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检索条件"机构=Dept. of Electrical and Computer Engineering and Technology"

共 55666 条记录，以下是441-450 订阅

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Improving Timing Quality Through Net Topology Optimization in Global Routing 37

Improving Timing Quality Through Net Topology Optimization i...

引用

37th IEEE International System-on-Chip Conference, SOCC 2024

作者： Yang, Jayoung Kim, Taewhan Seoul National University Dept. of Electrical and Computer Engineering Seoul Korea Republic of

ISBN: (纸本)9798350377569

Net topology construction is a major task in global routing of physical design since its outcome impacts the overall performance of the chips. The conventional global routers optimize the net topology considering wirelength, congestion, and timing. In this work, we propose a systematic and practical net topology refinement method to reinforce the timing robustness throughout the whole design process. Precisely, we develop a couple of effective techniques of net topology refinement that can be tightly linked to any of existing commercial and academic global routing tools: (1) a fast net topology exploration and (2) a parallel acceleration of net topology tuning. Through experiments, it is shown that our proposed technique is able to considerably enhance the timing metrics with no degradation in power consumption and wirelength. © 2024 IEEE.

关键词： Topology

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A Hybrid approach for Deepfake Detection using CNN-RNN

A Hybrid approach for Deepfake Detection using CNN-RNN

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

作者： Antad, Sonali Arthamwar, Vaishnavi Vijay Deshmukh, Rohan Kishor Chame, Akshay Uttam Chhangani, Harshita Pawan Vishwakarma Institute of Technology Dept. of Computer Engineering Pune India

ISBN: (纸本)9798350373783

Reliable deepfake detection of falsified videos generated by deep learning is still a pressing challenge with its growing popularity. Deep adversarial neural networks, which educate on film and target faces to ahead facial resources and facial expressions to targets, underneath DF can without difficulty idiot facial recognition structures the usage of revealed, 3-D photos mask, or video recordings from the valid consumer's face to sensors. We make significant contributions, including building adaptive datasets, comparing primitive and deep models for training, and tunable CNN-RNN tools. The overall evaluation model shows that the results are better than the findings of the most advanced methods. It was acknowledged that Deep faking today greatly impacts our environment;It is done by placing faces on original images/videos using deep neural networks. Together with the sharing of other misinformation via digital social networks, deepfake has formed digital fakery, which has become a real problem of negative social impact and as such there is a great need for successful measures to be taken to examine Deep Fakes [1]. The approach in this research generalizes by capturing physical cues in body shape and size. Our method combines CNN for spatial analysis, capturing facial features and skin texture, and LSTM and RNN for temporal analysis, monitoring expression progress. This method provides stable performance on complex deepfake videos. The modular design allows for future expansion, such as the integration of audio analysis. © 2024 IEEE.

关键词： Convolutional neural networks

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DC Fault Detection Using Time Derivative of High-Pass Filter Response in MTDC Systems 50

DC Fault Detection Using Time Derivative of High-Pass Filter...

引用

50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Khan, Saad Ahmed Ukil, Abhisek Nair, Nirmal-Kumar C. Dept. of Electrical Computer and Software Engineering The University of Auckland Auckland New Zealand

ISBN: (纸本)9781665464543

In the DC fault event, DC link capacitors immediately discharge a colossal current of high frequency. These large currents of high frequencies can instantly damage the components attached to the DC transmission system. Therefore, a rapid and robust DC fault detection topology is necessary, facilitating protection coordination in forthcoming Multi-Terminal Direct Current (MTDC) systems. This study introduces a novel fault detection technique using the time derivative of the High Pass Filter (HPF) response to identify fault currents in just a few microseconds (µs). This topology enables protection coordination for modern MTDC systems. Furthermore, this technique can identify different types of DC faults and remains unaffected by AC faults and load variation. © 2024 IEEE.

关键词： Chebyshev filters

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Experimental Evaluation of OTFS for Underwater Acoustic Communications 100

Experimental Evaluation of OTFS for Underwater Acoustic Comm...

引用

100th IEEE Vehicular technology Conference, VTC 2024-Fall

作者： Xue, Yukang Zhu, Xiyuan Khan, Uzma Zheng, Y. Rosa Lehigh University Dept. of Electrical & Computer Engineering BethlehemPA United States

ISBN: (纸本)9798331517786

This paper applies the Orthogonal Time-Frequency Space (OTFS) modulation to underwater mobile acoustic communications where the communication channel suffers from severe multipath and Doppler effects simultaneously. Field experiments have been conducted with acoustic transmission at a center frequency of 115 kHz and a data rate of 11.5 kilosymbols per second. We propose a new Turbo Decision Feedback Equalizer and Decoder (TDFED) for the receiver and the experimental results demonstrate the superior performance of the proposed TDFED algorithm over existing MMSE equalizers. © 2024 IEEE.

