Lempel-Ziv-Welch Research Articles

A Developed Compression Scheme to Optimize Data Transmission in Wireless Sensor Networks

Improving performance is an important issue in Wireless Sensor Networks (WSN). WSN has many limitations including network performance. The research question is how to reduce the amount of data transmitted to improve network performance? The work will include one of the dictionary compression methods which is Lempel Ziv Welch(LZW). One problem with the dictionary method is that the token size is fixed. The LZW dictionary method is not very useful with little data, because it loses many bytes when storing small-sized tokens. From the results obtained, the best compression ratios were in the proposed algorithm. The proposed work suggests using a dynamic size token where the tokens are classified according to their size(one byte, two bytes, or three bytes). The main idea of the proposed work is based on increasing the frequency of data to increase the compression ratio. To increase the frequency of data, the work suggests keeping the amount of incremental reading data instead of keeping the whole real data. Because the climate reading data changes very slowly, the amount of change would be frequent.

Secure Electronic Commerce Transactions Using Pythagorean Triple-based Cryptography and Audio Steganography

The rapidly increasing utilization of Information Technology (IT) in electronic commerce and other aspects of human existence has led to a rise in worries about the privacy and safe-ty/security of those involved. Due to the alarmingly high frequency with which data or user privacy breaches occur today, conducting online transactions can represent a major threat to buyers (and even business owners') privacy. Existing solutions to these security issues are still vulnerable to classic phishing scams. As a result, the construction of a robust and effi-cient audio steganography system for secure electronic commerce transactions is presented in this work. The developed system first encrypts sensitive financial data using the Pythagorean Triple-based Cryptographic (PTC) algorithm, then compresses the resulting cipher-text using the Lempel Ziv Welch (LZW) and Huffman's Compression Algorithms, and finally embeds the compressed file (using one-dimensional Discrete Cosine Transform (DCT)) in a suitable audio-file cover to produce an output (stego file) that is indistinguishable from the cover file. This output can then be exchanged between communicating entities across open networks. The new system has a little or no alteration in its outputs, as evidenced by the Signal-to-Noise Ratio (SNR), in the experimental results gotten.

LSD-induced increase of Ising temperature and algorithmic complexity of brain dynamics

A topic of growing interest in computational neuroscience is the discovery of fundamental principles underlying global dynamics and the self-organization of the brain. In particular, the notion that the brain operates near criticality has gained considerable support, and recent work has shown that the dynamics of different brain states may be modeled by pairwise maximum entropy Ising models at various distances from a phase transition, i.e., from criticality. Here we aim to characterize two brain states (psychedelics-induced and placebo) as captured by functional magnetic resonance imaging (fMRI), with features derived from the Ising spin model formalism (system temperature, critical point, susceptibility) and from algorithmic complexity. We hypothesized, along the lines of the entropic brain hypothesis, that psychedelics drive brain dynamics into a more disordered state at a higher Ising temperature and increased complexity. We analyze resting state blood-oxygen-level-dependent (BOLD) fMRI data collected in an earlier study from fifteen subjects in a control condition (placebo) and during ingestion of lysergic acid diethylamide (LSD). Working with the automated anatomical labeling (AAL) brain parcellation, we first create "archetype" Ising models representative of the entire dataset (global) and of the data in each condition. Remarkably, we find that such archetypes exhibit a strong correlation with an average structural connectome template obtained from dMRI (r = 0.6). We compare the archetypes from the two conditions and find that the Ising connectivity in the LSD condition is lower than in the placebo one, especially in homotopic links (interhemispheric connectivity), reflecting a significant decrease of homotopic functional connectivity in the LSD condition. The global archetype is then personalized for each individual and condition by adjusting the system temperature. The resulting temperatures are all near but above the critical point of the model in the paramagnetic (disordered) phase. The individualized Ising temperatures are higher in the LSD condition than in the placebo condition (p = 9 × 10-5). Next, we estimate the Lempel-Ziv-Welch (LZW) complexity of the binarized BOLD data and the synthetic data generated with the individualized model using the Metropolis algorithm for each participant and condition. The LZW complexity computed from experimental data reveals a weak statistical relationship with condition (p = 0.04 one-tailed Wilcoxon test) and none with Ising temperature (r(13) = 0.13, p = 0.65), presumably because of the limited length of the BOLD time series. Similarly, we explore complexity using the block decomposition method (BDM), a more advanced method for estimating algorithmic complexity. The BDM complexity of the experimental data displays a significant correlation with Ising temperature (r(13) = 0.56, p = 0.03) and a weak but significant correlation with condition (p = 0.04, one-tailed Wilcoxon test). This study suggests that the effects of LSD increase the complexity of brain dynamics by loosening interhemispheric connectivity-especially homotopic links. In agreement with earlier work using the Ising formalism with BOLD data, we find the brain state in the placebo condition is already above the critical point, with LSD resulting in a shift further away from criticality into a more disordered state.

