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2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022

作者： Zhu, Hu Yuan, Wei Wang, Chunxiang Yang, Ming Shanghai Jiao Tong University Department of Automation Shanghai200240 China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai200240 China University of Michigan-Shanghai Jiao Tong University Joint Institute Shanghai Jiao Tong University Shanghai200240 China

ISBN: (纸本)9781665481090

The tractor-trailer vehicle is a typical model of unmanned logistics vehicle in factories. The reverse movement of tractor-trailer vehicles is open-loop unstable, which limits its scope of use. In this paper, a practical tractor-trailer vehicle system is designed to realize the automatic reverse path-tracking task for unmanned tractor-trailer logistics vehicles. Firstly, an estimation system based on a single-line LiDAR is designed to measure the link angle. Then, a tracking system based on a cascade control is designed to track the reverse path accurately. Finally, the entire system is configured on a real tractor-trailer vehicle. The experimental results of the real tractor-trailer vehicle show that the system can track any reverse path that meets the kinematic constraints. And the mean absolute error (MAE) of the tracking path is within 25cm after the system is stabilized. © 2022 IEEE.

关键词： Tractors (truck)

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Depth-aware Fusion Method based on Image and 4D Radar Spectrum for 3D Object Detection

arXiv

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arXiv 2025年

作者： Sun, Yue Qian, Yeqiang Wang, Chunxiang Yang, Ming The Global Institute of Future Technology Shanghai Jiao Tong University Shanghai200240 China The Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

Safety and reliability are crucial for the public acceptance of autonomous driving. To ensure accurate and reliable environmental perception, intelligent vehicles must exhibit accuracy and robustness in various environments. Millimeter-wave radar, known for its high penetration capability, can operate effectively in adverse weather conditions such as rain, snow, and fog. Traditional 3D millimeter-wave radars can only provide range, Doppler, and azimuth information for objects. Although the recent emergence of 4D millimeter-wave radars has added elevation resolution, the radar point clouds remain sparse due to Constant False Alarm Rate (CFAR) operations. In contrast, cameras offer rich semantic details but are sensitive to lighting and weather conditions. Hence, this paper leverages these two highly complementary and cost-effective sensors, 4D millimeter-wave radar and camera. By integrating 4D radar spectra with depth-aware camera images and employing attention mechanisms, we fuse texture-rich images with depth-rich radar data in the Bird’s Eye View (BEV) perspective, enhancing 3D object detection. Additionally, we propose using GAN-based networks to generate depth images from radar spectra in the absence of depth sensors, further improving detection accuracy. Copyright © 2025, The Authors. All rights reserved.

关键词： Radar imaging

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Temperature Prediction for Stored Grain: A Multi- model Fusion Approach Based on Machine Learning

Temperature Prediction for Stored Grain: A Multi- model Fusi...

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Electronic Technology and information Science (ICETIS), International Conference on

作者： Donghao Chen Binkun Liu Key Laboratory of Grain Information Processing and Control Henan University of Technology Zhengzhou China Henan Engineering Laboratory of Grain Condition Intelligent Detection and Application Henan University of Technology Zhengzhou China

ISBN: (数字)9798350388343

ISBN: (纸本)9798350388350

Temperature fluctuations significantly affect microorganism growth and pest activities in grain pile, precise monitoring and forecasting temperature of stored grain are essential for maintaining the quality and safety of grain storage. This paper proposes a multi-model fusion approach to predict grain temperature using historical temperature data of stored grain and meteorological data from the region. Firstly, four distinct machine learning models, namely Adaboost, decision tree, extra trees, and random forest, are fine-tuned through parameter optimization to enhance their predictive capabilities respectively; Subsequently, these optimized models are fused to form different ensemble models, which are compared for prediction accuracy to obtain the optimal fusion model. In essence, the fusion process integrates the predictions of each individual model as new feature inputs into the fusion models. Furthermore, random forest is utilized to identify the key factors influencing grain temperature, providing insights into the importance of different influencing factors. The experimental results demonstrate that the proposed fusion models can achieve higher prediction accuracy and robustness compared with the single-model prediction methods. Additionally, the analysis of feature importance also offers empirical evidence for understanding the factors influencing grain temperature.

