Design and Development of Bioinformatics Feature Based DNA Sequence Data Compression Algorithm

This paper conducts a comparative analysis of the impact of unsparsing by data scaling on lossless data compression methods. The most commonly used data compression algorithms such as gzip, zlib, bzip2, and lzma are tested by their compression efficiency in unsparsed datasets by the performance metrics such as compression time, decompression time, and compression ratio for both the original and scaled datasets. Five different data scaling techniques are investigated to transform the original data into unsparsed data such as min-max, robust, absolute max, standardization, and normalization. Our research reveals relationships between data scaling methodologies and compression performance, with disparities in compression efficiency and computational complexity. Fur-thermore, we investigate the effects of scaling on compression ratio and provide results for improving the understanding of the factors that influence lossless data compression methods for non-sparse tabular data.

An antidictionary is particularly useful for data compression, and on-line electrocardiogram (ECG) lossless compression algorithms using antidictionaries have been proposed. They work in real-time with constant memory and give better compression ratios than traditional lossless data compression algorithms, while they only deal with ECG data on a binary alphabet. This paper proposes on-line ECG lossless compression for a given data on a finite alphabet. The proposed algorithm gives not only better compression ratios than those algorithms but also uses less computational space than they do. Moreover, the proposed algorithm work in real-time. Its effectiveness is demonstrated by simulation results.

Data compression techniques in IoT-enabled wireless body sensor networks: A systematic literature review and research trends for QoS improvement

<p>The need of data compression at smart Edge/Fog-based gateways is undeniable as data compression can significantly reduce the amount of data that has to be transmitted over a network. This, in turn, has a direct impact on reducing transmission latency and increasing network bandwidth. In time-critical and data-sensitive IoT applications such as healthcare, lossless data compression is preferable as compressed data can be recovered without losing any information. However, it is not an easy task to choose a proper lossless data compression algorithm for IoT applications as each lossless data compression method has its own advantages and disadvantages. This paper focuses on the analysis of lossless data compression algorithms run at smart Edge/Fog gateways. Widely used lossless data compression is run at different hardware which is often used as smart Fog/Edge gateways. The latency of data compression and the compression rate in different cases of input data sizes are analyzed. The paper provides guidelines for choosing a proper lossless data compression algorithm for time-critical IoT applications. <br /></p>

Summary form only given. Large aperture static imaging spectrometer (LASIS) is a new kind of interferometer spectrometer with the advantages of high throughput and large field of view. The LASIS data contains spatial information in principle component along spatial direction and spectral information in modulated component along Optical Path Difference (OPD) direction. LASIS data have clearly parallel displacement of spatial information among continuous frames. It is a unique characteristic for LASIS data compared with other kinds of interferometer spectrometer such as SMII data and GITFS data. Although dispersive spectrometer, such as AVIRIS data, also contains some spatial information, each frame in a group shows the same ground view with different wavelengths spectrum without any motion displacement among inter frames. Based on these characteristics, we propose a lossless data compression method named Dual-direction Prediction Vector Quantization (DPVQ). With a dual-direction prediction on both spatial and spectral direction, redundancy in LASIS data is largely removed by minimizing the prediction residue in DPVQ. Then a fast vector quantization (VQ) avoiding codebook splitting process is applied after prediction. Considering time efficiency, the dual-direction prediction and VQ in DPVQ are optimized to reduce the calculations, so that optimized prediction saves 60% running time and fast VQ saves about 25% running time with a similar quantization quality compared with classical generalized Lloyd algorithm (GLA). Experimental results, shown in Fig. 2, indicate that DPVQ can achieve a maximal Compression Ratio (CR) at about 3.4, which outperforms many existing lossless compression algorithms.

Telemetric information is great in size, requiring extra room and transmission time. There is a significant obstruction of storing or sending telemetric information. Lossless data compression (LDC) algorithms have evolved to process telemetric data effectively and efficiently with a high compression ratio and a short processing time. Telemetric information can be packed to control the extra room and association data transmission. In spite of the fact that different examinations on the pressure of telemetric information have been conducted, the idea of telemetric information makes pressure incredibly troublesome. The purpose of this study is to offer a subsampled and balanced recurrent neural lossless data compression (SB-RNLDC) approach for increasing the compression rate while decreasing the compression time. This is accomplished through the development of two models: one for subsampled averaged telemetry data preprocessing and another for BRN-LDC. Subsampling and averaging are conducted at the preprocessing stage using an adjustable sampling factor. A balanced compression interval (BCI) is used to encode the data depending on the probability measurement during the LDC stage. The aim of this research work is to compare differential compression techniques directly. The final output demonstrates that the balancing-based LDC can reduce compression time and finally improve dependability. The final experimental results show that the model proposed can enhance the computing capabilities in data compression compared to the existing methodologies.

