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arXiv 2020年

作者： Wu, Dongrui Xu, Yifan Lu, Bao-Liang 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 The Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Brain Science and Technology Research Center Qing Yuan Research Institute Shanghai Jiao Tong University 800 Dong Chuan Road Shanghai200240 China

A brain-computer interface (BCI) enables a user to communicate with a computer directly using brain signals. The most common non-invasive BCI modality, electroencephalogram (EEG), is sensitive to noise/artifact and suffers between-subject/within-subject non-stationarity. Therefore, it is difficult to build a generic pattern recognition model in an EEG-based BCI system that is optimal for different subjects, during different sessions, for different devices and tasks. Usually, a calibration session is needed to collect some training data for a new subject, which is time-consuming and user unfriendly. Transfer learning (TL), which utilizes data or knowledge from similar or relevant subjects/sessions/devices/tasks to facilitate learning for a new subject/session/device/task, is frequently used to reduce the amount of calibration effort. This paper reviews journal publications on TL approaches in EEG-based BCIs in the last few years, i.e., since 2016. Six paradigms and applications - motor imagery, event-related potentials, steady-state visual evoked potentials, affective BCIs, regression problems, and adversarial attacks - are considered. For each paradigm/application, we group the TL approaches into cross-subject/session, cross-device, and cross-task settings and review them separately. Observations and conclusions are made at the end of the paper, which may point to future research directions. Copyright © 2020, The Authors. All rights reserved.

关键词： Brain computer interface

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Fast and accurate single-image depth estimation on mobile devices, mobile AI 2021 challenge: Report

arXiv

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arXiv 2021年

作者： Ignatov, Andrey Malivenko, Grigory Plowman, David Shukla, Samarth Timofte, Radu Zhang, Ziyu Wang, Yicheng Huang, Zilong Luo, Guozhong Yu, Gang Fu, Bin Wang, Yiran Li, Xingyi Shi, Min Xian, Ke Cao, Zhiguo Du, Jin-Hua Wu, Pei-Lin Ge, Chao Yao, Jiaoyang Tu, Fangwen Li, Bo Yoo, Jung Eun Seo, Kwanggyoon Xu, Jialei Li, Zhenyu Liu, Xianming Jiang, Junjun Chen, Wei-Chi Joya, Shayan Fan, Huanhuan Kang, Zhaobing Li, Ang Feng, Tianpeng Liu, Yang Sheng, Chuannan Yin, Jian Benavides, Fausto T. Computer Vision Lab ETH Zurich Switzerland Ltd AI Witchlabs Switzerland Tencent GY-Lab China Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology China Nanjing Artificial Intelligence Chip Research Institute of Automation Chinese Academy of Sciences China Black Sesame Technologies Inc. Singapore Singapore Visual Media Lab KAIST Korea Republic of Harbin Institute of Technology China Peng Cheng Laboratory China Multimedia and Computer Vision Laboratory National Cheng Kung University Taiwan Samsung Research UK United Kingdom OPPO Research Institute China ETH Zurich Switzerland

Depth estimation is an important computer vision problem with many practical applications to mobile devices. While many solutions have been proposed for this task, they are usually very computationally expensive and thus are not applicable for on-device inference. To address this problem, we introduce the first Mobile AI challenge, where the target is to develop an end-to-end deep learning-based depth estimation solutions that can demonstrate a nearly real-time performance on smartphones and IoT platforms. For this, the participants were provided with a new large-scale dataset containing RGB-depth image pairs obtained with a dedicated stereo ZED camera producing high-resolution depth maps for objects located at up to 50 meters. The runtime of all models was evaluated on the popular Raspberry Pi 4 platform with a mobile ARM-based Broadcom chipset. The proposed solutions can generate VGA resolution depth maps at up to 10 FPS on the Raspberry Pi 4 while achieving high fidelity results, and are compatible with any Android or Linux-based mobile devices. A detailed description of all models developed in the challenge is provided in this paper. Copyright © 2021, The Authors. All rights reserved.

