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SSRN 2022年

作者： Chen, Kai Yang, Chunfeng Zhou, Zhengyuan Liu, Yao Ji, Tianjiao Sun, Weiya Chen, Yang School of Cyber Science and Engineering Southeast University Nanjing210096 China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing210096 China The College of Software Engineering Southeast University Nanjing210096 China Laboratory of Image Science and Technology The School of Computer Science and Engineering Southeast University Nanjing210096 China Jiangsu Key Laboratory of Molecular and Functional Imaging Department of Radiology Zhongda Hospital Southeast University Nanjing210009 China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing School of Computer Science and Engineering Southeast University Nanjing210096 China NHC Key Laboratory of Medical Virology and Viral Diseases National Institute for Viral Disease Control and Prevention Chinese Center for Disease Control and Prevention Beijing China Beijing Institute of Tracking and Communication Technology Beijing100094 China

Modeling spatio-temporal sequences is an important topic yet challenging for existing neural networks. Most of the current spatio-temporal sequence prediction methods usually capture features separately in temporal and spatial dimensions or employ multiple mutually independent local spatio-temporal graphs to represent a spatio-temporal sequence. The first kind of method mentioned above is difficult to mine the complex spatio-temporal correlations, while the other is limited for the accuracy of long-term predictions. To handle these issues, this paper proposes a Transformer-Improved Graph Convolution Network for spatio-temporal prediction. Specifically, the temporal location encoding method is exploited to derive the spatio-temporal characteristics of the sequence utilizing a spatio-temporal feature fusion network. In addition, a spatio-temporal attention network is developed to enhance the spatio-temporal correlation of the sequence, and the dynamic spatial features of sequence are further extracted through the adaptive graph convolution network. A private dataset and a public dataset are employed to demonstrate the performance of the proposed TIGC-Net. The qualitative and quantitative results show that the proposed TIGC-Net can extract dynamic spatiotemporal properties more effectively, enhance the spatio-temporal correlation of sequences and improve the prediction accuracy compared with four stateof-the-art. © 2022, The Authors. All rights reserved.

关键词： Convolution

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Switched High-Gain Based Interval Observer Design for Harmonic Oscillator System

Switched High-Gain Based Interval Observer Design for Harmon...

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Chinese Control Conference (CCC)

作者： Tianhang Ming Xiaoling Wang Bo Liu Housheng Su College of Automation & AI Nanjing University of Posts and Telecommunications Nanjing P. R. China Jiangsu Engineering Lab for IOT Intelligent Robots (IOTRobot) Nanjing China College of Information Engineering Minzu University of China Beijing P. R. China Key Laboratory of Image Processing and Intelligence Control of Ministry of Education Huazhong University of Science and Technology Wuhan P. R. China

In this paper, we revisit the interval observer design problem of harmonic oscillator system with unknown but bounded external disturbance and measurement noise. With the bounding information of the external disturbance and measurement noise, a high-gain interval observer is firstly defined and constructed for the harmonic oscillator system to realize the interval estimation on the state and the robust control of the system. Then, a switched high-gain interval observer is proposed to reduce the influence of the measurement noise for a tighter interval width on the state estimation. Finally, some numerical simulations are given to verify the theoretical results.

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Design of interval type-2 fuzzy logic controller for mobile wheeled inverted pendulum

Design of interval type-2 fuzzy logic controller for mobile ...

