Deterministic Signals Research Articles

Joint short-term prediction of polar motion and length of day with multi-task deep learning methods

Accurate prediction of Earth orientation parameters (EOPs) is critical for astro-geodynamics, high-precision space navigation, and positioning. However, the current model prediction accuracy for EOPs is significantly lower than the geodetic technical solutions, which can adversely affect certain high-precision real-time users. Deep learning neural networks, precisely one-dimensional convolutional neural networks (1DCNN), and long short-term memory (LSTM) can automatically learn arbitrary complex mappings from inputs to outputs and support multiple inputs and outputs. These are powerful features that offer a lot of promise for time series forecasting, which makes this method suitable to predict simultaneously the Earth rotation parameters (ERP). The computational strategy follows multiple steps. First, using the singular spectrum analysis SSA, the deterministic time-varying signal of the ERP time series can be more precisely and reasonably detected and modeled. Then the reconstructed series and its corresponding residuals are used for 1DCNN training and prediction. However, first, we develop a multivariate multi-step 1DCNN model with a multi-output strategy using three different scenarios including the ocean angular momentum (OAM), atmospheric angular momentum (AAM), and hydrological angular momentum (HAM), to predict both the deterministic and the stochastic part for (PMx, PMy) components of PM. Then the best case with fewer errors is chosen to predict the ERP at the same time in the short term. The results of 3 years of prediction experiments based on the EOP 14 C04 series using 1DCNN are compared with LSTM and show that the proposed model can predict both the deterministic and the stochastic parts for the three parameters at the same time with significant improvements in the ERP for short-term prediction. Compared with alternative methods analyzed in the Second EOP Prediction Comparison Campaign (2nd EOP PCC), the 1DCNN model achieves comparable or even better results: 0.26 mas for PMx, 0.28 mas for PMy, and 0.022 ms for LOD on the first day of prediction, and 1.93 mas for PMx, 1.28 mas for PMy, and 0.13 ms for LOD for the last day of prediction horizon.Graphical abstract

A sensitivity curve approach to tuning a pulsar timing array in the detection era

Abstract As pulsar timing arrays (PTAs) transition into the detection era of the stochastic gravitational wave background (GWB), it is important for PTA collaborations to review and possibly revise their observing campaigns. The detection of a “single source” would be a boon for gravitational astrophysics, as such a source would emit gravitational waves for millions of years in the PTA frequency band. Here we present generic methods for studying the effects of various observational strategies, taking advantage of detector sensitivity curves, i.e., noise-averaged, frequency-domain detection statistics. The statistical basis for these methods is presented along with myriad examples of how to tune a detector towards single, deterministic signals or a stochastic background. We demonstrate that trading observations of the worst pulsars for high cadence campaigns on the best pulsars increases sensitivity to single sources at high frequencies while hedging losses in GWB and single source sensitivity at low frequencies. We also find that sky-targeted observing campaigns yield minimal sensitivity improvements compared with other PTA tuning options. Lastly, we show the importance of the uncorrelated half of the GWB, i.e. the pulsar-term, as an increasingly prominent sources of noise and show the impact of this emerging noise source on various PTA configurations.

Signal demixing using multi-delay multi-layer reservoir computing

Brain circuitry involves a large number of recurrent feedback loops whose dynamics depend on interaction delays. Brain-inspired reservoir computing leverages the rich recurrent dynamics of interconnected units for performing tasks on inputs. In particular, time-delay reservoir computing uses the high-dimensional transient dynamics in nonlinear delayed feedback loop architectures for e.g. time series prediction and speech classification. The modification of the dynamical properties of delay-differential systems through the inclusion of multiple delays has also recently been shown to improve the performance of time-delay reservoir computing. Here we explore another aspect of such neuro-inspired computing of fundamental and technological importance: the ability to separate and predict two signals in a mixture, where each has some intrinsic predictability due to its underlying dynamics. This is illustrated using multi-delay and multi-layer reservoir computing with chaotic input signal mixtures. In contrast to Independent Component Analysis and related unsupervised learning techniques, the context here consists in the parallel supervised learning of the dynamics for each signal in order to predict each of them beyond the training set. Further, the superposition of the chaotic signals into a single input channel adds to the difficulty of the task. We quantify and explain this performance with various signals emanating from both deterministic and stochastic systems. Additionally, we explore the architecture of deep time-delay reservoir computers. Our findings demonstrate that multi-delay reservoir computing can learn and predict the future of two superimposed deterministic signals. Prediction—and thus separation—accuracy can be significantly higher in single and multi-layer time-delay reservoir computing when the first layer contains multiple delays. Bandpass filtering of the mixed signal to remove lower and higher frequencies improved the prediction by a few percent. In some cases, paradoxically, increasing the proportion of one chaotic signal in the mixture can actually help the learning of another chaotic signal, and thus slightly improve its prediction.

