A dynamical systems approach to time series analysis

Great range of electrocardiogram (ECG) signal processing methods can be found in the literature. In addition, the importance of gender differences in physiological activities was also identified in various conditions. This article aims to provide a comprehensive evaluation of linear and nonlinear ECG parameters to indicate suitable signal processing approaches which can show significant differences between men and women. These differences were investigated in two conditions: (i) during rest condition, and (ii) during the affective image inducements. A wide range of parameters from time-, frequency-, wavelet-, and nonlinear-techniques were examined. Applying the Wilcoxon rank sum test, significant differences between two genders were inspected. The analysis was performed on 47 college students at rest condition and while subjects watching four types of affective pictures, including sadness, happiness, fear, and peacefulness. The impact of these emotions on the results was also investigated. The results indicated that 72.95% and 72.61% of all features were significantly different between male and female in rest condition and affective inducements, respectively. In addition, the highest percentage of the significant difference between ECG parameters of men and women was achieved using nonlinear characteristics. Considering all features together, the highest significant difference between two genders was achieved for negative emotions, including sadness and fear. In conclusion, the results of this study emphasized the importance of gender role in cardiac responses during rest condition and different emotional states. Since these gender differences are well manifested by nonlinear signal processing techniques, dynamical gender-specific ECG system may improve the automatic emotion recognition accuracies.

: New nonlinear signal processing and modeling techniques were examined. Three key issues formed the focus of the project: 1 the incorporation of symmetries into the modeling process. Such symmetries might be deduced from fundamental principles, or inferred from observations. The incorporation of such symmetries leads to simpler, more robust models, with fewer free parameters. 2 The design of coupling terms for synchronizing the model with driving signals from the system of interest This is the first analytical result of its kind, and gives sufficient conditions for guaranteeing that the model will synchronize. 3 The successful use of symbolic time series analysis techniques to perform parameter estimation for spatiotemporal (distributed) systems. It was shown that the symbol statistics from a single site time series could be used for the parameter fitting even when the underlying system was turbulent.

Several signal analysis systems reported in the literature have benefited from the integration of artificial intelligence (AI) and signal processing. This paper examines a growing class of these systems in which the AI component is used to control signal-processing operations. Several philosophical arguments are presented to support the development of these so called coupled systems. Three different types of coupled-system control schemes are identified and these are illustrated using examples from the literature. The benefits and improvements to traditional methods of signal processing achieved using coupled control schemes are also identified and are again illustrated with examples.

With the rapid development of intelligent transportation, autonomous driving has become a core direction of technological innovation. Its safe and efficient operation relies on precise processing of multi-source sensor signals. Traditional signal processing methods, limited by manual feature design, often fail to extract complete features and suppress noise in complex traffic environments. This leads to perception biases and decision-making delays, posing safety hazards and restricting the implementation of autonomous driving technology. By combining literature review and technical analysis, this paper focuses on the integration of deep learning and autonomous driving signal processing, aiming to explore how to enhance the efficiency and reliability of signal processing through deep learning algorithms. It concludes that deep learning, through its adaptive feature learning ability, can significantly improve the real-time performance and accuracy of signal processing. However, challenges such as data scarcity, poor model interpretability, and high computational costs still exist. Future research should focus on technological breakthroughs such as lightweight model design and multi-sensor fusion to promote the large-scale application of deep learning in autonomous driving signal processing.

Detection and classification of rotorcraft targets are of great significance not only in civil fields but also in defense. However, up to now, it is still difficult for the traditional radar signal processing methods to detect and distinguish rotorcraft targets from various types of moving objects. Moreover, it is even more challenging to classify different types of helicopters. As the development of high-precision radar, classification of moving targets by micro-Doppler features has become a promising research topic in the modern signal processing field. In this paper, we propose to use the deep convolutional neural networks (DCNNs) in rotorcraft detection and helicopter classification based on Doppler radar signals. We apply DCNN directly to raw micro-Doppler spectrograms for rotorcraft detection and classification. The proposed DCNNs can learn the features automatically from the micro-Doppler signals without introducing any domain background knowledge. Simulated data are used in the experiments. The experimental results show that the proposed DCNNs achieve superior accuracy in rotorcraft detection and superior accuracy in helicopter classification, outperforming the traditional radar signal processing methods.

Global bifurcations in Rayleigh-Bénard convection. Experiments, empirical maps and numerical bifurcation analysis

Harmonics generated by nonlinear loads pollute the power system and affect the operation of equipment connected to it. Hence, harmonic mitigation is of prime concern to a power system engineer. Artificial Neural Network (ANN) is a nonlinear signal processing technique, which is built from interconnected elementary processors called neurons. In this article, a Hopfield Neural Network (HNN) based control algorithm for shunt compensator in a power distribution system is realized. The Hopfield network is modeled using energy minimization principle and consists of “n” interconnected neurons. The HNN is used to estimate different harmonic components present in distribution system operating with nonlinear loads. It also provides suitable control signals to the shunt compensator for compensation of various power quality issues such as power factor correction, load balancing, and harmonic reduction in the distribution system. Detailed experimental results are presented along with simulation studies on the prototype model developed in the laboratory and these results demonstrate the feasibility of the proposed method of control in DSTATCOM. The comparison of the HNN-based compensation technique with a popular and effective control algorithm based on Least Means Square (LMS) is also presented in this article.

