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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281-290 订阅

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FPGA Implementation of Real Time Video signal processing Using Sobel, Robert, Prewitt and Laplacian Filters 25

FPGA Implementation of Real Time Video Signal Processing Usi...

引用

25th signal processing and Communications Applications conference (SIU)

作者： Onat, Emrah SDT Ankara Turkey

ISBN: (纸本)9781509064946

In this paper, hardware implementation of edge detection at real time video signals using Sobel, Robert, Prewitt and Laplacian filters based on FPGA is explained. Besides, filters are compared in many ways. Edge detection is an elemantary and fundamental tool for image segmentation and feature extraction. Very high speed hardware like FPGA's are used to implement the image and video processing algorithms for improving the performance of processing systems. Algorithms are implemented on the Xilinx Zynq 7000. The video input signals come from a laptop's HDMI interface to FPGA in order to filter and the detected edges arc displayed on a HDMI display screen.

关键词： real time video processing image processing sobel filter robert filter prewitt filter laplacian filter edge detection FPGA

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Application workload model generation methodologies for system-level design exploration

Application workload model generation methodologies for syst...

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conference on design and architectures for signal and image processing

作者： Jukka Saastamoinen Jari Kreku Communication Platforms VTT Technical Research Center of Finland Oulu Finland

As most of the applications of embedded system products are realized in software, the performance estimation of software is crucial for successful system design. Significant part of the functionality of these applications is based on services provided by the underlying software libraries. Often used performance evaluation technique today is the system-level performance simulation of the applications and platforms using abstracted workload and execution platform models. The accuracy of the software performance results is dependent on how closely the application workload model reflects actual software as a whole. This paper presents a methodology which combines compiler based user code workload model generation with workload extraction of pre-compiled libraries, while exploiting an overall approach and execution platform model developed previously. Benefit of the proposed methodology compared to earlier solution is experimented using a set of benchmarks.

关键词： Libraries Load modeling Accuracy XML Kernel Instruction sets

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Collagen image Compression Using the JPEG-based Predictive Lossless Coding Scheme 9

Collagen Image Compression Using the JPEG-based Predictive L...

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9th Annual Summit and conference of the Asia-Pacific-signal-and-Information-processing-Association (APSIPA ASC)

作者： Fu, Szu-Wei Ding, Jian-Jiun Huang, Ying-Wun Hsiao, Ching-Wen Chen, Hsin-Hui Natl Taiwan Univ Grad Inst Commun Engn Taipei 10617 Taiwan

ISBN: (纸本)9781538615423

Collagen second harmonic generation (SHG) images are helpful for the diagnosis of cancer and diabetes. In this paper, we develop the JPEG-based predictive lossless image coding (JPLIC) algorithm to encode SHG images. The main difference between collagen SHG images and other images is that they have very irregular texture and the correlations among adjacent pixels are very low. Therefore, conventional lossless compression algorithms, including JPEG-LS and CALIC, which are based on predicting current pixel values from neighbors, may not be suitable for collagen SHG image compression. Therefore, instead of applying adjacent pixels, the proposed algorithm predicts pixel values by JPEG. After prediction, we use adaptive arithmetic coding together with context modeling to encode the residue. Simulations show that the proposed JPLIC algorithm has better performance than other lossless compression methods for collagen SHG images. Moreover, the proposed algorithm is also suitable for nearly lossless image compression and noise-like image compression.

关键词： image coding Transform coding Prediction algorithms Quantization (signal) Context modeling Algorithm design and analysis Cancer

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Parallelization of an ultrasound reconstruction algorithm for non destructive testing on multicore CPU and GPU

Parallelization of an ultrasound reconstruction algorithm fo...

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conference on design and architectures for signal and image processing

作者： Antoine Pédron Lionel Lacassagne Franck Bimbard Stéphane Le Berre LIST CEA Gif-sur-Yvette France Institut d'Electronique Fondamentale UMR 8622 Université Paris-Sud 11 Orsay France

The CIVA software platform developed by CEA-LIST offers various simulation and data processing modules dedicated to non-destructive testing (NDT). In particular, ultrasonic imaging and reconstruction tools are proposed, in the purpose of localizing echoes and identifying and sizing the detected defects. Because of the complexity of data processed, computation time is now a limitation for the optimal use of available information. In this article, we present performance results on parallelization of one computationally heavy algorithm on general purpose processors (GPP) and graphic processing units (GPU). GPU implementation makes an intensive use of atomic intrinsics. Compared to initial GPP implementation, optimized GPP implementation runs up to ×116 faster and GPU implementation up to ×631. This shows that, even with irregular workloads, combining software optimization and hardware improvements, GPU give high performance.

