A Model for Classification of Breast Cell Density Using ANN and Shift Invariance Wavelet Transform ConvNet

2019 ◽  
pp. 60-69
Author(s):  
Sulaiman Sadiya ◽  
C. A. Hafsath
Keyword(s):  
Wavelet Transform ◽  
Cell Density ◽  
Shift Invariance

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.

Detection and Classification of Brain Tumor from MRI Medical Image using Wavelet Transform and PSO based LLRBFNN Algorithm

2018 ◽  
Vol 6 (1) ◽  
pp. 18-23 ◽  
Author(s):  
T.Gopi Krishna ◽  
◽  
K.V.N. Sunitha ◽  
S. Mishra ◽  
◽  
...  
Keyword(s):  
Brain Tumor ◽  
Wavelet Transform ◽  
Medical Image

FINITE IMPULSE RESPONSE DOUBLE DENSITY FILTER BANKS AND FRAMELETS

2013 ◽  
Vol 11 (01) ◽  
pp. 1350010
Author(s):  
ASHOKA JAYAWARDENA ◽  
PAUL KWAN
Keyword(s):  
Wavelet Transform ◽  
Impulse Response ◽  
Filter Bank ◽  
Filter Banks ◽  
Finite Impulse Response ◽  
Wavelet Filters ◽  
Density Filter ◽  
Shift Invariance ◽  
Factorization Methods ◽  
Invariant Properties

In this paper, we focus on the design of oversampled filter banks and the resulting framelets. The framelets obtained exhibit improved shift invariant properties over decimated wavelet transform. Shift invariance has applications in many areas, particularly denoising, coding and compression. Our contribution here is on filter bank completion. In addition, we propose novel factorization methods to design wavelet filters from given scaling filters.

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