Use Of Compression Algorithms Research Articles

DETERMINING OPTIMAL COMPRESSION ALGORITHM FOR FILES OF DIFFERENT FORMATS

The aim of the article is to highlight possible areas of the use of compression algorithms in various fields. The article investigates the efficiency of the compression of files of different formats and provides recommendations on the optimal compression algorithms for specific scenarios. The research was carried out empirically: compression algorithms were implemented at the software level, the programs were used to compress files of various formats, the size of the resulting files was determined, and a comparison of the efficiency of the methods was made. Research results. The article presents a table of file sizes after compression using different compression algorithms, calculates compression ratios for each case, determines the average compression ratios for each compression algorithm, analyses the efficiency of compression algorithms, and identifies the optimal compression algorithms for files of different formats. The scientific novelty of this work is an integrated approach to comparing compression algorithms by compression ratios on different file formats and the study of the combination of Huffman and LZ78 algorithms, which has not been widely studied before. This allows us to gain a deeper understanding of the process of compressing files of different formats and identify effective algorithms for specific data types. The analysis can contribute to the development and improvement of file compression methods and have practical applications in various fields, such as data storage and transmission, file compression, and improving the performance of information processing systems. The practical significance of the work lies in its potential usefulness for various fields. It provides recommendations and conclusions on the selection of efficient file compression algorithms for different file formats. This can have a positive impact on data storage and transmission, data processing speed, software development, and multimedia data. Using optimal compression algorithms helps reduce file size, saves resources, and improves user experience.

Efficient Data Management in Agricultural IoT: Compression, Security, and MQTT Protocol Analysis.

The integration of Internet of Things (IoT) technology into agriculture has revolutionized farming practices by using connected devices and sensors to optimize processes and facilitate sustainable execution. Because most IoT devices have limited resources, the vital requirement to efficiently manage data traffic while ensuring data security in agricultural IoT solutions creates several challenges. Therefore, it is important to study the data amount that IoT protocols generate for resource-constrained devices, as it has a direct impact on the device performance and overall usability of the IoT solution. In this paper, we present a comprehensive study that focuses on optimizing data transmission in agricultural IoT solutions with the use of compression algorithms and secure technologies. Through experimentation and analysis, we evaluate different approaches to minimize data traffic while protecting sensitive agricultural data. Our results highlight the effectiveness of compression algorithms, especially Huffman coding, in reducing data size and optimizing resource usage. In addition, the integration of encryption techniques, such as AES, provides the security of the transmitted data without incurring significant overhead. By assessing different communication scenarios, we identify the most efficient approach, a combination of Huffman encoding and AES encryption, to strike a balance between data security and transmission efficiency.

Adaptive scene-aware deep attention network for remote sensing image compression

Deep neural networks have been successfully used in image processing and have benefitted image compression technology. On this basis, deeper and more extensive use of compression algorithms in remote sensing applications was investigated in our study. As a general residual block does not effectively utilize the connections between channels and spatial regions in the reconstruction process, an attention mechanism codec framework based on the channel-spatial attention residual block was introduced. Consequently, the network can account for the interdependence between channels and enhance specific spatial areas. Simultaneously, the feature importance map makes the network focus on valuable information, especially for small objects and remote sensing images with many tiny details, which is crucial to describe their contours. Introducing the attention mechanism commonly used in target detection can effectively recover the missing detailed information for remote sensing compressed images. Moreover, to utilize the underlying information of the various scene categories and characterize the feature differences between scenes in remote sensing images, an adaptive scene-aware module was introduced to enhance the feature representation in remote sensing images of different scenes. The experimental results show that the proposed method has superior subjective and objective effects in remote sensing image compression tasks.

Compressing learner language: An information-theoretic measure of complexity in SLA production data

We present a proof-of-concept study that sketches the use of compression algorithms to assess Kolmogorov complexity, which is a text-based, quantitative, holistic, and global measure of structural surface redundancy. Kolmogorov complexity has been used to explore cross-linguistic complexity variation in linguistic typology research, but we are the first to apply it to naturalistic second language acquisition (SLA) data. We specifically investigate the relationship between the complexity of second language (L2) English essays and the amount of instruction the essay writers have received. Analysis shows that increased L2 instructional exposure predicts increased overall complexity and increased morphological complexity, but decreased syntactic complexity (defined here as less rigid word order). While the relationship between L2 instructional exposure and complexity is robust across a number of first language (L1) backgrounds, L1 background does predict overall complexity levels.

