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International symposium on Intelligent signal processing and Communication Systems (lSPACS)

作者： Gao, Minjuan Zhang, Xuande ShaanXi Univ Sci & Technol Sch Elect & Control Engn Xian Peoples R China ShaanXi Univ Sci & Technol Shool Elect Informaiton & Artif Intelligence Xian Peoples R China

ISBN: (纸本)9781728130385

These years has witnessed the success of deep learning methods in computer vision. The approximation capabilities of neural networks is partly responsible for these success, and active function is crucial for the approximation capability. Motivated by the success of deep learning, this paper presents an Actived Edge Strength Similarity (AESSIM) based image quality assessment algorithm. Numerical experiments on the public datasets indicates that AESSIM is quite competitive in assessing performance.

关键词： image quality assessment active function edge strength

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An Algebraic Expert System with Neural Network Concepts for Cyber, Big Data and Data Migration 19

An Algebraic Expert System with Neural Network Concepts for ...

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19th ieee International symposium on signal processing and Information Technology (ISSPIT)

作者： Rudd-Orthner, Richard N. M. Mihaylova, Lyudmilla Univ Sheffield Dept Automat Control & Syst Engn Sheffield S Yorkshire England MASS KSA Riyadh Saudi Arabia

ISBN: (纸本)9781728153414

This paper describes a machine assistance approach to grading decisions for values that might be missing or need validation, using a mathematical algebraic form of an Expert System, instead of the traditional textual or logic forms and builds a neural network computational graph structure. This Experts System approach is also structured into a neural network like format of: input, hidden and output layers that provide a structured approach to the knowledge-base organization, this provides a useful abstraction for reuse for data migration applications in big data, Cyber and relational databases. The approach is further enhanced with a Bayesian probability tree approach to grade the confidences of value probabilities, instead of the traditional grading of the rule probabilities, and estimates the most probable value in light of all evidence presented. This is ground work for a Machine Learning (ML) experts system approach in a form that is closer to a Neural Network node structure.

关键词： Artificial intelligence Knowledgebase Expert Systems Neural Network Information Assurance

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The Importance of Space and Time for signal processing in Neuromorphic Agents: The Challenge of Developing Low-Power, Autonomous Agents That Interact With the Environment

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ieee signal processing MAGAZINE 2019年第6期36卷 16-28页

作者： Indiveri, Giacomo Sandamirskaya, Yulia Univ Zurich Inst Neuroinformat Zurich Switzerland Swiss Fed Inst Technol Zurich Switzerland Univ Zurich Zurich Switzerland ERC Brussels Belgium

Artificial neural networks (ANNs) and computational neuroscience models have made tremendous progress, enabling us to achieve impressive results in artificial intelligence applications, such as image recognition, natural language processing, and autonomous driving. Despite this, biological neural systems consume orders of magnitude less energy than today's ANNs and are much more flexible and robust. This adaptivity and efficiency gap is partially explained by the computing substrate of biological neural processing systems that is fundamentally different from the way today?s computers are built. Biological systems use in-memory computing elements operating in a massively parallel way rather than time-multiplexed computing units that are reused in a sequential fashion. Moreover, the activity of biological neurons follows continuous-time dynamics in real, physical time instead of operating on discrete temporal cycles abstracted away from real time.

关键词： Neurons Neuromorphics Synapses Biological neural networks Program processors Power demand Artificial neural networks Biological system modeling Adaptation models computational neuroscience

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image Semantic Segmentation Based on the GAN Auxiliary Network

Image Semantic Segmentation Based on the GAN Auxiliary Netwo...

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symposium of image, signal processing, and Artificial Vision (STSIVA)

作者： Jinshuo Zhang Zhicheng Wang Songyan Zhang Gang Wei Zhiqiang Xiong Meng Yang CAD Research Center Tongji University Shanghai China China Ship Development and Design Center Wuhan China

ISBN: (纸本)9781665426565

To enhance the performance of existing deep neural networks in semantic segmentation while preserving efficiency at the same time, a semantic segmentation network with the help of GAN (Generative Adversarial Networks) is proposed. The method consists of a generator and a discriminator. The segmentation results obtained from the generator are encoded and then fed into the discriminator to obtain the pixel-wise uncertainty values. Such uncertainty values are taken as weights for the calculation of CEGU (Cross-Entropy with GAN Uncertainty) to help the optimization of the generator. The discriminator is removed after training. Experiment results show that the mean IoU (Intersection over Union) scores of the segmentation results grow by 4.7% and 3.2% respectively on ResNet-50 and ResNet18, after using the GAN auxiliary method along with the CEGU. It shows that such a GAN auxiliary network can significantly improve the performance of basic end-to-end methods with various backbones on the semantic segmentation task, without introducing extra computation cost in the test phase.