关键词： Graphics processing unit Instruction sets image reconstruction Software algorithms Kernel Testing Optimization

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Session 4: signal processing on reconfigurable architectures

Session 4: Signal processing on reconfigurable architectures

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conference on design and architectures for signal and image processing

作者： Tomasz Kryjak AGH University of Science and Technology PL

The aim of this session is to present recent advances in signal processing implemented on field programmable reconfigurable arrays (FPGA) and coarse grained reconfigurable arrays (CGRA). In the first paper a hardware architecture of the Soft-output CANcellation (SCAN) algorithm used to decode polar codes is presented. This codes achieve the capacity of various communication channels for infinite code-lengths. The proposed design allows to obtain higher data rate than other solutions, as well as less decoding iterations. In the second paper a real-time FPGA module for automatic audio indexing is presented. It is based on the Mel-Frequency Cepstral Coefficients (MFCC) features. The Xilinx System Generator tool was used for implementation. Experiments proved that the system is able to provide an 80% classification accuracy for 4 music genres. In the last paper an IEEE-802.11a/g orthogonal frequency-division multiplexing receiver block for wireless communication systems is presented. It was implemented on CGRA processor/coprocessor model and synthesized for an FPGA device. The authors report speed-ups from x2.8 to x9.3 over RISC processors solutions.

关键词： Arrays Field programmable gate arrays Array signal processing Hardware Real-time systems Indexing

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Optimal MIMO Array Configuration for Ultrawideband Microwave Imaging 25

Optimal MIMO Array Configuration for Ultrawideband Microwave...

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25th signal processing and Communications Applications conference (SIU)

作者： Kocamis, M. Burak Oktem, Figen S. Orta Dogu Tekn Univ Elekt & Elekt Muhendisligi Bolumu TR-06800 Ankara Turkey

ISBN: (纸本)9781509064946

Wideband microwave imaging systems are recently used in various applications including airport security, surveillance, through-wall imaging and medicine. For the operation of such high-resolution systems, sparse multiple-input-multiple output (MIMO) arrays are of interest to reduce the hardware complexity and cost of conventional planar arrays. In this paper, we present a method for the optimal design of two-dimensional MIMO arrays in near-field imaging. Using a statistical framework, the optimality criterion is defined based on the image reconstruction quality. A modified version of the sequential backward selection algorithm is used to optimize the criterion over all possible locations of desired number of antenna elements. The designs obtained with the algorithm are compared with the commonly used sparse array configurations such as Mills Cross array and curvilinear structure composed array in terms of image reconstruction quality.

关键词： near-field imaging MIMO radar sparse array optimal design image reconstruction

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Exploring the concurrent execution of HEVC intra encoding algorithms for heterogeneous multi core architectures

Exploring the concurrent execution of HEVC intra encoding al...

引用

conference on design and architectures for signal and image processing

作者： Jens Brandenburg Benno Stabernack Video Coding & Analytics Department Heinrich Hertz Institute Berlin Germany

By introducing novel algorithms in the emerging high efficiency video coding (HEVC) standard average bitrate savings of 23% have been achieved in comparison to the H.264/AVC high profile reference encoder for the all intra configuration at the cost of an additional complexity increase. This high algorithmic complexity requires the development and integration of innovative approaches, like different parallelization strategies, heterogeneous designs and/or algorithmic optimizations, resulting in a complex design space. Early design verifications as well as performance evaluations are crucial to realize successful solutions, which meet the functional and non-functional constrains respectively. In this paper we propose a SystemC based heterogeneous multi-core model of an HEVC intra encoder, which is used to explore different design aspects and alternatives. Due to its cycle accurate nature the model is well suited to facilitate various performance evaluations and to drive HW/SW co-optimizations of the explored system, as we will discuss in this paper.

关键词： Encoding Estimation Transforms Prediction algorithms Standards Algorithm design and analysis Optimization

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Millimeter Wave Radar image Denoising with Complex Nonseparable Oversampled Lapped Transform 9

Millimeter Wave Radar Image Denoising with Complex Nonsepara...