Design and Implementation of Digitization Resource Sharing System Based on Hadoop Platform

The basic idea of Hadoop is to use multiple independently working computers to form a cluster of computers that can be stored and calculated logically, and do not require other expensive facilities to run, but integrate cheap equipment together. Hadoop technology is produced in the context of big data, to achieve the mass storage of big data, the most prominent advantage is the cost advantage. Based on Hadoop architecture, this paper designs the LZW compression algorithm for improving the storage efficiency of teaching resources, including the algorithm principle and algorithm flow. The system functions of resource collection, storage, management, migration and retrieval are designed. and designed the function module of each system. The function modules of the system form a whole system through parameter transfer, which has low coupling property. The research results assist the construction of an integrated sharing system of storage, management, transfer and retrieval of teaching resources to meet the demands of teaching resource sharing in the era of big data.

Comparison of Huffman Algorithm and Lempel Ziv Welch Algorithm in Text File Compression

The development of data storage hardware is very rapidly over time. In line with the development of storage hardware, the amount of digital data shared on the internet is increasing every day. That way no matter how big the size of the storage device we have, of course, it will only be a matter of time until that storage space is exhausted. Therefore, in terms of maximizing storage space, a technique called compression emerged. This study focuses on a comparative analysis of 2 Lossless compression technique algorithms, namely the Huffman algorithm and Lempel Ziv Welch (LZW). A number of test files with different file types are applied to both algorithms that are compared. The performance of the algorithm is determined based on the comparison of space saving and compression time. The test results showed that the Lempel Ziv Welch (LZW) algorithm was superior to Huffman’s algorithm in .txt file type compression and .csv, the average space savings produced were 63.85% and 77.56%. The degree of compression speed that each algorithm produces is directly proportional to the file size.

Verification of role of data scanning direction in image compression using fuzzy composition operations

A digital image is a numerical representation of visual perception that can be manipulated according to specifications. In order to reduce the cost of storage and transmission, digital images are compressed. Depending upon the quality of reconstruction, compression methods are categorized as Lossy and Lossless compression. The lossless image compression techniques, where the exact recovery of data is possible, is the most challenging task considering the tradeoff between the compression ratio achieved and the quality of reconstruction. The inherent data redundancies like interpixel redundancy and coding redundancy in the image are exploited for this purpose. The interpixel redundancy is treated by decorrelation using Run-length Encoding, Predictive Coding, and other Transformation Coding techniques. While entropy-based coding can be achieved using Huffman codes, arithmetic codes, and the LZW algorithm, which eliminates the coding redundancy. During the implementation of these sequential coding algorithms, the direction used for data scanning plays an important role. A study of various image compression techniques using sequential coding schemes is presented in this paper. The experimentation on 100 gray-level images comprising 10 different classes is carried out to understand the effect of the direction of scanning of data on its compressibility. Depending upon this study the interrelation between the maximum length of the Run and compression achieved similarly the resultant number of Tuples and compression achieved is reported. Considering the fuzzy nature of these relations, fuzzy composition operations like max-min, min-max, and max-mean compositions are used for decision-making. In this way, a rational comment on which data scanning direction is suitable for a particular class of images is made in the conclusion.

