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arXiv 2021年

作者： Xiong, Jian Gao, Hao Wang, Miaohui Li, Hongliang Ngan, King Ngi Lin, Weisi College of Telecommunications and Information Engineering Nanjing University of Posts and Telecommunications Nanjing210003 China College of Artificial Intelligence Nanjing University of Posts and Telecommunications Nanjing210003 China School of Computer Science and Cyberspace Security Hainan University China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu611731 China School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore

In video-based dynamic point cloud compression (V-PCC), 3D point clouds are projected onto 2D images for compressing with the existing video codecs. However, the existing video codecs are originally designed for natural visual signals, and it fails to account for the characteristics of point clouds. Thus, there are still problems in the compression of geometry information generated from the point clouds. Firstly, the distortion model in the existing rate-distortion optimization (RDO) is not consistent with the geometry quality assessment metrics. Secondly, the prediction methods in video codecs fail to account for the fact that the highest depth values of a far layer is greater than or equal to the corresponding lowest depth values of a near layer. This paper proposes an advanced geometry surface coding (AGSC) method for dynamic point clouds (DPC) compression. The proposed method consists of two modules, including an error projection model-based (EPM-based) RDO and an occupancy map-based (OM-based) merge prediction. Firstly, the EPM model is proposed to describe the relationship between the distortion model in the existing video codec and the geometry quality metric. Secondly, the EPM-based RDO method is presented to project the existing distortion model on the plane normal and is simplified to estimate the average normal vectors of coding units (CUs). Finally, we propose the OM-based merge prediction approach, in which the prediction pixels of merge modes are refined based on the occupancy map. Experiments tested on the standard point clouds show that the proposed method achieves an average 9.84% bitrate saving for geometry compression. Copyright © 2021, The Authors. All rights reserved.

关键词： Signal distortion

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Effective Analysis of Hot Spots in Hub Protein Interfaces Based on Random Forest 15th

Effective Analysis of Hot Spots in Hub Protein Interfaces Ba...

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15th International Conference on Intelligent Computing, ICIC 2019

作者： Lin, Xiaoli Zhou, Fengli Hubei Key Laboratory of Intelligent Information Processing and Real-Time Industrial System School of Computer Science and Technology Wuhan University of Science and Technology Wuhan430065 China Information and Engineering Department of City College Wuhan University of Science and Technology Wuhan430083 China

ISBN: (纸本)9783030269685

In the protein-protein interactions, hub proteins are the key factor to maintain stability of protein-protein interactions and exert the protein biological function. Conventional methods mainly focus on the topological structure and gene expression of hub proteins, while we mainly discuss the hot spots of hub protein interfaces. In order to evaluate the performance of the classification models, the importance of feature variables is analyzed by using the average precision descent curve and the average Gini coefficient descent curve. In addition, the margin box-plot is used to measure the certainty of the classification models. The experimental results show that the error rate of random forest method is lower, and our classification model has higher reliability. © 2019, Springer Nature Switzerland AG.

关键词： Gene expression

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A Faithful Deep Sensitivity Estimation for Accelerated Magnetic Resonance Imaging

arXiv

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arXiv 2022年

作者： Wang, Zi Fang, Haoming Qian, Chen Shi, Boxuan Bao, Lijun Zhu, Liuhong Zhou, Jianjun Wei, Wenping Lin, Jianzhong Guo, Di Qu, Xiaobo Department of Electronic Science Biomedical Intelligent Cloud R&D Center Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance National Institute for Data Science in Health and Medicine Xiamen University Xiamen361005 China Fudan University Xiamen361006 China Department of Radiology The First Affiliated Hospital of Xiamen University Xiamen361005 China Department of Radiology Zhongshan Hospital affiliated to Xiamen University Xiamen361005 China School of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China

