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Articoli di riviste sul tema "Data compression"

Autore: Grafiati

Pubblicato: 4 giugno 2021

Ultima modifica: 25 luglio 2025

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1

Shevchuk, Yury Vladimirovich. "Memory-efficient sensor data compression." Program Systems: Theory and Applications 13, no. 2 (2022): 35–63. http://dx.doi.org/10.25209/2079-3316-2022-13-2-35-63.

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We treat scalar data compression in sensor network nodes in streaming mode (compressing data points as they arrive, no pre-compression buffering). Several experimental algorithms based on linear predictive coding (LPC) combined with run length encoding (RLE) are considered. In entropy coding stage we evaluated (a) variable-length coding with dynamic prefixes generated with MTF-transform, (b) adaptive width binary coding, and (c) adaptive Golomb-Rice coding. We provide a comparison of known and experimental compression algorithms on 75 sensor data sources. Compression ratios achieved in the tes

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Sharath, R., Sinha Shivani, B.I Supreeth, Hebbar B.C. Varun, and B. Santhosh. "Distributed Data Compression using Cloud Computing Approach." Journal of Optical Communication Electronics 5, no. 2 (2019): 5–10. https://doi.org/10.5281/zenodo.2656008.

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<em>Storage and data trafficking have grown a great deal over the past decade. Therefore, there is a need to reduce the size of the data for better speed and proper utilization of the bandwidth. There are compressions that happen online as well as some happen statically. The online based compression is in greater demand, since an online platform is much easier and faster. Static methods have a requisition of the entire file to be present at the time of transmission, while online compressions don’t. Compressions make use of an algorithm to effectively shrink the data. Depending upon the s

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Saidhbi, Sheik. "An Intelligent Multimedia Data Encryption and Compression and Secure Data Transmission of Public Cloud." Asian Journal of Engineering and Applied Technology 8, no. 2 (2019): 37–40. http://dx.doi.org/10.51983/ajeat-2019.8.2.1141.

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Data compression is a method of reducing the size of the data file so that the file should take less disk space for storage. Compression of a file depends upon encoding of file. In lossless data compression algorithm there is no data loss while compressing a file, therefore confidential data can be reproduce if it is compressed using lossless data compression. Compression reduces the redundancy and if a compressed file is encrypted it is having a better security and faster transfer rate across the network than encrypting and transferring uncompressed file. Most of the computer applications rel

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Nithya, P., T. Vengattaraman, and M. Sathya. "Survey On Parameters of Data Compression." REST Journal on Data Analytics and Artificial Intelligence 2, no. 1 (2023): 1–7. http://dx.doi.org/10.46632/jdaai/2/1/1.

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The rapid development in the hardware and the software gives rise to data growth. This data growth has numerous impacts, including the need for a larger storage capacity for storing and transmitting. Data compression is needed in today’s world because it helps to minimize the amount of storage space required to store and transmit data. Performance measures in data compression are used to evaluate the efficiency and effectiveness of data compression algorithms. In recent times, numerous data compression algorithms are developed to reduce data storage and increase transmission speed in this inte

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5

Chen, Xinyu, Jiannan Tian, Ian Beaver, et al. "FCBench: Cross-Domain Benchmarking of Lossless Compression for Floating-Point Data." Proceedings of the VLDB Endowment 17, no. 6 (2024): 1418–31. http://dx.doi.org/10.14778/3648160.3648180.

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While both the database and high-performance computing (HPC) communities utilize lossless compression methods to minimize floating-point data size, a disconnect persists between them. Each community designs and assesses methods in a domain-specific manner, making it unclear if HPC compression techniques can benefit database applications or vice versa. With the HPC community increasingly leaning towards in-situ analysis and visualization, more floating-point data from scientific simulations are being stored in databases like Key-Value Stores and queried using in-memory retrieval paradigms. This

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6

Bernstein, Herbert J., Alexei Soares, Kimberly Horvat, and Jean Jakoncic. "Massive Compression for High Data Rate Macromolecular Crystallography (HDRMX): Impact on Diffraction Data and Subsequent Structural Analysis." Structural Dynamics 12, no. 2_Supplement (2025): A147. https://doi.org/10.1063/4.0000456.

