Quantization and Compressive Sensing

Quantization is an essential step in digitizing signals, and, therefore, an indispensable component of any modern acquisition system. This book chapter explores the interaction of quantization and compressive sensing and examines practical quantization strategies for compressive acquisition systems. Specifically, we first provide a brief overview of quantization and examine fundamental performance bounds applicable to any quantization approach. Next, we consider several forms of scalar quantizers, namely uniform, non-uniform, and 1-bit. We provide performance bounds and fundamental analysis, as well as practical quantizer designs and reconstruction algorithms that account for quantization. Furthermore, we provide an overview of Sigma-Delta ($\Sigma\Delta$) quantization in the compressed sensing context, and also discuss implementation issues, recovery algorithms and performance bounds. As we demonstrate, proper accounting for quantization and careful quantizer design has significant impact in the performance of a compressive acquisition system.Comment: 35 pages, 20 figures, to appear in Springer book "Compressed Sensing and Its Applications", 201

Scalable processing and autocovariance computation of big functional data

This is the peer reviewed version of the following article: Brisaboa NR, Cao R, Paramá JR, Silva-Coira F. Scalable processing and autocovariance computation of big functional data. Softw Pract Exper. 2018; 48: 123–140 which has been published in final form at https://doi.org/10.1002/spe.2524 . This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited.[Abstract]: This paper presents 2 main contributions. The first is a compact representation of huge sets of functional data or trajectories of continuous-time stochastic processes, which allows keeping the data always compressed even during the processing in main memory. It is oriented to facilitate the efficient computation of the sample autocovariance function without a previous decompression of the data set, by using only partial local decoding. The second contribution is a new memory-efficient algorithm to compute the sample autocovariance function. The combination of the compact representation and the new memory-efficient algorithm obtained in our experiments the following benefits. The compressed data occupy in the disk 75% of the space needed by the original data. The computation of the autocovariance function used up to 13 times less main memory, and run 65% faster than the classical method implemented, for example, in the R package.This work was supported by the Ministerio de Economía y Competitividad (PGE and FEDER) under grants [TIN2016-78011-C4-1-R; MTM2014-52876-R; TIN2013-46238-C4-3-R], Centro para el desarrollo Tecnológico e Industrial MINECO [IDI-20141259; ITC-20151247; ITC-20151305; ITC-20161074]; Xunta de Galicia (cofounded with FEDER) under Grupos de Referencia Competitiva grant ED431C-2016-015; Xunta de Galicia-Consellería de Cultura, Educación e Ordenación Universitaria (cofounded with FEDER) under Redes grants R2014/041, ED341D R2016/045; Xunta de Galicia-Consellería de Cultura, Educación e Ordenación Universitaria (cofounded with FEDER) under Centro Singular de Investigación de Galicia grant ED431G/01.Xunta de Galicia; D431C-2016-015Xunta de Galicia; R2014/041Xunta de Galicia; ED341D R2016/045Xunta de Galicia; ED431G/0

Loghub: A Large Collection of System Log Datasets towards Automated Log Analytics

Logs have been widely adopted in software system development and maintenance because of the rich system runtime information they contain. In recent years, the increase of software size and complexity leads to the rapid growth of the volume of logs. To handle these large volumes of logs efficiently and effectively, a line of research focuses on intelligent log analytics powered by AI (artificial intelligence) techniques. However, only a small fraction of these techniques have reached successful deployment in industry because of the lack of public log datasets and necessary benchmarking upon them. To fill this significant gap between academia and industry and also facilitate more research on AI-powered log analytics, we have collected and organized loghub, a large collection of log datasets. In particular, loghub provides 17 real-world log datasets collected from a wide range of systems, including distributed systems, supercomputers, operating systems, mobile systems, server applications, and standalone software. In this paper, we summarize the statistics of these datasets, introduce some practical log usage scenarios, and present a case study on anomaly detection to demonstrate how loghub facilitates the research and practice in this field. Up to the time of this paper writing, loghub datasets have been downloaded over 15,000 times by more than 380 organizations from both industry and academia.Comment: Dateset available at https://zenodo.org/record/322717

Quantitative Evaluation of Dense Skeletons for Image Compression

Skeletons are well-known descriptors used for analysis and processing of 2D binary images. Recently, dense skeletons have been proposed as an extension of classical skeletons as a dual encoding for 2D grayscale and color images. Yet, their encoding power, measured by the quality and size of the encoded image, and how these metrics depend on selected encoding parameters, has not been formally evaluated. In this paper, we fill this gap with two main contributions. First, we improve the encoding power of dense skeletons by effective layer selection heuristics, a refined skeleton pixel-chain encoding, and a postprocessing compression scheme. Secondly, we propose a benchmark to assess the encoding power of dense skeletons for a wide set of natural and synthetic color and grayscale images. We use this benchmark to derive optimal parameters for dense skeletons. Our method, called Compressing Dense Medial Descriptors (CDMD), achieves higher-compression ratios at similar quality to the well-known JPEG technique and, thereby, shows that skeletons can be an interesting option for lossy image encoding