Lossless Compression

The Canterbury Corpus provides a standardized set of files for evaluating the effectiveness of lossless data compression algorithms. Researchers utilize this benchmark to compare the performance of different compression methods, analyze compression ratios achieved, and conduct statistical analysis on the results. The corpus comprises diverse file types, enabling a comprehensive assessment of compression algorithms across various data domains. Detailed documentation, including descriptions of the corpora and compression methods employed, facilitates consistent and reproducible research in lossless data compression.

Hadoop与LZO压缩 Hadoop是一个开源框架，主要用于处理和存储大规模数据，由Apache软件基金会开发。在大数据处理领域，Hadoop以其分布式计算模型（MapReduce）和可扩展性而闻名。为了提高数据存储和传输效率，Hadoop支持多种压缩格式，其中之一就是LZO（Lempel-Ziv-Oberhumer）。 LZO是一种快速的无损数据压缩算法，由Uwe Ligges创建，其主要特点是压缩和解压缩速度快，但压缩率相对较低。在Hadoop中，LZO压缩被广泛用于减少数据存储空间和提高网络传输效率，尤其在实时或近实时的数据处理场景中表现突出。 在Hadoop中实现LZO压缩，通常需要两个关键组件：一是Java库，用于处理LZO压缩和解压缩的逻辑；二是Native库，这是一个C编译的库，提供高性能的压缩和解压缩功能。\"hadoop-lzo.zip\"这个压缩包集成了这两个组件，使得用户可以直接将LZO引入到Hadoop项目中。 hadoop-lzo-0.4.20.jar是这个压缩包中的核心文件，包含了Hadoop对LZO压缩的支持。该JAR文件包括了Hadoop与LZO交互所需的类和接口，例如org.apache.hadoop.io.compress.LzoCodec，负责在MapReduce任务中进行LZO压缩和解压缩。 要将LZO引入Hadoop项目，开发者需将hadoop-lzo-0.4.20.jar添加到项目的类路径中，并在core-site.xml配置文件中设置io.compression.codecs属性来注册LZO codec： io.compression.codecs com.hadoop.compression.lzo.LzoCodec,com.hadoop.compression.lzo.LzopCodec io.compression.codec.lzo.class com.hadoop.compression.lzo.LzoCodec

This document provides various examples of basic compressed sensing using the MATLAB function linprog. The following examples demonstrate how to apply compressed sensing techniques to different types of signals: ECG Signal Compression K-sparse Signal Recovery Audio Signal Compression Encrypted Data Recovery Image Compression via L1 minimization Each section includes practical code examples, with step-by-step explanations to help you understand how to implement compressed sensing in different applications.