A high capacity reversible data hiding scheme in efficient LZW compression

A high capacity reversible data hiding scheme in efficient LZW compression

A Modified LZW Algorithm Based on a Character String Parallel Search in Cluster-Based Telemetry Data Compression

The volume of telemetry data is gradually increasing, both because of the increasingly larger number of parameters involved, and the use of higher sampling frequencies. Efficient data compression schemes are therefore needed in space telemetry systems to improve transmission efficiency and reduce the burden of required spacecraft resources, in particular regarding their transmitter power. In our primary study, a D-CLU algorithm was proposed to perform lossless compression for telemetry data, and achieve better performance. However, a limitation of this algorithm is that the compression time may become longer when the clustering head (CH) and outlier (which are compressed by LZW algorithm) numbers increase. To reduce compression delay, this paper proposed a modified character string (MCS) parallel search strategy for LZW algorithm (denoted by MCS-based LZW). The proposed MCS-based LZW algorithm designs coding principle, dictionary update rule and search strategy according to the character string matching results. Example verification and simulation results show that the proposed algorithm can effectively decrease the dictionary search times, and thus reduce the compression time.

Feedback Overhead in the FDD Downlink Massive MIMO System

One of the fundamental problems in massive multiple input multiple output (MMIMO) systems based on the frequency division duplexing (FDD) mode is the acquisition of the channel state information at the base station (BS). Indeed, in these systems, the reciprocity of the uplink (UL) and downlink (DL) channels is not applicable. Thus, the users are supposed, after having estimated their subchannels, to feedback them to the BS. Since the size of the information to be returned is proportional to the number of base station antennas that is very large, then we witness an excess of the number to be returned for each user. It is therefore essential to find a way to reduce this quantity, which affects the overall performance of the system. In this article, we propose two new MMIMO-OFDM-FDD-DL schemas based on the Givens rotation quantization used in the 802.11ac standard to reduce the amount of information to be returned for each user. The first is a fusion between the Givens rotation (GR) and the arithmetic coding (AC) technique, and the second is based on the combination of the GR method and the universal compression technique, Lempel Ziv Welch (LZW). The proposed schemes named GR & AC and GR & LZW (with an improvement of this proposal based on a modified version of LZW (LZWM) named GR & LZWM) for the first and second, respectively, are compared and both can achieve a compression ratio of up to 95% (or even 98%) and 83%, considerably exceeding recent advanced techniques.

A Novel Hybrid Medical Data Compression Using Huffman Coding and LZW in IoT

Today, telemedicine has become very essential, because it provides the possibility for the health centers, hospitals, and research centers to exchange medical and diagnostic data, via IoT. Since the volume of the generated medical information in IoT is very high, transmitting this data via channels of limited bandwidth is time-consuming; thus, the information should be compressed before transmission. Todays, some of the methods compress data significantly, but quality of the restored data in these methods is very low. Considering the importance of images and information for diagnostic and medical applications, desired quality is of great importance. Thus, this study tries to present a hybrid medical information compression technique such that quality of the restored information is desired and compression is efficient. This compression technique combines two lossless compression methods, including Huffman encoding and Lempel-Ziv-Welch (LZW) that rearranges information. In this combination, a binary information arrangement is used between Hoffman and LZW techniques so that the integration of binary information reaches an information mapping and, while simplifying the mapping, is completely different for each piece of information and makes this article stand out. The simulation results of the proposed method show that medical information including signal, text, and image is reduced by an average of 37.85%. Remarkably, quality of the restored image is not degraded.

Multipurpose medical image watermarking for effective security solutions.