For many computerized applications, data compression is a standard requirement. In order to minimize the capacity needed for that data, it decreases the redundancy in data representation and thus therefore reduces the connectivity cost by efficiently utilizing the available bandwidth. There are a range of algorithms for data compression that are used to compress various formats of data. There are also sets of different compression algorithms for a single data form, which use different approaches. This paper assesses lossless algorithms for compression of data and compares their performance. To assess the performance of compressing text data, images, audio, a set of selected algorithms are used. Experimental findings and comparisons of algorithms for lossless compression using methods for compression. It gives contrasting conclusions on the size and time ratios toward existing research. Different information that can be compressed not only text but it can be Audio, pictures and video information. In this article, there are variety of various forms of lossless compression algorithms discussed.KeywordsData compressionLossless data compressionText compressionImage compressionAudio compression

In many bandwidth constrained applications, lossless compression may be unnecessary, as only two to three times of compression can be achieved. An alternative way to save bandwidth is to adopt perceptually lossless compression, which can attain eight times or more compression without loss of important information. In this research, our first objective is to compare and select the best compression algorithm in the literature to achieve 8:1 compression ratio with perceptually lossless compression for still images. Our second objective is to demonstrate error concealment algorithms that can handle corrupted pixels due to transmission errors in communication channels. We have clearly achieved the above objectives using realistic images.

With the development of modern computer technology, the growth of purchase funds for storage equipment is far behind the growth of data. How to effectively use the limited storage resources to store data becomes the focus of our research and the data compression technology is the key to solve this problem. The traditional compression technology on the CPU platform could not meet the requirements for processing massive data in the compression rate and the energy cost. The GPU provides a new solution for its strong parallel computation ability. In this paper we present an implementation of Parallel Matching Lempel-Ziv-Storer-Szymanski (PMLZSS), a high speed lossless data compression algorithm by using CUDA framework. The basic idea of our implementation of the PMLZSS algorithm on GPUs is the introduction of a paralleled matrix matching. The data needed to be compressed are divided into multiple dictionary strings and pre-read strings as the vertical axis and horizontal axis of the matrices, respectively. All of the matrices are paralleled matched in the different blocks. Compared with the traditional serial CPU platform LZSS compression algorithm and BZIP2 compression algorithm, the experimental data shows that on the premise of the basic compression rate unchanged, relative to the serial LZSS, the compression speed of PMLZSS is improved about 16x, while to the BZIP2, about 12x.

Wavelet representation of a signal is efficient for process data compression. An on‐line compression algorithm based on Haar wavelets is proposed here. As a new data point arrives, the algorithm computes all the approximation coefficients and updates the multiresolution tree before it prepares to receive the next data point. An efficient bookkeeping and indexing scheme improves compression ratio more significantly than batch‐mode wavelet compression. Reconstruction algorithms and historian format for this bookkeeping are developed. Various analytical results on the bounds on compression ratio and sum of the square error that can be achieved using this algorithm are derived. Experimental evaluation over two sets of plant data shows that wavelet compression is superior to conventional interpolative methods (such as boxcar, backward slope, and SLIM3) in terms of quality of compression measured both in time and frequency domain and that the proposed on‐line wavelet compression algorithm performs better than the batch‐mode wavelet compression algorithm due to the efficient indexing and bookkeeping scheme. The on‐line algorithm combines the high quality of compression of wavelet‐based methods and on‐line implementation of interpolative compression algorithms at the same time.

A prototype chip, called RD53A, has been designed by the RD53 collaboration to face the very high hit and trigger rate requirements (up to 3 GHz/cm2 and 1 MHz, respectively) of the High Luminosity LHC experiment upgrades. In this paper, an improved algorithm for data compression, capable of sustaining the very high data volume and proposed to be implemented in the periphery of the chip, is presented: it exploits Run Length Encoding (RLE) and Variable Length Coding (VLC) to compact chip pixel hit patterns. The compression and decompression algorithms are implemented with MATLAB, and the performance is calculated taking into account the RD53A data readout implementation and its chip simulation and verification framework (called VEPIX53). In all considered cases, the results show that the RLE and VLC combination achieves a data compression ratio between 1.57 and 1.62, resulting in a bitstream size reduction between 36.2% and 38.4% with respect to the rate of the current data transmission format.

