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14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022

作者： Tayem, Nizar Hussain, Ahmed A Moinuddeen, AbuMuhammad Radaydeh, Redha M. Alghazo, Jaafar M. Texas A & M Univ. Commerce Dept. of Engineering & Technology United States Prince Mohammad Univ. Dept. of Electrical Engineering Saudi Arabia Texas A & M Univ. Commerce Dept. of Computer Science United States Virginia Military Institute Electrical & Computer Engg. Dept. United States

ISBN: (纸本)9781665487719

Two-dimensional direction of arrival estimation is a computationally complex problem that has been the focus of research in the area of array signal processing for several decades now. This paper proposes a novel2D DOA azimuth and elevation angle estimation algorithm for multiple RF noncoherent sources using an L-shaped array antenna configuration. The proposed method employs a MUSIC-like estimation algorithm in conjunction with QR decomposition to improve the accuracy of 2D DOA estimation and provide much lower computational complexity when compared with existing methods such as the Capon method. It does not require knowing the number of sources in advance and pairs the estimated azimuth and elevation angles automatically for multiple sources. Simulation results are presented to validate the efficacy of the proposed method and its performance is compared with the Capon method. © 2022 IEEE.

关键词： Computational complexity

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Visual Traits-Based Recommendation System for Proactive Retailing in Physical Store Environment

Visual Traits-Based Recommendation System for Proactive Reta...

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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Kh'ng, Xin Yi Ooi, Boon Yaik Teoh, Sheng Kang Ooi, Boon Sheng Chen, Yen-Lin Universiti Tunku Abdul Rahman Department of Computer Science Kampar Malaysia Xilnex Web Bytes Sdn. Bhd. Penang Malaysia National Taipei University of Technology Dept. of Computer Science and Information Engineering Taipei Taiwan

ISBN: (纸本)9798350324174

A comprehensive visual traits-based recommendation system is designed for proactive retailing in a physical store environment. The proposed system utilizes computer vision algorithms to analyze various visual traits of customers, including facial features, clothing and accessories, to provide targeted product recommendations. The system does not require customers to provide any personal information, making it a less intrusive and more hassle-free approach compared to the conventional membership approach. By providing tailored product suggestions, the system offers retailers the opportunity to enhance their customers' shopping experience and increase sales. This paper provides a detailed description of the system's design and setup. © 2023 IEEE.

关键词： Recommender systems

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Optical Mark Recognition with Object Detection and Clustering

Optical Mark Recognition with Object Detection and Clusterin...

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International Conference on electrical engineering and Information & Communication Technology (ICEEICT)

作者： Kaifa Tabassum Zhalok Rahman Dept. of Computer Science and Engineering Shahjalal University of Science and Technology Dhaka Bangladesh

ISBN: (数字)9798350385779

ISBN: (纸本)9798350385786

OMR, or optical mark recognition, is the automated analysis of human-marked documents. Presently, many competitive examinations rely on multiple-choice questions, and the answers to these questions are recorded in OMR sheets. In this study, we present an automated answer detection system from an OMR sheet image using computer vision techniques. Typically, evaluating an OMR sheet from an image involves scanning, extracting data, and interpreting the results through various image processing techniques. However, traditional approaches have limitations. They cannot detect empty responses or recognize multiple choices properly without a specified layout or total number of questions stated. We took a unique approach by employing an object detection model, YOLOv8n, to detect and classify OMR marks. To address overlapping bounding boxes, we implemented the DBSCAN clustering algorithm to effectively group the columns and determine question order. Our system is designed to work with multiple layouts, as it can detect columns through clustering. Furthermore, it can identify unanswered questions as well as multiple marked options. To train and evaluate our model, we created a custom dataset with five different OMR sheet layouts. We achieved a remarkable precision of 96.5%, a recall of 99.8%, and an mAP (mean average precision) of 97.3%. These results demonstrate the efficacy of our approach to OMR sheet analysis.

关键词： Integrated optics Image recognition Codes Text analysis Optical recording Layout Clustering algorithms

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VLC Transceiver SoC Design for Real-Time Indoor-Tracking in Smart Hospitals

VLC Transceiver SoC Design for Real-Time Indoor-Tracking in ...

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IEEE World Forum on Internet of Things (WF-IoT)

作者： Zijin Pan Tian Lang Gang Chen Albert Wang Dept. of Electrical and Computer Engineering University of California Riverside Riverside CA USA

ISBN: (数字)9798350373011

ISBN: (纸本)9798350373028

Visible light communication (VLC) technique is well developed and proved to be a practical solution for real-time indoor tracking application, especially in hospitals where radiofrequency (RF) wireless technologies are forbidden due to potential interference hazards. As a continuous work for the LED-based VLP/PLC hybrid indoor tracking system, a fully integrated system-on-chip (SoC) design for the optical transceiver is proposed and discussed in this paper. The SoC integrated with transmitter, receiver and power supply circuits was designed on a commercial 45nm CMOS integrated circuit technology enabling the modulation and demodulation process for multiple frequencies. This work validated the feasibility of developing an efficient and low-cost tracking system using LED VLC technology for future smart hospitals.

