Data Compression Coding Research Articles

MicroGravity Explorer Kit (MGX): An Open-Source Platform for Accessible Space Science Experiments

The study of microgravity, a condition in which an object experiences near-zero weight, is a critical area of research with far-reaching implications for various scientific disciplines. Microgravity allows scientists to investigate fundamental physical phenomena influenced by Earth’s gravitational forces, opening up new possibilities in fields such as materials science, fluid dynamics, and biology. However, the complexity and cost of developing and conducting microgravity missions have historically limited the field to well-funded space agencies, universities with dedicated government funding, and large research institutions, creating a significant barrier to entry. This paper presents the MicroGravity Explorer Kit’s (MGX) design, a multifunctional platform for conducting microgravity experiments aboard suborbital rocket flights. The MGX aims to democratize access to microgravity research, making it accessible to high school students, undergraduates, and researchers. To ensure that the tool is versatile across different scenarios, the authors conducted a comprehensive literature review on microgravity experiments, and specific requirements for the MGX were established. The MGX is designed as an open-source platform that supports various experiments, reducing costs and accelerating development. The multipurpose experiment consists of a Jetson Nano computer with multiple sensors, such as inertial sensors, temperature and pressure, and two cameras with up to 4k resolution. The project also presents examples of codes for data acquisition and compression and the ability to process images and run machine learning algorithms to interpret results. The MGX seeks to promote greater participation and innovation in space sciences by simplifying the process and reducing barriers to entry. The design of a platform that can democratize access to space and research related to space sciences has the potential to lead to groundbreaking discoveries and advancements in materials science, fluid dynamics, and biology, with significant practical applications such as more efficient propulsion systems and novel materials with unique properties.

Method for testing the stability of an autoregressive model of the vocal tract and adjusting its parameters

Within the framework of the traditional direction of research in the field of acoustic measurements, an autoregressive model of the vocal tract as a key link in the human speech apparatus is considered. The acute problem of ensuring the stability of the autoregressive model in systems with adaptation of its parameters to the observed speech signal of short duration is pointed out. To overcome this problem, the task was set of testing the stability of the autoregressive model and adjusting its parameters based on the results of this testing. The study is based on the author’s method of formant analysis of vowel sounds of speech through the synthesis of a recursive shaping filter in the free oscillation mode. To solve sated task, a method is proposed for testing the stability and adjusting the parameters of the autoregressive model of the vocal tract based on a two-stage algorithm for its transformation. At the first stage of transformation, the stability of the autoregressive model is tested using the impulse response of the shaping filter. At the second stage, if the stability of the autoregressive model is violated, its impulse response is modified by element-by-element multiplication by a variable exponential value that asymptotically converges to zero. A regular algorithm has been developed for recalculating the modified impulse response into an adjusted vector of autoregressive parameters at the second stage of transformation. Based on the results of experimental testing of the proposed method, it was concluded that guaranteed stability of the autoregressive model of the vocal tract has been achieved with minimal distortion in the frequency domain. The results obtained are useful in the development and modernization of automatic speech recognition systems, digital speech communications, artificial intelligence and other information systems that use data compression and speech coding based on an autoregressive model of the vocal tract in automatic speech signal processing.

Lossless text compression by means of binary-coded ternary number representation

A lossless data compression code based on a binary-coded ternary number representation is investigated. The code is complete, universal, synchronizable, and supports a direct search in a compressed file. A simple monotonous encoding and very fast decoding algorithms are constructed owing to code properties. Experiments show that in natural language text compression, the new code outperforms byte-aligned codes SCDC and RPBC either in compression ratio or in decoding speed.

A Fast and Accurate Lempel–Ziv Complexity Indicator Based on Data Compression and Multiscale Coding for Recognition of Bearing Fault Severity

A Fast and Accurate Lempel–Ziv Complexity Indicator Based on Data Compression and Multiscale Coding for Recognition of Bearing Fault Severity

Exploiting data compression to improve reliability of phase-modulated holographic data storage.

