scholarly journals Wavelet Transform for IoT – A Signal Processing Perspective

2021 ◽  
Author(s):  
Indrakshi Dey
Keyword(s):  
Wavelet Transform ◽  
Wavelet Packet ◽  
Low Frequency ◽  
Wavelet Packet Transform ◽  
Discrete Wavelet ◽  
Energy Correlation ◽  
Reconstructed Signal ◽  
Generalized Framework ◽  
Frequency Components ◽  
Initial Results

<div>Denoising of signals in an Internet-of-Things (IoT) network is critically challenging owing to the diverse nature of the nodes generating them, environments through which they travel, characteristics of noise plaguing the signals and the applications they cater to. In order to address the abovementioned challenges, we conceptualize a generalized framework combining wavelet packet transform (WPT) and energy correlation analysis. WPT decomposes both the low-frequency and high-frequency components of the received signals in different time scales and wavelet spaces. Noise components are identified, removed through filtering and the signal components are predicted back after filtering using inverse wavelet packet transform (IWPT). Next energy of the reconstructed signal components are compared with that of the original transmitted signal to modify the characteristics of the decomposed signal components. Using the modified details, the signal components are reconstructed back again and the noise components are filtered out. This process is repeated until noise is completely removed. Initial results suggest that, our proposed framework offers improvement in error probability performance of a medium-scale IoT network over traditional discrete wavelet transform (DWT) and WPT based techniques by around 3 dB and 7 dB respectively.</div>

scholarly journals Wavelet Transform for IoT – A Signal Processing Perspective

2021 ◽  
Author(s):  
Indrakshi Dey
Keyword(s):  
Wavelet Transform ◽  
Wavelet Packet ◽  
Low Frequency ◽  
Wavelet Packet Transform ◽  
Discrete Wavelet ◽  
Energy Correlation ◽  
Reconstructed Signal ◽  
Generalized Framework ◽  
Frequency Components ◽  
Initial Results

<div>Denoising of signals in an Internet-of-Things (IoT) network is critically challenging owing to the diverse nature of the nodes generating them, environments through which they travel, characteristics of noise plaguing the signals and the applications they cater to. In order to address the abovementioned challenges, we conceptualize a generalized framework combining wavelet packet transform (WPT) and energy correlation analysis. WPT decomposes both the low-frequency and high-frequency components of the received signals in different time scales and wavelet spaces. Noise components are identified, removed through filtering and the signal components are predicted back after filtering using inverse wavelet packet transform (IWPT). Next energy of the reconstructed signal components are compared with that of the original transmitted signal to modify the characteristics of the decomposed signal components. Using the modified details, the signal components are reconstructed back again and the noise components are filtered out. This process is repeated until noise is completely removed. Initial results suggest that, our proposed framework offers improvement in error probability performance of a medium-scale IoT network over traditional discrete wavelet transform (DWT) and WPT based techniques by around 3 dB and 7 dB respectively.</div>

Energy Distribution Analysis Regarding the Crack Size in a Rotating Shaft

2019 ◽  
Vol 24 (3) ◽  
pp. 418-425
Author(s):  
Cristina Cristina Castejon ◽  
Marıa Jesus Gomez ◽  
Juan Carlos Garcia-Prada ◽  
Eduardo Corral
Keyword(s):  
Wavelet Transform ◽  
Crack Detection ◽  
Wavelet Packet ◽  
Critical Role ◽  
Wavelet Packet Transform ◽  
Discrete Wavelet ◽  
Distribution Analysis ◽  
Rotating Shaft ◽  
Vibration Signals ◽  
Machine Monitoring

Maintenance is critical to avoid catastrophic failures in rotating machinery, and the detection of cracks plays a critical role because they can originate failures with costly processes of reparation, especially in shafts. Vibration signals are widely used in machine monitoring and fault diagnostics. The most critical issue in machine monitoring is the suitable selection of the vibration parameters that represent the condition of the machine. Discrete Wavelet Transform, and one of its recursive forms, called Wavelet Packet Transform, provide a high potential for pattern extraction. Several factors must be selected and taken into account in the Wavelet Transform application such as the level of decomposition, the suitable mother wavelet, and the level basis or features. In this work, the dynamic response of a shaft with different levels of crack is studied. The evolution of energy of the vibration signals obtained from the rotating shaft and the frequencies where maximum increments of energy appear with the crack are analyzed. The results allow the conclusion that changes in energies computed by means of the Wavelet Packet Transform can be successfully used for crack detection.

