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International Conference on Control, Automation and Information Sciences (ICCAIS)

作者： Linh Van Ma Muhammad Ishfaq Hussain JongHyun Park Jeongbae Kim Moongu Jeon School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology Republic of Korea Department of Mechanical-IT Convergence System Engineering Pusan National University Busan Republic of Korea

We investigate the application of ByteTrack in the realm of multiple object tracking. ByteTrack, a simple tracking algorithm, enables the simultaneous tracking of multiple objects by strategically incorporating detections with a low confidence threshold. Conventionally, objects are initially associated with high confidence threshold detections. When the association between objects and detections becomes ambiguous, ByteTrack extends the association to lower confidence threshold detections. One notable drawback of the existing ByteTrack approach is its reliance on a fixed threshold to differentiate between high and low-confidence detections. In response to this limitation, we introduce a novel and adaptive approach. Our proposed method entails a dynamic adjustment of the confidence threshold, leveraging insights derived from overall detections. Through experimentation, we demonstrate the effectiveness of our adaptive confidence threshold technique while maintaining running time compared to ByteTrack.

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Adaptive Confidence Threshold for ByteTrack in Multi-Object Tracking

arXiv

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arXiv 2023年

作者： Van Ma, Linh Hussain, Muhammad Ishfaq Park, JongHyun Kim, Jeongbae Jeon, Moongu School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology Korea Republic of Department of Mechanical-IT Convergence System Engineering Pusan National University Busan Korea Republic of

关键词： Object detection

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Free-space beam shaping and steering based on a silicon optical phased array

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Photonics Research 2023年第12期11卷 2093-2099页

作者： WENBO REN QINGQING LIANG JIJUN FENG HAIPENG LIU JIANHUA HU SHUO YUAN XINCHENG XIA WEI JIANG QIWEN ZHAN HEPING ZENG Shanghai Key Laboratory of Modern Optical System Engineering Research Center of Optical Instrument and System(Ministry of Education)School of Optical-Electrical and Computer EngineeringUniversity of Shanghai for Science and TechnologyShanghai 200093China Chongqing Key Laboratory of Precision Optics Chongqing Institute of East China Normal UniversityChongqing 401120China State Key Laboratory of Precision Spectroscopy East China Normal UniversityShanghai 200241China

In this study,we present a method for free-space beam shaping and steering based on a silicon optical phased array,which addresses the theoretical limitation of traditional bulk *** theoretically analyze the beam propagation properties with changes in the applied *** beam profiles can be shaped by varying the phase combination,while a high-order quasi-Bessel beam can be generated with a cubic change to the phase *** simulated results are validated further experimentally,and they match one another *** steering can be achieved with a field of view as large as 140°,which has potential benefits for practical *** presented method is expected to have broad application prospects for optical communications,free-space optical interconnects,and light detection and ranging.

关键词： optical beam optics

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An Explainable Artificial Intelligence Framework for the Predictive Analysis of Hypo and Hyper Thyroidism Using Machine Learning Algorithms

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Human-Centric Intelligent systems 2023年第3期3卷 211-231页

作者： Hossain, Md. Bipul Shama, Anika Adhikary, Apurba Raha, Avi Deb Uddin, K. M. Aslam Hossain, Mohammad Amzad Islam, Imtia Murad, Saydul Akbar Munir, Md. Shirajum Bairagi, Anupam Kumar Department of Information and Communication Engineering Noakhali Science and Technology University Noakhali Bangladesh Department of System Engineering University of South Alabama Mobile USA Computer Science and Engineering Discipline Khulna University Khulna Bangladesh Mandy Dental College and Hospital Dhaka Bangladesh School of Computer Science and Computer Engineering The University of Southern Mississippi Hattiesburg USA Department of Electrical and Computer Engineering Old Dominion University Norfolk USA

