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DNA Strand Displacement Based on Nicking Enzyme for DNA Logic Circuits 1

13th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2018

作者： Wang, Gaiying Wang, Zhiyu Yan, Xiaoshan Liu, Xiangrong School of Information Science and Technology Xiamen University XiamenFujian361005 China Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China School of Automation Huazhong University of Science and Technology WuhanHubei430074 China

ISBN: (数字)9789811328268

ISBN: (纸本)9789811328251

DNA strand displacement is widely used in the construction of DNA molecule computational models. In this work, nicking enzyme is used as the input of the logic calculation model for it can cut one strand of a double-stranded DNA at a specific recognition nucleotide sequences known as a restriction site. Based on this, a variety of logic gates are designed and implemented, and a multi-person voting circuit is constructed. © 2018, Springer Nature Singapore Pte Ltd.

关键词： Enzymes

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Heterogeneous belief formation method based on BP neural network 2

Heterogeneous belief formation method based on BP neural net...

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2nd International Conference on Electronic Information Technology and Computer Engineering, EITCE 2018

作者： Zhuang, Yifan Wang, Hongwei Chen, Xi School of Automation Huazhong University of Science and Technology Wuhan430074 China School of Management Huazhong University of Science and Technology Wuhan430074 China Image Processing and Intelligent Control Key Laboratory of Education Ministry of China Wuhan430074 China

The BDI model has always been the focus of subject modeling research, which includes three kinds of thinking states of the rational subject: Belief, Desire and Intention. Belief is the cognition of agent to the world;it is a collection of environmental information, other agent information, and its own information that the agent has;and it is also the basis of the agent's thinking activity. Due to differences in the individual's living environment and experience, the formation of heterogeneous beliefs is an important issue in the BDI model study. This article divides individual belief set into two parts: knowledge belief and achievable belief. This article proposes an overall framework for the formation of individual heterogeneity beliefs: First, the individual's knowledge experience is modeled, and the empirical knowledge is structured and quantified into binary propositions;then the BP neural network learn and memory propositions of different combinations to form heterogeneous beliefs. Experiments show that this method can simulate the heterogeneity of individual beliefs caused by the individual's own experience, and can realize the belief generation mechanism of gradual information flow, limited attention and heterogeneous priors. © The Authors, published by EDP Sciences, 2018.

关键词： Neural networks

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Harmony-Search and Otsu based System for Coronavirus Disease (COVID-19) Detection using Lung CT Scan images

arXiv

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arXiv 2020年

作者： Rajinikanth, V. Dey, Nilanjan Raj, Alex Noel Joseph Hassanien, Aboul Ella Santosh, K.C. Raja, N. Sri Madhava Department of Electronics and Instrumentation Engineering St. Joseph’s College of Engineering Chennai600119 India Department of Information Technology Techno India College of Technology KolkataWest Bengal700156 India Key Laboratory of Digital Signal and Image Processing of Guangdong Province Department of Electronic Engineering College of Engineering Shantou University Shantou Guangdong China Faculty of Computers and Artificial Intelligence Cairo University Egypt Department of Computer Science University of South Dakota 414 E Clark St VermillionSD57069 United States

Pneumonia is one of the foremost lung diseases and untreated pneumonia will lead to serious threats for all age groups. The proposed work aims to extract and evaluate the Coronavirus disease (COVID-19) caused pneumonia infection in lung using CT scans. We propose an image-assisted system to extract COVID-19 infected sections from lung CT scans (coronal view). It includes following steps: (i) Threshold filter to extract the lung region by eliminating possible artifacts;(ii) image enhancement using Harmony-Search-Optimization and Otsu thresholding;(iii) image segmentation to extract infected region(s);and (iv) Region-of-interest (ROI) extraction (features) from binary image to compute level of severity. The features that are extracted from ROI are then employed to identify the pixel ratio between the lung and infection sections to identify infection level of severity. The primary objective of the tool is to assist the pulmonologist not only to detect but also to help plan treatment process. As a consequence, for mass screening processing, it will help prevent diagnostic burden. Copyright © 2020, The Authors. All rights reserved.

关键词： Coronavirus

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REFUGE2 CHALLENGE: A TREASURE TROVE FOR MULTI-DIMENSION ANALYSIS AND EVALUATION IN GLAUCOMA SCREENING

