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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781-790 订阅

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FPGA implementation of a wideband Gaussian random number generator with low resource utilization

FPGA implementation of a wideband Gaussian random number gen...

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IEEE signal processing and Communications Applications (SIU)

作者： Cansu Şen Soner Yeşil Mühendislik Grup Başkanlığı Aselsan A.Ş. Ankara Türkiye

This work presents a low-complexity hardware implementation of highly accurate wide-band Gaussian noise generator based on the Ziggurat method. Different from the previous works in the literature, the Ziggurat Algorithm is modified such that the computations are done over only three equal regions, yielding important savings in the resource utilization. For the verification of the design, Chi-square Goodness-of-Fit, Anderson Darling, and Kolmogorov-Smirnov tests have been applied together with the bit error rate (BER) measurements on the ZedBoard XC7Z020. Regarding the resource utilization, bandwidth and accuracy requirements, several architectures and their implementation results are presented. Implementation in Xilinx Zynq 7045-3 SoC device resulted in 205 Slices, 14 DSPs and 1-BRAM as for the lowest resource utilization, and 330MHz as for the highest signal bandwidth.

关键词： Field programmable gate arrays Dogs Generators signal to noise ratio Resource management Digital signal processing Hardware

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Background modelling, analysis and implementation for thermographic images

Background modelling, analysis and implementation for thermo...

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Workshops on image processing Theory, Tools and Applications, IPTA

作者： Irida Shallari Qaiser Anwar Muhammad Imran Mattias O'Nils Department of Electronics Design Mid Sweden University Sundsvall Sweden

Background subtraction is one of the fundamental steps in the image-processing pipeline for distinguishing foreground from background. Most of the methods have been investigated with respect to visual images, in which case challenges are different compared to thermal images. Thermal sensors are invariant to light changes and have reduced privacy concerns. We propose the use of a low-pass IIR filter for background modelling in thermographic imagery due to its better performance compared to algorithms such as Mixture of Gaussians and K-nearest neighbour, while reducing memory requirements for implementation in embedded architectures. Based on the analysis of four different image datasets both indoor and outdoor, with and without people presence, the learning rate for the filter is set to 3×10-3 Hz and the proposed model is implemented on an Artix-7 FPGA.

关键词： Adaptation models IIR filters Lighting Analytical models Thermal sensors Computational modeling Memory management

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JOINT FREQUENCY AND PRF AGILITY WAVEFORM design AND signal COHERENT ACCUMULATION AGAINST CROSS-PULSE REPEATER DECEPTION JAMMING

JOINT FREQUENCY AND PRF AGILITY WAVEFORM DESIGN AND SIGNAL C...

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IET International Radar conference 2023, IRC 2023

作者： Zhu, Endi Li, Yachao Wang, Jiadong Ding, Jiabao Wei, Jingyi Ding, Mingyue National Key Laboratory of Radar Signal Processing Xi'an China Academy of Advanced Interdisciplinary Research Xi'an China

ISBN: (纸本)9781839539954

Active deception jamming plays a dangerous role for the moving target detection in modern radar system. The false target generated by active deception jamming is extremely confusing for target identification. Frequency and pulse repetition frequency (PRF) agile waveforms have been widely applied in radar systems to improve anti-jamming capabilities of radar. However, the random phase fluctuation caused by frequency and PRF agility leads to the range-azimuth coupling of echoes, which makes it difficult to estimate motion parameters and achieve target coherent accumulation. In this article, in order to improve the detection performance of moving target in interference environment, we propose a novel joint frequency and PRF agility waveform (JFPAW) with characteristics of equivalent coherent waveform (ECW). In especial, the echo signals are divided into different groups according to the PRF of ECW, and then coarse velocity estimation of the target is performed based on minimum image entropy in the coherent echoes of ECW. After range walk correction in all groups by coarse velocity, an improved nonuniform discrete Fourier transform (NDFT) algorithm for coherent integration of the JFPAW is proposed to effectively detect the moving target in complex electromagnetic environments. © The Institution of Engineering & Technology 2023.

