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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641-650 订阅

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Network discovery using content and homophily

Network discovery using content and homophily

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International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Steven T. Smith Rajmonda S. Caceres Kenneth D. Senne Molly McMahon Timothy Greer MIT Lincoln Laboratory Lexington MA USA

ISBN: (纸本)9781509041183

A new approach for targeted graph sampling is proposed in which graph sampling and classification occur together, and content-based homophily is exploited to achieve improved classification performance. The application of network discovery of relevant content is considered using an approach that may be generalized to a broad class of vertex properties. The resulting procedure provides the initial step of a graph analytic processing chain whose performance is directly affected by the quality of graph sampling. The performance of the algorithm is measured with real network data and content observed on a social media site. Precision-Recall performance improvements of 30% are demonstrated with this dataset, compared to a baseline approach that does not exploit homophily. Because real-world graphs grow exponentially, this performance improvement may have a significant impact on graph analytic algorithms with sensitivities to the graph sampling quality.

关键词： Inference algorithms Media Social network services Mathematical model Detection algorithms image edge detection Algorithm design and analysis

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Parallel Monte Carlo Search for Hough Transform 22

Parallel Monte Carlo Search for Hough Transform

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22nd International conference on Computing in High Energy and Nuclear Physics, CHEP 2016

作者： Lopes, Raul H. C. Franqueira, Virginia N.L. Reid, Ivan D. Hobson, Peter R. College of Engineering Design and Physical Sciences Brunei University London UxbridgeUB8 3PH United Kingdom College of Engineering and Technology University of Derby DerbyDE22 1GB United Kingdom

We investigate the problem of line detection in digital image processing and in special how state of the art algorithms behave in the presence of noise and whether CPU efficiency can be improved by the combination of a Monte Carlo Tree Search, hierarchical space decomposition, and parallel computing. The starting point of the investigation is the method introduced in 1962 by Paul Hough for detecting lines in binary images. Extended in the 1970s to the detection of space forms, what came to be known as Hough Transform (HT) has been proposed, for example, in the context of track fitting in the LHC ATLAS and CMS projects. The Hough Transform transfers the problem of line detection, for example, into one of optimization of the peak in a vote counting process for cells which contain the possible points of candidate lines. The detection algorithm can be computationally expensive both in the demands made upon the processor and on memory. Additionally, it can have a reduced effectiveness in detection in the presence of noise. Our first contribution consists in an evaluation of the use of a variation of the Radon Transform as a form of improving theeffectiveness of line detection in the presence of noise. Then, parallel algorithms for variations of the Hough Transform and the Radon Transform for line detection are introduced. An algorithm for Parallel Monte Carlo Search applied to line detection is also introduced. Their algorithmic complexities are discussed. Finally, implementations on multi-GPU and multicore architectures are discussed. © Published under licence by IOP Publishing Ltd.

关键词： Hough transforms

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design of a time mode SerDes using differential pulse position modulation (DPPM)

Design of a time mode SerDes using differential pulse positi...

引用

IEEE Canadian conference on Electrical and Computer Engineering

作者： Taha Mehrabi Kaamran Raahemifar Electrical and Computer Engineering Department Ryerson University Toronto Canada

Time mode signal processing (TMSP) is the implementation of analog signal processing functions using the most basic element available, namely, propagation delay. Moreover, a time to digital converter (TDC), an important block of time mode circuits, could be used to convert the time difference between two edges of two signals into a sequence of digital numbers. In this paper, a new time mode SerDes for high data rate chip to chip communication utilizing differential pulse position modulation (DPPM) as the primary method of data encoding was proposed. The proposed design substantiates an improvement in the bandwidth and simplifies the circuit complexity of the currently used serializer de-serializers (SerDes). The complete proposed design was tested in TSMC 65 nm Complementary metal-oxide-semiconductor (CMOS) technology;it achieved a data rate of 5 Gbps running at the input clock frequency of 1.25 GHz.

关键词： Clocks Delays Computer architecture Receivers CMOS technology image edge detection conferences

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Session 5: image processing on multicore plateforms

Session 5: Image processing on multicore plateforms

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conference on design and architectures for signal and image processing

作者： Pierre Langlois Polytechnique Montréal (Canada)

ISBN: (纸本)9781509030859

This session proposes four papers, that, although all related to image processing, offer a great variety of algorithms, application domains and implementation targets. The algorithms include matrix processing, optical flow, the computation of image features with the Histogram of Oriented Gradients and hyperspectral imaging. The implementation targets include Intel processors, ASIPs, FPGAs and a Massively Parallel Processor Array. The session can thus appeal to all researchers interested in the implementation of computationally intensive image processing algorithms on a variety of platforms.

关键词： image processing histograms Optic Flow Multi-core processors massively parallel processors Platform algorithms image processing algorithm Targets image feature

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design and simulation of enhanced 64-bit Vedic multiplier

Design and simulation of enhanced 64-bit Vedic multiplier

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IEEE Jordan conference on Applied Electrical Engineering and Computing Technologies (AEECT)

作者： Syed Zohaib Hassan Naqvi Department of Electronic Engineering University of Engineering & Technology Taxila Pakistan

Immense growth in technology and ever-increasing computational complexities in image and signal processing algorithms requires robust and efficient hardware software co-design methodologies. Multiplication operation forms the core of such extensively used techniques like correlation, convolution filtering etc. and it is one of the major contributing factors for deteriorating the system performance in terms of latency and throughput. This paper presents a design and implementation framework of enhanced multiplier based on prehistoric “Indian Vedic mathematics sutras”. Presented architecture is based on Vedic multiplication. The computation of partial multiplication is performed in parallel manner and further added to generate the result. In this work, a module of single carry save adder for performance enhancement replaces multiple adders. The partial multiplication terms are accustomed by concatenation. The Proposed design is simulated and implemented using Xilinx ISE design Suit 14.5. Comparative analysis demonstrates that our proposed architecture for multiplication produce better results even for higher bits in terms of speed.

