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arXiv 2019年

作者： Ren, Hai-Peng Tian, Kun Bai, Chao Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xian University of Technology Xian710048 China School of Mechatronics Engineering Xi’an Technological University Xian710048 China

Secure communication using hyperchaos has a better potential performance, but hyperchaotic impulse circuits synchronization is a challenging task. In this paper, an impulse control method is proposed for the synchronization of two hyperchaotic Chen circuits. The sufficient conditions for the synchronization of hyperchaotic systems using the impulse control are given. The upper bound of the impulse interval is derived to assure the synchronization error system to be asymptotically stable. Simulation and circuit experiment show the correctness of the analysis and feasibility of the proposed method. Copyright © 2019, The Authors. All rights reserved.

关键词： Synchronization

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Application of information Technologies and Quantitative Light-Induced Fluorescence for the Assessment of Early Caries Treatment Outcomes

Application of Information Technologies and Quantitative Lig...

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International Conference on Developments in esystems Engineering (DESE)

作者： Elena Maslak Bakhtinur Khudanov Darya Krivtsova Tatyana Tsoy Volgograd State Medical University Volgograd Russia Tashkent State Dental Institute Ministry of Innovative Development Tashkent Uzbekistan Laboratory of Intelligent Robotic Systems (LIRS) Higher Institute of Information Technology and Intelligent Systems Kazan Federal University Kazan Russia

ISBN: (数字)9781728130217

ISBN: (纸本)9781728130224

information technologies (IT) and Quantitative light-induced fluorescence (QLF) are advanced technologies for dental caries detection, quantification and monitoring. Application of IT and QLF technologies to assess and monitor early dental caries stages are described in this article. In the prospective study 110 initial caries lesions in permanent teeth of 10-16-year-old children were monitored during 12 months period after the treatment. Qraycam device based on QLF technology and Q-Ray software v.1.24 (AIOBIO, South Korea) was used to assess and quantify caries lesions before and in 3, 6, 9 and 12 months after the treatment. Two treatment methods were randomly applied: caries infiltration (the first group) and peptide amelogenin application (the second group). Differences in the treatment outcomes between the groups were assessed by Student-t-test at p-value

关键词： Lesions Fluorescence Dentistry Monitoring Teeth Pediatrics Visualization

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Localization of Deep Inpainting Using High-Pass Fully Convolutional Network

Localization of Deep Inpainting Using High-Pass Fully Convol...

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International Conference on Computer Vision (ICCV)

作者： Haodong Li Jiwu Huang Guangdong Key Laboratory of Intelligent Information Processing and Shenzhen Key Laboratory of Media Security National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society China

ISBN: (数字)9781728148038

ISBN: (纸本)9781728148045

Image inpainting has been substantially improved with deep learning in the past years. Deep inpainting can fill image regions with plausible contents, which are not visually apparent. Although inpainting is originally designed to repair images, it can even be used for malicious manipulations, e.g., removal of specific objects. Therefore, it is necessary to identify the presence of inpainting in an image. This paper presents a method to locate the regions manipulated by deep inpainting. The proposed method employs a fully convolutional network that is based on high-pass filtered image residuals. Firstly, we analyze and observe that the inpainted regions are more distinguishable from the untouched ones in the residual domain. Hence, a high-pass pre-filtering module is designed to get image residuals for enhancing inpainting traces. Then, a feature extraction module, which learns discriminative features from image residuals, is built with four concatenated ResNet blocks. The learned feature maps are finally enlarged by an up-sampling module, so that a pixel-wise inpainting localization map is obtained. The whole network is trained end-to-end with a loss addressing the class imbalance. Extensive experimental results evaluated on both synthetic and realistic images subjected to deep inpainting have shown the effectiveness of the proposed method.

关键词： Forensics Feature extraction Convolution Kernel Machine learning Computer vision Probability

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A nonlinear impulsive control system with impulse time windows and un-fixed coefficient of impulsive intensity 6

A nonlinear impulsive control system with impulse time windo...

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6th International Conference on information, Cybernetics, and Computational Social systems, ICCSS 2019

作者： Feng, Yuming Wang, Zitao Liu, Xiaoyu Zhang, Wei Chongqing Engineering Research Center of Internet of Things Intelligent Control Technology Chongqing Three Gorges University Wanzhou Chongqing404100 China School of Computer Science and Engineering Chongqing Three Gorges University Wanzhou Chongqing404100 China Key Laboratory of Intelligent Information Processing and Control Chongqing Three Gorges University Wanzhou Chongqing404100 China School of Three Gorges Artificial Intelligence Chongqing Three Gorges University Wanzhou Chongqing404100 China

ISBN: (纸本)9781728158570

Based on the fact that there always exists error in the impulsive intensity, which leads to the fact that the coefficient is un-fixed, and in the occurrence of the impulses, we propose a new model, which is called nonlinear impulsive control system with impulse time windows and un-fixed coefficient of impulsive intensity. We find sufficient conditions for ensuring its stability by using Lypunov's method. We choose chaotic Lorenz system and Chua's system as numerical examples to show the effectiveness of the results, by employing such method, the systems are controlled. © 2019 IEEE.

