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Algorithms, Computing and Artificial Intelligence (ACAI), International Conference on

作者： Tianyu Huang Zhijiong Wang Guannan Hu Sio Hang Pun Hung Chun Li Mang I Vai Department of Electrical and Computer Engineering Joint Laboratory of Zhuhai UM Science and Technology Research Institute - Lingyange Semiconductor Incorporated University of Macau Macao China Department of Electrical and Computer Engineering Joint Laboratory of Zhuhai UM Lingyange Semiconductor Incorporated Science and Technology Research Institute - Lingyange Semiconductor Incorporated University of Macau Macao China Joint Laboratory of Zhuhai UM Lingyange Semiconductor Incorporated Science and Technology Research Institute - Lingyange Semiconductor Incorporated Zhuhai China Department of Electrical and Computer Engineering Joint Laboratory of Zhuhai UM Institute of Microelectronics Science and Technology Research Institute - Lingyange Semiconductor Incorporated University of Macau Macao China

ISBN: (数字)9798331529314

ISBN: (纸本)9798331529321

Electrocardiogram (ECG) is an important non-invasive technique for diagnosing cardiovascular diseases (CVD). After acquiring the patients' raw ECG signal data, signal processing is essential for the diagnosis. Conventional ECG signal processing is usually performed serially, which takes a long time to process signals with large amounts of data. In this study, we have proposed a GPGPU-based algorithm acceleration system to improve the efficiency of ECG signal processing. This system utilizes OpenCL for parallel algorithm development on GPGPU. GPGPU executes certain steps in the QRS complex detection algorithm via parallel computing, thereby accelerating the algorithmic process. Experimental results showed that the system achieved a speedup of 1.279 compared to serial computing when processing 39 ECG signal sequences in parallel. In addition, our system has been equipped with a progressive transmission mechanism and a function that intelligently determines whether an algorithm can be accelerated. These features make this system practical not only in biomedical signal processing but also in engineering and research fields with large amounts of data.

关键词： Signal processing algorithms Signal processing Electrocardiography Cardiovascular diseases Biomedical communication Parallel algorithms Detection algorithms Artificial intelligence

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General and Offset-Resistant Physical-Layer Acknowledgement Approach for Cross-Technology Communication

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IEEE Transactions on Communications 2025年

作者： Yao, Shumin Zhao, Qinglin Zhou, MengChu Feng, Li Zhang, Peiyun Albeshri, Aiiad Macau University of Science and Technology School of Computer Science and Engineering Avenida Wei Long Taipa China Pengcheng Laboratory Department of Broadband Communication Shenzhen518066 China New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States Nanjing University of Information Science & Technology Engineering Research Center of Digital Forensics of Ministry of Education School of Computer Science Nanjing210044 China King Abdulaziz University Department of Computer Science Jeddah21481 Saudi Arabia

Cross-technology communication (CTC) enables direct communications among devices with heterogeneous wireless technologies (e.g., Bluetooth, WiFi, ZigBee, etc.), thereby reducing the cost and complexity of their interconnections. However, CTC is essentially unreliable due to the technology heterogeneity, and most existing CTC designs do not provide acknowledgment (ACK) feedback to ensure reliable data transmission. Few ACK designs are only applicable to feedback for ZigBee-WiFi pair and are vulnerable to sampling offsets that inherently exist in CTC. In this work, we propose a General and Offset-resistant Physical-layer ACK approach, called GOP-ACK, to support reliable feedback with practical considerations. Its core idea lies in encoding ACK messages with offset-resistant signal that has two benefits: 1) it can be adapted to a wide range of CTC scenarios with minimal adjustment, and 2) it can be effortlessly and robustly detected even in the presence of sampling offsets. We offer practical guidelines to tackle key deployment challenges related to signal construction, efficient and robust transmission, and effective firmware module reuse, enabling the application of GOP-ACK to specific CTC scenarios. Based on these guidelines, we implement two design cases: ZigBee-to-BLE and ZigBee-to-WiFi feedback, and propose a theoretical model to analyze their performance. We then conduct real-world experiments and extensive simulations to verify its feasibility and superiority over the state of the art, thereby enhancing the practicality of CTC greatly. © 1972-2012 IEEE.

