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7th IEEE Advanced Information Technology, Electronic and Automation control Conference, IAEAC 2024

作者： Wang, Yixuan Li, Wei Huazhong University of Science and Technology School of Artificial Intelligence and Automation Wuhan China Image Processing and Intelligent Control Key Laboratory of the Education Ministry of China Wuhan China

ISBN: (纸本)9798350339161

Alzheimer's disease (AD) is the most prevalent form of dementia, and early diagnosis is crucial for delaying and treating AD. Resting-state functional magnetic resonance imaging (rs-fMRI), a widely used medical imaging technique, offers rich temporal and spatial data, which has led researchers to explore various feature extraction methods based on rs-fMRI images for AD identification. However, the related work still suffers from insufficient utilization of temporal and spatial information which leads to unsatisfactory early diagnosis. In this study, we propose a dual-branch fusion model to extract spatial-temporal features from rs-fMRI images. Our proposed model can extract temporal features at different levels. We developed a Class Activation Sequence (CAS) branch, which is a structure that emphasizes the function of each temporal node throughout the whole time series. Additionally, we created a time-domain local branch for local feature extraction. Further, we designed a fusion module for the model to describe temporal contextual relationships and fuse features at various levels. We tested the performance of the model on the ADNI dataset, and the experimental results show that compared with other algorithms, the dual-branch fusion model achieves higher classification accuracy on several classification tasks including early diagnosis, which proves the advantage of the dual-branch fusion model in temporal and spatial feature extraction for rs-fMRI images, and our work also provides a foundation for the temporal domain characterization of rs-fMRI images. © 2024 IEEE.

关键词： Feature extraction

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A Spatio-Temporal Scale Based Affective Neural Network for Emotion Detection in Conversation

A Spatio-Temporal Scale Based Affective Neural Network for E...

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2023 China Automation Congress, CAC 2023

作者： Li, Jiang Wang, Xiaoping Lei, Shanglin Zeng, Zhigang Wuhan430074 China Wuhan430074 China Key Laboratory of Image Processing and Intelligent Control Ministry of Education Wuhan430074 China Key Laboratory of Brain-inspired Intelligent Systems Hubei Province Wuhan430074 China

ISBN: (纸本)9798350303759

Emotion Detection in Conversation (EDC) has been implemented in numerous domains and is under increasing attention. The EDC task is highly challenging due to the need to incorporate both the context of the utterance and the identity of the participant. Some existing EDC approaches employ redundant network structures and inadequately model contextual utterances, resulting in limited performance gains. In this work, we devise a Spatio-Temporal scale based Affective neural Network (STANet) in a simplistic and efficacious manner. STANet consists of two main components, i.e., spatial scale network and temporal scale network, which are applied to extract spatial scale and temporal scale information in the conversation, respectively. Prior to spatio-temporal scale modeling, we encode the identity information of participants to examine its impact on the performance of emotion detection. We conduct substantial comparative experiments and ablation studies on three conversational emotion datasets, and the results demonstrate that our STANet realizes superior performance without taking advantage of external knowledge. © 2023 IEEE.

关键词： Emotion Recognition

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Memory Analysis for Memristors and Memristive Recurrent Neural Networks

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IEEE/CAA Journal of Automatica Sinica 2020年第1期7卷 96-105页

作者： Gang Bao Yide Zhang Zhigang Zeng Key Laboratory of Cascaded Hydropower Stations Operation and Control Electrical Engineering and New EnergyChina Three Gorges UniversityYichang 443002and Hubei Key Laboratory of Applied Mathematics(Hubei University)Wuhan 430074China Department of Medical Engineering California Institute of TechnologyPasadenaCalifornia 91125 USA School of Artificial Intelligence and Automation Huazhong University of Science and Technologyand also with the Key Laboratory of Image Processing and Intelligent Control of Education Ministry of ChinaWuhan 430074China

Traditional recurrent neural networks are composed of capacitors, inductors, resistors, and operational *** neural networks are constructed by replacing resistors with memristors. This paper focuses on the memory analysis,i.e. the initial value computation, of memristors. Firstly, we present the memory analysis for a single memristor based on memristors’ mathematical models with linear and nonlinear ***, we present the memory analysis for two memristors in series and parallel. Thirdly, we point out the difference between traditional neural networks and those that are memristive. Based on the current and voltage relationship of memristors, we use mathematical analysis and SPICE simulations to demonstrate the validity of our methods.

