joseph's Institute of Technology Department of Electronics and Communication Engineering Chennai India K. Ramakrishnan College of Technology Department of Electronics and Communication Engineering Trichy India

ISBN: (纸本)9798331534950

In B5G networks, accurate symbol timing estimate and estimation of the carrier frequency offset are both absolutely necessary. Particularly in highly mobile and interference-ridden settings, these factors are of paramount importance for sustaining high-throughput, dependable wireless communication. Estimating symbol timing and carrier frequency offset in OTFS can be difficult due to factors including the time-varying nature of Doppler shifts and interference from surrounding cells as well as the non-stationary nature of wireless channels. In such changing settings, traditional algorithms often fail to produce reliable estimations. To account for frequency changes caused by things like Doppler effects and drift, this research uses Machine Learning-Based Carrier Frequency Prediction Analysis (ML-CFPA) to implement a dynamic carrier frequency offset estimation and correction system. To preserve signal coherence and lower interference in B5G settings, carrier frequency offset adjustment is essential. Communication solutions with high performance are required for these applications to ensure they can function normally despite the complexity and changeability of their contexts. The outcomes demonstrate the higher performance of the algorithm in contrast to conventional approaches, highlighting its applicability to B5G networks. Its potential as a crucial enabling technology for the B5G future has been solidified by these simulations, which verify its capacity to maintain reliable communication under a variety of real-world conditions. © 2024 IEEE.

关键词： Graphical user interfaces

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IoT based crop rotation and soil nutrition analysis

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Materials Today: Proceedings 2022年 64卷 590-597页

作者： Raj, V.Arun koppula, Neeraja Lavanya, M. Manjari, R.Kavin Department of Electronics and Communication Engineering Mepco Schlenk Engineering College Sivakasi India Department of Computer Science and Engineering Geethanjali College of Engineering and Technology Hyderabad India

Crop revolution is the act of growing a progression of various sorts of harvests in a similar region rather than the one yield framework. This strategy for development normally recharges the dirt supplements and lessens synthetic contamination because of overabundance utilization of manures. Large scale supplements like Nitrogen (N), Phosphorous (P) and Potassium (K), soil pH, temperature and mugginess assume a significant part in deciding the harvest to be developed straightaway. In our proposed framework, the degree of full scale supplements NPK and pH is observed in light of the force of variety change in the dirt example arrangement utilizing a variety sensor. Along with this, the temperature, humidity and soil moisture are also monitored to maintain the soil health. The data sensed by these sensors is stored on the cloud and analysed based on which suggestions for growth of the suitable crop are made. A web portal is designed which displays a list of particular crops suitable for the tested soil. Additionally, the current soil moisture level, atmospheric temperature and humidity are also displayed. This system is designed so that any individual can implement crop rotation technique in their back yards, roof top garden or in their own farms with reduced usage of fertilizers. © 2022

关键词： Crop rotation

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Image Processing Techniques for Detecting Steel Defects with MATLAB and Machine Learning 1

Image Processing Techniques for Detecting Steel Defects with...

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1st International Conference on Advanced Computing and Emerging Technologies, ACET 2024

作者： Dankan Gowda, V. Mary, P. Arockia Ramanan, S.V. Kumar, N.M.G. Kumar, Pullela Svvsr Abdullah, Ibrahim Bms Institute of Technology and Management Department of Electronics and Communication Engineering Karnataka Bangalore India V.S.B. Engineering College Department of Information Technology Tamilnadu Karur India Ppg Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India Mohan Babu University Sree Vidyanikethan Engineering College Department of Electrical and Electronics Engineering Andhra Pradesh Tirupathi India Aditya College of Engineering and Technology Department of Computer Science & Engineering Andhra Pradesh Surampalem India Al-Turath University College Department of Computer Science Baghdad Iraq

ISBN: (纸本)9798350367720

This paper focuses on the detection of surface defects in the steel manufacturing industry since these defects inhibit the quality of the manufactured products and the efficiency of the manufacturing processes. Conventional inspection techniques require a extensive amount of work to be done at the inspection sites and perhaps associated with several kinds of errors. The current research work describes an automated system for determining the defects in the steel using image processing and integrating it with computational intelligence in MATLAB. The proposed method consists of pre-processing where the images are enhanced, feature extraction where characteristics of the defects are obtained and classification utilizing the machine learning models. The efficiency of the proposed approach is proved using experiments on the dataset of steel images and a substantial increase in the precision of the defects' recognition compared to other techniques. Thus, the' findings of the apply demonstrate that the use of image processing and machine learning can offer a strong system for real time defect identification in steel production. © 2024 IEEE.

