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18th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2017

作者： Shankar, Subramanian Shiva Pinxing, Lin Herkersdorf, Andreas Wild, Thomas Intel Technology Asia Pte. Ltd. Singapore Institute for Integrated Systems Technische Universität München Germany

ISBN: (纸本)9781538631515

Member State Bitmask Technique (MSBT) is a hardware oriented transition compression technique which can compress the redundant transitions in a finite automaton. While the compressed automaton is stored in on-chip memories;a dedicated hardware accelerator performs signature matching by comparing the network streams against the compressed automaton at line rate. The MSBT consists of three functional steps which include the intra-state transition compression, state grouping and the inter-state transition compression. The state grouping algorithm which is currently used in MSBT is not compression aware and results in sub-optimal transition compression. To address this weakness, a compression aware Divide and Conquer state grouping method is proposed in this paper, which can efficiently group states that improves the transition compression in MSBT. Experimental evaluation of the proposed state grouping method, results in a reduced on-chip memory usage of the order of 10-30%. The reduction in the memory usage allows to accommodate more signatures in on-chip memories and perform signature matching with them at line rate. © 2017 IEEE.

关键词： Automata Clustering algorithms Hardware System-on-chip Payloads compression algorithms Complexity theory system-on-chip compression algorithms Automata Payload Martius scarlet blue trichrome Clustering algorithms Complexity theory Computer hardware bit map line rate Divide and conquer compression finite state machines

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MANIPULATION AND COMPOSITING OF MC-DCT COMPRESSED VIDEO

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IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 1995年第1期13卷 1-11页

作者： CHANG, SF MESSERSCHMITT, DG COLUMBIA UNIV CTR TELECOMMUN RESNEW YORKNY 10027 UNIV CALIF BERKELEY DEPT EECSBERKELEYCA 94720

Many advanced video applications require manipulations of compressed video signals. Popular video manipulation functions include overlap (opaque or semitransparent), translation, scaling, linear filtering, rotation, and pixel multiplication. In this paper, we propose algorithms to manipulate compressed video in the compressed domain. Specifically, we focus on compression algorithms using the discrete cosine transform (DCT) with or without motion compensation (MC). compression systems of such kind include JPEG, motion JPEG, MPEG, and H.261. We derive a complete set of algorithms for all aforementioned manipulation functions in the transform domain, in which video signals are represented by quantized transform coefficients. Due to a much lower data rate and the elimination of decompression/compression conversion, the transform-domain approach has great potential in reducing the computational complexity. The actual computational speedup depends on the specific manipulation functions and the compression characteristics of the input video, such as the compression rate and the nonzero motion vector percentage. The proposed techniques can be applied to general orthogonal transforms, such as discrete trigonometric transform. For compression systems incorporating MC (such as MPEG), we propose a new decoding algorithm to reconstruct the video in the transform domain and then perform the desired manipulations in the transform domain. The same technique can be applied to efficient video transcoding (e.g., from MPEG to JPEG) with minimal decoding.

关键词： Video compression Transform coding Discrete transforms Discrete cosine transforms Decoding Maximum likelihood detection compression algorithms Motion compensation Computational complexity Transcoding

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Dynamic-CoMPI: dynamic optimization techniques for MPI parallel applications

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JOURNAL OF SUPERCOMPUTING 2012年第1期59卷 361-391页

作者： Filgueira, Rosa Carretero, Jesus Singh, David E. Calderon, Alejandro Nunez, Alberto Univ Carlos III Madrid Dept Comp Sci Madrid Spain

This work presents an optimization of MPI communications, called Dynamic-CoMPI, which uses two techniques in order to reduce the impact of communications and non-contiguous I/O requests in parallel applications. These techniques are independent of the application and complementaries to each other. The first technique is an optimization of the Two-Phase collective I/O technique from ROMIO, called Locality aware strategy for Two-Phase I/O (LA-Two-Phase I/O). In order to increase the locality of the file accesses, LA-Two-Phase I/O employs the Linear Assignment Problem (LAP) for finding an optimal I/O data communication schedule. The main purpose of this technique is the reduction of the number of communications involved in the I/O collective operation. The second technique, called Adaptive-CoMPI, is based on run-time compression of MPI messages exchanged by applications. Both techniques can be applied on every application, because both of them are transparent for the users. Dynamic-CoMPI has been validated by using several MPI benchmarks and real HPC applications. The results show that, for many of the considered scenarios, important reductions in the execution time are achieved by reducing the size and the number of the messages. Additional benefits of our approach are the reduction of the total communication time and the network contention, thus enhancing, not only performance, but also scalability.

