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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Xusheng Zhang Tianling Lyu Yijie Shi Xinyun Zhong Zhan Wu Yan Xi Shijie Wang Yang Chen Wentao Zhu School of Software Southeast University Nanjing China Zhejiang Lab Hangzhou China Laboratory of Image Science and Technology the School of Computer Science and Engineering Southeast University Nanjing China First-Imaging Tech. Shanghai China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing the Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Nanjing China

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

The application of CBCT systems in intraoperative environments has become increasingly common, but concurrent CBCT systems are unsuitable for situations that require a large longitudinal imaging FoV, such as orthopedics. To increase longitudinal coverage, we developed a dual-source CBCT (DS-CBCT) system in which two ray sources are symmetrically placed along the central plane. After further analyzing its geometric characteristics, we propose an analytical reconstruction algorithm termed DT-FDK specialized for DS-CBCT, which combines cone-beam rebinning and two rays with smaller cone angles among the four conjugate rays to further suppress cone beam artifacts. The system design and reconstruction algorithm are tested on both simulated and real-scanned data. Results show that the DS-CBCT can expand the effective imaging volume by 37.1% compared to single-source CBCT systems. The reconstructed images by DT-FDK also show improved image quality compared to traditional reconstruction algorithms.

关键词： image quality Orthopedic surgery Imaging Reconstruction algorithms Engineering in medicine and biology image reconstruction System analysis and design

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Neighborhood Interval Observer Based Coordination Control for Multi-agent Systems with Disturbances ⁎

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IFAC-PapersOnLine 2020年第2期53卷 10994-10999页

作者： Xiaoling Wang Guo-Ping Jiang Wen Yang Housheng Su Xiaofan Wang College of Automation Nanjing University of Posts and Telecommunications Nanjing 210023 China and also with Jiangsu Engineering Lab for IOT Intelligent Robots (IOTRobot) Nanjing 210023 China Key Laboratory of Advanced Control and Optimization for Chemical Processes East China University of Science and Technology Shanghai 200237 China School of Artifcial Intelligence and Automation Image Processing and Intelligent Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Luoyu Road 1037 Wuhan 430074 China Department of Automation Shanghai University Shanghai 200072 China

This paper focuses on multi-agent systems with uncertain disturbances, in which only the bounding functions on the disturbances and the bounds on the initial state of each agent are known. By designing a neighborhood interval observer for this kind of multi-agent system, the estimation of the sum of the relative state of each agent associated with itself and its neighbors is frstly realized. Then, on the basis of these estimated information, local control algorithm is designed to drive the system to achieve bounded consensus.

关键词： Multi-agent system disturbance neighborhood interval observer bounded consensus

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Lightweight Real-time Semantic Segmentation Network with Efficient Transformer and CNN

arXiv

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arXiv 2023年

作者： Xu, Guoan Li, Juncheng Gao, Guangwei Lu, Huimin Yang, Jian Yue, Dong The Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing210023 China The Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou215006 China The School of Communication and Information Engineering Shanghai University Shanghai200444 China Jiangsu Key Laboratory of Image and Video Understanding for Social Safety Nanjing University of Science and Technology Nanjing210049 China The Kyushu Institute of Technology Kitakyushu804-8550 Japan The School of Computer Science and Technology Nanjing University of Science and Technology Nanjing210049 China

In the past decade, convolutional neural networks (CNNs) have shown prominence for semantic segmentation. Although CNN models have very impressive performance, the ability to capture global representation is still insufficient, which results in suboptimal results. Recently, Transformer achieved huge success in NLP tasks, demonstrating its advantages in modeling long-range dependency. Recently, Transformer has also attracted tremendous attention from computer vision researchers who reformulate the image processing tasks as a sequence-to-sequence prediction but resulted in deteriorating local feature details. In this work, we propose a lightweight real-time semantic segmentation network called LETNet. LETNet combines a U-shaped CNN with Transformer effectively in a capsule embedding style to compensate for respective deficiencies. Meanwhile, the elaborately designed Lightweight Dilated Bottleneck (LDB) module and Feature Enhancement (FE) module cultivate a positive impact on training from scratch simultaneously. Extensive experiments performed on challenging datasets demonstrate that LETNet achieves superior performances in accuracy and efficiency balance. Specifically, It only contains 0.95M parameters and 13.6G FLOPs but yields 72.8% mIoU at 120 FPS on the Cityscapes test set and 70.5% mIoU at 250 FPS on the CamVid test dataset using a single RTX 3090 GPU. Source code will be available at https://***/IVIPLab/LETNet. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Multiscale Low-Frequency Memory Network for Improved Feature Extraction in Convolutional Neural Networks

arXiv

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arXiv 2024年

作者： Wu, Fuzhi Wu, Jiasong Kong, Youyong Yang, Chunfeng Yang, Guanyu Shu, Huazhong Carrault, Guy Senhadji, Lotfi Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Bangladesh Laboratoire Traitement du Signal et de l'Image Univ Rennes France Centre de Recherche en Information Biomédicale Sino-français CRIBs France Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing Southeast University Bangladesh