关键词： Computer architecture Mathematics Adders conferences Propagation delay Digital signal processing Field programmable gate arrays

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A new multiplicative nonnegative matrix factorization method for unmixing hyperspectral images combined with multispectral data

A new multiplicative nonnegative matrix factorization method...

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European signal processing conference (EUSIPCO)

作者： Yasmine Kheira Benkouider Moussa Sofiane Karoui Yannick De ville Shahram Hosseini Centre des Techniques Spatiales Arzew Algeria Laboratoire Signaux et Images Universite des Sciences et de la Technologie Oran Algeria UPS-OMP CNRS Universite de Toulouse Toulouse France Universite Federale Toulouse Midi-Pyrenees Toulouse Occitanie FR

In these investigations, a novel algorithm is proposed for linearly unmixing hyperspectral images combined with multispectral data. This algorithm, which is used to unmix the considered hyperspectral image, is founded on nonnegative matrix factorization. It minimizes, with new multiplicative update rules, a novel cost function, which includes multispectral data and a spectral degradation model between these data and hyperspectral ones. The considered multispectral variables are also used to initialize the proposed algorithm. Tests, using synthetic data, are carried out to assess the performance of our algorithm and its robustness to spectral variability between the processed data. The obtained results are compared to those of state of the art methods. These tests prove that the proposed algorithm outperforms all other used approaches.

关键词： Hyperspectral imaging signal processing algorithms Algorithm design and analysis Cost function Spatial resolution Data models

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Automatic radar waveform recognition based on time-frequency analysis and convolutional neural network

Automatic radar waveform recognition based on time-frequency...

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IEEE International conference on Acoustics, Speech and signal processing

作者： Chao Wang Jian Wang Xudong Zhang Department of Electronic Engineering Tsinghua University China

ISBN: (纸本)9781509041183

In this paper, we apply the idea of deep learning to radar waveform recognition. Since the frequency variation with time is the most essential distinction among radar signals with different modulation types, we transform one-dimensional radar signals into time-frequency images (TFIs) using time-frequency analysis and design a convolutional neural network to recognize the frequency variation patterns exhibited in TFIs. Furthermore, we analyze the statistical characteristics of the noise in TFIs and introduce a naive approach to reduce its influence on the frequency variation patterns. Simulation results demonstrate the impressive recognition rate under very low SNR conditions and the strong generalization ability of our proposed recognition method.

关键词： radar waveform recognition deep learning convolutional neural network time-frequency image noise reduction Frequency variation Time frequency analysis Noise reduction RADAR signalS Machine Learning modulation type Recognition (Psychology) radar Frequency analysis waveforms low SNR Neural network

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Parallelized Stochastic Gradient Markov Chain Monte Carlo algorithms for non-negative matrix factorization

Parallelized Stochastic Gradient Markov Chain Monte Carlo al...

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International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Umut Şimşekli Alain Durmus Roland Badeau Gaël Richard Éric Moulines A. Taylan Cemgil LTCI Université Paris-Saclay Paris France Center of Applied Mathematics École Polytechnique France Department of Computer Engineering Boğaziçi University İstanbul Turkey

Stochastic Gradient Markov Chain Monte Carlo (SG-MCMC) methods have become popular in modern data analysis problems due to their computational efficiency. Even though they have proved useful for many statistical models, the application of SG-MCMC to non-negative matrix factorization (NMF) models has not yet been extensively explored. In this study, we develop two parallel SG-MCMC algorithms for a broad range of NMF models. We exploit the conditional independence structure of the NMF models and utilize a stratified sub-sampling approach for enabling parallelization. We illustrate the proposed algorithms on an image restoration task and report encouraging results.

关键词： Stochastic processes Computational modeling Algorithm design and analysis Monte Carlo methods Extrapolation Matrix decomposition Probabilistic logic

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Special session 1 automotive parallel computing challenges - architectures, applications and tricks

Special session 1 automotive parallel computing challenges -...

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conference on design and architectures for signal and image processing

作者： Walter Stechele Tomasz Kryjak Lionel Lacassagne Dominique Houzet Martin Danek Technical University of Munich Germany AGH University of Science and Technology Krakow Poland University Pierre and Marie Curie Paris 6 France Grenoble University France daiteq Czech Republic

ISBN: (纸本)9781509030859

The focus of this special session is on computational challenges and solutions related to automotive parallel computing. The five papers cover aspects of machine learning, FPGA-based hardware acceleration, memory optimization, and multi-core systems. Application areas include image and radar processing, as well as AUTOSAR applications.

关键词： PARALLEL processing (COMPUTERS) automotive engineering images imagery (Psychotherapy) ARCHITECTURE Solutions aspect

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Special session 2 Computer vision and image analysis

Special session 2 Computer vision and Image analysis

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conference on design and architectures for signal and image processing

作者： Patrice Delmas Rachel Ababou Auckland University New Zealand St Cyr Coetquidan France

ISBN: (纸本)9781509030859

Computer vision and image analysis have taken advantage of five decades of intense development. The complexity of the algorithms in this field can now benefit from dramatically increasing computational power to offer viable applications going from embedded systems to cloud computing. This special session proposes three papers dealing with the implementation of computer vision and image analysis applications on embedded systems.

关键词： images computer vision image analysis Embedded systems computational power

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