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检索条件"任意字段=2021 International Conference on Advanced Algorithms and Signal Image Processing, AASIP 2021"

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Distributed Analysis Dictionary Learning Based on Consensus Constraints

Distributed Analysis Dictionary Learning Based on Consensus ...

引用

IEEE international conference on signal and image processing (ICSIP)

作者： Liu Yang Jing Dong Jie Zhang Xiaoqing Luo Jian Guan College of Electrical Engineering and Control Science Nanjing Tech University Nanjing China College of Electrical and Information Engineering Zhengzhou University of Light Industry Zhengzhou China School of Artificial Intelligence and Computer Science Jiangnan University Wuxi China College of Computer Science and Technology Harbin Engineering University Harbin China

ISBN: (纸本)9781665446006

Distributed dictionary learning is to learn a global dictionary so that all data distributed in a network has sparse representation in the domain of the dictionary. Existing works are based on sparse synthesis model. We consider this problem based on the sparse analysis model, and propose a distributed analysis dictionary learning (ADL) algorithm using consensus constraints. In particular, local dictionaries corresponding to local data at each node are introduced, and the distributed ADL problem is formulated as a minimization problem with consensus constraints on local dictionaries and the global dictionary. An optimization method consisting of a sparse coding stage and a dictionary update stage is then developed. Experimental results have shown that the performance of the proposed algorithm is comparable to centralized ADL algorithms.

关键词： Analytical models Dictionaries image coding conferences Optimization methods Distributed databases Machine learning

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A Novel Dictionary Management Scheme of LZW Compression Algorithm Based on Insignificant Dictionary Area

A Novel Dictionary Management Scheme of LZW Compression Algo...

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IEEE international conference on signal and image processing (ICSIP)

作者： Xiaoyong Cheng Bing Li Jiaxin Wu Wenhui Li Chao Luo Jiayi Su School of Microelectronics Southeast University School of Cyber Science and Engineering Southeast University School of Software Engineering Southeast University Southeast University Shenzhen Research Institute China

ISBN: (纸本)9781665446006

In order to avoid the possible degradation of the compression ratio, the dictionary management scheme is employed when the dictionary of Lempel-Ziv-Welch (LZW) algorithm is filled. Dictionary reset (DR) and least recently used (LRU) are two dictionary management schemes widely used. However, DR trades compression ratio for less time overhead and LRU obtains a high compression ratio while time and memory are sacrificed. This paper proposes a novel insignificant dictionary area LZW (IDA-LZW) which has a high and stable compression ratio while maintaining acceptable compression time and excellent decompression time. The proposed dictionary management scheme called IDA consists of dictionary partition and impact factor (IF) which returns the dictionary area should be cleared for new entries. Three variants of IDA-LZW and LZW with DR and LRU are implemented and show that the proposed scheme achieves the best average compression while consuming reasonable resources. Furthermore, comparisons are made with the results in the paper recently published to prove the proposed scheme is valuable.

关键词： Degradation Dictionaries image coding conferences Memory management Compression algorithms

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Learn to Look Around: Deep Reinforcement Learning Agent for Video Saliency Prediction

Learn to Look Around: Deep Reinforcement Learning Agent for ...

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IEEE Visual Communications and image processing (VCIP)

作者： Yiran Tao Yaosi Hu Zhenzhong Chen Wuhan Univeristy Wuhan China

ISBN: (纸本)9781728173221

In the video saliency prediction task, one of the key issues is the utilization of temporal contextual information of keyframes. In this paper, a deep reinforcement learning agent for video saliency prediction is proposed, designed to look around adjacent frames and adaptively generate a salient contextual window that contains the most correlated information of keyframe for saliency prediction. More specifically, an action set step by step decides whether to expand the window, meanwhile a state set and reward function evaluate the effectiveness of the current window. The deep Q-learning algorithm is followed to train the agent to learn a policy to achieve its goal. The proposed agent can be regarded as plug-and-play which is compatible with generic video saliency prediction models. Experimental results on various datasets demonstrate that our method can achieve an advanced performance.