Digital medical images contain important information regarding patient’s health and very useful for diagnosis. Even a small change in medical images (especially in the region of interest (ROI)) can mislead the doctors/practitioners for deciding further treatment. Therefore, the protection of the images against intentional/unintentional tampering, forgery, filtering, compression and other common signal processing attacks are mandatory. This manuscript presents a multipurpose medical image watermarking scheme to offer copyright/ownership protection, tamper detection/localization (for ROI (region of interest) and different segments of RONI (region of non-interest)), and self-recovery of the ROI with 100% reversibility. Initially, the recovery information of the host image’s ROI is compressed using LZW (Lempel-Ziv-Welch) algorithm. Afterwards, the robust watermark is embedded into the host image using a transform domain based embedding mechanism. Further, the 256-bit hash keys are generated using SHA-256 algorithm for the ROI and eight RONI regions (i.e. RONI-1 to RONI-8) of the robust watermarked image. The compressed recovery data and hash keys are combined and then embedded into the segmented RONI region of the robust watermarked image using an LSB replacement based fragile watermarking approach. Experimental results show high imperceptibility, high robustness, perfect tamper detection, significant tamper localization, and perfect recovery of the ROI (100% reversibility). The scheme doesn’t need original host or watermark information for the extraction process due to the blind nature. The relative analysis demonstrates the superiority of the proposed scheme over existing schemes.

Layered Lossless Compression Method of Massive Fault Recording Data

In order to overcome the problems of large error and low precision in traditional power fault record data compression, a new layered lossless compression method for massive fault record data is proposed in this paper. The algorithm applies LZW (Lempel Ziv Welch) algorithm, analyzes the LZW algorithm and existing problems, and improves the LZW algorithm. Use the index value of the dictionary to replace the input string sequence, and dynamically add unknown strings to the dictionary. The parallel search method is to divide the dictionary into several small dictionaries with different bit widths to realize the parallel search of the dictionary. According to the compression and decompression of LZW, the optimal compression effect of LZW algorithm hardware is obtained. The multi tree structure of the improved LZW algorithm is used to construct the dictionary, and the multi character parallel search method is used to query the dictionary. The multi character parallel search method is used to query the dictionary globally. At the same time, the dictionary size and update strategy of LZW algorithm are analyzed, and the optimization parameters are designed to construct and update the dictionary. Through the calculation of lossless dictionary compression, the hierarchical lossless compression of large-scale fault record data is completed. Select the optimal parameters, design the dictionary size and update strategy, and complete the lossless compression of recorded data. The experimental results show that compared with the traditional compression method, under this compression method, the mean square error percentage is effectively reduced, and the compression error and compression rate are eliminated, so as to ensure the integrity of fault record data, achieve the compression effect in a short time, and achieve the expected goal.

Disbursal of Text Steganography in the Space of Double-Secure Algorithm

Hackers reveal sensitive information from encrypted conversations as the globe has become one community. As a result, steganography and cryptography are used together to safeguard sensitive data more effectively than previous approaches and newly developed algorithms. A double-secure algorithm for text steganography (DSTS) is the safest approach compared to the paragraph hiding method (PHM) and capital alphabet shape encoding (CASE) method. In this context, the paragraph hiding method uses ASCII coding, which is not a standard method, and the CASE method has a security issue that if someone understands its applicability, it can be easily attacked. DSTS algorithm overcomes its limitations by using UTF-16 coding and enhances the security of CASE with a one-time pad (OTP). The one-time pad is a theoretically unbreakable cryptographic method. Still, it is not feasible for long messages, however, it is usable for short messages such as a password or intelligence agency’s more secure messages. DSTS degrades the average hiding capacity in a PHM from 2.06% to 1% due to UTF-16 coding. Using the UTF-8 method, the authors have improved MDSTS and the CMPHM by 6.49% and 7.76% hiding capacity, which outperforms in existing procedures; in the CMPHM, there is one reason for the improvement in hiding capacity is using the LZW compression method. CMHPM algorithm optimization standard deviation is less than that of the HPM, DSTS, and MDSTS, ultimately increasing speed.