Due to the interference of complex noise in holographic channels and the limitation of phase retrieve algorithms, the reliability of phase-modulated holographic data storage (PHDS) is seriously threatened, especially for multi-level phase modulation. A method for improving data reliability of PHDS is proposed by applying lossless data compression and low-density parity-check (LDPC) codes, which can eliminate data redundancy and correct errors effectively. We allocate the space saved by compression to store more LDPC parity bits and develop a method to determine the LDPC code rate and a method to manage the free space. Our method does not require the characteristics of the reconstructed phase distribution, which simplifies the statistical analysis and calculation. Simulation and experimental results demonstrate that our method greatly decreases the bit error rate (BER) and decoding iterations, and boosts the decoding success probability. For instance, when the phase error rate is 0.029 and the compression rate is 0.6, our method reduces the BER by 87.8%, the decoding iterations by 84.3%, and improves the decoding success probability by 93%. Our method enhances both data reliability and storage efficiency in PHDS.

A neurodynamic approach for nonsmooth optimal power consumption of intelligent and connected vehicles

This paper investigates a class of power consumption minimization and equalization for intelligent and connected vehicles cooperative system. Accordingly, a distributed optimization problem model related to power consumption and data rate of intelligent and connected vehicles is presented, where the power consumption cost function of each intelligent and connected vehicle may be nonsmooth, and the corresponding control variable is subject to the constraints generated by data acquisition, compression coding, transmission and reception. We propose a distributed subgradient-based neurodynamic approach with projection operator to achieve the optimal power consumption of intelligent and connected vehicles. By differential inclusion and nonsmooth analysis, it is confirmed that the state solution of neurodynamic system converges to the optimal solution of the distributed optimization problem. With the help of the algorithm, all intelligent and connected vehicles asymptotically reach a consensus on an optimal power consumption. Simulation results show that the proposed neurodynamic approach is capable of effectively solving the problem of power consumption optimal control for intelligent and connected vehicles cooperative system.

Inter-node compression with LDPC joint source–channel coding for highly correlated sources

This paper investigates a new communication system where two nodes want to disseminate highly correlated contents to a single destination and can be applied for densely deployed wireless sensors networks applications. Motivated by their capacity-achieving performance and existing practical implementations, the proposed communication scheme is fully based on Low-Density Parity-Check (LDPC) codes for data compression and channel coding. More specifically, we consider a network of two correlated binary sources with two orthogonal communication phases. Data are encoded at the first source with an LDPC channel code and broadcast in the first phase. Based on the first source received data, the second source computes the correlation vector and applies a Joint Source–Channel (JSC) LDPC code, which output is communicated in the second phase. At the receiver, the whole network is mapped on a joint factor graph over which an iterative message-passing joint decoder is proposed. The aim of the joint decoder is to exploit the residual correlation between the sources for better estimation. Simulation results are investigated and compared to the theoretical limits and to an LDPC-based distributed coding system where no inter-node compression is applied.

Creation of a video data reliability assessment model for compression coding technology in the conditions of errors in the data transmission channel

The technology of compression coding of video data for information communication systems of the air segment is being studied. Distinctive features of the developed technology are the following: structural decomposition of the information space by structural feature; the use of a two-hierarchical scheme of statistical coding. A feature of the proposed technology is the use of a statistical approach in the process of forming the informational part of the codegram and the marker (identifier) of the code structure in the original code sequence. This makes it possible to create conditions for localizing the action of errors that occur in the process of video data delivery in the air segment info-communication systems. A model for evaluating the effectiveness of the proposed video data compression coding technology is created. A feature of the created model is the use of both quantitative and qualitative indicators of data distortions of the video information resource under the conditions of errors in the data transmission channels. A comparative analysis of the developed technology with lossless coding methods (Huffman method) and existing compression coding technologies built on the basis of the JPEG platform is carried out. The advantages of the proposed technology are the provision of localization of the action of errors due to the use of additional service information (identifiers) about the code structure, which is formed in the process of statistical coding of the data of the video information resource.

A Delay-Efficient Deep Learning Approach for Lossless Turbo Source Coding

Lossless turbo source coding with decremental/incremental redundancy is a variable-length source coding scheme which employs turbo codes for data compression. Although the scheme offers low compression rates and lends itself to joint source-channel coding, it suffers from a large delay in the encoding phase. The delay is imposed by several tentative encoding-decoding procedures performed at the encoder to search for the minimum compression length. In this work, we apply machine learning to provide a highly accurate estimate of the proper compression length. The encoder starts its search from this estimated length, thus the delay of turbo source coding will decrease considerably. The preliminary results show a four-fold reduction in the encoding delay at the expense of a negligible increase in the compression rate.