Magnetic resonance imaging (MRI) is an essential diagnostic tool that suffers from prolonged scan time. To alleviate this limitation, advanced fast MRI technology attracts extensive research interests. Recent deep learning has shown its great potential in improving image quality and reconstruction speed. Faithful coil sensitivity estimation is vital for MRI reconstruction. However, most deep learning methods still rely on pre-estimated sensitivity maps and ignore their inaccuracy, resulting in the significant quality degradation of reconstructed images. In this work, we propose a Joint Deep Sensitivity estimation and Image reconstruction network, called JDSI. During the image artifacts removal, it gradually provides more faithful sensitivity maps with high-frequency information, leading to improved image reconstructions. To understand the behavior of the network, the mutual promotion of sensitivity estimation and image reconstruction is revealed through the visualization of network intermediate results. Results on in vivo datasets and radiologist reader study demonstrate that, for both calibration-based and calibrationless reconstruction, the proposed JDSI achieves the state-of-the-art performance visually and quantitatively, especially when the acceleration factor is high. Additionally, JDSI owns nice robustness to patients and autocalibration signals. Copyright © 2022, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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3R: Word and Phoneme Edition based Data Augmentation for Lexical Punctuation Prediction

3R: Word and Phoneme Edition based Data Augmentation for Lex...

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International Conference on Computational Intelligence and Security

作者： Aihua Zheng Naipeng Ye Xiao Wang Xiao Song Key Lab of Intelligent Computing and Signal Processing of Ministry of Education Hefei China Peking University Shenzhen Institute Shenzhen China Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei China

Existing Lexical Punctuation Prediction methods are mainly trained on the standard clean data while losing the generalization in practical automatic speech recognition (ASR) system with ubiquitous transcription errors. To bridge the gap between clean training data and noisy testing data, we propose three random (3R) data augmentation strategies: random word deletion (RWD), random word substitution (RWS), and random phoneme edition (RPE) in both word and phoneme levels on the training dataset. Specifically, we contribute an acoustically similar vocabulary with phoneme level editions for acoustically similar word substitution. In addition, we first introduce the RoBERTa-large model into a punctuation prediction task to capture the semantics and the long-distance dependencies in language. Extensive experiments on the English dataset IWSLT2011 yield to a new state-of-the-art comparing to the prevalent punctuation prediction methods.

关键词： Training Vocabulary Semantics Training data Predictive models Noise measurement Security

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High performance polarization management devices based on thin-film lithium niobate

arXiv

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arXiv 2021年

作者： Lin, Zhongjin Lin, Yanmei Li, Hao Xu, Mengyue He, Mingbo Ke, Wei Li, Zhaohui Wang, Dawei Yao, X. Steve Yu, Siyuan Cai, Xinlun State Key Laboratory of Optoelectronic Materials and Technologies School of Electronics and Information Technology Sun Yat-sen University Guangzhou510275 China Department of Electrical and Computer Engineering The University of British Columbia VancouverBCV6T 1Z4 Canada Photonics Information Innovation Center Hebei Provincial Center for Optical Sensing Innovations College of Physics Science and Technology Hebei University Baoding071002 China

High-speed polarization management is highly desirable for many applications, such as remote sensing, telecommunication, and medical diagnosis. However, most of the approaches for polarization management rely on bulky optical components that are slow to respond, cumbersome to use, and sometimes with high drive voltages. Here, we overcome these limitations by harnessing photonic integrated circuits based on thin-film lithium niobate platform. We successfully realize a portfolio of thin-film lithium niobate devices for essential polarization management functionalities, including arbitrary polarization generation, fast polarization measurement, polarization scrambling, and automatic polarization control. The present devices feature ultra-fast control speed, low drive voltages, low optical losses and compact footprints. Using these devices, we achieve high fidelity polarization generation with a polarization extinction ratio up to 41.9 dB, fast polarization scrambling with a scrambling rate up to 65 Mrad/s, and endless polarization control with a tracking speed up to 10 Krad/s, all of which are best results in integrated optics. The demonstrated devices unlock a drastically new level of performance and scales in polarization management devices, leading to a paradigm shift in polarization management. © 2021, CC BY.