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World Congress on Intelligent Control and Automation (WCICA)

作者： MyongHyok Ri Jian Huang Songhyok Ri HyonSu Yun ChangSik Kim Department of Control Science KimChaek University of Technology Pyongyang Wuhan Democratic People's Republic of Korea Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan CHINA Department of Computer Science Hamhung University of Chemical Industry Hamhung Wuhan Democratic People's Republic of Korea Department of Computer Science KimChaek University of Technology Pyongyang Democratic People's Republic of Korea

ISBN: (纸本)9781467384155

In this paper, a novel interval type-2 fuzzy logic controller (IT2FLC) is proposed for controlling a mobile wheeled inverted pendulum(MWIP) with model uncertainties and external disturbances. The MWIP is a typical underactuated system which is generally composed of two subsystems, an invert pendulum and a mobile robot system. The IT2FLC proposed in this paper is composed of a balancing controller, a velocity controller and a yaw steering controller. The proposed integrated IT2FLC is linear combination of the three individual IT2FLCs which uses simplified type reduce method. The proposed controller has simple structure and requires less computation, also it is expected to have robustness to uncertainties and external disturbances in the practical implementation. Considering the model uncertainties and external disturbances, simulation results proved the effectiveness and robustness of the proposed IT2FLC compared with type-1 fuzzy logic controller (T1FLC).

关键词： Fuzzy logic Wheels Uncertainty Robustness Mobile communication Control systems Automation

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A Multimodal Registration and Fusion Diagnostic System Based on Multi-scale Feature 20th

A Multimodal Registration and Fusion Diagnostic System Based...

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20th International Forum on Digital TV and Wireless Multimedia communications, IFTC 2023

作者： Jing, Hongyi Liu, Jiannan Han, Jing Zhai, Guangtao Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai China Department of Oral and Maxillofacial Head and Neck Oncology Shanghai Ninth People’s Hospital Shanghai Jiao Tong University School of Medicine Shanghai China National Center for Stomatology National Clinical Research Center for Oral Diseases Shanghai Key Laboratory of Stomatology College of Stomatology Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9789819736256

The rapid growth in manufacturing and multimodal medical imaging industries, driven by evolving user needs, has led to an increasing demand for advanced digital medical imaging technology and corresponding diagnostic analysis systems. Designing human-centered algorithm systems, including digital consultation, intelligent medical care, and medical imaging for disease diagnosis and treatment tracking, becomes imperative. Given the variations in acquisition equipment and imaging technologies across different modal medical images, disparities in initial pose and spatial characteristics arise. To address this, a multimodal medical image registration algorithm is employed to harmonize data coordinates into a unified spatial coordinate system. This paper proposes a feature analysis and key point extraction network based on deep learning. Methods such as matrix decomposition and unsupervised iterative registration are utilized to achieve precise multimodal medical image registration. Leveraging this registration framework, the study focuses on analyzing latent mandibular deviation as a target disease. Features from diverse modal medical images are extracted and fused through a multi-level feature structure network, facilitating disease classification diagnosis and subsequent parameter analysis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Medical imaging

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A NON-CONTRAST CT BASED ALGORITHM FOR DETECTING THE PANCREATIC LESIONS INCLUDING VARIOUS PATHOLOGICAL TYPES

A NON-CONTRAST CT BASED ALGORITHM FOR DETECTING THE PANCREAT...

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第十六届国际体视学与图像分析学术会议

作者： Lidu LIANG Haojie ZHANG Tian YAN Qian LU Chenjie ZHOU Shulong LI Xinchang Chinese Medicine Hospital General Surgery Center Second Department of Hepatobiliary SurgeryGuangdong Provincial Research Center for Artificial Organ and Tissue EngineeringZhujiang HospitalSouthern Medical University School of Biomedical Engineering Guangdong Provincial Key Laboratory of Medical Image ProcessingSouthern Medical University Ultrasound Department Yancheng Third People's Hospital Pancreatic Surgery Department Fudan University Shanghai Cancer Center

We aims to study the pancreatic lesion detection algorithm based on non-contrast CT,in order to achieve low-cost and accurate automatic detection of pancreatic *** constructed an advanced Faster RCNN(aFaster RCNN) based pancreatic lesion detection model based on non-contrast CT,using Faster RCNN as the benchmark *** this work,we propose a CBAM-Resnet50 feature extraction network based on interactive attention,and combine it with a new bounding box regression loss function that comprehensively considers the constraints of lesion shape and anatomical *** model was trained and tested using patients from four clinical centers in China,and the test results were higher than all compared *** method can effectively extract pancreatic lesion features from non-contrast CT scans to detect pancreatic lesion areas.

