Multiscale Decomposition Research Articles

Enhanced nucleate boiling of Novec 649 on thin metal foils via laser-induced periodic surface structures

The low boiling temperature of dielectric fluids makes them suitable for satisfying the cooling requirements of advanced high-energy-density devices commonly found in microelectronics. However, the high wettability/wickability of such coolants significantly affects the boiling dynamics, opening new research questions on the crucial role played by micro- and nanostructures on the boiling surface. While different studies point out the importance of microscale grooves, pillars and cavities to promote nucleation and enhance boiling heat transfer coefficient (HTC) for water, the scientific data to support such observations for dielectric fluids is sparse. In this regard, the present work provides an experimental investigation of the nucleate boiling enhancement of Novec 649 on thin metallic stainless-steel foils with tailored, laser-induced periodic surface structures (LIPSS). Femtosecond laser surface texturing was utilized to produce LIPSS with a periodicity of approx. 720 nm, while selected samples additionally included microscale grooves with a depth of 19.6 µm and a width of 25 µm, overlapped with the LIPSS features (labelled as GLIPSS). During boiling performance evaluation, high-speed IR thermography was used to measure the transient temperature fields of the boiling surface. LIPSS samples exhibited a reduction in wall superheat at the onset of boiling from 28 K to about 16 K, resulting in an HTC enhancement of about +65 %. Analysis of IR data was performed through multiscale proper orthogonal decomposition (mPOD) and revealed that the HTC enhancement in the presence of LIPSS is related to a faster heat transfer dynamics (dominant frequency: +54 %) occurring at a smaller spatial scale (dominant mean active site diameter: −12.5 %) and larger spatial density (dominant mean active site density: +39 %). Furthermore, the results for GLIPSS samples revealed a weak impact of micro features to the boiling performance of Novec 649, implying that sub-microscale surface topography is crucial to promote boiling in applications using dielectrics.

The effect of near-fault earthquakes and wavelet-generated artificial near-fault earthquakes on the seismic behavior of rockfill dams

Strong earth vibrations may destroy high asphalt concrete core rockfill dams (ACCRDs) and cause unanticipated economic and social losses. Due to favorable geographical, geological, and environmental characteristics, ACCRDs are always near ruptured fault zones. Compared to far-field and non-pulse earthquakes, near-fault (NF) earthquakes with pulse effect may put high dams under stress. However, seismic design and dynamic assessments of high ACCRDs under pulse-type earthquake stimulation are less popular than NF earthquake recordings. The fundamental explanation is that most earthquake data pertain to non-pulse-type seismic occurrences, whereas pulse-type earthquakes have more sophisticated seismology. This work uses the finite element approach to illustrate the seismic behavior of the ACCRD under pulse-type NF, non-pulse-type NF, and far-fault (FF) earthquake excitations. The pulse-type artificial NF earthquakes are generated using wavelet multi-scale decomposition incorporation (WMSDI), a simplified wavelet theory-based approach. Next, the WMSDI approach of artificial NF earthquake synthesis is tested and compared to earthquake data and structure responses. Forward-directivity pulse-type and fling-step pulse-type earthquakes may induce higher horizontal accelerations and stresses of the high ACCRD than FF earthquakes. FS and FD earthquake excitations with high PGV/PGA ratios cause severe residual deformation and relative settlement ratio to the high ACCRD. The dynamic response of the high ACCRD under pulse-type artificial NF earthquakes also depends on the pulse parameters of the artificial equivalent pulse mathematical models. Thus, pulse-type NF earthquake seismology should be included in rockfill dam seismic design assessments.

High-quality computational ghost imaging with multi-scale light fields optimization

High-quality computational ghost imaging under low sampling rates has always attracted much attention and plays an important role in practical applications. In this paper, a novel optical field optimization method based on multi-scale light fields singular value decomposition which can greatly reduce the number of computational ghost imaging measurements is proposed. The computational ghost imaging measurement matrix is derived from the components obtained by singular value decomposition of self-designed special measurement matrices. When the measurement matrix is fully sampled, high-quality reconstructed image can be obtained. Similarly, when the measurement matrix is under-sampled, it is still possible to obtain high-quality reconstructed image and show the performance of multi-resolution imaging. Simulation and experimental results show that our method can obtain high-quality computational ghost imaging, even at low sampling rates, and as the number of splicing matrices increases, the number of measurements is further reduced.

