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12th IEEE International Conference on Control, Automation and Information Sciences, ICCAIS 2023

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

ISBN: (纸本)9798350328783

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. © 2023 IEEE.

关键词： Object detection

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Wavelet method optimised by ant colony algorithm used for extracting stable and unstable signals in intelligent substations

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CAAI Transactions on Intelligence Technology 2022年第2期7卷 292-300页

作者： Tianyan Jiang Xiao Yang Yuan Yang Xi Chen Maoqiang Bi Jianfei Chen School of Electrical and Electronic Engineering Chongqing University of TechnologyChongqingChina DepartmentState Key Laboratory of Power Transmission Equipment&System and New Technology Chongqing UniversityChongqingChina Department of Electrical and Computer Engineering University of MarylandCollege ParkMarylandUSA

Partial discharge(PD)signals are an important index to evaluate the operation state of intelligent *** correct distinction of PD pulse and interference pulse has become a challenging *** of the noise and the low signal-to-noise ratio,the stable signals become *** selection of a wavelet basis,the selection rule of thresholdλand the design of the threshold function are the key factors affecting the final denoising ***,an enhanced ant colony optimisition wavelet(ACOW)algorithm was applied to find the global optimal threshold through the continuous derivative threshold function and the ant colony optimisation(ACO)*** the same time the efficiency of adaptive search calculation,was also significantly *** method of the ACOW algorithm was compared with the soft wavelet method,gradient-based wavelet method and the genetic optimisation wavelet(GOW)*** these four methods to denoise four typical signals,different mean square errors(MSE),magnitude errors(ME)and time costs were ***,the results show that the ACOW method can achieve the minimum MSE and has less time *** generates significantly smaller waveform distortion than the other three threshold estimation *** addition,the high efficiency and good quality of the output signals are beneficial to the diagnosis of local discharge signals in intelligent substations.

关键词： algorithm noise method

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GNN-Assisted BiG-AMP: Joint Channel Estimation and Data Detection for Massive MIMO Receiver

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IEEE Transactions on Wireless Communications 2025年第6期24卷 4631-4646页

作者： Liu, Zishen Wu, Nan He, Dongxuan Yuan, Weijie Li, Yonghui Quek, Tony Q. S. School of Information and Electronics Beijing Institute of Technology Beijing100081 China School of System Design and Intelligent Manufacturing Shenzhen Key Laboratory of Robotics and Computer Vision Southern University of Science and Technology Shenzhen518055 China School of Electrical and Information Engineering The University of Sydney SydneyNSW2008 Australia Singapore University of Technology and Design 487372 Singapore Department of Electronic Engineering Kyung Hee University Yongin17104 Korea Republic of

In this paper, we develop a graph neural network (GNN)-assisted bilinear inference approach to enhance the receiver performance of the MIMO system through message passing-based joint channel estimation and data detection (JCD). Specifically, based on the bilinear generalized approximate message passing (BiG-AMP) framework and conditional correlation of signal, we propose a GNN-assisted BiG-AMP (GNN-BiGAMP) approach, which integrates a GNN module into the data-detection-loop to compensate the inaccurate marginal likelihood approximation. By leveraging the coupling between the channel and received symbols, a bilinear GNN-assisted BiG-AMP (BiGNN-BiGAMP) JCD receiver is further proposed. This method incorporates two GNNs with similar graph representation into the bilinear posterior estimation loops, which not only compensates for approximation errors but also alleviates performance loss due to premature variance convergence, thereby enhancing the receiver performance significantly. To fully exploit the supervised information from channel estimation and data detection, we propose a multitask learning based training scheme, which coordinates GNNs with different tasks in two loops. Simulation results show that our proposed GNN-assisted JCD receivers significantly outperform other JCD counterparts in terms of both channel estimation and data detection. © 2025 IEEE. All rights reserved.