Data compression is an important part of information security because compressed data is more secure and easy to handle. Effective data compression technology creates efficient, secure, and easy-to-connect data. There are two types of compression algorithm techniques, lossy and lossless. These technologies can be used in any data format such as text, audio, video, or image file. The main objective of this study was to reduce the physical space on the various storage media and reduce the time of sending data over the Internet with a complete guarantee of encrypting this data and hiding it from intruders. Two techniques are implemented, with data loss (Lossy) and without data loss (Lossless). In the proposed paper a hybrid data compression algorithm increases the input data to be encrypted by RSA (Rivest-Shamir-Adleman) cryptography method to enhance the security level and it can be used in executing lossy and lossless compacting Steganography methods. This technique can be used to decrease the amount of every transmitted data aiding fast transmission while using slow internet or take a small space on different storage media. The plain text is compressed by the Huffman coding algorithm, and also the cover image is compressed by Discrete wavelet transform DWT based that compacts the cover image through lossy compression in order to reduce the cover image's dimensions. The least significant bit LSB will then be used to implant the encrypted data in the compacted cover image. We evaluated that system on criteria such as percentage Savings percentage, Compression Time, Compression Ratio, Bits per pixel, Mean Squared Error, Peak Signal to Noise Ratio, Structural Similarity Index, and Compression Speed. This system shows a high-level performance and system methodology compared to other systems that use the same methodology.

Computer systems and micro architecture researchers have proposed using hardware data compression units within the memory hierarchies of microprocessors in order to improve performance, energy efficiency, and functionality. However, most past work, and all work on cache compression, has made unsubstantiated assumptions about the performance, power consumption, and area overheads of the proposed compression algorithms and hardware. In this work, I present a lossless compression algorithm that has been designed for fast on-line data compression, and cache compression in particular. The algorithm has a number of novel features tailored for this application, including combining pairs of compressed lines into one cache line and allowing parallel compression of multiple words while using a single dictionary and without degradation in compression ratio. We reduced the proposed algorithm to a register transfer level hardware design, permitting performance, power consumption, and area estimation.

In recent research we proposed GP-zip, a system which uses evolution to find optimal ways to combine standard compression algorithms for the purpose of maximally losslessly compressing files and archives. The system divides files into blocks of predefined length. It then uses a linear, fixed-length representation where each primitive indicates what compression algorithm to use for a specific data block. GP-zip worked well with heterogonous data sets, providing significant improvements in compression ratio compared to some of the best standard compression algorithms. In this paper we propose a substantial improvement, called GP-zip*, which uses a new representation and intelligent crossover and mutation operators such that blocks of different sizes can be evolved. Like GP-zip, GP-zip* finds what the best compression technique to use for each block is. The compression algorithms available in the primitive set of GP-zip* are: Arithmetic coding (AC), Lempel-Ziv-Welch (LZW), Unbounded Prediction by Partial Matching (PPMD), Run Length Encoding (RLE), and Boolean Minimization. In addition, two transformation techniques are available: the Burrows-Wheeler Transformation (BWT) and Move to Front (MTF). Results show that GP-zip* provides improvements in compression ratio ranging from a fraction to several tens of percent over its predecessor.

Problem statement: Compression is useful because it helps reduce the consumption of expensive resources, such as hard disk space or transmission bandwidth. For effective data compression, the compression algorithm must be able to predict future data accurately in order to build a good probabilistic model for compression. Lossless compression is essential in cases where it is important that the original and the decompressed data be identical, or where deviations from the original data could be deleterious. Approach: Prediction by Partial Matching (PPM) data compression technique had utmost performance standard and capable of very good compression on a variety of data. In this research, we had introduced PPM technique to compress the data and implemented the algorithm on Altera FLEX10K FPGA device that allows for efficient hardware implementation. The PPM algorithm was modeled using the hardware description language VHDL. Results: Functional simulations were commenced to verify the functionality of the system with both 16-bit input and 32-bit input. The FPGA utilized 1164 logic cells with a maximum system frequency of 95.3MHz on Altera FLEX10K. Conclusion: The proposed approach is computationally simple, accurate and exhibits a good balance of flexibility, speed, size and design cycle time.