关键词： Q-learning Visual communication image processing Predictive models Prediction algorithms Task analysis Context modeling

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Optimal Mining on Type Control policy for Cloud Environment

Optimal Mining on Type Control policy for Cloud Environment

引用

IEEE international conference on signal and image processing (ICSIP)

作者： Zhao Zhang Zhi Yang Wenfa Li Xuehui Du Henan Province Key Laboratory of Information Security Zhengzhou China Beijing Union University Beijing China

ISBN: (纸本)9781665446006

In order to strengthen the cloud system’s ability to type control data resources, The information flow control model can more effectively protect the confidentiality and integrity of users' data in the cloud environment, and can prevent system vulnerabilities or attacks by illegal users outside the cloud system. However, in the process of implementing the type control system, too much manpower is often required to formulate rules to divide user security type labels. Unreasonable security type label division will directly affect system security and usability. In order to solve the problem of information flow control security type label distribution in the process of authorization system migration in a scientific way. This chapter proposes a bottom-up information flow access control security type label optimization mining method. The category domain label mining algorithm based on Louvain community discovery algorithm and the secret level mining algorithm based on genetic algorithm are used to obtain the optimal approximate solution to Information flow control security type label optimization mining problem (IFCSLMP). The results show that the proposed scheme can effectively dig out the effective information flow control security type label from the access control matrix.

关键词： image processing Process control Approximation algorithms Control systems Data models Data mining Data communication

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A Hybrid Feature Selection Method Based on Binary Differential Evolution and Feature Subset Correlation for Microarray Data∗

A Hybrid Feature Selection Method Based on Binary Differenti...

引用

2022 IEEE international conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Xie, Weidong Li, Wei Fang, Yushan Chi, Yuhuan Yu, Kun Northeastern University Computer Science and Engineering Shenyang China Northeastern University Key Laboratory of Intelligent Computing in Medical Image Shenyang China Northeastern University Medicine and Bioinformation Engineering Shenyang China

ISBN: (纸本)9781665468190

Obtaining essential genes from microarray data that can diagnose diseases can be very useful for researchers to understand diseases and develop drugs. However, the high computational cost due to the 'curse of dimensionality' and the high redundancy among features limit the application of evolutionary algorithms to the feature selection problem for high-dimensional data. This paper proposes a two-stage hybrid feature selection method to address this problem. In the first stage, a simple and efficient filtering me thod is used to initially filter redundant features, reduce the feature dimensionality, and reduce the search space of the evolutionary algorithm in the second stage. In the second stage, we propose an improved differential evolution algorithm. We redesign the binary quantization of the differential evolution algorithm for the characteristics of microarray data and improve the algorithm's variation process to balance the algorithm's search efficiency and accuracy. In addition, we define the redundancy of feature subsets and add it to the fitness function to reduce the redundancy of the final feature subsets. The proposed method is compared with classical feature selection methods and advanced hybrid feature selection methods on eight publicly available microarray data, and the effectiveness and advancement of the proposed method are demonstrated. © 2022 IEEE.

关键词： Feature Selection

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Reducing the Root-Mean-Square Error at signal Restoration using Discrete and Random Changes in the Sampling Rate for the Compressed Sensing Problem

Reducing the Root-Mean-Square Error at Signal Restoration us...

引用

Siberian conference on Control and Communications (SIBCON)

作者： Olesya A. Kozhemyak Oleg V. Stukach National Research Tomsk Polytechnic University Tomsk Russia National Research University Higher School of Economics Moscow Russia Novosibirsk State Technical University NETI Novosibirsk Russia

The data revolution will continue in the near future and move from centralized big data to "small" datasets. This trend stimulates the emergence not only new machine learning methods but algorithms for processing data at the point of their origin. So the Compressed Sensing Problem must be investigated in some technology fields that produce the data flow for decision making in real time. In the paper, we compare the random and constant frequency deviation and highlight some circumstances where advantages of the random deviation become more obvious. Also, we propose to use the differential transformations aimed to restore a signal form by discrets of the differential spectrum of the received signal. In some cases for the investigated model, this approach has an advantage in the compress of information.

关键词： image coding Machine learning algorithms signal restoration Process control Market research signal reconstruction Real-time systems

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Structure-Enhanced Attentive Learning For Spine Segmentation From Ultrasound Volume Projection images

Structure-Enhanced Attentive Learning For Spine Segmentation...

