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Message Passing Based Moving Target Localization in Multistatistic Systems With Unknown Time and Frequency Offsets

IEEE Transactions on Aerospace and Electronic Systems 2025年

作者： Li, Wenjuan Jin, Ming Guo, Qinghua Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo315211 China University of Wollongong School of Electrical Computer and Telecommunications Engineering WollongongNSW2522 Australia

It is challenging to localize a moving target using a multistatic system with unknown time delay (TD) and Doppler shift (DS) measurement offsets caused by asynchronization between transmitters and receivers. To address this issue, we develop a low complexity iterative message passing localization method using TD and DS measurements. In particular, to handle the unknown offsets, we formulate a joint estimation problem of target position, velocity and unknown offsets based on the maximum likelihood (ML) principle, which is nonlinear and nonconvex. Then, by transforming the objective function and relevant constraints into a product of multiple local functions, a message passing algorithm is developed. To handle the complex local functions in message updates, belief propagation (BP) and variational message passing (VMP) are combined. Comparisons with state-of-the-art methods demonstrate that, with much lower complexity, the proposed method delivers superior performance. © 2022 IEEE.

关键词： Iterative methods

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Model Uncertainty Estimation Based on a Dual-Module Neural Network Architecture 23

Model Uncertainty Estimation Based on a Dual-Module Neural N...

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23rd International Conference on Machine Learning and Cybernetics, ICMLC 2024

作者： Guo, Weiqing Zhang, Tianlun Peng, Dunlu Wang, Debby D. School of Science and Technology Hong Kong Metropolitan Universit Hong Kong Hong Kong School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai China

ISBN: (纸本)9798331528041

In recent years, deep learning has achieved remarkable success in various fields, including biology and medicine. However, the interpretability and robustness still face challenges, as erroneous predictions can lead to serious consequences in critical applications. This paper introduces a new framework for estimating model uncertainty that can enhance the decision-making process. Compared to existing uncertainty estimation methods like Bayesian neural networks and Monte Carlo dropout, this paper provides a more direct way to model uncertainty by directly estimating the output variance through a dual-module neural network architecture. Without the need for multiple forward passes or complex Bayesian inference, it can reduce computational intensity. In specific, this framework consists of a prediction module for input processing and an uncertainty module for estimating prediction uncertainty. We demonstrate the efficiency of our method through applications in medical image segmentation and protein-ligand binding affinity prediction. By incorporating model uncertainty estimation, the proposed framework not only improves the accuracy of deep learning models, but also significantly reduces the computational burden which is especially beneficial for processing large datasets. © 2024 IEEE.

关键词： Prediction models

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A Vision-Based People Identification System Using Gait Recognition for Industrial Environments 22

A Vision-Based People Identification System Using Gait Recog...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Peyron, Jason Ramotsoela, Daniel Hancke, Gerhard Department of Electrical Engineering University of Cape Town Cape Town South Africa Department of Computer Science City University of Hong Kong Hong Kong

ISBN: (纸本)9798331527471

This paper presents a vision-based people identification system designed for industrial environments, focusing on continuous user identification through gait recognition. Leveraging the CASIA-B gait dataset, a renowned resource in gait recognition research, our system adopts an enhanced spatiotemporal representation known as the Gait Energy Image (GEI), a binary silhouette-based gait representation, as its primary feature. The gait recognition model is built upon a ResNet architecture, integrating all essential components for effective identification. Our system demonstrates superior or comparable performance to existing algorithms in the literature for normal walking styles, with opportunities for further enhancements in the bag sequence identification. © 2024 IEEE.

关键词： Machine vision

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Triplet Loss-less Center Loss Sampling Strategies in Facial Expression Recognition Scenarios 57

Triplet Loss-less Center Loss Sampling Strategies in Facial ...

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57th Annual Conference on Information sciences and Systems, CISS 2023

作者： Rajoli, Hossein Lotfi, Fatemeh Atyabi, Adham Afghah, Fatemeh Clemson University Holcombe Department of Electrical and Computer Engineering ClemsonSC United States University of Colorado Colorado Springs Department of Computer Science Colorado Springs United States

ISBN: (纸本)9781665451819

Facial expressions convey massive information and play a crucial role in emotional expression. Deep neural network (DNN) accompanied by deep metric learning (DML) techniques boost the discriminative ability of the model in facial expression recognition (FER) applications. DNN, equipped with only classification loss functions such as Cross-Entropy cannot compact intra-class feature variation or separate inter-class feature distance as well as when it gets fortified by a DML supporting loss item. The triplet center loss (TCL) function is applied on all dimensions of the sample's embedding in the embedding space. In our work, we developed three strategies: fully-synthesized, semi-synthesized, and prediction-based negative sample selection strategies. To achieve better results, we introduce a selective attention module that provides a combination of pixel-wise and element-wise attention coefficients using high-semantic deep features of input samples. We evaluated the proposed method on the RAF-DB, a highly imbalanced dataset. The experimental results reveal significant improvements in comparison to the baseline for all three negative sample selection strategies. © 2023 IEEE.