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New higher-count-rate, integrating, large area X-ray detectors with framing rates as high as 17,400 images per second are beginning to be available. Data from these detectors are always compressed losslessly, but systems may not keep up with these data rates, and the files may still be larger than seems necessary. We propose that such MX experiments will require lossy compression algorithms to keep up with data throughput and capacity for long-term storage, but note that some information may be lost. Indeed, one might employ dramatic lossy compression only for archiving of data after structure

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7

Ryabko, Boris. "Time-Universal Data Compression." Algorithms 12, no. 6 (2019): 116. http://dx.doi.org/10.3390/a12060116.

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Nowadays, a variety of data-compressors (or archivers) is available, each of which has its merits, and it is impossible to single out the best ones. Thus, one faces the problem of choosing the best method to compress a given file, and this problem is more important the larger is the file. It seems natural to try all the compressors and then choose the one that gives the shortest compressed file, then transfer (or store) the index number of the best compressor (it requires log m bits, if m is the number of compressors available) and the compressed file. The only problem is the time, which essen

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8

Mishra, Amit Kumar. "Versatile Video Coding (VVC) Standard: Overview and Applications." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 10, no. 2 (2019): 975–81. http://dx.doi.org/10.17762/turcomat.v10i2.13578.

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Information security includes picture and video compression and encryption since compressed data is more secure than uncompressed imagery. Another point is that handling data of smaller sizes is simple. Therefore, efficient, secure, and simple data transport methods are created through effective data compression technology. Consequently, there are two different sorts of compression algorithm techniques: lossy compressions and lossless compressions. Any type of data format, including text, audio, video, and picture files, may leverage these technologies. In this procedure, the Least Significant

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9

McGeoch, Catherine C. "Data Compression." American Mathematical Monthly 100, no. 5 (1993): 493. http://dx.doi.org/10.2307/2324310.

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10

Helman, D. R., and G. G. Langdon. "Data compression." IEEE Potentials 7, no. 1 (1988): 25–28. http://dx.doi.org/10.1109/45.1889.

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11

Lelewer, Debra A., and Daniel S. Hirschberg. "Data compression." ACM Computing Surveys 19, no. 3 (1987): 261–96. http://dx.doi.org/10.1145/45072.45074.

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12

McGeoch, Catherine C. "Data Compression." American Mathematical Monthly 100, no. 5 (1993): 493–97. http://dx.doi.org/10.1080/00029890.1993.11990441.

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13

Bookstein, Abraham, and James A. Storer. "Data compression." Information Processing & Management 28, no. 6 (1992): 675–80. http://dx.doi.org/10.1016/0306-4573(92)90060-d.

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14

Ko, Yousun, Alex Chadwick, Daniel Bates, and Robert Mullins. "Lane Compression." ACM Transactions on Embedded Computing Systems 20, no. 2 (2021): 1–26. http://dx.doi.org/10.1145/3431815.

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This article presents Lane Compression, a lightweight lossless compression technique for machine learning that is based on a detailed study of the statistical properties of machine learning data. The proposed technique profiles machine learning data gathered ahead of run-time and partitions values bit-wise into different lanes with more distinctive statistical characteristics. Then the most appropriate compression technique is chosen for each lane out of a small number of low-cost compression techniques. Lane Compression’s compute and memory requirements are very low and yet it achieves a comp

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15

Yang, Le, Zhao Yang Guo, Shan Shan Yong, Feng Guo, and Xin An Wang. "A Hardware Implementation of Real Time Lossless Data Compression and Decompression Circuits." Applied Mechanics and Materials 719-720 (January 2015): 554–60. http://dx.doi.org/10.4028/www.scientific.net/amm.719-720.554.