The thyroid gland is the crucial organ in the human body, secreting two hormones that help to regulate the human body’s metabolism. Thyroid disease is a severe medical complaint that could be developed by high Thyroid Stimulating Hormone (TSH) levels or an infection in the thyroid tissues. Hypothyroidism and hyperthyroidism are two critical conditions caused by insufficient thyroid hormone production and excessive thyroid hormone production, respectively. Machine learning models can be used to precisely process the data generated from different medical sectors and to build a model to predict several diseases. In this paper, we use different machine-learning algorithms to predict hypothyroidism and hyperthyroidism. Moreover, we identified the most significant features, which can be used to detect thyroid diseases more precisely. After completing the pre-processing and feature selection steps, we applied our modified and original data to several classification models to predict thyroidism. We found Random Forest (RF) is giving the maximum evaluation score in all sectors in our dataset, and Naive Bayes is performing very poorly. Moreover selecting the feature by using the feature importance method RF provides the best accuracy of 91.42%, precision of 92%, recall of 92% and F1-score of 92%. Further, by analyzing the characteristics and behavior of the dataset, we identified the most important features (TSH, T3, TT4, and FTI) of the dataset. In terms of accuracy and other performance evaluation criteria, this study could advocate the use of effective classifiers and features backed by machine learning algorithms to detect and diagnose thyroid disease. Finally, we did some explainability analysis of our best classifier to understand the internal black-box of our machine learning model and datasets. This study could further pave the way for the researcher as well as healthcare professionals to analyze thyroid disease in real time applications.

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FIRE: A Dataset for Feedback Integration and Refinement Evaluation of Multimodal Models 38

FIRE: A Dataset for Feedback Integration and Refinement Eval...

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38th Conference on Neural Information Processing systems, NeurIPS 2024

作者： Li, Pengxiang Gao, Zhi Zhang, Bofei Yuan, Tao Wu, Yuwei Harandi, Mehrtash Jia, Yunde Zhu, Song-Chun Li, Qing Beijing Key Laboratory of Intelligent Information Technology School of Computer Science & Technology Beijing Institute of Technology China State Key Laboratory of General Artificial Intelligence BIGAI China State Key Laboratory of General Artificial Intelligence Peking University China Department of Electrical and Computer System Engineering Monash University Australia Guangdong Laboratory of Machine Perception and Intelligent Computing Shenzhen MSU-BIT University China Department of Automation Tsinghua University China

Vision language models (VLMs) have achieved impressive progress in diverse applications, becoming a prevalent research direction. In this paper, we build FIRE, a feedback-refinement dataset, consisting of 1.1M multi-turn conversations that are derived from 27 source datasets, empowering VLMs to spontaneously refine their responses based on user feedback across diverse tasks. To scale up the data collection, FIRE is collected in two components: FIRE-100K and FIRE-1M, where FIRE-100K is generated by GPT-4V, and FIRE-1M is freely generated via models trained on FIRE-100K. Then, we build FIRE-Bench, a benchmark to comprehensively evaluate the feedback-refining capability of VLMs, which contains 11K feedback-refinement conversations as the test data, two evaluation settings, and a model to provide feedback for VLMs. We develop the FIRE-LLaVA model by fine-tuning LLaVA on FIRE-100K and FIRE-1M, which shows remarkable feedback-refining capability on FIRE-Bench and outperforms untrained VLMs by 50%, making more efficient user-agent interactions and underscoring the significance of the FIRE dataset. © 2024 Neural information processing systems foundation. All rights reserved.

