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17th International Convention on Rehabilitation Engineering and Assistive technology, i-CREATe 2024

作者： Hemakom, Apit Sanguantrakul, Jongsook Suwannarat, Arpa Seesutas, Sangvorn Israsena, Pasin National Electronics and Computer Technology Center Biomedical Electronics and Systems Research Team Pathumthani Thailand

ISBN: (纸本)9798350355154

Measuring electroencephalography (EEG) from the scalp (scalp-EEG) in real-world environments presents challenges due to the cumbersome size and weight of traditional equipment, limiting its practicality for daily use. To overcome this, we develop a personalized ear-EEG system utilizing advanced digital fabrication techniques. The system comprises an EEG ear shell that securely houses electrodes within the ear canal and a compact, portable signal recording unit. Constructed from medical-grade resin and silver beads coated with PDMS/CNT composite, the ear shell and electrodes ensure comfort and safety. The ear shell is precisely tailored using 3D scanning and printing processes, facilitating rapid, reproducible production from various materials. This method significantly reduces development time compared to traditional manual fabrication. Our ear-EEG system effectively captures standard EEG signals, including alpha rhythm, steady-state visual evoked potentials (SSVEP), and auditory steady-state responses (ASSR). © 2024 IEEE.

关键词： Electroencephalography

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A Survey on Logic-Locking Characteristics and Attacks

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Journal of The Institution of Engineers (India): Series B 2024年第4期105卷 1073-1087页

作者： Subbiah, Karthik Chinnathevar, Sujatha Electronics and Communication Engineering SSM Institute of Engineering and Technology Tamilnadu Dindigul India Computer Science and Engineering SSM Institute of Engineering and Technology Tamilnadu Dindigul India

Integrated circuits (ICs) are ubiquitous and a crucial component of electronic systems, from satellites and military hardware to consumer devices and cell phones. The computing system’s foundation of trust is the IC. Most semiconductor businesses are shifting to fabless manufacturing and outsourcing to foundries worldwide as integrated circuit feature sizes continue to decrease. That puts the design business at risk for several things, such as unauthorized overproduction, resale on the black market, and illegal copying brought on by intellectual property theft. Logic locks offer one solution in which the chip’s actual functioning is "locked" with a key only known to the inventor. The design will only function as intended if specific keys are pressed. Unlocking overproduced chips should be impossible for supply chain attackers since designers open them after manufacturing them. Logical locks against the risk of overproduction are the main subject of this research. We examine current locking systems, define features based on crucial processing, and identify commonalities and discrepancies between the employed attacker models. This research paper is intended to assist scientists, IP distributors, and SoC developers in rapidly investigating and comprehending the most recent technologies that should be considered and analyzed for additional research on logic-locking techniques. © The Institution of Engineers (India) 2024.

关键词： Locks (fasteners)

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Monitoring COVID-19 environment: a real-time facial mask detection using YOLO models

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Research on Biomedical Engineering 2025年第1期41卷 1-18页

作者： Pandiyan, P. Thangaraj, Rajasekaran V, Balasubramanian V. K, Manavalasundaram S, Balakrishnan Department of Electrical and Electronics Engineering KPR Institute of Engineering and Technology Coimbatore India Department of Computer Science and Engineering Nandha Engineering College Erode India Department of Information Technology Velalar College of Engineering and Technology Erode India Department of Computer Science and Engineering Aarupadai Veedu Institute of Technology Chennai India Department of Computer Science and Engineering Sri Ramakrishna Institute of Technology Coimbatore India

Purpose: The rapid spread of COVID-19 has resulted in significant harm and impacted tens of millions of people globally. In order to prevent the transmission of the virus, individuals often wear masks as a protective measure for themselves and others. Coronavirus protection guidelines have been published by the World Health Organization (WHO). According to WHO standards, COVID-19 can be prevented by wearing a mask in public places and congested regions. In these places, it is very difficult to personally check to see if people are wearing face masks or not. Methods: The objective of this research work is to build a powerful, efficient, and real-time approach for detecting people not wearing masks. Three cutting-edge object identification models, namely YOLOv4, Tiny-YOLOv4, and YOLOv5, are employed in this study for the identification of masked faces. Result: The proposed YOLOv5 model is evaluated using real-time images captured using a smartphone or tablet. The test images include both single and multiple people with and without masks. The YOLOv5 model achieved recognition accuracy of 88.90% with an average detection speed of 0.0316 s per image, whereas the YOLOv4 and Tiny-YOLOv4 produced recognition accuracy of 82.24% and 74.80% with an average detection speed of 0.0530 s and 0.0541 s per image, respectively. Conclusion: The comparative performance suggests that the YOLOv5 model has a maximum recognition accuracy of 88.90% in face mask identification tasks compared to other models such as the YOLOv4 and Tiny-YOLOv4. © The Author(s), under exclusive licence to The Brazilian Society of Biomedical Engineering 2025.

