scholarly journals Implementation and Evaluation of Data-Compression Algorithms for Irregular-Grid Iterative Methods on the PEZY-SC Processor

2016 ◽  
Author(s):  
Naoki Yoshifuji ◽  
Ryo Sakamoto ◽  
Keigo Nitadori ◽  
Jun Makino
Keyword(s):  
Data Compression ◽  
Iterative Methods ◽  
Compression Algorithms ◽  
Irregular Grid ◽  
Evaluation Of Data

Data compression algorithms for biomedical images

2021 ◽  
Author(s):  
Francisco Cunha ◽  
Sérgio Faria ◽  
João Parracho ◽  
Lucas Thomaz ◽  
Pedro Assunção
Keyword(s):  
Data Compression ◽  
Biomedical Images ◽  
Compression Algorithms

scholarly journals A framework for data compression and damage detection in structural health monitoring applied on a laboratory three-story structure

2016 ◽  
Vol 8 (2) ◽  
pp. 129
Author(s):  
Manoel Afonso Pereira de Lima ◽  
Claudomiro Sales ◽  
Adam Santos ◽  
Reginaldo Santos ◽  
Moisés Silva ◽  
...  
Keyword(s):  
Structural Health Monitoring ◽  
Damage Detection ◽  
Data Compression ◽  
Health Monitoring ◽  
Principal Component ◽  
Component Analysis ◽  
Strong Impact ◽  
Story Structure ◽  
Compression Algorithms ◽  
Structural Health

Structural Health Monitoring (SHM) is an important technique used to preserve many types of structures in the short and long run, using sensor networks to continuously gather the desired data. However, this causes a strong impact in the data size to be stored and processed. A common solution to this is using compression algorithms, where the level of data compression should be adequate enough to allow the correct damage identification. In this work, we use the data sets from a laboratory three-story structure to evaluate the performance of common compression algorithms which, then, are combined with damage detection algorithms used in SHM. We also analyze how the use of Independent Component Analysis, a common technique to reduce noise in raw data, can assist the detection performance. The results showed that Piecewise Linear Histogram combined with Nonlinear PCA have the best trade-off between compression and detection for small error thresholds while Adaptive PCA with Principal Component Analysis perform better with higher values.

scholarly journals FPGA implementation of Lempel-Ziv data compression

2021 ◽  
Vol 10 (2) ◽  
pp. 99
Author(s):  
Gody Mostafa ◽  
Abdelhalim Zekry ◽  
Hatem Zakaria
Keyword(s):  
Data Compression ◽  
Compression Ratio ◽  
Digital Communication ◽  
Compression Rate ◽  
High Compression Ratio ◽  
Compression Algorithms ◽  
High Compression ◽  
Space Saving ◽  
Fpga Chip ◽  
Lossless Data Compression

When transmitting the data in digital communication, it is well desired that the transmitting data bits should be as minimal as possible, so many techniques are used to compress the data. In this paper, a Lempel-Ziv algorithm for data compression was implemented through VHDL coding. One of the most lossless data compression algorithms commonly used is Lempel-Ziv. The work in this paper is devoted to improve the compression rate, space-saving, and utilization of the Lempel-Ziv algorithm using a systolic array approach. The developed design is validated with VHDL simulations using Xilinx ISE 14.5 and synthesized on Virtex-6 FPGA chip. The results show that our design is efficient in providing high compression rates and space-saving percentage as well as improved utilization. The Throughput is increased by 50% and the design area is decreased by more than 23% with a high compression ratio compared to comparable previous designs.

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