关键词： Embeddings

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Pedestrian Group Detection with K-Means and DBSCAN Clustering Methods

Pedestrian Group Detection with K-Means and DBSCAN Clusterin...

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2022 IEEE International Conference on Electro Information Technology, eIT 2022

作者： Chen, Mingzuoyang Banitaan, Shadi Maleki, Mina Li, Yichun University of Detroit Mercy Dept. of Electrical & Computer Engineering & Computer Science DetroitMI United States

ISBN: (纸本)9781665480093

The development of autonomous vehicles has made real-time pedestrian detection and tracking an important research area for protecting human lives and improving society. A key challenge in this area is to improve pedestrian detection accuracy while reducing the processing time for tracking. This challenge can be addressed by detecting and tracking pedestrian groups since pedestrians move in groups. This paper proposes a method of detecting pedestrian groups using unsupervised machine learning methods. After detecting the pedestrians, K-Means and DBSCAN clustering methods were used to detect pedestrian groups based on the coordinates of pedestrians' bounding boxes. Simulation results of the MOT17 dataset indicate that both clustering techniques could reduce detection and tracking processing times. However, K-Means clustering is more effective than DBSCAN clustering in detecting pedestrian groups as measured by the Silhouette Coefficient score and Adjusted Rand Index (ARI). © 2022 IEEE.

关键词： Object detection

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Development of a Smart IOT Enabled Arduino Glove Supporting Sign Language Communication 26

Development of a Smart IOT Enabled Arduino Glove Supporting ...

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26th International Conference on computer and Information Technology, ICCIT 2023

作者： Zaman, Muhammad Shahriar Victor Biswas, Richard Faculty of Science and Technology Department of Computer Science and Engineering Dhaka1229 Bangladesh Faculty of Engineering Department of Electrical and Electronic Engineering 408/1 Kuratoli Dhaka1229 Bangladesh

ISBN: (纸本)9798350359015

Deafness is an unfortunate issue of our society impairing many people. Around 3 million people in Bangladesh suffer from partial or complete deafness. One key challenge for these individuals is communicating with regular people. Various sign languages are available but very few people are knowledgeable about it. Keeping this issue in consideration this project has been developed. The smart Arduino glove allows the user to express their ideas by translating their fingerspelling as well as the gestures into comprehensible messages. The system also allows identification of common sentences by identifying simple gestures. The suggested system if applied in daily life can bring a positive change in the lives of deaf-mute individuals. © 2023 IEEE.

关键词： Accelerometer Alphabets Deaf Digits Fingerspelling Flex sensor Gesture Recognition I2C MPU-6050 Mute PAJ7620 Sign language

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Optimizing Multidocument Summarization by Blending Reinforcement Learning Policies

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第3期4卷 416-427页

作者： Su, DiJia Su, Difei Mulvey, John M. Poor, H.Vincent Princeton University Department of Electrical and Computer Engineering PrincetonNJ08540 United States University of British Columbia Department of Computer Science VancouverBCV6Z1T8 Canada Princeton University Department of Operation Research and Financial Engineering PrincetonNJ08540 United States

We consider extractive summarization within a cluster of related texts (multidocument summarization). Unlike single-document summarization, redundancy is particularly important because sentences across related documents might convey overlapping information. Thus, sentence extraction in such a setting is difficult because one will need to determine which pieces of information are relevant while avoiding unnecessary repetitiveness. To solve this difficult problem, we propose a novel reinforcement learning-based method Policy Blending with maximal marginal relevance and Reinforcement Learning (PoBRL) for solving multidocument summarization. PoBRL jointly optimizes over the following objectives necessary for a high-quality summary: importance, relevance, and length. Our strategy decouples this multiobjective optimization into different subproblems that can be solved individually by reinforcement learning. Utilizing PoBRL, we then blend each learned policies to produce a summary that is a concise and a complete representation of the original input. Our empirical analysis shows high performance on several multidocument datasets. Human evaluation also shows that our method produces high-quality output. © 2020 IEEE.

关键词： Reinforcement learning

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A Rule and Network Dual-Driven Dynamic Scheduling Approach for Complex Flexible Manufacturing Job Shop 7

A Rule and Network Dual-Driven Dynamic Scheduling Approach f...