关键词： Graph neural networks

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AI-Assisted Markerless Activity Tracking system for Supporting Aging and Wellness

AI-Assisted Markerless Activity Tracking System for Supporti...

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2023 IEEE Microwaves, Antennas, and Propagation Conference, MAPCON 2023

作者： Abedi, Hajar Ansariyan, Ahmad Morita, Plinio P Wong, Alexander Boger, Jennifer Shaker, George University of Waterloo Electrical and Computer Engineering Waterloo Canada University of Waterloo School of Public Health Sciences Waterloo Canada University of Waterloo System Design Engineering Waterloo Canada

ISBN: (纸本)9798350328264

This paper presents a mm-Wave radar-based system to create a novel method of tracking seniors and monitoring them using ambient wireless signals. Radar sensors, coupled with deep learning models, facilitate the identification of various physical activities without requiring physical contact or the use of wearable devices. The system employs range-Doppler maps derived from a real-life in-home activities dataset for training deep learning networks. The gated recurrent units (GRU) model is chosen for real-time implementation due to its optimal trade-off between speed and accuracy. The system achieves an overall accuracy of 93% for trained subjects, with 86% accuracy for classifying the in-home physical activities of a new subject. Additionally, the system captures the frequency of washroom use, sedentary, duration of sleep, active and out-of-home periods, the level of activity performed by the subject over a period of time, current activity state, and gait parameters. © 2023 IEEE.

关键词： activity recognition aging mm-wave radar personalized care sequential deep learning

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Scar-Aware Late Mechanical Activation Detection Network for Optimal Cardiac Resynchronization Therapy Planning 22

Scar-Aware Late Mechanical Activation Detection Network for ...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Xing, Jiarui Wang, Shuo Petal, Amit R. Bilchick, Kenneth C. Epstein, Frederick H. Zhang, Miaomiao School of Medicine University of Virginia Health System United States University of Virginia Health System Department of Biomedical Engineering United States University of Virginia Department of Electrical and Computer Engineering United States University of Virginia Department of Computer Science United States

ISBN: (纸本)9798331520526

Accurate identification of late mechanical activation (LMA) regions is crucial for optimal cardiac resynchronization therapy (CRT) lead implantation. However, existing approaches using cardiac magnetic resonance (CMR) imaging often over-look myocardial scar information, which may be mistakenly identified as delayed activation regions. To address this issue, we propose a scar-aware LMA detection network that simultaneously detects myocardial scar and prevents LMA localization in these scarred regions. More specifically, our model integrates a pre-trained scar segmentation network using late gadolinium enhancement (LGE) CMRs into a LMA detection network based on highly accurate strain derived from displacement encoding with stimulated echoes (DENSE) CMRs. We introduce a novel scar-aware loss function that utilizes the segmented scar information to discourage false-positive detections of late activated areas. Our model can be trained with or without paired LGE data. During inference, our model does not require the input of LGE images, leveraging learned patterns from strain data alone to mitigate false-positive LMA detection in potential scar regions. We evaluate our model on subjects with and without myocardial scar, demonstrating significantly improved LMA detection accuracy in both scenarios. Our work paves the way for improved CRT planning, potentially leading to better patient outcomes. © 2025 IEEE.

关键词： Cardiac resynchronization therapy

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Alzheimer’s Disease and Frontotemporal Dementia: Differential Diagnosis Using Electroencephalogram Signal 1

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13th National Technical Symposium on Unmanned system Technology, NUSYS 2023

作者： Rostamikia, Mehran Sarbaz, Yashar Farzamnia, Ali Biomedical System Modeling Lab Biomedical Engineering Department Electrical and Computer Engineering Faculty University of Tabriz Tabriz Iran Faculty of Engineering Universiti Malaysia Sabah Sabah Kota Kinabalu Malaysia School of Computing and Engineering University of Huddersfield Huddersfield United Kingdom

