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Interference mitigation and target detection for automotive FMCW radar with range-Doppler sparse regularization

science China(Information sciences) 2024年第9期67卷 342-343页

作者： Yan HUANG Yunxuan WANG Xiao ZHOU Hui ZHANG Yuan MAO Guisheng LIAO Wei HONG State Key Lab of Millimeter Waves Southeast University Department of Computer Science University College London National Key Lab of Radar Signal Processing Xidian University

The invention of vehicles has shaped our modern society and accelerated our economy while causing many tragic incidents simultaneously. Some advanced driver-assistance systems(ADAS) based on surrounding environment se... 详细信息

关键词： Radar interference

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TBIA-DBNet: A Two-Branch Image-Adaptive DBNet for Scene Text Detection in Real-World Foggy Scenes 27th

TBIA-DBNet: A Two-Branch Image-Adaptive DBNet for Scene Tex...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Liu, Zhaoxi Zhou, Gang He, Runlin Zhang, Mengnan Jia, Zhenhong Ma, Jing Key Laboratory of Signal Detection and Processing Department of Computer Science and Technology Xinjiang University Urumqi China

ISBN: (纸本)9783031781186

Though deep learning-based scene text detection methods have achieved promising results on conventional datasets, these methods are unable to maintain optimal performance in adverse weather conditions, such as foggy weather. To alleviate this problem, we propose a Two-Branch Image-Adaptive DBNet (TBIA-DBNet) framework. Specifically, to avoid missing discriminable features from the original image in one branch, we design an Image Enhancement Network (IENet) in another branch. Additionally, we design a Fusion Module based on Coordinate Attention (FMCA) to fully integrate original and enhanced features. Experimental results demonstrate that TBIA-DBNet significantly enhances scene text detection performance in foggy weather. Notably, it improves detection accuracy by nearly 10% in real-world foggy weather conditions compared to existing methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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Deformable Multi-Scale Network for Snow Removal in Video 27th

Deformable Multi-Scale Network for Snow Removal in Video

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27th International Conference on Pattern Recognition, ICPR 2024

作者： He, Runlin Zhou, Gang Xue, Tianhao Liu, Zhaoxi Jia, Zhenhong Key Laboratory of Signal Detection and Processing Department of Computer Science and Technology Xinjiang University Urumqi China

ISBN: (纸本)9783031781247

Snowfall severely degrades outdoor video visibility while reducing the performance of subsequent vision tasks. Although video recovery methods based on deep learning have achieved amazing accomplishments, video snow removal still faces problems such as varying scales and intricate trajectories of snowflakes, which makes it difficult to remove snowflakes and easy to create artifacts on moving objects. To address these issues, we propose a deformable multi-scale video desnowing network. Specifically, we design a multi-scale pseudo-3D residual block(MSRB-P3D) that can effectively remove snowflakes of different scales. Furthermore, a deformable large kernel attention 3Dblock(D-LKA 3Dblock) is introduced to capture the inter-frame dynamic information and reduce the artifacts. Due to the scarcity of dataset, we proposed a new dataset named Synthetic and Real Snowy Video Dataset(SRSVD). Extensive experiments have proven that our proposed method not only outperforms other state-of-the-art methods on both synthetic and real snowy videos, but also effectively improves performance on subsequent vision task. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Snow

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Real-Time Smart Healthcare System Based on Edge-Internet of Things and Deep Neural Networks for Heart Disease Prediction

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第1期49卷 93-104页

作者： Hameurlaine, Messaoud Moussaoui, Abdelouhab Bensalah, Mustapha LEIMA Laboratory Tissemsilt University Tissemsilt Algeria Computer Science Department Faculty of Sciences Ferhat Abbas University - Sétif 1 Algeria Signal Processing Laboratory Military Polytechnic School Algiers Algeria

With technological advancements, smart health monitoring systems have become increasingly vital and popular. The rise of smart homes, appliances, and medical systems, along with the pivotal role of the Internet of Things (IoT), is significantly enhancing healthcare services by improving data processing and predictive capabilities. IoT not only aids in predicting heart disease but also supports emergency responses. However, traditional data transfer methods are inefficient in terms of time and energy, resulting in high latency and consumption. Edge computing, alongside deep learning methods, provides effective solutions with superior performance. This paper introduces a Real-Time Smart Healthcare System utilizing Edge-Internet of Things and Deep Learning. The primary objective of this system is to monitor patient health changes, predict heart disease, and automate medication administration in real time. The study presents a DNN-based prediction model that leverages edge computing and IoT. This model processes health data from IoT devices, while edge devices deliver timely health predictions to doctors and patients via edge and cloud servers. The proposed system is evaluated on performance parameters, demonstrating superior results compared to other methods. By integrating edge computing, IoT, and deep learning, this system enables efficient real-time health monitoring and prediction, benefiting both healthcare professionals and patients. It demonstrates exceptional performance with an accuracy of 96.15%, precision of 92.86%, recall (sensitivity) of 97.50%, and an F1-score of 94.87%. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Electronic health record

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CGAPoseNet+GCAN: A Geometric Clifford Algebra Network for Geometry-aware Camera Pose Regression

CGAPoseNet+GCAN: A Geometric Clifford Algebra Network for Ge...

