A New Algorithm for Data Compression Optimization

Chapter 1 - Introduction

One of the goals of telemedicine is to enable remote visualization and browsing of medical volumes. There is a need to employ scalable compression schemes and efficient client-server models to obtain interactivity and an enhanced viewing experience. First, we present a scheme that uses JPEG2000 and JPIP (JPEG2000 Interactive Protocol) to transmit data in a multi-resolution and progressive fashion. The server exploits the spatial locality offered by the wavelet transform and packet indexing information to transmit, in so far as possible, compressed volume data relevant to the clients query. Once the client identifies its volume of interest (VOI), the volume is refined progressively within the VOI from an initial lossy to a final lossless representation. Contextual background information can also be made available having quality fading away from the VOI. Second, we present a prioritization that enables the client to progressively visualize scene content from a compressed file. In our specific example, the client is able to make requests to progressively receive data corresponding to any tissue type. The server is now capable of reordering the same compressed data file on the fly to serve data packets prioritized as per the client's request. Lastly, we describe the effect of compression parameters on compression ratio, decoding times and interactivity. We also present suggestions for optimizing JPEG2000 for remote volume visualization and volume browsing applications. The resulting system is ideally suited for client-server applications with the server maintaining the compressed volume data, to be browsed by a client with a low bandwidth constraint.

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

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.

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.

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.

The development of telecommunications technology in the era of globalization happens very rapidly to help people to communication and one of the communication technology is using telephone device. However, the absence of security in securing data/information of delivery Short Message Service (SMS) that is confidential causes the message easily stolen by unauthorized parties. SMS sent via the BTS will be accepted Message Service Center (SMSC) been causing a crack misuse of the information. One way to secure the data from the data communication process is by cryptography. This research using descriptive statistical research methods. This research was conducted by analyzing the security by implementing the RC6 algorithm in cryptography and compression applications based on android SMS. The RC6 algorithm is an symmetric cryptographic algorithm that use the same key in encryption and decryption of SMS message. The encryption process that changes the number bits of data affects the number of SMSs that need to be sent, so text compression is required. The Huffman algorithm is a lossless data compression algorithm in which data compression does not eliminate one byte and is stored as originally. So the result of decompress is same with original ciphertext. Testing is done using Blackbox Method.This research was conducted to solve the security problem of sending SMS with cryptography and send data can be compressed to save SMS cost.Keywords : Cryptography, Encryption, Decryption, SMS, Mobile, RC6, Huffman,android

Energy saving is an important issue in wireless sensor networks (WSNs) since the nodes are typically powered by batteries with a limited capacity and the energy is limited. As data communication usually consumes much energy and bandwidth, decreasing energy consumption can be generally achieved by reducing the communication of data, for instance, through data compression. Thus, it is an important research issue that how to decrease energy consumption as well as maximize the lifetime of WSNs and increase the efficiency of data communication through data compression on the WSNs nodes where energy supply, memory space and processing resources are constrained. Considering the basic idea of edge operator in the field of image processing and the characteristic that the data stream collected by the nodes of WSNs is time series data, this paper proposes a nonthreshold-based node level algorithm of data compression over the WSNs. The algorithm, with simple calculating and low complexity, compresses the time series data collected by sensor nodes into many piecewise linear representations by extracting some points named edge-points that can indicate the trends of time series data, and especially dose not require any prior knowledge of the monitored objects as well as any predefined threshold value related to the time series data. The experiments on real public sensor data series show that the proposed algorithm can compress data effectively and reduce the communication of data obviously. Moreover, compared with some other data compression algorithms, the proposed algorithm appears better compression performance, reconstructed error and stability which allows the algorithm being applied to the collected data series with different fluctuation characteristics. Consequently, it can save the energy of wireless communication of senor nodes and prolong the lifetime of WSNs better.

The rapid growth of smart grid systems has resulted in an exponential increase in data volume. This has led to significant challenges for efficient storage and transmission, thereby necessitating the development of advanced data compression techniques. Traditional techniques often struggle to maintain a balance between compression efficiency and data integrity, particularly when dealing with diverse and large datasets. To address this issue, this paper presents an adaptive data compression algorithm based on discrete wavelet transform (DWT) and Horse Herd Optimization (HHO). The proposed technique significantly improves storage by dynamically optimizing key performance metrics, such as signal-to-noise ratio (SNR), mean square error (MSE), and compression ratio (CR). By employing a robust optimization algorithm, it effectively addresses the computational challenges of processing large-scale and real-time data. This adaptability ensures the algorithm is well-suited for dynamic smart grid environments, providing a scalable and reliable solution to modern data management demands. This technique uses HHO to find the optimal thresholding for smart grid data compression. In general, DWT-based data compression is carried out using a universal threshold for ignoring particular wavelet coefficients. But, the performance of data compression varies for different threshold values. Hence, selecting an optimal threshold is a challenging task for data compression. Therefore, to solve this issue, an effective optimization algorithm is needed. In this work, a Multi-Objective Horse Herd Optimization (MO-HHO) algorithm is proposed to find the optimal threshold. The suggested MO-HHO algorithm accurately determines the global optimum threshold. Hence, it maintains a good compromise between SNR, MSE and CR. The effectiveness of the proposed algorithm is examined using three different data sets. Various datasets from the IEEE Power quality wave data, Household Electric Power Consumption data and Real-time dataset from an experimental set-up were used to test the proposed algorithm. The outcome demonstrates that the suggested MO-HHO algorithm performs better than the other conventional methods.

