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Generating Synthetic Light-Adapted Electroretinogram Waveforms Using Artificial Intelligence to Improve Classification of Retinal Conditions in UnderRepresented Populations

arXiv

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arXiv 2024年

作者： Kulyabin, Mikhail Zhdanov, Aleksei Maier, Andreas Loh, Lynne Estevez, Jose J. Constable, Paul A. Pattern Recognition Lab Department of Computer Science Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Engineering School of Information Technologies Telecommunications and Control Systems Ural Federal University Yekaterinburg Russia Flinders University College of Nursing and Health Sciences Caring Futures Institute AdelaideSA Australia

Visual electrophysiology is often used clinically to determine the functional changes associated with retinal or neurological conditions. Te full-feld fash electroretinogram (ERG) assesses the global contribution of the outer and inner retinal layers initiated by the rods and cone pathways depending on the state of retinal adaptation. Within clinical centers, reference normative data are used to compare clinical cases that may be rare or underpowered within a specifc demographic. To bolster either the reference dataset or the case dataset, the application of synthetic ERG waveforms may ofer benefts to disease classifcation and case-control studies. In this study and as a proof of concept, artifcial intelligence (AI) to generate synthetic signals using generative adversarial networks is deployed to upscale male participants within an ISCEV reference dataset containing 68 participants, with waveforms from the right and left eye. Random forest classifers further improved classifcation for sex within the group from a balanced accuracy of 0.72–0.83 with the added synthetic male waveforms. Tis is the frst study to demonstrate the generation of synthetic ERG waveforms to improve machine learning classifcation modelling with electroretinogram waveforms. Copyright © 2024, The Authors. All rights reserved.

关键词： Ophthalmology

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Neural Dynamic Fault-Tolerant Scheme for Collaborative Motion Planning of Dual-Redundant Robot Manipulators

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IEEE Transactions on Neural Networks and Learning Systems 2024年第6期36卷 11189-11201页

作者： Zhijun Zhang Zhongwen Cao Xingru Li School of Automation Science and Engineering South China University of Technology Guangzhou China Key Library of Autonomous Systems and Network Control Ministry of Education Guangzhou China Institute for Super Robotics (Huangpu) Guangzhou China Jiangxi Thousand Talents Plan Nanchang University Nanchang China College of Computer Science and Engineering Jishou University Jishou China Guangdong Artificial Intelligence and Digital Economy Laboratory (Pazhou Laboratory) Guangzhou China Shaanxi Provincial Key Laboratory of Industrial Automation School of Mechanical Engineering Shaanxi University of Technology Hanzhong China School of Information Science and Engineering Changsha Normal University Changsha China Institute of Artificial Intelligence and Automation Guangdong University of Petrochemical Technology Maoming China

To avoid the task failure caused by joint breakdown during the collaborative motion planning of dual-redundant robot manipulators, a neural dynamic fault-tolerant (NDFT) scheme is proposed and applied. To do so, a joint fault-tolerant strategy is first designed, and it is formulated as a time-varying equality constraint. Second, combining the robot position and orientation control, joint limit constraint, joint fault-tolerant equality constraint, and considering the repetitive motion optimization criterion, a fault-tolerant framework for the dual-redundant robot manipulators based on quadratic programming (QP) is constructed. Then, a varying-parameter recurrent neural network (VP-RNN) is designed to solve the QP issue. The fault-tolerant framework and the VP-RNN constitute NDFT scheme. With the NDFT scheme, the impact of faulty joints on the whole system can be remedied by healthy joints, thereby the end-effectors of the robot can complete the given end-effector task. Finally, computer simulations and physical experiments are implemented to verify the availability, physical realizability, and accuracy of the proposed NDFT scheme in the collaborative execution of end-effector tasks. Comparative experimental results with conventional repetitive motion planning schemes based on neural networks show higher accuracy and smaller joint angle drift.