关键词： Temperature sensors Temperature measurement Temperature distribution Humidity Predictive models Prediction algorithms Robustness

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Learning with Open-world Noisy Data via Class-independent Margin in Dual Representation Space

arXiv

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arXiv 2025年

作者： Pan, Linchao Gao, Can Zhou, Jie Wang, Jinbao College of Computer Science and Software Engineering Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China

Learning with Noisy Labels (LNL) aims to improve the model generalization when facing data with noisy labels, and existing methods generally assume that noisy labels come from known classes, called closed-set noise. However, in real-world scenarios, noisy labels from similar unknown classes, i.e., open-set noise, may occur during the training and inference stage. Such open-world noisy labels may significantly impact the performance of LNL methods. In this study, we propose a novel dual-space joint learning method to robustly handle the open-world noise. To mitigate model overfitting on closed-set and open-set noises, a dual representation space is constructed by two networks. One is a projection network that learns shared representations in the prototype space, while the other is a One-Vs-All (OVA) network that makes predictions using unique semantic representations in the class-independent space. Then, bi-level contrastive learning and consistency regularization are introduced in two spaces to enhance the detection capability for data with unknown classes. To benefit from the memorization effects across different types of samples, class-independent margin criteria are designed for sample identification, which selects clean samples, weights closed-set noise, and filters open-set noise effectively. Extensive experiments demonstrate that our method outperforms the state-of-the-art methods and achieves an average accuracy improvement of 4.55% and an AUROC improvement of 6.17% on CIFAR80N. Copyright © 2025, The Authors. All rights reserved.

关键词： Contrastive Learning

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Multi-TransPathoNet: A transfer learning-based neural network for pathology image classification with multi-Task learning 3

Multi-TransPathoNet: A transfer learning-based neural networ...

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3rd International Conference on Advanced Algorithms and Signal Image processing, AASIP 2023

作者： Deng, Xin Li, Min Ren, Xiaojun Zheng, Xiuyan Liang, Fei He, Liang Zhu, Min School of Software Xinjiang University China School of Information Science and Engineering Xinjiang University China Xinjiang Key Laboratory of Clinical Genetic Testing and Biomedical Information China Xinjiang Clinical Research Center for Precision Medicine of Digestive System Tumor China Department of Pathology Karamay Central Hospital China Xinjiang Key Laboratory of Signal Detection and Processing China Dept. of Electron. Eng. and Beijing National Research Center for Information Science and Technology Tsinghua University China

ISBN: (纸本)9781510668522

Histopathological images are one of the common diagnostic tools used in the medical field. However, the complex cellular and tissue structures inside colorectal cancer histopathological images make it difficult for physicians to perform efficient cancer diagnosis and quantitative analysis. Therefore, this study proposed a multi-Task learning-based pathology image classification method Multi-TransPathoNet (A transfer learning-based neural network for pathology image classification with multi-Task learning, Multi-TransPathoNet) for detection and visualization output of colorectal cancer histopathology images, thus helping to improve medical efficiency. To verify the practical application value of this method, this study used the paracancerous, highly differentiated, and high-moderately differentiated histopathological images which are more difficult to distinguish in colorectal cancer as the dataset for classification test, and our method obtained 92% accuracy. Besides, we compared with the single-Task learning models Resnet50 and Vgg19 respectively, which also have good performance in image classification tasks, to comprehensively evaluate the proposed model's performance. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Computer aided diagnosis

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Time-of-Flight Camera Based Trailer Hitch Detection for Automatic Reverse Hanging system 6th

Time-of-Flight Camera Based Trailer Hitch Detection for Auto...

