Branch prediction based on universal data compression algorithms

2002 ◽  
Author(s):  
E. Federovsky ◽  
M. Feder ◽  
S. Weiss
Keyword(s):  
Data Compression ◽  
Branch Prediction ◽  
Compression Algorithms

Branch prediction based on universal data compression algorithms

1998 ◽  
Vol 26 (3) ◽  
pp. 62-72 ◽  
Author(s):  
Eitan Federovsky ◽  
Meir Feder ◽  
Sholomo Weiss
Keyword(s):  
Data Compression ◽  
Branch Prediction ◽  
Compression Algorithms

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.

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