Separating deterministic and stochastic gravitational wave signals in realistic pulsar timing array datasets

Recent observations by pulsar timing arrays (PTAs) suggest the presence of gravitational wave (GW) signals that potentially originate from supermassive black hole binaries (SMBHBs). These binaries can generate two kinds of signals: a stochastic gravitational wave background (GWB) or a deterministic continuous gravitational wave (CGW). The ability to correctly recognize and separate them is crucial for accurate signal recovery and astrophysical interpretation. This paper is aimed at investigating the interaction between stochastic GWB and deterministic CGW signals with the analysis pipelines currently available. We focus on understanding potential misinterpretations and biases in the parameter estimation when these signals are analysed separately or altogether. To this end, we performed several realistic simulations based on the European PTA 24.8yr dataset. We first injected either a GWB or a CGW into five datasets (of three GWB realisations and two CGW realisations) with identical noise. We analysed each signal type independently and then we analysed data sets containing both a stochastic GWB and a single resolvable CGW. We compared the parameter estimations using different search models, including Earth term (ET) only or combined Earth and pulsar term (ET+PT) CGW templates, along with correlated or uncorrelated power law GWB templates. We show that when searched for independently, the GWB and CGW signals can be misinterpreted (i.e. they can be confused with each other) and only a combined search is able to recover the true signal present. For datasets containing both a GWB and a CGW, failure to account for the latter biases the recovery of the GWB; however, when we perform a combined search, both GWB and CGW parameters can be recovered without any strong bias. Care must be taken with the method used to perform combined searches on these multi-component datasets, as the CGW PT can be misinterpreted as a common uncorrelated red noise. However, this can be avoided by conducting direct searches for a correlated GWB plus a CGW (ET+PT). Our study underscores the importance of combined searches to ensure unbiased recovery of GWB parameters in the presence of strong CGWs. This is crucial to accurately interpreting the signal recently found in PTA data and it is a first step towards a robust framework for disentangling stochastic and deterministic GW components in more sensitive datasets in the future.

Neural Network-Based Sliding Mode Control for Semi-Markov Jumping Systems With Singular Perturbation.

The primary focus of this article centers around the application of sliding mode control (SMC) to semi-Markov jumping systems, incorporating a dynamic event-triggered protocol (ETP) and singular perturbation. The underlying semi-Markov singularly perturbed systems (SMSPSs) exhibit mode switching behavior governed by a semi-Markov process, wherein the variation of this process is regulated by a deterministic switching signal. To simultaneously reduce the triggering rate and uphold the system performance, a novel parameter-based dynamic ETP is established. This protocol incorporates weight estimation of a radial basis function neural network (RBFNN) and introduces two internal dynamic variables. Following the Lyapunov's theory, sufficient criteria are established for ensuring the mean-square exponential stability of the resulting system. Additionally, an SMC scheme based on the convergence factor is designed to fulfill reachability conditions. Finally, two examples are carried out to validate the solvability and applicability of the attained control methodology.