关键词： Manipulators Robots Fault tolerant systems Fault tolerance End effectors Planning Kinematics Manipulator dynamics Jacobian matrices Collaboration

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Liver Tumor Segmentation of CT Image by Using Deep Fully Convolutional Network 1

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3rd International Conference on Machine Learning for Cyber Security, ML4CS 2020

作者： Jin, Lingmin Ma, Rui Zhao, Meng Teng, Shenghua Li, Zuoyong College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao266590 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350121 China

ISBN: (数字)9783030622237

ISBN: (纸本)9783030622220

Accurate segmentation of liver tumors is an important guarantee for the success of liver cancer surgery, where convolutional network has been a type of popular method. However, the performance of the traditional convolutional network is limited by the network depth. To improve the accuracy of liver tumor segmentation, we propose a cascaded deep fully convolutional network (DFCN) which uses ResNet as the basis network followed by side output layer in the upsampling stage to fuse multi-scale image features. For better localizing the liver tumors, the segmentation result is further refined by a fully connected conditional random field. Experimental results show that the proposed method achieves higher segmentation accuracy than several state-of-the-art methods. © 2020, Springer Nature Switzerland AG.

关键词： Convolution

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Research of Virtual Simulation Experiment Course Resource Intelligent Information System Construction 2

Research of Virtual Simulation Experiment Course Resource In...

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2nd IEEE International Conference on Power, Electronics and computer Applications, ICPECA 2022

作者： Wang, Yangping Dang, Jianwu Yong, Jiu Wang, Wenrun Yue, Biao Jin, Fangrui Computer Science and Technology Experimental Teaching Demonstration Center of National Lanzhou Jiaotong University National Virtual Simulation Experiment Teaching Center of Railway Traffic Information and Control Lanzhou Jiaotong University Gansu Prov. Eng. Research Center for Artificial Intelligence and Graphics and Image Processing Lanzhou730070 China National Virtual Simulation Experiment Teaching Center of Railway Traffic Information and Control Lanzhou Jiaotong University College of Intelligence and Computing Tianjin University Computer Science and Technology Experimental Teaching Demonstration Center of National Lanzhou Jiaotong University Gansu Prov. Eng. Research Center for Artificial Intelligence and Graphics and Image Processing Lanzhou730070 China College of Electronic and Information Engineering Lanzhou Jiaotong University Gansu Provincial Engineering Research Center for Artificial Intelligence and Graphics and Image Processing Lanzhou730070 China

ISBN: (纸本)9781665442763

In order to enable the construction of virtual simulation experiment resources in colleges and universities to keep up with the development of professional technology, this paper proposes to build a 'diversified virtual simulation experiment teaching resource construction system' through exploration and research. With the help of virtual reality, AR, panoramic roaming, video and other technologies, it provides a unified development framework, development documents and case templates for professional teachers to independently develop virtual simulation experiment teaching resources in line with undergraduate teaching, and helps teachers quickly build virtual simulation experiment resources in accordance with unified standards. It effectively solves the problems that it is difficult for college teachers to independently develop virtual simulation courseware and the technology is not unified. © 2022 IEEE.

关键词： Virtual reality

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TokenHPE: Learning Orientation Tokens for Efficient Head Pose Estimation via Transformers

TokenHPE: Learning Orientation Tokens for Efficient Head Pos...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Cheng Zhang Hai Liu Yongjian Deng Bochen Xie Youfu Li National Engineering Research Center for E-Learning Central China Normal University Wuhan China College of Computer Science Beijing University of Technology Beijing China Engineering Research Center of Intelligence Perception and Autonomous Control Ministry of Education Beijing China Department of Mechanical Engineering City University of Hong Kong Kowloon Hong Kong China

Head pose estimation (HPE) has been widely used in the fields of human machine interaction, self-driving, and attention estimation. However, existing methods cannot deal with extreme head pose randomness and serious occlusions. To address these challenges, we identify three cues from head images, namely, neighborhood similarities, significant facial changes, and critical minority relationships. To leverage the observed findings, we propose a novel critical minority relationship-aware method based on the Transformer architecture in which the facial part relationships can be learned. Specifically, we design several orientation tokens to explicitly encode the basic orientation regions. Meanwhile, a novel token guide multiloss function is designed to guide the orientation tokens as they learn the desired regional similarities and relationships. We evaluate the proposed method on three challenging benchmark HPE datasets. Experiments show that our method achieves better performance compared with state-of-the-art methods. Our code is publicly available at https://***/zc2023/TokenHPE.