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This paper presents a hardware implementation of real time data compression and decompression circuits based on the LZW algorithm. LZW is a dictionary based data compression, which has the advantage of fast speed, high compression, and small resource occupation. In compression circuit, the design creatively utilizes two dictionaries alternately to improve efficiency and compressing rate. In decompression circuit, an integrated State machine control module is adopted to save hardware resource. Through hardware description and language programming, the circuits finally reach function simulation

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Kuncoro, Adam Prayogo, Dinar Mustofa, Dwi Krisbiantoro, and Tarwoto Tarwoto. "DIGITAL DATA SECURITY WITH APPLICATION OF CRYPTOGRAPHY AND DATA COMPRESSION TECHNIQUES." Jurnal Teknik Informatika (Jutif) 4, no. 5 (2023): 995–99. http://dx.doi.org/10.52436/1.jutif.2023.4.5.659.

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The need for digital data security is to ensure that the data and information we have are confidential and can only be accessed by authorized users. And no one can change the information in it, thus ensuring complete accuracy. The functions of data security are confidentiality, authentication, integrity, and anti-repudiation. Compression techniques are used to protect digital data because they aim to make less storage space and allow us to transfer more data over the internet. This study aims to plan to prove the application of a combination of 2 (two) techniques, namely compression and crypto

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P, Srividya. "Optimization of Lossless Compression Algorithms using Multithreading." Journal of Information Technology and Sciences 9, no. 1 (2023): 36–42. http://dx.doi.org/10.46610/joits.2022.v09i01.005.

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The process of reducing the number of bits required to characterize data is referred to as compression. The advantages of compression include a reduction in the time taken to transfer data from one point to another, and a reduction in the cost required for the storage space and network bandwidth. There are two types of compression algorithms namely lossy compression algorithm and lossless compression algorithm. Lossy algorithms find utility in compressing audio and video signals whereas lossless algorithms are used in compressing text messages. The advent of the internet and its worldwide usag

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P, Srividya. "Optimization of Lossless Compression Algorithms using Multithreading." Journal of Information Technology and Sciences 9, no. 1 (2023): 36–42. http://dx.doi.org/10.46610/joits.2023.v09i01.005.

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Abstract (sommario):

The process of reducing the number of bits required to characterize data is referred to as compression. The advantages of compression include a reduction in the time taken to transfer data from one point to another, and a reduction in the cost required for the storage space and network bandwidth. There are two types of compression algorithms namely lossy compression algorithm and lossless compression algorithm. Lossy algorithms find utility in compressing audio and video signals whereas lossless algorithms are used in compressing text messages. The advent of the internet and its worldwide usag

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Chithra, P. L., and S. Lakshmi Bala. "Sculpting Efficiency: Hierarchical Codec Framework for 3D LiDAR Point Cloud Data." Indian Journal Of Science And Technology 18, no. 11 (2025): 904–21. https://doi.org/10.17485/ijst/v18i11.1583.

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Objectives: A novel and effective 3D compression strategy is proposed for Point Cloud Data (PCD) from Light Detection and Ranging (LiDAR) sensors. The proposed methodology called 3DPCD-TPK Codec: t-distributed Stochastic Neighbour Embedding (t-SNE), Density-Based Spatial Clustering of Applications with Noise (DBSCAN), and K-dimensional Tree (K-D Tree) for 3D PCD Compression. 3DPCD-TPK codec achieves effective compression of 3D PCD by combining t-SNE for dimensionality reduction, DBSCAN for clustering, and K-D Tree for effective spatial indexing. To accomplish lossless compression of PCD, LZ4 c

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20

Pandey, Anukul, Barjinder Singh Saini, and Butta Singh. "ELECTROCARDIOGRAM DATA COMPRESSION TECHNIQUES IN 1D/2D DOMAIN." Biomedical Engineering: Applications, Basis and Communications 33, no. 02 (2021): 2150011. http://dx.doi.org/10.4015/s1016237221500113.