OPTIMIZATION OF RADIO SYSTEM STRUCTURES FOR DIRECTION FINDING OF SIGNAL SOURCES WITH COMPLETELY KNOWN PARAMETERS

Context. Direction finders are critical components of radar and radio navigation systems, particularly when installed onboard UAVs. The increasing use of unmanned systems has heightened the need for precise direction finding and wide-angle unambiguous measurements. The primary challenge is to strike a balance between achieving high accuracy and maintaining a broad range of unambiguous measurement angles. Objective. To simultaneously enhance direction finding accuracy and expand the range of unambiguous measurement angles through the statistical synthesis of functionally deterministic signal processing methods in multichannel direction finders. Method. The approach is grounded in the statistical theory of optimization for radio remote sensing and radar systems. For the specific type of signals considered in this study, represented by functional-deterministic models, the likelihood function is constructed, and its maxima are determined for various configurations of multi-antenna direction finders. The statistical synthesis results are validated through simulation and in-situ experiments. Results. Theoretical analysis and simulation modeling confirm that in dual-antenna direction finders, there is a trade-off between high resolution and the range of unambiguous direction finding angles. An improved signal processing method is developed for a four-antenna direction finder, utilizing a pair of high-gain and a pair of low-gain antennas. To achieve the maximum possible bearing accuracy within the range of unambiguous direction finder measurements, a new signal processing method is synthesized for a sixelement radio receiver, combining the processing of signals in two amplitude direction finders and one phase direction finder. Conclusions. The proposed approach achieves an optimal balance between resolution and angle range, making it particularly suitable for onboard systems of unmanned aerial vehicles. Simulation results confirm the effectiveness of the proposed method, highlighting its potential for implementation in modern radio systems.

СИНТЕЗ КРИТЕРІЮ РІВНЯ СИНХРОНІЗМУ В ХАОТИЧНІЙ СИСТЕМІ ЗВ’ЯЗКУ ІЗ ДОПОМІЖНИМ ГЕНЕРАТОРОМ

Currently, the use of deterministic chaos signals in information transmission systems represents a promising area of scientific and applied research due to their properties, including wide bandwidth, noise-like behavior, high information capacity, resistance to strong interference, and the ability to synchronize signal generators on both the transmitting and receiving sides.This paper presents a simulation model of a binary information transmission system in the Matlab/Simulink environment, based on the Rucklidge nonlinear dynamic system operating in chaotic mode. During the transmission of binary messages, unidirectional chaotic synchronization of structurally identical Rucklidge systems occurs on the receiving side. Modulation of the chaotic channel oscillations is achieved by altering the bifurcation parameters of the nonlinear transmitter system. The identification of received logical levels and the information message is accomplished by determining the level of chaotic synchronization between the two nonlinear dynamic Rucklidge systems on the receiving side. The applied criterion for the similarity of the evolution of chaotic systems on both the receiving and transmitting sides determines the accuracy of logical level identification. The selection of the synchronization criterion directly impacts the system’s tactical characteristics, including immunity to interference, sensitivity to nonlinear distortions, bandwidth, and more.The evolution of chaotic generators is proposed to be compared by utilizing the energy difference of signals from driven nonlinear dynamic systems. Several implementations of the energy criterion were considered, and their impact on the parameters of information signal transmission under conditions of additive interference and nonlinear channel distortions was modeled. As a result of this research, a universal criterion for the level of chaotic synchronization is proposed, involving the normalized energy difference of the signals from the main and auxiliary driven chaotic generators across all phase variables. Research on the system model for various levels of interference and distortions has shown that the proposed criterion is weakly sensitive to additive noise up to -10 dB and nonlinear channel signal distortions, such as bilateral clipping and step changes in the transmission line up to 20%. This finding allows for the application of this method in logical level identification amid strong interference and moderate nonlinear distortions.

Quenching and Suppression of Limit Cycles in 3x3 Nonlinear Systems

For several decades, the importance and weight-age of prediction of nonlinear self-sustained oscillations or Limit Cycles (LC) and their quenching by signal stabilization have been discussed which is confined to Single Input and Single Output (SISO) system. However, for the last five to six decades, the analysis of 2x2 Multi Input and Multi Output (MIMO) Nonlinear Systems gained importance in which a lot of literature available. In recent days few literatures are available which addresses the exhibition of LC and their quenching/suppression in 3x3 MIMO Nonlinear systems. Poor performances in many cases like Load Frequency Control (LFC) in multi area power system, speed and position control in robotics, automation industry and other occasions have been observed which draws attention of Researchers. The complexity involved, in implicit nonmemory type and memory type nonlinearities, it is extremely difficult to formulate the problem in particular for 3x3 systems. Under this circumstance, the harmonic linearization/ harmonic balance reduces the complexity considerably. Still the analytical expressions are so complex which loses the insight into the problem particularly for memory type nonlinearity in 3x3 system. Hence in the present work a novel graphical method has been developed for prediction of limit cycling oscillations in a 3x3 nonlinear system. The quenching of such LC using signal stabilization technique using deterministic (Sinusoidal) and random (Gaussian) signals has been explored. Suppression LC using pole placement technique through arbitrary selection and optimal selection of feedback Gain Matrix K with complete state controllability condition and Riccati Equation respectively. The method is made further simpler assuming a 3x3 system exhibits the LC predominantly at a single frequency, which facilitates clear insight into the problem and its solution. The proposed techniques are well illustrated with example and validated/substantiated by digital simulation (a developed program using MATLAB codes) and use of SIMULINK Tool Box of MATLAB software. The Signal stabilization with Random (Gaussian) Signals and Suppression LC with optimal selection of state feedback matrix K using Riccati Equation for 3x3 nonlinear systems have never been discussed elsewhere and hence it claims originality and novelty. The present work has the brighter future scope of: i. Adapting the Techniques like Signal Stabilization and Suppression LC for 3x3 or higher dimensional nonlinear systems through an exhaustive analysis. ii. Analytical/Mathematical method may also be developed for signal stabilization using both deterministic and random signals based on Dual Input Describing function (DIDF) and Random Input Describing Function (RIDF) respectively. iii. The phenomena of Synchronization and De-synchronization can be observed/identified analytically using Incremental Input Describing Function (IDF), which can also be validated by digital simulations.