关键词： computer operating systems

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R3-Net: A deep network for multi-oriented vehicle detection in aerial images and videos

arXiv

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arXiv 2018年

作者： Li, Qingpeng Mou, Lichao Xu, Qizhi Zhang, Yun Zhu, Xiao Xiang State Key Laboratory of Virtual Reality Technology and Systems Beijing Key Laboratory of Digital Media School of Computer Science and Engineering Beihang University Beijing100191 China Remote Sensing Technology Institute German Aerospace Center Wessling82234 Germany Signal Processing in Earth Observation Technical University of Munich Munich80333 Germany Canada Research Chair Laboratory in Advanced Geomatics Image Processing Department of Geodesy and Geomatics Engineering University of New Brunswick FrederictonNBE3B 5A3 Canada

Vehicle detection is a significant and challenging task in aerial remote sensing applications. Most existing methods detect vehicles with regular rectangle boxes and fail to offer the orientation of vehicles. However, the orientation information is crucial for several practical applications, such as the trajectory and motion estimation of vehicles. In this paper, we propose a novel deep network, called rotatable region-based residual network (R3-Net), to detect multi-oriented vehicles in aerial images and videos. More specially, R3-Net is utilized to generate rotatable rectangular target boxes in a half coordinate system. First, we use a rotatable region proposal network (R-RPN) to generate rotatable region of interests (R-RoIs) from feature maps produced by a deep convolutional neural network. Here, a proposed batch averaging rotatable anchor (BAR anchor) strategy is applied to initialize the shape of vehicle candidates. Next, we propose a rotatable detection network (R-DN) for the final classification and regression of the R-RoIs. In RDN, a novel rotatable position sensitive pooling (R-PS pooling) is designed to keep the position and orientation information simultaneously while downsampling the feature maps of RRoIs. In our model, R-RPN and R-DN can be trained jointly. We test our network on two open vehicle detection image datasets, namely DLR 3K Munich Dataset and VEDAI Dataset, demonstrating the high precision and robustness of our method. In addition, further experiments on aerial videos show the good generalization capability of the proposed method and its potential for vehicle tracking in aerial videos. The demo video is available at https://***/xCYD-tYudN0. Copyright © 2018, The Authors. All rights reserved.

关键词： Remote sensing

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Improving the detection of mtbi via complexity analysis in resting - state magnetoencephalography

Improving the detection of mtbi via complexity analysis in r...

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IEEE International Workshop on Imaging Systems and Techniques (IST)

作者： Marios Antonakakis Stavros I. Dimitriadis Andrew C. Papanicolaou George Zouridakis Michalis Zervakis Digital Image and Signal Processing Laboratory School of Electronic and Computer Engineering Technical University of Crete Chania Greece Neuroinformatics Group Aristotle University Thessaloniki Greece Cardiff University Brain Research Imaging Centre (CUBRIC) School of Psychology Cardiff University Cardiff United Kingdom Departments of Pediatrics and Anatomy and Neurobiology University of Tennessee Health Science Center Memphis TN USA Neuroscience Institute Le Bonheur Children's Hospital Memphis TN USA Departments of Engineering Technology Computer Science Biomedical Engineering and Electrical and Computer Engineering University of Houston Houston TX USA

ISBN: (纸本)9781509018185

Diagnosis of mild Traumatic Brain Injury (mTBI) is difficult due to the variability of obvious brain lesions using imaging scans. A promising tool for exploring potential biomarkers for mTBI is magnetoencephalography which has the advantage of high spatial and temporal resolution. By adopting proper analytic tools from the field of symbolic dynamics like Lempel-Ziv complexity, we can objectively characterize neural network alterations compared to healthy control by enumerating the different patterns of a symbolic sequence. This procedure oversimplifies the rich information of brain activity captured via MEG. For that reason, we adopted neural-gas algorithm which can transform a time series into more than two symbols by learning brain dynamics with a small reconstructed error. The proposed analysis was applied to recordings of 30 mTBI patients and 50 normal controls in δ frequency band. Our results demonstrated that mTBI patients could be separated from normal controls with more than 97% classification accuracy based on high complexity regions corresponding to right frontal areas. In addition, a reverse relation between complexity and transition rate was demonstrated for both groups. These findings indicate that symbolic complexity could have a significant predictive value in the development of reliable biomarkers to help with the early detection of mTBI.

