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2024 IEEE Students Conference on engineering and Systems, SCES 2024

作者： Rahull, G. Ram Nivas, D. Vinisha, V. Yeshwanthkumar, S. Shivasanthosh, B. KPR Institute of Engineering and Technology Electronics and Communication Engineering Tamil Nadu Coimbatore641407 India

ISBN: (纸本)9798350374711

The rapid expansion of the automobile industry has brought to light critical safety issues, requiring for extreme caution throughout the assembly and manufacturing of vehicles. Defect identification in automotive components has therefore gained significant importance. This paper offers a novel method of deep learning for defect detection in auto accessories, moving from manual inspection to machine vision approaches. The core components of this system comprise YOLOv5, web cameras, and Raspberry Pi boards. The primary aim is to seamlessly detect and categorize defects within the manufactured automobile accessory products during the production phase. The YOLOv5 algorithm serves as the foundation for defect identification in automobile spare parts. Unlike employing other models for detecting the spare parts this research work relies on the implementation of YOLOv5 due to its suitability for real-time object detection tasks. Additionally, as YOLOv5 allows for the modification of accuracy criteria based on application requirements unlike predefined rates associated with other models. It is significant that the implementation of YOLOv5's advanced computer vision capabilities, the system ensures precise differentiation between defective and non-defective items, thereby guaranteeing that only top-quality products meet the stringent standards for distribution. Moreover, our system seamlessly transmits the gathered data to an Internet of Things (IoT) platform, leveraging Raspberry Pi boards and web cameras for integration. This IoT integration empowers real-time monitoring and comprehensive data analysis. Our technology significantly enhances product quality, optimizes manufacturing processes, and improves the company's competitive advantage in the automobile accessory market by enabling rapid decision-making and prompt corrective action, thus enhancing overall product quality, manufacturing efficiency, and the company's competitive edge in the automotive accessory market. ©

关键词： Automotive industry

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A Comparative Analysis of 8-Bit Parallel Prefix Adder Architectures 5

A Comparative Analysis of 8-Bit Parallel Prefix Adder Archit...

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5th International Conference on Smart electronics and communication, ICOSEC 2024

作者： Bharathi, M. Madhurima, V. Sandhyakumari, G. Poornima, M. Tabassum, Saleha Mohan Babu University Electronics and Communication Engineering Andhra Pradesh Tirupati India S V College of Engineering Electronics and Communication Engineering AndraPradesh Tirupati India Siddartha Institute of Science and Technology Electronics and Communication Engineering AndraPradesh Puttur India Ksrmce Electrical and Electronics Engineering AndraPradesh Kadapa India

ISBN: (纸本)9798331504403

Digital adders are crucial parts of contemporary computer systems that influence the efficiency of specific arithmetic processes. Parallel prefix adders, like Sparse Kogge, Hancarlson and Brent Kung enhance performance by calculating results leading to faster and more efficient arithmetic operations compared to traditional adders that process data sequentially. This paper thoroughly compares four 8 bit designs;Sparse Kogge, Han Carlson, Brent Kung and Binary Coded Decimal (BCD) adder. Evaluation criteria include propagation latency (ns) power consumption (W) and area utilization (LUTS). When comparing the Brent Kung adder with the BCD adder there is a 18.33% increase in propagation delay but a significant 83.87% decrease in power consumption and a fifty percent reduction in area usage. The primary focus of the Hancarlson adder is power efficiency leading to an 87.10% drop in power usage but a substantial 93.49% increase in propagation latency. The Sparse Kogge adder strikes a balance with a 58.06% decrease in power consumption and a moderate 18.75% reduction in area utilization while maintaining a propagation delay, as the BCD adder. These results illustrate trade-offs between various adder architectures in terms of power, latency, and area utilization. These designs are designed and simulated using Xilinx Vivado 2022.2 software. © 2024 IEEE.

关键词： Binary codes

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An Automatic Diagnostic Tool for Autism Spectrum Disorder using Structural Magnetic Resonance Imaging and a Tailored Binary Coded Genetic Algorithm

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IEIE Transactions on Smart Processing and Computing 2024年第3期13卷 236-242页

作者： Sachnev, Vasily Suresh, Mahanand Belathur Department of Information Communication and Electronics Engineering Catholic University of Korea Korea Republic of Department of Information Science and Engineering Computing Sri Jayachamarajendra College of Engineering JSS Science and Technology University Mysuru India

In this paper, we propose an automatic diagnostic tool for autism based on machine learning and structural MRI. A set of 989 relevant features extracted from structural magnetic resonance imaging (MRI) present in the ABIDE database was used. The tool has two steps: searching for the best set of features using a tailored binary coded genetic algorithm and then using the selected set of features to train a classifier using an extreme learning machine. The trained classifier can efficiently identify autism spectrum disorder (ASD) versus healthy controls. The proposed tailored binary coded genetic algorithm uses a statistical selective crossover designed specifically for the classification problem, which significantly speeds up the genetic algorithm and improves the classification of ASD versus healthy controls. Extensive experiments with the proposed automatic autism diagnostic tool clearly indicate the advantages of the proposed method compared to previous approaches. Copyrights © 2024 The Institute of electronics and Information Engineers.

