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14th IEEE International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, HNICEM 2022

作者： Torres, Ronnier Franz H. Garcia, Ramon Mapua University School of Electrical Electronics and Computer Engineering Manila City Philippines

ISBN: (纸本)9781665464932

Vehicle related accidents happens at urban areas, this includes, national roads, municipal roads, provincial roads and crowded boulevard streets and most of the captured vehicle license plate by the CCTV that is involve in a hit and run incident was blurred and not clear enough to identify and trace the perpetrators. Several studies have presented to restore a blurred image by performing different deconvolution techniques. Most of this researches focuses only on removing the blur effect on a non-moving object and mostly in a situation where there is an enough light in the surrounding and the blur parameter was either added or setup intentionally or user-defined. In this paper, the study aims to restore a blurred image specifically on a license plate of a moving test vehicle using Lucy-Richardson Algorithm from a real video footage of a surveillance camera subjected in different light condition. In addition to that, the vehicle will also be subjected to different speeds to know its effect on its restoration. The study will significantly test whether the deconvolution algorithm can restore a real motion blurred license plate image exposed at different lighting conditions test in different car speeds. © 2022 IEEE.

关键词： Restoration

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Biomedical System-on-Chip Design with Integrated Body-Coupled Powering

Biomedical System-on-Chip Design with Integrated Body-Couple...

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2023 IEEE Biomedical Circuits and Systems Conference, BioCAS 2023

作者： Zhu, Yingjie Fong, Jia Yi Lin, Longyang Yoo, Jerald Li, Jiamin Southern University of Science and Technology School of Microelectronics Shenzhen China National University of Singapore Department of Electrical and Computer Engineering Singapore

ISBN: (纸本)9798350300260

Capacitive Body-Coupled Powering (BCP) has been demonstrated as an effective power delivery solution for wearable biomedical system-on-chips (SoC), thanks to its improved path loss compared to the RF-based transmission in the body area, and its insusceptibility to the body shadowing effect. Integrating BCP with biomedical SoCs could unlock potentials for a power-sustainable and long-lasting system design. In this paper, starting from the motivation for BCP, we will identify the challenges in the integration of BCP with other building blocks of the SoC (e.g., sensing front-end, transceiver). After reviewing and analyzing the existing techniques for BCP and a BCP-compatible SoC design, the design considerations and optimizations will be discussed. © 2023 IEEE.

关键词： bio-signal sensing biomedical system-on-chip body-coupled communication body-coupled powering

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Blockchain-Enabled SAGIN Communication for Disaster Prediction and Management

Blockchain-Enabled SAGIN Communication for Disaster Predicti...

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2023 IEEE International Conference on Dependable, Autonomic and Secure Computing, 2023 International Conference on Pervasive Intelligence and Computing, 2023 International Conference on Cloud and Big Data Computing, 2023 International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023

作者： Okoumassoun, Tagnon Privael Ridhawi, Ismaeel Al Abbas, Ali Al-Oqily, Ibrahim School of Electrical Engineering and Computer Science University of Ottawa Canada Kuwait College of Science and Technology Kuwait The Hashemite University Jordan

ISBN: (纸本)9798350304602

The Sixth Generation (6G) network will rely on the integration and cooperation of a plethora of communication and networking systems. Space-air-ground integrated networks (SA-GIN) which integrate satellites, aerial networks, and terrestrial communication, can provide seamless and continuous delivery of services and applications with high levels of performance. While SAGIN provides promising solutions for data collection and communication, it poses security concerns given that it may allow untrusted devices to be part of the cooperative system. In this paper we introduce a blockchain-enabled SAGIN framework that authenticates both devices and the data collected by SAGIN participants. Federated Learning (FL) is supported by the framework to enable network distribution and accurate real-time data analysis. As a proof-of-concept, the solution considers a use-case for earthquake event prediction and monitoring. Satellite sensors are used to collect data and identify the presence of thermal anomalies used for earthquake forecasting. Furthermore, a post-earthquake monitoring technique is used which relies on Unmanned Aerial Vehicles (UAVs) to provide access to remote places in order to collect high quality images. The collected images are then processed locally on UAV swarms or through edge devices for real-time decision making. An experimental study is conducted on models and approaches needed to increase prediction accuracy for earthquake events. © 2023 IEEE.

