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SHO-FA: Robust Compressive Sensing With Order-Optimal Complexity, Measurements, and Bits

IEEE TRANSACTIONS ON INFORMATION THEORY 2016年第12期62卷 7419-7444页

作者： Bakshi, Mayank Jaggi, Sidharth Cai, Sheng Chen, Minghua Chinese Univ Hong Kong Inst Network Coding Hong Kong Hong Kong Peoples R China Chinese Univ Hong Kong Dept Informat Engn Hong Kong Hong Kong Peoples R China

Suppose x is any exactly k-sparse vector in R-n. We present a class of sparse matrices A, and a corresponding algorithm that we call short and fast(1) (SHO-FA) that, with high probability over A, can reconstruct x from Ax. The SHO-FA algorithm is related to the invertible bloom lookup tables recently introduced by Goodrich et al., with two important distinctions-SHO-FA relies on linear measurements, and is robust to noise. The SHO-FA algorithm is the first to simultaneously have the following properties: 1) it requires only O(k) measurements;2) the bit precision of each measurement and each arithmetic operation is O(log(n) + P) (here, 2(-P) corresponds to the desired relative error in the reconstruction of x);3) the computational complexity of decoding is O(k) arithmetic operations and that of encoding is O(n) arithmetic operations;and 4) if the reconstruction goal is simply to recover a single component of x instead of all of x, with significant probability over A, this can be done in constant time. All the above constants are independent of all problem parameters other than the desired probability of success. For a wide range of parameters, these properties are information-theoretically order-optimal. In addition, our SHO-FA algorithm works over fairly general ensembles of sparse random matrices, and is robust to random noise and (random) approximate sparsity for a large range of k. In particular, suppose the measured vector equals A(x + z) + e, where z and e correspond to the source tail and measurement noise, respectively. Under reasonable statistical assumptions on z and e, our decoding algorithm reconstructs x with an estimation error of O(parallel to z parallel to(2) + parallel to e parallel to(2)). The SHO-FA algorithm works with high probability over A, z, and e, and still requires only O(k) steps and O(k) measurements over O(log(n))-bit numbers. This is in contrast to most existing algorithms that focus on the worst case z model, where it is known that Ome

关键词： Compressed sensing compression algorithms algorithm design and analysis graph theory signal processing algorithms

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GreedyGD: Enhanced Generalized Deduplication for Direct Analytics in IoT

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2024年第4期20卷 6954-6962页

作者： Hurst, Aaron Lucani, Daniel E. Zhang, Qi Aarhus Univ DIGIT Ctr DK-8200 Aarhus Denmark Aarhus Univ Dept Elect & Comp Engn DK-8200 Aarhus Denmark

The exponential growth of data generated by the Internet of Things presents significant challenges for data communication, storage, and analytics. Consequently, organizations often face high costs when attempting to leverage their own data. Novel techniques that holistically optimize data storage and analytics in IoT systems are therefore required. One promising approach is generalized deduplication (GD), which is a lossless compression technique that delivers high compression while also enabling low-cost random access directly on compressed data. In this article, we introduce GreedyGD, a novel GD data compression algorithm that offers reliable, efficient data analytics, along with more compression and faster runtime compared to previous GD compressors. Evaluating GreedyGD on 18 real-world datasets revealed excellent performance: a 11.2x speed-up, 1.6x more compression, and more accurate and reliable analytics while using 4x less data compared to previous GD compressors.

关键词： Costs Internet of Things Data analysis Reliability Data compression Compressors compression algorithms Clustering data analytics data compression generalized deduplication IoT

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Constraints on Variable Bit-Rate Video for ATM Networks

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 1992年第4期2卷 361-372页

作者： Reibman, Amy R. Haskell, Barry G. AT&T Bell Labs Holmdel NJ 07733 USA

We consider constraints on the encoded bit rate of a video signal that are imposed by a channel and encoder and decoder buffers. We present conditions that ensure that the video encoder and decoder buffers do not overflow or underflow when the channel can transmit a variable bit rate. Using these conditions and a commonly proposed network-user contract, we examine the effect of a network policing function on the allowable variability in the encoded video bit rate. We describe how these ideas might be implemented in a system that controls both the encoded and transmitted bit rates. Finally, we present the performance of video that has been encoded using the derived constraints for the leaky bucket channel.