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2024 IEEE Winter Conference on Applications of computer Vision, WACV 2024

作者： Pepe, Alberto Lasenby, Joan Buchholz, Sven University of Cambridge Signal Processing and Communications Lab United Kingdom Technical University Department of Computer Science and Media Brandenburg Germany

ISBN: (纸本)9798350318920

We introduce CGAPoseNet+GCAN, which enhances CGAPoseNet, an architecture for camera pose regression, with a Geometric Clifford Algebra Network (GCAN). With the addition of the GCAN we obtain a geometry-aware pipeline for camera pose regression from RGB images only. CGAPoseNet employs Clifford Geometric Algebra to unify quaternions and translation vectors into a single mathematical object, the motor, which can be used to uniquely describe camera poses. CGAPoseNet can obtain comparable results to other approaches without the need of expensive tuning of the loss function or additional information about the scene, such as 3D point clouds, which might not always be available. CGAPoseNet, however, like several approaches in the literature, only learns to predict motor coefficients, and it is unaware of the mathematical space in which predictions sit in and of their geometrical meaning. By leveraging recent advances in Geometric Deep Learning, we modify CGAPoseNet with a GCAN: proposals of possible motor coefficients associated with a camera frame are obtained from the InceptionV3 backbone, and the GCAN downsamples them to a single motor through a sequence of layers that work in G4,00. The network is hence geometry-aware, has multivector-valued inputs, weights and biases and preserves the grade of the objects that it receives in input. CGAPoseNet+GCAN has almost 4 million fewer trainable parameters, it reduces the average rotation error by 41% and the average translation error by 8.8% compared to CGAPoseNet. Similarly, it reduces rotation and translation errors by 32.6% and 19.9%, respectively, compared to the best performing PoseNet strategy. CGAPoseNet+GCAN reaches the state-of-the-art results on 13 commonly employed datasets. To the best of our knowledge, it is the first experiment in GCANs applied to the problem of camera pose regression. © 2024 IEEE.

关键词： Cameras

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Effect of Pectoral Muscles on CNN based Mammographic Cancer Detection 18

Effect of Pectoral Muscles on CNN based Mammographic Cancer ...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Dhimann, Santoresh Kumari Sawhney, Tinny Yadav, Rakesh Kumar Maharishi University of Information Technology Department of Computer Science and Engineering Lucknow India University of Jammu Digital Signal Processing Lab Department of Electronics Jammu India

ISBN: (纸本)9789380544519

Cancer is attributed to abnormal growth of tissue cells in any part of the living body. The number of cancer patients is increasing worldwide. The reason of the development of abnormal cells within the body is still unknown to the researchers. The most common is breast cancer and it is affective affecting one out of every ten women. The reason of breast lesions formation may be calcification or mass deposit and digital mammography is generally used for verifying it. It is a cheap, non-toxic, and user-friendly test, but needs expert opinion for ascertaining the presence of cancer. The accuracy of mammographic detection varies between 80% and 90%. Recently, Convolutional neural networks (CNNs) have shown their application in automatic detection of the cancer, but inclusion of pectoral muscle in the mammogram reduces the efficiency of the CNN system as the texture of the muscle resembles with cancerous tissues. Further, CNN based detection requires high resolution images which in turn requires state of art technology-based machines, generally, not available in rural area-based clinics. The focus of the proposed research is to investigate effect of low-quality blurred input mammograms on the detection of pectoral muscle boundary, so that the pectoral muscle removed mammograms can be applied to CNN system for automatic prediction of cancerous images. The investigations carried out have shown that low quality blurred images can also be employed for detecting cancerous tissues provided the pectoral muscle is removed from the mammograms before using CNN based classifier. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： cancer detection CNN Hough lines mammogram pectoral muscle

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Particle Flow Gaussian Sum Particle Filter 48

Particle Flow Gaussian Sum Particle Filter

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48th IEEE International Conference on Acoustics, Speech and signal processing, ICASSP 2023

作者： Comandur, Karthik Li, Yunpeng Nannuru, Santosh Iiit Hyderabad Signal Processing and Communication Research Centre India University of Surrey Department of Computer Science United Kingdom

ISBN: (纸本)9781728163277

The particle flow Gaussian particle filter (PFGPF) uses an invertible particle flow to generate a proposal density. It approximates the predictive and posterior distributions as Gaussian densities. In this paper, we use a bank of PFGPF filters to construct a Particle flow Gaussian sum particle filter (PFGSPF), which approximates the prediction and posterior as Gaussian mixture model. This approximation is useful in complex estimation problems where a single Gaussian approximation is inadequate. We compare the performance of this proposed filter with the PFGPF and others in challenging numerical simulations. © 2023 IEEE.