Suppression Limit Cycles in 2x2 Nonlinear Systems with Memory Type Nonlinearities

For several decades, the importance and weight-age of prediction of nonlinear self-sustained oscillations or Limit Cycles (LC) and their quenching by signal stabilization have been discussed, which is confined to Single Input and Single Output (SISO) systems. However, for the last five to six decades, the analysis of 2x2 Multi Input and Multi Output (MIMO) Nonlinear Systems gained importance in which a lot of literature available. In recent days’ people have started discussing suppression of LC which limits the performance of most of the physical systems in the world. It is a formidable task to suppress the limit cycles for 2x2 systems with memory type nonlinearity in particular. Backlash is one of the nonlinearities commonly occurring in physical systems that limit the performance of speed and position control in robotics, automation industry and other occasions like Load Frequency Control (LFC) in multi area power systems. The feasibility of suppression of such nonlinear self-oscillations has been explored in case of the memory type nonlinearities. Backlash is a common memory type nonlinearity which is an inherent Characteristic of a Governor, used for usual load frequency control of an inter-connected power system and elsewhere. Suppression LC using pole placement technique through arbitrary selection and optimal selection of feedback Gain Matrix K with complete state controllability condition and Riccati Equation respectively and is done through state feedback. The Governing equation is d/dt [X(t)] =(A-BK) X: which facilitates the determination of feedback gain matrix K for close loop Poles / Eigen values placement where the limit cycles are suppressed/eliminated in the general multi variable systems. The complexity involved in implicit non-memory type or memory type nonlinearities, it is extremely difficult to formulate the problem for 2x2 systems. Under this circumstance, the harmonic linearization/harmonic balance reduces the complexity considerably. Still the analytical expressions are so complex which loses the insight into the problem particularly for memory type nonlinearity in 2x2 system and the method is made further simpler assuming a 2x2 system exhibits the LC predominately at a single frequency. Hence in the proposed work an alternative attempt has been made to develop a graphical method for the prediction of Limit Cycling Oscillations in 2x2 memory type Nonlinear systems which not only reduces the complexity of formulations but also facilitates clear insight into the problem and its solution. The present techniques are well illustrated with an example and validated / substantiated by digital simulation (developed program using MATLAB codes) and use of SIMULINK Tool Box of MATLAB software. The present work has the brighter future scope of: Adapting the Techniques like Signal Stabilization and Suppression LC for 3x3 or higher dimensional nonlinear systems through an exhaustive analysis. Analytical/Mathematical methods may also be developed for signal stabilization using both deterministic and random signals based on Dual Input Describing function (DIDF) and Random Input Describing Function (RIDF) respectively. The phenomena of Synchronization and De-synchronization can be observed/identified analytically using Incremental Input Describing Function (IDF), which can also be validated by digital simulations.

The Influence of Dopaminergic Medication on Regularity and Determinism of Gait and Balance in Parkinson's Disease: A Pilot Analysis.