关键词： Image enhancement

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Crop Classification using Convolutional Neural Network 15

Crop Classification using Convolutional Neural Network

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Kavitha, K. Gopalakrishnan, K. Balaji, S. Jeevanantham, J. Aakhila Hayathunisa, M. Department of Information Technology Velalar College of Engineering Erode India Department of Computer Science and Engineering Kongu Engineering College Erode India Department of Electronics and Communication Kongu Engineering College Erode India

ISBN: (纸本)9798331300579

Deep learning is a powerful machine learning technique that has the potential to revolutionize agriculture. By leveraging deep learning models, we can develop innovative solutions to critical agricultural challenges, such as crop classification, weed percentage estimation, and crop quality identification. Deep learning models can be trained on remote sensing data to accurately classify different crop types. This information can be used by farmers and agricultural stakeholders to monitor and manage their crops more effectively. For example, crop classification models can be used to identify areas of a field that are underperforming or at risk of disease. Weed infestations can significantly reduce crop yields and quality. Deep learning models can be trained on high-resolution imagery and sensor data to accurately estimate weed percentages within fields. This information can be used to develop precise and timely weed management strategies, reducing the need for excessive herbicide usage and minimizing the environmental impact. Assessing crop quality is crucial for optimizing harvest decisions and ensuring the delivery of high-quality produce to consumers. Deep learning techniques, such as image analysis and spectroscopy, can be employed to identify various quality parameters in crops, such as size, color, ripeness, and the presence of diseases or blemishes. This information can help farmers make informed decisions about harvesting, storage, and distribution. Assessing crop quality is crucial for optimizing harvest decisions and ensuring the delivery of high-quality produce to consumers. Deep learning techniques, such as image analysis and spectroscopy, can be employed to identify various quality parameters in crops, such as size, color, ripeness, and the presence of diseases or blemishes. The continued development and integration of deep learning technologies into agriculture hold promise for advancing global food production and agricultural sustainability. © Grenze Sc

关键词： Weed control

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GPU Side-Channel Attack Classification for Targeted Secure Shader Mitigation

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SN computer Science 2024年第8期5卷 1148页

作者： Lungu, Nelson Patra, Sudhansu Shekhar Mishra, Manoj Ranjan Dash, Bibhuti Bhusan Sasmal, Gopal Charan Pattnayak, Parthasarathi Singh, Satyendr Gourisaria, Mahendra Kumar Department of Electrical and Electronics Engineering University of Zambia Lusaka Zambia School of Computer Applications Kalinga Institute of Industrial Technology (KIIT) Deemed to be University Bhubaneswar India MCA Department Trident Academy of Creative Technology Bhubaneswar India Computer Science and Engineering Department BML Munjal University Gurugram India School of Computer Engineering Kalinga Institute of Industrial Technology (KIIT) Deemed to be University Bhubaneswar India

Graphics processing units (GPUs) provide massively parallel processing capabilities, enabling accelerated computation across diverse applications. However, their parallel architecture and shared resources also introduce significant security vulnerabilities, especially side-channel attacks that can compromise sensitive data. This article presents a systematic classification of GPU side-channel attack vectors, including power analysis, timing analysis, electromagnetic analysis, and combined attacks. This taxonomy proposes countermeasures for the NVIDIA GeForce RTX 3090 GPU to mitigate these threats. The countermeasures encompass hardware techniques such as isolated execution environments, secure memory partitioning, and hardware random number generators to reduce the attack surface. Software methods, including constant-time algorithms, data-independent execution paths, masking, and oblivious memory access, are also implemented to eliminate timing and cache side channels. Comprehensive experiments demonstrate the effectiveness of the countermeasures, achieving up to 95% reduction in power analysis leakage, 90% in timing analysis, and 92% in electromagnetic analysis with acceptable performance overheads. The secure shader programming framework provides robust mitigation against GPU side-channel attacks while maintaining efficiency and compatibility with the RTX 3090 architecture. This work contributes vital insights and solutions for analysing GPU security vulnerabilities, designing focused countermeasures, and enabling confidential computing leveraging modern GPU capabilities. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Graphics processing units security Hardware countermeasures NVIDIA GeForce RTX 3090 Secure shader Side-channel attacks Software mitigation

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Deep Optimal VGG16 Based COVID-19 Diagnosis Model

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computers, Materials & Continua 2022年第1期70卷 43-58页

作者： M.Buvana K.Muthumayil S.Senthil kumar Jamel Nebhen Sultan S.Alshamrani Ihsan Ali Department of Computer Science and Engineering PSNA College of Engineering and TechnologyDindigul624622India Department of Information Technology PSNA College of Engineering and TechnologyDindigul624622India Department of Electrical and Electronics Engineering New Prince Shri Bhavani College of Engineering and TechnologyChennai600073India Prince Sattam Bin Abdulaziz University College of Computer Engineering and SciencesAlkharj11942Saudi Arabia Department of Information Technology College of Computer and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer System and Technology Faculty of Computer Science and Information TechnologyUniversity of MalayaKuala Lumpur50603Malaysia

Coronavirus(COVID-19)outbreak was first identified in Wuhan,China in December *** was tagged as a pandemic soon by the WHO being a serious public medical *** spite of the fact that the virus can be diagnosed by qRT-PCR,COVID-19 patients who are affected with pneumonia and other severe complications can only be diagnosed with the help of Chest X-Ray(CXR)and Computed Tomography(CT)*** this paper,the researchers propose to detect the presence of COVID-19 through images using Best deep learning model with various *** features like Speeded-Up Robust Features(SURF),Features from Accelerated Segment Test(FAST)and Scale-Invariant Feature Transform(SIFT)are used in the test images to detect the presence of *** optimal features are extracted from the images utilizing DeVGGCovNet(Deep optimal VGG16)model through optimal learning *** task is accomplished by exceptional mating conduct of Black Widow *** this strategy,cannibalism is *** this phase,fitness outcomes are rejected and are not satisfied by the proposed *** results acquired from real case analysis demonstrate the viability of DeVGGCovNet technique in settling true issues using obscure and testing ***16 model identifies the imagewhich has a place with which it is dependent on the distinctions in *** impact of the distinctions on labels during training stage is studied and predicted for test *** proposed model was compared with existing state-of-the-art models and the results from the proposed model for disarray grid estimates like Sen,Spec,Accuracy and F1 score were promising.