关键词： MPI library Parallel techniques Clusters architectures compression algorithms Collective I/O Adaptive systems Heuristics

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Sparsity Order Estimation for Compressed Sensing System Using Sparse Binary Sensing Matrix

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IEEE ACCESS 2022年 10卷 33370-33392页

作者： Thiruppathirajan, S. Narayanan, Lakshmi R. Sreelal, S. Manoj, B. S. Indian Space Res Org ISRO Vikram Sarabhai Space Ctr VSSC Thiruvananthapuram 695022 Kerala India Indian Inst Space Sci & Technol IIST Thiruvananthapuram 695547 Kerala India

We present a composite Compressed Sensing system for the acquisition and recovery of compressible signals, where a sparse Binary Sensing Matrix aids Sparsity Order Estimation, and a Gaussian Sensing Matrix aids reconstruction. The Binary Sensing Matrix is deterministic and is adapted according to the varying nature of the sparsity order. We estimate the sparsity order by exploiting the sparse structure of the Binary Sensing Matrix and the statistics of the obtained measurements. We refine the estimates of the sparsity order using a Kalman filter with a discrete Markov model that characterizes the sparsity order variation. A Binary Sensing Matrix-Aided Orthogonal Matching Pursuit is developed for faster recovery of compressible signals. Simulation results on real-world and synthetic data demonstrate the merits of the proposed sparsity order estimation and recovery methods compared to other existing methods. Our proposed methods are practical and recover compressible signals at least 25% faster than the existing methods.

关键词： Sensors Sparse matrices Matching pursuit algorithms Estimation Size measurement Noise measurement Volume measurement Compressed sensing compression algorithms discrete cosine transforms greedy algorithms Kalman filters Markov processes maximum likelihood estimation signal reconstruction sparse matrices vibration measurement

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Normal-Based Bas-Relief Modelling via Near-Lighting Photometric Stereo

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COMPUTER GRAPHICS FORUM 2020年第1期39卷 204-219页

作者： Wei, M. Song, Z. Nie, Y. Wu, J. Ji, Z. Guo, Y. Xie, H. Wang, J. Wang, F. L. Nanjing Univ Aeronaut & Astronaut Sch Comp Sci & Technol Nanjing Peoples R China Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China Hangzhou Dianzi Univ Sch Comp Sci & Technol Hangzhou Peoples R China Educ Univ Hong Kong Dept Math & Informat Technol Hong Kong Peoples R China Open Univ Hong Kong Sch Sci & Technol Hong Kong Peoples R China

We present a near-lighting photometric stereo (NL-PS) system to produce digital bas-reliefs from a physical object (set) directly. Unlike both the 2D image and 3D model-based modelling methods that require complicated interactions and transformations, the technique using NL-PS is easy to use with cost-effective hardware, providing users with a trade-off between abstract and representation when creating bas-reliefs. Our algorithm consists of two steps: normal map acquisition and constrained 3D reconstruction. First, we introduce a lighting model, named the quasi-point lighting model (QPLM), and provide a two-step calibration solution in our NL-PS system to generate a dense normal map. Second, we filter the normal map into a detail layer and a structure layer, and formulate detail- or structure-preserving bas-relief modelling as a constrained surface reconstruction problem of solving a sparse linear system. The main contribution is a WYSIWYG (i.e. what you see is what you get) way of building new solvers that produces multi-style bas-reliefs with their geometric structures and/or details preserved. The performance of our approach is experimentally validated via comparisons with the state-of-the-art methods.

关键词： compression algorithms modelling computational geometry curves and surfaces

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DESIGN OF A FULL-FEATURED, SOLID-STATE TELEPHONE ANSWERING DEVICE

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IEEE TRANSACTIONS ON CONSUMER ELECTRONICS 1989年第3期35卷 681-686页

作者： FERRIS, G AIZNER, M DSP Group Inc. Emeryville CA USA

The authors discuss the use of advanced digital signal processing (DSP) algorithms to implement sophisticated audio processing functions and microcontroller tasks on a low-cost digital signal processor, enabling OEM (original equipment manufacturer) vendors to build cost-effective solid-state telephone answering devices. Sophisticated data compression algorithms and a low-cost programmable DSP processor were used to develop a powerful set of telephone-answering-machine chips. The DSP-based chip sets have been integrated in devices that offer incoming message recording times from 5 to 25 min, outgoing messages, time/date voice stamping and many other advanced features. A key to the project was finding application areas where DSP software could replace the traditional hardware while providing higher performance at lower cost. Speech processing along with telephony provided that environment. Aspects of hardware and software design are discussed.

关键词： Speech processing Telephone ORIGINAL EQUIPMENT MANUFACTURER incoming data optoelectronic module Digital signal processors Computer hardware chip set HIGH-PERFORMANCE signal processor compression algorithms

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When Bloom Filters Are No Longer Compact: Multi-Set Membership Lookup for Network Applications

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IEEE-ACM TRANSACTIONS ON NETWORKING 2016年第6期24卷 3326-3339页

作者： Qiao, Yan Chen, Shigang Mo, Zhen Yoon, Myungkeun Google Inc Mountain View CA 94043 USA Univ Florida Dept Comp & Informat Sci & Engn Gainesville FL 32611 USA Kookmin Univ Seoul 02707 South Korea