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Electrocardiogram (ECG) is one of the best representatives of physiological signal that provides the state of the autonomic nervous system, primarily responsible for the cardiac activity. The ECG data compression plays a significant role in localized digital storage or efficient communication channel utilization in telemedicine applications. The lossless and lossy compression system’s compressor efficiency depends on the methodologies used for compression and the quality measure used to evaluate distortion. Based on domain ECG, data compression can be performed either one-dimensional (1D) or t

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A. Sapate, Suchit. "Effective XML Compressor: XMill with LZMA Data Compression." International Journal of Education and Management Engineering 9, no. 4 (2019): 1–10. http://dx.doi.org/10.5815/ijeme.2019.04.01.

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22

Chandak, Shubham, Kedar Tatwawadi, Idoia Ochoa, Mikel Hernaez, and Tsachy Weissman. "SPRING: a next-generation compressor for FASTQ data." Bioinformatics 35, no. 15 (2018): 2674–76. http://dx.doi.org/10.1093/bioinformatics/bty1015.

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Abstract Motivation High-Throughput Sequencing technologies produce huge amounts of data in the form of short genomic reads, associated quality values and read identifiers. Because of the significant structure present in these FASTQ datasets, general-purpose compressors are unable to completely exploit much of the inherent redundancy. Although there has been a lot of work on designing FASTQ compressors, most of them lack in support of one or more crucial properties, such as support for variable length reads, scalability to high coverage datasets, pairing-preserving compression and lossless com

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23

Zirkind, Givon. "AFIS data compression." ACM SIGSOFT Software Engineering Notes 32, no. 6 (2007): 8. http://dx.doi.org/10.1145/1317471.1317480.

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Zirkind, Givon. "AFIS data compression." ACM SIGGRAPH Computer Graphics 41, no. 4 (2007): 1–36. http://dx.doi.org/10.1145/1331098.1331103.

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McCluskey, E. J., D. Burek, B. Koenemann, et al. "Test data compression." IEEE Design & Test of Computers 20, no. 2 (2003): 76–87. http://dx.doi.org/10.1109/mdt.2003.1188267.

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Hernaez, Mikel, Dmitri Pavlichin, Tsachy Weissman, and Idoia Ochoa. "Genomic Data Compression." Annual Review of Biomedical Data Science 2, no. 1 (2019): 19–37. http://dx.doi.org/10.1146/annurev-biodatasci-072018-021229.

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Recently, there has been growing interest in genome sequencing, driven by advances in sequencing technology, in terms of both efficiency and affordability. These developments have allowed many to envision whole-genome sequencing as an invaluable tool for both personalized medical care and public health. As a result, increasingly large and ubiquitous genomic data sets are being generated. This poses a significant challenge for the storage and transmission of these data. Already, it is more expensive to store genomic data for a decade than it is to obtain the data in the first place. This situat

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27

Mattsson, A. Geo. "DATA ON COMPRESSION." Journal of the American Society for Naval Engineers 13, no. 2 (2009): 422. http://dx.doi.org/10.1111/j.1559-3584.1901.tb03391.x.

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McGillis, Peggy, Mina Nichols, and Britt Terry. "[Data] Compression Theory." EDPACS 25, no. 8 (1998): 16. http://dx.doi.org/10.1201/1079/43236.25.8.19980201/30193.9.

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Berger, Jens, Ulrich Frankenfeld, Volker Lindenstruth, et al. "TPC data compression." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 489, no. 1-3 (2002): 406–21. http://dx.doi.org/10.1016/s0168-9002(02)00792-1.

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Farruggia, Andrea, Paolo Ferragina, Antonio Frangioni, and Rossano Venturini. "Bicriteria Data Compression." SIAM Journal on Computing 48, no. 5 (2019): 1603–42. http://dx.doi.org/10.1137/17m1121457.