关键词： Radio frequency Hospitals Wireless networks Light emitting diodes Transceivers Real-time systems System-on-chip Optical transmitters Visible light communication Demodulation

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DDoS Attack using SYN Flooding: A Case Study 9

DDoS Attack using SYN Flooding: A Case Study

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9th International Conference on Computing for Sustainable Global Development, INDIACom 2022

作者： Kukreti, Sambhavi Modgil, Sumit Kumar Gehlot, Neha Kumar, Vinod Shree Guru Gobind Singh Tricentenary University Dept. of Computer Science and Engineering Gurgaon India

ISBN: (纸本)9789380544441

Undoubtedly, technology has not only transformed our world of work and lifestyle, but it also carries with it a lot of security challenges. The Distributed Denial-of-Service (DDoS) attack is one of the most prominent attacks witnessed by cyberspace of the current era. This paper outlines several DDoS attacks, their mitigation stages, propagation of attacks, malicious codes, and finally provides redemptions of exhibiting normal and DDoS attacked scenarios. A case study of a SYN flooding attack has been exploited by using Metasploit. The utilization of CPU frame length and rate have been observed in normal and attacked phases. Preliminary results clearly show that in a normal scenario, CPU usage is about 20%. However, in attacked phases with the same CPU load, CPU execution overhead is nearly 90% or 100%. Thus, through this research, the major difference was found in CPU usage, frame length, and degree of data flow. Wireshark tool has been used for network traffic analyzer. © 2022 Bharati Vidyapeeth, New Delhi.

关键词： Denial-of-service attack

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Machine Learning Approaches for Survival Prediction in Covid-19 Patients: A Comparative Analysis

Machine Learning Approaches for Survival Prediction in Covid...

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Advances in Computing,Communication, electrical, and Smart Systems (iCACCESS), International Conference on

作者： Md. Muhiminul Islam M. I. R. Shuvo Dept. of Computer Science and Engineering Khulna University of Engineering & Technology Khulna Bangladesh

The COVID-19 pandemic has underscored the significance of accurately predicting patient survival in order to promptly administer efficient medical care. The augmentation of biological and healthcare service data volume has been found to enhance the precision of disease prognosis, survival prediction, and other clinical assessments. Numerous biological characteristics serve as the underlying factors contributing to the etiology of numerous diseases. Therefore, it is imperative to have precise medical data that possesses appropriate characteristics in order to facilitate an analysis that exhibits exceptional clinical accuracy. In order to effectively analyze data, it is imperative to employ a machine learning model that is both exact and accurate in predicting sickness or survival outcomes. An expeditious and accurate assessment of the disease's magnitude is crucial during a particular phase of a pandemic, such as the Covid-19 outbreak. The primary aim of this research is to employ machine learning methodologies in order to forecast the survival outcomes of individuals diagnosed with COVID-19. This will be accomplished by using a publicly available dataset comprising various medical attributes pertaining to 383,499 COVID-19 patients, which was collected and made accessible by the Directorate General of Epidemiology, Secretariat of Health in Mexico. Various machine learning techniques, including Regression methods, Artificial Neural Networks, Random Forest Classifier, Support Vector Machine, AdaBoost, and XGBoost, are employed on the dataset that has undergone diverse preprocessing procedures. The experimental findings demonstrated that the system yielded numerous advantages in comparison to previous efforts in the same field.

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Smart Traffic Management in Green VANETs: Ant-Inspired Routing Strategies with Local Search Optimization

Smart Traffic Management in Green VANETs: Ant-Inspired Routi...

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2024 International Conference on Recent Innovation in Smart and Sustainable Technology, ICRISST 2024

作者： Thangam, S. Jahnavi, M. Chandana, K. Dheeraj, K. Janet Kumari, J Jesy Saleem Raja, A. Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Bengaluru India University of Technology and Applied Sciences-Shinas College of Computing and Information Sciences It Department Oman

ISBN: (纸本)9798350309140

Green Vehicular Ad-hoc Networks (VANETs) are gaining significance in smart mobility because of growing concerns about the environment. The research introduces a novel method for managing traffic in such networks by combining ant-inspired routing methods with local search optimization approaches. The solution, which draws inspiration from the efficient path-finding abilities of ants, facilitates decentralized communication and sharing of traffic data among cars. It allows rapid adaptation to changing traffic circumstances and environmental elements. Local search optimization enhances routing decisions by considering the near vicinity of each vehicle. Extensive simulations demonstrate notable enhancements in traffic efficiency, decreased congestion, and improved energy conservation as compared to conventional routing methods. The suggested solutions also enhance the development of environmentally friendly and enduring transportation networks. © 2024 IEEE.

