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International IEEE conference on signal-image Technologies and Internet-Based System

作者： Yangjun Chen Yibin Chen Department Applied Computer Science University of Winnipeg MAN Canada

A graph reachability query, as one of the primary tasks in numerous applications, is to find whether two given data objects, u and v, are related in any way in a large and complex dataset. Formally, the query is about to find if v is reachable from u in a directed graph which is large in size. In this paper, we focus ourselves on building a reachability labeling for large directed graphs, in order to process reachability queries efficiently. A new approach is proposed to compress transitive closure to support reachability checkings. The approach consists of two schemes, called Core-I labeling and Core-ii labeling, respectively. For a graph G with n nodes and e edges, the labeling time of Core-I is bounded by O(n + e + t¿min{b, s}), where b is the number of the leaf nodes of a spanning tree of G, t is the number of non-tree edges (edges that do not appear in the spanning tree) and s is the number of the start nodes of all non-tree edges in G. The space overhead is bounded by O(n + s¿min{b, s}) and the querying time is O(log(min{b, s})). Core-ii needs O(n + e + t¿min{b, s} + d¿s¿logmin{b, s}) labeling time and O(n + d¿s) space, where d is the number of the end nodes of all non-tree edges in G. But the query time is reduced to O(1). Experiments have been performed, showing that our method is promising.

关键词： Labeling Tree graphs Sparse matrices Internet image coding Computer science Application software Query processing

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Fast double-parallel image processing based on FPGA

Fast double-parallel image processing based on FPGA

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IEEE International conference on Vehicular Electronics and Safety (ICVES)

作者： Ye Li Qingming Yao Bin Tian Wencong Xu State Key Laboratory of Intelligent Control and Management of Complex Systems Institute of Automation Chinese Academy and Sciences Beijing China School of Mechanical Electrical and Information Engineering Shandong University Weihai China

Due to FPGA's flexibility and parallelism, it is popular for accelerating image processing. In this paper, a double-parallel architecture based on FPGA has been exploited to speed up median filter and edge detection tasks, which are essential steps during image processing. The double-parallel scheme includes an image-level parallel and an operation-level parallel. The image-level parallel is a high-level parallel which divides one image into different parts and processes them concurrently. The operation-level parallel, which is embedded in each image-level parallel thread, fully explores every parallel part inside the concrete algorithms. The corresponding design is based on a DE2 Development Board which contains a CYCLONE ii FPGA device. Meanwhile, the same task has also been implemented on PC and DSP for performance comparison. Despite the fact that operating frequencies of used PC and DSP are much higher than FPGA's, FPGA costs less time per computed image than both of them. By taking advantage of the double-parallel technique, the speed/frequency ratio of FPGA is 202 times faster than PC and 147 times faster than DSP. Finally, a detailed discussion about different platforms is conducted, which analyzes advantages and disadvantages of used computing platforms. This paper reveals that the proposed double-parallel scheme can dramatically speed up image processing methods even on a low-cost FPGA platform with low frequency and limited resources, which is very meaningful for practical applications.

关键词： image edge detection Field programmable gate arrays Digital signal processing Hardware Kernel Detectors

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Filtering of Noise in Audio/Voice signal

Filtering of Noise in Audio/Voice Signal

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International conference on Contemporary Computing and Informatics (IC3I)

作者： Rishma Thakur Manoj Kumar Pandey Neeraj Gupta Electronics & Communication Engineering M.Tech. Student Amity School of Engineering & Technology Haryana India Electronics & Communication Engineering Assistant Professor Amity School of Engineering & Technology Haryana India

ISBN: (数字)9781538668948

ISBN: (纸本)9781538668955

Filter plays a vital role in electronics and communication system. Filters are used to pass the desired frequency signals and rejects undesired frequency signal. There are several applications where digital filtering techniques are used such as data communication, voice or audio filtering, video processing, image processing and many more. The basic idea behind this paper is to design different types of infinite impulse response digital filters such as Butterworth filter (BWF), Chebyshev type I filter (CSFT-1), Chebyshev type ii filter (CSFT-2) and elliptic filter (EF) and to compare their performance to find out best suitable type for the filtering of voice signal, using LabVIEW simulation.

