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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791-800 订阅

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Transform Learning Algorithm Based on the Probability of Representation of signals

Transform Learning Algorithm Based on the Probability of Rep...

引用

European signal processing conference

作者： Gayatri Parthasarathy G. Abhilash Department of Electronics and Communication Engineering National Institute of Technology Calicut Kerala India

Compressed sensing is a signal acquisition scheme that measures signals at sub-Nyquist rate amenable to sparse recovery, with high probability, from a reduced set of measurements. One of the main requirements of compressive sensing is the sparsity of the class of signals of interest in some basis. A method to construct a sparsifying basis for a class of signals using information theoretic measures is proposed in this paper. The algorithm constructs the sparsifying basis from a known non-sparsifying basis by concentrating the probability distribution of the basis in the representation of a class of signals. Simulation studies using speech and image signals confirm that the basis constructed using the proposed method results in an improved sparsity of the signals with thresholded coefficients but without degrading the signal quality.

关键词： Transforms Entropy signal processing algorithms Training Optimization Dictionaries Algorithm design and analysis

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A Highly Efficient and Comprehensive image processing Library for C++-based High-Level Synthesis

A Highly Efficient and Comprehensive Image Processing Librar...

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FSP 2017;Fourth International Workshop on FPGAs for Software Programmers

作者： M. Akif Oezkan Oliver Reiche Frank Hannig Juergen Teich Friedrich-Alexander University Erlangen-Nurnberg (FAU)

ISBN: (纸本)9783800744435

Field Programmable Gate Arrays (FPGAs) are proved to be among the most suitable architectures for image processing applications. However, accelerating algorithms using FPGAs is a time-consuming task and needs expertise. Whereas the recent advancements in High-Level Synthesis (HLS) promise to solve this problem, today's HLS tools require apt hardware descriptions of algorithms to be able to provide favorable implementations. A solution is developing highly parameterizable and optimized HLS libraries for the fundamental image processing components. Another solution is providing a higher level of abstraction in the form of a Domain-Specific Language (DSL) and a corresponding efficient back end for hardware design. In this paper, we provide a highly efficient and parameterizable C++ library for image processing applications, which would be the cornerstone for both approaches. In our library, nodes of a stream-based data flow graph can be described as C++ objects for specified functions, and the whole application can be efficiently parallelized just by defining a global constant as the parallelization factor. Moreover, the key hardware design elements, i. e., line buffers and sliding windows with different border handling patterns, can be utilized individually to ease the design of more complicated applications.

关键词： level of abstraction computer hardware design image processing hue lightness saturation Libraries high level synthesis Field programmable gate arrays data flow graphs Cornerstones Abstraction algorithms

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Multiple-input multiple-output (MIMO) MRI: An efficient pulse design algorithm to combine parallel excitation and parallel imaging

Multiple-input multiple-output (MIMO) MRI: An efficient puls...

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IEEE International conference on Acoustics, Speech and signal processing

作者： Xianglun Mao David J. Love Joseph V. Rispoli Thomas M. Talavage School of Electrical & Computer Engineering Purdue University IN 47907 USA Weldon School of Biomedical Engineering Purdue University IN 47907 USA

ISBN: (纸本)9781509041183

Magnetic resonance imaging (MRI) plays a critical role in medicine today. Over the last few years, there has been an interest in techniques that use multiple coils on one side of the imaging system such as parallel excitation or parallel imaging to improve performance in high-field MRI. In this paper, we explore the use of multiple coils at both the transmit and receive sides for enhanced imaging. We term this a multiple-input multiple-output (MIMO) MRI system. We use tools from the well-developed MIMO communication models and propose a novel radio-frequency (RF) pulse design method for multiple coils. We show that our MIMO MRI techniques can provide an improved signal-to-noise ratio (SNR) in the reconstructed image.

关键词： MRI MIMO parallel excitation parallel imaging RF pulse design parallel excitation multiple input multiple output systems Magnetic Resonance Imaging C7orf49 gene RF pulse multicoil signal to noise ratios design Algorithm

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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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5th ENRI International Workshop on ATM/CNS, EIWAC 2017

5th ENRI International Workshop on ATM/CNS, EIWAC 2017

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5th ENRI International Workshop on ATM/CNS, EIWAC 2017

ISBN: (纸本)9789811370854

The proceedings contain 19 papers. The special focus in this conference is on ATM/CNS. The topics include: GBAS Interoperability and Multi-constellation/Multi-frequency Trials;ADS-B Latency Estimation Technique for Surveillance Performance Assessment;Jamming and Spoofing Protection for ADS-B Mode S Receiver Through Array signal processing;Automatic Detection of Airport Runway Area Based on Super-Pixel PolSAR image Classification;wireless-Optical signal Converter Utilizing Stacked-Patch Antennas and Electro-Optic Substrates;Multiuser 5G Hot-Spots Based on 60Â GHz Leaky-Wave Antennas and WDM Radio-over-Fiber;Millimeter-Wave Imaging for FOD Detection;300-GHz Bistatic Radar System by Radio Over Fiber Network Technology;HF-START: Application in Aid of Radio Communications/Navigation;future Airspace design by Dynamic Sectorization;an Approach for Attribute- and Performance-Based Evaluation of Interdependent Critical Infrastructures;trajectory Mathematical Distance Applied to Airspace Major Flows Extraction;aircraft Safety Analysis Using Generalized Polynomial Chaos;robust Optimal Trajectory Planning Under Uncertain Winds and Convective Risk;optimal Location of Dynamic Military Areas Within Civil Aviation Traffic;Coordinated Validation for SWIM Concept-Oriented Operation to Achieve Interoperability;Development of a Ground Subsystem Prototype for Ground-Based Augmentation System (GBAS) Approach Service Type D (GAST-D) and the Evaluation of Its Performance in a Low Magnetic Latitude Region.

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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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