Background/Objectives: Understanding how dual-tasking and Parkinson's disease medication affect gait and balance regularity can provide valuable insights to patients, caregivers, and clinicians regarding frailty and fall risk. However, dual-task gait and balance studies in PD most often only employ linear measures to describe movement regularity. Some have used nonlinear techniques to analyze PD performances, but only in the on-medication state. Thus, it is unclear how the nonlinear aspects of gait or standing balance are affected by PD medication. This study aimed to assess how dopaminergic medication influenced the regularity and determinism of joint angle and anterior-posterior (AP) and medial-lateral (ML) center of pressure (COP) path time-series data while single- and dual-tasking in PD. Methods: Sixteen subjects with PD completed single- and dual-task gait and standing balance trials for 3 min off and on dopaminergic medication. Sample entropy and percent determinism were calculated for bilateral hip, knee, and shoulder joints, and the AP and ML COP path. Results: There were no relevant medication X task interactions for either the joint angles series or the balance series. Instead, the results supported independent effects of medication, dual-tasking, or standing with eyes closed. Balance task difficulty (i.e., eyes open vs. eyes closed) was detected by the nonlinear analyses, but the nonlinear measures yielded opposing results such that standing with eyes closed simultaneously yielded less regular and more deterministic signals. Conclusions: When juxtaposed with previous findings, these results suggest that medication-induced functional improvements in people with PD might be accompanied by a shift from lesser to greater signal consistency, and the effects of dual-tasking and standing with eyes closed were mixed. Future studies would benefit from including both linear and nonlinear measures to better describe gait and balance performance and signal complexity in people with PD.

Advanced Frequency Analysis of Signals with High-Frequency Resolution

In today’s era, it is important to analyze and utilize various signals in industrial or laboratory applications. Measured signals provide critical information about the controlled system, which can be contained precisely within a narrow frequency range. Many methods and algorithms exist to process such signals in both the time and frequency domains. In particular, signal processing in the frequency domain is primary in industrial practice because dominant components within a specific narrow frequency band are sought. The discrete Fourier transformation (DFT) algorithm is the tool used in practice to find these frequency components. The DFT algorithm provides the full frequency spectrum with a higher number of calculation steps, and its spectrum frequency resolution is low. Therefore, research has focused on finding a method to achieve high-frequency spectrum resolution. An important factor in selecting the technique was that such an algorithm should be implementable on a microprocessor-based system under harsh industrial conditions. Research results showed that the DFT ZOOM method meets these requirements. The frequency zoom has many advantages but requires some modification. It is implemented in high-performance analyzers, but a thorough and detailed description of the respective algorithm is lacking in technical articles and literature. This article mathematically and theoretically describes the modified frequency zoom algorithm in detail. The steps of the frequency zoom, from creating an analytical signal through frequency shifting and decimation to the frequency analysis of the signal, are realized. The algorithm allows for the analysis of a signal with high-frequency resolution in a limited frequency band. A significant modification of DFT ZOOM is that of using the Hilbert transform to create an analytic signal. This resolves the aliasing issue caused by the overlap between fundamental and sideband spectra. Results from processing deterministic and stochastic signals using the modified DFT ZOOM are presented. The presented experimental results contribute to a more detailed frequency analysis of the signal. As part of this scientific research, the issues of frequency zoom were thoroughly addressed, solving the partial problems of this algorithm, both in theory and in the context of signal theory.

Statistical Synthesis and Analysis of Functionally Deterministic Signal Processing Techniques for Multi-Antenna Direction Finder Operation

This manuscript focuses on the process of measuring the angular positions of radio sources using radio engineering systems. This study aims to improve the accuracy of measuring the angular positions of sources that radiate functionally determined signals and to expand the range of the unambiguous operation angles for multi-antenna radio direction finders. To achieve this goal, the following tasks were addressed: (1) defining the models of signals, noise, and their statistical characteristics, (2) developing the theoretical foundations of statistical optimization methods for measuring the angular positions of radio sources in multi-antenna radio direction finders, (3) optimizing the structures of radio direction finders with different configurations, (4) analyzing the accuracy and range of the unambiguous measurement angles in the developed methods, and (5) conducting experimental measurements to confirm the main results. The methods used are based on the statistical theory of optimization for remote sensing and radar systems. For the specified type of signals, given by functionally deterministic models, a likelihood function was constructed, and its maxima were determined for different multi-antenna direction finder configurations. The results of statistical synthesis were verified through simulation modeling and experiments. The primary approach to improving measurement accuracy and expanding the range of unambiguous angles involves combining antennas with different spatial characteristics and optimally integrating classical radio direction-finding methods. The following results were obtained: (1) theoretical studies and simulation modeling confirmed the existence of a contradiction between high resolution and the width of the range of the unambiguous measurements in two-antenna radio direction finders, (2) an improved signal processing method was developed for a four-antenna radio direction finder with a pair of high-gain and a pair of low-gain antennas, and (3) to achieve maximum direction-finding accuracy within the unambiguous measurement range, a new signal processing method was synthesized for a six-element radio receiver, combining processing in two amplitude direction finders and one phase direction finder. This work provides a foundation for further theoretical studies, highlights the specifics of combining engineering measurements in direction-finding systems, and offers examples of rapid verification of new methods through computer modeling and experimental measurements.