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Revolutionizing electric robot charging infrastructure through federated transfer learning and data route optimization

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Cluster Computing 2025年第6期28卷 1-14页

作者： Hayawi, Kadhim Sajid, Junaid Malik, Asad Waqar Trabelsi, Zouheir College of Interdisciplinary Studies Computational Systems Zayed University Abu Dhabi United Arab Emirates School of Electrical Engineering and Computer Science (SEECS) National University of Sciences and Technology (NUST) Islamabad Pakistan Department of Electrical and Computer Engineering National Strategic Planning and Analysis Research Center Mississippi State University Starkville USA Department of Information System and Security United Arab Emirates University Al Ain United Arab Emirates

The growing prevalence of electric robots within industrial settings calls for innovative solutions to address the limitations in the existing charging infrastructure, ultimately improving the efficiency and sustainability of these robotic systems. However, internet connectivity, which may be required for prior scheduling and charging, is often unavailable due to security concerns, harsh conditions, operational control requirements, and regulatory compliance. Protecting sensitive data, ensuring reliability, maintaining production control, and adhering to industry-specific regulations drive the decision to isolate internal networks from the internet. In such scenarios, locating a suitable charging spot for ERs, particularly during urgent situations with critically low battery levels, becomes challenging. This paper proposes a scheduling algorithm that relies on an ER-to-ER mechanism for charging at available stations. Moreover, since the process relies on ad-hoc communication susceptible to potential malicious actions by intermediate entities, it poses a risk to the overall process. To mitigate this risk, we propose a machine learning-driven method to detect malicious connections, safeguarding the network’s stability. Moreover, to reduce network congestion resulting from routing requests, we have modified the Grey-wolf optimization algorithm specifically for industrial settings. This modification enables the efficient scheduling of charging requests at optimal stations, all while prioritizing the privacy of robots.

关键词： Anomaly detection Charging stations Data routing Electric robots Federated transfer learning Grey wolf optimization

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When SAM2 meets video camouflaged object segmentation: a comprehensive evaluation and adaptation

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Visual Intelligence 2025年第1期3卷 1-14页

作者： Zhou, Yuli Sun, Guolei Li, Yawei Xie, Guo-Sen Benini, Luca Konukoglu, Ender Computer Vision Laboratory ETH Zürich Zürich Switzerland Department of Informatics University of Zürich Zürich Switzerland Integrated System Laboratory ETH Zürich Zürich Switzerland School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Department of Electrical Electronic and Information Engineering University of Bologna Bologna Italy

This study investigates the application and performance of the Segment Anything Model 2 (SAM2) in the challenging task of video camouflaged object segmentation (VCOS). VCOS involves detecting objects that blend seamlessly in the surroundings for videos due to similar colors and textures and poor light conditions. Compared to the objects in normal scenes, camouflaged objects are much more difficult to detect. SAM2, a video foundation model, has shown potential in various tasks. However, its effectiveness in dynamic camouflaged scenarios remains under-explored. This study presents a comprehensive study on SAM2’s ability in VCOS. First, we assess SAM2’s performance on camouflaged video datasets using different models and prompts (click, box, and mask). Second, we explore the integration of SAM2 with existing multimodal large language models (MLLMs) and VCOS methods. Third, we specifically adapt SAM2 by fine-tuning it on the video camouflaged dataset. Our comprehensive experiments demonstrate that SAM2 has the excellent zero-shot ability to detect camouflaged objects in videos. We also show that this ability could be further improved by specifically adjusting SAM2’s parameters for VCOS.

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Driver Fatigue Detection Using OpenCV and Dlib Library

Driver Fatigue Detection Using OpenCV and Dlib Library

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2023 IEEE Asia-Pacific Conference on computer Science and Data engineering, CSDE 2023

作者： Shen, Kuang Ramli, Rusyaizila Chuah, Joong Huang Chai, Grace Mei Ting School of Electronics and Computer Science University of Southampton Southampton United Kingdom University of Southampton Malaysia Carbon Neutrality Research Group Johor Iskandar Puteri79100 Malaysia University of Malaya Faculty of Engineering Department of Electrical Engineering Kuala Lumpur50603 Malaysia University of Southampton Malaysia Smart Manufacturing System Reasearch Group Johor Iskandar Puteri79100 Malaysia

ISBN: (纸本)9798350341072

Driver fatigue and distraction are major contributors to fatal road accidents worldwide. To enhance vehicle safety, this article proposes a fatigue detection system using EAR and MAR algorithms to identify signs of fatigue, such as eye closure and yawning. When prolonged eye closure or yawning is detected, the system issues an alert to the driver. It leverages OpenCV and Dlib libraries for implementation. Additionally, the system detects driver distraction by monitoring head swivels, counting left and right movements. It's worth noting that Dlib may yield facial landmark deviations for non-frontal faces. To mitigate this, the study selected 69 videos from the YawDD database, focusing on near-frontal face views, achieving a 91.3% accuracy rate. © 2023 IEEE.