GIF IMAGE HARDWARE COMPRESSORS

Increasing requirements for data transfer and storage is one of the crucial questions now. There are several ways of high-speed data transmission, but they meet limited requirements applied to their narrowly focused specific target. The data compression approach gives the solution to the problems of high-speed transfer and low-volume data storage. This paper is devoted to the compression of GIF images, using a modified LZW algorithm with a tree-based dictionary. It has led to a decrease in lookup time and an increase in the speed of data compression, and in turn, allows developing the method of constructing a hardware compression accelerator during the future research.

Compression and Encryption of Vital Signals Using an SoC-FPGA

This article presents the implementation of a remote monitoring system of biomedical signals with cybersecurity support and compression of vital sign signals and data of the patient. This system uses a low-cost microsystem for encrypting and compressing the information using the Lempel–Ziv–Welch (LZW) lossless compression algorithm and the Advanced Encryption Standard (AES). In this case, WolfSSL library is used for the implementation of the Transport Layer Security (TLS) protocol, whose encryption function is accelerated by the AES processor designed on a System on Chip - Field Programmable Gate Array (SoC-FPGA) device. A multiparameter board and an SoC-FPGA development board make up the vital signs measurement system, which was calibrated and verified by a commercial patient simulator. Data transmission tests were carried out from the measurement system to the monitoring application developed in LabVIEW and implemented on a personal computer (PC), where vital signs and data of the patient are decrypted and decompressed. Also, it was possible to verify a significant improvement in the performance of the TLS connection. From the results obtained, it can be concluded that the designed microsystem allows to compress, encrypt, and transmit biomedical data in real-time and without loss of information. The microsystem is very suitable for e-health platforms and/or e-health devices that use unsecured communication networks with limited bandwidth.

Engineering Practical Lempel-Ziv Tries

The Lempel-Ziv 78 ( LZ78 ) and Lempel-Ziv-Welch ( LZW ) text factorizations are popular, not only for bare compression but also for building compressed data structures on top of them. Their regular factor structure makes them computable within space bounded by the compressed output size. In this article, we carry out the first thorough study of low-memory LZ78 and LZW text factorization algorithms, introducing more efficient alternatives to the classical methods, as well as new techniques that can run within less memory space than the necessary to hold the compressed file. Our results build on hash-based representations of tries that may have independent interest.

Distributed compression and decompression for big image data: LZW and Huffman coding

In today’s era, digital data are being created and transmitted primarily in the form of images and videos. Storing such a huge number of images and transmitting them requires a lot of computer resources such as storage and bandwidth. So, instead of storing the image data as is, if we compress and store it, it saves a lot of resources. Image compression is the act of removing the maximum possible redundant data from an image and maintaining only the non-redundant data. To compress and decompress such big image data, a distributed environment with a map-reduce paradigm using Hadoop distributed file system and Apache Spark is used. In addition to these, Microsoft Azure cloud environment with infrastructure as a service is also used. Various setups such as a single system, 1 + 4 node cluster, 1 + 15 node cluster, and 1 + 18 node cluster cloud infrastructure are used to show the time comparisons among these setups with the self-created large image dataset. On these four self-made clusters, more than 100 million (109,670,400) images are compressed and decompressed; the execution times are compared with two of the traditional image compression methods: Lempel-Ziv-Welch (LZW) and Huffman coding. Both the LZW and Huffman coding are lossless image compression techniques. LZW removes both spatial and coding redundancies and whereas the Huffman coding removes only coding redundancy. These two compression techniques: LZW and Huffman are just placeholders, these can be replaced with any other compression technique for large image data. In our work, we have used compression ratio, average root mean square error (ARMSE), and average peak signal to noise ratios to validate that the compression and decompression process for each technique is exactly the same irrespective of the number of systems used, distributed or not.