关键词： iiR filters Finite impulse response filters Cutoff frequency Frequency response Chebyshev approximation Filtering theory

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image Deconvolution and Point-spread Function Reconstruction with STARRED: A wavelet-based Two-channel Method Optimized for Light-curve Extraction

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ASTRONOMICAL JOURNAL 2024年第2期168卷 55页

作者： Millon, Martin Michalewicz, Kevin Dux, Frederic Courbin, Frederic Marshall, Philip J. Stanford Univ Kavli Inst Particle Astrophys & Cosmol Stanford CA 94305 USA Stanford Univ Dept Phys Stanford CA 94305 USA Imperial Coll London Dept Math London SW7 2AZ England European Southern Observ Alonso de Cordova 3107 Vitacura Santiago Chile Ecole Polytech Fed Lausanne EPFL Inst Phys Lab Astrophys Observ Sauverny CH-1290 Versoix Switzerland SLAC Natl Accelerator Lab Menlo Pk CA USA

We present starred, a point-spread function (PSF) reconstruction, two-channel deconvolution, and light-curve extraction method designed for high-precision photometric measurements in imaging time series. An improved resolution of the data is targeted rather than an infinite one, thereby minimizing deconvolution artifacts. In addition, starred performs a joint deconvolution of all available data, accounting for epoch-to-epoch variations of the PSF and decomposing the resulting deconvolved image into a point source and an extended source channel. The output is a high-signal-to-noise-ratio, high-resolution frame combining all data and the photometry of all point sources in the field of view as a function of time. Of note, starred also provides exquisite PSF models for each data frame. We showcase three applications of starred in the context of the imminent LSST survey and of JWST imaging: (i) the extraction of supernovae light curves and the scene representation of their host galaxy;(ii) the extraction of lensed quasar light curves for time-delay cosmography;and (iii) the measurement of the spectral energy distribution of globular clusters in the "Sparkler," a galaxy at redshift z = 1.378 strongly lensed by the galaxy cluster SMACS J0723.3-7327. starred is implemented in jax, leveraging automatic differentiation and graphics processing unit acceleration. This enables the rapid processing of large time-domain data sets, positioning the method as a powerful tool for extracting light curves from the multitude of lensed or unlensed variable and transient objects in the Rubin-LSST data, even when blended with intervening objects.

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Use of different time-domain techniques for metabolite maps reconstruction in /sup 1/H-MRSI

Use of different time-domain techniques for metabolite maps ...

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Annual International conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： M. Sansone M. Cesarelli P. Bifulco M. Bracale G. Tedeschi S. Bonavita F. Di Salle Department of Electronic Engineering and Telecommunications Bioengineering Unit University of Napoli Federico II Naples Italy Institute of Neurological Sciences Second University of Naples Italy Institute of Radiology University of Napoli Federico II Naples Italy

ISBN: (纸本)0780364651

/sup 1/H-MRSI can provide in vivo information about specific metabolites in biological tissues. The main applications of this technique concern brain studies. However, routine clinical applications are still limited by a variety of factors such as the long acquisition time, poor spatial resolution, the presence of undesired signals coming from water and fat, difficulties in data-processing. These problems can be addressed with two different approaches: the design of new excitation pulse sequences, and the improvement of data-processing techniques. In this preliminary study the latter approach has been adopted. Echoes are acquired without any suppression pulses sequence: the undesired signal coming from water is attenuated by means of an opportune processing, using a filtering technique based on SVD. A specific algorithm for peak detection based upon iterative fitting (VARPRO) was developed. Maps relative to the four main metabolites in the brain are reconstructed and presented.