关键词： Decision making

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Disturbance Analysis of an Interconnected Power Systems 26

Disturbance Analysis of an Interconnected Power Systems

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26th International Conference on electrical Machines and Systems, ICEMS 2023

作者： Yu, Linxin Min, Fuhong Cheng, Yizi Xu, Yanwu Nanjing Normal University School of Electrical and Automation Engineering Nanjing China Nanjing Normal University School of Computer Science and Technology Nanjing China

ISBN: (纸本)9798350317589

This paper studies the chaotic phenomena in interconnected power systems considering load disturbances and electromagnetic power disturbances. The discrete implicit mapping method, which is different from traditional methods, is employed to analyze the path of the system from period to chaos. Compared with traditional methods, it can improve the accuracy, which helps to deeply understand the mechanism and triggering conditions of chaotic phenomena in power systems. The results are valuable to prevent and suppress chaotic oscillations in power system, and have strong practical significance. © 2023 IEEE.

关键词： Electric power system interconnection

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Runtime Detection of Adversarial Attacks in AI Accelerators Using Performance Counters

arXiv

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arXiv 2025年

作者： Rahaman, Habibur Chatterjee, Atri Bhunia, Swarup School of Electrical and Computer Engineering University of Florida United States

Rapid adoption of AI technologies raises several major security concerns, including the risks of adversarial perturbations, which threaten the confidentiality and integrity of AI applications. Protecting AI hardware from misuse and diverse security threats is a challenging task. To address this challenge, we propose SAMURAI, a novel framework for safeguarding against malicious usage of AI hardware and its resilience to attacks. SAMURAI introduces an AI Performance Counter (APC) for tracking dynamic behavior of an AI model coupled with an on-chip Machine Learning (ML) analysis engine, known as TANTO (Trained Anomaly Inspection Through Trace Observation). APC records the runtime profile of the low-level hardware events of different AI operations. Subsequently, the summary information recorded by the APC is processed by TANTO to efficiently identify potential security breaches and ensure secure, responsible use of AI. SAMURAI enables real-time detection of security threats and misuse without relying on traditional software-based solutions that require model integration. Experimental results demonstrate that SAMURAI achieves up to 97% accuracy in detecting adversarial attacks with moderate overhead on various AI models, significantly outperforming conventional software-based approaches. It enhances security and regulatory compliance, providing a comprehensive solution for safeguarding AI against emergent threats. © 2025, CC BY-NC-SA.

关键词： Adversarial machine learning

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DENSE-Guided Deep Motion networks Accounted by Large Rotations to Improve Myocardial Strain Analysis from Routine Cine MRI 22

DENSE-Guided Deep Motion Networks Accounted by Large Rotatio...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Lei, Pengcheng Xing, Jiarui Fang, Faming Epstein, Frederick H. Zhang, Miaomiao University of Virginia Department of Electrical and Computer Engineering United States School of Computer Science and Technology East China Normal University Shanghai China University of Virginia Health System Department of Biomedical Engineering United States