Deep learning and Convolutional Neural Networks (CNNs) have driven major transformations in diverse research areas. However, their limitations in handling low-frequency information present obstacles in certain tasks like interpreting global structures or managing smooth transition images. Despite the promising performance of transformer structures in numerous tasks, their intricate optimization complexities highlight the persistent need for refined CNN enhancements using limited resources. Responding to these complexities, we introduce a novel framework, the Multiscale Low-Frequency Memory (MLFM) Network, with the goal to harness the full potential of CNNs while keeping their complexity unchanged. The MLFM efficiently preserves low-frequency information, enhancing performance in targeted computer vision tasks. Central to our MLFM is the Low-Frequency Memory Unit (LFMU), which stores various low-frequency data and forms a parallel channel to the core network. A key advantage of MLFM is its seamless compatibility with various prevalent networks, requiring no alterations to their original core structure. Testing on imageNet demonstrated substantial accuracy improvements in multiple 2D CNNs, including ResNet, MobileNet, EfficientNet, and ConvNeXt. Furthermore, we showcase MLFM's versatility beyond traditional image classification by successfully integrating it into image-to-image translation tasks, specifically in semantic segmentation networks like FCN and U-Net. In conclusion, our work signifies a pivotal stride in the journey of optimizing the efficacy and efficiency of CNNs with limited resources. This research builds upon the existing CNN foundations and paves the way for future advancements in computer vision. Our codes are available at https://***/AlphaWuSeu/MLFM. © 2024, CC BY-NC-ND.

关键词： Computer vision

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Cooperative MIMO Channel Modeling and Multi-Link Spatial Correlation Properties

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IEEE Journal on Selected Areas in communications 2012年第2期30卷 388-396页

作者： Xiang Cheng Cheng-Xiang Wang Haiming Wang Xiqi Gao Xiao-Hu You Dongfeng Yuan Bo Ai Qiang Huo Ling-Yang Song Bing-Li Jiao School of Electronics and Computer Science Peking University Beijing China Key Laboratory of Wireless Sensor Network & Communication Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences China State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University China Joint Research Institute for Signal and Image Processing School of Engineering & Physical Sciences Heriot-Watt University Edinburgh UK School of Information Science and Engineering South East University Najing China School of Information Science and Engineering Shandong University Jinan China State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing China

In this paper, a novel unified channel model framework is proposed for cooperative multiple-input multiple-output (MIMO) wireless channels. The proposed model framework is generic and adaptable to multiple cooperative MIMO scenarios by simply adjusting key model parameters. Based on the proposed model framework and using a typical cooperative MIMO communication environment as an example, we derive a novel geometry-based stochastic model (GBSM) applicable to multiple wireless propagation scenarios. The proposed GBSM is the first cooperative MIMO channel model that has the ability to investigate the impact of the local scattering density (LSD) on channel characteristics. From the derived GBSM, the corresponding multi-link spatial correlation functions are derived and numerically analyzed in detail.

关键词： MIMO Correlation Channel models Scattering Educational institutions Fading Adaptation models

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Understanding the Robustness of 3D Object Detection with Bird'View Representations in Autonomous Driving

Understanding the Robustness of 3D Object Detection with Bir...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Zijian Zhu Yichi Zhang Hai Chen Yinpeng Dong Shu Zhao Wenbo Ding Jiachen Zhong Shibao Zheng Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Dept. of Comp. Sci. and Tech. THBI Lab Institute for AI Tsinghua University BNRist Center Zhongguancun Laboratory Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Information Materials and Intelligent Sensing Laboratory of Anhui Province School of Computer Science and Technology Anhui University SAIC Motor AI Lab

3D object detection is an essential perception task in autonomous driving to understand the environments. The Bird's-Eye-View (BEV) representations have significantly improved the performance of 3D detectors with camera inputs on popular benchmarks. However, there still lacks a systematic understanding of the robustness of these vision-dependent BEV models, which is closely related to the safety of autonomous driving systems. In this paper, we evaluate the natural and adversarial robustness of various representative models under extensive settings, to fully understand their behaviors influenced by explicit BEV features compared with those without BEV. In addition to the classic settings, we propose a 3D consistent patch attack by applying adversarial patches in the 3D space to guarantee the spatiotemporal consistency, which is more realistic for the scenario of autonomous driving. With substantial experiments, we draw several findings: 1) BEV models tend to be more stable than previous methods under different natural conditions and common corruptions due to the expressive spatial representations; 2) BEV models are more vulnerable to adversarial noises, mainly caused by the redundant BEV features; 3) Camera-LiDARfusion models have superior performance under different settings with multi-modal inputs, but BEV fusion model is still vulnerable to adversarial noises of both point cloud and image. These findings alert the safety issue in the applications of BEV detectors and could facilitate the development of more robust models.

