The regional features of precipitation variation trends over Sichuan in China by the ESMD method

Based on a precipitation time series from 49 meteorological stations in Sichuan Province during the period from 1961 to 2011, the multi-scale characteristics of precipitation variability are analyzed using the extreme-point symmetric mode decomposition method (ESMD). Regional differences in variation trends and change-points were also preliminarily discussed. The results indicated that in the last 50+ years, the overall precipitation in Sichuan Province has exhibited a significant non-linear downward trend, and its changes have clearly exhibited an inter-annual scale (quasi-3 and quasi-8-year) and interdecadal scale (quasi-13-year). The variance contribution rates of each component demonstrated that the inter-annual change had a strong influence on the overall precipitation change in Sichuan Province, and the reconstructed inter-annual variation trend could describe the fluctuation state of the original precipitation during the study period. The reconstructed interdecadal variability revealed that the climate mode in Sichuan Province had divided into three distinct variation periods with 1973 and 1998 as the boundaries. Furthermore, there were regional differences in the non-linear changes and change-points of precipitation. In addition, in order to study the relations between the changing more or less of rising or decrease and meteorological station’s geographical position (latitude, longitude and elevation) i.e., the Cokriging interpolation technique is applied directly to precipitation variation trend components through ESMD decomposition. At the same time, the results also suggested that the ESMD method can effectively reveal variations in long-term precipitation sequences at different time scales and can be used for the complex diagnosis of non-linear and non-stationary signal changes.

A High-Resolution Dyadic Transform for Non-Stationary Signal Analysis

This article shows a new Te-transform and its periodogram for applications that mainly exhibit stochastic behavior with a signal-to-noise ratio lower than −30 dB. The Te-transform is a dyadic transform that combines the properties of the dyadic Wavelet transform and the Fourier transform. This paper also provides another contribution, a corollary on the energy relationship between the untransformed signal and the transformed one using the Te-transform. This transform is compared with other methods used for the analysis in the frequency domain, reported in literature. To perform the validation, the authors created two synthetic scenarios: a noise-free signal scenario and another signal scenario with a signal-to-noise ratio equal to −69 dB. The results show that the Te-transform improves the sensitivity in the frequency spectrum with respect to previously reported methods.

Evolution of turbulent kinetic energy during the entire sandstorm process

An adaptive segmented stationary method for non-stationary signal is proposed to reveal the turbulent kinetic energy evolution during the entire sandstorm process observed at the Qingtu Lake Observation Array. Sandstorm which is a common natural disaster is mechanically characterized by a particle-laden two-phase flow experiencing wall turbulence, with an extremely high Reynolds number and significant turbulent kinetic energy. Turbulence energy transfer is important to the understanding of sandstorm dynamics. This study indicates that large-/very-large-scale coherent structures originally exist in the rising stage of sandstorms with a streamwise kinetic energy of 75 % rather than gradually forming. In addition to carrying a substantial portion of energy, the very-large-scale-motions are active structures with strong nonlinear energy transfer. These structures gain energy from strong nonlinear interaction. As sandstorm evolves, these large structures are gradually broken by quadratic phase coupling, with the energy fraction reducing to 40 % in the declining stage. The nonlinear process in the steady and declining stages weakens and maintains a balanced budget of energy. The systematic bispectrum results provide a new perspective for further insight of sandstorms.

A CEEMDAN–Assisted Deep Learning Model for the RUL Estimation of Solenoid Pumps

This paper develops a data-driven remaining useful life prediction model for solenoid pumps. The model extracts high-level features using stacked autoencoders from decomposed pressure signals (using complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm). These high-level features are then received by a recurrent neural network-gated recurrent units (GRUs) for the RUL estimation. The case study presented demonstrates the robustness of the proposed RUL estimation model with extensive empirical validations. Results support the validity of using the CEEMDAN for non-stationary signal decomposition and the accuracy, ease-of-use, and superiority of the proposed DL-based model for solenoid pump failure prognostics.

On a new Wigner-Ville distribution associated with linear canonical transform

Linear canonical transform as a general integration transform has been considered into Wigner-Ville distribution (WVD) to show more powerful ability for non-stationary signal processing. In this paper, a new WVD associated with linear canonical transform (WVDL) and integration form of WVDL (IWVDL) are presented. First, the definition of WVDL is derived based on new autocorrelation function and some properties are investigated in details. It removes the coupling between time and time delay and lays the foundation for signal analysis and processing. Then, based on the characteristics of WVDL over time-frequency plane, a new parameter estimation method, IWVDL, is proposed for linear modulation frequency (LFM) signal. Two phase parameters of LFM signal are estimated simultaneously and the cross term can be suppressed well by integration operator. Finally, compared with classical WVD, the simulation experiments are carried out to verify its better estimation and suppression of cross term ability. Error analysis and computational cost are discussed to show superior performance compared with other WVD in linear canonical transform domain. The further application in radar imaging field will be studied in the future work.

Semg Based Recognition Of Hand Motions For Lower Limb Prostheses

Aim: On multiple muscle locations, surface electromyography (sEMG) signals were recorded to predict the effect of different hand movements. Background: Myoelectric information is a non-stationary signal, so extracting correct features is important to boost any myoelectric control devices' performance. The myoelectric signal is an electrical activity recorded by a surface electrode at various movements of the muscles. Objective: The study presented pattern recognition classification methods to select an excellent algorithm for controlling the SEMG signal. Method: Various time domain and frequency domain parameters were extracted prior to conduct the classifier test. Result: For the evaluation of the results for the recorded data (of all six movements), confusion matrix for neural network, support vector machine (SVM), DT, and linear discriminant analysis (LDA) classifiers is presented. Conclusion: This present study will be a step in analyzing different problems for developing lower limb prostheses.