关键词： Complexity theory Time series analysis Sensitivity Integrated circuits Indexes Brain Laplace equations

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Quality Inspection of Phalaenopsis Hybrids Using Hyperspectral Band Selection Techniques

Quality Inspection of Phalaenopsis Hybrids Using Hyperspectr...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Yen-Chieh Ouyang Bo-Han Chen Meng-Chueh Lee Tsang-Sen Liu Mang Ou-Yang Hsian-Min Chen Chao-Cheng Wu Chia-Hsien Wen Min-Shao Shih Chein-I Chang Yung-Jhe Yan Department of Electrical Engineering National Chung Hsing University Taiwan Institute of communication Engineering National Chung Hsing University Taiwan Taiwan Agriculture Research Institute Nantou Taiwan Department of Electrical and Computer Engineering National Chiao-Tung University Hsinchu City Taiwan Center for Quantitative Imaging in Medicine Taichung Veterans General Hospital Taiwan Department of Electrical Engineering National Taipei University of Technology Taipei Taiwan Department of Computer Science and Information Management Providence University Taiwan Remote Sensing Signal and Image Processing Laboratory University of Maryland Baltimore MD USA

Fusarium wilt on Phalaenopsis is a disease that makes farmers suffer seriously. Although Phalaenopsis does not die immediately with Fusarium wilt, it seriously decreases the quality that buyers cannot accept. In this paper, we introduce an emerging method to detect Fusarium wilt at the base of Phalaenopsis stems. The detection model divides Phalaenopsis samples into two categories, healthy and infection. The band selection (BS) processing technique based on band prioritization (BP) is applied to extract significant bands and eliminate redundant bands. Subsequently, some algorithms which are constrained energy minimization (CEM), spectral information divergence(SID) and SeQuential N-FINDER to detect the Fusarium wilt, and we hope the research would help farmers decrease their losses.

关键词： Hyperspectral imaging Support vector machines Diseases Signal to noise ratio Hybrid fiber coaxial cables Correlation

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Improved rice algorithm of lossless compression for on-board images

Improved rice algorithm of lossless compression for on-board...

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International Conference on Multimedia Technology

作者： Wei, Yong-Wang Ding, Qing-Hai Luo, Hai-Bo Shenyang Institute of Automation Chinese Academy of Science Shenyang 110016 China Graduate School of Chinese Academy of Science Beijing 100049 China Key Laboratory of Optical-Electronics Information Processing Chinese Academy of Science Shenyang 110016 China Key Laboratory of Image Understanding and Computer Vision Liaoning Province 110016 China Electrical and Information Engineering College Beihua University Jilin 132021 China Research Institute of General Development and Demonstration of Equipment Equipment Academy of Air Force Beijing100076 China

ISBN: (纸本)9781424478743

Aiming at the problems of the relatively lower compression ratio and the difficulty in hardware implementation regarding Rice lossless compression algorithm for the Space-borne Remote-sensing images, an improved Rice method is proposed in which the length of predictor FIFO is determined by the size of input images and the global unicode for differences is used. The improved Rice algorithm is mainly composed of two steps, MED prediction for the whole row data and Rice global uniform entropy coding. The statistical results show that it can significantly reduce the spatial redundancy of the adjacent pixels and the mean value of image differences. The compression bit rate is reduced approximately 0.4581 bpp(bit/pixel) against the original Rice algorithm. Furthermore, the new way reserves the all detail information of the input images throughout the whole encoding process. Because we split the all samples with the same mode, the proportion of the identifier of the splitting mechanic in the output data stream is reduced and the compression ratio is increased. The improvement can reduce the encoding complexity and be easily implemented for hardware application. ©2010 IEEE.

关键词： Signal encoding

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T-TIME: Test-Time Information Maximization Ensemble for Plug-and-Play BCIs

arXiv

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arXiv 2024年

作者： Li, Siyang Wang, Ziwei Luo, Hanbin Ding, Lieyun Wu, Dongrui 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 Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen China Henan Key Laboratory of Brain Science and Brain Computer Interface Technology School of Electrical and Information Engineering Zhengzhou University China School of Civil and Hydraulic Engineering Huazhong University of Science and Technology Wuhan430074 China

