Analysis of Wavelet Transform Multiscale Products for Step Detection and Estimation

1998 ◽  
Author(s):  
Brian M. Sadler ◽  
Ananthram Swami
Keyword(s):  
Wavelet Transform ◽  
Step Detection ◽  
Detection And Estimation ◽  
Multiscale Products

TIME-SCALE APPROACH FOR CHIRP DETECTION II

2010 ◽  
Vol 08 (01) ◽  
pp. 89-107
Author(s):  
MARCELA MORVIDONE
Keyword(s):  
Wavelet Transform ◽  
Gravitational Waves ◽  
Time Scale ◽  
Matched Filter ◽  
Random Noise ◽  
Joint Detection ◽  
New Approach ◽  
Detection And Estimation ◽  
Filter Approach ◽  
Interferometric Detectors

A new approach for joint detection and estimation of signals embedded in stationary random noise is considered and compared to previously studied methods, for the subclass of amplitude and frequency modulated signals. The method is a "reassigned" version of the Hough-wavelet transform and it is compared to the matched filter approach and the Hough-wavelet transform itself. Extensions to previous results obtained with these two methods are also presented. As target application, the problem of gravitational waves at interferometric detectors is considered. Our main conclusion is that there is no gain in introducing the reassignment operation.

Detection and Estimation of Heteroscedastic Noise by Means of the Wavelet Transform

1997 ◽  
Vol 34 (2) ◽  
pp. 73-75 ◽  
Author(s):  
Christian R. Mittermayr ◽  
Erwin Rosenberg ◽  
Manfred Grasserbauer
Keyword(s):  
Wavelet Transform ◽  
Detection And Estimation

Classification of Acceleration Waveforms during Walking by Wavelet Transform

1997 ◽  
Vol 36 (04/05) ◽  
pp. 356-359 ◽  
Author(s):  
M. Sekine ◽  
M. Ogawa ◽  
T. Togawa ◽  
Y. Fukui ◽  
T. Tamura
Keyword(s):  
Wavelet Transform ◽  
Wavelet Analysis ◽  
Center Of Gravity ◽  
Dynamic Responses ◽  
Sequential Data ◽  
Acceleration Signal ◽  
Continuous Dynamic

Abstract:In this study we have attempted to classify the acceleration signal, while walking both at horizontal level, and upstairs and downstairs, using wavelet analysis. The acceleration signal close to the body’s center of gravity was measured while the subjects walked in a corridor and up and down a stairway. The data for four steps were analyzed and the Daubecies 3 wavelet transform was applied to the sequential data. The variables to be discriminated were the waveforms related to levels -4 and -5. The sum of the square values at each step was compared at levels -4 and -5. Downstairs walking could be discriminated from other types of walking, showing the largest value for level -5. Walking at horizontal level was compared with upstairs walking for level -4. It was possible to discriminate the continuous dynamic responses to walking by the wavelet transform.

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