Analysis of the impact of data compression on condition monitoring algorithms for ball screws

The overall equipment effectiveness (OEE) is a management ratio to evaluate the added value of machine tools. Unplanned machine downtime reduces the operational availability and therefore, the OEE. Increased machine costs are the consequence. An important cause of unplanned machine downtimes is the total failure of ball screws of the feed axes due to wear. Therefore, monitoring of the condition of ball screws is important. Common concepts rely on high-frequency acceleration sensors from external control systems to detect a change of the condition. For trend and detailed damage analysis, large amounts of data are generated and stored over a long time period (>5 years), resulting in corresponding data storage costs. Additional axes or machine tools increase the data volume further, adding to the total storage costs. To minimize these costs, data compression or source coding has to be applied. To achieve maximum compression ratios, lossy coding algorithms have to be used, which introduce distortion in a signal. In this work, the influence of lossy coding algorithms on a condition monitoring algorithm (CMA) using acceleration signals is investigated. The CMA is based on principal component analysis and uses 17 features such as standard deviation to predict the preload condition of a ball screw. It is shown that bit rate reduction through lossy compression algorithms is possible without affecting the condition monitoring - as long as the compression algorithm is known. In contrast, an unknown compression algorithm reduces the classification accuracy of condition monitoring by about 20 % when coding with a quantizer resolution of 4 bit/sample.

Performance evaluation of arithmetic coding data compression for internet of things applications

<span style="font-size: 9pt; font-family: 'Times New Roman', serif;">Wireless Sensor Network (WSN) is known for its autonomous sensors, where it has been found to be useful during the development of Internet of Things (IoT) devices. However, WSN is also known for its limited energy supply and memory space, as it carries small-sized batteries and memory space. Hence, a data compression approach has been introduced in this paper with the purpose of solving this particular issue. This paper focused on the performance of the Arithmetic Coding algorithm. Temperature (Temp), Sea-Level Pressure (Pressure), stride interval (Stride), and heart rate (BPM) were chosen as the dataset in this project. Based on the results, the compression ratio of Temp, Pressure, Stride, and BPM were 0.428, 0.255, 0.217, and 0.159 respectively. From this analysis, BPM produced the best compression ratio. Undeniably, the Arithmetic Coding algorithm is one of the best methods to compress real-world datasets. Hence, by using this approach, it can reduce the usage of energy and memory space.</span><table class="MsoTableGrid" style="width: 444.85pt; border-collapse: collapse; border: none; mso-border-alt: solid windowtext .5pt; mso-yfti-tbllook: 1184; mso-padding-alt: 0in 5.4pt 0in 5.4pt;" width="593" border="1" cellspacing="0" cellpadding="0"><tbody><tr style="mso-yfti-irow: 0; mso-yfti-firstrow: yes; mso-yfti-lastrow: yes; height: 63.4pt;"><td style="width: 290.6pt; border: none; border-top: solid windowtext 1.0pt; mso-border-top-alt: solid windowtext .5pt; padding: 0in 5.4pt 0in 5.4pt; height: 63.4pt;" valign="top" width="387"><p class="MsoNormal" style="margin-top: 6.0pt; text-align: justify;"><span style="font-size: 9.0pt; color: black; mso-bidi-font-style: italic;">Wireless Sensor Network (WSN) is known for its autonomous sensors, where it has been found to be useful during the development of Internet of Things (IoT) devices. However, WSN is also known for its limited energy supply and memory space, as it carries small-sized batteries and memory space. Hence, a data compression approach has been introduced in this paper with the purpose of solving this particular issue. This paper focused on the performance of the Arithmetic Coding algorithm. Temperature (Temp), Sea-Level Pressure (Pressure), stride interval (Stride), and heart rate (BPM) were chosen as the dataset in this project. Based on the results, the compression ratio of Temp, Pressure, Stride, and BPM were 0.428, 0.255, 0.217, and 0.159 respectively. From this analysis, BPM produced the best compression ratio. Undeniably, the Arithmetic Coding algorithm is one of the best methods to compress real-world datasets. Hence, by using this approach, it can reduce the usage of energy and memory space.</span></p></td></tr></tbody></table>

Improved Efficiency on Adaptive Arithmetic Coding for Data Compression Using Range-Adjusting Scheme, Increasingly Adjusting Step, and Mutual-Learning Scheme