关键词： Thin films

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A Balanced Cost Fault-Tolerant Scheduling Algorithm in Heterogeneous Real-Time Systems

A Balanced Cost Fault-Tolerant Scheduling Algorithm in Heter...

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IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Jing Liu Yifu Zhang Yang Liu Pei Yang Cen Chen Ziqi Zhu Zeng Zeng College of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China Institute for Infocomm Research Singapore

ISBN: (纸本)9781728176499;9781728176505

High reliability is required for many safety-critical and real-time applications. It is difficult to avoid permanent faults during the executing process of tasks. Once such faults are not processed in time, real-time tasks cannot be completed within the specified time, which may lead to catastrophic consequences. In order to deal with the problem of permanent fault tolerance in heterogeneous real-time systems, in this paper, we propose a balanced cost fault-tolerant scheduling algorithm. First, the algorithm uses the task computation time and the average communication time of its successor nodes as the ranking factors when calculating task priority, so that the priority of the task with larger average communication time improved; second, a task cost table is established, and tasks are allocated to appropriate processors according to the values in the task cost table in processor allocation stage. Experimental results show that the proposed algorithm can effectively improve the system reliability and reduce the completion time.

关键词： Fault tolerance Program processors Scheduling algorithms High performance computing Conferences Fault tolerant systems Real-time systems

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Three-Dimensional Imaging by Interferenceless Optical Scanning Holography

SSRN

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SSRN 2022年

作者： Liu, Jung-Ping Tsai, Chen-Ming Poon, Ting-Chung Tsang, P.W.M. Zhang, Yaping Department of Photonics Feng Chia University 100 Wenhwa Rd. Seatwen Taichung40724 Taiwan Digital Optics Center Feng Chia University 100 Wenhwa Rd. Seatwen Taichung40724 Taiwan Bradley Department of Electrical and Computer Engineering Virginia Tech BlacksburgVA24061 United States Department of Electrical Engineering City University of Hong Kong Hong Kong Yunnan Provincial Key Laboratory of Modern Information Optics Kunming University of Science and Technology Yunnan Kunming650500 China

Optical scanning holography (OSH) can perform high quality, speckle-free three-dimensional (3D) holographic imaging. However, conventional OSH system contains an interferometer, which is sensitive to environmental disturbances. Here, we developed an interferenceless OSH (IOSH) system to overcome this issue. In IOSH, the interferometer is replaced by a digital micro device (DMD) together with a projection module. The Fresnel zone plates are directly displayed on the DMD and projected to the object target. The high frame-rate DMD can serve as both the display device and the scanning device, and thus the system is compact, robust, and low cost. We have studied the system performance by analyzing the 3D complex point spread function. The ability of 3D imaging by IOSH has also been demonstrated experimentally. © 2022, The Authors. All rights reserved.

关键词： Imaging systems

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A Paired Phase and Magnitude Reconstruction for Advanced Diffusion-Weighted Imaging

arXiv

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arXiv 2022年

作者： Qian, Chen Wang, Zi Zhang, Xinlin Shi, Boxuan Jiang, Boyu Tao, Ran Li, Jing Ge, Yuwei Kang, Taishan Lin, Jianzhong Guo, Di Qu, Xiaobo The Department of Electronic Science Biomedical Intelligent Cloud R&D Center Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance National Institute for Data Science in Health and Medicine Xiamen University China The United Imaging Healthcare Shanghai China Xingaoyi Medical Equipment Co. Ltd. Yuyao China Department of Radiology Zhongshan Hospital of Xiamen University School of Medicine Xiamen University Xiamen China The School of Computer and Information Engineering Xiamen University of Technology Xiamen China