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IEEE international conference on Acoustics, Speech and signal processing

作者： Rui Zhao Zixun Huang Tianshan Liu Frank H. F. Leung Sai Ho Ling De Yang Timothy Tin-Yan Lee Daniel P.K. Lun Yong-Ping Zheng Kin-Man Lam The Hong Kong Polytechnic University Hong Kong China School of Biomedical Engineering University of Technology Sydney NSW Australia

ISBN: (纸本)9781728176055;9781728176062

Automatic spine segmentation, based on ultrasound volume projection imaging (VPI), is of great value in clinical applications to diagnose scoliosis in teenagers. In this paper, we propose a novel framework to improve the segmentation accuracy on spine images via structure-enhanced attentive learning. Since the spine bones contain strong prior knowledge of their shapes and positions in ultrasound VPI images, we propose to encode this information into the semantic representations in an attentive manner. We first revisit the self-attention mechanism in representation learning, and then present a strategy to introduce the structural knowledge into the key representation in self-attention. By this means, the network explores both the contextual and structural information in the learned features, and consequently improves the segmentation accuracy. We conduct various experiments to demonstrate that our proposed method achieves promising performance on spine image segmentation, which shows great potential in clinical diagnosis.

关键词： image segmentation Ultrasonic imaging Shape conferences Semantics Imaging signal processing algorithms

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Intelligent Traffic Management for Emergency Vehicles using Convolutional Neural Network

Intelligent Traffic Management for Emergency Vehicles using ...

引用

international conference on advanced Computing and Communication Systems (ICACCS)

作者： S. Deepajothi D.Palanival Rajan P. Karthikeyan S. Velliangiri Guru Nanak Institute of Technology Hyderabad CMR Engineering College Hyderabad india Parul University Gujarat india CMR Institute of Technology Hyderabad india

The structure of the road network and the rapid growth of urbanization are becoming increasingly complex. The intersection delay is the main factor that affects the productivity of urban road traffic as the bottleneck of traffic growth. A fair signal control system could help relieve congestion on the highways. Suppose the privilege of preference for the emergency is not assured. In that case, the delay in traffic at the collision may increase, which could hardly indicate the reliable, safe and rapid output as a priority of public transport or any emergency vehicle. We have proposed the Convolutional neural network (CNN) based traffic management for emergency vehicles. CNN model is deployed in the Raspberry-Pi. CNN model will accept the video from the traffic road and take quick decision to allow the emergency vehicles. The proposed method improves accuracy over traditional image processing algorithm and reduces cost.

关键词： Productivity Law enforcement Roads Snow image processing Focusing Delays

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Wind Vector Inversion of ADS-B Data Based on Improved PSO-PF Algorithm

Wind Vector Inversion of ADS-B Data Based on Improved PSO-PF...

引用

international conference on Guidance, Navigation and Control, ICGNC 2022

作者： Chen, Wantong Wu, Duo Ren, Shiyu Diao, Tianru Tianjin Key Laboratory of Intelligent Signal and Image Processing Civil Aviation University of China Tianjin300300 China School of Electronic Information and Automation Civil Aviation University of China Tianjin300300 China

ISBN: (纸本)9789811966125

At present, inversing wind vector from aircraft trajectory captured by automatic dependent surveillance-broadcast (ADS-B) is one of the main solutions to obtain wind field in aviation meteorology. However, some algorithms in this scheme still have some shortcomings, especially the poor performance when the aircraft turns at a small angle. In this paper, the geometric structure of ADS-B data is used to improve particle swarm optimized particle filter algorithm (PSO-PF) to inverse wind vectors in small area of single aircraft data and large area of multi-aircraft data respectively. The mean absolute error of wind estimation under different turning angles is evaluated. Experimental results show that the algorithm is feasible, and it still has a good inversion effect when the aircraft turns at a small angle. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Aircraft

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IoT Based Communication System for Autonomous Electric Vehicles

IoT Based Communication System for Autonomous Electric Vehic...

引用

international conference on signal processing and Communication (ICSC)

作者： Vishwajit Rahatal Pratik More Minesh Salunke Sahil Makeshwar Radhika D. Joshi Pune(COEP) Pune India

ISBN: (纸本)9781665427401

This paper discusses the development of an Internet of Things (IoT)-based communication system for Electric Vehicles (EVs) that utilizes a communication protocol that is bandwidth-efficient, lightweight, and uses the least amount of battery power. This system was designed using the Message Queuing Telemetry Transport (MQTT) protocol, which is a lightweight protocol that consumes very little system power. A Quality of Service (QoS) levels comparison was carried out to determine the suitable QoS level for this system. This paper proposes a Lane Detection System which is an advanced Driver-Assistance System (ADAS) feature, and its parameters, such as radius of curvature, the off-center distance of the vehicle, and curve direction, are calculated and communicated via the communication system. In the Lane Detection System, color selection and edge detection techniques were used. This system can detect lanes in real-time and store and send data to a remote location for vehicle monitoring.

关键词： Protocols Lane detection image edge detection Quality of service signal processing Electric vehicles Real-time systems

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