关键词： non-contrast CT pancreatic cancer lesion detection

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One-pass multi-task networks with cross-task guided attention for brain tumor segmentation

arXiv

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arXiv 2019年

作者： Zhou, Chenhong Ding, Changxing Wang, Xinchao Lu, Zhentai Tao, Dacheng School of Electronic and Information Engineering South China University of Technology Guangzhou510000 China Department of Computer Science Stevens Institute of Technology Hoboken United States Guangdong Provincial Key Laboratory of Medical Image Processing School of Biomedical Engineering Southern Medical University Guangzhou China UBTech Sydney Artificial Intelligence Centre School of Information Technologies Faculty of Engineering and Information Technologies University of Sydney DarlingtonNSW2008 Australia

Class imbalance has emerged as one of the major challenges for medical image segmentation. The model cascade (MC) strategy, a popular scheme, significantly alleviates the class imbalance issue via running a set of individual deep models for coarse-to-fine segmentation. Despite its outstanding performance, however, this method leads to undesired system complexity and also ignores the correlation among the models. To handle these flaws in the MC approach, we propose in this paper a light-weight deep model, i.e., the One-pass Multi-task Network (OM-Net) to solve class imbalance better than MC does, while requiring only one-pass computation for brain tumor segmentation. First, OM-Net integrates the separate segmentation tasks into one deep model, which consists of shared parameters to learn joint features, as well as task-specific parameters to learn discriminative features. Second, to more effectively optimize OM-Net, we take advantage of the correlation among tasks to design both an online training data transfer strategy and a curriculum learning-based training strategy. Third, we further propose sharing prediction results between tasks, which enables us to design a cross-task guided attention (CGA) module. By following the guidance of the prediction results provided by the previous task, CGA can adaptively recalibrate channel-wise feature responses based on the category-specific statistics. Finally, a simple yet effective post-processing method is introduced to refine the segmentation results of the proposed attention network. Extensive experiments are conducted to demonstrate the effectiveness of the proposed techniques. Most impressively, we achieve state-of-the-art performance on the BraTS 2015 testing set and BraTS 2017 online validation set. Using these proposed approaches, we also won joint third place in the BraTS 2018 challenge among 64 participating teams. The code is publicly available at https://***/chenhong-zhou/OM-Net. Copyright © 2019, The Author

关键词： Magnetic resonance imaging

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High Information Density and Low Coverage Data Storage in DNA with Efficient Channel Coding Schemes

arXiv

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arXiv 2024年

作者： Ding, Yi He, Xuan Nguyen, Tuan Thanh Song, Wentu Yakhini, Zohar Yaakobi, Eitan Pan, Linqiang Tang, Xiaohu Cai, Kui Information Coding and Transmission Key Lab of Sichuan Province Southwest Jiaotong University Sichuan Chengdu611756 China Cluster Singapore University of Technology and Design 487372 Singapore Faculty of Computer Science Technion - Israel Institute of Technology Haifa3200003 Israel School of Computer Science RUNI Herzliya4615200 Israel Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Hubei Wuhan430074 China

DNA-based data storage has been attracting significant attention due to its extremely high data storage density, low power consumption, and long duration compared to conventional data storage media. Despite the recent advancements in DNA data storage technology, significant challenges remain. In particular, various types of errors can occur during the processes of DNA synthesis, storage, and sequencing, including substitution errors, insertion errors, and deletion errors. Furthermore, the entire oligo may be lost. In this work, we report a DNA-based data storage architecture that incorporates efficient channel coding schemes, including different types of error-correcting codes (ECCs) and constrained codes, for both the inner coding and outer coding for the DNA data storage channel. We also carried out large scale experiments to validate our proposed DNA-based data storage architecture. Specifically, 1.61 and 1.69 MB data were encoded into 30,000 oligos each, with information densities of 1.731 and 1.815, respectively. It has been found that the stored information can be fully recovered without any error at average coverages of 4.5 and 6.0, respectively. This experiment achieved the highest net information density and lowest coverage among existing DNA-based data storage experiments (with standard DNA), with data recovery rates and coverage approaching theoretical optima. Copyright © 2024, The Authors. All rights reserved.