关键词： Decision feedback equalizers

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Kinematic Cycle Consistency Using Wearables and RF Data for Improved Human Skeleton Estimation 58

Kinematic Cycle Consistency Using Wearables and RF Data for ...

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58th Asilomar Conference on Signals, Systems and computers, ACSSC 2024

作者： Salehin, Sultanus Kearney, Sean Gurbuz, Z. The University of Alabama Dept. of Electrical & Computer Engineering TuscaloosaAL United States

ISBN: (纸本)9798350354058

Radar-based health monitoring has the potential to enable 24/7 in-home ambient sensing to increase accessibility to care in a privacy-protective, low-cost fashion. This paper focuses on the assessment of mobility and gait for early warning, post-treatment monitoring, and emergency response to health conditions affecting human movement. Current technology has focused on wearables for monitoring personalized health, but wearables are not effective if not worn. In this case radar can provide a backup source of health information, that can be utilized standalone or in conjunction with the information provided by a wearable. This paper explores the estimation of gait parameters and human pose (i.e. skeleton) using RF sensing alone as well as how wearables can be utilized with RF to improve estimation accuracy. In particular, we propose the concept of kinematic cycle consistency using RF and wearable-estimated kinematics to improve skeleton estimation accuracy. Our results are validated using a gold standard Vicon optical tracking system. © 2024 IEEE.

关键词： Musculoskeletal system

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Knee Osteoarthritis detection and classification using a customized CenterNet with DenseNet201 3

Knee Osteoarthritis detection and classification using a cus...

引用

3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Shirisha, N. Shilpa, K. Meghana, C.H. Rahman, Mohammed Abdul MLR Institute of Technology Dept. of Computer Science and Engineering Hyderabad India MLR Institute of Technology Dept. of Computer Science and Engineering Nalgonda India

ISBN: (纸本)9798331523893

Knee osteoarthritis (KOA) is a prevalent joint disorder diagnosed using imaging modalities like MRI, CT scans, and X-rays, with X-rays being the most cost-effective. Early detection is crucial for effective management. This study presents an automated deep learning approach to detect and classify KOA severity based on the Kellgren-Lawrence (KL) grading system using single posteroanterior standing knee X-ray images. Utilizing the Osteoarthritis Initiative dataset, we employed transfer learning to fine-tune DenseNet-201, enhancing model performance. Additionally, knowledge distillation was applied to reduce computational complexity while maintaining accuracy. Our model achieved over 95% accuracy on both testing and cross-validation datasets, outperforming existing methods. This approach offers a reliable tool for early KOA diagnosis and grading, potentially aiding clinical decision-making © 2025 IEEE.

关键词： Diagnostic radiography

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Automatic Recognition of Non-standard Number Plates using YOLOv8 18

Automatic Recognition of Non-standard Number Plates using YO...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Tupsounder, Asaavi Patwari, Renuka Ambati, Roja Chavarkar, Arya Shirsat, Kavita Vidyalankar Institute of Technology Dept. of Computer Engineering Mumbai India

ISBN: (纸本)9789380544519

A critical feature of vehicle movement applications is the detection and identification of a vehicle's License Plate (LP). Despite technological and algorithm developments, differences in LP properties by nation, such as number system, colors, character language, fonts, and size, necessitate more study to enhance detection and recognition. Despite substantial study, many systems function in well-defined contexts, necessitating the use of expensive equipment to capture photos from slow-moving vehicles or generate high-quality shots. This study describes a deep learning-based automated approach for LP detection and recognition, which is further separated into detection and character recognition. Yolov8 (You Only Look Once Version-8) was utilized for automatic pixel separation and gradient computation, making complicated model training easier. Using deep learning, Easy OCR (Optical Character Recognition) was utilized to retrieve text from number plates. Easy OCR (Optical Character Recognition) was utilized to extract text from license plates, with deep learning algorithms employed to effectively read text in difficult settings. For image and video processing, OpenCV (Open-Source computer Vision Library) was employed. The project titled 'Automatic Number Plate Using YOLOv8 Model' leverages the advanced capabilities of the YOLOv8 model to develop a robust system for automated number plate recognition, serving as the core component for real-time object detection and number plate localization within images and video streams. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： ANPR (Automatic Number Plate Recognition) Deep learning Image Recognition Non-Standard OpenCV YOLOv8

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Impact Analysis of Cyberattacks in Electric Propulsion Systems for Hybrid Tracked Vehicles

Impact Analysis of Cyberattacks in Electric Propulsion Syste...