PSD estimation and modal parameter identification for random vibrations with blade tip timing measurement

Abstract Blade tip timing (BTT) is a vibration measurement technique for blade health monitoring. Most of the existing BTT analysis methods are suitable for deterministic vibration signals but are ineffective for random vibration signals that often occur in practice. Statistical analysis of BTT data is significant for random vibration analysis and improving blade monitoring efficiency. This study proposes a compressive model for power spectral density (PSD) estimation and modal parameter identification. The efficiencies of three compressive sensing algorithms, including the least absolute shrinkage and selection operator (Lasso), nonnegative least squares (NLS), and nonnegative orthogonal matching pursuit, are compared. The effects of the duration of the signal and the frequency resolution on the quality of the estimated PSD and the identified parameters are discussed. According to the analysis, to obtain accurate damping ratios, it is recommended that the duration of the signal be greater than 3000 revolutions. A Q criterion based on the half-power bandwidth is proposed to determine the set of frequency resolutions. Numerical and field tests were conducted to verify the proposed method. The results indicate that the NLS algorithm is recommended to use. The root-mean-square errors of the identified natural frequencies and damping ratios obtained by the proposed method were 0.065 Hz and 0.023%, respectively. The proposed method was verified at different rotational speeds in a field test, demonstrating the capability of the method over a wide rotational speed range and providing more opportunities to detect blade damage.

Dynamic protocol-based sliding mode control for nonlinear semi-Markovian jump systems with deterministic switching

Dynamic protocol-based sliding mode control for nonlinear semi-Markovian jump systems with deterministic switching

Deep Reinforcement Learning for Ecological and Distributed Urban Traffic Signal Control with Multi-Agent Equilibrium Decision Making

Multi-Agent Reinforcement Learning (MARL) has shown strong advantages in urban multi-intersection traffic signal control, but it also suffers from the problems of non-smooth environment and inter-agent coordination. However, most of the existing research on MARL traffic signal control has focused on designing efficient communication to solve the environment non-smoothness problem, while neglecting the coordination between agents. In order to coordinate among agents, this paper combines MARL and the regional mixed-strategy Nash equilibrium to construct a Deep Convolutional Nash Policy Gradient Traffic Signal Control (DCNPG-TSC) model, which enables agents to perceive the traffic environment in a wider range and achieves effective agent communication and collaboration. Additionally, a Multi-Agent Distributional Nash Policy Gradient (MADNPG) algorithm is proposed in this model, which is the first time the mixed-strategy Nash equilibrium is used for the improvement in the Multi-Agent Deep Deterministic Policy Gradient algorithm traffic signal control strategy to provide the optimal signal phase for each intersection. In addition, the eco-mobility concept is integrated into MARL traffic signal control to reduce pollutant emissions at intersections. Finally, simulation results in synthetic and real-world traffic road networks show that DCNPG-TSC outperforms other state-of-the-art MARL traffic signal control methods in almost all performance metrics, because it can aggregate the information of neighboring agents and optimize the agent’s decisions through gaming to find an optimal joint equilibrium strategy for the traffic road network.