关键词： Channel coding

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image retrieval based on color features integrated with anisotropic directionality

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Journal of Systems Engineering and Electronics 2010年第1期000卷

作者： Jing Bai Xiaohua Wang and Licheng Jiao key laboratory of Intelligent Perception and image Understanding of Ministry of Education of China Institute of Intelligent Information processing Xidian University Xi’an 710071 P. R. China Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian UniversityXi'an 710071P. R. China

A novel image retrieval approach based on color features and anisotropic directional information is proposed for content based image retrieval systems (CBIR). The color feature is described by the color histogram (CH), which is translation and rotation invariant. However, the CH does not contain spatial information which is very important for the image retrieval. To overcome this shortcoming, the subband energy of the lifting directionlet transform (L-DT) is proposed to describe the directional information, in which L-DT is characterized by multi-direction and anisotropic basis functions compared with the wavelet transform. A global similarity measure is designed to implement the fusion of both color feature and anisotropic directionality for the retrieval process. The retrieval experiments using a set of COREL images demonstrate that the higher query precision and better visual effect can be achieved.

关键词： image retrieval color histogram directionlet transform anisotropic similarity

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Metric Learning Based Rolling Bearing Faults Diagnosis with Curvelet Transform

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Journal of Physics: Conference Series 2019年第1期1229卷

作者： Ziming Lu Jiang-Wen Xiao Zhengyi Huang School of Automation Huazhong University of Science and Technology Wuhan 430074 China Key Laboratory of Image Processing and Intelligent Control (Huazhong University of Science and Technology) Ministry of Education China

Rolling bearing faults are among the primary causes of breakdown in mechanical equipment. Aiming at the vibration signals of rolling bearing which are non-stationary and easy to be disturbed by noise, a novel fault diagnosis method based on curvelet transform and metric learning is proposed. This method consists of 3 parts. The first one is feature engineering which includes reshaping the original timing features of rolling bearings, employing curvelet transform to transform reshaped features and making its coefficients as the new features. Curvelet transform can analyse the original signal from many angles. The second one is employing metric learning to map these new features into special embedding space. The last one is applying KNN classifier to detect the rolling bearing faults. Metric learning can effectively improve the performance of KNN by learning a mapping matrix to modify the distribution of samples. The proposed method overcomes the problems such as the subjectivity and blindness of manual feature extraction, poor coupling in each stage and sensitive to the effect of noise. Extensive simulations based on several data-sets show that the our method has better performance on bearing fault diagnosis than traditional methods.

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Dynamic Ca2+ sensitivity of BK channels

Dynamic Ca2+ sensitivity of BK channels

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The 10th Chinese Symposium on Calcium Signaling(第十届全国钙信号和细胞功能研讨会)

作者： Pan-Pan Hou Feng Xiao Hao-Wen Liu Ming Yuchi Guo-Hui Zhang Ying Wu Wei Wang Wen-Ping Zeng Ming-Yue Ding Zheng-Xing Wu Jian-Ming Cui Jiu-Ping Ding Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Ministry of Education Department of Biomedical Engineering College of Life Science and Technology Wuhan Hubei China Department of pharmacology Soochow University college of pharmaceutical Sciences Suzhou 215123China Key Laboratory of Molecular Biophysics Huazhong University of Science and Technology Ministry of Education College of Life Science and Technology Wuhan Hubei China Department of Biomedical Engineering Center for the Investigation of Membrane Excitability DisordersCardiac Bioelectricity and Arrhythmia Center Washington University St Louis MO 63130 USA

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