引用

2024 NDIA Michigan Chapter Ground Vehicle Systems engineering and technology Symposium, GVSETS 2024

作者： Muriithi, Grace Papari, Behnaz Arsalan, Ali Khan, Asif Buraimoh, Elutunji Ozkan, Gokhan Timilsina, Laxman Edrington, Christopher Dept. Automotive Engineering Clemson University SC United States Dept. Electrical and Computer Engineering Clemson Universit United States

In the age of advancing digitalization and integrating complex electronics within modern vehicles, hybrid tracked vehicles (HTVs) are increasingly becoming susceptible to cybersecurity threats. Particularly vulnerable are the control units, which have become prime targets for adversarial exploitation due to their pivotal role in vehicle functionality. To address these security concerns, this study demonstrates two distinct yet equally harmful scenarios using replay and DoS attacks to uncover and evaluate vulnerabilities within the Energy Management System (EMS) of HEVs. In the replay attack scenario, the adversary surreptitiously alters the SoC control messages emanating from the Battery Management System (BMS). The attack is calibrated to strategically time and sequence the message replays across various operational states using reinforcement learning, thereby maintaining apparent legitimacy within expected SoC ranges to evade detection. The DoS attack, however, presents a more direct threat by targeting the generator's revolution speed sensors. By compromising these sensors, the attack disrupts the generator and causes subsequent engine shutdowns, compelling the battery to singularly meet the vehicle's power supply demands. Formal attack models are developed and subsequently deployed on a simulated HTV platform within MATLAB/Simulink to analyze the impacts of these cyberattacks. The simulation results illustrate the impacts of the simulated cyberattacks, further reinforcing the importance of resilient and adaptive security measures for the control layer. © Rights reserved by the National Defense Industrial Association (NDIA) Michigan Chapter, authors and their respected organizations.

关键词： Battery management systems

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Design of a Brain Controlled Robotic Arm: Initial Experimental Testing 21

Design of a Brain Controlled Robotic Arm: Initial Experiment...

引用

21st International Multi-Conference on Systems, Signals and Devices, SSD 2024

作者： Aljedaani, Lama Taha Abdelhedi, Fatma Aldahasi, Renad Abdullah Batheeb, Amal Abdullah Engineering King Abdulaziz University Dept. of Electrical and Computer Jeddah Saudi Arabia

ISBN: (纸本)9798350374131

In recent years, a significant leap has been made in various techniques to control robotics to assist people with upper limb amputations. Individuals with limb amputations have been facing significant challenges in their daily lives, where the simple and daily tasks become much more complex and take longer time, or even impossible, without depending on other people. Furthermore, traditional assistive devices often require significant physical effort, and they may be unable to perform different tasks efficiently, thus restricting the amputee's independence. Consequently, there is a need for an advanced assistive device to provide greater control and functionality to improve upper limb amputee independence. The Brain-Controlled Robotic Arm project aims to develop an advanced assistive device that allows individuals with disabilities to control a robotic arm using their brain signals. The project applies brain-computer interface technology (BCI) that allows the human brain to interact with a digital computer through electroencephalogram (EEG) technology. In this project, the robotic arm utilizes an electroencephalogram (EEG) to capture brain signals and then process those signals to get the required signal in order to control the robotic arm. © 2024 IEEE.

关键词： Brain computer interface

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IMU-Based Model for Precise Toothbrush Stroke and Orientation Tracking

IMU-Based Model for Precise Toothbrush Stroke and Orientatio...

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2024 IEEE Region 10 Conference, TENCON 2024

作者： Islam Bhuyan, Mohammad Ariful Uddin, Md Mayen Kim, Hyeon Cheol University of Ulsan Dept. of Electrical Electronic and Computer Engineering Ulsan Korea Republic of

ISBN: (纸本)9798350350821

This paper presents a model designed for precise orientation estimation and stroke distance tracking of toothbrush activity. The model leverages Inertial Measurement Unit (IMU) technology, which includes accelerometers and gyroscopes, to capture detailed motion data during the activity. The proposed Corrective Fusion Filter Technique (CFFT) helps integrate accelerometer and gyroscope data with several corrective steps to estimate orientation precisely, and the combined sensor data accurately measures stroke distance. This filtering method also minimizes sensor noise and drifting issues, enhancing the model's reliability and performance. © 2024 IEEE.

关键词： Accelerometers

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