关键词： Firmware

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OVER-PARAMETERIZED MODEL OPTIMIZATION WITH POLYAK-LOJASIEWICZ CONDITION

OVER-PARAMETERIZED MODEL OPTIMIZATION WITH POLYAK-LOJASIEWIC...

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11th International Conference on Learning Representations, ICLR 2023

作者： Chen, Yixuan Shi, Yubin Dong, Mingzhi Yang, Xiaochen Li, Dongsheng Wang, Yujiang Dick, Robert P. Lv, Qin Zhao, Yingying Yang, Fan Gu, Ning Shang, Li China and Shanghai Key Laboratory of Data Science School of Computer Science Fudan University Shanghai China School of Mathematics Statistics The University of Glasgow Glasgow United Kingdom Microsoft Research Asia Shanghai China Department of Engineering Science University of Oxford Oxford United Kingdom Department of Electrical Engineering and Computer Science University of Michigan Michigan United States Department of Computer Science University of Colorado Boulder BoulderCO United States School of Microelectronics Fudan University Shanghai China

This work pursues the optimization of over-parameterized deep models for superior training efficiency and test performance. We first theoretically emphasize the importance of two properties of over-parameterized models, i.e., the convergence gap and the generalization gap. Subsequent analyses unveil that these two gaps can be upper-bounded by the ratio of the Lipschitz constant and the Polyak-Lojasiewicz (PL) constant, a crucial term abbreviated as the condition number. Such discoveries have led to a structured pruning method with a novel pruning criterion. That is, we devise a gating network that dynamically detects and masks out those poorly-behaved nodes of a deep model during the training session. To this end, this gating network is learned via minimizing the condition number of the target model, and this process can be implemented as an extra regularization loss term. Experimental studies demonstrate that the proposed method outperforms the baselines in terms of both training efficiency and test performance, exhibiting the potential of generalizing to a variety of deep network architectures and tasks. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Network architecture

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Impact of Motorcycle Placement on Travel Time and Traffic Dynamics at Urban Intersections

Impact of Motorcycle Placement on Travel Time and Traffic Dy...

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Innovative research in Applied science, engineering and Technology (IRASET),Conference on

作者： Ayoub Charef Zahi Jarir Mohamed Quafafou LAMIGEP Research Laboratory EMSI Moroccan School of Engineering Sciences Marrakech Morocco Laboratory of Computer Systems Engineering (LISI) Cadi Ayyad University Marrakech Marrakech Morocco Department of Computer Science University of Aix-Marseille Marseille France

ISBN: (数字)9798350309508

ISBN: (纸本)9798350309515

This paper delves into a comprehensive exploration of the impact of motorcycle positioning on travel times at signalized intersections in urban settings. Leveraging a synergistic approach that integrates data analysis and VISSIM simulation, the study focuses on three key intersections in Marrakech, dissecting traffic dynamics during distinct time intervals. The analysis reveals consistent trends, showcasing that Scenario 2, where motorcycles are separated to the right of cars, consistently yields lower travel times compared to Scenario 1, where motorcycles navigate in front of cars. VISSIM simulations visually underscore these findings, emphasizing the efficiency gains achieved through the introduction of dedicated motorcycle lanes. The discussion articulates the significance of these results, offering insights into the potential benefits of segregated motorcycle traffic for urban traffic planning. This research contributes valuable perspectives and actionable considerations for optimizing intersection efficiency and reducing travel times in the context of urban mobility.

关键词： Data analysis Correlation Navigation Simulation Urban planning Motorcycles Market research

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NARCE: A Mamba-Based Neural Algorithmic Reasoner Framework for Online Complex Event Detection

arXiv

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arXiv 2025年

作者： Han, Liying Dong, Gaofeng Ouyang, Xiaomin Kaplan, Lance Cerutti, Federico Srivastava, Mani Department of Electrical and Computer Engineering University of California Los Angeles United States Department of Computer Science and Engineering Hong Kong University of Science and Technology China US Army DEVCOM Army Research Laboratory United States Department of Information Engineering University of Brescia Italy Amazon United States

