检索结果-内蒙古大学图书馆

arXiv 2021年

作者： Wu, Dongrui Xu, Jiaxin Fang, Weili Zhang, Yi Yang, Liuqing Xu, Xiaodong Luo, Hanbin Yu, Xiang The Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Zhejiang Lab Hangzhou311121 China The School of Civil and Hydraulic Engineering Huazhong University of Science and Technology Wuhan430074 China The College of Public Administration Huazhong University of Science and Technology Wuhan430074 China The University of Michigan Ann ArborMI48109 United States The School of Management and Sino European Institute for Intellectual Property Huazhong University of Science and Technology Wuhan430074 China

Physiological computing uses human physiological data as system inputs in real time. It includes, or significantly overlaps with, brain-computer interfaces, affective computing, adaptive automation, health informatics, and physiological signal based biometrics. Physiological computing increases the communication bandwidth from the user to the computer, but is also subject to various types of adversarial attacks, in which the attacker deliberately manipulates the training and/or test examples to hijack the machine learning algorithm output, leading to possible user confusion, frustration, injury, or even death. However, the vulnerability of physiological computing systems has not been paid enough attention to, and there does not exist a comprehensive review on adversarial attacks to them. This paper fills this gap, by providing a systematic review on the main research areas of physiological computing, different types of adversarial attacks and their applications to physiological computing, and the corresponding defense strategies. We hope this review will attract more research interests on the vulnerability of physiological computing systems, and more importantly, defense strategies to make them more secure. Copyright © 2021, The Authors. All rights reserved.

关键词： Machine learning

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Efficient spatialtemporal context modeling for action recognition?

arXiv

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arXiv 2021年

作者： Cao, Congqi Lu, Yue Zhang, Yifan Jiang, Dongmei Zhang, Yanning Natl. Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Provincial Key Lab on Speech and Image Information Processing School of Computer Science Northwestern Poly-technical University Xi'an710129 China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing100190 China

Contextual information plays an important role in action recognition. Local operations have difficulty to model the relation between two elements with a long-distance interval. However, directly modeling the contextual information between any two points brings huge cost in computation and memory, especially for action recognition, where there is an additional temporal dimension. Inspired from 2D criss-cross attention used in segmentation task, we propose a recurrent 3D criss-cross attention (RCCA-3D) module to model the dense long-range spatiotemporal contextual information in video for action recognition. The global context is factorized into sparse relation maps. We model the relationship between points in the same line along the direction of horizon, vertical and depth at each time, which forms a 3D criss-cross structure, and duplicate the same operation with recurrent mechanism to transmit the relation between points in a line to a plane finally to the whole spatiotemporal space. Compared with the non-local method, the proposed RCCA-3D module reduces the number of parameters and FLOPs by 25% and 30% for video context modeling. We evaluate the performance of RCCA-3D with two latest action recognition networks on three datasets and make a thorough analysis of the architecture, obtaining the optimal way to factorize and fuse the relation maps. Comparisons with other state-of-the-art methods demonstrate the effectiveness and efficiency of our model. Copyright © 2021, The Authors. All rights reserved.

关键词： Machine learning

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Blip-Up Blip-Down Circular EPI (BUDA-cEPI) for Distortion-Free dMRI with Rapid Unrolled Deep Learning Reconstruction

arXiv

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arXiv 2023年

作者： Yarach, Uten Chatnuntawech, Itthi Liao, Congyu Teerapittayanon, Surat Iyer, Siddharth Srinivasan Kim, Tae Hyung Haldar, Justin Cho, Jaejin Bilgic, Berkin Hu, Yuxin Hargreaves, Brian Setsompop, Kawin Radiologic Technology Department Associated Medical Sciences Chiang Mai University Chiang Mai Thailand National Nanotechnology Center National Science and Technology Development Agency Pathum Thani Thailand Department of Radiology Stanford University StanfordCA United States Department of Electrical Engineering Stanford University StanfordCA United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States Department of Computer Engineering Hongik University Seoul Korea Republic of Signal and Image Processing Institute Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los AngelesCA United States Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital CharlestownMA United States Department of Radiology Harvard Medical School BostonMA United States Department of Bioengineering Stanford University StanfordCA United States Packard Electrical Engineering Building 350 Jane Stanford Way StanfordCA94305 United States