MWDINet: A multilevel wavelet decomposition interaction network for stock price prediction

Stock price prediction is a classical interdisciplinary issue drawn from finance, computer science, econometrics, and mathematics. Most stock price data are nonlinear, nonstationary, and highly complex, making stock price prediction challenging. Recently, deep neural networks (DNNs) have demonstrated powerful learning capabilities and have yielded notable results in stock price prediction tasks. Most existing deep learning solutions, however, only consider time-domain information or lack effective modeling of frequency-domain information, thus failing to effectively utilize both time-domain and frequency-domain information of the data. Meanwhile, existing methods ignore autocorrelated errors in the stock price forecasting task due to missing valid information data, i.e., they do not consider the correlation between the error at the current time step and the error at the previous time step, which undermines the standard maximum likelihood estimation (MLE) assumption, thereby weakening the model’s performance. We propose a multilevel wavelet decomposition interaction network (MWDINet), an end-to-end framework for stock price prediction. MWDINet employs the multiscale wavelet decomposition interaction module (MWDI-Block) and the Hull Moving Average module (HMA-Block) to extract the data’s frequency-domain and time-domain information, respectively. In MWDI-Block, the traditional signal processing method of Maximum Overlapping Discrete Wavelet Transform (MODWT) is seamlessly embedded into a deep learning framework (named DMODWT). The DMODWT algorithm not only automatically extracts the frequency-domain information from the data, but also fine-tunes the wavelet filters. With HMA-Block, we improve the Hull Moving Average (HMA), commonly used in the industry, into a deep learning module, which learns how changes in different markets over time. Inspired by the research on correcting autocorrelated errors in linear models in econometrics, we further design a deep difference module (DIF-Block) to correct autocorrelated errors and thus improve the prediction performance of the model. Moreover, all components are integrated seamlessly in a unified end-to-end framework. Extensive experiments on real-world datasets demonstrate that MWDINet outperforms the state-of-the-art models and has remarkable potential in stock price prediction.

Infrared and Visible Image Fusion: Methods, Datasets, Applications, and Prospects

Infrared and visible light image fusion combines infrared and visible light images by extracting the main information from each image and fusing it together to provide a more comprehensive image with more features from the two photos. Infrared and visible image fusion has gained popularity in recent years and is increasingly being employed in sectors such as target recognition and tracking, night vision, scene segmentation, and others. In order to provide a concise overview of infrared and visible picture fusion, this paper first explores its historical context before outlining current domestic and international research efforts. Then, conventional approaches for infrared and visible picture fusion, such as the multi-scale decomposition method and the sparse representation method, are thoroughly introduced. The advancement of deep learning in recent years has greatly aided the field of picture fusion. The outcomes of the fusion have a wide range of potential applications due to the neural networks’ strong feature extraction and reconstruction skills. As a result, this research also evaluates deep learning techniques. After that, some common objective evaluation indexes are provided, and the performance evaluation of infrared and visible image fusion is introduced. The common datasets in the areas of infrared and visible image fusion are also sorted out at the same time. Datasets play a significant role in the advancement of infrared and visible image fusion and are an essential component of infrared and visible image fusion testing. The application of infrared and visible image fusion in many domains is then simply studied with practical examples, particularly in developing fields, used to show its application. Finally, the prospect of the current infrared and visible image fusion field is presented, and the full text is summarized.