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Deep learning of Hash Method for Fast Image Retrieval

Deep learning of Hash Method for Fast Image Retrieval

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2021 China Automation Congress, CAC 2021

作者： Peng, Taiying Wang, Tian Liu, Kexin Zhang, Mengyi Shi, Peng Snoussi, Hichem School of Automation Science and Electrical Engineering Beihang University Beijing China Institute of Artificial Intelligence Beihang University Beijing China College of Electrical Engineering and Control Science Nanjing Tech University Nanjing China School of Mathematics and Computer Science Fujian Normal University Fujian China Institute Charles Delaunay-LM2S University of Technology of Troyes Troyes France

ISBN: (纸本)9781665426473

Most of the image retrieval technology are complex and advanced methods to improve the accuracy of *** also have deep net layers, which costs much time to caculate the result. When we need deal with complex image task, it will be hard to complete the fast image retrieval. This paper presents a convolutional neural network combining with the fast encoding technology under the hash algorithm to improve fast retrieval time and accuracy. In order to keep the existing accuracy and retrieval speed of the network, we present a Hash method path of the network combining witn the dual *** clustering analysis and AlexNet,we get lots of high dimensional feature. Then these feature is put into Hash-coding *** algorithm will output binary feature to match the target image. Through learning similar graph pairs, Hash net work shorten the distance between the similar images, and achieve fast *** it realizes image retrieval and outputs the most similar *** the search task. © 2021 IEEE

关键词： Image enhancement

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Architecture Descriptions Analysis Based on Text Mining and Crawling Technology 3

Architecture Descriptions Analysis Based on Text Mining and ...

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3rd IEEE International Conference on Frontiers Technology of Information and computer, ICFTIC 2021

作者： Zheng, Tong Zhao, Dongmiao Wang, Xingtian Gao, Weijun Miao, Sheng Qingdao University College of Computer Science and Technology Qingdao China Qingdao University of Technology Innovation Institute for Sustainable Maritime Architecture Research and Technology Qingdao China Faculty of Environmental Engineering University of Kitakyushu Kitakyushu Japan Qingdao University of Technology School of Information and Control Engineering Qingdao China

ISBN: (纸本)9781665406055

Nowadays, text mining technology is developed rapidly and employed by various contexts, such as healthcare, public opinion analysis, and document retrieval. Architectural design descriptions usually include large amount of massive text, which means information and knowledge are hidden. Since more and more attention are paid to the sustainable development, this paper research the main building design factors on nature aspect by using text mining. Collecting and processing 41,062 descriptions of architecture projects from website, the frequency of occurrence words with nature aspect are analyzed in 11 architecture categories respectively. The top 100 words of frequency are taken to constitute the word cloud, and the design factors on natural environment aspect could be summarized as area, light, landscape, structure, material, façade, site, water, wind, and view. This will provide architects guidance on dealing with the contradiction between building and nature. It will help us to further explore the relationship between architectural design and natural environment and realize a green and sustainable future. © 2021 IEEE.

关键词： Architectural design

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Latent multi-view subspace clustering based on Laplacian regularized representation

Latent multi-view subspace clustering based on Laplacian reg...