Objective: An electroencephalogram (EEG)-based brain-computer interface (BCI) enables direct communication between the human brain and a computer. Due to individual differences and non-stationarity of EEG signals, such BCIs usually require a subject-specific calibration session before each use, which is time-consuming and user-unfriendly. Transfer learning (TL) has been proposed to shorten or eliminate this calibration, but existing TL approaches mainly consider offline settings, where all unlabeled EEG trials from the new user are available. Methods: This paper proposes Test-Time Information Maximization Ensemble (T-TIME) to accommodate the most challenging online TL scenario, where unlabeled EEG data from the new user arrive in a stream, and immediate classification is performed. T-TIME initializes multiple classifiers from the aligned source data. When an unlabeled test EEG trial arrives, T-TIME first predicts its labels using ensemble learning, and then updates each classifier by conditional entropy minimization and adaptive marginal distribution regularization. Our code is publicized. Results: Extensive experiments on three public motor imagery based BCI datasets demonstrated that T-TIME outperformed about 20 classical and state-of-the-art TL approaches. Significance: To our knowledge, this is the first work on test time adaptation for calibration-free EEG-based BCIs, making plug-and-play BCIs possible. © 2024, CC BY.

关键词： Calibration

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Video Emotion Recognition using Hand-Crafted and Deep Learning Features

Video Emotion Recognition using Hand-Crafted and Deep Learni...

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Asian Conference on Affective Computing and Intelligent Interaction (ACII Asia)

作者： Xiaohan Xia Jiamu Liu Tao Yang Dongmei Jiang Wenjing Han Hichem Sahli NPU-VUB Joint AVSP Research Lab School of Computer Science Northwestern Polytechnical University (NPU) Shaanxi Key Lab on Speech and Image Information Processing 127 Youyi Xilu Xi’an China Samsum R&D Institute China-Beijing Floor 18 Sun Palace Building 12A Sun Palace Road Chaoyang District Beijing China Interuniversity Microelectronics Centre(IMEC) Kepeldreef 75 Heverlee Belgium

ISBN: (纸本)9781538653128

In this paper, we present our system designed for the video emotion recognition task of the Multimodal Emotion Challenge (MEC 2017). Histogram of Oriented Gradients (HOG), face shape (SHAPE), and geometric (GEO) features are extracted from the detected face images as hand-crafted video features. A pre-trained VGG-Face model is fine-tuned with the face images and emotion labels from the training set of CHEAVD 2.0, the outputs of the penultimate fully-connected layer (FC6) and the last fully-connected layer (FC7) are adopted as Deep Convolutional Neural Network (DCNN) based features. For each video clip, the hand-crafted features and DCNN based features are input into corresponding hidden Markov models (HMMs, one for each emotion class), respectively, for the initial emotion recognitions. The output logarithm likelihood probabilities from the HMMs are then ranked, and the orders constitute an eight-dimensional feature vector as inputs to a Naive Bayes classifier for decision fusion. Experimental results on the CHEAVD 2.0 database show that the combination of FC6, GEO, SHAPE and HOG features obtains the highest macro average precisions (MAPs) on both the validation set (46.61%) and test set (43.88%), which are 12.51% and 22.18% higher than the baseline results, respectively.

关键词： Face Emotion recognition Shape Feature extraction Hidden Markov models Task analysis

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Undefined character processing system using character structure analysis by mathematical morphology

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Systems and computers in Japan 2000年第9期31卷 60-71页

作者： Zhu, Qing Kodate, Akihisa Kim, Woontae Urano, Yoshiyori Tominaga, Hideyoshi Telecom. Adv. Organization of Japan Waseda Research Center Tokyo 169-0051 Japan Global Info. Telecommunication Inst. Waseda University Tokyo 169-0051 Japan Dept. Electronics Info. Commun. E. Waseda University Tokyo 169-8555 Japan Beijing Computer Institute Waseda University Telecom. Adv. Organization of Japan Waseda Research Center Info. Processing Society of Japan School of Science and Engineering Waseda University Hanover University Germany Inst. Image Electronics Engineers J. Gyeonggi University Inst. Image Electronics Engineers J. Korea Inst. Telematics Electronics

In this paper, we propose a character processing system (undefined character processing system) that handles characters for which fonts or character code systems have not been provided on the terminals (undefined characters). The undefined characters include characters historically not installed in the input/output environment, such as ancient characters or characters used by minority groups, and characters not used in a particular language system environment. The proposed system uses morphology to analyze the structures of the target undefined characters, generates codes which describe the characters' shapes based on the analysis results, and reconstructs the characters from these codes. A prototype of this system was constructed. The results of the structural analysis of the undefined characters and their reconstructions verified the effectiveness of the proposed system.

关键词： image reconstruction

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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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