关键词： Gaussian mixture Gaussian particle filter Gaussian sum particle filer invertible particle flow PFGPF

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Improving ECAPA-TDNN Performance with Coordinate Attention

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Journal of Shanghai Jiaotong University (science) 2024年 1-7页

作者： Liu, Shuanghong Song, Zhida He, Liang School of Computer Science and Technology Xinjiang University Urumqi830017 China Xinjiang Key Laboratory of Signal Detection and Processing Urumqi830017 China Department of Electronic Engineering and Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China

The current mainstream networks, such as squeeze and excitation residual neural network (SE-ResNet) and emphasized channel attention, propagation and aggregation based time delay neural network (ECAPA-TDNN), enhance the capability of speaker embedding extractors to extract more discriminative speaker embeddings by incorporating squeeze and excitation (SE) attention within the convolutional blocks. However, the SE attention focuses solely on encoding inter-channel information, overlooking the importance of spatial positional information and time-frequency information, which are crucial for the model’s performance. In this paper, we first experimentally compare the effectiveness of several mainstream attention mechanisms in the computer vision domain for the ECAPA-TDNN model. Next, we focus on the substantial improvements that coordinate attention (CA) brings to the ECAPA-TDNN model. The introduction of CA can help the model embed time-frequency information into the channel representation. Even without using AS-Norm, our proposed model achieves relative reductions of about 5.3% equal error rate (EER) and 5.5% minimum detection cost function (minDCF) on both the Voxceleb-O and Voxceleb-H test sets compared to the ECAPA-TDNN baseline model. In addition, the EER is relatively reduced by 9.46% on the CN-Celeb1 test set. This result strongly demonstrates that the CA module can effectively improve the generalization ability of the ECAPA-TDNN model. © Shanghai Jiao Tong University 2024.

关键词： Neural network models

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Registration Error-resistant Track-to-track Association for Multiple Sensors 2

Registration Error-resistant Track-to-track Association for ...

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2nd IEEE International Conference on signal, Information and Data processing, ICSIDP 2024

作者： Zhang, Shuyang Gao, Chang Zhang, Qingfu Yan, Junkun Jia, Tianyi Wang, Rongrong Liu, Hongwei Xidian University National Key Laboratory of Radar Signal Processing Xi'an China City University of Hong Kong Department of Computer Science Kowloon Hong Kong

ISBN: (纸本)9798331515669

The process of Multi-sensor Track-to-Track Association (MTTA) involves the association of tracks observed by multiple sensors, which are of the same target. This process serves as a foundation for enhancing track continuity and refining state estimation via the fusion of multi-sensor information. Predominantly, the existing methods employ statistical modeling of target motion to devise association gates between tracks. However, these methods encounter a decrease in association accuracy when faced with unknown registration errors. To address this challenge, a data-driven approach is proposed, which reconfigures track association into a classification problem. This approach harnesses target kinematic characteristics and track trajectory features extracted at the track level. Subsequently, these features are employed in constructing a track association algorithm based on decision trees. Experiments are conducted on a publicly available dataset, Multi-source Track Association Dataset (MTAD) derived from real data. The results have demonstrated superior association performance, particularly under conditions of inadequate system registration. © 2024 IEEE.

关键词： Information fusion

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Improving Speaker Verification Back-End with Graph Neural Networks

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Journal of Shanghai Jiaotong University (science) 2025年 1-9页

作者： Chen, Jinfeng Fang, Zhihua He, Liang School of Computer Science and Technology Xinjiang University Urumqi830017 China Xinjiang Key Laboratory of Signal Detection and Processing Urumqi830017 China School of Intelligence Science and Technology Xinjiang University Urumqi830017 China Department of Electronic Engineering Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China

Currently, research on speaker verification tasks is primarily concentrated on enhancing deep speaker models to extract high-quality speaker embeddings. Nevertheless, this speaker embeddings can be regarded as potential graph structures. Therefore, this paper introduces a graph neural network-based speaker verification back-end approach, treating the speaker embeddings derived from the front-end as graph structures and leveraging graph neural networks to uncover the relationships among embeddings, thereby achieving superior-quality speaker embeddings. In addition, we propose a group updating method to solve the problem of memory overflow when the number of nodes is excessive. Extensive experiments and ablation studies were carried out on the VoxCeleb dataset, and the experimental outcomes validate the effectiveness of our proposed graph neural network speaker back-end in significantly boosting the performance of speaker verification systems. (Figure presented.) © Shanghai Jiao Tong University 2025.

关键词： Graph embeddings

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