Detection of Gate Valve Leaks through the Analysis Fractal Characteristics of Acoustic Signal

This paper considers the possibility of using monofractal and multifractal analysis of acoustic signals to detect water leaks through gate valves. Detrended fluctuation analysis (DFA) and multifractal detrended fluctuation analysis (MF-DFA) were used. Experimental studies were conducted on a ½-inch nominal diameter wedge valve, which was fitted to a ¾-inch nominal diameter steel pipeline. The water leak was simulated by opening the valve. The resulting leakage rates for different valve opening conditions were 5.3, 10.5, 14, 16.8, and 20 L per minute (L/min). The Hurst exponent for acoustic signals in a hermetically sealed valve is at the same level as a deterministic signal, while the width of the multifractal spectrum closely matches that of a monofractal process. When a leak occurs, turbulent flow pulsations appear, and with small leak sizes, the acoustic signals become anticorrelated with a high degree of multifractality. As the leakage increases, the Hurst exponent also increases and the width of the multifractal spectrum decreases. The main contributor to the multifractal structure of leak signals is small, noise-like fluctuations. The analysis of acoustic signals using the DFA and MF-DFA methods enables determining the extent of water leakage through a non-sealed gate valve. The results of the experimental studies are in agreement with the numerical simulations. Using the Ansys Fluent software (v. 19.2), the frequencies of flow vortices at different positions of gate valve were calculated. The k-ω SST turbulence model was employed for calculations. The calculations were conducted in a transient formulation of the problem. It was found that as the leakage decreases, the areas with a higher turbulence eddy frequency increase. An increase in the frequency of turbulent fluctuations leads to enhanced energy dissipation. Some of the energy from ordered processes is converted into the energy of disordered processes.

Machine learning evaluation in the Global Event Processor FPGA for the ATLAS trigger upgrade

The Global Event Processor (GEP) FPGA is an area-constrained, performance-critical element of the Large Hadron Collider's (LHC) ATLAS experiment. It needs to very quickly determine which small fraction of detected events should be retained for further processing, and which other events will be discarded. This system involves a large number of individual processing tasks, brought together within the overall Algorithm Processing Platform (APP), to make filtering decisions at an overall latency of no more than 8ms. Currently, such filtering tasks are hand-coded implementations of standard deterministic signal processing tasks.In this paper we present methods to automatically create machine learning based algorithms for use within the APP framework, and demonstrate several successful such deployments. We leverage existing machine learning to FPGA flows such as hls4ml and fwX to significantly reduce the complexity of algorithm design. These have resulted in implementations of various machine learning algorithms with latencies of 1.2 μs and less than 5% resource utilization on an Xilinx XCVU9P FPGA. Finally, we implement these algorithms into the GEP system and present their actual performance.Our work shows the potential of using machine learning in the GEP for high-energy physics applications. This can significantly improve the performance of the trigger system and enable the ATLAS experiment to collect more data and make more discoveries. The architecture and approach presented in this paper can also be applied to other applications that require real-time processing of large volumes of data.

Testing for periodicity at an unknown frequency under cyclic long memory, with applications to respiratory muscle training

A frequent problem in applied time series analysis is the identification of dominating periodic components. A particularly difficult task is to distinguish deterministic periodic signals from periodic long memory. In this paper, a family of test statistics based on Whittle’s Gaussian log-likelihood approximation is proposed. Asymptotic critical regions and bounds for the asymptotic power are derived. In cases where a deterministic periodic signal and periodic long memory share the same frequency, consistency and rates of type II error probabilities depend on the long-memory parameter. Simulations and an application to respiratory muscle training data illustrate the results.

One-Bit DoA Estimation for Deterministic Signals Based on -Norm Minimization

One-Bit DoA Estimation for Deterministic Signals Based on -Norm Minimization

Spectral analysis of solar-irradiance fluctuations

Solar-irradiance fluctuations possess a power-law spectrum with two different slopes in the intermediate (1/day <f<1/h) and high [1/h <f<1/(2 min)] frequency (f) regimes. This spectrum is a combination of a deterministic (latitude-dependent) variation of daylight duration and a stochastic component arising from various environmental attenuation factors. We propose a principled approach to decompose the solar-irradiance time series into the deterministic location-dependent clear sky and the stochastic environmental attenuation signals. Such decomposition permits one to systematically analyze the solar photovoltaic power-fluctuation spectra after accounting for the strong geographic dependence, failing which no baseline standard exists with which to compare spectra between disparate geographic locations. Our analysis for two different locations shows that the stochastic environmental factors determine the spectral power-law slope in the high-frequency range, while the deterministic clear-sky signal plays a role in the intermediate frequency range. We close by discussing its implications for solar photovoltaic power production, in particular, for the geographic smoothing of fluctuations. Published by the American Physical Society 2024