Two-Stage Deep Ensemble Paradigm Based on Optimal Multi-scale Decomposition and Multi-factor Analysis for Stock Price Prediction

Two-Stage Deep Ensemble Paradigm Based on Optimal Multi-scale Decomposition and Multi-factor Analysis for Stock Price Prediction

Distribution of Active Faults and Lithospheric Discontinuities in the Himalayan-Tibetan Orogenic Zone Identified by Multiscale Gravity Analysis

The lithospheric structure of the Tibetan Plateau and its adjacent area is a hot topic in geodynamic research. It is important to reveal the mechanism of crustal deformation and tectonic evolution of the study area. In this study, the techniques of wavelet multiscale decomposition and field edge detection were used to study the discontinuities of the lithosphere revealed by multilevel Bouguer gravity anomalies. Specifically, we evaluated the depth characteristics of the major active faults in the study area and identified 15 deep major faults that cut through the lithosphere. They are Chaman fault, Shyok suture zone, Altyn-Tagh fault, Karakash fault, Karakoram fault, Talas-Fergana fault, Kashgarr-Yeshgar transfer system, Rushan-Pshart suture zone, Sangri-Nacuo fault, Main Frontal thrust, Burmese fold belt, Yadong-Gulu fault, Gaoligong fault, Sagaing fault and Nujiang fault. We have also elucidated the tectonic mechanisms of two famous geodynamic phenomena in the Pamir Plateau. The first is the intense intermediate depth seismicity beneath Pamir-Hindukush. It cannot simply be described as the rupture of a subducted residual plate, which could be divided into two distinct tectonic units. One belongs to the Indian plate, the other to the Eurasian plate. Secondly, the mechanism of intense seismicity confined to the western upper crust of the Pamir Plateau could be explained as significant fragmentation of crustal material. Finally, and most importantly, we summarized the coupling mechanism between deep geodynamics and horizontal deformation as observed by modern geodetic techniques. In the upper mantle, the leading edge of the subducting Indian plate reached the SW boundary of Tarim basin and forms a closed structure in western Himalaya. Then, the Tibetan Plateau underwent a tectonic escape towards the east under the continuous compression between the Indian and Eurasian plates. During the process of tectonic escape, the role of the N–S direction normal faults in the Himalayan tectonic zone is limited.

Analysis of the information overlap between the PIV and [formula omitted] chemiluminescence signals in turbulent flames using a sparse sensing framework

The purpose of this study is to quantify the information overlap between the OH* chemiluminescence signal and the three-dimensional Particle Image Velocimetry (PIV) signal for a set of bluff-body stabilized, swirling and non-swirling turbulent methane/air flames issued from the Cambridge/Sandia Stratified Swirl Burner. The time-resolved velocity data was collected using high-speed stereoscopic PIV operated at 3 kHz. This sampling rate was sufficiently high to enable accurate tracking of the large-scale spatial and temporal features of the flow. To investigate flame-flow interactions, high-speed (8 kHz) OH* chemiluminescence imaging was applied on the same set of flames. The two datasets are first independently analyzed using multi-scale Proper Orthogonal Decomposition (mPOD) for the purpose of detecting the relevant flow and heat release rate structures. The resulting modes are qualitatively compared to assess if it is possible to identify the same flow structures from the two datasets. Finally, the information overlap is quantified by predicting the velocity field from the chemiluminescence signal using a sparse sensing framework. Sparse sensing is a mathematical technique that leverages the low-dimensional representation of a physical phenomenon to predict the entire state of the system using few measurements. The results show that the velocity and chemiluminescence mPOD modes display broadly similar features, from which is possible to retrieve the same flow instabilities in the corresponding frequency range. Moreover, the prediction of the velocity field obtained using chemiluminescence as input of the sparse sensing model achieves a good level of accuracy, pointing to an important degree of information overlap between the two signals.

Frequency characteristics of axisymmetric conical boattail models with different slant angles

This study focuses on the unsteady behavior of the flow around axisymmetric conical boattail models under low-speed conditions. Particle image velocimetry was conducted on the symmetric plane for four boattail models with angles of 0°, 10°, 16°, and 22°. Different data processing techniques, including variational mode decomposition (VMD), fast Fourier transform, proper orthogonal decomposition (POD), and multiscale proper orthogonal decomposition (mPOD), were applied to understand the effects of boattail angles on the characteristic frequency of the wake flow. Our results indicated that vortex shedding, bubble pumping, and the rotation of vortex shedding are the three dominant modes for four boattail configurations. However, the energy of vortex shedding mode becomes comparable to that of bubble pumping for the model with the boattail angle of 22°. The orientation of the shear layer remarkably changes near the boattail surfaces for different angles, which is connected to the flow behavior on the surface. This study also suggests that VMD and mPOD are advantageous data-driven methods for analyzing turbulent flows.