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BRAMBLE, JOHN M., H. K. HUANG, and MARK D. MURPHY. "Image Data Compression." Investigative Radiology 23, no. 10 (1988): 707–12. http://dx.doi.org/10.1097/00004424-198810000-00001.

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Tyrygin, I. Ya. "?-Entropy data compression." Ukrainian Mathematical Journal 44, no. 11 (1992): 1473–79. http://dx.doi.org/10.1007/bf01071523.

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Goldberg, Mark A. "Image data compression." Journal of Digital Imaging 11, S1 (1998): 230–32. http://dx.doi.org/10.1007/bf03168323.

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Goldberg, Mark A. "Image data compression." Journal of Digital Imaging 10, S1 (1997): 9–11. http://dx.doi.org/10.1007/bf03168640.

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Tao, Dingwen, Sheng Di, Hanqi Guo, Zizhong Chen, and Franck Cappello. "Z-checker: A framework for assessing lossy compression of scientific data." International Journal of High Performance Computing Applications 33, no. 2 (2017): 285–303. http://dx.doi.org/10.1177/1094342017737147.

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Because of the vast volume of data being produced by today’s scientific simulations and experiments, lossy data compressor allowing user-controlled loss of accuracy during the compression is a relevant solution for significantly reducing the data size. However, lossy compressor developers and users are missing a tool to explore the features of scientific data sets and understand the data alteration after compression in a systematic and reliable way. To address this gap, we have designed and implemented a generic framework called Z-checker. On the one hand, Z-checker combines a battery of data

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Ochoa, Idoia, Mikel Hernaez, and Tsachy Weissman. "Aligned genomic data compression via improved modeling." Journal of Bioinformatics and Computational Biology 12, no. 06 (2014): 1442002. http://dx.doi.org/10.1142/s0219720014420025.

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With the release of the latest Next-Generation Sequencing (NGS) machine, the HiSeq X by Illumina, the cost of sequencing the whole genome of a human is expected to drop to a mere $1000. This milestone in sequencing history marks the era of affordable sequencing of individuals and opens the doors to personalized medicine. In accord, unprecedented volumes of genomic data will require storage for processing. There will be dire need not only of compressing aligned data, but also of generating compressed files that can be fed directly to downstream applications to facilitate the analysis of and inf

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Nemetz, Tibor, and Pál Papp. "Increasing data security by data compression." Studia Scientiarum Mathematicarum Hungarica 42, no. 4 (2005): 343–53. http://dx.doi.org/10.1556/sscmath.42.2005.4.1.

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We analyze the effect of data-compression on security of encryption both from theoretical and practical point of view. It is demonstrated that data-compression essentially improves the security of encryption, helps to overcome technical difficulties. On the other side, it makes crypt-analysis more difficult and causes extra problems. At present data-compression applied rarely and frequently defectively. We propose a method which eliminates the negative effects. Our aim is initiate data compression as an aid for data security. To this end we provide an overview of the most frequently used crypt

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Lee, Chun-Hee, and Chin-Wan Chung. "Compression Schemes with Data Reordering for Ordered Data." Journal of Database Management 25, no. 1 (2014): 1–28. http://dx.doi.org/10.4018/jdm.2014010101.

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Although there have been many compression schemes for reducing data effectively, most schemes do not consider the reordering of data. In the case of unordered data, if the users change the data order in a given data set, the compression ratio may be improved compared to the original compression before reordering data. However, in the case of ordered data, the users need a mapping table that maps the original position to the changed position in order to recover the original order. Therefore, reordering ordered data may be disadvantageous in terms of space. In this paper, the authors consider tw

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Hayati, Anis Kamilah, and Haris Suka Dyatmika. "THE EFFECT OF JPEG2000 COMPRESSION ON REMOTE SENSING DATA OF DIFFERENT SPATIAL RESOLUTIONS." International Journal of Remote Sensing and Earth Sciences (IJReSES) 14, no. 2 (2018): 111. http://dx.doi.org/10.30536/j.ijreses.2017.v14.a2724.