ISBN: (数字)9789819720279

ISBN: (纸本)9789819720262

Dementia is a neurological disorder that affects a person’s cognitive and social skills, leading to a decline in their overall mental functioning. Frontotemporal dementia (FTD) and Alzheimer’s disease (AD) are two common types of dementia. Frontotemporal dementia mostly affects the frontal and temporal lobes of the brain. These areas are responsible for executive functions, decision-making, language, behavior regulation, and personality traits. Alzheimer’s disease primarily damages the cerebral cortex, which is responsible for higher cognitive functions such as memory, language, and perception. The electroencephalogram (EEG) signal has many advantages in diagnosing these disorders, including low cost and high temporal resolution. This study compares AD and FTD patients with healthy subjects by extracting specific features from EEG signals. Two machine learning algorithms were used for the separation, Support Vector Machines (SVMs) and k-Nearest Neighbors (KNNs), and 10-Fold Cross-Validation was applied to validate the performance of this method and an accuracy of 91.2% (sd = 7.8) was achieved using the SVM classifier for diagnosing the disease and 71.1% (sd = 8.6) for classifying AD and FTD. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Support vector machines

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An RFID-Based Battery-Less Vibration Monitoring system for electrical Appliances

An RFID-Based Battery-Less Vibration Monitoring System for E...

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2022 International Symposium on Information Technology and Digital Innovation, ISITDI 2022

作者： Song, Zequn Sun, Ran Rahmadya, Budi Takeda, Shigeki Engineering Graduate School Of Science And Engineering Ibaraki University Department Of Electrical And Electronic System Hitachi Japan Engineering Ibaraki University Department Of Electrical And Electronic System Hitachi Japan Andalas University Computer System Department Padang Indonesia

ISBN: (数字)9781665461160

ISBN: (纸本)9781665461160

This paper presents a vibration monitoring system for electrical appliances. This system is based on RFID sensors and edge processing technologies. For long-term monitoring, two different operation modes referred to as standby and active modes are introduced. The difference between the two modes is radio wave radiation times. The standby mode is useful to reduce energy consumption and temperature increase of an RFID reader, and amount of data uploaded to a network. This mode also detects a beginning of a vibration event caused by the motor of an electrical appliance. The standby mode subsequently triggers the active mode. The active mode accurately monitors the vibration event and keeps the measured data only for the active mode. Experiments for monitoring a refrigerator demonstrate that the proposed modes enable efficient vibration detections. This system can prevent unintended COVID-19 vaccine disposals caused by the problematic operation and management of refrigerators. © 2022 IEEE.

关键词： COVID-19

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Cross-modal change detection flood extraction based on self-supervised contrastive pre-training

Cross-modal change detection flood extraction based on self-...

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2024 ISPRS Technical Commission I Mid-term Symposium on Intelligent Sensing and Remote Sensing Application

作者： Feng, Wenqing Guan, Fangli Sun, Chenhao Xu, Wei School of Computer Science Hangzhou Dianzi University Hangzhou China Electrical & Information Engineering School Changsha University of Science & Technology Changsha China Information System and Management College National University of Defense Technology Changsha China

Flood extraction is a critical issue in remote sensing analysis. Accurate flood extraction faces challenges such as complex scenes, image differences across modalities, and a shortage of labeled samples. Traditional supervised deep learning algorithms demonstrate promising prospects in flood extraction. They mostly rely on abundant labeled data. However, in practical applications, there is a scarcity of available labeled samples for flood change regions, leading to an expensive acquisition of such data for flood extraction. In contrast, there is a wealth of unlabeled data in remote sensing images. Self-supervised contrastive learning (SSCL) provides a solution, allowing learning from unlabeled data without explicit labels. Inspired by SSCL, we utilized the open-source CAU-Flood dataset and developed a framework for cross-modal change detection in flood extraction (CMCDFE). We employed the Barlow Twin (BT) SSCL algorithm to learn effective visual feature representations of flood change regions from unlabeled cross-modal bi-temporal remote sensing data. Subsequently, these well-initialized weight parameters were transferred to the task of flood extraction, achieving optimal accuracy. We introduced the improved CS-DeepLabV3+ network for extracting flood change regions from cross-modal bi-temporal remote sensing data, incorporating the CBAM dual attention mechanism. By demonstrating on the CAU-Flood dataset, we proved that fine-tuning with only a pre-trained encoder can surpass widely used ImageNet pre-training methods without additional data. This approach effectively addresses downstream cross-modal change detection flood extraction tasks. © 2024 Wenqing Feng et al.