关键词： Numerical methods

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A reconfigurable system learning for data classification using parallel processing

A reconfigurable system learning for data classification usi...

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2015 International Conference on Artificial Intelligence, ICAI 2015 - WORLDCOMP 2015

作者： Moreira, E.M. Maciel, C.D. Moreira, E.M. Zanoni, F.S. Federal Institute of Education Science and Technology of São Paulo São João da Boa Vista São Paulo Brazil Signal Processing Laboratory Department of Electrical Engineering University of São Paulo São Carlos São Paulo Brazil Systems Engineering and Information Technology Institute Federal University of Itajubá Itajubá Minas Gerais Brazil

ISBN: (纸本)1601324073

This paper presents a System Learning with a task scheduler, which makes possible the utilization of several classification and validation methods, allowing the distribution of tasks between the module systems. This architecture is structured of such way that the classifications obtained through a specific technique can be reutilized in parallel by the same algorithm or by other techniques, producing new classifications through the refinement of the results achieved and expanding the use in databases with different characteristics. The results demonstrated that through a learning system, the complexity of the analysis of great databases is minimized, allowing to verify basis with different structures and to increase the methods applied in the analysis of each structure. It favors the comparison between the methodologies and provides more reliable results. © 2019 ICAI 2015 - WORLDCOMP 2015. All rights reserved.

关键词： Classification (of information)

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Learning common harmonic waves on stiefel Manifold – A new mathematical approach for brain network analyses

arXiv

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arXiv 2020年

作者： Chen, Jiazhou Han, Guoqiang Cai, Hongmin Yang, Defu Laurient, Paul J. Styner, Martin Wu, Guorong School of Computer Science and Engineering South China University of Technology Guangzhou China Department of Psychiatry University of North Carolina Chapel Hill United States Intelligent Information Processing Laboratory Hangzhou Dianzi University Hangzhou China Department of Radiology Wake Forest School of Medicine United States Department of Computer Science University of North Carolina Chapel Hill United States

Converging evidence shows that disease-relevant brain alterations do not appear in random brain locations, instead, its spatial pattern follows large-scale brain networks. In this context, a powerful network analysis approach with a mathematical foundation is indispensable to understand the mechanism of neuro-pathological events spreading throughout the brain. Indeed, the topology of each brain network is governed by its native harmonic waves, which are a set of orthogonal bases derived from the Eigen-system of the underlying Laplacian matrix. To that end, we propose a novel connectome harmonic analysis framework to provide enhanced mathematical insights by detecting frequency-based alterations relevant to brain disorders. The backbone of our framework is a novel manifold algebra appropriate for inference across harmonic waves that overcomes the limitations of using classic Euclidean operations on irregular data structures. The individual harmonic difference is measured by a set of common harmonic waves learned from a population of individual Eigen-systems, where each native Eigen-system is regarded as a sample drawn from the Stiefel manifold. Specifically, a manifold optimization scheme is tailored to find the common harmonic waves which reside at the center of Stiefel manifold. To that end, the common harmonic waves constitute the new neurobiological bases to understand disease progression. Each harmonic wave exhibits a unique propagation pattern of neuropathological burdens spreading across brain networks. The statistical power of our novel connectome harmonic analysis approach is evaluated by identifying frequency-based alterations relevant to Alzheimer’s disease, where our learning-based manifold approach discovers more significant and reproducible network dysfunction patterns compared to Euclidian methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Harmonic analysis

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ORB-Based Fast Anti-Viewing Image Feature Matching Algorithm

ORB-Based Fast Anti-Viewing Image Feature Matching Algorithm

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2018 Chinese Automation Congress, CAC 2018

作者： Oi, Liangliang Liu, Wei Liu, Ding Crystal Growth Equipment and System Integration Engineering Research Center Laboratory for Complex System Control and Intelligent of Information Processing Xi'An University of Technology Xi'an710048 China

ISBN: (纸本)9781728113128

Aiming at the problem that the original ORB algorithm is difficult to solve the effective matching problem under large angle of view transformation. This paper proposes an image feature matching algorithm based on ORB to resist the change of view according to the principle of ASIFT. The basic idea of the algorithm is as follows, Firstly, the longitude angle and latitude angle caused by the rotation of the camera are simulated, and the simulated image sequence set which is deformed by the change of the optical axis direction of the camera is accurately simulated. Then the feature points are extracted and mapped them into the original image by combining the FAST feature point detection method with the BRISK feature descriptors. Finally, the Brute-Force Matcher and the Random Sampling Consistency (RANSAC) method were used for feature matching and optimization. The experimental results show that the proposed algorithm can solve the problem that the image features cannot be matched under the large change of viewing angle (greater than 60°), and it has a significant improvement in real-time performance compared with ASIFT. © 2018 IEEE.

