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11th International Conference on Intelligent Control and Information processing, ICICIP 2021

作者： Ye, Tiange Lan, Rushi Luo, Xiaonan Guilin University of Electronic Technology Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin China

ISBN: (纸本)9781665425155

In this paper, we propose a new method for colon cancer classification from histopathological images, which can automatically analyze a given whole slide image (WSI). We usually classify cancer classification by referring a WSI, which is typically 20000 × 20000 pixels. The cost of obtaining WSIs with annotating cancer regions is very high. Multiple-instance learning (MIL) is a variant of supervised learning in which the instances in a bag share a single class label. That is, MIL only needs unannotated WSI. In recent years, MIL has developed a hard attention mechanism which has achieved good performance. However, this hard attention mechanism cannot notice the interior of each patch, i.e., it lacks soft attention mechanism. In this paper, a soft, sequential, spatial, top-down attention mechanism (which we abbreviate as S3TA) is used to make up for the lack of MIL attention mechanism. Finally, our experiments show that by varying the number of attention steps in S3TA, we achieved a better accuracy of 93.6% than the old model. © 2021 IEEE.

关键词： image enhancement

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Design of Smart Home System Based on ZigBee Technology 1

Design of Smart Home System Based on ZigBee Technology

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1st IEEE International Conference on Data Intelligence and Innovative Application, DIIA 2024

作者： Liang, Yinfan Luo, Xiaonan Guilin University of Electronic Technology Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin China

ISBN: (纸本)9798331504434

With the rise of the Internet of Things (loT) and digital homes, smart home technology has also advanced. Smart homes can proactively sense people's needs, providing users with a more comfortable and secure living environment, while also saving energy and enhancing safety, thereby greatly improving the quality of life. Thanks to loT technology, smart home devices can connect to the home network, enabling data sharing and centralized control. This project applies ZigBee and MQTT protocols to the field of smart homes, achieving intelligent control and remote control of devices such as lighting, appliances, and security. The ZigBee protocol is used for networking, connecting terminal nodes in the facility layer with the coordinator node, while the MQTT protocol is used to connect the coordinator node to the internet. This project has realized remote control, intelligent control, and smart security of the home system, providing users with a more convenient and comfortable home experience. © 2024 IEEE.

关键词： Smart homes

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Conformal Geometric Algebraic Description of Flight Collision Detection Algorithm Based on Cylinder Protection Zone 3

Conformal Geometric Algebraic Description of Flight Collisio...

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3rd International Symposium on Aerospace Engineering and Systems, ISAES 2024

作者： Wu, Haobo Jiao, Weidong Civil Aviation University of China Tianjin Key Laboratory of Intelligent Signal and Image Processing Tianjin China

ISBN: (纸本)9798350350418

In order to provide sufficient reaction time for air traffic controllers and pilots, a Flight Conflict Detection Algorithm (FCDA) based on Cylindrical Protection Zone (CPZ) in the Conformal Geometric Algebra (CGA) is proposed. Initially, the CGA description of the CPZ is represented by using the CGA forms of the central circle and central axis segments of the cylinder. Then based on the inner and outer product and geometric product operations of CGA, the positional relationship of the two CPZs is judged to determine whether a flight conflict occurs or not. Subsequently, we compared the performance of flight conflict detection methods based on rectangular protection zones, spherical protection zones, and cylindrical protection zones through experiments in CGA and Euclidean space, respectively, the experimental results show that in CGA, the proposed method has a faster speed than the flight conflict method based on cuboids, and is basically equivalent to the method based on spherical protection zones. Significantly, all CGA-based methodologies demonstrated superior speed over their Euclidean space counterparts. Considering the propensity of rectangular and spherical zones to elicit false alarms due to their expansive coverage of airspace, the CGA-CPZ-FCDA method emerges as the superior choice. © 2024 IEEE.

关键词： Spheres

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Frame rate up-conversion for depth-based 3D video

Frame rate up-conversion for depth-based 3D video

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2012 13th IEEE International Conference on Multimedia and Expo, ICME 2012

作者： Lu, Qingchun Fang, Xiangzhong Xu, Chong Wang, Yongzhe Institute of Image Communication and Information Processing Shanghai Jiao Tong University Shanghai 200240 China Shanghai Key Labs of Digital Media Processing and Communication Shanghai 200240 China

ISBN: (纸本)9781467316590

A novel frame rate up-conversion (FRUC) scheme for depth-based 3D video is proposed. Differing from the existing conventional FRUC methods which are designed for two dimensional (2D) video only, the proposed method is designed for depth-based 3D video, which increases the frame rate of both color sequences and its associated depth maps by jointly considering the image intensity, depth information, and spatial-temporal correlations. The proposed method contains motion estimation, block irregular segmentation (BIS), depth-constrained motion vector post-processing, forward and backward motion compensation, and edge-preserved combination. Experimental results show that the intermediate color and depth images interpolated by our proposed method provide a good image quality both objectively and subjectively. Moreover, compared with conversional FRUC methods, the color and depth map sequences up-converted by the proposed method are more suitable for further virtual view synthesis. © 2012 IEEE.

