Understanding the Relationship between Inflation and Growth: A Wavelet Transformation Approach in the Case of Bangladesh

World Economy ◽  
2016 ◽  
Vol 40 (9) ◽  
pp. 1918-1933 ◽  
Author(s):  
Gazi Salah Uddin ◽  
Ahmed Taneem Muzaffar ◽  
Mohamed Arouri ◽  
Bo Sjö
Keyword(s):  
Wavelet Transformation ◽  
Transformation Approach ◽  
The Relationship

Parameters Identification for Nonlinear Duffing System Based on Wavelet Transformation

2013 ◽  
Vol 357-360 ◽  
pp. 1524-1530
Author(s):  
Shi Zhou ◽  
Dong Mei Huang ◽  
Wei Xin Ren ◽  
Qiong Li Wang
Keyword(s):  
Forced Vibration ◽  
Wavelet Transformation ◽  
Recognition Accuracy ◽  
Vibration Signal ◽  
Least Square Method ◽  
Least Square ◽  
Instantaneous Amplitude ◽  
Approximate Analytical Expression ◽  
Duffing System ◽  
The Relationship

Continuous wavelet transformation is made to identify the parameters of damped harmonic forced vibration Duffing system. With the aid of conversion relationship between the scale and frequency, the solution of nonlinear Duffing equation is adopted by average method, which gained approximate analytical expression for instantaneous amplitude and instantaneous frequency of the system. The nonlinear stiffness coefficient and natural frequency can be gained by least square method and the relationship between recognition accuracy and parameter selection are summarized in the article. Parameter identification method of harmonic forced vibration system is proposed in this paper. Studying the wavelet ridge and corresponding scale by segments to filter out the affects of the simple harmonic motion, to extract systems free vibration signal and to achieve the goal of identifying system parameters.

Wavelet Transforms and Multirate Filtering

2002 ◽  
pp. 86-104 ◽  
Author(s):  
Raghuveer Rao
Keyword(s):  
Image Processing ◽  
Wavelet Transform ◽  
Wavelet Transforms ◽  
Wavelet Transformation ◽  
Discrete Wavelet ◽  
Orthogonal Wavelet ◽  
Seminal Paper ◽  
Wavelet Expansions ◽  
Square Integrable Function ◽  
The Relationship

One of the most fascinating developments in the field of multirate signal processing has been the establishment of its link to the discrete wavelet transform. Indeed, it is precisely this link that has been responsible for the rapid application of wavelets in fields such as image compression. The objective of this chapter is to provide an overview of the wavelet transform and develop its link to multirate filtering. The birth of the field of wavelet transforms is now attributed to the seminal paper by Grossman and Morlet (1984) detailing the continuous wavelet transform or CWT. The CWT of a square integrable function is obtained by integrating it over regions defined by translations and dilations of a windowing function called the mother wavelet. The idea of representing functions or signals in terms of dilations can be found even in engineering articles dating back by several years, for example, Helstrom (1966). However, Grossman and Morlet’s formulation was more complete and was motivated by potential application to modeling seismic data. The next step of significance was the discovery of orthogonal wavelet basis functions and their role in defining multi-resolution representations (Daubechies 1988; Meyer 1992). Daubechies also provided a method for constructing compactly supported wavelets. Mallat (1989) established the fact that coefficients of orthogonal wavelet expansions can be obtained through multirate filtering which paved the way for widespread investigation of using wavelet transforms in signal and image processing applications. The objective of the chapter is to provide an overview of the relationship between multirate filtering and wavelet transformation. We begin with a brief account of the CWT, then go through the discrete wavelet transformation (DWT) followed by derivation of the relationship between the DWT and multirate filtering. The chapter concludes with an account of selected applications in digital image processing.

scholarly journals The Capacity of the Hybridizing Wavelet Transformation Approach With Data-Driven Models for Modeling Monthly-Scale Streamflow