关键词： Vehicular ad hoc networks

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Comparative Study of Transmission Expansion Planning with Conventional and Unconventional High Surge Impedance Loading (HSIL) Lines

Comparative Study of Transmission Expansion Planning with Co...

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IEEE General Meeting Power& Energy Society

作者： Bhuban Dhamala Mona Ghassemi Dept. of Electrical and Computer Engineering The University of Texas at Dallas Richardson TX USA

ISBN: (数字)9798350381832

ISBN: (纸本)9798350381849

The strategic planning of transmission expansion is paramount in ensuring the reliability and efficiency of power systems, particularly in the context of growing electricity demand and the integration of renewable energy sources. This paper investigates the utilization of unconventional high surge impedance loading (HSIL) lines in transmission expansion planning (TEP) and offers a comparative analysis of their performance against conventional line-based TEP methods. Commencing with a 17-bus 500 kV test system known for its robust operation under normal and all single contingencies at different loading scenarios, the objective is to connect a new load at a new location. Meticulously examining and comparing the number of lines and right of way (ROW) required for both methods while maintaining uniform conductor weight per circuit, the effectiveness of unconventional HSIL lines within the TEP context is assessed.

关键词： Renewable energy sources Power transmission lines Systems operation Loading Contingency management Strategic planning Conductors Impedance Reliability Surges

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3D human skeleton keypoint detection using RGB and dept. image

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Transactions of the Korean Institute of electrical Engineers 2021年第9期70卷 1354-1361页

作者： Jeong, Jeongseok Park, Byeongjun Yoon, Kyoungro Dept. of Smart ICT Convergence Engineering Konkuk University Korea Republic of Dept. of Computer Science and Engineering Konkuk University Korea Republic of

As computing technology advances, tasks those are used to judge human behavior with the eyes are turning into tasks those computers try to judge human behavior through keypoint detection. Accordingly, in this paper, we propose a 3D human skeleton keypoint detection system using RGB and dept. images acquired by Azure Kinect's RGB camera and dept. camera, respectively. The 3D human skeleton keypoint detection system proposed in this paper detects 2D human skeleton keypoints from RGB images, and uses dept. value acquired to project the detected 2D human skeleton keypoints onto the dept. image. However, when detecting 3D human skeleton keypoints in such method, the human skeleton keypoints are projected onto the surface of human body. To solve this problem, the skeleton keypoints provided by Azure Kinect is used to calibrate the dept. value of the extracted keypoints. Copyright © The Korean Institute of electrical Engineers.

关键词： Cameras

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Testing a Transistor-Level Programmable Fabric: Challenges and Solutions

Testing a Transistor-Level Programmable Fabric: Challenges a...

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VLSI Test Symposium

作者： Apurva Jain Thomas Broadfoot Carl Sechen Yiorgos Makris Electrical and Computer Engineering Dept. The University of Texas at Dallas Richardson TX USA

ISBN: (数字)9798350363784

ISBN: (纸本)9798350363791

Test vector generation for a TRAnsistor-level Programmable (TRAP) fabric faces a number of feasibility and efficiency challenges. The former are caused by (i) the use of bi-directional pass transistors, which are beyond the capabilities of commercial Automatic Test Pattern Generation (ATPG) tools, and (ii) the design specifics of TRAP, which result in certain stuck-at faults not being logically testable and calling for a quiescent current-based test solution instead. The latter are caused by the fact that ATPG tools are oblivious to (i) the difference between programming bits and regular inputs, which results in lengthy test application times, and (ii) the role that different modules in the architecture of TRAP play in establishing logic circuits, which results in lengthy unguided exploration of a very large functional space to establish appropriate vector justification and response propagation paths. To address these challenges, we explore an array of solutions including (i) employing TRAP instances where bi-directional transistors are replaced by uni-directional ones, (ii) generating custom IDDQ tests, (iii) expressing test application time as the optimization objective of an Integer Linear Program (ILP) formulation, and (iv) leveraging design knowledge, resulting in perfect stuck-at fault coverage of TRAP and an order-of-magnitude savings in test application time.

关键词： Bidirectional control Programmable logic arrays Automatic test pattern generation Very large scale integration Programming Vectors Fabrics

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