关键词： Motion estimation

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Liver Tumor image Segmentation Method Based on U-Net Network 1

Liver Tumor Image Segmentation Method Based on U-Net Network

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1st IEEE International Conference on Data Intelligence and Innovative Application, DIIA 2024

作者： Lu, Dinghong Luo, Xiaonan Guilin University of Electronic Technology Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin China

ISBN: (纸本)9798331504434

Liver tumors are the most common and fatal ones globally, bringing great harm to human life. In order to ascertain the precise location, size, and volume of tumors, the conventional method of manual segmentation necessitates that physicians delineate the tumor boundaries within the patients' abdominal CT scans, a process that demands considerable expenditure of time and effort on the part of the medical staff. A method harnessing the U-Net neural network, predicated on deep learning, has been introduced in this study to augment the accuracy and efficiency of liver tumor segmentation in imaging. The U-Net network has been enhanced with the incorporation of Adam optimization and callback mechanisms to bolster the training efficacy and preserve the most effective model. Furthermore, the implementation of the binary crossentropy loss function has been pivotal for optimizing the neural network's convergence and predictive accuracy, which in turn has led to enhanced segmentation results. The experimental findings suggest that the proposed method is capable of automating the segmentation of liver tumor images, including the precise delineation of tumors and those with indistinct margins, achieving a segmentation accuracy of 99.64%. © 2024 IEEE.

关键词： Computerized tomography

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A uniform solution to the independent set problem through tissue P systems with cell separation

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Frontiers of Computer Science 2012年第4期6卷 477-488页

作者： Xingyi ZHANG Xiangxiang ZENG Bin LUO Zheng ZHANG Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and TechnologyAnhui UniversityHefei 230039China Department of Computer Science Xiamen UniversityXiamen 361005China Key Laboratory of Image Processing and Intelligent Control Department of Control Science and EngineeringHuazhong University of Science and TechnologyWuhan 430074China

Membrane computing is an emergent branch of natural computing, which is inspired by the structure and the functioning of living cells, as well as the organization of cells in tissues, organs, and other higher order structures. Tissue P systems are a class of the most investigated computing mod- els in the framework of membrane computing, especially in the aspect of efficiency. To generate an exponential resource in a polynomial time, cell separation is incorporated into such systems, thus obtaining so called tissue P systems with cell separation. In this work, we exploit the computational efficiency of this model and construct a uniform family of such tissue P systems for solving the independent set problem, a well-known NP-complete problem, by which an efficient so- lution can be obtained in polynomial time.

关键词： membrane computing tissue P system cell sep- aration independent set problem

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Nonlinear Terminal Guidance for Hypervelocity Vehicles Impact in Specific Direction and Speed

Nonlinear Terminal Guidance for Hypervelocity Vehicles Impac...

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第25届中国控制与决策会议

作者： Kun Hu Lei Liu Yongji Wang Zhishen Wang Key Laboratory of Ministry of Education for Image Processing & Intelligent Control Department of Control Science & Engineering Huazhong University of Science & Technology

ISBN: (纸本)9781467355322

The terminal guidance problem of a hypervelocity gliding vehicle to intercept a stationary target in the planar scenario is considered. In addition to impact position accuracy, the guidance law must meet the impact angle and speed demand. This problem is formulated as an infinite-time horizon nonlinear regulator problem, and solved with the state-dependent Riccati equation (SDRE) control technique. We convert the system to a linear-like structure with state-dependent coefficient (SDC) matrices and derive a closed-loop state-feedback control law using the SDRE method. A new state is introduced concerning the impact speed constraint. By rotating the coordinate system, the guidance scheme is extended to satisfy arbitrary impact angle. The state weighting matrix is chosen as the function of time-to-go to include the distance information between the vehicle and target. The numerical simulations are carried out for different impact angles and speeds, the results of which verify the effectiveness of the proposed guidance approach.

关键词： Terminal Guidance, Nonlinear Regulator, State-dependent Riccati Equation

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On-line signature verification using wavelet packet

On-line signature verification using wavelet packet

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2011 International Joint Conference on Biometrics, IJCB 2011

作者： Wang, Kaiyue Wang, Yunhong Zhang, Zhaoxiang Laboratory of Intelligent Recognition and Image Processing Beijing Key Laboratory of Digital Media Beihang University Beijing 100191 China

ISBN: (纸本)9781457713583

In this paper, we propose a novel approach for on-line signature verification using wavelet packet. Signatures are first normalized and resampled, thus they have the same number of sample points. Then, several types of local features are extracted, so that wavelet transform can be applied on them. After that, we conduct experiments to select the best local features, wavelet bases and wavelet packet settings. Also, experiments are carried out to verify the reliability and efficiency of our approach, which performs better than discrete wavelet transform and competes with the state-of-arts. © 2011 IEEE.