引用

6th International Conference on Cognitive systems and Signal processing, ICCSIP 2021

作者： Liu, Yaqi Wang, Chunxiang Yuan, Wei Yang, Ming Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China University of Michigan - Shanghai Jiao Tong University Joint Institute Shanghai Jiao Tong University Shanghai200240 China

ISBN: (纸本)9789811692468

With the development of autonomous vehicles, trailer-mounted self-driving cars are widely used for goods transportation. However, self-driving cars still use a manual way to hitch a trailer. To address this problem, this paper proposes a two-stage trailer hitch detection algorithm using a Time-of-Flight (ToF) camera to hitch the vehicle to a trailer automatically. The algorithm is divided into two parts: trailer detection and trailer hitch detection. An algorithm based on a 2D color image is used for trailer detection. To hitch a trailer successfully, the accurate pose of the trailer hitch is needed. This paper proposes an algorithm that combines coarse matching and fine matching in trailer hitch detection, which provides the 3D coordinate and the pose of the hitch. The method’s performance is evaluated on real data. The experiments show the proposed method achieves high accuracy and high efficiency. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Light trailers

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Event-Triggered Distributed Multitarget Tracking With Unknown Clutter Rate and Detection Profile

引用

IEEE Transactions on Aerospace and Electronic systems 2025年

作者： Zheng, Litao Yang, Feng Shi, Lihong Guo, Zhengyu Cai, Yunze Shanghai Jiao Tong University Department of Automation Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Northwestern Polytechnical University School of Automation Xi'an710129 China Key Lab of Information Fusion Technology Ministry of Education of China Xi'an710129 China National Key Laboratary of Air-based Information Perceptian and Fusion Luoyang China State Key Laboratory of Submarine Geoscience Shanghai201913 China

This paper addresses communication bandwidth reduction and energy efficiency enhancement of a peer-to-peer sensor network for distributed multitarget tracking under unknown clutter rate and detection profile. We propose an event-triggered distributed bootstrap cardinalized probability hypothesis density (CPHD) filter, where the local filter consists of an estimator that estimates the clutter and detection parameters, and a tracker that estimates the number and states of targets. Specifically, the proposed approach enforces each sensor to broadcast its local posterior density only when it has high estimation quality and significant information gain, wherein the former is determined by the outputs of the local estimator and tracker, while the latter is measured via the Jensen-Shannondivergence. Then, we develop a modified weighted arithmetic average fusion method to separately fuse the cardinality distributions and intensity functions, in which the fusion weights are constructed based on estimation qualities and triggering thresholds, which is incorporated into the distributed flooding scheme for distributed averaging. Additionally, the Gaussian mixture implementations of the proposed event-triggered distributed bootstrap CPHD filter are given, where only Gaussian components with high weights are applied, thus reducing the effect of false alarms and, consequently, achieving a partial consensus across the network. Finally, the effectiveness of the proposed algorithm is validated through simulation experiments. © 1965-2011 IEEE.

关键词： Gaussian distribution

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Big-Moe: Bypassing Isolated Gating For Generalized Multimodal Face Anti-Spoofing

Big-Moe: Bypassing Isolated Gating For Generalized Multimoda...

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Yingjie Ma Zitong Yu Xun Lin Weicheng Xie Linlin Shen College of Computer Science and Software Engineering Shenzhen University Great Bay University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Guangdong Provincial Key Laboratory of Intelligent Information Processing

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In the domain of facial recognition security, multimodal Face Anti-Spoofing (FAS) is essential for countering presentation attacks. However, existing technologies encounter challenges due to modality biases and imbalances, as well as domain shifts. Our research introduces a Mixture of Experts (MoE) model to address these issues effectively. We identified three limitations in traditional MoE approaches to multimodal FAS: (1) Coarse-grained experts’ inability to capture nuanced spoofing indicators; (2) Gated networks’ susceptibility to input noise affecting decision-making; (3) MoE’s sensitivity to prompt tokens leading to overfitting with conventional learning methods. To mitigate these, we propose the Bypass Isolated Gating MoE (BIG-MoE) framework, featuring: (1) Fine-grained experts for enhanced detection of subtle spoofing cues; (2) An isolation gating mechanism to counteract input noise; (3) A novel differential convolutional prompt bypass enriching the gating network with critical local features, thereby improving perceptual capabilities. Extensive experiments on four benchmark datasets demonstrate significant generalization performance improvement in multimodal FAS task. The code is released at https://***/murInJ/BIG-MoE.

