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A Speech Cryptosystem Using the New Chaotic System with a Capsule-Shaped Equilibrium Curve

Computers, Materials & Continua 2023年第6期75卷 5987-6006页

作者： Mohamad Afendee Mohamed Talal Bonny Aceng Sambas Sundarapandian Vaidyanathan Wafaa Al Nassan Sen Zhang Khaled Obaideen Mustafa Mamat Mohd Kamal Mohd Nawawi Faculty of Informatics and Computing Universiti Sultan Zainal AbidinMalaysia Department of Computer Engineering University of SharjahSharjah26666UAE Department of Mechanical Engineering Universitas Muhammadiyah TasikmalayaIndonesia Research and Development Centre Vel Tech UniversityAvadiChennaiIndia School of Artificial Intelligence and Automationand Key Laboratory of Image Processingand Intelligent Control of Education Ministry of ChinaHuazhong University of Scienceand TechnologyWuhan430074China Institute of Strategic Industrial Decision Modeling School of Quantitative SciencesUniversiti Utara Malaysia06010UUM SintokKedahMalaysia

In recent years,there are numerous studies on chaotic systems with special equilibrium curves having various shapes such as circle,butterfly,heart and *** paper describes a new 3-D chaotic dynamical system with a capsule-shaped equilibrium *** proposed chaotic system has two quadratic,two cubic and two quartic nonlinear *** is noted that the proposed chaotic system has a hidden attractor since it has an infinite number of equilibrium *** is also established that the proposed chaotic system exhibits multi-stability with two coexisting chaotic attractors for the same parameter values but differential initial states.A detailed bifurcation analysis with respect to variations in the system parameters is portrayed for the new chaotic system with capsule equilibrium *** have shown MATlab plots to illustrate the capsule equilibrium curve,phase orbits of the new chaotic system,bifurcation diagrams and *** an engineering application,we have proposed a speech cryptosystem with a numerical algorithm,which is based on our novel 3-D chaotic system with a capsule-shaped equilibrium *** proposed speech cryptosystem follows its security evolution and implementation on Field Programmable Gate Array(FPGA)*** results show that the proposed encryption system utilizes 33%of the FPGA,while the maximum clock frequency is 178.28 MHz.

关键词： Chaos chaotic systems hidden attractors multi-stability bifurcation speech cryptosystem FPGA design

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DIFFERENCE IN UPTAKE PATTERN HETEROGENEITY AND REPRODUCIBILITY OF RADIOMICS FEATURES BETWEEN FAPI AND FDG PET/CT IMAGING OF TUMORS 21

DIFFERENCE IN UPTAKE PATTERN HETEROGENEITY AND REPRODUCIBILI...

引用

21st IEEE International Symposium on Biomedical Imaging (ISBI)

作者： Xu, Tianmeng Huang, Yanchao Ang, Tingting Chu, Shulin Luo, Peiyin Wang, Shunfang Zhang, Yufeng Lu, Lijun Tang, Ganghua Lv, Wenbing Yunnan Univ Sch Inormat Kunming 650504 Yunnan Peoples R China Yunnan Univ Yunnan Key Lab Intelligent Syst & Comp Kunming 650504 Yunnan Peoples R China Southern Med Univ Lab Qual Control & Evaluat Radiopharmaceut Dept Med Nanfang Hosp Guangzhou 510515 Guangdong Peoples R China Southern Med Univ Sch Biomed Engn Guangzhou 510515 Guangdong Peoples R China Southern Med Univ Guangdong Prov Key Lab Med Image Proc Guangzhou 510515 Guangdong Peoples R China

ISBN: (纸本)9798350313345;9798350313338

To investigate the difference in uptake pattern heterogeneity between fluorodeoxyglucose (FDG) and fibroblast activation protein inhibitor (FAPI) PET/CT and the reproducibility of radiomics features with respect to different calculation methods of standardized uptake value (SUV). 210 patients with various tumors were involved. Among the extracted 497 radiomics features, there are only 20 features that exhibited an ICC difference of more than 0.25 between the two tracers when using different SUV calculation methods, revealing the similar influence of SUV calculation methods. In contrast, the ICC values for most texture features were generally lower (0.22-0.39) between FDG and FAPI PET images, indicating a large difference in uptake pattern heterogeneity between the two tracers. Our results demonstrated that the influence of SUV calculation methods on the reproducibility of FDG and FAPI PET features is similar. The uptake pattern heterogeneity characterized by radiomics features between FDG and FAPI PET images is significantly different.