The target motion parameter estimation is an important index to measure the performance of radar system. Accurate estimation of acceleration parameter has important influence on improving the detection and tracking performance of high maneuvering target. When radar transmits a signal with fixed frequency, the maneuvering target echo signal can be approximated as linear frequency modulation signal. Therefore, the acceleration parameter estimation of maneuvering target is transformed into the parameter estimation of linear frequency modulation signal. In this paper, a novel method for acceleration parameter estimation of maneuvering target is proposed based on the compressed sensing theory. The linear frequency modulation signal has sparse characteristic in fractional Fourier transform domain and compressed sensing theory asserts that we can employ nonadaptive linear projections to preserve the structure of the signal and reconstruct the signal with these projections using an optimization process. Theoretical analysis and experimental results verify that the method can effectively solve the precision problem of parameter estimation caused by the large sampling rate in the traditional signal processing method, and effectively reduce the data storage and transmission pressure in radar signal processing.

Bogies are an important component of high-speed trains. The level of mechanical performance of bogies has a major influence on the safety and reliability of high-speed train. Therefore, conducting fault diagnoses on bogies with big data is very important. Fault mechanisms of bogies are very complex, and feature signals are nonobvious. For these reasons, fault information of bogies cannot be effectively extracted using the traditional signal processing method. Therefore, this paper adopted the deep neural network to recognize faults in bogies. The deep neural network offers numerous benefits in this context. Using deep neural networks, fault information in a signal spectrum can be extracted in a selfadaptive method. This technique is free of dependence on extensive signal processing knowledge and diagnostic experience. Compared with the traditional intelligent diagnosis method, the deep neural network can obtain a higher diagnostic accuracy. Additionally, the deep neural network does not depend on the sample size, and it can obtain high diagnostic accuracy even when the sample size is relatively small. It also achieves very high diagnostic accuracy applied to high-speed trains with different speeds and different faults, which shows that the method is extensively applicable. Furthermore, the recognition accuracy rate of the deep neural network under normal conditions can reach 100%. This method provides a new paradigm for fault diagnosis of the high-speed train with big data and plays an important role in this field.

It is well known that underwater acoustic channels are particularly complicated and difficult for typical signal processing procedures due to the time-varying, homogeneous volume, rough boundaries, and the abundance of interference noise sources in these channels. Most traditional methods of statistical signal processing employ simplified assumptions (linear, stationary, Gaussian) for the sake of mathematical tractability that inevitably lead to inadequate performance.

In the digital era, efficient image processing is indispensable for a wide range of applications including medical imaging, remote sensing, surveillance, and autonomous systems. Signal processing theory plays a pivotal role in enhancing image quality, extracting relevant features, and enabling real-time analysis. This article provides a comprehensive study of optimization strategies in image processing that are grounded in classical and modern signal processing techniques. It explores the application of Fourier, wavelet, and statistical transform methods, adaptive filtering, and deep learning signal representations. Emphasis is placed on computational efficiency, denoising, and feature retention to improve system performance and accuracy across multiple domains.

Dynamical systems generate complex signals. Very often, traditional sonar signal processing in both the time domain and frequency domain are not adequate enough to characterize the complex dynamics of underwater sources that generate, radiate and/or reflect sonar signals. Recent advances in signal processing are aimed at capitalizing on the inherent complexities in a signal structure by applying information theoretic/chaotic processing analysis tools to reveal signal dynamics, otherwise unseen when traditional (linear) signal processing methods are applied. Here examples are presented of periodicities (i.e., limit cycles), quasi-periodicites (2-torus and n-torus motion) and nonperiodic (chaotic) motion as applied to signals-of-interest in underwater acoustic applications. It is demonstrated how nonlinear diagnostic tools, when appropriately applied to underwater acoustic time series measurements, can be used for extracting new information about system dynamics.

A comprehensive overview on signal processing and artificial intelligence techniques applications in classification of power quality disturbances

Magnetosensory evoked potentials: Consistent nonlinear phenomena

We present an investigation of how topological data analysis (TDA) can be applied to condition-based monitoring (CBM) of wind turbines for energy generation.TDA is a branch of data analysis focusing on extracting mean- ingful information from complex datasets by analyzing their structure in state space and computing their underlying topo- logical features. By representing data in a high-dimensional state space, TDA enables the identification of patterns, anoma- lies, and trends in the data that may not be apparent through traditional signal processing methods. For this study, wind turbine data was acquired from a wind park in Norway via standard vibration sensors at different lo- cations of the turbine’s gearbox. Both the vibration acceler- ation data and its frequency spectra were recorded at infre- quent intervals for a few seconds at high frequency and fail- ure events were labelled as either gear-tooth or ball-bearing failures. The data processing and analysis are based on a pipeline where the time series data is first split into intervals and then transformed into multi-dimensional point clouds via a time-delay embedding. The shape of the point cloud is an- alyzed with topological methods such as persistent homol- ogy to generate topology-based key health indicators based on Betti numbers, information entropy and signal persistence. Such indicators are tested for CBM and diagnosis (fault de- tection) to identify faults in wind turbines and classify them accordingly. Topological indicators are shown to be an in- teresting alternative for failure identification and diagnosis of operational failures in wind turbines.