Screening of sleep movement disorders via EMG signal analysis

It is estimated that 50 to 70 million Americans suffer from a chronic sleep disorder, which hinders their daily life, affects their health, and incurs a significant economic burden to society. Untreated Periodic Leg Movement (PLM) or Rapid Eye Movement Behaviour Disorder (RBD) could lead to a three to four-fold increased risk of stroke and Parkinson’s disease respectively. These risks bring about the need for less costly and more available diagnostic tools that will have great potential for detection and prevention. The goal of this study is to investigate the potentially clinically relevant but under-explored relationship of the sleep-related movement disorders of PLMs and RBD with cerebrovascular diseases. Our objective is to introduce a unique and efficient way of performing non-stationary signal analysis using sparse representation techniques. To fulfill this objective, at first, we develop a novel algorithm for Electromyogram (EMG) signals in sleep based on sparse representation, and we use a generalized method based on Leave-One-Out (LOO) to perform classification for small size datasets. In the second objective, due to the long-length of these EMG signals, the need for feature extraction algorithms that can localize to events of interest increases. To fulfill this objective, we propose to use the Non-Negative Matrix Factorization (NMF) algorithm by means of sparsity and dictionary learning. This allows us to represent a variety of EMG phenomena efficiently using a very compact set of spectrum bases. Yet EMG signals pose severe challenges in terms of the analysis and extraction of discriminant features. To achieve a balance between robustness and classification performance, we aim to exploit deep learning and study the discriminant features of the EMG signals by means of dictionary learning, kernels, and sparse representation for classification. The classification performances that were achieved for detection of RBD and PLM by means of implicating these properties were 90% and 97% respectively. The theoretical properties of the proposed approaches pertaining to pattern recognition and detection are examined in this dissertation. The multi-layer feature extraction provide strong and successful characterization and classification for the EMG non-stationary signals and the proposed sparse representation techniques facilitate the adaptation to EMG signal quantification in automating the identification process.

Screening of sleep movement disorders via EMG signal analysis

It is estimated that 50 to 70 million Americans suffer from a chronic sleep disorder, which hinders their daily life, affects their health, and incurs a significant economic burden to society. Untreated Periodic Leg Movement (PLM) or Rapid Eye Movement Behaviour Disorder (RBD) could lead to a three to four-fold increased risk of stroke and Parkinson’s disease respectively. These risks bring about the need for less costly and more available diagnostic tools that will have great potential for detection and prevention. The goal of this study is to investigate the potentially clinically relevant but under-explored relationship of the sleep-related movement disorders of PLMs and RBD with cerebrovascular diseases. Our objective is to introduce a unique and efficient way of performing non-stationary signal analysis using sparse representation techniques. To fulfill this objective, at first, we develop a novel algorithm for Electromyogram (EMG) signals in sleep based on sparse representation, and we use a generalized method based on Leave-One-Out (LOO) to perform classification for small size datasets. In the second objective, due to the long-length of these EMG signals, the need for feature extraction algorithms that can localize to events of interest increases. To fulfill this objective, we propose to use the Non-Negative Matrix Factorization (NMF) algorithm by means of sparsity and dictionary learning. This allows us to represent a variety of EMG phenomena efficiently using a very compact set of spectrum bases. Yet EMG signals pose severe challenges in terms of the analysis and extraction of discriminant features. To achieve a balance between robustness and classification performance, we aim to exploit deep learning and study the discriminant features of the EMG signals by means of dictionary learning, kernels, and sparse representation for classification. The classification performances that were achieved for detection of RBD and PLM by means of implicating these properties were 90% and 97% respectively. The theoretical properties of the proposed approaches pertaining to pattern recognition and detection are examined in this dissertation. The multi-layer feature extraction provide strong and successful characterization and classification for the EMG non-stationary signals and the proposed sparse representation techniques facilitate the adaptation to EMG signal quantification in automating the identification process.

Time-Frequency Feature Analysis

Most of the real-world signals in nature are non-stationary, i.e., their statistics are time variant. Extracting the time-varying frequency characteristics of a signal is very important in understanding the signal better, which could be of immense use in various applications such as pattern recognition and automated-decision making systems. In order to extract meaningful time-frequency (TF) features, a joint TF analysis is required. The proposed work is an attempt to develop a generalized TF analysis methodology that exploits the benefits of TF distribution (TFD) in pattern classification systems as related to discriminant feature detection and classification. Our objective is to introduce a unique and efficient way of performing non-stationary signal analysis using adaptive and discriminant TF techniques. To fulfill this objective, in the first point, we build a novel TF matrix (TFM) decomposition that increases the effectiveness of segmentation in real-world signals. Instantaneous and unique features are extracted from each segment such that they successfully represent joint TF structure of the signal. In the second point, based on the above technique, two unique and novel discriminant TF analysis methods are proposed to perform an improved and discriminant feature selection of any non-stationary signals. The first approach is a new machine learning method that identifies the clusters of the discriminant features to compute the presence of the discriminative pattern in any given signal, and classify them accordingly. The second approach is a discriminant TFM (DTFM) framework, which is a combination of TFM decomposition and the discriminant clustering techniques. The developed DTFM analysis automatically identifies the differences between different classes as the distinguishing structure, and uses the identified structure to accurately classify and locate the discriminant structure in the signal. The theoretical properties of the proposed approaches pertaining to pattern recognition and detection are examined in this dissertation. The extracted TF features provide strong and successful characterization and classification of real and synthetic non-stationary signals. The proposed TF techniques facilitate the adaptation of TF quantification to any feature detection technique in automating the identification process of discriminatory TF features, and can find applications in many different fields including biomedical and multimedia signal processing.