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The huge size of remote sensing data implies the information technology infrastructure to store, manage, deliver and process the data itself. To compensate these disadvantages, compressing technique is a possible solution. JPEG2000 compression provide lossless and lossy compression with scalability for lossy compression. As the ratio of lossy compression getshigher, the size of the file reduced but the information loss increased. This paper tries to investigate the JPEG2000 compression effect on remote sensing data of different spatial resolution. Three set of data (Landsat 8, SPOT 6 and Pleia

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Guerra, Aníbal, Jaime Lotero, José Édinson Aedo, and Sebastián Isaza. "Tackling the Challenges of FASTQ Referential Compression." Bioinformatics and Biology Insights 13 (January 2019): 117793221882137. http://dx.doi.org/10.1177/1177932218821373.

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The exponential growth of genomic data has recently motivated the development of compression algorithms to tackle the storage capacity limitations in bioinformatics centers. Referential compressors could theoretically achieve a much higher compression than their non-referential counterparts; however, the latest tools have not been able to harness such potential yet. To reach such goal, an efficient encoding model to represent the differences between the input and the reference is needed. In this article, we introduce a novel approach for referential compression of FASTQ files. The core of our

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Dahunsi, F. M., O. A. Somefun, A. A. Ponnle, and K. B. Adedeji. "Compression Techniques of Electrical Energy Data for Load Monitoring: A Review." Nigerian Journal of Technological Development 18, no. 3 (2021): 194–208. http://dx.doi.org/10.4314/njtd.v18i3.4.

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In recent years, the electric grid has experienced increasing deployment, use, and integration of smart meters and energy monitors. These devices transmit big time-series load data representing consumed electrical energy for load monitoring. However, load monitoring presents reactive issues concerning efficient processing, transmission, and storage. To promote improved efficiency and sustainability of the smart grid, one approach to manage this challenge is applying data-compression techniques. The subject of compressing electrical energy data (EED) has received quite an active interest in the

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Mansyuri, Umar. "KOMPRESI DATA TEKS DENGAN METODE RUN LENGTH ENCODING." Jurnal Ilmiah Sistem Informasi 1, no. 2 (2021): 102–9. http://dx.doi.org/10.46306/sm.v1i2.13.

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One method of using data compression is by using a method called Run Length Encoding (RLE), especially image data. The RLE method is one of the simplest lossless types of data compression schemes and is based on the simple principle of data encoding. The RLE method is very suitable for compressing data containing repetitive characters such as simple graphic images. The compressed data are 28 RGB (Red, Green, Blue) images and 28 grayscale images in jpg, png, bmp, and tiff formats, respectively. Image data is compressed with an encoder and decoder program using the RLE algorithm in the matlab ap

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N V A, P., P. M Francis, and B. Prasad Kumar. "Efficient Test Data Compression Techniques using Viterbi Architecture." International Journal of Scientific Engineering and Research 1, no. 1 (2013): 36–39. https://doi.org/10.70729/6130912.

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Satria, Gunawan Zain, Nirwana, Baso Kaswar Andi, Suhartono, and Rahman Patta Abd. "Implementation of Text Compression using Adaptive Shannon-Fano Algorithm." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 3 (2020): 3984–90. https://doi.org/10.35940/ijeat.C6383.029320.

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This study aims to implement the Shannon-fano Adaptive data compression algorithm on characters as input data. This study also investigates the data compression ratio, which is the ratio between the number of data bits before and after compression. The resulting program is tested by using black-box testing, measuring the number of character variants and the number of types of characters to the compression ratio, and testing the objective truth with the Mean Square Error (MSE) method. The description of the characteristics of the application made is done by processing data in the form of a coll

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Kontoyiannis, I. "Pointwise redundancy in lossy data compression and universal lossy data compression." IEEE Transactions on Information Theory 46, no. 1 (2000): 136–52. http://dx.doi.org/10.1109/18.817514.

