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检索条件"任意字段=2017 Conference on Design and Architectures for Signal and Image Processing, DASIP 2017"

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design and implementation of a novel human-machine interactive healthcare system for visual reproduction test

Design and implementation of a novel human-machine interacti...

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2nd IET International conference on Biomedical image and signal processing (ICBISP 2017)

作者： Anran Qi Wanjin Li Anna Zheng Linmi Tao Beijing University of Posts and Telecommunications Beijing Beijing CN University of Michigan Ann Arbor MI US Tsinghua University Beijing Beijing CN

ISBN: (纸本)9781785613197

Cognitive impairment is a noteworthy problem in the healthcare domain due to an aging population. The existing methods to assess mild cognitive impairment such as Visual Reproduction experiment is via paper and pen, and the results are read and diagnosed by doctors. This classic method only collects static characteristics of visual memory and loses details of human behavior in testing. In this paper, we design a novel interactive system coupled with a digital pen and paper. The system records the dynamics information of drawing behavior during the experiments without changing the consistence of tester experience. Thus these dynamic data can be used to show a correlation with the quality of test completion based on the previous Visual Reproduction research. Meanwhile, functions such as real-time recording and offline management highly improve the efficiency and accuracy of medical judgment. Additionally, new matrixes obtained by calculating dynamic data may be important consideration for future phenotype and human behavior association studies. This human-interactive system reveals the dynamic information which can assist speculation of quality of drawing scripts and also can benefit medical researchers on effective evaluation of the Visual Reproduction experiment results.

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Software design of DC insulator surface pollution analysis device

Software design of DC insulator surface pollution analysis d...

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2017 EPTC Power Transmission and Transformation Technology conference

作者： Yang Zhang Ran Jia Hui Liu Chao Zhou Rong Liu State Grid Shan Dong Electric Power Research Institute Jinan China

ISBN: (纸本)9781510872738

Surface pollution monitoring and evaluation of DC line insulators is an important ways to improve the pertinence and efficiency of anti-pollution flashover measures. The design and development of software for surface pollution analysis of DC insulators based on the synergetic evaluation of surface leakage current and visible light images are presented. Through the collection, processing and optimization comparison of the visible light image signal and the DC leakage current signal of the DC insulator surface, the pollution detection and pollution flashover warning of the insulators on the DC line are realized.

关键词： DC INSULATOR POLLUTION ANALYSIS SOFTWARE LEAKAGE CURRENT image RECOGNITION computer software insulators image recognition leakage currents direct current surface leakage current pollution detection and control Surfaces

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Good features to track for RGBD images

Good features to track for RGBD images

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IEEE International conference on Acoustics, Speech and signal processing

作者： Maxim Karpushin Giuseppe Valenzise Frederic Dufaux LTCI Telecom ParisTech - Universite Paris-Saclay France L2S CNRS - CentraleSupelec - Universite Paris-Sud France

ISBN: (纸本)9781509041183

RGBD (texture-plus-depth) image representation enriches traditional 2D content with additional geometrical information, having the potential to improve the performance of many computer vision tasks. In image matching, this has been partially studied by considering how depth maps can help render feature descriptors more distinctive. However, little has been done to design keypoint detection approaches able to leverage the availability of depth information. In this paper, we propose a novel and robust approach for detecting corners from RGBD images. Our method modifies a classical corner detection strategy, based on local second-order moment matrices, by computing derivatives in a coordinate system which reflects the local properties of object surfaces. Our results demonstrate a higher stability to out-of-plane rotations of the proposed RGBD corner detector both in terms of feature repeatability and in a visual odometry application.

关键词： RGBD texture+depth corner detection keypoint extraction local features Corner detection images local image feature Coordinate system Plane rotation corner detector computer vision

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A Synthesis Flow for Fast Convolution Unit Based on Molecular Reactions 9

A Synthesis Flow for Fast Convolution Unit Based on Molecula...

