A novel variable filter band discrete wavelet transform: Theory and principle

Author(s):  
Zhong Zhang ◽  
Hiroshi Toda ◽  
Takashi Imamura ◽  
Tetsuo Miyake

It is well-known that a mother wavelet for the discrete wavelet transform (DWT) has the band-pass filter characteristic with octave width in the frequency domain and can be used for octave analysis. However, it is possible that the octave analysis is not necessarily the most suitable to match the analysis signal. In this study, in order to construct the most suitable basis to match the analysis signal, a novel variable-filter band discrete wavelet transform (VFB-DWT) is proposed. It is achieved by using variable-band filters instead of conventional decomposition and reconstruction sequences, which are designed in consideration of the real signal characteristics. Additionally, it is proven that perfect reconstruction of the analysis signal by VFB-DWT is guaranteed using the perfect shift invariant theorem that underlies the theory of the PTI-CDWT having base DWT.

Atmosphere ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 615
Author(s):  
Chang Lai ◽  
Wei Li ◽  
Jiyao Xu ◽  
Xiao Liu ◽  
Wei Yuan ◽  
...  

An algorithm has been developed to isolate the gravity waves (GWs) of different scales from airglow images. Based on the discrete wavelet transform, the images are decomposed and then reconstructed in a series of mutually orthogonal spaces, each of which takes a Daubechies (db) wavelet of a certain scale as a basis vector. The GWs in the original airglow image are stripped to the peeled image reconstructed in each space, and the scale of wave patterns in a peeled image corresponds to the scale of the db wavelet as a basis vector. In each reconstructed image, the extracted GW is quasi-monochromatic. An adaptive band-pass filter is applied to enhance the GW structures. From an ensembled airglow image with a coverage of 2100 km × 1200 km using an all-sky airglow imager (ASAI) network, the quasi-monochromatic wave patterns are extracted using this algorithm. GWs range from ripples with short wavelength of 20 km to medium-scale GWs with a wavelength of 590 km. The images are denoised, and the propagating characteristics of GWs with different wavelengths are derived separately.


Author(s):  
Sidi M. Berri ◽  
J. M. Klosner

Abstract This paper investigates a new strategy for early detection of defects in a power transmission pair of spur gears. Sensitivity to local defects is enhanced by processing the signal as follows. The orthogonal discrete wavelet transform (ODWT) of the band-pass filtered averaged signal is first obtained. This is followed by thresholding in the wavelet domain, thereby removing the low amplitude noise contribution. The inverse wavelet transform then essentially reconstructs the component of the signal that is due to the defect. Experimental results demonstrate the efficiency of this procedure.


2018 ◽  
Vol 154 ◽  
pp. 01046
Author(s):  
Yusuf A Amrulloh ◽  
Jawahir A K Haq

Breath sound recordings from pediatric subjects pose more processing complications. Children, especially the younger ones, are not able to follow instructions to stay calm during recording. This makes their recordings not only contain stationary artifacts but also non-stationary artifacts such as movement of subjects and their heartbeats. Further, the breath sounds from pediatric subjects also have lower magnitude compared to adults. In this work, we proposed to address those problems by developing a method to remove the artifacts from breath sound recordings. We implemented a combination of a Butterworth band pass filter and a discrete wavelet filter. We tested three types of wavelets (Coiflet, Symlet and Daubechies). Ten level decompositions and a set of hard thresholds were implemented in our work. Our results show that our developed method was capable of removing the artifacts significantly while maintaining the signal of interest. The highest signal to noise ratio improvement (10.65dB) was achieved by 32 orders Symlet.