关键词： Discrete Fourier transforms

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Multi-scale spot segmentation with selection of image scales

Multi-scale spot segmentation with selection of image scales

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IEEE International conference on Acoustics, Speech and signal processing

作者： Bertha Mayela Toledo Acosta Antoine Basset Patrick Bouthemy Charles Kervrann Inria Centre Rennes - Bretagne Atlantique France

ISBN: (纸本)9781509041183

Detecting spot-like objects of different sizes in images is needed in many applications. Multiple image scales must then be handled for reliable spot segmentation. We define an original criterion based on the a contrario approach and the LoG scale-space framework to automatically select the meaningful scales. We then design a coarse-to-fine multi-scale spot segmentation scheme involving a locally adaptive thresholding across scales, to come up with the final map of segmented spots. We report experimental results on simulated and real images of different types, and we demonstrate that our method outperforms other existing methods.

关键词： Multi-scale spot detection automatic scale selection a contrario approach object segmentation Object segmentation images Spot Spots Dermal denticles spot fish imagery (Psychotherapy) BALANCES multi-scale segmentation Segmentation image scale simulating

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design of Orthonormal Haar-like Features for Fast Pattern Matching

Design of Orthonormal Haar-like Features for Fast Pattern Ma...

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European signal processing conference

作者： Izumi Ito Karen Egiazarian School of Engineering Tokyo Institute of Technology Tokyo Japan Signal Processing Laboratory Tampere University of Technology Tampere Finland

The goal of pattern matching is to find small parts of an image that are similar to a given template. Matching in transform-domain (such as Haar, Walsh-Hadamard, etc.) is more efficient that matching in the spatial domain. However, it has a limitation: the template size is restricted to be a power of two to apply fast computational algorithms of transforms. In this paper, fast pattern matching method based on orthonormal tree-structured Haar transform (OTSHT) is proposed. It allows to overcome the above-mentioned limitation of the template size. Two types of tree structures are considered in this paper: balanced tree and logarithmic tree. It is demonstrated that the proposed method with the balanced tree structure is computationally more efficient.

关键词： Built-in self-test Pattern matching Transforms Strips Binary trees signal processing

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Directional graph weight prediction for image compression

Directional graph weight prediction for image compression

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IEEE International conference on Acoustics, Speech and signal processing

作者： Francesco Verdoja Marco Grangetto Universita degli Studi di Torino Dept. of Computer Science Italy

ISBN: (纸本)9781509041183

Graph-based models have recently attracted attention for their potential to enhance transform coding image compression thanks to their capability to efficiently represent discontinuities. Graph transform gets closer to the optimal KLT by using weights that represent inter-pixel correlations but the extra cost to provide such weights can overwhelm the gain, especially in the case of natural images rich of details. In this paper we provide a novel idea to make graph transform adaptive to the actual image content, avoiding the need to encode the graph weights as side information. We show that an approach similar to spatial prediction can be used to effectively predict graph weights in place of pixels;in particular, we propose the design of directional graph weight prediction modes and show the resulting coding gain. The proposed approach can be used jointly with other graph based intra prediction methods to further enhance compression. Our comparative experimental analysis, carried out with a fully fledged still image coding prototype, shows that we are able to achieve significant coding gains.

关键词： image compression graph transform directional prediction image compression Data compression coding gain Line graph

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Loop Overhead Reduction Techniques for Coarse Grained Reconfigurable architectures

Loop Overhead Reduction Techniques for Coarse Grained Reconf...

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Euromicro Symposium on Digital System design

作者： Kanishkan Vadivel Mark Wijtvliet Roel Jordans Henk Corporaal Department of Electrical Engineering Eindhoven University of Technology Eindhoven The Netherlands Department of Astrophysics/IMAPP Radboud University Nijmegen The Netherlands

Due to their flexibility and high performance, Coarse Grained Reconfigurable Array (CGRA) are a topic of increasing research interest. However, CGRAs also have the potential to achieve very high energy efficiency in comparison to other reconfigurable architectures when hardware optimizations are applied. Some of these optimizations are common for more traditional processors but can also lead to large efficiency gains for reconfigurable architectures. This paper investigates three hardware based loop optimization techniques that can significantly improve the energy efficiency of CGRAs. The three techniques are evaluated on processing kernels from the image processing domain as well as an industrial computer vision application. Energy consumption and area estimates are obtained using a CGRA synthesized with a commercial 40nm library. For the three applied techniques (zero-overhead loop accelerator, single-cycle loop support, and loop buffers) the simulation results show overall energy gains of 6.8% for zero-overhead loop support, 13.2% for ZOLA combined with single-cycle loop support and 18.3% for a combination of all optimizations.

关键词： Program processors Computer architecture Hardware Schedules Optimization Field programmable gate arrays Kernel

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Transform Learning Algorithm Based on the Probability of Representation of signals

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

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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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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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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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