关键词： COVID-19

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Real-Time Multi-objects Detection Using YOLOv7 for Advanced Driving Assistant Systems 2nd

Real-Time Multi-objects Detection Using YOLOv7 for Advanced ...

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2nd International Conference on Innovations in Data Analytics, ICIDA 2023

作者： Solunke, Babruvan R. Gengaje, Sachin R. Computer Science and Engineering Walchand Institute of Technology Solapur India Electronics and Computer Engineering Walchand Institute of Technology Solapur India

ISBN: (纸本)9789819734658

Accurate and efficient multi-object localization and categorization is one of the key needs for applications of robotic vision, intelligent military surveillance systems, security, and ADAS. It is a significant and complex issue with computer vision that has received a lot of attention. Due to the development of self-driving cars, smart video monitoring, face recognition, and several people tracking services, there is a great demand for quick and precise object detection systems. These methods locate each object by drawing bounding box around it in addition to identify and classify every object in an image or video frame. One of the basic needs for driverless vehicles and many modern driving aid technologies is the ability to recognize and interpret all stationary and moving objects surrounding a vehicle under varying driving and weather circumstances. Convolutional neural network (CNN) technology can provide safety in modern vehicles. This paper analyzes the recent deep learning-based object detection methods, challenges and presents an improved general framework for real-time multi-object detections in ADAS-based on YLOv7 and CNN. We compared the YOLOv7 approach with earlier versions in the YOLO family, and it was found that YOLOv7 outperforms all earlier real-time object detectors to obtain a reliable speed between 5 and 160 frames per second and improved accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Object detection

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"Survey and analysis of blockchain technologies with respect to: properties, algorithms, architecture, models, evolution and framework"

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Multimedia Tools and Applications 2025年第14期84卷 13571-13615页

作者： Tiwari, Nikita Ranjan, Prakash Biswal, Pradeep Kumar Barde, Chetan Sinha, Neha Department of Computer Science and Engineering Indian Institute of Information Technology Bhagalpur Bhagalpur India Department of Electronics and Communication Engineering Indian Institute of Information Technology Bhagalpur Bhagalpur India

The growing realm of blockchain technology has captivated researchers and practitioners alike with its promise of decentralized, secure, and transparent transactions. This paper presents a comprehensive survey and analysis of various blockchain technologies, focusing on their fundamental properties, architectural designs, operational models, evolutionary trends, and overarching frameworks. Through an extensive review of existing literature and empirical studies, this research synthesizes the multifaceted landscape of blockchain, elucidating key concepts such as consensus mechanisms, smart contracts, scalability, interoperability, and privacy protocols. By examining the evolution of blockchain from its inception to its current state and beyond, this paper delineates the trajectory of technological advancements and identifies emerging paradigms. Furthermore, it critically evaluates the strengths and limitations of diverse blockchain architectures and models, offering insights into their applicability across different domains and use cases. Finally, this study culminates in the proposal of a comprehensive framework that encapsulates the essential elements necessary for understanding, implementing, and harnessing the transformative potential of blockchain technologies. Through this holistic analysis, this research contributes to a deeper understanding of blockchain ecosystems and provides valuable guidance for future research directions and practical implementations. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Blockchain

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Deep learning and machine learning based highly accurate reflection prediction model for multi layers anti-reflection coatings

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Optical and Quantum electronics 2025年第1期57卷 1-22页

作者： Oktay, Semih Duru, İremnur Bakır, Halit Tabaru, Timuçin Emre Department of Electrical-Electronics Engineering Sivas University of Science and Technology Sivas Turkey Computer Engineering Department Sivas University of Science and Technology Sivas Turkey

This study focuses on creating an accurate reflection prediction model that will guide the design of filters with multilayer Anti-Reflection Coating (ARC) to optimize the thickness parameters using Machine Learning (ML) and Deep Learning (DL) techniques. This model aims to shed light on the design process of a multilayer optical filter, making it more cost-effective by providing faster and more precise production. In creating this model, a dataset containing data obtained from 3000 (1500 Ge–Al2O3, 1500 Ge–SiO2) simulations previously performed on a computer based on the thicknesses of multilayer structural materials was used. The data are generated using Computational Electromagnetic simulation software based on the Finite-Difference Time-Domain method. To understand the mechanism of the proposed model, two different two-layer coating simulations were studied. While Ge was used as the substrate in both coatings, Al2O3 and SiO2 were used as the second layers. The data set consists of the 3–5 µm and 8–12 µm bands typical for the mid-wave infrared (MWIR) and long-wave infrared (LWIR) bands and includes reflectance values for wavelengths ranging between these spectra. In the specified 2-layer data set, the average reflectance was obtained with a minimum of 0.36 at 515 nm Ge and 910 nm SiO2 thicknesses. This value can be increased by adapting the proposed model to more than 2 layers. Six ML algorithms and a DL model, including artificial neural networks and convolutional neural networks, are evaluated to determine the most effective approach for predicting reflectance properties. Furthermore, in the proposed model, a hyperparameter tuning phase is used in the study to compare the efficiency of ML and DL methods to generate dual-band ARC and maximize the prediction accuracy of the DL algorithm. To our knowledge, this is the first time this has been implemented in this field. The results show that ML models, particularly decision tree (MSE: 0.00000069, RMSE: 0.00083), rand

关键词： Antireflection coatings

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CropSmart-A Crop Prediction Application Using Machine Learning 12th

CropSmart-A Crop Prediction Application Using Machine Learni...