Current machine learning models excel in short-span perception tasks but struggle to derive high-level insights from long-term observation, a capability central to understanding complex events (CEs). CEs, defined as sequences of short-term atomic events (AEs) governed by spatiotemporal rules, are challenging to detect online due to the need to extract meaningful patterns from long and noisy sensor data while ignoring irrelevant events. We hypothesize that state-based methods are well-suited for CE detection, as they capture event progression through state transitions without requiring long-term memory. Baseline experiments validate this, demonstrating that the state-space model Mamba outperforms existing architectures. However, Mamba’s reliance on extensive labeled data, which are difficult to obtain, motivates our second hypothesis: decoupling CE rule learning from noisy sensor data can reduce data requirements. To address this, we propose NARCE, a framework that combines Neural Algorithmic Reasoning (NAR) to split the task into two components: (i) learning CE rules independently of sensor data using synthetic concept traces generated by LLMs and (ii) mapping sensor inputs to these rules via an adapter. Our results show that NARCE outperforms baselines in accuracy, generalization to unseen and longer sensor data, and data efficiency, significantly reducing annotation costs while advancing robust CE detection. © 2025, CC BY.

关键词： Labeled data

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Exploring Gender Dynamics in Motorcycle Traffic Violations Through Gender Detection Technology

Exploring Gender Dynamics in Motorcycle Traffic Violations T...

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International Conference on Intelligent Computing in Data sciences (ICDS)

ISBN: (数字)9798350351200

ISBN: (纸本)9798350351217

Urban traffic management faces significant challenges due to non-compliance with traffic rules, particularly among motorcycle riders. This study introduces an innovative approach employing the YOLOv9 object detection framework to monitor and analyze motorcycle violations at busy intersections in Marrakech. By leveraging advanced machine learning techniques, the study aimed to detect helmet usage, traffic light violations, and assess gender-based differences in violation rates. Data were collected using smartphone cameras at strategic locations during peak traffic times across selected days. The captured video footage was analyzed using the YOLOv9 model, which was pretrained on diverse traffic scenes to enhance its accuracy and reliability in real-time object detection. The findings reveal that the automated system not only aligns closely with manual counting but also offers greater consistency and efficiency. Key results indicated a pronounced gender disparity in violation frequencies and provided insights into the temporal patterns of traffic rule infractions. Moreover, the study highlighted minor discrepancies due to environmental factors, which were systematically addressed to refine the detection process. Contextual factors in interpreting detection results.

关键词： Accuracy Motorcycles Manuals Object detection Streaming media Cameras Real-time systems Safety Reliability Monitoring

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Enhancing Road Safety: Automated Traffic Violation Detection and Counting System Using YOLO Algorithm

Enhancing Road Safety: Automated Traffic Violation Detection...

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Mediterranean Smart Cities Conference (MSCC)

ISBN: (数字)9798350374001

ISBN: (纸本)9798350374018

Ensuring compliance with traffic regulations, such as wearing helmets and obeying traffic signals, is crucial for enhancing road safety, particularly among motorcycle riders. In this study, we propose an automated approach for detecting helmet wearing and traffic light violations using computer vision techniques. Our methodology involves leveraging the YOLO-v8 object detection model pretrained on the COCO dataset to identify motorcycles, persons, traffic lights, and helmets in video footage captured at intersections in Marrakech. We conducted manual counting as a benchmark for evaluating the performance of our automated system. Our results demonstrate a strong alignment between our automated approach and manual counting for both helmet detection and traffic light violations. However, occasional discrepancies were observed, particularly during specific time slots characterized by high motorcycle speeds. Contextual factors such as traffic density and vehicle speed were identified as influencing factors. Despite these challenges, our automated system shows promise as a valuable tool for monitoring and enforcing traffic regulations. Ongoing refinement and optimization are essential to address these challenges and enhance the accuracy and reliability of automated detection systems. Our study highlights the potential of automated technologies in improving road safety measures and underscores the importance of considering contextual factors in interpreting detection results.

关键词： Head Motorcycles Manuals Benchmark testing Regulation Road safety Safety Reliability Monitoring Optimization

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Optimizing Traffic Flow and Pedestrian Safety: The Role of Crosswalk Distance in Roundabout Design

Optimizing Traffic Flow and Pedestrian Safety: The Role of C...

