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The purpose of this paper is to examine the volatility of daily returns in a sample of developed and emerging equity markets at different time scales through wavelet decomposition. Such information is vital for international investors who have different time horizons for their investment decisions and trading strategies.

The wavelet technique used here allows the return series to be viewed at different frequency by decomposing the series into different time horizons known as time scales. The decomposed return series enable investigation of return variability at different return intervals.

In an analysis at different time scales, there is no evidence to suggest that the return dynamics of developed and emerging markets are different. In both types of markets, return variance is time scale dependent, satisfying a pure power law process, and the variability in returns is more likely to be due to the dynamics at the lower time scales. While emerging markets generally exhibit a higher level of volatility, the relative contribution from each time scale is quite similar to that of the developed markets.

The difference in the return dynamics between emerging and developed markets is observed at the lowest time scale. This is an indication that differences in the return dynamics between the two types of markets may be more likely in the short term (high frequency) rather than in the long term. A plausible reason for this is speculative trading. Such information is vital for international investors who have different time horizons for their investment decisions and trading strategies.

The purpose of this manuscript is to detect heart fault using Electrocardiogram. Mutually low and high frequency noises such as electromyography (EMG) and power line interference (PLI) degrades the performance of ECG signals.

The ECG record depicts the procedural electrical movement of the heart, which is non-invasive foot age obtained by placing surface electrodes on designated locations of the patient’s skin. The main concept of this manuscript is to present a novel filtering method to cancel the unwanted noises in ECG signal. Here, intrinsic time scale decomposition (ITD) is introduced to suppress the effect of PLI from ECG signals.

In the existing ITD, the gain control parameter is a constant value; however, in this paper it is an adaptive feature that varies according to certain constraints. Simulation outcomes show that the proposed method effectively reduces the effect of PLI and quantitatively express the effectiveness with different evaluation metrics.

The results found by the proposed method are compared with Fourier decomposition technique and eigen value decomposition methods (EDM) to validate the effectiveness of the proposed method.

The purpose of this paper is to develop a directional and roll control system for unmanned combat air vehicle (UCAV) automatic takeoff roll, with the objective of keeping the UCAV along the runway centerline and keeping the wings level, especially when there is a crosswind.

The nonlinear model of the UCAV during takeoff roll is established. The model is linearized about the lateral‐directional equilibrium point at different forward speeds. The approximate directional model and roll model are extracted using time‐scale decomposition technique. Then the directional control law and roll control law are developed using gain scheduling approach. Nose wheel steering, differential brake and rudder are used as the primary directional control device at low, medium and high speeds, respectively, according to both the qualitative and quantitative analysis of their control effectiveness at different speeds. A priority matrix is developed to determine the secondary control device which is used if the primary control device fails, thus the directional control system can have a certain degree of fault tolerance.

This work developed the directional control law and roll control law by using gain scheduling approach. Experimental results verified that the developed directional and roll control system has high robustness and satisfactory fault tolerance: it can guarantee a safe takeoff under a 50 ft/sec crosswind, even if one directional control device fails, which satisfies the relevant criteria in MIL‐HDBK‐1797.

The directional and roll control system developed can be easily applied to practice and can steer the UCAV during takeoff roll safely, which will considerably increase the autonomy of the UCAV.

The paper shows how time‐scale decomposition technique is employed to extract the approximate directional model and roll model, which simplifies model analysis and control law design. A fault‐tolerant directional control system is designed to improve safety during takeoff.

The aim of this research is to explore multiscale hedging strategies among cryptocurrencies, commodities, and GCC stocks. Particularly, this is done by evaluating the connectedness among these asset classes covering a period with COVID-19 implications. Using the wavelet approach, the present study aims to recommend whether there exist different time horizon-based hedging abilities across the asset classes.

The approach used in this study is a multiscale decomposition of time series based on wavelets of daily prices of 13 asset classes. Since the wavelet analysis allows to decompose the time series into its frequency components at different time scales by a filtering process the study covered 1-day, 8-day, and 64-day time horizons to examine the hedging properties across those asset classes.

The results of this study show that hedging effectiveness differs among stock markets over time. In some cases, cryptocurrencies may keep their hedging properties across time while in others they switch from safe haven to hedge devices. In almost all cases, the three main cryptocurrencies showed diversifying properties as was observed by the multiscale correlation and hedge ratio estimations. In a competing sense, gold showed safe haven properties across time than cryptocurrencies except at an 8-day time scale where hedge ratios were low, positive and statistically different from zero that could be interpreted as a good hedge device in the medium term.

Though this research has considered a set of thirteen asset classes, it was limited to a period in which most cryptocurrencies started trading for the first time which reduces the number of observations compared to Bitcoin prices and stable coins such as Ethereum, Ripple, and Bitcoin Cash. Also, the research was focused on the GCC stock markets which may have different results as compared to other regional markets of Asia or Latin America. A comparative analysis in future could be another area of research in future.

This study has some significant policy implications. The cryptocurrency market is severely affected by demand and risk shocks to crude oil prices during the COVID-19 period. From the investor's point of view, diversification benefits can be obtained by combining cryptocurrencies along with oil-related products during episodes of financial turmoil and COVID-19 pandemic. The GCC region is constantly endeavoring to adopt more scientific tools and mechanisms of investment, and therefore, this study's results will provide some useful directions to the government, policymakers, financial institutions, and investors.

The current study covers a big bunch of 13 assets spanning across financial and real assets. This is based on literature gap and hence, will be a significant addition to the existing literature. Moreover, the GCC region is emerging as a global investment hub and this study will provide investors dynamic hedging strategies across these asset classes.