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 101993-102006 ◽  
Author(s):  
Sinan Jasim Hadi ◽  
Mustafa Tombul ◽  
Sinan Q. Salih ◽  
Nadhir Al-Ansari ◽  
Zaher Mundher Yaseen
Keyword(s):  
Wavelet Transformation ◽  
Data Driven ◽  
Monthly Scale ◽  
Transformation Approach

scholarly journals Investigating the Causal Linkages among Inflation, Interest Rate, and Economic Growth in Pakistan under the Influence of COVID-19 Pandemic: A Wavelet Transformation Approach

2021 ◽  
Vol 14 (6) ◽  
pp. 277
Author(s):  
Muhammad Azmat Hayat ◽  
Huma Ghulam ◽  
Maryam Batool ◽  
Muhammad Zahid Naeem ◽  
Abdullah Ejaz ◽  
...  
Keyword(s):  
Interest Rate ◽  
Granger Causality ◽  
Wavelet Transformation ◽  
Multi Scale ◽  
Long Run ◽  
Causality Tests ◽  
Short Run ◽  
The Interest Rate ◽  
The Relationship ◽  
Cross Wavelet

This research is the earliest attempt to understand the impact of inflation and the interest rate on output growth in the context of Pakistan using the wavelet transformation approach. For this study, we used monthly data on inflation, the interest rate, and industrial production from January 1991 to May 2020. The COVID-19 pandemic has affected economies around the world, especially in view of the measures taken by governmental authorities regarding enforced lockdowns and social distancing. Traditional studies empirically explored the relationship between these important macroeconomic variables only for the short run and long run. Firstly, we employed the autoregressive distributed lag (ARDL) cointegration test and two causality tests (Granger causality and Toda–Yamamoto) to check the cointegration properties and causal relationship among these variables, respectively. After confirming the long-run causality from the ARDL bound test, we decomposed the time series of growth, inflation, and the interest rate into different time scales using wavelet analysis which allows us to study the relationship among variables for the very short run, medium run, long run, and very long run. The continuous wavelet transform (CWT), the cross-wavelet transform (XWT), cross-wavelet coherence (WTC), and multi-scale Granger causality tests were used to investigate the co-movement and nature of the causality between inflation and growth and the interest rate and growth. The results of the wavelet and multi-scale Granger causality tests show that the causal relationship between these variables is not the same across all time horizons; rather, it is unidirectional in the short-run and medium-run but bi-directional in the long-run. Therefore, this study suggests that the central bank should try to maintain inflation and the interest rate at a low level in the short run and medium run instead of putting too much pressure on these variables in the long-run.

A wavelet transformation approach for multi-source gravity fusion: Applications and uncertainty tests

2016 ◽  
Vol 128 ◽  
pp. 18-30 ◽  
Author(s):  
Yongliang Bai ◽  
Dongdong Dong ◽  
Shiguo Wu ◽  
Zhan Liu ◽  
Guangxu Zhang ◽  
...  
Keyword(s):  
Wavelet Transformation ◽  
Transformation Approach ◽  
Fusion Applications

scholarly journals Wavelet Domain Multidictionary Learning for Single Image Super-Resolution

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Xiaomin Wu ◽  
Jiulun Fan ◽  
Jian Xu ◽  
Yanzi Wang
Keyword(s):  
Wavelet Transformation ◽  
Super Resolution ◽  
Principal Component ◽  
Discrete Wavelet ◽  
Single Image ◽  
Image Super Resolution ◽  
Single Image Super Resolution ◽  
Relationship Of ◽  
The Given ◽  
The Relationship

Image super-resolution (SR) aims at recovering the high-frequency (HF) details of a high-resolution (HR) image according to the given low-resolution (LR) image and some priors about natural images. Learning the relationship of the LR image and its corresponding HF details to guide the reconstruction of the HR image is needed. In order to alleviate the uncertainty in HF detail prediction, the HR and LR images are usually decomposed into 4 subbands after 1-level discrete wavelet transformation (DWT), including an approximation subband and three detail subbands. From our observation, we found the approximation subbands of the HR image and the corresponding bicubic interpolated image are very similar but the respective detail subbands are different. Therefore, an algorithm to learn 4 coupled principal component analysis (PCA) dictionaries to describe the relationship between the approximation subband and the detail subbands is proposed in this paper. Comparisons with various state-of-the-art methods by experiments showed that our proposed algorithm is superior to some related works.

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