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Global Aeronautical engineering and Satellite Technology (GAST), International Conference on

ISBN: (数字)9798350371598

ISBN: (纸本)9798350371604

This research investigates the impact of pedestrian crossings on the speed characteristics and capacity of roundabouts, with a primary focus on enhancing traffic flow. Unsignalized intersections and roundabouts conventionally grant pedestrians priority, which can substantially affect operational efficiency. The paper underscores the vital significance of strategically placing crosswalks to minimize disruptions to vehicular movement and prevent *** study collected data from roundabouts located in Marrakech, Morocco, utilizing mobile devices to record vehicle and pedestrian counts, as well as their passage times. In addition to field data, Vissim simulations were employed to improve precision. Our research specifically delves into how pedestrians influence the capacity of exit lanes at *** central objective is to ascertain the optimal spacing between roundabouts and crosswalks to maximize their capacity. The findings provide valuable insights for urban planning and traffic management, addressing the intricate relationship between pedestrian flow, crosswalk placement, and the overall efficiency of roundabouts.

关键词： Pedestrians Satellites Urban planning Oral communication Mobile handsets Data models Safety

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Dynamic Online Modulation Recognition using Incremental Learning

Dynamic Online Modulation Recognition using Incremental Lear...

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International Conference on Computing, Networking and Communications (ICNC)

作者： Ali Owfi Ali Abbasi Fatemeh Afghah Jonathan Ashdown Kurt Turck Holcombe Department of Electrical and Computer Engineering Clemson University Clemson SC USA Computer Science Department Vanderbilt University Nashville TN USA Air Force Research Laboratory Rome NY USA

ISBN: (数字)9798350370997

ISBN: (纸本)9798350371000

Modulation recognition is a fundamental task in communication systems as the accurate identification of modulation schemes is essential for reliable signal processing, interference mitigation for coexistent communication technologies, and network optimization. Incorporating deep learning (DL) models into modulation recognition has demonstrated promising results in various scenarios. However, conventional DL models often fall short in online dynamic contexts, particularly in class incremental scenarios where new modulation schemes are encountered during online deployment. Retraining these models on all previously seen modulation schemes is not only time-consuming but may also not be feasible due to storage limitations. On the other hand, training solely on new modulation schemes often results in catastrophic forgetting of previously learned classes. This issue renders DL-based modulation recognition models inapplicable in real-world scenarios because the dynamic nature of communication systems necessitate the effective adaptability to new modulation schemes. This paper addresses this challenge by evaluating the performance of multiple Incremental Learning (IL) algorithms in dynamic modulation recognition scenarios, comparing them against conventional DL-based modulation recognition. Our results demonstrate that modulation recognition frameworks based on IL effectively prevent catastrophic forgetting, enabling models to perform robustly in dynamic scenarios.

关键词： Wireless communication Training Adaptation models Accuracy Heuristic algorithms Computational modeling Modulation

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Tenacity prediction of polyester materials based on an ensemble soft measurement model

Tenacity prediction of polyester materials based on an ensem...

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2024 China Automation Congress, CAC 2024

作者： Duan, Jiaxu Wang, Jun Tao, Youji Ma, Zhiping Wu, Xin Hu, Zhou State Key Laboratory of Environmental Adaptability for Industrial Products China National Electric Apparatus Research Institute Co. Ltd Guangzhou China Kinte Materials Science and Technology Co. Ltd Guangzhou China Department of Computer Science Hunan University of Technology and Business Changsha China School of Electronic Information and Electrical Engineering Yangtze University Jingzhou China

ISBN: (纸本)9798350368604

In the development process of polyester powder coating materials, it has great guiding significance for the development of new materials as the polyester material properties can be predicted through the given monomer structure and formula. In order to meet the requirements of rapid development of polyester materials, this paper establishes a prediction method based on an ensemble soft measurement model (ESM) for one key property-tenacity in prompting the acceleration of development cycle of polyester products. The processed data employed to train and test the model were collected from a local material company, and it covers over 100 kinds of polymerized resins. Firstly, the molecular expressions of monomers are converted to SMILES codes and further translated to ECFP fingerprints. Then, the principal component analysis (PCA) method is adopted to extract the elements with higher importance in each fingerprint. Finally, the ensemble model that consists of 4 machine learning models was established to make the multi-classification decision in tenacity prediction. The effectiveness of the prediction model is evaluated by comparing the predicted and the practical tensile test results. The results demonstrate that this approach achieves better performance in the material property prediction as well as generalization capability. © 2024 IEEE.

关键词： Tensile testing

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