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9th International conference on Wireless Communications and signal processing (WCSP)

作者： Zhuang, Yuchen Ge, Lulu Wei, Wei Zhao, Jing You, Xiaohu Zhang, Chuan Lab Efficient Architectures Digital Commun & Sign Nanjing Jiangsu Peoples R China Southeast Univ Natl Mobile Commun Res Lab Nanjing Jiangsu Peoples R China Nanjing Univ State Key Lab Coordinat Chem Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781538620625

Nowadays, molecular computing and artificial intelligence have drawn intensive attentions from both academia and industry. Therefore, people begin to seek possible combination of these two promising areas. In this paper, with the aid of chemical reaction networks (CRNs), a design methodology of realizing the key block of fast convolution unit (FCU) in convolutional neural networks (CNNs) is proposed. In order to address the difficulty when "compiling" a complex traditional circuit into CRNs, this paper devotes itself in offering a systematic method to construct the n-parallel FCUs, where a 3-parallel FCU case is employed as a running example. Theoretical analysis, numerical simulation, and complexity comparison have proved the validity of this method and ensured its convenience, robustness, and efficiency. Though preliminary, the proposed method can be readily employed to design other arithmetic blocks of CNNs. Thanks to the high parallelism of CRNs, it is believed that this method offers another perspective to address the complexity of CNN implementation, especially when the system scale is large.

关键词： Chemical reaction networks (CRNs) convolutional neural networks (CNNs) fast convolution units (FCUs)

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Proceedings of International conference on Recent Advancement on Computer and Communication 1st ed. 2018

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丛书名： Lecture Notes in Networks and Systems

2018年

作者： Basant Tiwari Vivek Tiwari Kinkar Chandra Das Durgesh Kumar Mishra Jagdish C. Bansal

ISBN: (数字)9789811081989

ISBN: (纸本)9789811081972

The book is a compilation of best papers presented at International conference on Recent Advancement in Computer and Communication (ICRAC 2017) organized by IMPLab Research and Innovation Foundation, Bhopal, India. The book covers all aspects of computers and communication techniques including pervasive computing, distributed computing, cloud computing, sensor and adhoc network, image, text and speech processing, pattern recognition and pattern analysis, digital signal processing, digital electronics, telecommunication technologies, robotics, VLSI technologies, embedded system, satellite communication, digital signal processing, and digital communication. The papers included are original research works of experts from industry, government centers and academic institutions; experienced in engineering, design and research.

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Compressive multispectral model for spectrum sensing in cognitive radio networks

Compressive multispectral model for spectrum sensing in cogn...

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European signal processing conference (EUSIPCO)

作者： A. Jeison Marín T. Jose I. Martinez Leonardo Betancur Henry Arguello Universidad Pontificia Bolivariana Medellin CO Universidade Rey Juan Carlos Universidad Pontificia Bolivariana

ISBN: (数字)9780992862671

ISBN: (纸本)9781538607510

Cognitive Radio (CR) is one of the most promising techniques for optimizing the spectrum usage. However, the large amount of data of spectral information that must be processed to identify and assign spectral resources increases the channel assignment times, therefore worsening the quality of service for the devices using the spectrum. Compressive Sensing (CS) is a digital processing technique that allows the reconstruction of sparse or compressible signals using fewer samples than those required traditionally. This paper presents a model that addresses the Spectral Sensing problem in Cognitive Radio using Compressive Sensing as an effective way of decreasing the number of samples required in the sensing process. This model is based on Compressive Spectral Imaging (CSI) architectures where a centralized spectrum manager selects what power data must be delivered by the different wireless devices using binary patterns, and builds a multispectral data cube image with the geographical and spectral data power information. The results show that this multispectral data cube can be built with only a 50% of the samples generated by the devices and, therefore reducing the data traffic dramatically.

关键词： Sensors Mathematical model Data models image coding signal processing Optimization Europe

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Parallelization strategies for fast factorized backprojection SAR on embedded multi-core architectures

Parallelization strategies for fast factorized backprojectio...

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IEEE International conference on Microwaves, Communications, Antennas and Electronic Systems, COMCAS

作者： M. Wielage F. Cholewa C. Riggers P. Pirsch H. Blume Institute of Microelectronic Systems Leibniz Universität Hannover Hannover Germany

ISBN: (纸本)9781538631706

This paper presents parallelization strategies for the implementation of imaging algorithms for synthetic aperture radar (SAR). Great emphasis is placed on time-domain based algorithms, namely the Global Backprojection Algorithm (GBP) and its accelerated version, the Fast Factorized Backprojection Algorithm (FFBP). Multi-core platforms are selected for implementation as some combine good performance results with moderate power consumption. The implemented algorithms support several types of parallelization, as the stages of the algorithms can be handled sequentially or interleaved. For the GBP algorithm three different data distribution schemes are investigated. For the FFBP algorithm a successive stage calculation method is compared with a combined calculation method. The performance is exemplary evaluated on the low cost/energy, yet powerful multi-core platform Odroid-XU4. All parallelization strategies show an almost linear speed-up with the number of used cores. Even though a specific multi-core platform is investigated, the design decisions are applicable for general multi-core architectures.