arXiv

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arXiv 2022年

作者： Fang, Huihui Li, Fei Wu, Junde Fu, Huazhu Sun, Xu Son, Jaemin Yu, Shuang Zhang, Menglu Yuan, Chenglang Bian, Cheng Lei, Baiying Zhao, Benjian Xu, Xinxing Li, Shaohua Fumero, Francisco Sigut, José Almubarak, Haidar Bazi, Yakoub Guo, Yuanhao Zhou, Yating Baid, Ujjwal Innani, Shubham Guo, Tianjiao Yang, Jie Orlando, José Ignacio Bogunović, Hrvoje Zhang, Xiulan Xu, Yanwu The REFUGE2 Challenge Australia State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-Sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science Guangzhou China Intelligent Healthcare Unit Baidu Inc. Beijing China The Institute of High Performance Computing Agency for Science Technology and Research Singapore Yatiris Group PLADEMA Institute CONICET UNICEN Tandil Argentina Christian Doppler Lab for Artificial Intelligence in Retina Department of Ophthalmology and Optometry Medical University of Vienna Vienna Austria VUNO Inc Seoul Korea Republic of Tencent HealthCare Tencent Shenzhen China Computer Vision Institute College of Computer Science and Software Engineering of Shenzhen University Shenzhen China School of Biomedical Engineering Health Science Center Shenzhen University China Xiaohe Healthcare ByteDance Guangdong Guangzhou510000 China School of Biomedical Engineering Shenzhen University China College of Computer Science & Software Engineering Shenzhen University China Department of Computer Science and Systems Engineering Universidad de La Laguna Spain Saudi Electronic University Saudi Arabia King Saud University Saudi Arabia Institute of Automation Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China SGGS Institute of Engineering and Technology India Institute of Medical Robotics Shanghai Jiao Tong University China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China

With the rapid development of artificial intelligence (AI) in medical image processing, deep learning in color fundus photography (CFP) analysis is also evolving. Although there are some open-source, labeled datasets of CFPs in the ophthalmology community, large-scale datasets for screening only have labels of disease categories, and datasets with annotations of fundus structures are usually small in size. In addition, labeling standards are not uniform across datasets, and there is no clear information on the acquisition device. Here we release a multi-annotation, multi-quality, and multi-device color fundus image dataset for glaucoma analysis on an original challenge-Retinal Fundus Glaucoma Challenge 2nd Edition (REFUGE2). The REFUGE2 dataset contains 2000 color fundus images with annotations of glaucoma classification, optic disc/cup segmentation, as well as fovea localization. Meanwhile, the REFUGE2 challenge sets three sub-tasks of automatic glaucoma diagnosis and fundus structure analysis and provides an online evaluation framework. Based on the characteristics of multi-device and multi-quality data, some methods with strong generalizations are provided in the challenge to make the predictions more robust. This shows that REFUGE2 brings attention to the characteristics of real-world multi-domain data, bridging the gap between scientific research and clinical application. © 2022, CC BY-NC-ND.

关键词： Color

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LIGHT YOLO FOR HIGH-SPEED GESTURE RECOGNITION

LIGHT YOLO FOR HIGH-SPEED GESTURE RECOGNITION

引用

IEEE International Conference on image processing

作者： Zihan Ni Jia Chen Nong Sang Changxin Gao Leyuan Liu Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan China

This paper proposes an efficient model named Light YOLO for hand gesture recognition on the embedded platforms. Light YOLO improves accuracy, speed, and model size, in three aspects. To deal with the small scale gestures in practical applications, we strengthen the YOLOv2 with a spatial refinement module to obtain fine-grained features. To accelerate the refined network, we propose a selective-dropout channel pruning approach to prune the redundancy convolution kernels in the network. Moreover, we introduce a dataset for hand gesture recognition in complex scenes. The experimental results on this dataset show that the proposed Light YOLO significantly improve the YOLOv2 network, i.e., accuracy from 96.80% to 98.06%, speed form 40PFS to 125FPS, and size form 250M to 4MB.

关键词： Gesture recognition Feature extraction Detectors Acceleration Taylor series Kernel Cost function

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Multi-Scale YOLOv2 for Hand Detection in Complex Scenes

Multi-Scale YOLOv2 for Hand Detection in Complex Scenes

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International Conference on control, Automation, Robotics and Vision (ICARCV)

作者： Jia Chen Zihan Ni Nong Sang Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan China

This paper presents a model named Multi-Scale YOLOv2 (MS-YOLOv2) for hand detection in complex scenes. The proposed MS-YOLOv2 is implemented by introducing three modules to YOLOv2, including a Multi-Scale Feature Refinement Module to acquire fine-grained features, a Channel Importance Evaluation Module to recalibrate feature channels and a Hard Example Punishment Module to get rid of hand interference areas. Experiment results show that the proposed MS-YOLOv2 makes much performance improvement to YOLOv2, but with little computational complexity gain. On our dataset, the proposed MS-YOLOv2 can achieve 98.2% of AP and 97.9% of AR. Moreover, on the VIVA challenge, the proposed MS-YOLOv2 achieves AP/AR of 85.1%/45.8% at Level-1 and 80.1%/45.9% at Level-2.