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12th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2024

作者： Madugula, Vidya Perumandla, Anshu Likhitha, Singari Gupta, Manu Dholvan, Mohan Department of Electronics and Computer Engineering Sreenidhi Institute of Science and Technology Hyderabad India

ISBN: (纸本)9789819601424

India is a country whose primary sources of income are agriculture and farming. Varied soil in the nation enables farmers to grow a wide range of crops all year round. The agricultural industry has been the focus of in-depth investigation in the past several years due to recent technological advances, including machine learning and smart computing. The proposed work aims to develop a web application for crop selection supporting farmers and improving agricultural productivity. Various supervised machine learning classifiers are used for crop prediction based on environmental factors and soil conditions. Performance metrics are included in the evaluation of several machine learning models. The results attained show that the bagging classifier achieved better results for crop prediction with an accuracy of 97%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Smart agriculture

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Image Description Generation using Deep Learning: A Comprehensive Overview

Image Description Generation using Deep Learning: A Comprehe...

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2024 IEEE International Conference on Information technology, electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Jain, Shubh Zawar, Siddhant Rupchandani, Yash Chimanna, M.A. Pune Institute of Computer Technology Department of Electronics & Telecommunication Pune India

ISBN: (纸本)9798350382693

This survey paper explores the field of image description generation using deep learning, a critical intersection of computer vision and natural language processing. The paper provides a comprehensive overview of technical aspects, methods, datasets, and evaluation metrics relevant to image captioning. It delves into the challenges faced in automatically generating accurate descriptions for image content, emphasizing the importance of developing more effective methods in this domain. The integration of deep neural networks, specifically Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), is highlighted as a key enabler for achieving high-quality natural language descriptions of images. Various commonly used classification models and datasets are discussed, shedding light on the diverse range of approaches and resources available to researchers. Additionally, the paper presents a summary of reviewed research papers, showcasing the breadth and depth of advancements in image captioning and target recognition. It concludes with an invitation to future research, highlighting the vast potential and opportunities in this dynamic field. © 2024 IEEE.

关键词： Convolutional neural networks

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Advanced Aerial Medical Logistics: Cold Chain Supply and Organ Delivery Drone 4

Advanced Aerial Medical Logistics: Cold Chain Supply and Org...

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4th International Conference on Advancement in electronics and Communication Engineering, AECE 2024

作者： Bhayje, Sarvesh Kharche, Shubhangi Shetty, Sanmitha Gawade, Ved Sakhare, Sakshi Sies Graduate School of Technology Department of Electronics & Computer Science India

ISBN: (纸本)9798350364729

Organ transplantation is a life-saving medical procedure, but the increasing global demand for transplantable organs has exposed significant challenges, particularly in the timely and secure transportation of organs between donors and recipients. The success of these procedures often hinges on rapid and reliable organ delivery, where delays or mishandling can mean the difference between life and death. To address these challenges, the use of cold chain supply drones has emerged as a promising solution. By leveraging autonomous aerial technology, drones can transport organs efficiently while maintaining strict temperature control, crucial for organ viability. This survey paper explores the latest advancements in drone technology for organ transport, the legal frameworks governing their use, and the primary obstacles faced by the industry. Our research demonstrates a payload capacity of 1.2 kg for organ delivery drones and identifies quadcopters as the optimal platform. We further address temperature regulation within the organ storage box, ensuring precise preservation during transit. By combining stability analysis, payload capacity, and temperature control, our project aims to revolutionize organ delivery, making it more efficient, reliable, and accessible. © 2024 IEEE.

关键词： Drones

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Deep Learning Based Lipreading Assistant

Deep Learning Based Lipreading Assistant

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2024 OPJU International technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Somkuwar, Sameer Rathi, Amey Todankar, Somesh Yelalwar, R.G. Pune Institute of Computer Technology Department of Electronics and Telecommunication Pune India

ISBN: (纸本)9798350373783

Lipreading, the ability to understand speech by observing lip movements, offers a valuable communication modality in noisy environments and for individuals with hearing loss. However, the inherent ambiguity and subtlety of lip information present challenges for accurate interpretation. This paper surveys the evolution of lipreading models, exploring how they address these challenges. We examine the transition from traditional methods with separate feature extraction and prediction stages to recent advancements in deep learning. We analyze how these models capture the temporal context of lip movements, a crucial factor in lipreading proficiency. By investigating the strengths and limitations of existing models, this survey aims to identify potential future directions for lipreading technology, paving the way for more robust and accurate speech understanding from visual cues. © 2024 IEEE.

关键词： Speech recognition

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