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7th International Conference on Robotics, Control and Automation engineering, RCAE 2024

作者： Xu, Kai Xu, Binzi Huang, Dengchao Wang, Chun Anhui Polytechnic University School of Electrical Engineering Wuhu241000 China Huaibei Normal University School of Computer Science and Technology Huaibei235000 China

ISBN: (纸本)9798350355642

Traditional manually designed dispatching rules (DRs) cannot cope with the variety and rapid changes in production environments due to their inflexibility and lack of adaptability. Meanwhile, the development in manufacturing intelligence and informatization allows for storing a vast amount of scheduling data during production. Consequently, a novel approach emerges in constructing scheduling models and decision-making strategies based on historical data. Following this idea, this paper proposes a dual-driven dynamic scheduling approach for complex flexible manufacturing job shops, utilizing a combination of manually designed DRs and backpropagation neural network (BPNN) technology. Specifically, six different production scenarios are designed to optimize different objectives, simulating the actual complex manufacturing environment. Then, a real-time scheduling mapping network model is developed with the production states as input and selected DRs as output by training the BPNN. The results demonstrate the effectiveness and practicality of the proposed method in resolving dynamic scheduling issues of complex flexible manufacturing job shops, surpassing traditional manual DRs. © 2024 IEEE.

关键词： Flexible manufacturing systems

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Cut to the Chase: A Fast-Decoding Scheme for Symbiotic Backscatter Multi-User NOMA Systems 16

Cut to the Chase: A Fast-Decoding Scheme for Symbiotic Backs...

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16th International Conference on Wireless Communications and Signal Processing, WCSP 2024

作者： Wen, Zhen Ding, Haiyang Elkashlan, Maged Li, Dong Yuen, Chau Moualeu, Jules M. Chen, Mingkai Liu, Zhongwei School of Information and Communications National University of Defense Technology Wuhan China School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom School of Computer Science and Engineering Macau University of Science and Technology China School of Electrical and Electronic Engineering Nanyang Technological University Singapore School of Electrical and Information Engineering University of the Witwatersrand Johannesburg South Africa

ISBN: (纸本)9798350390643

This paper proposes a fast-decoding scheme based on the successive interference cancellation (SIC) framework for symbiotic backscatter multi-user non-orthogonal multiple access (NOMA) systems, which aims to decode the desired primary NOMA signal and the backscatter signal for the end-users in an efficient manner. Under the proposed decoding framework, we derive the symbiotic constraints on the decoding thresholds and power allocation ratios to guarantee the minimum decoding times and a shorter decoding times than the conventional solutions, respectively. Moreover, we derive the corresponding successful fast-decoding probability at high transmit signal-to-noise ratio (SNR). By keeping the primary channel statistics or the decoding threshold for the backscatter signal sufficiently low, our results demonstrate that the decoding speed of the proposed decoding scheme is most certainly faster than the conventional ones. © 2024 IEEE.

关键词： Signal to noise ratio

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Extrinsic and Temporal Calibration of Automotive Radar and 3-D LiDAR in Factory and On-Road Calibration Settings

IEEE Open Journal of Intelligent Transportation Systems

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IEEE Open Journal of Intelligent Transportation Systems 2023年 4卷 708-719页

作者： Lee, Chia-Le Hou, Chun-Yu Wang, Chieh-Chih Lin, Wen-Chieh Institute of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu300 Taiwan National Yang Ming Chiao Tung University Department of Electrical and Computer Engineering Hsinchu300 Taiwan Industrial Technology Research Institute Mechanical and Mechatronics Systems Research Laboratories Hsinchu31040 Taiwan National Yang Ming Chiao Tung University Department of Computer Science Hsinchu300 Taiwan

While automotive radars are widely used in ADAS and autonomous driving, extrinsic and temporal calibration of automotive radars with other sensors is still daunting due to the sparsity, uncertainty, and missing elevation angles of automotive radar measurements. We propose a target-based calibration approach of 3D automotive radar and 3D LiDAR that performs extrinsic and temporal calibration in both factory and on-road settings. In factory calibration settings, a map is built with precise target poses;target trajectories are estimated based on map-based target localization in which the accuracy of both nearby and faraway target pose estimates can be ensured. The spatial and temporal relationships between radar and LiDAR measurements are established with target trajectories to accomplish extrinsic and temporal calibration. The proposed data collection procedure provides sufficient motion for analyzing time delay between sensors and can significantly reduce the data collection effort and time. There is 52.3% distance error reduction after time delay compensation in the experiment, which shows the improvements of temporal calibration. In on-road calibration settings, the metal objects with semantic labels, such as traffic signs, are selected as calibration targets. Although there could be insufficient correspondences to infer the missing dimension of planar radar for six DoF extrinsic calibration as demonstrated in factory calibration settings, the three extrinsic parameters and the time delay are shown still to be accurate. We validated the proposed method using the nuScenes datasets, which provide sensor measurements, poses, and HD map. With twenty-two data logs, each has over 1000 correspondences, the result of extrinsic parameters reaches centimeter-level accuracy compared with the offered benchmark. The time delay compensation improves 1 meter error for radar tracking in a 20 m/s vehicle case and improves mapping quality in real world data. © 2020 IEEE.

关键词： Calibration

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