Many important network functions require online membership lookup against a large set of addresses, flow labels, signatures, and so on. This paper studies a more difficult, yet less investigated problem, called multi-set membership lookup, which involves multiple (sometimes in hundreds or even thousands) sets. The lookup determines not only whether an element is a member of the sets but also which set it belongs to. To facilitate the implementation of multi-set membership lookup in on-die memory of a network processor for line-speed packet inspection, the existing work uses the variants of Bloom filters to encode set IDs. However, through a thorough analysis of the mechanism and the performance of the prior art, much to our surprise, we find that Bloom filters-which were originally designed for encoding binary membership information-are actually not efficient for encoding set IDs. This paper takes a different solution path by separating membership encoding and set ID storage in two data structures, called index filter and set-id table, respectively. With a new ID placement strategy called uneven candidate-entry distribution and a two-level design of an index filter, we demonstrate through analysis and simulation that when compared with the best existing work, our new approach is able to achieve significant memory saving under the same lookup accuracy requirement, or achieve significantly better lookup accuracy under the same memory constraint.

关键词： Computational efficiency approximation algorithms compression algorithms

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Enabling Reliable Change Detection for Independently Compressed SAR Images

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2017年第8期55卷 4785-4794页

作者： McGuinness, Christopher Balster, Eric Univ Dayton Dept Elect & Comp Engn Dayton OH 45469 USA

This paper develops distortion metrics for compressed synthetic aperture radar (SAR) imagery from change detection test statistics. These metrics are used to predict lossy image compression's impact on change detection performance. The metrics do not require the intended change detection comparison image to provide these benefits. An SAR compression system leveraging the distortion metrics is proposed. The system generates a bad-pixel mask highlighting potential false alarms that are generated due to compression and are subsequently discarded in the change detection process. The proposed system's performance is demonstrated through noncoherent change detection analysis after JPEG2000 and JPEG image compression. Similarly, a coherent change detection system is evaluated after JPEG2000 image compression. For noncoherent change detection at large compression ratios (CRs) using JPEG2000, the proposed system provides a 33% reduction in false alarms at a 0.1 probability of detection as well as the ability to maintain near-distortionless false alarm rates across a wide range of CRs. At a 0.1 probability of detection for coherent change detection, the system provides a 37% reduction in false alarms at modest CRs. The coherent change detection system is also demonstrated to maintain low false alarm rates across a range of CRs.

关键词： Change detection algorithms compression algorithms synthetic aperture radar (SAR)

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Kronecker Compressive Sensing

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2012年第2期21卷 494-504页

作者： Duarte, Marco F. Baraniuk, Richard G. Rice Univ Dept Elect & Comp Engn Houston TX 77005 USA

Compressive sensing (CS) is an emerging approach for the acquisition of signals having a sparse or compressible representation in some basis. While the CS literature has mostly focused on problems involving 1-D signals and 2-D images, many important applications involve multidimensional signals;the construction of sparsifying bases and measurement systems for such signals is complicated by their higher dimensionality. In this paper, we propose the use of Kronecker product matrices in CS for two purposes. First, such matrices can act as sparsifying bases that jointly model the structure present in all of the signal dimensions. Second, such matrices can represent themeasurement protocols used in distributed settings. Our formulation enables the derivation of analytical bounds for the sparse approximation of multidimensional signals and CS recovery performance, as well as a means of evaluating novel distributed measurement schemes.

关键词： Compressed sensing compression algorithms hyperspectral imaging multidimensional signal processing video compression

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Geometric inhomogeneous random graphs

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THEORETICAL COMPUTER SCIENCE 2019年 760卷 35-54页

作者： Bringmann, Karl Keusch, Ralph Lengler, Johannes Max Planck Inst Informat Saarbrucken Germany Swiss Fed Inst Technol Inst Theoret Comp Sci Zurich Switzerland

Real-world networks, like social networks or the internet infrastructure, have structural properties such as large clustering coefficients that can best be described in terms of an underlying geometry. This is why the focus of the literature on theoretical models for real-world networks shifted from classic models without geometry, such as Chung-Lu random graphs, to modern geometry-based models, such as hyperbolic random graphs. With this paper we contribute to the theoretical analysis of these modern, more realistic random graph models. Instead of studying directly hyperbolic random graphs, we use a generalization that we call geometric inhomogeneous random graphs (GIRGs). Since we ignore constant factors in the edge probabilities, GIRGs are technically simpler (specifically, we avoid hyperbolic cosines), while preserving the qualitative behavior of hyperbolic random graphs, and we suggest to replace hyperbolic random graphs by this new model in future theoretical studies. We prove the following fundamental structural and algorithmic results on GIRGs. (1) We provide a sampling algorithm that generates a random graph from our model in expected linear time, improving the best-known sampling algorithm for hyperbolic random graphs by a substantial factor O (root n). (2) We establish that GIRGs have clustering coefficients in Omega(1), (3) we prove that GIRGs have small separators, i.e., it suffices to delete a sublinear number of edges to break the giant component into two large pieces, and (4) we show how to compress GIRGs using an expected linear number of bits. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Real-world networks Random graph models Hyperbolic random graphs Sampling algorithms compression algorithms Clustering coefficient

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