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arXiv 2019年

作者： Song, Wei Shen, Yifei Li, Liping Niu, Kai Zhang, Chuan Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei China National Mobile Communications Research Laboratory Southeast University Nanjing China Key Laboratory of Universal Wireless Communication Ministry of Education Beijing Univer- sity of Posts and Telecommunications Beijing100876 China

Similar to existing codes, puncturing and shortening are two general ways to obtain an arbitrary code length and code rate for polar codes. When some of the coded bits are punctured or shortened, it is equivalent to a situation in which the underlying channels of the polar codes are different. Therefore, the quality of bit channels with puncturing or shortening differ from the original qualities, which can greatly affect the construction of polar codes. In this paper, a general construction of polar codes is studied in two aspects: 1) the theoretical foundation of the construction;and 2) the hardware implementation of polar codes encoders. In contrast to the original identical and independent binary-input, memoryless, symmetric (BMS) channels, these underlying BMS channels can be different. For binary erasure channel (BEC) channels, recursive equations can be employed assuming independent BMS channels. For all other channel types, the proposed general construction of polar codes is based on the existing Tal-Vardy's procedure. The symmetric property and the degradation relationship are shown to be preserved under the general setting, rendering the possibility of a modification of Tal-Vardy's procedure. Simulation results clearly show improved error performance with re-ordering using the proposed new procedures. In terms of hardware, a novel pruned folded encoder architecture is proposed which saves the computation for the beginning frozen bits. Implementation results show the pruned encoder achieve 28% throughput improvement. Copyright © 2019, The Authors. All rights reserved.

关键词： signal encoding

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Cross-Modality Coherent Registration of Whole Mouse Brains

Research Square

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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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Android Oriented Image Visualization Exploratory Search

Android Oriented Image Visualization Exploratory Search

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2019国际计算机前沿大会

作者： Jianquan Ouyang Hao He Minnan Chu Dong Chen Huanrong Tang College of Information Engineering Xiangtan University Key Laboratory of Intelligent Computing and Information Processing Ministry of Education College of Information Engineering Xiangtan University

When using traditional image search engines, smartphone users often complain about their poor user interface including poor user experience,and weak interaction. Moreover, users are unable to find a desired picture partly due to the unclear key words. This paper proposes the word-bag co-occurrence scheme by defining the correlation between images. Through exploratory search, the search range can be expanded and help users refine retrieval of the expected images. Firstly, the proposed scheme applied the bag of visual words(BoVW) vector by processing images on Hadoop. Secondly, similarity matrix was constructed to organize the image data. Finally, the images in which users were interested was visually displayed on the android mobile phone via exploratory search. Comparing the proposed method to current methods by testing with image data sets on ImageNet, the experimental results show that the former is superior to the latter on visual representation, and the proposed scheme can provide a better user experience.

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Learning Target-Oriented Dual Attention for Robust RGB-T Tracking

Learning Target-Oriented Dual Attention for Robust RGB-T Tra...

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IEEE International Conference on Image processing

作者： Rui Yang Yabin Zhu Xiao Wang Chenglong Li Jin Tang Key Lab of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei China Institute of Physical Science and Information Technology Anhui University Hefei China

ISBN: (纸本)9781538662502;9781538662496

RGB-Thermal object tracking attempts to locate target object using complementary visual and thermal infrared data. Existing RGB-T trackers fuse different modalities by robust feature representation learning or adaptive modal weighting. However, how to integrate dual attention mechanism for visual tracking is still a subject that has not been studied yet. In this paper, we propose two visual attention mechanisms for robust RGB-T object tracking. Specifically, the local attention is implemented by exploiting the common visual attention of RGB and thermal data to train deep classifiers. We also introduce the global attention, which is a multimodal target-driven attention estimation network. It can provide global proposals for the classifier together with local proposals extracted from previous tracking result. Extensive experiments on two RGB-T benchmark datasets validated the effectiveness of our proposed algorithm.

关键词： Target tracking Visualization Training Feature extraction Proposals Object tracking Estimation

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Transfer Learning for EEG-Based Brain-Computer Interfaces: A Review of Progress Made Since 2016

arXiv

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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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Adaptive Algorithm Based on Reversible Data Hiding Method for JPEG Images 7th

Adaptive Algorithm Based on Reversible Data Hiding Method fo...

引用

7th EAI International Conference on Cloud computing, CloudComp 2016 and 1st EAI International Conference on Security and Privacy in New computing Environments, SPNCE 2016

作者： Zhang, Hao Yin, Zhaoxia Zhang, Xinpeng Chen, Jianpeng Wang, Ruonan Luo, Bin Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei230601 China School of Communication and Information Engineering Shanghai University Shanghai200072 China

ISBN: (纸本)9783319696041

This paper presents an adaptive reversible information hiding algorithm that can maintain thee JPEG file sizes by using RLC (Run Length Coding) AC coefficient coded for embedding, the key point is to choose the appropriate number of participation to hide information. By calculating the maximum storage capacity of the image at different system, select the appropriate RLC pairs to rotate and embed data. In the extraction stage, by calculating the sequence of the original RLC pairs status, then consult the mapping relationships between the current sequence and the original RLC pairs sequence, we extract the secret message and recover the original image. Test results proved that the proposed method can improve the rate-distortion performance to some extent. © 2018, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Adaptive algorithms

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An improved lossless data hiding scheme in JPEG bitstream by VLC mapping 1

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13th International Conference on Mobile Ad-hoc and Sensor Networks, MSN 2017