Purpose: We implemented the blip-up, blip-down circular echo planar imaging (BUDA-cEPI) sequence with readout and phase partial Fourier to reduced off-resonance effect and T2* blurring. BUDA-cEPI reconstruction with S-based low-rank modeling of local k-space neighborhoods (S-LORAKS) is shown to be effective at reconstructing the highly under-sampled BUDA-cEPI data, but it is computationally intensive. Thus, we developed an ML-based reconstruction technique termed "BUDA-cEPI RUN-UP" to enable fast reconstruction. Methods: BUDA-cEPI RUN-UP – a model-based framework that incorporates off-resonance and eddy current effects was unrolled through an artificial neural network with only six gradient updates. The unrolled network alternates between data consistency (i.e., forward BUDA-cEPI and its adjoint) and regularization steps where U-Net plays a role as the regularizer. To handle the partial Fourier effect, the virtual coil concept was also incorporated into the reconstruction to effectively take advantage of the smooth phase prior, and trained to predict the ground-truth images obtained by BUDA-cEPI with S-LORAKS. Results: BUDA-cEPI with S-LORAKS reconstruction enabled the management of off-resonance, partial Fourier, and residual aliasing artifacts. However, the reconstruction time is approximately 225 seconds per slice, which may not be practical in a clinical setting. In contrast, the proposed BUDA-cEPI RUN-UP yielded similar results to BUDA-cEPI with S-LORAKS, with less than a 5% normalized root mean square error detected, while the reconstruction time is approximately 3 seconds. Conclusion: BUDA-cEPI RUN-UP was shown to reduce the reconstruction time by ~88x when compared to the state-of-the-art technique, while preserving imaging details as demonstrated through DTI application. © 2023, CC BY.

关键词： Magnetic resonance imaging

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Local Patch Network with Global Attention for Infrared Small Target Detection

arXiv

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arXiv 2021年

作者： Chen, Fang Gao, Chenqiang Liu, Fangcen Zhao, Yue Zhou, Yuxi Meng, Deyu Zuo, Wangmeng School of Communication and Information Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China Chongqing Key Laboratory of Signal and Information Processing Chongqing University of Posts and Telecommunications Chongqing400065 China Macau Institute of Systems Engineering Macau University of Science and Technology Taipa999078 China School of Mathematics and Statistics Xi’an Jiaotong University Shanxi Xi’an710049 China School of Computer Science and Technology Harbin Institute of Techonlogy Heilongjiang Harbin47822 China

Infrared small target detection plays an important role in the infrared search and tracking applications. In recent years, deep learning techniques were introduced to this task and achieved noteworthy effects. Following general object segmentation methods, existing deep learning methods usually processed the image from the global view. However, the imaging locality of small targets and extreme class-imbalance between the target and background pixels were not well-considered by these deep learning methods, which causes the low-efficiency on training and high-dependence on numerous data. A local patch network (LPNet) with global attention is proposed in this paper to detect small targets by jointly considering the global and local properties of infrared small target images. From the global view, a supervised attention module trained by the small target spread map is proposed to suppress most background pixels irrelevant with small target features. From the local view, local patches are split from global features and share the same convolution weights with each other in a patch net. By leveraging both the global and local properties, the data-driven framework proposed in this paper has fused multi-scale features for small target detection. Extensive synthetic and real data experiments show that the proposed method achieves the state-of-the-art performance compared with existing both conventional and deep learning methods. © 2021, CC BY.

关键词： Infrared imaging

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Semantic knowledge transfer for semi-supervised medical image segmentation

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Engineering Applications of Artificial Intelligence 2025年 158卷

作者： Shiwei Zhou Haifeng Zhao Leilei Ma Dengdi Sun Key Laboratory of Intelligent Computing & Signal Processing (ICSP) Ministry of Education Anhui University Hefei 230601 China Anhui Provincial Key Laboratory of Multimodal Cognitive Computation Anhui University Hefei 230601 China School of Computer Science and Technology Anhui University Hefei 230601 China School of Artificial Intelligence Anhui University Hefei 230601 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei 230026 China

In semi-supervised medical image segmentation, due to possible differences in information content and distribution between labeled and unlabeled data, dealing with the two separately usually prevents knowledge transfer from labeled to unlabeled data. This prevents the model from effectively sharing learned information between the two types of data. To alleviate this problem, we train labeled and unlabeled data as a whole. Semantic mixing of labeled and unlabeled data is achieved by selecting and exchanging some of the region images of both through a mask to generate complementary input views. In addition, due to the limited labeled data, the unlabeled data has a weak ability to distinguish categories in the feature space. Traditional methods rely on pixel positions to generate positive and negative samples for contrastive learning to solve this problem, but relying on pixel position sampling can easily lead to semantic inconsistency, which affects the effect of feature learning; therefore, to address this problem, we propose an innovative labeled data-guided inter-class contrastive learning strategy, which extracts the category features from labeled and unlabeled data and exploits the accurate category information in the labeled data to guide contrastive learning, while introducing a similarity-based ranking weighting mechanism. Combining the two designs, we propose a new semantic knowledge transfer framework for semi-supervised medical image segmentation. Experiments demonstrate a significant improvement in our model compared to State of the Art (SOTA) on the Automatic Cardiac Diagnosis Challenge (ACDC) dataset and the Left Atrium (LA) dataset.

