Time-varying Filter Research Articles

Adaptive model predictive control scheme for partially internal thermally coupled air separation column

Partially Internal Thermally Coupled Air Separation Column (P-ITCASC) is a highly energy-efficient and cost-effective technology. However, its complex dynamics resulting from thermal coupling pose a challenge to the operating stability of this technology. This article, therefore, proposes an adaptive model predictive control (AMPC) scheme for the P-ITCASC process. The controller incorporates an auto-regressive and exogenous inputs (ARX) model, a linear time-varying Kalman filter, and a recursive polynomial model estimator (RPME) algorithm. Within RPME, ARX polynomial models are identified and utilized to estimate the time-varying parameters and update the prediction model of the process. The process states are observed through the Kalman filter, and the constrained receding horizon optimization problem is solved using quadratic programming. This control scheme ensures the closed-loop system's feasibility and stability in the presence of output/input constraints. An adaptive generic model control, model predictive control, and adaptive internal model control schemes were also designed for benchmarking study. Numerical simulations show that AMPC is more efficient in handling nonlinear dynamics and maintaining product concentration to desired set points compared to other control schemes.

Attenuation of non-stationary random noise in ground penetrating radar data based on time-varying filtering

The attenuation of random noise is significant in the enhancement of the signal-to-noise ratio in ground penetrating radar (GPR) data. However, the non-stationary nature of random noise poses significant challenges to most conventional denoising methods. We propose a time-varying bandpass filter (TVBF) based on the local maxima multiple synchrosqueezing transform (LMMSST), referred to as TVBFLMMSST, for the attenuation of non-stationary random noise in GPR data. The LMMSST method is introduced to achieve highly compressed time–frequency representation (TFR). By integrating an instantaneous frequency trajectory detection algorithm, we design the TVBF to successfully separate useful signals from random noise. The efficacy of the TVBFLMMSST is validated through synthetic and field experiments, demonstrating its capability to effectively isolate components of useful signals and noise signals. Compared to classical denoising methods, TVBFLMMSST exhibits robust performance in attenuating random noise while preserving the amplitude of useful signals.

Analysis and denoising of electric noise in phased array ultrasonic testing system

Phased array ultrasonic testing (PAUT) is an advanced technique used for non-destructive testing (NDT) to detect and assess the integrity of materials or structures. In composite materials with significant acoustic attenuation, electrical noise significantly impacts PAUT detection accuracy, highlighting a crucial issue. This paper presents a comprehensive analysis of electrical noise, explains the relationship between electrical noise and inspection parameters, and proposes recommendations to reduce it. A time-varying filter empirical mode decomposition (TVF-EMD) joint wavelet thresholding method is proposed for denoising electrical noise after time-corrected gain (TCG). The ultrasonic echo signal goes through TVF-EMD decomposition, resulting in intrinsic mode functions (IMFs). Energy entropy is utilized to identify the signal-dominant IMFs, and wavelet thresholding is applied to further reduce noise within these selected IMFs. The proposed method effectively removes noise and ensures signal integrity by validating simulated and experimental signals.

Recursive Filtering Under Probabilistic Encoding-Decoding Schemes: Handling Randomly Occurring Measurement Outliers.

This article focuses on the recursive filtering problem for networked time-varying systems with randomly occurring measurement outliers (ROMOs), where the so-called ROMOs denote a set of large-amplitude perturbations on measurements. A new model is presented to describe the dynamical behaviors of ROMOs by using a set of independent and identically distributed stochastic scalars. A probabilistic encoding-decoding scheme is exploited to convert the measurement signal into the digital format. For the purpose of preserving the filtering process from the performance degradation induced by measurement outliers, a novel recursive filtering algorithm is developed by using the active detection-based method where the "problematic" measurements (i.e., the measurements contaminated by outliers) are removed from the filtering process. A recursive calculation approach is proposed to derive the time-varying filter parameter via minimizing such the upper bound on the filtering error covariance. The uniform boundedness of the resultant time-varying upper bound is analyzed for the filtering error covariance by using the stochastic analysis technique. Two numerical examples are presented to verify the effectiveness and correctness of our developed filter design approach.

Hadamard transformation based frequency response mismatch calibration for a 4-channel TIADC

The time-interleaved analog-to-digital converter (TIADC) is one of the most effective solutions for improving the sampling rate of acquisition systems. However, the frequency response mismatch between channels can seriously degrade the performance of the system. In this paper, we propose a calibration method for frequency response mismatches in 4-channel TIADC systems. Following the construction of a four-channel TIADC model using a four-order Hadamard matrix, the error signals were extracted using three time-invariant filters and subsequently subtracted from the output to complete the calibration. It uses the Hadamard transform instead of the complex exponential modulator used in conventional time-varying filter methods, which can further simplify the signal processing without the necessity of complicated calculations. We have employed a stimulated four-channel TIADC system to validate the effectiveness of the proposed calibration technique. Moreover, in a commercial 12.5 GSPS four-channel TIADC system, it is also demonstrated that this approach is effective in improving the spurious-free dynamic range of the system.