Context-based adaptive arithmetic coding (CAAC) has high coding efficiency and is adopted by the majority of advanced compression algorithms. In this paper, five new techniques are proposed to further improve the performance of CAAC. They make the frequency table (the table used to estimate the probability distribution of data according to the past input) of CAAC converge to the true probability distribution rapidly and hence improve the coding efficiency. Instead of varying only one entry of the frequency table, the proposed range-adjusting scheme adjusts the entries near to the current input value together. With the proposed mutual-learning scheme, the frequency tables of the contexts highly correlated to the current context are also adjusted. The proposed increasingly adjusting step scheme applies a greater adjusting step for recent data. The proposed adaptive initialization scheme uses a proper model to initialize the frequency table. Moreover, a local frequency table is generated according to local information. We perform several simulations on edge-directed prediction-based lossless image compression, coefficient encoding in JPEG, bit plane coding in JPEG 2000, and motion vector residue coding in video compression. All simulations confirm that the proposed techniques can reduce the bit rate and are beneficial for data compression.

ВДОСКОНАЛЕННЯ МЕТОДУ КОМПРЕСІЇ ДАНИХ НА ОСНОВІ КОДУ ФІБОНАЧЧІ

This article discusses the effectiveness of data compression based on Fibonacci code. The prob-lem of using variable-length code for data compression is studied on the example of a test message encoding. The general principle of data compression, its variants are considered and the connection between the distribution of the frequency of the use of certain letters and the possibility of applying compression on the basis of codes of dynamic size is analyzed. Also, the general principle of constructing the coding tables for this method is considered, and practical calculations of the efficiency of the method for cases of symbols distribution according to statistics and at a uniform distribution of symbols are carried out. Based on calculations of compression efficiency in different cases, the main disadvantages of the method are outlined for further improvement of the algorithm. Taking into account certain deficiencies, the compression method on the basis of Fibonacci codes has been improved, which ensures the increase of the efficiency of real data compression.A method for increasing the compression efficiency by means of Fibonacci code with a large number of unique symbols is offered and the analysis of the efficiency of the proposed method compared with the standard method has been conducted. It has been determined that the improved excep-tion method has a higher percentage of compression over the standard one, but the results are significantly dependent on the frequency division of the symbols. The improved method is directed on the compression of texts using multiple languages, special symbols, as well as on the compression of files of any format where the standard method has a small or even negative compression ratio.

Fast Algorithm of Truncated Burrows-Wheeler Transform Coding for Data Compression of Sensors

Lots of sensors in the IoT (Internet of things) may generate massive data, which will challenge the limited sensor storage and network bandwidth. So the study of big data compression is very useful in the field of sensors. In practice, BWT (Burrows-Wheeler transform) can gain good compression results for some kinds of data, but the traditional BWT algorithms are neither concise nor fast enough for the hardware of sensors, which will limit the BWT block size in a very small and incompetent scale. To solve this problem, this paper presents a fast algorithm of truncated BWT named “CZ-BWT algorithm” and implements it in the shareware named “ComZip.” CZ-BWT supports the BWT block up to 2 GB (or larger) and uses the bucket sort. It is very fast with the time complexity O(N) and fits the big data compression. The experiment results indicate that ComZip with the CZ-BWT filter is obviously faster than bzip2, and it can obtain better compression ratio than bzip2 and p7zip in some conditions. In addition, CZ-BWT is more concise than current BWT with SA (suffix array) sorts and fits the hardware BWT implementation of sensors.

Optimised cost considering Huffman code for biological data compression

Classical Huffman code has been widely used to compress biological datasets. Though a considerable reduction of size of data can be obtained by classical Huffman code, a more efficient encoding is possible by treating binary bits differently considering requirement of transmission time, energy consumption, and similar. A number of techniques have already modified the Huffman code algorithm to obtain optimal prefix-codes for unequal letter costs in order to reduce overall transmission cost (time). In this paper, we propose a new approach to improve compression performance of one such extension, the cost considering approach (CCA), by applying a genetic algorithm for optimal allocation of the codewords to the symbols. The idea of the proposed approach is to sacrifice some cost to minimise the total number of bits, hence, the genetic algorithm works by giving penalty on the cost. The performance of the approach is evaluated by using it to compress some standard biological datasets. The experiments show that the proposed approach improves the compression performance of the CCA considerably without increasing the cost significantly.