关键词： Dopant drift memory memristive neural networks memristor

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A new change detection method based on non-parametric density estimation and Markov random fields

A new change detection method based on non-parametric densit...

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MIPPR 2009 - Medical Imaging, Parallel processing of images, and Optimization Techniques: 6th International Symposium on Multispectral image processing and Pattern Recognition

作者： Wang, Guiting Fan, Yuanzhang Jiao, Licheng Institute of Intelligent Information Processing Xidian University Xi'an 710071 China Key Laboratory of Intelligent Perception and Image Understanding Ministry of Education of China China

ISBN: (纸本)9780819478085

A new change detection approach based on non-parametric density estimation and Markov random fields is proposed in this paper. As the concrete form of gray statistical distribution of remote sensing images is often difficult to be known, the non-parameter density estimation method does not need the specific forms in advance, and is especially suitable for the estimation problem of small samples, so we adopt the non-parametric density estimation method to obtain the precise estimation of the probability density of statistical distribution of differencing image in the paper, and then perform multitemporal remote sensing image change detection combining with MRF(Markov random fields)model for spatial smoothing. The final experimental results show that the proposed method is effective. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： Probability distributions

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A novel small dim infrared target detecting method based on grayscale merging and connected components analysis

A novel small dim infrared target detecting method based on ...

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2012 International Conference on Modelling, Identification and control, ICMIC 2012

作者： Zhang, Ying Wang, Min Niu, Shaoqing Key Laboratory of Image Processing and Intelligent Control of Education Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)9780956715715

Target detecting algorithm in infrared image is drawing extensive attention both at home and abroad, expecially when the infrared images own complex backgrounds and low resolution. How to make sure of the accuracy of infrared dim-small target detecting is the key point during the processing procedure. In this paper, a novel region of interest detecting algorithm is proposed for aerial small dim infrared target with clutter background. Different from traditional region of interest extraction method, we extract region of interest based on the grayscale merging algorithm and connected components analysising approach. First, the infrared image is reconstructed by separating the grayscale into several interval sections, then binarization is used to segment the processed image according to estimate the threshold value using minimum error probability;finally, analysis and select the connected components, to sift the accurate connected domain which contain the target. The validity of the proposed approach is demonstrated by experiment of infrared target region extraction. © 2012 Huazhong Univ of Sci & Tec.

关键词： Object detection

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基于第二代Bandelet域隐马尔可夫树模型的图像分割

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自动化学报 2009年第5期35卷 498-504页

作者：侯彪徐婧刘凤焦李成 Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an 710071 China

第二代Bandelet可以充分利用图像的内在几何正则性特点,并能自适应获得图像的最优表示.本文采用隐马尔可夫树(Hidden Markov tree,HMT)模型对图像的第二代Bandelet系数建模,通过多尺度参数训练和基于上下文的最大后验概率进行图像分割.... 详细信息

第二代Bandelet可以充分利用图像的内在几何正则性特点,并能自适应获得图像的最优表示.本文采用隐马尔可夫树(Hidden Markov tree,HMT)模型对图像的第二代Bandelet系数建模,通过多尺度参数训练和基于上下文的最大后验概率进行图像分割.为了评价本文方法的性能,我们分别选择合成纹理图像、航拍图像和SAR图像进行实验,并与小波域HMT模型分割方法(WD-HMTseg)和Contourlet域HMT模型分割方法(CHM Tseg)进行比较说明算法的有效性.实验结果表明本文方法不但在边缘准确性和区域一致性上有明显改进,而且也降低了纹理图的错分概率.

关键词：图像分割第二代Bandelet 隐马尔可夫树模型小波 Contourlet变换

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Directional Filter for SAR images Based on Nonsubsampled Contourlet Transform and Immune Clonal Selection

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International Journal of Automation and computing 2009年第3期6卷 245-253页

作者： Xiao-Hui Yang Li-Cheng Jiao Deng-Feng Li Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi＇an 710071 PRC Institute of Applied Mathematics School of Mathematics and Information Sciences Henan University Kaifeng 475004 PRC

A directional filter algorithm for intensity synthetic aperture radar （SAR） image based on nonsubsampled contourlet transform （NSCT） and immune clonal selection （ICS） is presented. The proposed filter mainly focuses on exploiting different features of edges and noises by NSCT. Furthermore, ICS strategy is introduced to optimize threshold parameter and amplify parameter adaptively. Numerical experiments on real SAR images show that there are improvements in both visual effects and objective indexes.