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 this study, the authors present an energy-efficient data compression protocol for data collection in wireless sensor networks (WSNs). WSNs are essentially constrained by motes’ limited battery power and networks bandwidth. The authors focus on data compression algorithms and protocol development to effectively support data compression for data gathering in WSNs. Their design of compressed data-stream protocol (CDP) is generic in the sense that other lossless or lossy compression algorithms can be easily ‘plugged’ into the proposed protocol system without any changes to the rest of the CDP. This design intends to support various different WSN applications where users may prefer more specific compression algorithms, tailored to the sensing data characteristics in question, to their general algorithm. CDP is not only able to significantly reduce energy consumptions of data gathering in multi-hop WSNs, but also able to reduce sensor network traffic and thus avoid congestion accordingly. The proposed CDP is implemented on the tinyOS platform using the nesC programming language. To evaluate their work, the authors conduct simulations via TOSSIM and PowerTOSSIM-z with real-world sensor data. The results demonstrate the significance of CDP.

In data compression or source coding algorithms, input sequences of symbols are converted to shorter sequences while the original information remains unchanged. One of the well-known data compression algorithms is Deflate which is designed based on the LZ method. Deflate method has three different modes where its second mode is applicable for real-time applications. In this mode, a certain static table of Huffman codes is employed during the coding procedure. In this paper, a new version of deflate algorithm is proposed and implemented in hardware. In the proposed method, a new basic coding table is employed. This table is modified adaptively based on the input sequence. Simulation results show that in this adaptive algorithm, the coding performance is improved. In the hardware implementation of the new method, through some parallelism concepts, we try to improve the hardware utilization and throughput.

Data compression is an essential process due to the need to reduce the average time required to send messages and reduce the data size for storage purposes. There is a vital need for lossless compression especially for text and binary compression because it is important to ensure that the restructured text is identical to the original text. The predictive by partial matching (PPM) data compression scheme has set the performance standard in lossless compression throughout the past decade. PPM is chosen as it is capable of very good compression on a variety of data. In this paper, we present the realization of data compression using PPM on Altera FLEX10K FPGA device that allows for efficient hardware implementation. The PPM algorithm for binary data compression was successfully written and modeled in VHDL. The design is followed by the timing analysis and circuit synthesis for the validation, functionality and performance of the designated circuit which supports the practicality, advantages and effectiveness of the proposed hardware realization for the application. The designed was verified using both 16- bit input and 32-bit input. The hardware prototype utilized 1164 logic cells with a maximum system frequency of 95.3MHz.

Building entirely digital Holter monitoring systems is now pursued and development of digital electrocardiographs is progressing. Conventional Holter electrocardiographs are devices that record electrocardiograms on magnetic tape in the form of an analog signal, which implies a number of problems related to accuracy (waveform distortion) and vibration of movable parts (such as motors), as well as weight and size of the device. This paper discusses a simple Holter electrocardiograph that converts 24-hour electrocardiograms into digital signals, with provision for noise suppression, elimination of baseline fluctuation, easy diagnostics, storing data on media, and data transfer. An entirely digital design for such a device would contribute to circuit integration and would solve the problem of miniaturization by using IC memory cards as the media. However, to record a digital ECG signal for 24 hours, 64.8 MBytes of memory is required for the standard pattern of 2 channels, 12 bits, and 250-Hz sampling. IC memory cards currently available lack this capacity, and an algorithm for data compression is necessary. In this paper, a simple algorithm is proposed for R-R interval extraction in a compact Holter monitoring system, as well as a data compression algorithm that provides adaptive regulation of compression ratio and accuracy. Simulation results are reported.

In digital communication while transmit the data it is well desire that the transmitting data bits should be as minimum as possible, so to compress the data there are several technique used. In this paper we have implemented a Shannon-fano algorithm for data compression through VHDL coding. Using VHDL implementation we can easily observe that how many bits we can save or how much data gets compressed during transmission, and we can also see the encoding of the respective symbol of transmit data. In the field of data compression the Shannon-fano algorithm is used, this algorithm is also used in an implode compression method which are used in zip file or .rar format. To implement this algorithm in VHDL we use ModelSim SE 6.4 simulators and to synthesize these code Quartus-II tool has been used.