Wavelet Tree ensembles with Machine Learning and its classification

Wavelet trees are compact data structures in computational geometry. In the past, it was used as an essential tool for handling the size of data, data compression, indexing and for many more applications. Machine learning algorithms are used for classification of data and its analysis. In this article, we discuss the scope of machine learning with wavelet trees, wavelet entropy, wavelet matrix and wavelet packets. The study concludes that machine learning applications with wavelet tree is a better choice in terms storage and classification of data. The proposed methodology consists of three techniques for making the data more efficient. It consists of LZW Compression techniques, Wavelet tree, and machine learning algorithm SVM. In this methodology compression with classification process is done for datasets. This proposed methodology performs with machine learningalgorithms in terms of classification of data. In future this method can be used for efficient searching and indexing of large data sets. The classified and compressed dataset perform the indexing with wavelet tree takes less searching time.

Image compression methods for efficient storage and transmission

This paper presents a detailed and comprehensive review of image compression methods, emphasizing their role in optimizing both storage and transmission efficiency across various domains, from everyday use in social media to specialized applications like medical imaging and satellite data processing. We systematically explore both traditional and contemporary image compression techniques, categorizing them into lossless and lossy methods, transform-based approaches, and the latest advancements in machine learning-based compression. Lossless compression techniques, including Run-Length Encoding (RLE), Huffman Coding, Lempel-Ziv-Welch (LZW), and the Portable Network Graphics (PNG) format, are discussed for their ability to preserve image quality perfectly, albeit at the cost of relatively lower compression ratios. Conversely, lossy compression methods, such as JPEG and fractal compression, offer significant file size reduction by discarding non-essential data, while still maintaining acceptable visual quality for many practical applications. We further delve into transform-based approaches like Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT), which form the backbone of popular standards such as JPEG and JPEG 2000, enabling more efficient data representation in the frequency domain. Additionally, the study highlights emerging machine learning and deep learning techniques, such as autoencoders and Generative Adversarial Networks (GANs), that are pushing the boundaries of image compression by achieving unprecedented compression ratios while minimizing perceptual loss in image quality. Through a comparative analysis, we evaluate these methods based on multiple performance metrics, including compression ratio, computational complexity, image fidelity (measured via Peak Signal-to-Noise Ratio, PSNR, and Structural Similarity Index, SSIM), and their practical applications across different industries. Our findings suggest that while traditional methods such as JPEG, PNG, and JPEG 2000 remain widely adopted due to their simplicity and efficiency, emerging techniques driven by deep learning show great potential in adapting to specific image characteristics, achieving higher compression ratios, and better preserving image quality under extreme compression. Finally, this paper identifies key challenges and trends in the field, such as the increasing computational demands of advanced techniques, the need for adaptive compression strategies, and the importance of standardization for broad industry adoption. We conclude that while traditional methods will continue to play a significant role, the future of image compression lies in the integration of machine learning and content-aware technologies that dynamically optimize compression performance across diverse image types and application contexts.

Analysis for Lossless Data Compression Algorithms for Low Bandwidth Networks

Analysis for loss less data compression delivers the relevant data about variations of them as well as to describe the possible causes for each algorithm and best performing data types. It describes the basic lossless techniques of data compression Huffman encodes, Arithmetic Encoding, and Lempel Ziv Encodings then briefly with their effectiveness under varying data types of Latin text, audio and video. These properties give the solution of which lossless compression algorithm more suitable compared to other from the Saving Percentage, compression ratio, time of compression and time of decompression with Low Bandwidth Network. Moreover here Lossless Data Compression Algorithms (LDCA) being implemented and tested Huffman compression, Arithmetic compression, and Lempel Ziv algorithms, the implemented result shows that LZW algorithm saves more size than that of the others two with text file, Huffman compression algorithm saves more file sizes and the time takes to compressed decompress is higher than that of other two for audio file type and finally Huffman performs greater on very huge data compressions that is due to much compressing capability.