关键词： Genetic algorithms

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Multi-Class Skin Cancer Detection Using Fusion of Textural Features Based CAD Tool

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Computers, Materials & Continua 2024年第12期81卷 4217-4263页

作者： Khushmeen Kaur Brar Bhawna Goyal Ayush Dogra Sampangi Rama Reddy Ahmed Alkhayyat Rajesh Singh Manob Jyoti Saikia Department of Electronics and Communication Engineering Chandigarh UniversityMohali140413India Chitkara University Institute of Engineering and Technology Chitkara UniversityChandigarh140401India Department of Physics&Electronics School of SciencesJAIN(Deemed to be University)Bangalore560001India College of Technical Engineering The Islamic UniversityNajaf54001Iraq Department of Electronics and Communication Engineering Uttaranchal Institute of TechnologyUttaranchal UniversityDehradun248007India Electrical and Computer Engineering Department University of MemphisMemphisTN 38152USA Biomedical Sensors&Systems Lab University of MemphisMemphisTN 38152USA

Skin cancer has been recognized as one of the most lethal and complex types of cancer for over a *** diagnosis of skin cancer is of paramount importance,yet the process is intricate and *** analysis and modeling of human skin pose significant difficulties due to its asymmetrical nature,the visibility of dense hair,and the presence of various substitute *** texture of the epidermis is notably different from that of normal skin,and these differences are often evident in cases of unhealthy *** a consequence,the development of an effective method for monitoring skin cancer has seen little ***,the task of diagnosing skin cancer from dermoscopic images is particularly *** is crucial to diagnose skin cancer at an early stage,despite the high cost associated with the procedure,as it is an expensive ***,the advancement of diagnostic techniques for skin cancer has been *** address this issue,there is a need for a more accurate and efficient method for identifying and categorizing skin cancer *** involves the evaluation of specific characteristics to distinguish between benign and malignant skin cancer *** present and evaluate several techniques for segmentation,categorized into three main types:thresholding,edge-based,and *** techniques are applied to a dataset of 200 benign and melanoma lesions from the Hospital Pedro Hispano(PH2)*** evaluation is based on twelve distinct metrics,which are designed to measure various types of errors with particular clinical ***,we assess the effectiveness of these techniques independently for three different types of lesions:melanocytic nevi,atypical nevi,and *** first technique is capable of classifying lesions into two categories:atypical nevi and melanoma,achieving the highest accuracy score of 90.00%with the Otsu(3-level)*** second technique also classifies lesions int

关键词： Melanoma computer-aided diagnosis segmentation PH2 ISIC(International Skin Imaging Collaboration) dermoscopy non-melanoma

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AppleDiseaseNet-B7: A Robust Solution for Apple Leaf Disease Classification and Model Evaluation 4

AppleDiseaseNet-B7: A Robust Solution for Apple Leaf Disease...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Mathias, Ajisha Raj, J. Relin Francis Raja, S.R. Krishnan, R. Santhana Balamuralikrishnan, G. Soundiraraj, N. Vel Tech Rangarajan Dr Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Avadi Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Hindustan Institute of Technology and Science Chennai India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India Psna College of Engineering and Technology Department of Electrical and Electronics Engineering Dindigul India

ISBN: (纸本)9798331540364

Apple leaf diseases, like cedar apple rust, black rot, and apple scab, pose severe obstacles to apple farming and impact crop productivity and quality. The sophisticated classification model AppleDiseaseNet-B7, known for its efficiency and outstanding performance, was created using the EfficientNetB7 architecture and is presented in this paper. The model improves its feature extraction capabilities by utilizing pretrained weights from ImageNet, which makes it a good fit for differentiating between various apple leaf diseases. AppleDiseaseNet-B7 incorporates numerous unique layers intended to increase classification precision. A Global Average Pooling layer streamlines feature maps, complicated patterns are captured by a dense layer with ReLU activation, overfitting is reduced by a dropout layer, and class prediction is accomplished by a final dense layer with softmax activation. The model's resilience is further increased using thorough data preparation, and augmentation approaches from the PlantVillage and Kaggle Apple Leaf Disease datasets. A comprehensive comparison is made between AppleDiseaseNet-B7's performance and other well-known models, including DenseNet121, ResNet50, and AlexNet. The analysis shows that AppleDiseaseNet-B7 regularly performs better than comparable models in terms of accuracy, precision, recall, and F1 score, among other criteria. This comparison demonstrates how well AppleDiseaseNet-B7 performs in classifying apple leaf diseases, providing a reliable and effective method for managing and detecting agricultural diseases. The study emphasizes how crucial sophisticated model selection is to enhancing crop health monitoring and disease control. © 2024 IEEE.