关键词： Cameras

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Indoor RGB-D object detection with the guidance of hand-held objects

Indoor RGB-D object detection with the guidance of hand-held...

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9th International Conference on Internet Multimedia Computing and Service, ICIMCS 2017

作者： Qiao, Leixian Zhu, Yaohui Li, Runze Min, Weiqing Jiang, Shuqiang Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9789811085291

In this paper, towards intelligent learning like human for robots, we propose a novel framework to detect indoor objects with the guidance of hand-held objects. In this framework, firstly, we leverage the segmentation algorithm to obtain hand-held objects proposals, and propose another segmentation algorithm based on depth information to obtain object proposals in the indoor scene. Second, to solve the problem of the diversity of data distribution between hand-held objects and indoor objects, we employ the unsupervised adaptive deep neural networks to learn adaptive features on the RGB and depth modality. To evaluate the proposed framework, we conduct experiments on the HOD-16 dataset and indoor scene dataset. The experimental results demonstrate that the proposed framework improves the performance of the indoor object detection in RGB, depth and fused modality respectively. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Object detection

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Deep learning for depression recognition with audiovisual cues: A review

arXiv

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arXiv 2021年

作者： He, Lang Niu, Mingyue Tiwari, Prayag Marttinen, Pekka Su, Rui Jiang, Jiewei Guo, Chenguang Wang, Hongyu Ding, Songtao Wang, Zhongmin Pan, Xiaoying Dang, Wei School of Computer Science and Technology Xi'an University of Posts and Telecommunications Shaanxi Xi'an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Shaanxi Xi'an710121 China Beijing100190 China Beijing100049 China Department of Computer Science Aalto University Espoo Finland School of Foreign Languages Northwest University Shaanxi Xi'an China School of Electronic Engineering Xi'an University of Posts and Telecommunications Xi'an China School of Electronics and Information Northwestern Polytechnical University Shaanxi Xi'an China Shaanxi Mental Health Center Shaanxi Xi'an710061 China

With the acceleration of the pace of work and life, people have to face more and more pressure, which increases the possibility of suffering from depression. However, many patients may fail to get a timely diagnosis due to the serious imbalance in the doctor-patient ratio in the world. Promisingly, physiological and psychological studies have indicated some differences in speech and facial expression between patients with depression and healthy individuals. Consequently, to improve current medical care, many scholars have used deep learning to extract a representation of depression cues in audio and video for automatic depression detection. To sort out and summarize these works, this review introduces the databases and describes objective markers for automatic depression estimation (ADE). Furthermore, we review the deep learning methods for automatic depression detection to extract the representation of depression from audio and video. Finally, this paper discusses challenges and promising directions related to automatic diagnosing of depression using deep learning technologies. © 2021, CC BY.

关键词： Diagnosis

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Reaching Non-Negative Edge Consensus of Networked Dynamical systems

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IEEE TRANSACTIONS ON CYBERNETICS 2018年第9期48卷 2712-2722页

作者： Wang, Xiao Ling Su, Housheng Chen, Michael Z. Q. Wang, Xiao Fan Chen, Guanrong College of Automation Nanjing University of Posts and Telecommunications Nanjing China School of Automation Image Processing and Intelligent Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Wuhan China School of Automation Nanjing University of Science and Technology Nanjing China Department of Automation Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Department of Electronic Engineering City University of Hong Kong Hong Kong

In this paper, the problem of non-negative edge consensus of undirected networked linear time-invariant systems is addressed by associating each edge of the network with a state variable, for which a distributed algorithm is constructed. Sufficient conditions referring only to the number of edges are derived for non-negative edge consensus of the networked systems. Subsequently, the linear programming method and a low-gain feedback technique are introduced to simplify the design of the feedback gain matrix for achieving the non-negative edge consensus. It is found that the low-gain feedback technique has a good effect on the non-negative edge consensus of the networked systems subject to input saturation. Numerical simulations are presented to verify the effectiveness of the theoretical results.

关键词： Distributed algorithm line graph low-gain feedback networked system non-negative edge consensus

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