关键词： Vehicle safety

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Imperceptible and Sparse Adversarial Attacks via a Dual-Population-Based Constrained Evolutionary Algorithm

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第2期4卷 268-281页

作者： Tian, Ye Pan, Jingwen Yang, Shangshang Zhang, Xingyi He, Shuping Jin, Yaochu Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Institutes of Physical Science and Information Technology Hefei230601 China Hefei Comprehensive National Science Center Institute of Artificial Intelligence Hefei230088 China Anhui University School of Computer Science and Technology Hefei230601 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Artificial Intelligence Hefei230601 China Anhui University Anhui Engineering Laboratory of Human-Robot Integration System and Intelligent Equipment School of Electrical Engineering and Automation Hefei230601 China Bielefeld University Faculty of Technology Bielefeld33619 Germany

The sparse adversarial attack has attracted increasing attention due to the merit of a low attack cost via changing a small number of pixels. However, the generated adversarial examples are easily detected in vision since the perturbation to each pixel is relatively large. To achieve imperceptible and sparse adversarial attacks, this article formulates a bi-objective constrained optimization problem, simultaneously minimizing the 0 and 2 distances to the original image, and proposes a dual-population-based constrained evolutionary algorithm to solve it. The proposed method solves the optimization problem by evolving two populations, where one population is responsible for finding feasible solutions (i.e., successful attacks) and the other one is to minimize both the 0 and 2 distances. Moreover, a population initialization strategy and two genetic operators are customized to accelerate the convergence speed. Experimental results indicate that the proposed method can achieve high success rates with low attack costs, and strikes a better balance between the 0 and 2 distances than state-of-the-art methods. © 2020 IEEE.

关键词： Evolutionary algorithms

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Contactless Respiratory Rate Monitoring Using FMCW Radar

Contactless Respiratory Rate Monitoring Using FMCW Radar

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2022 International Symposium on Electronics and Smart Devices, ISESD 2022

作者： Widiyasari, Diyah Ahmadi, Nur Adiono, Trio Purwarianti, Ayu Purnomo, Ariana Tulus Lin, Ding-Bing Bandung Institute of Technology School of Electrical Engineering and Informatics Bandung40132 Indonesia Bandung40132 Indonesia National Taiwan University of Science and Technology Department of Electronic and Computer Engineering Taipei Taiwan Sampoerna University Departement of Information System Jakarta Selatan12780 Indonesia

ISBN: (数字)9798350396607

ISBN: (纸本)9798350396607

Vital sign monitoring plays a very important role in the early diagnosis and prognosis of a disease, as well as for patient health management. Among other vital signs, respiratory rate (RR) is rarely recorded and often forgotten despite the fact that it is an essential predictor of serious events such as cardiac arrest and deteriorating clinical condition. In this paper, we propose a frequency-modulated continuous-wave (FMCW) radar for contactless RR monitoring. We employ FFT to calculate RR in frequency-domain and combine it with Kalman filter to smooth out the estimated RR. We use a public dataset measuring RR from 50 children under 13 years of age recorded with 60 GHz FMCW radar. The experimental results show that the proposed method yields MAE and RMSE of 3.20 and 4.10, respectively, with respect to the reference device. Incorporating Kalman filter reduces the percentage of error by 14.83% and 13.36% compared to without it. The overall results suggest that radar-based technology can be potentially used for contactless RR monitoring which is suitable for those who cannot use contact-based monitoring devices. © 2022 IEEE.

关键词： Kalman filters

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