关键词： Bit rate Video compression Asynchronous transfer mode Decoding B-ISDN Control systems Contracts compression algorithms Algorithm design and analysis Streaming media

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Compressed Holistic ConvNet Representations for Detecting Loop Closures in Dynamic Environments

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IEEE ACCESS 2020年 8卷 60552-60574页

作者： Wang, Shuo Lv, Xudong Liu, Xiaomin Ye, Dong Harbin Inst Technol Sch Instrumentat Sci & Engn Harbin 150001 Peoples R China Harbin Inst Technol Sch Elect & Informat Engn Harbin 150001 Peoples R China Jiamusi Univ Informat & Elect Technol Inst Jiamusi 154002 Peoples R China

Detecting loop closures in dynamic environments is a severe challenge for the simultaneous localization and mapping (SLAM) system. Convolutional neural networks (ConvNet) could provide high-level and abstract representations extracted directly from images as image descriptors. Some novel ConvNet-based methods have been presented. In dynamic environments, they perform better than the state-of-the-art methods which use hand-crafted features. In this paper, (1) We proposed a flexible loop closure detection workflow based on the holistic representations;(2) In this workflow, a post-processing method is applied to the raw holistic ConvNet representations for redundant information compression;(3) In addition, a compression ratio is introduced in (2) to determine how much information will be retained depending on background change and moving objects. We evaluated our workflow in four open datasets. The experimental results demonstrate that the proposed workflow performs better than many state-of-the-art methods and ConvNet-based approaches.

关键词： Loop closure detection holistic representations descriptors convolutional neural network compression algorithms

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Structured Compressed Sensing: From Theory to Applications

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2011年第9期59卷 4053-4085页

作者： Duarte, Marco F. Eldar, Yonina C. Univ Massachusetts Dept Elect & Comp Engn Amherst MA 01003 USA Technion Israel Inst Technol Dept Elect Engn IL-32000 Haifa Israel

Compressed sensing (CS) is an emerging field that has attracted considerable research interest over the past few years. Previous review articles in CS limit their scope to standard discrete-to-discrete measurement architectures using matrices of randomized nature and signal models based on standard sparsity. In recent years, CS has worked its way into several new application areas. This, in turn, necessitates a fresh look on many of the basics of CS. The random matrix measurement operator must be replaced by more structured sensing architectures that correspond to the characteristics of feasible acquisition hardware. The standard sparsity prior has to be extended to include a much richer class of signals and to encode broader data models, including continuous-time signals. In our overview, the theme is exploiting signal and measurement structure in compressive sensing. The prime focus is bridging theory and practice;that is, to pinpoint the potential of structured CS strategies to emerge from the math to the hardware. Our summary highlights new directions as well as relations to more traditional CS, with the hope of serving both as a review to practitioners wanting to join this emerging field, and as a reference for researchers that attempts to put some of the existing ideas in perspective of practical applications.

关键词： Approximation algorithms compressed sensing compression algorithms data acquisition data compression sampling methods

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Transaction Fees Minimization in Blockchain-Based Home Delivery System

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IEEE ACCESS 2024年 12卷 109197-109209页

作者： Jandaeng, Chanankorn Chukleang, Tirajet Kongsen, Jonksuk Koad, Peeravit Thu, May Walailak Univ Informat Innovat Ctr Excellence IICE Nakhon Si Thammarat 80160 Thailand Walailak Univ Sch Informat Nakhon Si Thammarat 80160 Thailand Walailak Univ Sch Informat Management Informat Technol Nakhon Si Thammarat 80160 Thailand Cambodia Univ Technol & Sci Fac Engn Phnom Penh 121003 Cambodia

This study investigates the impact of Zlib compression on gas consumption within blockchain systems, focusing particularly on Ethereum transactions. By employing the Ethereum simulator Ganache, we simulate 100 realistic home delivery system datasets to evaluate the performance of compressed versus uncompressed data. The methodology encompasses rigorous statistical analysis to ensure robust results. Our findings reveal that using the Zlib algorithm to compress textual data exceeding 141 bytes before submitting transactions on the Ethereum network reduces the gasUsed while maintaining the system time unchanged. This demonstrates the effectiveness of data compression in optimizing transaction costs without affecting operational efficiency. Additionally, our research extends to analyzing real gasPrice trends on the Ethereum network. We propose a non-linear regression model that accurately predicts hourly gasPrice variations based on the day of the week and the specific time. This provides a valuable tool for users to plan transactions strategically. These insights enhance the understanding of blockchain dynamics and offer practical solutions for improving economic and system efficiency in blockchain operations.

关键词： Blockchains Smart contracts Open source software Cryptocurrency Costs Security compression algorithms Text compression algorithm transaction fees blockchain logistics home delivery

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IBU: An In-Block Update Address Mapping Scheme for Solid-State Drives

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IEEE ACCESS 2022年 10卷 4934-4947页

作者： Taghizadeh, Reza Gholami Marvasti, Mohammadreza Binesh Asghari, Seyyed Amir Taghizadeh, Ramin Gholami Nabavi, Morteza Savaria, Yvon Kharazmi Univ Dept Elect & Comp Engn Tehran *** Iran Polytech Montreal Dept Elect & Comp Engn Montreal PQ H3T 1J4 Canada

One of the most important features of solid-state drives (SSDs) is the ability to update data sectors out of place, performing garbage collection operations within its physical blocks, to mitigate limited flash memory lifespan. In SSDs, a firmware called the Flash Translation Layer (FTL) is used to hide such features from the operating system. One way to extend the lifespan of flash memories is to use data compression at the FTL level. In compression-based FTLs, the address mapping type has a significant effect on the efficiency and read/write speed. The type of address mapping in previous similar schemes is based on page-level address translation and requires high memory space for the mapping table. In this paper, we propose a compression-based hybrid FTL called In Block Update Address Mapping, in which the pages of each logical block are compressed, and fewer physical pages are written in physical blocks. Therefore, a small number of free pages remain. In the proposed scheme, the required memory of the mapping table has been reduced by 78% when compared to similar schemes. Moreover, extensive simulation results reported in this paper show that the proposed FTL scheme outperforms other FTL schemes in the read and write operations under realistic benchmark workloads.