Optimised cost considering Huffman code for biological data compression

Classical Huffman code has been widely used to compress biological datasets. Though a considerable reduction of size of data can be obtained by classical Huffman code, a more efficient encoding is possible by treating binary bits differently considering requirement of transmission time, energy consumption, and similar. A number of techniques have already modified the Huffman code algorithm to obtain optimal prefix-codes for unequal letter costs in order to reduce overall transmission cost (time). In this paper, we propose a new approach to improve compression performance of one such extension, the cost considering approach (CCA), by applying a genetic algorithm for optimal allocation of the codewords to the symbols. The idea of the proposed approach is to sacrifice some cost to minimise the total number of bits, hence, the genetic algorithm works by giving penalty on the cost. The performance of the approach is evaluated by using it to compress some standard biological datasets. The experiments show that the proposed approach improves the compression performance of the CCA considerably without increasing the cost significantly.

Penyandi Persepsi Isyarat Audio Berdasar pada Model Modulasi Frekuensi (FM)

Data compression or source coding has been playing an important role in many areas in daily life. There are several audio encoders available with various methods. Each audio encoder works at a certain range bitrate depending on the signal model used and the method implemented. Based on literature reviews it is to know that there are only a view of audio encoder working at bit rate below 16 kbit/s, especially for music signals. The togetherness in having harmonically components between the audio signals and frequency modulated signals is the basic idea of proposing the perceptual encoder based on FM signal model. In estimating and extracting FM parameters, the psychoacoustic model is used to exploit the perceptual nature of humans ears. The errors of FM modeling is distributed below the masking curve of psychoacoustic model. The developed encoder allows sending music signals on the GSM channel. The developed encoder has four cascaded main processes, namely sinusoid components extraction, harmonic components separation, FM parameters estimation, and FM parameter encoding. The simulation results show the proposed method is successful to encode some audio signal types.

Group Frames With Few Distinct Inner Products and Low Coherence

Frame theory has been a popular subject in the design of structured signals and codes in recent years, with applications ranging from the design of measurement matrices in compressive sensing, to spherical codes for data compression and data transmission, to spacetime codes for MIMO communications, and to measurement operators in quantum sensing. High-performance codes usually arise from designing frames whose elements have mutually low coherence. Building off the original "group frame" design of Slepian which has since been elaborated in the works of Vale and Waldron, we present several new frame constructions based on cyclic and generalized dihedral groups. Slepian's original construction was based on the premise that group structure allows one to reduce the number of distinct inner pairwise inner products in a frame with $n$ elements from $\frac{n(n-1)}{2}$ to $n-1$. All of our constructions further utilize the group structure to produce tight frames with even fewer distinct inner product values between the frame elements. When $n$ is prime, for example, we use cyclic groups to construct $m$-dimensional frame vectors with at most $\frac{n-1}{m}$ distinct inner products. We use this behavior to bound the coherence of our frames via arguments based on the frame potential, and derive even tighter bounds from combinatorial and algebraic arguments using the group structure alone. In certain cases, we recover well-known Welch bound achieving frames. In cases where the Welch bound has not been achieved, and is not known to be achievable, we obtain frames with close to Welch bound performance.

Comparative Study of Generalized Quantitative-Qualitative Inaccuracy Fuzzy Measures for Noiseless Coding Theorem and 1:1 Codes

In coding theory, we study various properties of codes for application in data compression, cryptography, error correction, and network coding. The study of codes is introduced in Information Theory, electrical engineering, mathematics, and computer sciences for the transmission of data through reliable and efficient methods. We have to consider how coding of messages can be done efficiently so that the maximum number of messages can be sent over a noiseless channel in a given time. Thus, the minimum value of mean codeword length subject to a given constraint on codeword lengths has to be founded. In this paper, we have introduced mean codeword length of orderαand typeβfor 1:1 codes and analyzed the relationship between average codeword length and fuzzy information measures for binary 1:1 codes. Further, noiseless coding theorem associated with fuzzy information measure has been established.

Layouts for improved hierarchical parallel computations

New layouts for the assignment of a set of n parallel processors to perform certain tasks in several hierarchically connected layers are suggested, leading, after some initialization phase, to the full exploitation of all of the processing power all of the time. This framework is useful for a variety of string theoretic problems, ranging from modular arithmetic used, among others, in Karp–Rabin type rolling hashes, as well as in cryptographic applications, and up to data compression and error-correcting codes.