ISBN: (纸本)9798331520526

Myocardial strain imaging provides a valuable tool for detecting subclinical left ventricular (LV) dysfunction and adding prognostic value in assessing various types of heart disease. Recent studies have utilized highly accurate strain-dedicated techniques, such as displacement encoding with stimulated echoes (DENSE), to train a deep learning (DL) framework to predict the myocardial displacements/deformations from routine cine balanced steady state free precession (bSSFP) images. However, these methods have shown limited performance in capturing the large rotational motion of the myocardium associated with twist and torsion over time, which are important aspects of myocardial mechanics. To address this gap, this paper introduces a novel DENSE-guided DL network that explicitly accounts for large rotational motion to further improve strain analysis of standard cine bSSFP images. Specifically, our proposed network includes two key components: (i) a time-series rotation estimation network employing a 3D convolutional encoder-decoder architecture to model the large rotational dynamics of the LV myocardium over time, and (ii) a radial motion prediction network based on deformable image registration. The output of these two sub-networks was integrated and refined through a fusion network to predict the final myocardial displacements, supervised by DENSE ground truth. Experimental results show that our method improves the accuracy of myocardial strain with effectively captured large rotations. © 2025 IEEE.

关键词： Rotation

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Geppetteau: Enabling haptic perceptions of virtual fluids in various vessel profiles using a string-driven haptic interface 23

Geppetteau: Enabling haptic perceptions of virtual fluids in...

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17th International Conference on Tangible, Embedded, and Embodied Interaction, TEI 2023

作者： Sagheb, Shahabedin Liu, Frank Wencheng Vuong, Alex Dai, Shiling Wirjadi, Ryan Bao, Yueming Likamwa, Robert Honors College Virginia Tech United States School of Electrical Computer and Energy Engineering Arizona State University United States

ISBN: (纸本)9781450399777

What we feel from handling liquids in vessels produces unmistakably fluid tactile sensations. These stimulate essential perceptions in home, laboratory, or industrial contexts. Feeling fluid interactions from virtual fluids would similarly enrich experiences in virtual reality. We introduce Geppetteau, a novel string-driven weight shifting mechanism capable of providing perceivable tactile sensations of handling virtual liquids within a variety of vessel shapes. These mechanisms widen the range of augmentable shapes beyond the state-of-the-art of existing mechanical systems. In this work, Geppetteau is integrated into conical, spherical, cylindrical, and cuboid shaped vessels. Variations of these shapes are often used for fluid containers in our day-to-day. We studied the effectiveness of Geppetteau in simulating fine and coarse-grained tactile sensations of virtual liquids across three user studies. Participants found Geppetteau successful in providing congruent physical sensations of handling virtual liquids in a variety of physical vessel shapes and virtual liquid volumes and viscosities. © 2023 ACM.

关键词： Virtual reality

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Transformer-based Text Classification on Unified Bangla Multi-class Emotion Corpus 25

Transformer-based Text Classification on Unified Bangla Mult...

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Sourav, Md Sakib Ullah Mahmud, Mohammad Sultan Zheng, Hua Aljaidi, Mohammad Talukder, Md. Simul Hasan Bin Sulaiman, Rejwan Nur, Abdullah Hafez Al-Qerem, Ahmad Shandong University of Finance and Economics School of Management Science and Engineering Jinan China Shenzhen University College of Computer Science and Software Engineering Senzhen China Software Engineering Institute of Guangzhou Department of Software Engineering Guangzhou510990 China Zarqa University Department of Computer Science Zarqa Jordan University of Business and Technology College of Engineering Jeddah21448 Saudi Arabia Dhaka University of Engineering and Technology Department of Electrical and Electronic Engineering Dhaka Bangladesh Northumbria University Newcastle Tayne School of Computer Science & Technology Northumbria United Kingdom International Islamic University Chittagong Department of Electronic and Telecommunication Engineering Chittagong Bangladesh Zarqa University Faculty of Information Technology Department of Computer Science Zarqa Jordan

ISBN: (纸本)9798331540012

Due to its importance in studying people's thoughts on various Web 2.0 services, emotion classification is a critical undertaking. Most existing research is focused on the English language, with little work on low-resource languages. Though sentiment analysis, particularly emotion classification in English, has received increasing attention in recent years, little study has been done in the context of Bangla, one of the world's most widely spoken languages. In this research, we propose a complete set of approaches for identifying and extracting emotions from Bangla texts. We provide a Bangla emotion classifier for six classes, i.e., anger, disgust, fear, joy, sadness, and surprise, from Bangla words using transformer-based models, which exhibit phenomenal results in recent days, especially for high-resource languages. The Unified Bangla Multi-class Emotion Corpus (UBMEC) is used to assess the performance of our models. UBMEC is created by combining two previously released manually labelled datasets of Bangla comments on six emotion classes with fresh manually labelled Bangla comments created by us. The corpus dataset and code we used in this work are publicly available. © 2024 IEEE.