Wind Energy ◽  
2019 ◽  
Vol 22 (11) ◽  
pp. 1581-1592 ◽  
Author(s):  
Daniel Strömbergsson ◽  
Pär Marklund ◽  
Kim Berglund ◽  
Juhamatti Saari ◽  
Allan Thomson

Author(s):  
BRANDON WHITCHER ◽  
PETER F. CRAIGMILE

We investigate the use of Hilbert wavelet pairs (HWPs) in the non-decimated discrete wavelet transform for the time-varying spectral analysis of multivariate time series. HWPs consist of two high-pass and two low-pass compactly supported filters, such that one high-pass filter is the Hilbert transform (approximately) of the other. Thus, common quantities in the spectral analysis of time series (e.g., power spectrum, coherence, phase) may be estimated in both time and frequency. Compact support of the wavelet filters ensures that the frequency axis will be partitioned dyadically as with the usual discrete wavelet transform. The proposed methodology is used to analyze a bivariate time series of zonal (u) and meridional (v) winds over Truk Island.


Author(s):  
M. Kalaiarasi ◽  
T. Vigneswaran

<p>Image compression is a key technology in the development of various multimedia and communication applications. Perfect reconstruction of the image without any loss in picture quality and data is very important. This can be achieved with the Discrete Wavelet Transform (DWT), which is an efficient tool for image compression and video compression. The lifting based DWT architecture has the advantage of lower computational complexities and also requires less memory compared to the conventional convolution method. The existing DWT architectures are represented in terms of folded, flipping and recursive structures. The various architectures are discussed in terms of memory, power consumption and operating frequency involved with the given size of image and required levels of decomposition. This paper presents a survey of these architectures for 2-dimensional and 3-dimensional Discrete Wavelet Transform. This study is useful for deriving an efficient method for improving the speed and hardware complexities of existing architectures.</p>


2017 ◽  
Vol 59 (6) ◽  
pp. 1841-1847 ◽  
Author(s):  
Muhammet Hilmi Nisanci ◽  
Ahmet Yahya Tesneli ◽  
Nigar Berna Tesneli ◽  
Fatih Ustuner ◽  
Ekrem Demirel ◽  
...  

2021 ◽  
Vol 4 (4) ◽  
Author(s):  
Artem Ihorovych Fironov ◽  
Vitaliy Viktorovych Levchenko

Access systems with face recognition is widely used today. They are used in many enterprises and institutes where it is necessary to control the flow of passing people.  Facially recognizable technical vision systems are important because they can be used to store specific individuals faces and use them for access control. As a result of analysis of same modern systems the variant of system there are additional functions is offered. The system consists of ESP-EYE module, with build-in wi-fi and Bluetooth modules, chip sensor camera “ OV2640” and LED display, which dasplays a notification for a person about granting or denying access, notifications are in two collors: geen and red respectively.. Also it has an emergency power supply in case of unforeseen situations. Wi-fi is used as a means of transmiting data from camera to the server. This transmition method of data transmition has several advantages over Bluetooth. It allows to the system to transfer data at a much higher speed and over a grater distance, it is also more secure, provides access to the internet and allows to control the system  remotely. All the listed advantages of this method of transmition give us a great variability in the operation and placement of the system. To recognize people system use a comparison method. It compares the person’s face with a database and, after processing it produces the result. To optimize and speed up this process, the system uses a method of image compression based on discrete wavelet transform. This method is the transmission of a signal through several filtres, usualy two. First, the signal is passed through a low-pass filter whis a pulse response g, resulting in an output signal in the form of a convolutional sum. At the same time the signal is decomposed by a high pass filter. The LPF gives an approximate shape of the output signal, and the HPF – the signal of difference or additional detail. Discrete wavelet transform in an oriented basis makes it possible to construct transformation matrices with a given number of filters ”m”, where “m” is in the general case a prime positive number. The simplest way to compare the two images is by substracting the brightness values of the two matrices and estimating the resulting matrix of differences using standard deviation. The use of standard deviation in combination with fiberboard in OB allows to speed up the process of face recognition in the system by discarding unncessary details, the absence of which minimaly harms the accuracy of the results. The advantages of this system are that it is less expensive, in compareson with existing analogs, less energy-consuming, easy to assemble and install, uses a relatively simple and at the same time quite accurate method of identidying a persons identity.


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