引用

International Workshop on Advanced Computational Intelligence (IWACI)

作者： Wei Guo Hangjun Che Man-Fai Leung Nankun Mu Xiangguang Dai Yuming Feng School of Electronic and Information Engineering Southwest University Chongqing China School of Electronic Information Engineering Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Southwest University Chongqing China School of Computing and Information Science Faculty of Science and Engineering Anglia Ruskin University Cambridge UK Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education College of Computer Science Chongqing University Chongqing China School of Computer Science and Engineering Chongqing Three Gorges University Chongqing China Key Laboratory of Intelligent Information Processing and Control Chongqing Three Gorges University Chongqing China

We propose a latent multi-view subspace clustering model based on Laplacian regularized representation. To emphasize the information of the representation matrix at the local level and reflect the grouping effect of clustering, a Laplacian regularization is imposed on the representation matrix. Additionally, To boost the efficiency o f c lustering, we apply nonnegative constraints on the representation matrix. A unified framework is designed to solve the proposed model by using ALM and LADMAP methods. The proposed model has excellent performance, as demonstrated by a number of experimental findings.

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Unsupervised Pedestrian Re-Id: Attention-Driven Framework with Clustering Optimization

SSRN

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SSRN 2023年

作者： Wang, Xuan Sun, Zhaojie Chehri, Abdellah Jeon, Gwanggil Song, Yongchao School of Computer and Control Engineering Yantai University Qingquan Road 30 Yantai264005 China Department of Mathematics and Computer Science Royal Military College of Canada KingstonONK7K 7B4 Canada Department of Embedded Systems Engineering Incheon National University 119 Academy-ro Yeonsu-gu Incheon22012 Korea Republic of

Unsupervised pedestrian re-identification (re-ID) is still a challenging task. Although convolutional neural networks (CNN) have had great success in pedestrian re-ID, they face the challenge of dealing with pose changes, occlusions, and lighting changes. To effectively tackle these challenges, it is imperative to prioritize the implementation of efficient sampling strategies. We propose an attention-driven framework with clustering optimization (AFC) to address the above issues. First, we introduce a new attention mechanism that enhances multi-scale spatial attention and reduces the number of trainable parameters. Then, we employed a straightforward and effective method of group sampling. In addition, we apply a clustering consensus approach to estimate pseudo-label similarity in continuous training and use temporal propagation and ensembles to improve pseudo-labels. Extensive experiments on Market-1501, DukeMTMC-reID, and MSMT17 have shown that our approach achieves significant performance gains in unsupervised pedestrian re-ID tasks, providing valuable insights for further research in this area. © 2023, The Authors. All rights reserved.

关键词： Pattern recognition

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Deep Learning and Multi-Modal Fusion for Real-Time Multi-Object Tracking: Algorithms, Challenges, Datasets, and Comparative Study

SSRN

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SSRN 2023年

Real-time multi-object tracking (MOT) is a complex task that involves the detection and tracking of multiple objects. After the objects are detected, they are assigned markers, and their trajectories are tracked in real-time. The scientific community is intrigued bythe possibilities of utilizing MOT technology in the context of smart cities. Their primary focus lies in the domains of intelligent transportation, detection of vehicles and pedestrians, crowd surveillance, and public safety. Deep learning techniques have been developed in recent years to effectively tackle the challenges of real-time MOT tasks and enhance tracking performance. Environmental perception within smart traffic applications relies heavily on sensor data fusion. In traffic scenarios, a combination of sensors and cameras can be used to detect and track targets, as well as collect data. However, it faces challenges when it comesto detecting and tracking objects that are in motion, have complex changes in appearance, or are in crowded scenes. This paper investigates the baseline standard for real-time Multiple Object Tracking tasks. Focusing on quantitative measures, we conduct a thorough and exhaustive analysis of commonly used benchmark datasets and metrics. In this study, we also examine established embedding techniques and real-time multi-object tracking algorithms. Each strategy will be categorized and evaluated based on a set of predetermined principles. Several MOT strategies are analyzed and represented visually. Finally, this paper provides an overview of the MOT mission’s present challenges and prospective objectives. © 2023, The Authors. All rights reserved.

关键词： Smart city

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