关键词： Fruits

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Design and Development of an e-Health Package for Voice Pathology Detection 5th

Design and Development of an e-Health Package for Voice P...

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5th International Conference on Computing and Network communications, CoCoNet 2023

作者： Rai, Shiksha Rishita, S. Lasya, Kandalam Srihari Vekkot, Susmitha Department of Electronics and Communication Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru560035 India

ISBN: (纸本)9789819747108

In this paper, a speech-based detection system is developed for voice pathologies like GERD, laryngitis, bulbar palsy, vocal fold leukoplakia, cyst, dysarthria, and aphasia. The e-health set-up comprises three modules for the detection of specific voice disorders leading to degradation in voice quality. Classification of these pathologies is performed using various machine learning algorithms before and after data balancing using SMOTE. The maximum performance is obtained for CatBoost with an accuracy of 56% for the detection of voice disorders before data balancing and 93% post-data balancing. An accuracy of 86.5% is obtained by random forest for detection of dysarthria and an accuracy of 88% is obtained for aphasia using the CatBoost classifier. Further, to make an improvement in the selected features we have customized a generalized feature set for the classification of voice disorders. All the components are effectively incorporated into a user-friendly e-health package, ensuring non-invasive, and precise diagnosis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electronic health record

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Remote Interference Source Localization: A Multi-UAV-Based Cooperative Framework

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Chinese Journal of electronics 2022年第3期31卷 442-455页

作者： WU Guangyu GU Jiangchun Department of Computer Science and Technology University of Science and Technology of China School of Communication Engineering Army Engineering University of PLA

Interference source localization with high accuracy and time efficiency is of crucial importance for protecting spectrum resources. Due to the flexibility of unmanned aerial vehicles(UAVs), exploiting UAVs to locate the interference source has attracted intensive research interests. The off-the-shelf UAV-based interference source localization schemes locate the interference sources by employing the UAV to keep searching until it arrives at the target. This obviously degrades time efficiency of localization. To balance the accuracy and the efficiency of searching and localization, this paper proposes a multi-UAV-based cooperative framework alone with its detailed scheme, where search and remote localization are iteratively performed with a swarm of UAVs. For searching, a low-complexity Q-learning algorithm is proposed to decide the direction of flight in every time interval for each UAV. In the following remote localization phase, a fast Fourier transformation based location prediction algorithm is proposed to estimate the location of the interference source by fusing the searching result of different UAVs in different time intervals. Numerical results reveal that in the proposed scheme outperforms the stateof-the-art schemes, in terms of the accuracy, the robustness and time efficiency of localization.

关键词： unmanned aerial vehicles low-complexity Q-learning algorithm path planning fast Fourier transforms remote interference source localization multi-robot systems UAV swarm mobile robots remote localization location prediction algorithm aerospace robotics autonomous aerial vehicles learning (artificial intelligence) fast Fourier transformation cooperative communication spectrum resources radiofrequency interference mobile radio flight direction multiUAV-based cooperative framework

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Universal and Reversible Gate Design in Quantum-dot Cellular Automata Nanotechnology

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Micro and Nanosystems 2023年第4期15卷 308-321页

作者： Sharma, Vijay Kumar Riyaz, Sadat School of Electronics and Communication Engineering Shri Mata Vaishno Devi University Katra182320 India