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Evolving multi-resolution pooling CNN for monaural singing voice separation

arXiv

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arXiv 2020年

作者： Yuan, Weitao Dong, Bofei Wang, Shengbei Unoki, Masashi Wang, Wenwu Tianjin Key Laboratory of Autonomous Intelligence Technology and Systems School of Computer Science and Technology Tianjin Polytechnic University Tianjin China School of Information Science Japan Advanced Institute of Science and Technology Japan Centre for Vision Speech and Signal Processing University of Surrey Guildford United Kingdom

Monaural Singing Voice Separation (MSVS) is a challenging task and has been studied for decades. Deep neural networks (DNNs) are the current state-of-the-art methods for MSVS. However, the existing DNNs are often designed manually, which is time-consuming and error-prone. In addition, the network architectures are usually pre-defined, and not adapted to the training data. To address these issues, we introduce a Neural Architecture Search (NAS) method to the structure design of DNNs for MSVS. Specifically, we propose a new multi-resolution Convolutional Neural Network (CNN) framework for MSVS namely Multi-Resolution Pooling CNN (MRP-CNN), which uses various-size pooling operators to extract multi-resolution features. Based on the NAS, we then develop an evolving framework namely Evolving MRP-CNN (E-MRP-CNN), by automatically searching the effective MRP-CNN structures using genetic algorithms, optimized in terms of a single-objective considering only separation performance, or multi-objective considering both the separation performance and the model complexity. The multiobjective E-MRP-CNN gives a set of Pareto-optimal solutions, each providing a trade-off between separation performance and model complexity. Quantitative and qualitative evaluations on the MIR-1K and DSD100 datasets are used to demonstrate the advantages of the proposed framework over several recent baselines. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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Resource-Efficient Neural Networks for Embedded Systems

arXiv

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arXiv 2020年

作者： Roth, Wolfgang Schindler, Günther Klein, Bernhard Peharz, Robert Tschiatschek, Sebastian Fröning, Holger Pernkopf, Franz Ghahramani, Zoubin Graz University of Technology Laboratory of Signal Processing and Speech Communication Austria Institute of Computer Engineering Heidelberg University Germany Institute of Theoretical Computer Science Graz University of Technology Austria Faculty of Computer Science University of Vienna Austria University of Cambridge Cambridge United Kingdom

While machine learning is traditionally a resource intensive task, embedded systems, autonomous navigation, and the vision of the Internet of Things fuel the interest in resourceefficient approaches. These approaches aim for a carefully chosen trade-off between performance and resource consumption in terms of computation and energy. The development of such approaches is among the major challenges in current machine learning research and key to ensure a smooth transition of machine learning technology from a scientific environment with virtually unlimited computing resources into everyday's applications. In this article, we provide an overview of the current state of the art of machine learning techniques facilitating these real-world requirements. In particular, we focus on resource-efficient inference based on deep neural networks (DNNs), the predominant machine learning models of the past decade. We give a comprehensive overview of the vast literature that can be mainly split into three non-mutually exclusive categories: (i) quantized neural networks, (ii) network pruning, and (iii) structural efficiency. These techniques can be applied during training or as post-processing, and they are widely used to reduce the computational demands in terms of memory footprint, inference speed, and energy efficiency. We also briefly discuss different concepts of embedded hardware for DNNs and their compatibility with machine learning techniques as well as potential for energy and latency reduction. We substantiate our discussion with experiments on well-known benchmark data sets using compression techniques (quantization, pruning) for a set of resource-constrained embedded systems, such as CPUs, GPUs and FPGAs. The obtained results highlight the difficulty of finding good trade-offs between resource efficiency and prediction quality. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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Cross-Modality Coherent Registration of Whole Mouse Brains

Research Square

引用

Research Square 2021年

作者： Peng, Hanchuan Qu, Lei Li, Yuanyuan Xie, Peng Liu, Lijuan Wang, Yimin Wu, Jun Liu, Yu Wang, Tao Li, Longfei Guo, Kaixuan Wan, Wan Ouyang, Lei Xiong, Feng Zheng, Yefeng Xu, Fang Gong, Hui Dong, Hongwei Luo, Qingming Bi, Guo-Qiang Hawrylycz, Michael Zeng, Hongkui Allen Institute for Brain Science United States Key Laboratory of Intelligent Computation & Signal Processing Ministry of Education Anhui University China SEU-ALLEN Joint Center Institute for Brain Intelligence Southeast University Bangladesh Institute for Brain and Intelligence Southeast University Bangladesh Tencent Jarvis Lab China CAS Key Laboratory of Brain Connectome and Manipulation China Britton Chance Center for Biomedical Photonics Wuhan National Laboratory for Optoelectronics MoE Key Laboratory for Biomedical Photonics Huazhong University of Science and Technology China Center for Integrative Connectomics Department of Neurobiology David Geffen School of Medicine UCLA United States Hainan University China Hefei National Laboratory for Physical Sciences at the Microscale School of Life Sciences University of Science and Technology of China China