关键词： Feature extraction Detectors Object detection Skin Color Gesture recognition

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Deep Learning for Sleep Stage Classification

Deep Learning for Sleep Stage Classification

引用

Chinese Automation Congress (CAC)

作者： Yang Wang Dongrui Wu Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan China

Scoring of sleep stages plays an important role in the diagnosis of sleep-related diseases. Scoring by visual inspection is time-consuming and heavily depends on the experience of experts. Thus, there is an urgent need for an automatic sleep stage classification system. This paper proposes a novel compact convolutional neural network (C-CNN) using only single-channel EEG signal. Compared with traditional machine learning approaches based on hand-engineered features, our approach provides an end-to-end solution that requires almost no prior knowledge and preprocessing while achieving better performance. Experiments on the expanded Sleep-EDF database verified its effectiveness and efficiency. In addition, we notice the issue of class imbalance in sleep stages, and a class-imbalance metric, the balanced classification accuracy (BCA), is introduced. At the cost of a little drop in accuracy, which is still higher than existing classification methods, the introduction of class-imbalance weights can significantly increase the BCA metric and result in a higher recall for each sleep stage. This paper also proposes a recurrent neural network based on the attention mechanism and bidirectional long short-term memory (LSTM), which provides better performance than C-CNN but requires more training time.

关键词： Feature extraction Sleep Electroencephalography Training Convolution Brain modeling Deep learning

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Multi-View broad learning system for primate oculomotor decision decoding

arXiv

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arXiv 2019年

作者： Shi, Zhenhua Chen, Xiaomo Zhao, Changming He, He Stuphorn, Veit Wu, Dongrui Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Zanvyl Krieger Mind and Brain Institute Johns Hopkins University School of Medicine Department of Neurobiology Stanford University StanfordCA United States Department of Neuroscience Johns Hopkins University BaltimoreMD United States

Multi-view learning improves the learning performance by utilizing multi-view data: data collected from multiple sources, or feature sets extracted from the same data source. This approach is suitable for primate brain state decoding using cortical neural signals. This is because the complementary components of simultaneously recorded neural signals, local field potentials (LFPs) and action potentials (spikes), can be treated as two views. In this paper, we extended broad learning system (BLS), a recently proposed wide neural network architecture, from single-view learning to multi-view learning, and validated its performance in monkey oculomotor decision decoding from medial frontal LFPs and spikes. We demonstrated that medial frontal LFPs and spikes in non-human primate do contain complementary information about the oculomotor decision, and that the proposed multi-view BLS is a more effective approach to classify the oculomotor decision, than several classical and state-of-the-art single-view and multi-view learning approaches. Copyright © 2019, The Authors. All rights reserved.

关键词： Electrophysiology

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Enhanced Moth-flame Optimization Based on Cultural Learning and Gaussian Mutation

引用

Journal of Bionic Engineering 2018年第4期15卷 751-763页

作者： Liwu Xu Yuanzheng Li Kaicheng Li Gooi Hoay Beng Zhiqiang Jiang Chao Wang Nian Liu State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical Engineering and Electronics Huazhong University of Science and Technology Wuhan 430074 China School of Automation Ministry of Education Key Laboratory of lmage Processing and Intelligence Control Huazhong University of Science and Technology Wuhan 430074 China Nanyang Technological University Singapore 639798 Singapore School of Hydropower and lnformation Engineering Huazhong University of Science and Technology Wuhan 430074 China China Institute of Water Resources and Hydropower Research Beijing 100038 China State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Beij'ing 102206 China

This paper presents an Enhanced Moth-Flame Optimization （EMFO） technique based on Cultural Learning （CL） and Gaussian Mutation （GM）. The mechanism of CL and the operator of GM are incorporated to the original algorithm of Moth-Flame Optimization （MFO）. CL plays an important role in the inheritance of historical experiences and stimulates moths to obtain information from flames more effectively, which helps MFO enhance its searching ability. Furthermore, in order to overcome the disadvantage of trapping into local optima, the operator of GM is introduced to MFO. This operator acts on the best flame in order to generate several variant ones, which can increase the diversity. The proposed algorithm of EMFO has been comprehensively evaluated on 13 benchmark functions, in comparison with MFO. Simulation results verify that EMFO shows a significant improvement on MFO, in terms of solution quality and algorithmic reliability.

关键词： bioinspired computing moth-flame optimization cultural learning Gaussian mutation benchmark functions

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$H_{\infty}$ consensus control of fractional-order multi-agent systems over the directed graph

$H_{\infty}$ consensus control of fractional-order multi-age...

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Chinese control Conference (CCC)

作者： Lin Chen Wen-Sen Wang Hao Wu Yan Lei Jiang-Wen Xiao Key Laboratory of Image Processing and Intelligent Control Ministry of Education State grid shanxi electric power research institute

In this paper, the H ∞ consensus of fractional-order multi-agent systems with directed communication graph is investigated. It's the first time to introduce the H ∞ control to investigate the consensus problem of the fractional-order multi-agent systems. In view of Mittag-Leffler stability theory and fractional Lyapunov directed method, a sufficient condition is presented to guarantee all the agents reach consensus with the desired H ∞ performance. Finally, the results are verified by several numerical simulations.

关键词： Multi-agent systems control systems Laplace equations Eigenvalues and eigenfunctions Power system stability Stability analysis Graph theory

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