关键词： Fluorodeoxyglucose (FDG) Fibroblast activation protein inhibitor (FAPI) Radiomics Reproducibility Uptake pattern heterogeneity PET/CT

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A Recipe for Scaling up Text-to-Video Generation with Text-free Videos

arXiv

引用

arXiv 2023年

作者： Wang, Xiang Zhang, Shiwei Yuan, Hangjie Qing, Zhiwu Gong, Biao Zhang, Yingya Shen, Yujun Gao, Changxin Sang, Nong Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology China Alibaba Group China Zhejiang University China Ant Group China

Diffusion-based text-to-video generation has witnessed impressive progress in the past year yet still falls behind text-to-image generation. One of the key reasons is the limited scale of publicly available data (e.g., 10M video-text pairs in WebVid10M vs. 5B image-text pairs in LAION), considering the high cost of video captioning. Instead, it could be far easier to collect unlabeled clips from video platforms like YouTube. Motivated by this, we come up with a novel text-to-video generation framework, termed TF-T2V, which can directly learn with text-free videos. The rationale behind is to separate the process of text decoding from that of temporal modeling. To this end, we employ a content branch and a motion branch, which are jointly optimized with weights shared. Following such a pipeline, we study the effect of doubling the scale of training set (i.e., video-only WebVid10M) with some randomly collected text-free videos and are encouraged to observe the performance improvement (FID from 9.67 to 8.19 and FVD from 484 to 441), demonstrating the scalability of Copyright © 2023, The Authors. All rights reserved.

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Csp-Net: Common Spatial Pattern Empowered Neural Networks for Eeg-Based Motor imagery Classification

SSRN

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SSRN 2024年

作者： Jiang, Xue Meng, Lubin Chen, Xinru Xu, Yifan Wu, Dongrui Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen518063 China

Electroencephalogram-based motor imagery (MI) classification is an important paradigm of non-invasive brain-computer interfaces. Common spatial pattern (CSP), which exploits different energy distributions on the scalp while performing different MI tasks, is very popular in MI classification. Convolutional neural networks (CNNs) have also achieved great success, due to their powerful learning capabilities. This paper proposes two CSP-empowered neural networks (CSP-Nets), which integrate knowledge-driven CSP filters with data-driven CNNs to enhance the performance in MI classification. CSP-Net-1 directly adds a CSP layer before a CNN to improve the input discriminability. CSP-Net-2 replaces a convolutional layer in CNN with a CSP layer. The CSP layer parameters in both CSP-Nets are initialized with CSP filters designed from the training data. During training, they can either be kept fixed or optimized using gradient descent. Experiments on four public MI datasets demonstrated that the two CSP-Nets consistently improved over their CNN backbones, in both within-subject and cross-subject classifications. They are particularly useful when the number of training samples is very small. Our work demonstrates the advantage of integrating knowledge-driven traditional machine learning with data-driven deep learning in EEG-based brain-computer interfaces. © 2024, The Authors. All rights reserved.

关键词： Electroencephalography

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Complementary Learning Subnetworks for Parameter-Efficient Class-Incremental Learning

arXiv

引用

arXiv 2023年

作者： Li, Depeng Zeng, Zhigang The School of Artificial Intelligence and Automation The Institute of Artificial Intelligence Huazhong University of Science and Technology China The Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan430074 China

In the scenario of class-incremental learning (CIL), deep neural networks have to adapt their model parameters to non-stationary data distributions, e.g., the emergence of new classes over time. However, CIL models are challenged by the well-known catastrophic forgetting phenomenon. Typical methods such as rehearsal-based ones rely on storing exemplars of old classes to mitigate catastrophic forgetting, which limits real-world applications considering memory resources and privacy issues. In this paper, we propose a novel rehearsal-free CIL approach that learns continually via the synergy between two Complementary Learning Subnetworks. Our approach involves jointly optimizing a plastic CNN feature extractor and an analytical feed-forward classifier. The inaccessibility of historical data is tackled by holistically controlling the parameters of a well-trained model, ensuring that the decision boundary learned fits new classes while retaining recognition of previously learned classes. Specifically, the trainable CNN feature extractor provides task-dependent knowledge separately without interference;and the final classifier integrates task-specific knowledge incrementally for decision-making without forgetting. In each CIL session, it accommodates new tasks by attaching a tiny set of declarative parameters to its backbone, in which only one matrix per task or one vector per class is kept for knowledge retention. Extensive experiments on a variety of task sequences show that our method achieves competitive results against state-of-the-art methods, especially in accuracy gain, memory cost, training efficiency, and task-order robustness. Furthermore, to make the non-growing backbone (i.e., a model with limited network capacity) suffice to train on more incoming tasks, a graceful forgetting implementation on previously learned trivial tasks is empirically investigated. Copyright © 2023, The Authors. All rights reserved.

关键词： Decision making

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Intermittent Sampled Data control for Time-Varying Formation-Containment of the Multiagent System with/without Time Delay

引用

COMPLEXITY 2021年第1期2021卷

作者： Chi, Ming Wang, Xu-Long Dou, Yangming Liu, Zhi-Wei Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Minist Educ Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China

Time-varying formation-containment problems for a second-order multiagent system (SOMAS) are studied via pulse-modulated intermittent control (PMIC) in this paper. A distributed control framework utilizing the neighbors' positions and velocities is designed so that leaders in the multiagent system form a formation, and followers move to the convex hull formed by each leader. Different from the traditional formation-containment problems, this paper applies the PMIC framework, which is more common and more in line with the actual control scenarios. Based on the knowledge of matrix theory, algebraic graph theory, and stability theory, some sufficient conditions are given for the time-varying formation-containment problem of the second-order multiagent system. Some numerical simulations are proposed to verify the effectiveness of the results presented in this paper.

