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Wavelet analysis of impact of renewable energy consumption and technological innovation on CO2 emissions: evidence from Portugal
This paper uncover a new perception of the dynamic interconnection between CO
2
emission and economic growth, renewable energy use, trade openness, and technological innovation in the Portuguese economy utilizing innovative Morlet wavelet analysis. The research applied continuous wavelet transform, wavelet correlation, the multiple and partial wavelet coherence, and frequency domain causality analyses are applied on variables of investigation using dataset between 1980 and 2019. The result of these analyses disclosed that the interconnection among the indicators progresses over time and frequency. The present analysis finds notable wavelet coherence and significant lead and lag interconnections in the frequency domain, while conflicting relationships among the variables are found in the time domain. The wavelet analysis according to economic viewpoint affirms that renewable energy consumption helps to curb CO
2
while trade openness, technological innovation, and economic growth contribute to CO
2
. The outcomes also proposed that renewable energy consumption decreases CO
2
in medium and long run in Portugal. Therefore, policymakers in Portugal should stimulate investment in renewable energy sources, establish restrictive laws, and enhance energy innovation.
Journal Article
A wavelet tour of signal processing : the sparse way
Mallat's book is the undisputed reference in this field - it is the only one that covers the essential material in such breadth and depth.- Laurent Demanet, Stanford UniversityThe new edition of this classic book gives all the major concepts, techniques and applications of sparse representation, reflecting the key role the subject plays in.
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Dynamic connection between inward foreign direct investment, renewable energy, economic growth and carbon emission in China: evidence from partial and multiple wavelet coherence
This paper presents a fresh understanding of the vigorous connection between inward FDI, renewable energy consumption, economic growth and carbon emission in the Chinese economy employing novel Morlet wavelet analysis. Wavelet correlation, continuous wavelet transform and partial and the multiple wavelet coherence analyses are applied on variables under study for data acquired during the period 1979 to 2017. The outcome of these analyses reveals that the connections among the variables progress over frequency and time. From the frequency domain point of view, the current study discovers noteworthy wavelet coherence and robust lead and lag linkages, although time domain reveals inconsistent associations among the considered variables. The wavelet analysis according to economic point of view supports that inward foreign direct investment (FDI) and renewable energy consumption help to enhance economic condition in Chinese economy. The results also suggested that inward FDI enhances the environmental degradation in medium and long run in China. The results emphasize the significance of having organized strategies by the policymakers to cope with huge environmental degradation occurred for a couple of decades in China.
Journal Article
Forensics feature analysis in quaternion wavelet domain for distinguishing photographic images and computer graphics
In this paper, a novel set of features based on Quaternion Wavelet Transform (QWT) is proposed for digital image forensics. Compared with Discrete Wavelet Transform (DWT) and Contourlet Wavelet Transform (CWT), QWT produces the parameters, i.e., one magnitude and three angles, which provide more valuable information to distinguish photographic (PG) images and computer generated (CG) images. Some theoretical analysis are done and comparative experiments are made. The corresponding results show that the proposed scheme achieves 18 percents’ improvements on the detection accuracy than Farid’s scheme and 12 percents than Özparlak’s scheme. It may be the first time to introduce QWT to image forensics, but the improvements are encouraging.
Journal Article
California's carbon market and energy prices: a wavelet analysis
Carbon price is a key variable in management and risk decisions in activities related to the burning of fossil fuels. Different major players in this market, such as polluters, regulators and financial actors, have different time horizons. We use innovative multivariate wavelet analysis tools, including partial wavelet coherence and partial wavelet gain, to study the link between carbon prices and final energy prices in the time and frequency dimensions in California's carbon market, officially known as the California cap-and-trade programme. We find that gasoline prices lead an anti-phase relation with carbon prices. This result is very stable at lower frequencies (close to 1-year period cycles), and it is also present before mid-2015 in the 20-34 weeks frequency band. Regarding electricity, we find that at about a 1-year period, a rise in carbon prices is reflected in higher electricity prices. We conclude that the first 5 years of compliance of the California cap-and-trade programme show that emissions trading is a significant measure for climate change mitigation, with visible rising carbon prices. The quantitative financial analytics we present supports the recent decision to extend the current market to 2030 without the need for complementary carbon pricing schemes.
This article is part of the theme issue 'Redundancy rules: the continuous wavelet transform comes of age'.
Journal Article
Signal Processing Algorithm Based on Discrete Wavelet Transform
The use of digital technologies for processing and diagnosing electrocardiogram signals using wavelet-analysis can significantly improve the efficiency and quality of parameter estimations of the pacemaker configuration during implantation. It is also efficient in the process of correction of functional modes of cardiac pacemaker and diagnostics to eliminate postoperative complications, etc. A special processing of complex cardio signals at a qualitatively new level is an indispensable condition for the decisive improvement of the processing of current values of diagnosed parameters, widespread use of digital instruments for sound and informed decision-making on the provision of medical care and the treatment of people with diseases of the cardiovascular system. The article discusses the approximation method. Digital technologies are implemented using MATLAB computing environment.
Journal Article
A comprehensive guide to selecting suitable wavelet decomposition level and functions in discrete wavelet transform for fault detection in distribution networks
The paper presents a comprehensive analysis of the IEEE-16 bus system under different operating conditions. It discusses the selection of suitable decomposition level and wavelet function for analyzing non-stationary signals to enhance power distribution network fault detection. MATLAB/Simulink is used to simulate the system, and transient fault current signals are processed with the MATLAB Wavelet Toolbox. The optimal decomposition level is determined by energy concentration, with the highest energy found in scales D9 (b4), D8 (b5), and D7 (b6), and D8 having the most concentration. Using MATLAB classifier learner, the article evaluates seven common mother wavelets with 53 wavelet functions, and sym3 is found to be the most efficient wavelet function in terms of training time, prediction speed, and accuracy of SVM classifiers. All fault types both symmetrical/unsymmetrical types, and various normal transient conditions such as load/capacitor/DG switching are detected/discriminated with nearly 100% accuracy at the midpoint of line 6–7 with various fault conditions, inception angles (0, 30, 45, 60, 90 and 120°) and a fault resistance of (5,10, 15, and 20 ohms). Additionally, 9 MW wind Farm is integrated at busbar 10, and various fault scenarios are simulated to assess system performance with 100% Accuracy.
Journal Article