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9th Annual Summit and conference of the Asia-Pacific-signal-and-Information-processing-Association (APSIPA ASC)

作者： Nagayama, Satoshi Muramatsu, Shogo Yamada, Hiroyoshi Sugiyama, Yuuichi Niigata Univ Grad Sch Sci & Technol Niigata Japan Niigata Univ Fac Engn Niigata Japan FUJITSU TEN LTD Kobe Hyogo Japan

ISBN: (纸本)9781538615423

In this work, we propose using Complex Nonseparable Oversampled Lapped Transform (CNSOLT) as a sparse representation method for denoising of complex radar image. CNSOLT is an image transform which is an extended version of the existing Nonseparable Oversampled Lapped Transform to the complex number field, and simultaneously satisfies non-separabe, redundant, linear-phase, and compact-support property. The lattice structure is able to constitute a Parseval tight frame with redundancy of rational numbers, and it is possible to design a dictionary with directional atomic images. In this work, we propose an image restoration method combining CNSOLT and an iterative shrinkage/thresholding algorithm (ISTA) modified for complex-valued signals. The performance evaluation is conducted through denoising of complex radar images. We also compare the performance with that of the complex K-SVD as a reference method.

关键词： Dictionaries image restoration Transforms Redundancy Radar imaging Noise reduction

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Fast and efficient signals recovery for deterministic compressive sensing: Applications to biosignals

Fast and efficient signals recovery for deterministic compre...

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conference on design and architectures for signal and image processing

作者： Andrianiaina Ravelomanantsoa Hassan Rabah Amar Rouane Institut Jean Lamour (IJL) UMR7198 Université de Lorraine Vandoeuvre-Les-Nancy France

Compressed sensing is a technique that is suitable for compressing and recovering signals having sparse representations in certain bases. Compressed sensing has been widely used to optimize the measurement process of power and bandwidth constrained systems like wireless body sensor network. The central issues with compressed sensing are mainly the construction of measurement matrices and the development of efficient recovery algorithms. In this paper, we proposed a simple and fast recovery algorithm which performed a thresholding in the discrete cosine transform domain. We validated it by recovering electrocardiogram and electromyogram signals taken from the Phyiobank database. The simulation and experimental results have shown that the proposed recovery algorithm was 25 and 12 times faster than orthogonal matching pursuit and stagewise orthogonal matching pursuit, respectively. In addition, depending on the compression ratio, the signal-to-noise ratio of recovered signals were improved up to 2 dB.

关键词： Discrete cosine transforms Atmospheric measurements Particle measurements Sparse matrices signal processing algorithms Matching pursuit algorithms

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Low-Cost DNN Hardware Accelerator for Wearable, High-Quality Cardiac Arrythmia Detection 31

Low-Cost DNN Hardware Accelerator for Wearable, High-Quality...

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31st IEEE International conference on Application-Specific Systems, architectures and Processors (ASAP)

作者： Loh, Johnson Wen, Jianan Gemmeke, Tobias Rhein Westfal TH Aachen Chair Integrated Digital Syst & Circuit Design Aachen Germany

ISBN: (纸本)9781728171470

This work implements a digital signal processing (DSP) accelerator for ECG signal classification. Targeting the integration into wearable devices for 24/7 monitoring, low energy consumption per classification is a key requirement, while maintaining a high classification accuracy at the same time. Co-optimization on algorithm and hardware level led to an architecture consisting mostly of convolution operations in the processing pipeline. The realized discrete wavelet transform and convolutional neural network (CNN) is utilized for continuous time-sequence classification in a sliding-window approach moving away from sample/batch-based processing typical for CNNs. In contrast to previous hardware realizations in this domain, the proposed design was validated using the benchmark dataset from the demanding CinC challenge 2017. The architecture achieves a competitive 0.781 F1-score with only 5597 trainable parameters reducing the computational complexity of state-of-the-art ECG-DNN software solutions by three orders of magnitude. Synthesis in a 22-nm FDSOI-CMOS technology features 0.783 mu J per solution meeting requirements for edge device operation at high-end classification performance.

关键词： electrocardiogram (ECG) classification low energy per solution digital signal processing (DSP) application-specific integrated circuit (ASIC) discrete wavelet transform (DWT) 1D convolutional neural networks (CNN)

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