scholarly journals Visualizing Processing of a differential Magnetic Sensor Signals By the Wavelet Transform

2000 ◽  
Vol 20 (1Supplement) ◽  
pp. 265-268
Author(s):  
H. KOCHI ◽  
S. HAYANO ◽  
Y. SAITO
Keyword(s):  
Wavelet Transform ◽  
Magnetic Sensor ◽  
Sensor Signals

scholarly journals Wavelet Transform for Signal Processing in Internet-of-Things (IoT)

2021 ◽  
Author(s):  
Indrakshi Dey ◽  
Shama Siddiqui
Keyword(s):  
Signal Processing ◽  
Wavelet Transform ◽  
Communication Networks ◽  
Wavelet Packet ◽  
Wavelet Packet Transform ◽  
Wireless Communication Networks ◽  
Impact Noise ◽  
Sensor Signals ◽  
Processing Steps ◽  
Primary Contribution

The primary contribution of this chapter is to provide an overview of different denoising methods used for signal processing in IoT networks from the perspectives of physical layer in the network. The chapter starts with the introduction to different kinds of noise that can be encountered in any kind of wireless communication networks, different kinds of wavelet transform and wavelet packet transform methods that can be used for denoising sensor signals in IoT networks and the different processing steps that are needed to be followed to accomplish wavelet packet transform for the sensor signals. Finally, a universal framework based on energy correlation analysis has been presented for denoising sensor signals in IoT networks, and such a framework can achieve considerable improvement in denoising performance reducing the effective noise correlation coefficient to 0.00001 or lower. Moreover, this method is found to be equally effective for Gaussian or impact noise or both.

scholarly journals Visualizing Processing of a differential Magnetic Sensor Signals By the Wavelets Transform

1999 ◽  
Vol 19 (Supplement1) ◽  
pp. 195-198
Author(s):  
H. KOCHI ◽  
S. HAYANO ◽  
Y. SAITO ◽  
T.L. KUNII
Keyword(s):  
Magnetic Sensor ◽  
Sensor Signals ◽  
Wavelets Transform

scholarly journals Inertial Sensor Signals Denoising with Wavelet Transform

2015 ◽  
Vol 7 (1) ◽  
pp. 57-64
Author(s):  
EDU Ioana-Raluca ◽  
◽  
ADOCHIEI Felix-Constantin ◽  
OBREJA Radu ◽  
ROTARU Constantin ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Inertial Sensor ◽  
Sensor Signals

scholarly journals A peak detection algorithm adopting magnetic sensor signal for rail spike location in tamping machine

2019 ◽  
Vol 11 (11) ◽  
pp. 168781401989157
Author(s):  
Meng Gao ◽  
Haifeng Wu ◽  
Yong Shen ◽  
Xia Wang ◽  
Yu Zeng
Keyword(s):  
Sliding Window ◽  
Detection Algorithm ◽  
Peak Detection ◽  
Magnetic Sensor ◽  
Window Length ◽  
Search Window ◽  
Location Error ◽  
Peak Detection Algorithm ◽  
Sensor Signals ◽  
Peak Search

When a tamping machine is tamping track ballasts under railway, it is necessary to determine the tamping positions in advance. This study proposes a peak detection algorithm to locate rail spikes with magnetic sensor signals, and then determine the tamping positions. In this algorithm, we have performed the downsampling, sliding window, threshold classification, and secondary peak search to complete peak detection. Especially, we discuss how a sliding window length, a downsampling frequency, a secondary-search-window length, and other parameters affect the performance of the algorithm. In experiments, we use a group of real magnetic sensor signals to evaluate the algorithm. Compared with traditional methods, the proposed algorithm can reduce the false positives and misses of peak detection to 0, while the maximum location error will not more than 1 cm.

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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