scholarly journals Data Reduction Techniques in Neural Recording Microsystems

10.5772/59662 ◽  
2015 ◽  
Author(s):  
Mohsen Judy ◽  
Alireza Akhavian ◽  
Farzad Asgarian
Keyword(s):  
Data Reduction ◽  
Neural Recording ◽  
Reduction Techniques

Flight Test Execution and Data Reduction Techniques of Exhaust Gas Reingestion on the CH-53K King Stallion

2021 ◽  
Author(s):  
Noopur Joshi ◽  
Noah Becker ◽  
Roger Tull ◽  
James Kenna ◽  
Christopher Adams ◽  
...  
Keyword(s):  
Data Reduction ◽  
Flight Test ◽  
Exhaust Gas ◽  
Test Execution ◽  
Reduction Techniques

Experiments and Experiences Using Filtering and Data Reduction Techniques for Imaging in Nondestructive Testing

1992 ◽  
pp. 567-573
Author(s):  
L. v. Bernus ◽  
F. Mohr ◽  
T. Schmeidl ◽  
H. Ermert ◽  
M. Pollakowski ◽  
...  
Keyword(s):  
Nondestructive Testing ◽  
Data Reduction ◽  
Reduction Techniques

Parallel Data Reduction Techniques for Big Datasets

2013 ◽  
pp. 72-93 ◽  
Author(s):  
Ahmet Artu Yıldırım ◽  
Cem Özdoğan ◽  
Dan Watson
Keyword(s):  
Big Data ◽  
Data Reduction ◽  
Memory Systems ◽  
Discrete Wavelet ◽  
Server Side ◽  
Advantages And Disadvantages ◽  
Reduction Techniques ◽  
Parallel Data ◽  
Processing Techniques

Data reduction is perhaps the most critical component in retrieving information from big data (i.e., petascale-sized data) in many data-mining processes. The central issue of these data reduction techniques is to save time and bandwidth in enabling the user to deal with larger datasets even in minimal resource environments, such as in desktop or small cluster systems. In this chapter, the authors examine the motivations behind why these reduction techniques are important in the analysis of big datasets. Then they present several basic reduction techniques in detail, stressing the advantages and disadvantages of each. The authors also consider signal processing techniques for mining big data by the use of discrete wavelet transformation and server-side data reduction techniques. Lastly, they include a general discussion on parallel algorithms for data reduction, with special emphasis given to parallel wavelet-based multi-resolution data reduction techniques on distributed memory systems using MPI and shared memory architectures on GPUs along with a demonstration of the improvement of performance and scalability for one case study.

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