关键词： Extraction

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Arrayed Waveguide Grating for Visible Light Application 14

Arrayed Waveguide Grating for Visible Light Application

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Holography, Diffractive Optics, and Applications XIV 2024

作者： Sun, Quancheng Feng, Zhihua Li, Yuhong Wang, Dongcheng Wang, Yan Liu, Haipeng Li, Junhao Yang, Cunliang Feng, Jijun Shanghai Key Laboratory of Modern Optical System Engineering Research Center of Optical Instrument and System Ministry of Education School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China School of Physics Xidian University Shaanxi Xi'an710071 China Nanjing Electronic Devices Institute Jiangsu210016 China

ISBN: (纸本)9781510682085

Miniaturized spectrometers, owing to their compact dimensions, high sensitivity, and low cost, have been extensively applied in environmental monitoring, biomedical diagnostics, industrial inspection, and other fields. However, conventional miniature spectrometers often face challenges in simultaneously achieving high spectral resolution, miniaturization, and a high degree of integration. As a pivotal component in integrated optics, the Arrayed Waveguide Grating (AWG) possesses advantages such as exceptional resolution, broad spectral bandwidth, and superior integrability, thus serving as a highly effective means for the design and realization of miniature spectrometers. Here, a highly uniform visible light arrayed waveguide grating with a central wavelength of 633nm was designed and fabricated. The device was based on a 300 nm silicon nitride platform, with the waveguide width of 300nm. The beam diverges at the input star coupler, then propagates through the arrayed waveguide, and finally converges on the image plane of the output star coupler. There are 4 output channels with a channel spacing of 10nm, and the spectral resolution is 5nm. This configuration can provide a uniform spectral output with a high stability, making it suitable for applications in the field of spectral analysis. © 2024 SPIE. All rights reserved.

关键词： Spectral resolution

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Tailoring Highly Confined Electric-field Based Optical Skyrmions

Tailoring Highly Confined Electric-field Based Optical Skyrm...

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2024 Photonics and Electromagnetics Research Symposium, PIERS 2024

作者： Chen, J. Shen, X. Zeng, Y. Zhan, Q. University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China University of Shanghai for Science and Technology Shanghai Key Laboratory of Modern Optical System Shanghai200093 China

ISBN: (纸本)9798350375909

Optical skyrmions are quasi-particles with nontrivial topological textures, which hold potential applications in optical information processing, transmission, and storage. There are various three-dimensional physical vectors to construct the optical skyrmions, such as spin skyrmions, Stokes skyrmions, Poynting vector skyrmions, and so on. Here, we present a method to construct tightly focused electric-field skyrmions based on the array antenna radiation theory. A multiple concentric-ring array of dipoles (MCAD) is placed at the focal region of the high numerical aperture (NA) lens to mimic the topological structure of the desired electric field skyrmion, and the radiation pattern of the MCAD is derived to form the incident fields at the pupil planes of the high NA lenses in a 4π focusing setup. The characteristics of the generated focal field are quantitatively evaluated by using the Debye diffraction integral theory. The results demonstrate that two types of electric field vector topologies, i.e., skyrmion and meron, can be created with adjustable polarity, vorticity, and helicity. Additionally, the normal direction of the plane containing the skyrmion or meron can also be customized, increasing the degrees of freedom in tailoring optical skyrmions. © 2024 IEEE.

关键词： Topology

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