作者： Du, Yang Yin, Zhaoxia Zhang, Xinpeng Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei230601 China School of Communication and Information Engineering Shanghai University Shanghai200072 China

ISBN: (数字)9789811088902

ISBN: (纸本)9789811088896

This paper proposed a lossless data hiding scheme by variable length code (VLC) mapping, which focused on embedding additional data into JPEG bitstream. The entropy-coded data in JPEG bitstream consists of a sequence of VLCs and the appended bits. Not all VLCs defined in the JPEG file header are used in the entropy-coded data and the replacement of unused-VLCs do nothing with image decompression. Hence, additional data can be embedded by mapping the unused-VLCs to the used-VLCs. To obtain higher embedding capacity, we improved the mapping rules in this paper. Employing the proposed mapping scheme, larger embedding capacity and no image distortion are both achieved while the filesize of JPEG is preserved after data embedding. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Mapping

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HMOE-Net: Hybrid Multi-scale Object Equalization Network for Intracerebral Hemorrhage Segmentation in CT Images

HMOE-Net: Hybrid Multi-scale Object Equalization Network for...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xizhi He Kai Chen Kai Hu Zhineng Chen Xuanya Li Xieping Gao Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Xiangtan China Affiliated Hospital of Xiangnan University Chenzhou China Institute of Automation Chinese Academy of Sciences Beijing China Baidu Inc. Beijing China College of Medical Imaging and Inspection Xiangnan University Chenzhou China

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

In this paper, we propose a novel Hybrid Multi-scale Object Equalization Network (HMOE-Net) to segment intracerebral hemorrhage (ICH) regions. In particular, we design a shallow feature extraction network (SFENet) and a deep feature extraction network (DFENet) to solve the problem of equalization learning of hybrid multi-scale object features. The multi-level feature extraction (MLFE) blocks are presented in DFENet to explore multi-level semantic features more effectively. Furthermore, we adopt a progressive feature extraction strategy combining SFENet and DFENet to further consider the differences of various ICH regions and achieve the equalization feature learning of multi-scale objects. To verify the effectiveness of HMOE-Net, we collect a clinical ICH dataset with a total of 500 CT cases from three hospitals for the evaluation. The experimental results show that HMOE-Net is superior to six state-of-the-art methods and achieves accurate segmentation for multi-scale ICH regions.

关键词： Feature extraction Semantics Image segmentation Data mining Hemorrhaging Computed tomography Convolution

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A multi-label classification method using a hierarchical and transparent representation for paper-reviewer recommendation

arXiv

引用

arXiv 2019年

作者： Zhang, Dong Zhao, Shu Duan, Zhen Chen, Jie Zhang, Yanping Tang, Jie Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Hefei230601 China Tsinghua University Department of Computer Science and Technology Beijing100084 China

Paper-reviewer recommendation task is of significant academic importance for conference chairs and journal editors. It aims to recommend appropriate experts in a discipline to comment on the quality of papers of others in that discipline. How to effectively and accurately recommend reviewers for the submitted papers is a meaningful and still tough task. Generally, the relationship between a paper and a reviewer often depends on the semantic expressions of them. Creating a more expressive representation can make the peer-review process more robust and less arbitrary. So, the representations of a paper and a reviewer are very important for the paper-reviewer recommendation. Actually, a reviewer or a paper often belongs to multiple research fields, which increases difficulty in paper-reviewer recommendation. In this paper, we propose a Multi-Label Classification method using a HIErarchical and transPArent Representation named Hiepar-MLC. Firstly, we introduce a HIErarchical and transPArent Representation (Hiepar) to express the semantic information of the reviewer and the paper. Hiepar is learned from a two-level bidirectional gated recurrent unit based network applying the attention mechanism. It is capable of capturing the two-level hierarchical information (word-sentence-document) and highlighting the elements in reviewers or papers to support the labels. This word-sentence-document information mirrors the hierarchical structure of a reviewer or a paper and captures the exact semantics of them. Then we transform the paper-reviewer recommendation problem into a Multi-Label Classification (MLC) issue, whose multiple research labels exactly guide the learning process. It's flexible that we can select any multi-label classification method to solve the paper-reviewer recommendation problem. Further, we propose a simple multi-label based reviewer assignment (MLBRA) strategy to select the appropriate reviewers. It's interesting that we also explore the paper-reviewer recom

关键词： Digital libraries

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Improved Lossless Data Hiding for JPEG Images Based on Histogram Modification

引用

Computers, Materials & Continua 2018年第6期55卷 495-507页

作者： Yang Du Zhaoxia Yin Xinpeng Zhang Key Laboratory of Intelligent Computing&Signal Processing Ministry of EducationAnhui UniversityHefei230601P.R.China Department of Computer Science Purdue UniversityWest lafayette47906USA School of Communication and Information Engineering Shanghai UniversityShanghai 200072P.R.China

This paper proposes a lossless and high payload data hiding scheme for JPEG images by histogram *** most in JPEG bitstream consists of a sequence of VLCs(variable length codes)and the appended *** VLC has a corresponding RLV(run/length value)to record the AC/DC *** achieve lossless data hiding with high payload,we shift the histogram of VLCs and modify the DHT segment to embed *** we sort the histogram of VLCs in descending order,the filesize expansion is *** paper’s key contribution includes:Lossless data hiding,less filesize expansion in identical pay-load and higher embedding efficiency.

关键词： Lossless data hiding histogram VLC JPEG.

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