关键词： Discrete wavelet transforms

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Dysfunction and recovery of the cortical connectome gradient and its association with gene expression profiles in methamphetamine and heroin use disorders

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Progress in Neuro-Psychopharmacology and Biological Psychiatry 2025年 139卷 111391页

作者： Du, Zhe Yang, Wenhan Wen, Xinwen Cai, Suping Liu, Jun Yuan, Kai School of Life Science and Technology Xidian University Shaanxi Xi'an 710126 China Department of Radiology Second Xiangya Hospital Central South University Changsha 410011 China Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing School of Information Engineering Inner Mongolia University of Science and Technology Inner Mongolia Baotou 014010 China Engineering Research Center of Molecular and Neuro Imaging Ministry of Education Shaanxi Xi'an China Ganzhou City Key Laboratory of Mental Health The Third People's Hospital of Ganzhou City Jiangxi Ganzhou 341000 China Xi'an Key Laboratory of Intelligent Sensing and Regulation of Trans-Scale Life Information School of Life Science and Technology Xidian University Shaanxi Xi'an 710126 China

Background: The hierarchy and segregation of community-based brain networks can be characterized by functional connectome gradient (FCG). Whether the cortical FCG was disrupted and could even be reversed after prolonged abstinence in substance use disorders (SUDs) remained unclear. Less is known about the underlying genetic basis. Methods: We utilized three independent datasets (n = 247) to investigate cortical FCG dysfunction and recovery after prolonged abstinence in methamphetamine use disorder (MUD) (n = 131), and validated in heroin use disorder (HUD) (n = 36). Furthermore, magnetic resonance spectroscopy (MRS) was employed to test the relationship between the glutamate in the ventral tegmental area (VTA) and cortical FCG. Postmortem gene expression was utilized to assess the transcriptional profiles related to cortical FCG alterations. Support vector regression (SVR) model based on the baseline cortical FCG was built to predict craving changes after long-term abstinence. Results: MUD and HUD individuals exhibited similar cortical FCG dysfunction and recovery at global and network levels, mainly in dorsal and ventral attentions network (DAN and VAN), frontoparietal network (FPN) and default mode network (DMN). The glutamate levels in VTA were correlated with the gradient distance between FPN and limbic network (LIM). The transcriptional profiles explained variance of the altered gradient pattern, with the most related genes enriched in synaptic signaling. Baseline cortical FCG significantly predicted craving changes after long-term abstinence. Conclusions: We provided novel insights into the understanding of SUD as treatable health conditions from cortical FCG changes, which could be considered as the potential biomarkers of craving changes after prolonged abstinence for SUDs. © 2025 Elsevier Inc.

关键词： Cortical functional connectome gradient Gene expression Glutamate Heroin use disorder Methamphetamine use disorder Support vector regression

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Enhancing Deep Learning-based Depression Level Estimation Based on Multi-task Learning 24

Enhancing Deep Learning-based Depression Level Estimation Ba...

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7th International Conference on Machine Learning and Machine Intelligence, MLMI 2024

作者： Ye, Qingxin Xie, Zhenming Sun, Hao Xin, Luyao Chen, Youwen Song, Jian Chen, Yen-Wei Provincial key Laboratory of Computational Science Huaqiao University Quanzhou China Intelligent Image Processing Lab Zhejiang University Hangzhou China Intelligent Image Processing Lab Ritsumeikan University Osaka Japan

ISBN: (纸本)9798400717833

In recent years, depression, as a serious mental illness, has received widespread attention from various sectors of society. How to identify depressive emotions in a timely manner and detect depression has become an urgent issue that needs to be addressed. Currently, many experts and scholars propose using deep learning methods to assess the level of depression. Compared to other deep learning models, Transformer models have parallel computing capabilities with their self-attention mechanism, enabling them to handle entire sequences and capture long-distance dependencies. After the success of Transformers in natural language processing, similar architectures have been introduced into the field of computer vision, promoting advancements in multi-modal research. However, how to integrate other information or tasks to improve assessment accuracy remains a challenge. In our team's preliminary work, we proposed a multi-modal adaptive fusion Transformer network for assessing depression levels. Inspired by multi-task learning, we introduced gender classification as a second auxiliary task alongside the main task of depression regression, forming a three-task learning framework including depression classification. This was done to enhance the model's performance. We validated the effectiveness of the proposed method by testing it on a public dataset (AVEC 2019 Detecting Depression with AI Sub-challenge). The experimental results show that our proposed method achieved a Concordance Correlation Coefficient (CCC) of 0.60448, which is 1.688% higher than the accuracy of the dual-task learning method (CCC = 0.5876). © 2024 Copyright held by the owner/author(s).

关键词： Multi-task learning

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