关键词： Directional filter nonsubsampled contourlet transform （NSCT） immune clonal selection optimization （ICSO） syntheticaperture radar （SAR）.

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Spectrum Sensing Based on Deep Learning Classification for Cognitive Radios

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China Communications 2020年第2期17卷 138-148页

作者： Shilian Zheng Shichuan Chen Peihan Qi Huaji Zhou Xiaoniu Yang Science and Technology on Communication Information Security Control Laboratory Jiaxing 314033China State Key Laboratory of Integrated Service Networks Xidian UniversityXi’an 710071China Key Laboratory of Intelligent Perception and Image Understanding Ministry of Education of ChinaXidian UniversityXi’an 710071China

Spectrum sensing is a key technology for cognitive *** present spectrum sensing as a classification problem and propose a sensing method based on deep learning *** normalize the received signal power to overcome the effects of noise power *** train the model with as many types of signals as possible as well as noise data to enable the trained network model to adapt to untrained new *** also use transfer learning strategies to improve the performance for real-world *** experiments are conducted to evaluate the performance of this *** simulation results show that the proposed method performs better than two traditional spectrum sensing methods,i.e.,maximum-minimum eigenvalue ratio-based method and frequency domain entropy-based *** addition,the experimental results of the new untrained signal types show that our method can adapt to the detection of these new ***,the real-world signal detection experiment results show that the detection performance can be further improved by transfer ***,experiments under colored noise show that our proposed method has superior detection performance under colored noise,while the traditional methods have a significant performance degradation,which further validate the superiority of our method.

关键词： spectrum sensing deep learning convolutional neural network cognitive radio spectrum management

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Flow rate control and resource allocation policy with security demands in OFDMA networks

Flow rate control and resource allocation policy with securi...

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10th World Congress on intelligent control and Automation, WCICA 2012

作者： Bo, Yang Zhu, Xingzheng Yue, Jianting Guan, Xinping School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Ministry of Education of China 800 Dongchuan Road Shanghai 200240 China Key Laboratory of Education Ministry for Image Processing and Intelligent Control China

ISBN: (纸本)9781467313988

OFDMA-based network performs well in maximizing the overall throughput, and meanwhile, problems with security requirements are focused to satisfy the increasing needs of confidential data transmissions. According to information theory, however, secure transmission undesirably antagonizes larger flow rate, thus a tradeoff is expected to balance these two matters. In this paper, we consider a downlink situation in OFDMA network, where all users request secure transmission from the base station yet the resources including power and subcarriers are limited such that an appropriate policy on resource allocation can play an instrumental role. Dynamic queueing and Lyapunov optimization are two highlights in this process. We utilize dynamic queueing so that merely instantaneous state of the network is enough, which is advantageous for easier implementations. On the other hand, by the means of Lyapunov optimization, we also prove our algorithm can obtain a performance of flow rate admission extremely close to the optimal. Applying this policy can reduce computational complexity significantly but still perform close to optimality. © 2012 IEEE.

关键词： Resource allocation

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Bootstrapping Autonomous Lane Changes with Self-supervised Augmented Runs 17th

Bootstrapping Autonomous Lane Changes with Self-supervised ...

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17th European Conference on Computer Vision, ECCV 2022

作者： Xiang, Xiang Key Lab of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9783031250682

In this paper, we want to strengthen an autonomous vehicle’s lane-change ability with limited lane changes performed by the autonomous system. In other words, our task is bootstrapping the predictability of lane-change feasibility for the autonomous vehicle. Unfortunately, autonomous lane changes happen much less frequently in autonomous runs than in manual-driving runs. Augmented runs serve well in terms of data augmentation: the number of samples generated from augmented runs in a single one is comparable with that of samples retrieved from real runs in a month. In this paper, we formulate the Lane-Change Feasibility Prediction problem and also propose a data-driven learning approach to solve it. Experimental results are also presented to show the effectiveness of learned lane-change patterns for the decision making. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Autonomous vehicles

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