Recent whole brain mapping projects are collecting large-scale 3D images using powerful and informative modalities, such as STPT, fMOST, VISoR, or MRI. Registration of these multi-dimensional whole-brain images onto a standard atlas is essential for characterizing neuron types and constructing brain wiring diagrams. However, cross-modality image registration is challenging due to intrinsic variations of brain anatomy and artifacts resulted from different sample preparation methods and imaging modalities. We introduced a cross-modality registration method, called mBrainAligner, which uses coherent landmark mapping as well as deep neural networks to align whole mouse brain images to the standard Allen Common Coordinate Framework atlas. We also built a single cell resolution atlas using the fMOST modality, and used our method to generate whole brain map of 3D full single neuron morphology and neuron cell types. © 2021, CC BY.

关键词： Brain mapping

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A protocol for structured illumination microscopy with minimal reconstruction artifacts

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Biophysics Reports 2019年第2期5卷 80-90页

作者： Junchao Fan Xiaoshuai Huang Liuju Li Shan Tan Liangyi Chen Key Laboratory of Image Processing and Intelligent Control of Ministry of Education of China School of Automation Huazhong University of Science and Technology State Key Laboratory of Membrane Biology Beijing Key Laboratory of Cardiometabolic Molecular MedicineInstitute of Molecular Medicine Peking University

The imaging rate of structured illumination microscopy(SIM) reached 188 Hz recently. As the exposure time decreases, the camera detects fewer virtual photons, while the noise level remains the same. As a result, the signal-to-noise ratio(SNR) decreases sharply. Furthermore, the SNR decreases further because of photobleaching and phototoxicity. This decreased quality of SIM raw data may lead to surprising artifacts with various causes, which may confuse a new user of SIM microscopy. We summarize three significant possible sources of severe artifacts in reconstructed super-resolution(SR)images. Ultrafast motion of a biological sample or an uneven illumination pattern is the most difficult to be identified. The estimated parameter could also be incorrect, leading to artifact of regular ***, reconstruction with the Wiener method generates stochastic artifacts due to the amplification of noise during the deconvolution process. To deal with these problems, we have established a protocol to reconstruct ultrafast SIM raw data obtained in low SNR conditions. First, we checked the quality of the raw data with the image J plugin SIMcheck before reconstruction. Then, a modified parameter estimation method was used to improve the precision of the parameters. Finally, an iterative algorithm was used for SIM reconstruction under low signal-to-noise ratio conditions. This procedure effectively suppressed the artifacts in the super-resolution images reconstructed from raw data of low signal-to-noise ratio.

关键词： Ultrafast SIM Low signal-to-noise ratio Reconstruction

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Control-oriented fault detection of solid oxide fuel cell system unknown input on fuel supply

Control-oriented fault detection of solid oxide fuel cell sy...

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作者： Wu, Xiao-long Xu, Yuan-wu Xue, Tao Shuai, Junchao Jiang, Jianhua Deng, Zhonghua Fu, Xiaowei Li, Xi School of Automation Key Laboratory of Education Ministry for Image Processing and Intelligent Control Huazhong University of Science & Technology Wuhan China College of Computer Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Wuhan China Wuhan Industrial Economic Development Research Center Wuhan China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen China

As the solid oxide fuel cell (SOFC) system work environment is a high-temperature environment for a long time, it is difficult to obtain the SOFC stack internal state change directly. When the fault occurs, it is difficult to determine where the fault occurs. Moreover, the existing literature ignores the impact of faults, which creates many problems for SOFC system control. Therefore, a state observer-based fault detection method, which is used to detect the input flow sensor fault and the fuel input fault, is proposed. Their advantage is that they do not need data processing. To realize the fault detection, the observer is used to track the changes of SOFC stack chamber temperature. To obtain the observer estimation parameter, an approach from the actual stack structure parameters is employed to approximate the observer parameters. The results show the proposed fault detect method can judge fuel input fault type quickly and shield the disturbances signals from the sensor effectively. The proposed method also can be used to other operating points or air input fault. © 2018 Chinese Automatic Control Society and John Wiley & Sons Australia, Ltd

关键词： Fault detection

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