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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"

共 891 条记录，以下是611-620 订阅

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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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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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High-dimensional optimization of color coded apertures for compressive spectral cameras

High-dimensional optimization of color coded apertures for c...

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European signal processing conference (EUSIPCO)

作者： Hoover Rueda Henry Arguello Gonzalo R. Arce Department of Electrical and Computer Enaineerinz University of Delaware Newark DE USA Department of Computer Science Universidad Industrial de Santander Colombia

A spectral image can be regarded as a three-dimensional cube where each pixel is a vector of intensities representing a spectral signature. Compressive spectral imaging (CSI) is a sensing and reconstruction framework, based on the fundamentals of the compressive sensing theory, which focuses on capturing spectral images efficiently, exploiting their highly correlated information by coding its spectral characteristics commonly using a black-and-white, grayscale or recently a color coded aperture. The distribution of the entries of the coded apertures determines the quality of the estimated spectral images. State of the art methods have used random coded apertures, and some optimization procedures have focused on the optimal design of horizontal sections of the coded apertures; however, they do not fully exploit the spatio-spectral correlations within the spectral images. To that end, in this paper, it is proposed a high-dimensional optimization procedure to design color coded apertures for CSI systems, which exploits not only the spectral correlations but also the spatial correlations within an spectral image. Simulations analyzing the conditioning of the sensing matrices, as well as the reconstruction quality of the attained spectral images show the improvement entailed by the proposed method.

关键词： Apertures image color analysis Optimization image coding Optical imaging Optical sensors

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Learning Fast Sparsifying Overcomplete Dictionaries

Learning Fast Sparsifying Overcomplete Dictionaries

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European signal processing conference

作者： Cristian Rusu John Thompson Institute for Digital Communications University of Edinburgh

In this paper we propose a dictionary learning method that builds an overcomplete dictionary that is computationally efficient to manipulate, i.e., sparse approximation algorithms have sub-quadratic computationally complexity. To achieve this we consider two factors (both to be learned from data) in order to design the dictionary: an orthonormal component made up of a fixed number of fast fundamental orthonormal transforms and a sparse component that builds linear combinations of elements from the first, orthonormal component. We show how effective the proposed technique is to encode image data and compare against a previously proposed method from the literature. We expect the current work to contribute to the spread of sparsity and dictionary learning techniques to hardware scenarios where there are hard limits on the computational capabilities and energy consumption of the computer systems.

关键词： Dictionaries Sparse matrices signal processing algorithms Machine learning Transforms Linear programming Approximation algorithms

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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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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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Demo: MPPA® manycore processor towards future ADAS system solutions

Demo: MPPA® manycore processor towards future ADAS system s...

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conference on design and architectures for signal and image processing

作者： Pierre-Edouard Beaucamps Frédéric Blanc Kalray KALRAY Grenoble FRANCE

This document describes a demonstration, proposed at dasip Demo Night session. This demonstration is running on a low-power manycore processor from Kalray, named MPPA ® and is highlighting capabilities of this n... 详细信息

关键词： image processing signal processing signal processing algorithms Filtering algorithms Advanced driver assistance systems Clustering algorithms Real-time systems

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An Advanced Embedded Architecture for Connected Component Analysis in Industrial Applications 20

An Advanced Embedded Architecture for Connected Component An...

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20th conference and Exhibition on design, Automation and Test in Europe (DATE)

作者： Tekleyohannes, Menbere Sadri, Mohammadsadegh Weis, Christian Wehn, Norbert Klein, Martin Siegrist, Michael Univ Kaiserslautern Kaiserslautern Germany Wipotec Wiege & Positioniersyst GmbH Kaiserslautern Germany

ISBN: (纸本)9783981537093

In recent years, connected component analysis (CCA) has become one of the vital image/video processing algorithms due to its wide-range applicability in the field of computer vision. Numerous applications such as pattern recognition, object detection and image segmentation involve connected component analysis. In the context of camera-based inspection systems, CCA plays an important role for quality assurance. State-of-the-art hardware architectures offer high performance implementations of CCA using field programmable gate arrays (FPGAs). However, due to their high memory-demand, most of these implementations inhibit a large resource utilization. In this paper, we propose a hybrid software-hardware architecture of CCA for an industrial application using Xilinx Zynq-7000 All Programmable System on Chip (SoC). By offloading the most resource consuming part of the algorithm to the embedded CPU, we achieved high performance, while reducing the required resources on the FPGA. Our proposed architecture saves more than 30% of on-chip memory (Block RAMs) compared to state-of-the-art hardware architectures without affecting the throughput. Furthermore, due to the embedded CPU, our system provides a versatile and highly flexible feature extraction at run-time without the necessity to reconfigure the FPGA.

关键词： Feature extraction Computer architecture Field programmable gate arrays Hardware Random access memory Throughput Algorithm design and analysis

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design of high secure visual secret sharing scheme for gray scale images

Design of high secure visual secret sharing scheme for gray ...

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International conference on Wireless Communications, signal processing and Networking (WiSPNET)

作者： Javvaji V. K. Ratnam P. Ramana Reddy T. Sreenivasulu Reddy Department of Electronics and Communication Engineering Jawaharlal Nehru Technological University Anantapur Ananthapuramu Andhra Pradesh India Department of Electronics and Communication Engineering Sri Venkateswara University Tirupati Andhra Pradesh India

ISBN: (纸本)9781509044436;9781509044429

In this paper, a (k, n)visual secret sharing scheme using Boolean and shift operations that provides high security to the secret image is designed. An algorithm is proposed to encode the original secret image to generate n share images using simple Boolean XOR and circular shift operations. The secret data cannot be revealed with any k - 1 or less number of share images. The security is provided to the original secret by encrypting this secret with a random image and distinct authentication id used for each share during generation of shares. The size of generated share images is same as that of original image and requires no pixel expansion. The proposed scheme eliminates security issues observed in the related techniques. Experimental results confirm the feasibility and security of the proposed technique.

关键词： Cryptography Visualization image reconstruction Stacking conferences Authentication

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The Application of Algebraic Geometry Theory in Computer image processing

The Application of Algebraic Geometry Theory in Computer Ima...

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International conference on Computer Technology, Electronics and Communication (ICCTEC)

作者： Lixiao Guo Ningbo Dahongying University Ningbo Zhejiang

ISBN: (纸本)9781538657850

This paper introduces a new mathematical tool of geometric algebra in computer vision research problems in geometric algebra as a new mathematical system combining the geometry and algebraic properties. In multidimensional signal vector processing and geometry, geometry modeling and geometric design has outstanding advantages in calculation. Based on the mathematical framework of geometric algebra, this paper focuses on computer vision 3D reconstruction after carrying out some exploratory researches, mainly studies the color image matching, the two views of the unipolar geometry and camera internal parameter calibration.

关键词： Algebra Color image color analysis Geometry Transforms Cameras image reconstruction

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