Background: Growing progress in the field of nanoelectronics necessitates ever more advanced nanotechnology due to the continued scaling of conventional devices. For the purpose of fabricating current integrated circuits (ICs), Quantum-dot cellular automata (QCA) nanotechnology is the most suitable substitute for complementary metal oxide semiconductor (CMOS) technology. The problem of short-channel secondary effects at the ultra-nanoscale level confronts CMOS technology. Aims: QCA nanotechnology overcomes the issues of conventional logic circuit design methods due to its numerous advantages. This research work aims to design an energy-efficient, reliable, universal, 3×3, and reversible logic gate for the implementation of various logical and Boolean functions in QCA nanotechnology. Objective: It is desirable for portable systems to have a small size, extremely low power consumption, and a clock rate in the terahertz. As a result, QCA nanotechnology is an incredible advancement for digital system applications and the design of future systems. Methods: This research article proposes a novel, ultra-efficient, multi-operative, 3×3 universal reversible gate implemented in QCA nanotechnology using precise QCA cell interaction. The proposed gate is used for the implementation of all the basic logic gates to validate its universality. The implementation of all thirteen standard Boolean functions establishes the proposed gate's multi-operational nature. The energy dissipation analysis of the design has been presented for the varying setups. Results: The proposed gate is area-efficient because it uses minimum QCA cells. Various logical and Boolean functions are effectively implemented using the proposed gate. The result analysis establishes the minimum energy dissipation of the proposed design and endorses it as an ultra-efficient design. Conclusion: The QCA cell interaction method demonstrates the best way to design a universal, reversible, and multi-operative gate. © 2023

关键词： Nanocrystals

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Resilient Framework for Next-Generation Heterogeneous Wireless Networks in Disaster Management 4

Resilient Framework for Next-Generation Heterogeneous Wirele...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Mathias, Ajisha Raj, J. Relin Francis Raja, S.R. Krishnan, R. Santhana Balamurugan, S. Rajkumar, G.Vinoth Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Avadi Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Hindustan Institute of Technology and Science Chennai India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Velammal College of Engineering and Technology Department of Mathematics Madurai India J.P. College of Engineering Department of Electronics and Communication Engineering Tenkasi India

ISBN: (纸本)9798331540364

The growing occurrence and concentration of natural and artificial disasters necessitate the development of robust communication systems capable of maintaining reliable and efficient connectivity under adverse conditions. Heterogeneous wireless networks (HSNs) have emerged as a promising solution, offering the flexibility to integrate various wireless technologies and adapt to changing environments. However, the inherent complexity and dynamic nature of HSNs present significant challenges in ensuring resilience and reliability, particularly in disaster scenarios where network infrastructure may be severely compromised. This research investigates a Resilient Framework for Next Generation Heterogeneous Wireless Networks in Disaster Management, focusing on enhancing communication reliability and efficiency during critical scenarios. The study introduces Network Coding Enhanced COPE (NC-COPE), a novel algorithm integrating network coding principles into the COPE framework. Simulation analysis compares NC-COPE with existing algorithms: Optimized Link State Routing (OLSR), Coding-Aware Routing (CAR), and Dynamic Source Routing (DSR) using metrics such as Packet Delivery Ratio (PDR), Routing Overhead, and Resilience to Node Failures. NC-COPE demonstrates superior performance in maintaining high PDR, reducing routing overhead, and enhancing network resilience under node failures. The findings underscore NC-COPE's potential to mitigate communication challenges in disaster management scenarios, highlighting its role in future wireless network deployments. © 2024 IEEE.

关键词： communication Reliability Disaster Management Heterogeneous Wireless Networks Network Coding Packet Delivery Ratio Resilient Framework Routing Overhead

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A Comprehensive Survey on Technologies in Video-based Event Detection and Recognition Using Machine Learning and Deep Learning Techniques 2

A Comprehensive Survey on Technologies in Video-based Event ...

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2nd IEEE International Conference on Networks, Multimedia and Information technology, NMITCON 2024

作者： Jyothi, H. Komala, M. Mallikarjunaswamy, S. Sjb Institute of Technology Dept of Electronics and Communication Engineering Bengaluru India Jss Academy of Technical Eduction Dept of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350372892

Video-based event recognition systems are integral to numerous applications ranging from security surveillance to automated content analysis and interaction in smart environments. This survey provides a comprehensive review of the current methodologies and technologies employed in video-based event recognition. The research begin by exploring the foundational concepts of video processing and event detection, highlighting the evolution of techniques from rule-based systems to advanced deep learning models. The core of the survey examines various approaches, including object recognition, motion analysis, and contextual understanding, which enable the identification and classification of events in video data. This research discusses the integration of multimodal data sources to enhance recognition accuracy and robustness. Additionally, the survey addresses the challenges faced in real-world deployments such as scalability, real-time processing demands, and privacy concerns. Future trends are contemplated, focusing on the potential impacts of emerging technologies like augmented reality and edge computing on the development of more sophisticated and efficient event recognition systems. This survey aims to provide a clear and structured overview of the field, encouraging further research and innovation in developing smarter, more responsive video-based event recognition technologies. © 2024 IEEE.

关键词： Video analysis

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