Fine-Grained and Multiple Classification for Alzheimer's Disease With Wavelet Convolution Unit Network.

In this article, we propose a novel wavelet convolution unit for the image-oriented neural network to integrate wavelet analysis with a vanilla convolution operator to extract deep abstract features more efficiently. On one hand, in order to acquire non-local receptive fields and avoid information loss, we define a new convolution operation by composing a traditional convolution function and approximate and detailed representations after single-scale wavelet decomposition of source images. On the other hand, multi-scale wavelet decomposition is introduced to obtain more comprehensive multi-scale feature information. Then, we fuse all these cross-scale features to improve the problem of inaccurate localization of singular points. Given the novel wavelet convolution unit, we further design a network based on it for fine-grained Alzheimer's disease classifications (i.e., Alzheimer's disease, Normal controls, early mild cognitive impairment, late mild cognitive impairment). Up to now, only a few methods have studied one or several fine-grained classifications, and even fewer methods can achieve both fine-grained and multi-class classifications. We adopt the novel network and diffuse tensor images to achieve fine-grained classifications, which achieved state-of-the-art accuracy for all eight kinds of fine-grained classifications, up to 97.30%, 95.78%, 95.00%, 94.00%, 97.89%, 95.71%, 95.07%, 93.79%. In order to build a reference standard for Alzheimer's disease classifications, we actually implemented all twelve coarse-grained and fine-grained classifications. The results show that the proposed method achieves solidly high accuracy for them. Its classification ability greatly exceeds any kind of existing Alzheimer's disease classification method.

Temperature Drift Compensation of Fiber Optic Gyroscopes Based on an Improved Method.

This study proposes an improved multi-scale permutation entropy complete ensemble empirical mode decomposition with adaptive noise (MPE-CEEMDAN) method based on adaptive Kalman filter (AKF) and grey wolf optimizer-least squares support vector machine (GWO-LSSVM). By establishing a temperature compensation model, the gyro temperature output signal is optimized and reconstructed, and a gyro output signal is obtained with better accuracy. Firstly, MPE-CEEMDAN is used to decompose the FOG output signal into several intrinsic mode functions (IMFs); then, the IMFs signal is divided into mixed noise, temperature drift, and other noise according to different frequencies. Secondly, the AKF method is used to denoise the mixed noise. Thirdly, in order to denoise the temperature drift, the fiber gyroscope temperature compensation model is established based on GWO-LSSVM, and the signal without temperature drift is obtained. Finally, the processed mixed noise, the processed temperature drift, the processed other noise, and the signal-dominated IMFs are reconstructed to acquire the improved output signal. The experimental results show that, by using the improved method, the output of a fiber optic gyroscope (FOG) ranging from -30 °C to 60 °C decreases, and the temperature drift dramatically declines. The factor of quantization noise (Q) reduces from 6.1269 × 10-3 to 1.0132 × 10-4, the factor of bias instability (B) reduces from 1.53 × 10-2 to 1 × 10-3, and the factor of random walk of angular velocity (N) reduces from 7.8034 × 10-4 to 7.2110 × 10-6. The improved algorithm can be adopted to denoise the output signal of the FOG with higher accuracy.

Intra-nucleus mosaic pattern (InMop) and whole-cell Haralick combined-descriptor for identifying and characterizing acute leukemia blasts on single cell peripheral blood images.