XMPP Based Applications under Low Bandwidth and High Latency Conditions

This paper discusses use of Extensible Messaging and Presence Protocol (XMPP) based applications under Low Bandwidth and High Latency conditions. Such conditions usually occur during communication between air-to-air or air-to-distant sea platforms. Considering the type of subject conditions XMPP applications discussed in this paper is mostly applicable to military and defense industry. In terms of XMPP applications IM, chat, message broadcasting, Customizable Forms (CF), archiving and database functionalities can be considered. Conditions of Low Bandwidth and High Latency require existing XMPP and data exchange mechanism to be revised or simplified. Otherwise XMPP communication is delayed or gets interrupted and the purpose of XMPP application is not properly met. This study discusses the use of compression algorithms to tackle this problem. Although quantitative facts are provided and referenced this paper is based on empirical approach and includes qualitative results.

A new compression algorithm of data provenance based on self-adaptive granularity

Usually, storage performance of data provenance is sustained through the use of compression algorithms. When provenance models with high repeatable rate and small variances are concerned, many current algorithms still face the challenge of avoiding redundancy. Adaptive Merge algorithm, featuring the optimisation of provenance factorisation, is introduced to compress provenance tree on the self-adaptive granularity basis. A kind of comprehensive solution regarding pointer-saving storage is provided here. The validity of Adaptive Merge algorithm is proven experimentally. Comparisons are made with Argument Factorisation to prove that our algorithm performs better in compression ratio. The extent of ratio enhancement depends on the features of data sets. The compression ratio of the provided provenance data set is increased by about 10%. Other data sets all show improvements in varying degrees. This suggests that by resolving pointer explosion issue, this new algorithm performs better and achieves a more reasonable compression ratio.

An example of mathematical authorship attribution

In this paper we discuss a novel mathematical approach to authorship attribution which we implemented recently to face a concrete problem of author recognition. The fundamental ideas for our methods came from statistical mechanics and information theory. We combine two approaches. Both of them use similarity measures between couples of texts as indicators of stylistic closeness: the first one is based on the comparison of frequencies of fixed length substrings (n-grams) throughout the texts; the second one relies on a suitable use of compression algorithms as relative entropy approximators, in the spirit of the so-called Ziv–Merhav theorem. The two methods were separately developed and then combined, together with a suitable and theoretically founded ranking analysis, to produce an original authorship attribution procedure that yielded very successful results on the specific problem to which it was applied. This ranking analysis could be of interest also in other application fields.

3D Echocardiography: A Review of the Current Status and Future Directions

Ultrasound technology has improved markedly in the past 10 to 15 years, prompting echocardiographers to extend its use in studying cardiac structure and function. New ultrasound equipment and techniques offer superior image quality, greater accuracy, and expanding capabilities. As a result, more and improved imaging modalities are available for evaluating cardiac anatomy, ventricular function, blood flow velocity, and valvular diseases. Threedimensional (3D) echocardiography offers the ability to improve and expand the diagnostic capabilities of cardiac ultrasound. However, as with any emerging technology, the enthusiasm to embrace a new technique must be tempered by a critical appraisal of the evidence supporting its use. It is essential to assess the limitations as well as the unique capabilities it provides. Cardiac imaging should be safe, accurate, versatile, comprehensive, and cost-effective, while providing important clinical information. Criteria for appropriate utilization should be based on current evidence and updated as new information becomes available. To justify the use of a new 3D modality, its unique contribution to clinical practice must be critically analyzed. In this article we review the status of 3D echocardiography, examine the evidence for its use in various clinical situations, and propose guidelines for appropriate application of this technique based on available evidence.

Orbit complexity and data compression

We consider data compression algorithms as a tool to get an approximate measure for the quantity of information contained in a string. By this it is possible to give a notion of orbit complexity for topological dynamical systems. In compact ergodic dynamical systems, entropy is almost everywhere equal to orbit complexity. The use of compression algorithms allows a direct estimation of the information content of the orbits.