关键词： COVID 19 multi-feature extraction vgg16 optimal learning rate

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Enhanced Mini-YOLOv7: A AI Approach for Safety Helmet Detection in the Construction Industry

Enhanced Mini-YOLOv7: A AI Approach for Safety Helmet Detect...

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2024 IEEE International Women in engineering (WIE) Conference on Electrical and computer engineering, WIECON-ECE 2024

作者： Blessie, A. Alice Sankarasubramanian, R.S. John, Joseph Krishnapriya, S. Bhuvanesh, A. Keerthanadevi, R. Government College of Engineering Department of Electronics and Communication Engineering Tirunelveli India Psg Institute of Technology and Applied Research Department of Mathematics Coimbatore India Gregorian Institute of Technology Department of Mechnanical Engineering Kottayam India Sri Eshwar College of Engineering Department of English Coimbatore India Psn College of Engineering and Technology Department of Electrical and Electronics Engineering Tirunelveli India Psn College of Engineering and Technology Department of Computer Science and Engineering Tirunelveli India

ISBN: (纸本)9798331535476

Wearing safety helmets is an effective way to keep construction workers safe on the job. Workers often opt to remove their helmets due to discomfort or a lack of security knowledge, leaving them vulnerable to unsaid threats. A worker's risk of injury from falls, both physical and otherwise, increases when they do not wear a safety helmet. Because detecting whether workers are wearing safety helmets is an important aspect of managing safety on construction sites, a fast and accurate detector is required. However, standard manual monitors aren't particularly popular, and attaching sensors to a safety helmet is a laborious operation. With that in mind, this study presents EM-YOLOv7, a DL method for autonomous safety helmet recognition in real-time that has been developed for use on construction sites. A multi-scale non-local attention module (MSNA) is incorporated into the system to improve detection accuracy in complicated situations. This module enhances feature recovery by aggregating semantic context across multiple scales. Further, to improve detection performance and handle mutual occlusion, a loss function named Wise-IoUv3 (WIoUv3) is used. This method adjusts the loss depending on the overlap between the predicted and ground truth bounding boxes. To train and test the model, we will utilize the safety helmet-wearing (SHWD) dataset, which includes a wide variety of construction workers and whether or not they wore helmets. The goal of this technique is to provide a 99% accurate solution for real-time helmet recognition, which would greatly improve safety management and decrease the likelihood of accidents on construction sites. © 2024 IEEE.

关键词： Construction industry

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Hierarchical Loss for Bi-Level Classification of Speech into Language and Dialects

Hierarchical Loss for Bi-Level Classification of Speech into...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Angra, Ananya Muralikrishna, H. Dileep, A.D. Thenkanidiyoor, Veena MANAS Lab SCEE Indian Institute of Technology Mandi Mandi India Department of Electronics and Communication Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal576104 India Department of Computer Science Engineering Indian Institute of Technology Dharwad Dharwad India Department of Computer Science Engineering National Institute of Technology Goa Goa India

ISBN: (纸本)9798350368741

Spoken dialect identification (DID) is a challenging task due to high similarities between the classes. This becomes further complicated when DID needs to be performed in a multilingual environment, especially when languages are from same language-family, as the scope for confusion is significantly high. In this paper, we propose multiple techniques to address this issue. These techniques motivate the DID system to respect the parent-child relationship between the languages and their dialects by performing bi-level classification of speech. Specifically, in our first approach, we train the language identification (LID) and DID models independently and then combine them together such that the output of LID model selects the DID model, which in turn decides the dialect. However, such independent training does not allow the model to learn the relations/similarities between dialects of different languages. To overcome this limitation, we propose to train an end-to-end DID model which is trained using dialects of all languages in the dataset. While such end-to-end training allows the model to learn inter-dialect similarities in a better way, it does not explicitly prevent a dialect from being misclassified into a dialect of a different language. To address this limitation, we propose a novel hierarchical loss, which motivates the model to maintain the parent-child relationship between language and dialects. Specifically, with the help of an auxiliary language classifier and a primary dialect classifier, the hierarchical loss penalizes the model heavily whenever parent-child relationship is not maintained, i.e., when predicted dialect does not belong to the predicted language. Experiments conducted on a set of closely related Indian languages shows that hierarchical loss based training leads to improvement in the performance. © 2025 IEEE.

关键词： Hierarchical loss Multi-lingual Spoken dialect identification

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