关键词： Training image segmentation Uncertainty Costs computational modeling Semantics Generative adversarial networks

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NSST Based MRI-PET/SPECT Color image Fusion Using Local Features Fuzzy Rules and NSML in YIQ Space 19

NSST Based MRI-PET/SPECT Color Image Fusion Using Local Feat...

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19th ieee International symposium on signal processing and Information Technology (ISSPIT)

作者： Ullah, Hikmat Zhao, Yaqin Wu, Longwen Abdalla, Fakheraldin Y. O. Mkindu, Hassan Harbin Inst Technol Sch Elect & Informat Engn Harbin 1500001 Peoples R China

ISBN: (纸本)9781728153414

An improved algorithm for the fusion of anatomical structural MRI (magnetic resonance imaging) and metabolic functional PET (positron emission tomography) or SPECT (single photon emission computed tomography) images is proposed here. First, if any of the source image is in RGB format, it is converted to YIQ color space. Then by taking full advantage of non-subsampled shearlet transform (NSST), both the input images (channel-Y and/or gray-scale) are decomposed into one low frequency subbands (LFS) and several high frequency subbands (HFS). Second, local spatial frequency (SF) and region energy (RE) based fuzzy pixel-rules are applied for the fusion of LFS. Where, the fused HFS coefficient is directly selected by computing and comparing novel sum-modified-Laplacian (NSML) of every HFS, to extract maximum and more useful information. Finally, the required fused image is achieved by inversed NSST and inversed YIQ transform. Experimental results obtained shows that the algorithm significantly achieved maximum feature information from the source images, enhance visual quality and contrast of the fused image, and decrease computational task compared to other state-of-art proposed methods.

关键词： MRI-PET and MRI-SPECT fusion Local-features Fuzzy-rules NSML NSST YIQ

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Evolving Dendritic Neuron Model by Equilibrium Optimizer Algorithm

Evolving Dendritic Neuron Model by Equilibrium Optimizer Alg...

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symposium of image, signal processing, and Artificial Vision (STSIVA)

作者： Chunzhi Hou Jiarui Shi Baohang Zhang Faculty of Engineering University of Toyama Toyama Japan

ISBN: (纸本)9781665426565

In recent years, the role of a single dendritic neural structures with non-linear localisation in computing has attracted a lot of attention from the industry. The dendritic neuron model (DNM) is an approximate logical neuron model based on dendrites, with branches of dendrites corresponding to three distributions in *** model is trained to assort data as needed by mimicking the mechanisms of transmitting information and biological nerves. Traditionally DNM models use error back propagation (BP) to optimise local minimum problems, but also degrade their performance. We now train it using an equilibrium optimizer based on physical phenomena inspired by control volume mass balance. Experimental results due to some real-world classification problems show that the mentioned algorithm can improve the accuracy of the DNM solution.

关键词： Proteins computational modeling Biological system modeling Prediction algorithms Data models Classification algorithms Dendrites (neurons)

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Research on an Two-Channel ACNN-LSTM Model for Financial Text Sentiment Analysis

Research on an Two-Channel ACNN-LSTM Model for Financial Tex...

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symposium of image, signal processing, and Artificial Vision (STSIVA)

作者： Hanxiao Shi Liqiang You Mimi Ren Xiaojun Li School of Management and E-Business Zhejiang Gongshang University Hangzhou China School of Science and Technology Zhejiang Gongshang University Hangzhou College of Commerce Hangzhou China

ISBN: (纸本)9781665426565

This paper proposes a sentiment analysis model based on two-channel attention-driven convolutional neural networks and long short term memory neural networks for financial text. Firstly, this paper uses two different word vector initialization methods to construct classification model by selecting different feature representations and taking full account of the relationship between words. Secondly, this paper adds Attention mechanism based on the context structure to analyze the text to obtain more hidden information. Finally, the experimental results show that our approach is feasible and effective.

关键词： Support vector machines Analytical models Sentiment analysis computational modeling Redundancy Neural networks Feature extraction

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Path Planning for an Omnidirectional Mobile Robot Based on Modified A * Algorithm with Energy Model

Path Planning for an Omnidirectional Mobile Robot Based on M...