关键词： Flash memories Data compression Writing Memory management Solid state drives Hardware compression algorithms Flash memory SSD flash translation layer garbage collection data compression in SSD

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2-DIMENSIONAL ENCODING BY FINITE-STATE ENCODERS

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IEEE TRANSACTIONS ON COMMUNICATIONS 1990年第3期38卷 341-347页

作者： SHEINWALD, D LEMPEL, A ZIV, J TECHNION ISRAEL INST TECHNOL DEPT COMP SCI IL-32000 HAIFA ISRAEL TECHNION ISRAEL INST TECHNOL DEPT ELECT ENGN IL-32000 HAIFA ISRAEL

Distortion-free compressibility of individual pictures by finite-state encoders is investigated. In a recent paper (see IEEE Trans. Inform. Theory, vol.32, no.1, p.1-8, 1986) the compressibility of a given picture I was defined and shown to be the asymptotically attainable lower bound on the compression ratio that can be achieved for I by any finite-state encoder. Here, a different and more direct approach is taken to prove similar results, which are summarized in a converse-to-coding theorem and a constructive-coding-theorem that leads to a universal asymptotically optimal compression algorithm.< >

关键词： Encoding Image coding Codes compression algorithms Entropy Decoding Transducers Conferences Computer science

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Distillation-Guided Residual Learning for Binary Convolutional Neural Networks

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022年第12期33卷 7765-7777页

作者： Ye, Jianming Wang, Jingdong Zhang, Shiliang Peking Univ Dept Comp Sci Sch Elect Engn & Comp Sci Beijing 100871 Peoples R China Microsoft Res Asia Beijing 100080 Peoples R China

It is challenging to bridge the performance gap between binary convolutional neural network (BCNN) and floating-point CNN (FCNN). This performance gap is mainly caused by the inferior modeling capability and training strategy of BCNN, which leads to substantial residuals in intermediate feature maps between BCNN and FCNN. To minimize the performance gap, we enforce BCNN to produce similar intermediate feature maps with the ones of FCNN. This intuition leads to a more effective training strategy for BCNN, i.e., optimizing each binary convolutional block with blockwise distillation loss derived from FCNN. The goal of minimizing the residuals in intermediate feature maps also motivates us to update the binary convolutional block architecture to facilitate the optimization of blockwise distillation loss. Specifically, a lightweight shortcut branch is inserted into each binary convolutional block to complement residuals at each block. Benefited from its squeeze-and-interaction (SI) structure, this shortcut branch introduces a fraction of parameters, e.g., less than 10% overheads, but effectively boosts the modeling capability of binary convolution blocks in BCNN. Extensive experiments on ImageNet demonstrate the superior performance of our method in both classification efficiency and accuracy, e.g., BCNN trained with our methods achieves the accuracy of 60.45% on ImageNet, better than many state-of-the-art ones.

关键词： Training Convolution Optimization Kernel Computer architecture Computational modeling Neural networks Binary neural network compression algorithms distillation

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A new adaptive coefficient scanning based on local and global prediction

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SIGNAL IMAGE AND VIDEO PROCESSING 2016年第1期10卷 189-197页

作者： Chen, Hsin-Hui Ding, Jian-Jiun Natl Taiwan Univ Grad Inst Commun Engn 1 Sec 4Roosevelt Rd Taipei 10617 Taiwan

In this paper, a new adaptive coefficient scanning scheme, which is called local-and global-prediction-based adaptive scanning (LGPAS), is described to improve the coding efficiency of discrete cosine transform (DCT)-based image compression methods including JPEG and H.264/AVC intra-coding, in which zigzag scanning is used. The coding performance is limited because the zigzag scan order ignores the statistical properties of the DCT coefficients. On the other hand, we adopt not only the global information but also the local information to perform learning and adaptively generate the scanning patterns, unlike the existing methods. Furthermore, we adopt variation prediction, nonzero probability estimation, and the proposed techniques of zigzag weighting and energy weighting matrices to generate the scanning pattern. On the basis of the local and global predictions for the probability distributions of the nonzero DCT coefficients in an image, the proposed LGPAS scheme can adaptively update the scan order patterns and thus achieves a higher entropy coding gain. Simulations show that the proposed scheme significantly outperforms the conventional zigzag scanning method and other existing adaptive scanning methods.

关键词： Adaptive scanning Intra-prediction JPEG compression algorithms Data compression

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