This paper surveys a variety of data compression methods spanning almost forty years of research, from the work of Shannon, Fano and Huffman in the late 40's to a technique developed in 1986. The aim of data compression is to reduce redundancy in stored or communicated data, thus increasing effective data density. Data compression has important application in the areas of file storage and distributed systems. Concepts from information theory, as they relate to the goals and evaluation of data compression methods, are discussed briefly. A framework for evaluation and comparison of methods is constructed and applied to the algorithms presented. Comparisons of both theoretical and empirical natures are reported and possibilities for future research are suggested. INTRODUCTION Data compression is often referred to as coding, where coding is a very general term encompassing any special representation of data which satisfies a given need. Information theory is defined to be the study of eff...

This paper describes an algorithm, called SEQUITUR, that identifies hierarchical structure in

Universal data compression algorithms fail to compress genetic sequences. It is due to the specificity of this particular kind of "text". We analyze in some details the properties of the sequences, which cause the failure of classical algorithms. We then present a lossless algorithm, biocompress-2, to compress the information contained in DNA and RNA sequences, based on the detection of regularities, such as the presence of palindromes. The algorithm combines substitutional and statistical methods, and to the best of our knowledge, lead to the highest compression of DNA. The results, although not satisfactory, gives insight to the necessary correlation between compression and comprehension of genetic sequences. 1 Introduction There are plenty of specific types of data which need to be compressed, for ease of storage and communication. Among them are texts (such as natural language and programs), images, sounds, etc. In this paper, we focus on the compression of a specific kin...

We address the problem of code size minimization in VLSI systems with embedded DSP processors. Reducing code size reduces the production cost of embedded systems. We use data compression methods to develop code size minimization strategies. We present a framework for code size minimization where the compressed data consists of a dictionary and a skeleton. The dictionary can be computed using popular text compression algorithms. We describe two methods to execute the compressed code that have varying performance characteristics and varying degrees of freedom in compressing the code. Experimental results obtained with a TMS320C25 code generator are presented. 1: Introduction An increasingly common micro-architecture for embedded systems is to integrate a microprocessor or microcontroller, a ROM and an ASIC all on a single integrated circuit (Figure 1). Such a micro-architecture can currently be found in such diverse embedded systems as FAX modems, laser printers and cellular telephones....

Sensor networks are fundamentally constrained b y the difficulty and energy expense of delivering information from sensors to sink. Our work has focused on garnerin g additional significant energ y improvements b y d ev isin g computationally-efficient lossless compression algorithms on the source node. These reduce the amount of data that must be passed through the network and to the sink, and thus have energy benefits that are multiplicative with the number of hops the data travels through the network. Currently, if sensor system designers want to compress acquired data, they must either develop application-specific compression algorithms or use off-the-shelf algorithms not designed for resource-constrained sensor nodes. This paper discusses the design issues involved with implementing, adapting, and customizing compression algorithms specifically geared for sensor nodes. While developing Sensor LZW (S-LZW) and some simple, but effective, variations to this algorithm, we show how different amounts of compression can lead to energy savings on both the compressing node and throughout the network and that the savings depends heavily on the radio hardware. To validate and evaluate our work, we apply it to datasets from several different real-world deployments and show that our approaches can reduce energy consumption by up to a factor of 4.5X across the network.

Although run-time reconfigurable systems have been shown to achieve very high performance, the speedups over traditional microprocessor systems are limited by the cost of configuration of the hardware. Current reconfigurable systems suffer from a significant overhead due to the time it takes to reconfigure their hardware. In order to deal with this overhead, and increase the compute power of reconfigurable systems, it is important to develop hardware and software systems to reduce or eliminate this delay. In this paper, we explore the idea of configuration compression and develop algorithms for reconfigurable systems. These algorithms, targeted to Xilinx Virtex series FPGAs with minimum modification of hardware, can significantly reduce the amount of data needed to transfer during configuration. In this work we have extensively researched the current compression techniques, including the Huffman coding, the Arithmetic coding and LZ coding. We have also developed different algorithms targeting different hardware structures. Our readback algorithm allows certain frames to be reused as a dictionary and sufficiently utilize the regularities within the configuration bitstream. In addition, we have developed frame reordering techniques that better uses the regularities by shuffling the sequence of the configuration. We have also developed the wildcard approach that can be used for true partial reconfiguration. The simulation results demonstrate that a factor of 4 compression ratio can be achieved.

Greedy off-line textual substitution refers to the following approach to compression or structural inference. Given a long textstring x, a substring w is identified such that replacing all instances of w in x except one by a suitable pair of pointers yields the highest possible contraction of x; the process is then repeated on the contracted textstring until substrings capable of producing contractions can no longer be found. This paper examines computational issues arising in the implementation of this paradigm and describes some applications and experiments.

this article tends to be answered by making a selection of queries more or less haphazardly to gain a feeling for what the collection contains.

We introduce a general framework which is suitable to capture an essence of compressed pattern matching according to various dictionary based compressions, and propose a compressed pattern matching algorithm for the framework. The goal is to find all occurrences of a pattern in a text without decompression, which is one of the most active topics in string matching. Our framework includes such compression methods as Lempel-Ziv family, (LZ77, LZSS, LZ78, LZW), byte-pair encoding, and the static dictionary based method. Technically, our pattern matching algorithm extends that for LZW compressed text presented by Amir, Benson and Farach. 1 Introduction Pattern matching is one of the most fundamental operations in string processing. The problem is to find all occurrences of a given pattern in a given text. A lot of classical or advanced pattern matching algorithms have been proposed (see [3, 2]). Data compression is another most important research topic, whose aim is to reduce its space u...

The overall throughput of automatic test equipment (ATE) is sensitive to the download time of test data. An effective approach to the reduction of the download time is to compress test data before the download. A compression algorithm for test data should meet two requirements: lossless and simple decompression. In this paper, we propose a new test data compression method that aims to fully utilize the unique characteristics of test data compression. The key idea of the proposed method is to perform the Burrows-Wheeler transformation on the sequence of test patterns, and then to apply run-length coding. The experimental results show that our compression method performs better than six other methods for compressing test data. The average compression ratio of the proposed method performed on five test data sets is 315, while that for the next best one, the LZW method, is 21.