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Technical Analysis on Application Layer Error Control in IPTV

引用

ZTE communications 2010年第1期000卷

作者： Xia Hongfei Lü Xiaopeng Liu Feng ZTE Corporation Nanjing 210012P.R.China Image Processing and Image Communication Laboratory of Jiangsu Province Nanjing University of Posts and TelecommunicationsNanjing 210003P. R. China

When they provide Internet Protocol Television (IPTV) services, IP networks face difficulties such as reduced transmission bandwidth, delay, jitter, and packet loss. A terminal-centric solution can improve the image quality by adding control policies like congestion control and error control to the receiving terminal and source code. An end-to-end application layer reliability method is usually required to satisfy IPTV requirements in the IP network. Application Layer Forward Error Correction (AL-FEC) technology solves the problem of packet loss, and can ensure the end-to-end reliability for IPTV services.

关键词：差错控制分析应用 IPTV 技术接收终端 IP网络服务终端互联网协议

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Heterogeneous two-Stream Network with Hierarchical Feature Prefusion for Multispectral Pan-Sharpening

Heterogeneous two-Stream Network with Hierarchical Feature P...

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IEEE International Conference on Acoustics, Speech and Signal processing

作者： Dong Wang Yunpeng Bai Bendu Bai Chanyue Wu Ying Li National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science Northwestern Polytechnical University Xi'an China School of Communication and Information Engineering Xi’an University of Posts and Telecommunications Xi'an China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Provincial Key Laboratory of Speech & Image Information Processing Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9781728176055;9781728176062

Multispectral (MS) pan-sharpening aims at producing a high spatial resolution (HR) MS image by fusing a single-band HR panchromatic (PAN) image and a corresponding MS image with low spatial resolution. In this paper, we propose a heterogeneous two-stream network (HTSNet) with hierarchical feature prefusion for MS pan-sharpening. The HTSNet employs a heterogeneous group of spatial and spectral streams for spatial and spectral information extraction, respectively. The spatial stream utilizes a 2D CNN for spatial information extraction from the PAN images, and the spectral stream obtains spectral feature cubes from the MS images by a 3D CNN. At the same time, a prefusion module is introduced to prefuse the spatial details with spectral information and transfer information between different streams, which can enhance later processing. In the experiment, the Gaofen-2 satellite dataset is utilized to compare the proposed method with the state-of-the-art MS pan-sharpening methods. Experimental results demonstrate the superiority of our HTSNet in terms of visual effect and quantitative qualities.

关键词： Satellites Three-dimensional displays Fuses Streaming media Signal processing Information retrieval Visual effects

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A delay system method to design of event-triggered control of networked control systems

A delay system method to design of event-triggered control o...

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IEEE Conference on Decision and Control

作者： Dong Yue Engang Tian Qing-Long Han Department of Control Science and Engineering and the Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Huazhong University of Science and Technology Hubei 430074 China School of Electrical and Automation Engineering Nanjing Normal University 78 Bancang Street Nanjing 210042 China Center for Intelligent and Networked Systems and the School of Information and Communication Technology Central Queensland University Rockhampton Qld 4702 Australia

ISBN: (纸本)9781612848006

The event-triggered H_(infinity) control design is investigated for networked control systems with uncertainties and transmission delays. A novel event-triggering scheme is proposed, which has some advantages over traditional ones with a continuous detector. Considering the effect of the transmission delay, a delay system model for the analysis is firstly constructed. Then, based on the model and Lyapunov functional method, criteria for the stability with an H_(infinity) norm bound and criteria for the co-design of both the feedback gain and the trigger parameters are derived. In order to solve the feedback gain and the trigger parameters, the linear matrix inequality technique is employed. From the simulation example, it can be concluded that the proposed event-triggering scheme is superior to some other event-triggering schemes in some existing literature.

关键词： feedback gain Networked control systems Event trigger Delay systems Scheme Co-design control systems design control

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EWT: Efficient Wavelet-Transformer for Single image Denoising

arXiv

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arXiv 2023年

作者： Li, Juncheng Cheng, Bodong Chen, Ying Gao, Guangwei Zeng, Tieyong The School of Communication & Information Engineering Shanghai University Shanghai China Jiangsu Key Laboratory of Image and Video Understanding for Social Safety Nanjing University of Science and Technology Nanjing China The School of Computer Science and Technology Xidian University Xian China The Department of Cyberspace Security Beijing Electronic Science & Technology Institute Beijing China The Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing China The Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou China The Department of Mathematics The Chinese University of Hong Kong New Territories Hong Kong

Transformer-based image denoising methods have achieved encouraging results in the past year. However, it must uses linear operations to model long-range dependencies, which greatly increases model inference time and consumes GPU storage space. Compared with convolutional neural network-based methods, current Transformer-based image denoising methods cannot achieve a balance between performance improvement and resource consumption. In this paper, we propose an Efficient Wavelet Transformer (EWT) for image denoising. Specifically, we use Discrete Wavelet Transform (DWT) and Inverse Wavelet Transform (IWT) for downsampling and upsampling, respectively. This method can fully preserve the image features while reducing the image resolution, thereby greatly reducing the device resource consumption of the Transformer model. Furthermore, we propose a novel Dual-stream Feature Extraction Block (DFEB) to extract image features at different levels, which can further reduce model inference time and GPU memory usage. Experiments show that our method speeds up the original Transformer by more than 80%, reduces GPU memory usage by more than 60%, and achieves excellent denoising results. All code will be public. © 2023, CC BY.

关键词： image denoising

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