关键词： Classification (of information)

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Sign-Based Distributed Learning with Byzantine Resilience based on Audit Mechanism

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IEEE Transactions on Information Forensics and Security 2025年 20卷 5774-5788页

作者： Li, Chengxi Xiao, Ming Skoglund, Mikael KTH Royal Institute of Technology Division of Information Science and Engineering School of Electrical Engineering and Computer Science Stockholm 10044 Sweden

In this paper, we study the problem of distributed learning (DL) with devices transmitting sign information of the local gradients to the server under communication constraints, where the devices are susceptible to Byzantine attacks. For this problem, a sign-based gradient descent method with majority vote and stochastic 1-bit quantization (Sign-M-stochastic) has been proposed very recently. However, the Byzantine resilience of Sign-M-stochastic is inherently limited, based on the fact that all Byzantine devices and honest devices participate equally in the training process. To overcome this drawback and enhance the resilience to Byzantine attacks, inspired by the audit-based distributed detection systems, we propose a novel DL method with an audit mechanism (DL-AM). In each iteration, the sign information of the local gradients are obtained by the devices from stochastic 1-bit quantization. All devices, partitioned into groups, send the sign information to the server through multiple paths, both directly and via other devices in the same group. This approach provides the server with additional information about the identities of the devices, which enables the server to form the global model update by aggregating the sign information of different devices with varying weights. We analyze the convergence performance of the proposed method from a theoretical perspective. Finally, numerical results demonstrate the superiority of DL-AM over the baseline methods. © 2005-2012 IEEE.

关键词： Audit mechanism Byzantine resilience convergence analysis distributed learning sign information

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Improving Cardiovascular Prediction Performance Using Machine Learning Based Feature Selection 17

Improving Cardiovascular Prediction Performance Using Machin...

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17th International Conference on Developments in eSystems engineering, DeSE 2024

作者： Naser, Marwah Abdulrazzaq Alsabah, Muntadher Abdulhussain, Sadiq H. Mahmmod, Basheera M. Hussain, Abir Al-Jumeily, Dhiya Department of Computer Engineering University of Baghdad Baghdad Iraq Medical Technical College Al Farahidi University Baghdad Iraq Department of Electrical Engineering University of Sharjah Sharjah United Arab Emirates School of Computer Science and Mathematics Liverpool John Moores University Liverpool United Kingdom

ISBN: (纸本)9798350368697

To date, cardiovascular disease (CVD) is responsible for a considerable number of deaths each year. Hence, developing an effective CVD prediction model is essential to reducing mortality rates. To this end, this paper makes use of different machine learning (ML) classifiers such as logistic regression (LG), K-nearest neighbor (KNN), support vector machine (SVM), gradient boosting (GB), and adaptive boosting (AdaB) to improve CVD prediction performance. In addition, this paper investigates the use of ML based on the ANOVA feature selection method and voting ensemble model by aggregating different ML classifiers to improve CVD prediction. The CVD performance is evaluated using key metrics such as accuracy, precision, recall, F1-score, and confusion matrix. The results demonstrate that the proposed approach achieves the highest CVD prediction accuracy compared to state-of-the-art methods recording 93.44%. The findings obtained in this paper suggest that the SVM ANOVA feature selection and ensemble approaches can be considered practical strategies for improving the prediction accuracy of CVD. © 2024 IEEE.

关键词： Prediction models

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