On effectiveness of lossless compression in transferring mHealth data files

Dzhagaryan, Armen; Milenkovic, Aleksandar

doi:10.1109/healthcom.2015.7454588

Cited by 5 publications

(2 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The effective upload and download throughputs and energy efficiencies depend on many factors, including the file size and type, selected compression utility, the compression level, network characteristics such as latency and throughput, as well as the smartphone's performance and energyefficiency. Whereas previous studies showed that compressed uploads and downloads can save time and energy in many typical file transfers initiated from smartphones Dzhagaryan and Milenkovic, 2015;Milenkovic et al, 2013b) there is not a single upload or download file transfer method that works the best for all data types and network conditions. To underscore this problem, we conduct a measurement-based study that evaluates the effectiveness of various data transfer options under different network conditions.…”

Section: File Transfers In Mobile Cloudmentioning

confidence: 96%

Analytical Models for Evaluating Effectiveness of Compressed File Transfers in Mobile Computing

Dzhagaryan

Milenkoviź

2016

Proceedings of the 6th International Joint Conference on Pervasive and Embedded Computing and Communication Systems

Self Cite

View full text Add to dashboard Cite

The importance of optimizing data transfers between mobile computing devices and the cloud is increasing with an exponential growth of mobile data traffic. Lossless data compression can be essential in increasing communication throughput, reducing communication latency, achieving energy-efficient communication, and making effective use of available storage. In this paper we introduce analytical models for estimating effective throughput and energy efficiency of uncompressed data transfers and compressed data transfers that utilize common compression utilities. The proposed analytical models are experimentally verified using state-of-the-art mobile devices. These models are instrumental in developing a framework for seamless optimization of data file transfers.

show abstract

Section: File Transfers In Mobile Cloudmentioning

confidence: 96%

Analytical Models for Evaluating Effectiveness of Compressed File Transfers in Mobile Computing

Dzhagaryan

Milenkoviź

2016

Proceedings of the 6th International Joint Conference on Pervasive and Embedded Computing and Communication Systems

Self Cite

View full text Add to dashboard Cite

show abstract

“…When a file is decompressed, lossless compression keeps the original file's quality. It is documented that lossless compression is used [2][3][4][5][6]. In this work, to remove the unnecessary portions that need a large amount of memory of digital photographs, the statistical technique is utilized for this task.…”

Section: Introductionmentioning

confidence: 99%

Digital-Image Dimension Reduction Via Analysis of Principal component

Fadhil

Hburi²,

Fleih³

et al. 2022

ejuow

View full text Add to dashboard Cite

An Image with high-resolution is associated with huge size data space because each information of the image is arranged into 2D picture elements' values, each of them containing its associated value of the RGB bits. The depiction of picture data makes it challenging to distribute picture files using the Internet. For Internet users, the time it takes to upload and download photos has all time been the main concern. A high-resolution image takes up more storage space, in addition to the data transit difficulty. The Analysis of Principal Component, or PCA for a brief notation, is a mathematical approach utilized to lessen the data dimensionality. It extracts the main pattern of a linear system using the factoring matrices technique. The objectives of this paper are to see how effective PCA is in reducing digital picture features and to investigate the (feature-reduced) images’ quality on comparison with different values of the variance. As per the synthesizing of the initial research, the dimension or size reduction technique through the Analysis of Principal Component typically involves of 4-important steps: (1) picture-data normalizing (2) matrix of the covariance calculating using picture-data. (3) discovering the picture-data projection (with fewer number of features) to a new basis use the Single Value Decomposition technique (SVD) (4) determining the picture-data projection (with fewer number of characteristics) to a new basis. According to testing results, the PCA approach considerably decreases the size of picture data while sustaining the original picture’s fundamental properties. This approach reduced file size by 35.3 percent for the best feature lowered quality. The upload time of picture files through the Internet has substantially improved, particularly for mobile device downloads.

show abstract

Principal component analysis to reduce dimension on digital image

2017

Procedia Computer Science

View full text Add to dashboard Cite

On effectiveness of lossless compression in transferring mHealth data files

Cited by 5 publications

References 7 publications

Analytical Models for Evaluating Effectiveness of Compressed File Transfers in Mobile Computing

Analytical Models for Evaluating Effectiveness of Compressed File Transfers in Mobile Computing

Digital-Image Dimension Reduction Via Analysis of Principal component

Principal component analysis to reduce dimension on digital image

Contact Info

Product

Resources

About