The purpose of this paper is to present the application of Hilbert–Huang transform (HHT) for fatigue damage feature characterisation in the time–frequency domain based on strain signals obtained from the automotive coil springs.

HHT was employed to detect the temporary changes in frequency characteristics of the vibration response of the signals. The extraction successfully reduced the length of the original signal to 40 per cent, whereas the fatigue damage was retained. The analysis process for this work is divided into three stages: signal characterisation with the application of fatigue data editing (FDE) for fatigue life assessment, empirical mode decomposition with Hilbert transform, an energy–time–frequency distribution analysis of each intrinsic mode function (IMF).

The edited signal had a time length of 72.5 s, which was 40 per cent lower than the original signal. Both signals were retained statistically with close mean, root-mean-square and kurtosis value. FDE improved the fatigue life, and the extraction did not affect the content and behaviour of the original signal because the editing technique only removed the minimal fatigue damage potential. HHT helped to remove unnecessary noise in the recorded signals. EMD produced sets of IMFs that indicated the differences between the original signal and mean of the signal to produce new components. The low-frequency energy was expected to cause large damage, whereas the high-frequency energy will cause small damage.

HHT and EMD can be used in the strain data signal analysis of the automotive component of a suspension system. This is to improve the fatigue life, where the extraction did not affect the content and behaviour of the original signal because the editing technique only removed the minimal fatigue damage potential.

The purpose of this study is to examine linkage between exchange rate, stock return and interest rate for India.

Using monthly data from January 2000 to December 2014, this study has scrutinized the linkage between exchange rate, stock return and interest rate using maximum overlap discrete wavelet transform (MODWT) which is very much appropriate when the variables are discrete in nature.

Our major findings indicate that the empirical relationship between these variables is not significant at lower scales. As we go on higher scales, there is a clear linkage between them, and three markets are associated with each other. Moreover, the direction and type of the relationship depends on the frequency bands, and finally with the help of Granger causality tests, we established a lead/lag relationship between stock price, exchange rate and interest rate.

The linkage between stock market, foreign exchange market and money market in case of emerging countries like India is more relevant because negative or positive shocks affecting one market may be transmitted quickly to another through contagious effect.

Little attention has been given to examine the link between stock return, exchange rate and interest rate in India. This study adopts a more sophisticated MODWT approach for examining the cross-correlation and causality.

In this chapter we perform a Monte Carlo simulation study of the errors-in-variables model examined in Ramsey, Gallegati, Gallegati, and Semmler (2010) by using a wavelet multiresolution approximation approach. Differently from previous studies applying wavelets to errors-in-variables problem, we use a sequence of multiresolution approximations of the variable measured with error ranging from finer to coarser scales. Our results indicate that multiscale approximations to the variable observed with error based on the coarser scales provide an unbiased asymptotically efficient estimator that also possess good finite sample properties.

The purpose of this paper is to estimate the relationship between stock prices and exchange rates of eight Asian countries. The analysis is based on methodologies that possess the ability to provide a complete representation of data series from both time and frequency perspectives simultaneously. In addition, instead of limiting the analysis to focus on the conditional mean of the response variable y in the regression equation, the authors investigate the extremes of distribution to reveal a range of hidden relationships between these variables.

Given the limitations of classical methodology of Pearson correlation and least-squares regression, this study estimates the relationship between stock prices and exchange rates through wavelet correlation and cross-correlation to serve as a protocol for different traders who view the market with different time resolutions. In addition, quantile regression technique robust to heteroscedasticity, skewness and leptokurtosis is used to understand the relationship between stock prices and a specified quantile of the exchange rates.

In accordance with the portfolio balance effect, it is observed that stock prices and exchange rates are negatively correlated at all frequencies. In particular, the negative correlation grows with higher time scales (lower frequency intervals). The findings from quantile regression also suggest that the coefficients are more inclined to be negative when exchange rates are extremely high.

The paper contributes to the literature by focussing on the multi-scale relationship between stock prices and exchange rates. In addition, it also analyzes the relationship between stock prices and a specified quantile of the exchange rates.

Continuous on-line monitoring of structural integrity are in priority in many engineering fields such as aerospace, automotive, civilian structures, and industrial applications. Of all these possible applications, the aerospace industry has one of the highest payoffs. Possible damage can lead to catastrophic failures and costly inspections. On the other hand, processing a signal consists of important feature from sensors measurements to reach the considered target. Typically, the sensors translate a physical phenomenon from one or many sources in temporal variations or in spatial variations. The purpose of this paper is to investigate damages, in terms of suddenly screw removal or in a small cut, detection in vibrating (clamped-free) aluminum beam structures using the empirical mode decomposition (EMD) method along with the Hilbert-Huang transformation (HHT). The perspective is to identify very small defects in real aircraft structures.

The proposed method deals with a new time-frequency signal processing analysis tool, for damages detection in a vibrating plate. An experimental test ring is used in order to excite a clamped-free aluminum plate. Two types of excitations are used. The first one is a harmonic excitation and the second one is a random excitation provided by an impact hammer. A hole and its filled by a screw with mass of 0.2 g, and a small cut is created, simulating a cut creation, are produced afterword, and the HHT is used in order to arise the developed oscillations, and to reveal hidden reflections in the data and to provide a high-resolution energy-time frequency spectrum.

The major finding was the clear amplitude increment either for screw removal or for cut creation, using the EMD process with the HHT, giving the possibility to detect them.

The use of the HHT to detect, using an experimental procedure, two different defects: a suddenly screw removal and a cut creation, in a clamped-free beam, excited by non-stationary and non-linear signals.