关键词： signal processing algorithms Apertures Synthetic aperture radar image quality Complexity theory Approximation algorithms Computer architecture

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An Embedded Solution for Multispectral Palmprint Recognition

An Embedded Solution for Multispectral Palmprint Recognition

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European signal processing conference

作者： Chao LI Yannick BENEZETH Keisuke NAKAMURA Randy GOMEZ Fan YANG LE2I UMR6306 CNRS Univ. Bourgogne Franche-Comte Dijon France Chinese Academy of Sciences Institute of Acoustics Beijing Chine Honda Research Institute Japan Japan

Palmprint based identification has attracted much attention in the past decades. In some real-life applications, portable personal authentication systems with high accuracy and speed efficiency are required. This paper presents an embedded palmprint recognition solution based on the multispectral image modality. We first develop an effective recognition algorithm by using partial least squares regression, then a FPGA prototype is implemented and optimized through high-level synthesis technique. The evaluation experiments demonstrate that the proposed system can achieve a higher recognition rate at a lower running cost comparing to the reference implementations.

关键词： signal processing algorithms Optimization Algorithm design and analysis Feature extraction Databases Pipelines

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Multi Denoising Approximate Message Passing for Optimal Recovery with Lower Computational Cost

Multi Denoising Approximate Message Passing for Optimal Reco...

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European signal processing conference

作者： Alessandro Perelli Mike E. Davies Institute for Digital Communications The University of Edinburgh (UK)

An emerging issue in large-scale inverse problems is constituted by the interdependency between computational and recovery performance;in particular in practical application, such as medical imaging, it is crucial to provide high quality estimates given bounds on computational time. While most work in this direction has gone down the lines of improving optimisation schemes, in this paper we are proposing and investigating a different approach based on a multi denoising approximate message passing (MultiD-AMP) framework for Compressive Sensing (CS) image reconstruction which exploits an hierarchy of denoisers by starting with a low fidelity model and then using the estimate as starting point for a higher fidelity models through an iterative reconstruction algorithm. MultiD-AMP achieves lower time complexity and same accuracy compared to using the same most accurate denoiser as in D-AMP. The novelty of our approach is based on exploiting the deterministic state evolution of AMP, which means the predictability of the recovery performances, to design a strategy for selecting the denoiser from a set ordered by both computational complexity and statistical efficiency. We apply the MultiD-AMP framework for image reconstruction given noisy Gaussian random linear measurements. Furthermore, we extend and show the applicability of MultiD-AMP for CS to image reconstruction.

关键词： Switches Noise reduction image reconstruction Noise measurement Training Discrete wavelet transforms Time complexity

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The Joint International Symposium on Artificial Intelligence and Natural Language processing, iSAI-NLP 2017

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The Joint International Symposium on Artificial Intelligence and Natural Language processing, iSAI-NLP 2017

ISBN: (纸本)9783319947020

The proceedings contain 22 papers. The special focus in this conference is on Artificial Intelligence and Natural Language processing. The topics include: Isan Dhamma Characters Segmentation and reading in Thai;ontology-assisted structural design flaw detection of object-oriented software;virtual reality application for animal cruelty education;anti-theft motorcycle system using face recognition by deep learning under concept on Internet of Things;balanced scorecard quality information dashboards model for competitive business advantage;criminal background check program with fingerprint;Jobs analysis for business intelligence skills requirements in the ASEAN region: A text mining study;the model of teenager’s internet usage behavior analysis using data mining;privacy-preserving reputation management in fully decentralized systems: Challenges and opportunities;cloud-based services for cooperative robot learning of 3D object detection and recognition;empirical testing of a technique for assessing prior knowledge in the field of research training;problem content table construction based on extracting Sym-Multi-Word-Co from texts;Unpredictable disaster evacuation guide for weak people by real-time sensor network, SNS and maps;Detection of normal and abnormal ECG signal using ANN;dynamic relation-based analysis of objective interestingness measures in association rules mining;image encryption using cellular neural network and matrix transformation;improved term weighting factors for keyword extraction in hierarchical category structure and Thai text classification;ontology-based classifiers for wikipedia article quality classification;thai Named-Entity Recognition using variational long short-term memory with conditional random field;cardiac arrhythmia classification using Hjorth Descriptors.

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