关键词： Time domain analysis image reconstruction signal processing Filtering In vivo Biological tissues Spatial resolution Iterative algorithms Magnetic resonance imaging Pathology

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Blocked Polynomial Deterministic Matrix for Compressed Sensing

Blocked Polynomial Deterministic Matrix for Compressed Sensi...

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International conference on Wireless Communications, Networking and Mobile Computing (WiCom)

作者： Xiaobo Li Ruizhen Zhao Shaohai Hu Institute of Information Science Beijing Jiaotong University Beijing China

Abstract-Compressed Sensing (CS), a new area of signal processing, seeks to reconstruct sparse or compressible signal from a small number of measurements. Mostly, random matrix is used as measurement matrix, such as Gaussian, Bernoulli and Fourier matrices. However, those matrices are difficult to implement in hardware, so deterministic matrices are proposed, such as polynomial deterministic matrix. In this paper, we proposed blocked polynomial deterministic matrix, and proved that it satisfied the Restricted Isometry Property (RIP). Also, the proposed matrix has several advantages in CS applications: (i) The time of constructing matrix is shorter and the space to store the matrix is smaller compared with polynomial deterministic matrix; (ii) It can broaden the range of measurement number. Experimental results demonstrate that the proposed matrix is useful in CS applications.

关键词： Sparse matrices Polynomials Size measurement image reconstruction Matching pursuit algorithms Compressed sensing Symmetric matrices

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Radiated noise sources location based on MVDR near-field focused beamforming

Radiated noise sources location based on MVDR near-field foc...

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IEEE conference on Industrial Electronics and applications (ICIEA)

作者： Jie Shi Bosheng Liu Haiyan Song Longlong Zhao Ming Li College of Underwater Acoustic Engineering Harbin Engineering University 150001 China 91388 army 93 unit Guangdong province Zhanjiang city 524022 China STMicroelectronics 28 Ang Mo Kio Industrial Park II Avenue 3 Singapore 569508

Noise sources image can be used to determine the spatial distribution of the major noise sources. Thereby we can adopt damping and noise-reducing measures pertinently. In the near-field, compared to the conventional beamforming (CBF), focused beamforming should compensates the time-delay difference and the magnitude attenuation by spherical wave rules according to the different curvature radiuses between the source and array elements, and the time-delay difference value is the 2-dimensional function of both bearing and range. In this paper, the MVDR near-field focused beamforming algorithm and wideband MVDR near-field focused beamforming algorithm are presented. The location performance of both single frequency line spectrum noise and wideband noise signal is improved markedly. Because of making use of various spectrum band signal energy fully, we can scale the noise sources spatial distribution more roundly.

关键词： Array signal processing Wideband Graphical models Distribution functions Integrated circuits Noise measurement Arrays

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An object-oriented synthetic aperture radar processor implemented using C++ classes

An object-oriented synthetic aperture radar processor implem...

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IEEE National conference on Aerospace and Electronics (NAECON)

作者： H.D. Maney Norden Systems Inc. Norwalk CT USA

By far, the most popular applications for object-oriented programming (OOP) are those dealing with graphical user interfaces. This is not surprising. An association between a visible element on a display and a computer language object seems natural. In the field of radar signal processing, however, the use of objects is less common. A primary reason for the lack of popularity of OOP for signal processing is the perception that OOP languages generate slower executing code than procedural languages. As OOP languages mature and computers become faster, this becomes less of a concern. The tools of OOP (data encapsulation, inheritance, polymorphism,) can be just as useful to radar signal processor developers as they are to user interface developers. They increase the level of abstraction available to the developer, allowing him or her to concentrate more on the solution to a problem and less on the implementation of it. The tools allow the developer to produce truly reusable code, reducing the amount of debugging needed in later projects. The end result can be higher developer productivity and more reliable code. This paper describes an implementation of a complete synthetic aperture radar (SAR) processor in C++. Raw radar data collected using the Norden AN/APG-76 Multi-mode Radar aboard Norden's Gulfstream ii test aircraft were used as input to the C++ processor. The processor was evaluated for speed and accuracy in comparison with an equivalent FORTRAN processor and the AN/APG-76 processor. This test indicated that the APG-76 processing speed was equivalent to 70 SGI/MIPS R4000 50/100's programmed in FORTRAN or C++. The ease or difficulties encountered in porting the processor to several machine types are documented. The resulting high-resolution radar image is compared with the AN/APG-76 real-time image. The major classes are described. Rationale for class designs is provided and the overall benefits of OOP are evaluated.< >