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Budiman, Gelar, Andriyan Bayu Suksmono, and Donny Danudirdjo. "Compressive Sampling with Multiple Bit Spread Spectrum-Based Data Hiding." Applied Sciences 10, no. 12 (2020): 4338. http://dx.doi.org/10.3390/app10124338.

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We propose a novel data hiding method in an audio host with a compressive sampling technique. An over-complete dictionary represents a group of watermarks. Each row of the dictionary is a Hadamard sequence representing multiple bits of the watermark. Then, the singular values of the segment-based host audio in a diagonal matrix are multiplied by the over-complete dictionary, producing a lower size matrix. At the same time, we embed the watermark into the compressed audio. In the detector, we detect the watermark and reconstruct the audio. This proposed method offers not only hiding the informa

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Song, Biao, Yuyang Fang, Runda Guan, Rongjie Zhu, Xiaokang Pan, and Yuan Tian. "Hierarchical Indexing and Compression Method with AI-Enhanced Restoration for Scientific Data Service." Applied Sciences 14, no. 13 (2024): 5528. http://dx.doi.org/10.3390/app14135528.

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In the process of data services, compressing and indexing data can reduce storage costs, improve query efficiency, and thus enhance the quality of data services. However, different service requirements have diverse demands for data precision. Traditional lossy compression techniques fail to meet the precision requirements of different data due to their fixed compression parameters and schemes. Additionally, error-bounded lossy compression techniques, due to their tightly coupled design, cannot achieve high compression ratios under high precision requirements. To address these issues, this pape

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Kaur, Harjit. "Image Compression Techniques with LZW method." International Journal for Research in Applied Science and Engineering Technology 10, no. 1 (2022): 1773–77. http://dx.doi.org/10.22214/ijraset.2022.39999.

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Abstract: Image compression is a technique which is used to reduce the size of the data. In other words, it means to remove the extra data from the available by applying some techniques and tricks which makes the data easy for storing and transmitting it over the transmission medium. The compression techniques are broadly divided into two categories. First one is Lossy Compression in which some of the data is lost while compressing it and second technique is lossless technique in which data is not lost after compressing it. These compression techniques can be applied on different image formats

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Daggubati, Siva Phanindra, Venkata Rao Kasukurthi, and Prasad Reddy. "Advancing Genomic Data Management through TCompress: An Innovative Method for Sequence Compression of Biological Sequence Compression Algorithm using Text-Compression Tools." Indian Journal Of Science And Technology 18, no. 23 (2025): 1811–17. https://doi.org/10.17485/ijst/v18i23.2790.

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Objectives: With the rapid growth of genetic data, there is an urgent need for efficient methods to compress biological sequences. This study aims to enhance the storage and retrieval of large volumes of genetic information by introducing TCompress, a new compression algorithm specifically designed for DNA sequences. Method: We developed a custom software tool called TCompress that converts biological sequences into binary format. This binary representation enables the use of standard text compression tools such as GenCompress, 7-Zip, GZip, and PeaZip. A detailed evaluation was conducted to me

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Aji Suryadi, Yanuar, and Gunawan. "Compressor Piping Design Effect on Vibration Data." Journal of Advanced Research in Fluid Mechanics and Thermal Sciences 88, no. 1 (2021): 94–108. http://dx.doi.org/10.37934/arfmts.88.1.94108.

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One of the systems for oil and gas production supports is the nitrogen compression system. Problem found that condition of the compressor has high vibration with the maximum overall the first compressor is 9,813 mm / s RMS, the second compressor is 7,439 mm / s RMS, the third compressor is 7,430 mm / s RMS, the fourth compressor is 13.47 mm / s RMS, the fifth compressor is 13,220 mm / s RMS, and sixth compressor already damaged. This research will discuss the nitrogen compression process in terms of the characteristics of the output fluid flow from the compressor using computational fluid dyna

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