Acute leukemia is usually diagnosed when a test of peripheral blood shows at least 20% of abnormal immature cells (blasts), a figure even lower in case of recurrent cytogenetic abnormalities. Blast identification is crucial for white blood cell (WBC) counting, which depends on both identifying the cell type and characterizing the cellular morphology, processes susceptible of inter- and intraobserver variability. The present work introduces an image combined-descriptor to detect blasts and determine their probable lineage. This strategy uses an intra-nucleus mosaic pattern (InMop) descriptor that captures subtle nuclei differences within WBCs, and Haralick's statistics which quantify the local structure of both nucleus and cytoplasm. The InMop captures WBC inner-nucleus structure by applying a multiscale Shearlet decomposition over a repetitive pattern (mosaic) of automatically-segmented nuclei. As a complement, Haralick's statistics characterize the local structure of the whole cell from an intensity co-occurrence matrix representation. Both InMoP and Haralick-based descriptors are calculated using the b-channel from Lab color-space. The combined-descriptor is assessed by differentiating blasts from nonleukemic cells with support vector machine (SVM) classifiers and different transformation kernels, in two public and independent databases. The first database-D1 (n = 260) is composed of healthy and acute lymphoid leukemia (ALL) single cell images, and second database-D2 contains acute myeloid leukemia (AML) blasts (n = 3294) and nonblast (n = 15,071) cell images. In a first experiment, blasts versus nonblast differentiation is performed by training with a subset of D2 (n = 6588) and testing in D1 (n = 260), obtaining a training AUC of 0.991 ± 0.002 and AUC = 0.782 for the independent validation. A second experiment automatically differentiates AML blasts (260 images from D2) from ALL blasts (260 images from D1), with an AUC of 0.93. In a third experiment, state-of-the-art strategies, VGG16 and RESNEXT convolutional neural networks (CNN), separate blast from nonblast cells in both databases. The VGG16 showed an AUC of 0.673 and the RESNEXT of 0.75. Reported metrics for all the experiments are area under the ROC curve (AUC), accuracy and F1-score.

Medical Diagnosis with Multimodal Image Fusion Techniques

Image Fusion is an effective approach utilized to draw out all the significant information from the source images, which supports experts in evaluation and quick decision making. Multi modal medical image fusion produces a composite fused image utilizing various sources to improve quality and extract complementary information. It is extremely challenging to gather every piece of information needed using just one imaging method. Therefore, images obtained from different modalities are fused Additional clinical information can be gleaned through the fusion of several types of medical image pairings. This study's main aim is to present a thorough review of medical image fusion techniques which also covers steps in fusion process, levels of fusion, various imaging modalities with their pros and cons, and the major scientific difficulties encountered in the area of medical image fusion. This paper also summarizes the quality assessments fusion metrics. The various approaches used by image fusion algorithms that are presently available in the literature are classified into four broad categories i) Spatial fusion methods ii) Multiscale Decomposition based methods iii) Neural Network based methods and iv) Fuzzy Logic based methods. the benefits and pitfalls of the existing literature are explored and Future insights are suggested. Moreover, this study is anticipated to create a solid platform for the development of better fusion techniques in medical applications.

Malignant melanoma detection using multi-scale image decomposition and a new ensemble-learning scheme

Malignant melanoma detection using multi-scale image decomposition and a new ensemble-learning scheme

Remote Sensing Pansharpening with TV-H−1 Decomposition and PSO-Based Adaptive Weighting Method

In remote sensing, owing to existing sensors’ limitations and the tradeoff between signal-to-noise ratio (SNR) and instantaneous field of view (IFOV), it is difficult to obtain a single image with good spectral and spatial resolution. Pansharpening (PS) is the technique for sharpening multispectral (MS) images by extracting structural and edge information of panchromatic (PAN) image. Multiscale decomposition methods are used for decomposing image in sub-bands but are affected by ringing artifacts, therefore the resultant image seems to be blurred and misregistered. The proposed method overcomes this drawback by decomposing PAN and four band MS image into cartoon and texture components with total variation (TV) Hilbert[Formula: see text] model. The particle swarm optimization (PSO) algorithm is used for finding the optimum weight for fusing texture and cartoon details of PAN and MS images. The proposed method is practically validated on both full-scale and reduced-scale. Robustness of our proposed approach is tested on different geographical areas such as hilly, urban, and vegetation areas. From the visual analysis and qualitative parameters, the proposed method is proved effective compared with other traditional approaches.