scholarly journals Comparison of different feature extraction methods for the analysis of uterine magnetomyography signals to predict term labor

2018 ◽  
Vol 7 (3.29) ◽  
pp. 1
Author(s):  
T Ananda Babu ◽  
Dr P. Rajesh Kumar
Keyword(s):  
Feature Extraction ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Wavelet Packet ◽  
Extraction Methods ◽  
Wavelet Packet Transform ◽  
Svm Classifier ◽  
Discrete Wavelet ◽  
Term Labor

The prediction of term labor by analyzing the uterine magnetomyographic signals attempted in this research. The existing works did not focus on the classification of the signals. Publicly available MIT-BIH database records were divided into term-labor and term-nonlabor groups. This research presents two methods for feature extraction, discrete wavelet transform and wavelet packet transform. Energy, standard deviation, variance, entropy and waveform length of transform coefficients used in the first method. The normalized logarithmic energy of wavelet coefficients from each packet of the total wavelet packet tree used as the feature space for the second method. The labor assessment done through the classification of the features by using five different classifiers for different mother wavelet families. Discrete wavelet transform features extracted using coif5 wavelet with random subspace classification gives the accuracy, precision and FPrates of 93.9286%, 94.2014% and 5.7986% respectively. Using sym8 wavelet for wavelet packet transform features classified with SVM classifier performed well with 95.8763% accuracy, 95.9719% precision and 4.0281% FPrate. The results obtained from the research will be helpful in term labor assessment and understanding the parturition process.  

A blind digital image watermarking method based on the dyadic wavelet packet transform and fast interval arithmetic techniques

2015 ◽  
Vol 13 (05) ◽  
pp. 1550040 ◽  
Author(s):  
Ryuji Ohura ◽  
Teruya Minamoto
Keyword(s):  
Digital Image ◽  
Interval Arithmetic ◽  
High Frequency ◽  
Image Watermarking ◽  
Wavelet Packet ◽  
Wavelet Packet Transform ◽  
Discrete Wavelet ◽  
Digital Image Watermarking ◽  
Dyadic Wavelet ◽  
Frequency Components

We propose a new digital image watermarking method based on the dyadic wavelet packet transform (DYWPT) and fast interval arithmetic (IA) techniques. Because the DYWPT has a redundant representation, like the dyadic wavelet transform (DYWT), the amount of information that the watermark must contain is greater than in the case of methods based on ordinary discrete wavelet transforms (DWTs) and the discrete wavelet packet transform (DWPT). However, the order of the high frequency components is not necessarily the same as the order of their frequency domain. Therefore, in our approach, we rearrange the order of the high frequency components in descending order of frequency components and embed a watermark selectively into higher frequency components. Our watermark is a ternary-valued logo that is embedded into higher frequency components through use of the DYWPT and fast IA techniques. We describe our watermarking procedure in detail and present experimental results demonstrating that our method produces watermarked images that have better quality and are robust with respect to various types of attacks, including marking, clipping, median filtering, contrast tuning (histeq and imadjust commands in the MATLAB Image Processing Toolbox), addition of Gaussian white noise, addition of salt & pepper noise, JPEG and JPEG 2000 compressions, rotation, and resizing.

The Fusion Algorithm of Infrared and Visible Images Based on Computer Vision

2014 ◽  
Vol 945-949 ◽  
pp. 1851-1855
Author(s):  
Ming Hui Deng ◽  
Jian Xin Kang ◽  
Yan Jun Li
Keyword(s):  
Wavelet Transform ◽  
Low Frequency ◽  
Source Image ◽  
Discrete Wavelet ◽  
Fusion Algorithm ◽  
Second Generation Wavelet Transform ◽  
Fused Image ◽  
Visible Images ◽  
Frequency Components ◽  
Second Generation Wavelet

Directionlet transform is a lattice-based skewed discrete wavelet transform. It has advantages of multi-directional and anisotropy compared with standard two-dimensional wavelet transform, thus, it is better at describing the characteristics of images. For the research focus of different-source image fusion, a novel fusion algorithm based on Directionlet transform was proposed, and the fusion speed was improved efficiently by combing the transform with a lifting scheme. Firstly, between transform direction and alignment direction, two registered source images were decomposed by using lifting Directionlet transform respectively in different times, thus anisotropic sub images were obtained. Then, the low frequency components were combined averagely and the selection principle of high frequency sub images were based on which has stronger anisotropic edge information. Finally, the fused image was obtained by using inverse Directionlet transform. Experimental results show that the fusion effect and speed are both better than standard wavelet transform and other second generation wavelet transform.

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