关键词： Convolutional codes Sensitivity Convolution Face recognition Noise Logic gates Feature extraction Security Speech processing Overfitting

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Design and Implementation of Telemedicine system Using Light Fidelity and PIC16F877A Microcontroller 14

Design and Implementation of Telemedicine System Using Light...

引用

14th International Conference on information Science and Technology, ICIST 2024

作者： Ezilarasan, M.R. Leung, Man-Fai Dai, Xiangguang Vel Tech Rangarajan Dr. Sagunthala R and D Instituteof Science and Technology Department of Electronics and Communication Engineering Chennai India School of Computing and Information Science Anglia Ruskin University Faculty of Science and Engineering Cambridge United Kingdom Information Processing and Control of Chongqing Municipal Institutions of Higher Education Chongqing Three Gorges University Key Laboratory of Intelligent Chongqing Wanzhou404100 China

ISBN: (纸本)9798350353334

Medical Body Area Networks (MBANs) increasingly utilize wireless communication technologies to enhance the freedom and convenience for patients and caregivers. Currently, radio frequency (RF) is the prevalent medium in healthcare applications;however, the potential for electromagnetic interference with precision medical equipment remains a concern. This study introduces a novel design and implementation of a medical healthcare information system using the newly developed Wireless Visible Light Communication (VLC) technology, also known as Light Fidelity (Li-Fi). Visible Light Emitting Diodes (LEDs), expected to soon surpass traditional incandescent and fluorescent bulbs as the primary light source due to their energy efficiency, facilitate this technology. In this research, patient health is monitored using sensors, with results displayed on an LCD. An LED lightbulb transmits the data as light signals, which are captured by a photo-detector at the receiver's end. This setup not only displays the output on an LCD but also alerts caregivers through an auditory signal, beeping every 15 seconds for respiration and every 30 seconds for heart rate monitoring. Moreover, these lighting fixtures serve dual functions: illumination and wireless data transmission, using the rapid switching capability of LEDs. This prototype VLC-based system offers a promising alternative for data services and monitoring in hospital areas where RF use is restricted. © 2024 IEEE.

关键词： Liquid crystal displays

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Unleashing Correlation and Continuity for Hyperspectral Reconstruction from RGB Images

arXiv

引用

arXiv 2025年

作者： Feng, Fuxiang Cong, Runmin Wei, Shoushui Zhang, Yipeng Li, Jun Kwong, Sam Zhang, Wei The School of Control Science and Engineering Shandong University Jinan250061 China The Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan250061 China The University of California Los Angeles United States The Hubei Key Laboratory of Intelligent Geo-Information Processing The School of Computer Science China University of Geo-sciences Wuhan430078 China The Lingnan University Hong Kong

Reconstructing Hyperspectral Images (HSI) from RGB images can yield high spatial resolution HSI at a lower cost, demonstrating significant application potential. This paper reveals that local correlation and global continuity of the spectral characteristics are crucial for HSI reconstruction tasks. Therefore, we fully explore these inter-spectral relationships and propose a Correlation and Continuity Network (CCNet) for HSI reconstruction from RGB images. For the correlation of local spectrum, we introduce the Group-wise Spectral Correlation Modeling (GrSCM) module, which efficiently establishes spectral band similarity within a localized range. For the continuity of global spectrum, we design the Neighborhood-wise Spectral Continuity Modeling (NeSCM) module, which employs memory units to recursively model the progressive variation characteristics at the global level. In order to explore the inherent complementarity of these two modules, we design the Patch-wise Adaptive Fusion (PAF) module to efficiently integrate global continuity features into the spectral features in a patch-wise adaptive manner. These innovations enhance the quality of reconstructed HSI. We perform comprehensive comparison and ablation experiments on the mainstream datasets NTIRE2022 and NTIRE2020 for the spectral reconstruction task. Compared to the current advanced spectral reconstruction algorithms, our designed algorithm achieves State-Of-The-Art (SOTA) performance. Copyright © 2025, The Authors. All rights reserved.

关键词： Convolutional neural networks

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