Objective: Multi-shot interleaved echo planer imaging can obtain diffusion-weighted images (DWI) with high spatial resolution and low distortion, but suffers from ghost artifacts introduced by phase variations between shots. In this work, we aim at solving the challenging reconstructions under inter-shot motions between shots and a low signal-to-noise ratio. Methods: An explicit phase model with paired phase and magnitude priors is proposed to regularize the reconstruction (PAIR). The former prior is derived from the smoothness of the shot phase and enforced with low-rankness in the k-space domain. The latter explores similar edges among multi-b-value and multi-direction DWI with weighted total variation in the image domain. Results: Extensive simulation and in vivo results show that PAIR can remove ghost artifacts very well under a high number of shots (8 shots) and significantly suppress the noise under the ultra-high b-value (4000 s/mm2). Conclusion: The explicit phase model PAIR with complementary priors has a good performance on challenging reconstructions under inter-shot motions between shots and a low signal-to-noise ratio. Significance: PAIR has great potential in advanced clinical DWI applications and brain function research. Copyright © 2022, The Authors. All rights reserved.

关键词： Clinical research

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Skeleton-Based Pre-Training With Discrete Labels for Emotion Recognition in IoT Environments

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IEEE Internet of Things Journal 2025年

作者： Zhang, Zhen Liang, Feng Wang, Wei Zeng, Runhao Leung, Victor C.M Hu, Xiping Shenzhen MSU-BIT University Guangdong-Hong Kong-Macao Joint Laboratory for Emotional Intelligence and Pervasive Computing Shenzhen China Shenzhen MSU-BIT University Artificial Intelligence Research Institute China Lanzhou University Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Gansu China Beijing Institute of Technology School of Medical Technology China University of British Columbia Department of Electrical and Computer Engineering Canada

Self-supervised emotion recognition leveraging skeleton-based data offers a promising approach for classifying emotional expressions within the extensive amount of unlabeled data gathered by sensors in the Internet of Things (IoT). Recent advancements in this field have been driven by contrastive learning-based or generative learning-based self-supervised methods, which effectively tackle the issue of sparsely labeled data. In emotion recognition tasks, the emotional high-level semantics embedded in the skeleton data are more important than the subtle joint movements. Compared to existing methods, discrete label prediction can encourage SSL models to abstract high-level semantics in a manner similar to human perception. However, it is challenging to comprehensively capture emotional expressed in skeleton data solely from joint-based features. Moreover, emotional information conveyed through body movements may include redundant details that hinder the understanding of emotional expression. To overcome these challenges, we propose a novel discrete-label-based emotion recognition framework named the Appendage-Informed Redundancy-ignoring (AIR) discrete label framework. First, we introduce the Appendage-Skeleton Partitioning (ASP) module, which leverages limb movement data from the original skeleton to explore emotional expression. Next, we propose the Appendage-refined Multi-scale Discrete Label (AMDL) module, which transforms traditional self-supervised tasks into classification tasks. This design continuously extracts emotional semantics from skeleton data during pre-training, functioning similarly to predicting categories and subsequently classifying samples. To further reduce the nonessential information in skeleton data that may negatively impact the generation of accurate emotional categories, we propose the Appendage Label Refinement (ALR) module. It refines the generated categories by using the relationships between the skeleton and the various appendages obtai

关键词： Labeled data

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Face Image Super-Resolution via Sparse Representation and Local Texture Constraints 15th

Face Image Super-Resolution via Sparse Representation and Lo...

引用

15th International Conference on Intelligent Computing, ICIC 2019

作者： Wang, Bao-Cheng Li, Bo School of Computer Science of Technology Wuhan University of Science of Technology WuhanHubei430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System WuhanHubei430065 China

ISBN: (纸本)9783030267629

In order to solve the problem brought by low-resolution images and to make better use of low-resolution images, this paper proposed a face super-resolution image reconstruction method based on sparse representation and local texture constraints. In the proposed method, sparse representation is used to reconstruct the super-resolution of face image. Moreover, the local texture of the reconstructed image is also used twice before and after reconstruction as constraints to obtain better details and make the reconstructed face image clearer. Experimental results show that this method can effectively reconstruct the corresponding high-resolution images from low-resolution images. © 2019, Springer Nature Switzerland AG.

关键词： Image reconstruction

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