关键词： Multi agent systems

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CSP-Net: Common Spatial Pattern Empowered Neural Networks for EEG-Based Motor imagery Classification

arXiv

引用

arXiv 2024年

作者： Jiang, Xue Meng, Lubin Chen, Xinru Xu, Yifan Wu, Dongrui Key Laboratory of The Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen518063 China

关键词： Convolutional neural networks

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Location and Capacity of Charging Station with Random Spatial-Temporal Distribution of Charging Demands

Location and Capacity of Charging Station with Random Spatia...

引用

IEEE International Conference on Cyber Technology in Automation, control, and intelligent Systems

作者： Man Jiang Zhong Cao Yinsheng Niu Xueyu Zhang Xiao-Kang Liu Qi-Fan Yuan CentralChina Branch of State Grid Corporation of China Wuhan China Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

This paper studies the problem of location and capacity for direct charging (DC) fast charging stations (CS). A new model with the random spatial-temporal distribution of charging demands is proposed to plan both the location and capacity for CSs and is modeled as a nonlinear optimization problem with a comprehensive cost function and multi-variable that can be solved by an improved genetic algorithm. Further, the trip chain of EV users is considered to describe the traveling characteristics based on the origin-destination (OD) matrix, and then the Voronoi diagram is used to divide the service area of each CS. The effectiveness of both the model and the improved genetic algorithm is demonstrated through the final simulation results and comparative analyses.

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Group Sparsity Mixture Model and Its Application on image Denoising

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IEEE TRANSACTIONS ON image processing 2022年 31卷 5677-5690页

作者： Liu, Haosen Li, Laquan Lu, Jiangbo Tan, Shan Huazhong Univ Sci & Technol Minist Educ Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Chongqing Univ Posts & Telecommun Sch Sci Chongqing 400065 Peoples R China SmartMore Corp Ltd Shenzhen 518063 Peoples R China HKUST GZ SmartMore Joint Lab Guangzhou 511455 Peoples R China

Prior learning is a fundamental problem in the field of image processing. In this paper, we conduct a detailed study on (1) how to model and learn the prior of the image patch group, which consists of a group of non-local similar image patches, and (2) how to apply the learned prior to the whole image denoising task. To tackle the first problem, we propose a new prior model named Group Sparsity Mixture Model (GSMM). With the bilateral matrix multiplication, the GSMM can model both the local feature of a single patch and the relation among non-local similar patches, and thus it is very suitable for patch group based prior learning. This is supported by the parameter analysis which demonstrates that the learned GSMM successfully captures the inherent strong sparsity embodied in the image patch group. Besides, as a mixture model, GSMM can be used for patch group classification. This makes the image denoising method based on GSMM capable of processing patch groups flexibly. To tackle the second problem, we propose an efficient and effective patch group based image denoising framework, which is plug-and-play and compatible with any patch group prior model. Using this framework, we construct two versions of GSMM based image denoising methods, both of which outperform the competing methods based on other prior models, e.g., Field of Experts (FoE) and Gaussian Mixture Model (GMM). Also, the better version is competitive with the state-of-the-art model based method WNNM with about x8 faster average running speed.

关键词： Gaussian mixture model group sparsity image denoising nonlocal similarity

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Multimodal Brain-Computer Interfaces: AI-powered Decoding Methodologies

arXiv

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arXiv 2025年

作者： Li, Siyang Wang, Hongbin Chen, Xiaoqing Wu, Dongrui The Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China The Henan Key Laboratory of Brain Science and Brain Computer Interface Technology School of Electrical and Information Engineering Zhengzhou University China

Brain-computer interfaces (BCIs) enable direct communication between the brain and external devices. This review highlights the core decoding algorithms that enable multimodal BCIs, including a dissection of the elements, a unified view of diversified approaches, and a comprehensive analysis of the present state of the field. We emphasize algorithmic advancements in cross-modality mapping, sequential modeling, besides classic multi-modality fusion, illustrating how these novel AI approaches enhance decoding of brain data. The current literature of BCI applications on visual, speech, and affective decoding are comprehensively explored. Looking forward, we draw attention on the impact of emerging architectures like multimodal Transformers, and discuss challenges such as brain data heterogeneity and common errors. This review also serves as a bridge in this interdisciplinary field for experts with neuroscience background and experts that study AI, aiming to provide a comprehensive understanding for AI-powered multimodal BCIs. © 2025, CC BY-NC-ND.

关键词： Brain computer interface

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