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symposium of image, signal processing, and Artificial Vision (STSIVA)

作者： Cong Liu Xiaobin Xu Xinhong Li Zhijie Pan Kai Hu You Shu College of Mechanical & Electrical Engineering Hohai University Changzhou China College of Mechanical and Electrical Engineering Nanjing University of Aeronautics and Astronautics Nanjing China Changzhou Changgong Electronic Technology Co. Ltd Changzhou China Jiangsu Lanling Chemical Group Co. Ltd Changzhou China

ISBN: (纸本)9781665426565

A path planning framework for an omnidirectional mobile robot based on modified A * algorithm with energy model is proposed. The kinematic model of the omnidirectional mobile robot is established, and the energy consumption model of the omnidirectional mobile robot during motion is established based on the detailed derivation of formulas and used to improve the A* algorithm. The derived results show that the extra power consumed in steering movement is only related to the steering angle.

关键词： Energy consumption Friction Conferences computational modeling Wheels Kinematics Path planning

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Network Situation Risk Assessment Based on Vulnerability Correlation Analysis

Network Situation Risk Assessment Based on Vulnerability Cor...

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symposium of image, signal processing, and Artificial Vision (STSIVA)

作者： Xinmeng Nan Ruiqi Chen Hongtao Tian Yupeng Liu Xi'an Modern Control Technology Research Institute Xi’an China China Unicom Xi'an branch Xi’an China Unit 32381 of the PLA Beijing China

ISBN: (纸本)9781665426565

For the question that situation assessment methods for the analysis of existing vulnerabilities are associated with the lack of analysis of vulnerability assessments, which leads to the poor accuracy assessment, the paper presents a method for network vulnerabilities associated with risk assessment situation analysis. The method improves the existing hierarchical network situation assessment, with the system being divided into three levels, which are loopholes at the bottom, host in the middle, and network system at the top. Based on the security risk indices, we calculate the vulnerability, the host, the entire network system risk index, and evaluate and analyze the security posture of the entire network, to solve the problem of inaccurate assessment. The experiments show that the method improves the accuracy of the assessment of network situation assessment greatly.

关键词： Analytical models Correlation Conferences computational modeling Predictive models Network security Risk management

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4.5 BioAIP: A Reconfigurable Biomedical AI Processor with Adaptive Learning for Versatile Intelligent Health Monitoring

4.5 BioAIP: A Reconfigurable Biomedical AI Processor with Ad...

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ieee International Conference on Solid-State Circuits (ISSCC)

作者： Jiahao Liu Zhen Zhu Yong Zhou Ning Wang Guanghai Dai Qingsong Liu Jianbiao Xiao Yuxiang Xie Zirui Zhong Hongduo Liu Liang Chang Jun Zhou University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9781728195490

ISBN: (纸本)9781728195506

Intelligent health monitoring devices automatically detect abnormalities in users' biomedical signals (e.g. arrhythmia from an ECG signal or a seizure from an EEG signal) through signal classification. Compared to conventional machine learning methods, neural-network-based AI classification methods are promising in achieving higher classification accuracy, but with significantly increased computational complexity, posing challenges to real-time performance and low power consumption. AI processors have been designed to accelerate neural networks for general AI applications such as image and voice recognition. They are not suitable for biomedical AI processing, which requires a combination of biomedical and AI processing hardware. In addition, the design redundancy for general AI applications results in large power consumption making it unsuitable for ultra-low-power health monitoring devices. There are also some biomedical AI processors such as ECG/EEG/EMG AI processors. However, they are customized for specific algorithms and tasks, prohibiting algorithm upgrades, limiting their applicability. In addition, prior designs lack adaptive learning to address the patient-to-patient variation issue. In this work, the BioAlP is proposed - a reconfigurable biomedical Al processor with adaptive learning. It has the following key features: 1) A reconfigurable biomedical Al processing architecture with reconfigurable neural network and biomedical processing engines to support versatile biomedical Al processing. 2) An event-driven biomedical Al processing architecture and approximate data compression technique to reduce power consumption. 3) An Al-based adaptive-learning architecture to address patient-to-patient variation. 4) A reconfigurable FIR engine reusing the neural-network engine to reduce the hardware overhead.

关键词： Adaptive learning Program processors Power demand Speech recognition Artificial intelligence Task analysis Monitoring

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