关键词： Radar signal processing Airborne radar Testing Application software Object oriented programming Graphical user interfaces Computer displays Computer languages signal generators Data encapsulation

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Feature Level Medical image Fusion with Deep Learning

Feature Level Medical Image Fusion with Deep Learning

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Evolutionary Algorithms and Soft Computing Techniques (EASCT), International conference on

作者： R. Prathipa R. Ramadevi Sathyabama Institute of Science and Technology Chennai India Department of Biomedical Engineering Saveetha School of Engineering Chennai India

In recent years, medical image fusion has emerged as a comprehensive analytical method that frequently employs various modification techniques. The objective of multi-modal medical image fusion is to enhance image quality while preserving specific characteristics by combining images from different imag- ing modalities. This field encompasses cutting-edge domains such as image processing, computer vision, pattern recognition, ma- chine learning, and artificial intelligence. Furthermore, medical image fusion has found extensive clinical applications, enabling healthcare professionals to gain a deeper understanding of lesions by integrating medical images from multiple modalities. In this study, we propose the utilization of a Grey Wolf algorithm, which comprises various learning models, to minimize squared error and optimize the process through weighted entropy during image fusion. The successful results are attributed to a hybridization of the Discrete wavelet Transform (DWT) and the Discrete Curvelet Transform (DCT). By employing Principal Component Analysis, Grey Wolf Optimization, and a Recurrent Neural Network, we have developed a novel fusion model for unsupervised categorization known as GWO-RNN. This model is designed to work with multi-modality images that incorporate structural and sparse constraints. The flexibility and precision of image fusion are further enhanced through correlation and covariance analysis using eigenvalues and eigenvectors. Matlab simulations of the proposed workbench are conducted, comparing it against state-of-the-art methods to assess performance metrics, including peak signal-to-noise ratio, mean square error, correlation, and accuracy. The proposed workbench not only meets but surpasses all performance benchmarks while also reducing computational overhead.

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Intelligent storm identification system using a hierarchical neural network

Intelligent storm identification system using a hierarchical...

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Canadian conference on Electrical and Computer Engineering (CCECE)

作者： Langi Ferens Kinsner Kect Sawatzky Department of Electrical and Computer Engineering University of Manitoba Winnipeg MAN Canada Atmospheric Environment Services Environment Canada Winnipeg MAN Canada InfoMagnetics Technologies Winnipeg MAN Canada

The paper presents a system to detect severe storm events from non-Doppler radar data using a hierarchical artificial neural network (HANN). The system incorporates three levels of data processing: (i) dimensionality reduction (by data slicing, fragmentation, and preprocessing), (ii) feature extraction in the form of codebooks, and (iii) pattern recognition and classification. We study various schemes of such a processing structure. In one scheme, the first level processing slices the volumetric radar data into a set of images representing radar echo intensity at constant altitudes and fragments the images into image blocks. The second level processing extracts features from the image blocks using a self-organizing feature map (SOFM) neural network. It results in a set of codebooks, which is used by a back propagation (BP) neural network at the third level processing for classification. We have used a limited set of 22 known storm events for our experiments and system development. Preliminary results show 100% correct classification of the storm set. The scheme has been implemented in an X-windows environment, which has been installed in the Atmospheric Environment Services, Winnipeg, Canada, for field tests.< >

关键词： Neural network applications Self-organizing feature maps Backpropagation Hierarchical systems Feature extraction image classification Pattern recognition Radar imaging/mapping Radar cross sections Knowledge based systems Intelligent systems Geophysical signal processing Weather forecasting

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