Infrared and visible light image fusion via pixel mean shift and source image gradient

Image fusion is performed to merge the information of different source images into one image. In this study, a fusion algorithm based on the pixel mean shift and source image gradient was proposed to make the background of the fused image clear and the target prominent. First, a pixel spatial average kernel function was designed to separate the image texture and the significant target during pixel density migration. In this study, a novel approach is presented that distinguishes itself from other methods in two distinct ways. Firstly, it segregates the texture details of dissimilar pixel regions and captures the texture characteristics of analogous pixel regions. Secondly, by utilizing the discrepancy between the minimum and maximum values of infrared and visible images, the proposed technique incorporates the source graph into the fusion process, thereby improving the precision of the information. Additionally, the feature reconstruction function was designed based on the layer features, which could adaptively concentrate the source image information on one image and further improve the clarity and information retention of the fused image. Finally, the results of the qualitative and quantitative comparison of our proposed method with other state-of-the-art methods available, obtained by applying them to public datasets, demonstrated the advantages of our algorithm. Our results retained more appearance details and significantly more target information.

Detecting linear trend changes in data sequences

We propose TrendSegment, a methodology for detecting multiple change-points corresponding to linear trend changes in one dimensional data. A core ingredient of TrendSegment is a new Tail-Greedy Unbalanced Wavelet transform: a conditionally orthonormal, bottom-up transformation of the data through an adaptively constructed unbalanced wavelet basis, which results in a sparse representation of the data. Due to its bottom-up nature, this multiscale decomposition focuses on local features in its early stages and on global features next which enables the detection of both long and short linear trend segments at once. To reduce the computational complexity, the proposed method merges multiple regions in a single pass over the data. We show the consistency of the estimated number and locations of change-points. The practicality of our approach is demonstrated through simulations and two real data examples, involving Iceland temperature data and sea ice extent of the Arctic and the Antarctic. Our methodology is implemented in the R package trendsegmentR, available from CRAN.

Additive genetic and environmental variation interact to shape the dynamics of seasonal migration in a wild bird population.

Dissecting joint micro-evolutionary and plastic responses to environmental perturbations requires quantifying interacting components of genetic and environmental variation underlying expression of key traits. This ambition is particularly challenging for phenotypically discrete traits where multiscale decompositions are required to reveal non-linear transformations of underlying genetic and environmental variation into phenotypic variation, and when effects must be estimated from incomplete field observations. We devised a joint multistate capture-recapture and quantitative genetic animal model, and fitted this model to full-annual-cycle resighting data from partially-migratory European shags (Gulosus aristotelis) to estimate key components of genetic, environmental and phenotypic variance in the ecologically critical discrete trait of seasonal migration versus residence. We demonstrate non-negligible additive genetic variance in latent liability for migration, resulting in detectable micro-evolutionary responses following two episodes of strong survival selection. Further, liability-scale additive genetic effects interacted with substantial permanent individual and temporary environmental effects to generate complex non-additive effects on expressed phenotypes, causing substantial intrinsic gene-by-environment interaction variance on the phenotypic scale. Our analyses therefore reveal how temporal dynamics of partial seasonal migration arise from combinations of instantaneous micro-evolution and within-individual phenotypic consistency, and highlight how intrinsic phenotypic plasticity could expose genetic variation underlying discrete traits to complex forms of selection.

Dynamic hedging strategies across assets and commodities – a wavelet analysis

PurposeThe 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.Design/methodology/approachThe 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.FindingsThe 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.Research limitations/implicationsThough 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.Practical implicationsThis 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.Originality/valueThe 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.

A novel feature extraction method based on TQWT and instantaneous energy variation for Parkinson’s disease detection

Voice disorder is an early symptom of Parkinson’s disease (PD), and extracting effective voice features is of great significance for PD detection. The voice features based on tunable Q-factor wavelet transform (TQWT) have promising performance in PD detection only using conventional energy and entropy features. To further develop the potential of TQWT, a novel feature extraction method named instantaneous energy variation based on TQWT (IEV-TQWT) is proposed. Firstly, TQWT is adopted to perform multi-scale decomposition to obtain the oscillation information of the voice signals. Next, IEV-TQWT features are obtained by calculating instantaneous energy variation vectors from the spectrograms of sub-signals. Finally, the IEV-TQWT features are input to multiple classifiers to verify the PD classification performance. The results have shown that IEV-TQWT features with explicit physical information have a significant improvement in classification performance. In addition, IEV-TQWT features outperform other advanced features on the CPPDD dataset and reach mainstream level on SPDD dataset, indicating that IEV-TQWT features can be used as an effective assisted diagnosis tool of PD.