Advanced forecasting of COVID-19 epidemic: Leveraging ensemble models, advanced optimization, and decomposition techniques

In the global effort to address the outbreak of the new coronavirus pneumonia (COVID-19) pandemic, accurate forecasting of epidemic patterns has become crucial for implementing successful interventions aimed at preventing and controlling the spread of the disease. The correct prediction of the course of COVID-19 outbreaks is a complex and challenging task, mainly because of the significant volatility in the data series related to COVID-19. Previous studies have been limited by the exclusive use of individual forecasting techniques in epidemic modeling, disregarding the integration of diverse prediction procedures. The lack of attention to detail in this situation can yield worse-than-ideal results. Consequently, this study introduces a novel ensemble framework that integrates three machine learning methods (kernel ridge regression (KRidge), Deep random vector functional link (dRVFL), and ridge regression) within a linear relationship (L-KRidge-dRVFL-Ridge). The optimization of this framework is accomplished through a distinctive approach, specifically adaptive differential evolution and particle swarm optimization (A-DEPSO). Moreover, an effective decomposition method, known as time-varying filter empirical mode decomposition (TVF-EMD), is employed to decompose the input variables. A feature selection technique, specifically using the light gradient boosting machine (LGBM), is also implemented to extract the most influential input variables. The daily datasets of COVID-19 collected from two countries, namely Italy and Poland, were used as the experimental examples. Additionally, all models are implemented to forecast COVID-19 at two-time horizons: 10- and 14-day ahead (t+10 and t+14). According to the results, the proposed model can yield higher correlation coefficient (R) for both case studies: Italy (t+10 = 0.965, t+14 = 0.961) and Poland (t+10 = 0.952, t+14 = 0.940) than the other models. The experimental results demonstrate that the model suggested in this paper has outstanding results in various kinds of complex epidemic prediction situations. The proposed ensemble model demonstrates exceptional accuracy and resilience, outperforming all similar models in terms of efficacy.

Time-varying Comb Filters to Improve SpeechPerception in Sensorineural Hearing LossSubjects

Time-varying Comb Filters to Improve SpeechPerception in Sensorineural Hearing LossSubjects

A multi-stage forecasting system for daily ocean tidal energy based on secondary decomposition, optimized gate recurrent unit and error correction

Tidal energy, as a new energy, has very high research potential and practical application value. For the characteristics of tidal energy such as nonstationarity and nonlinearity, a multi-stage forecasting system for daily ocean tidal energy is proposed. It is based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), refined composite multi-scale dispersion entropy (RCMDE), empirical mode decomposition based on time-varying filter modified by white shark optimizer (WSOTVFEMD), improved gate recurrent unit using parasitic salp swarm algorithm based on differential evolution (PDESSAGRU) and error correction using CNN (CNN-EC), named as ICEEMDAN-RCMDE-WSOTVFEMD-PDESSAGRU–CNN–EC. Firstly, decompose tidal energy into a series of intrinsic mode functions (IMFs) by ICEEMDAN, and divide IMFs into high-complexity and low-complexity components by RCMDE. Next, secondly decompose the reconstructed high-complexity components into high-complexity parts by WSOTVFEMD. Afterwards, separately predict each component of the high-complexity parts and the low-complexity components by PDESSA, and reconstruct the predicted results to obtain original predicting results. In the end, decompose the error into error IMFs (EIMFs) by ICEEMDAN, predict EIMFs with convolutional neural network (CNN) respectively to acquire error predicting results, and reconstruct original predicting results and error predicting results to acquire the final results. Taking the tidal energy of the American cities including San Francisco, Sitka, and Wauna as case studies, the results show that the proposed system has high prediction accuracy after experiments with 13 comparative models in each city.

Phase retrieval from integrated intensity of auto-convolution

Ultra-fast optical pulses are the most ephemeral sensing paradigm ever devised, examining events over incredibly brief timescales with broadband illumination. A consequence of sensing at timescales lower than a picosecond is that pulse characterization cannot be done with traditional analog-to-digital samplers and must be ascertained from integrating intensity sensors. Techniques for pulse characterization have been constructed using combinations of time-invariant and time-variant filter responses to create non-linear but invertible intensity datasets (Walmsley & Dorrer, 2009). In this paper, we develop a novel high-order phase retrieval technique to perform pulse characterization from a single-pixel integrating sensor measuring integrated intensity of auto-convolution (IIAC). We examine gradient descent’s ability to recover signals as a function of signal dimension and measurement count, and we demonstrate the effective use of iterative hard tensor thresholding as an initializer. Finally, we demonstrate IIAC recovery in a laboratory setting to recover the time profile of a complex laser pulse. We assert that the IIAC recovery solution demonstrated here simultaneously provides the optics community with a pulse characterization technique that scales to low-power microscopy systems and provides the optimization community with a physically motivated high-order phase retrieval problem enhanced by low-rank tensor processing.

Development and verification of a method to simulate non-stationary vehicle interior wind noise

As speeds and directions of the vehicle and wind change, the time-varying flow creates variations in wind noise, which can be referred to as non-stationary wind noise. To investigate people's perceptions of non-stationary wind noise inside the vehicle, a method to simulate the non-stationary wind noise is needed. Previously, a method was developed that used stationary wind noise recordings taken at several constant wind speeds and directions to form functions that relate the 1/3 octave sound pressure level with wind speed and direction. These functions are used to create time-varying filters based on provided time histories of wind speed and direction. To reduce the cost of taking many stationary measurements, an improved method was investigated. At each yaw angle, one speed sweep wind tunnel measurement was used to estimate the relationship between sound pressure level and wind speed. Two partially correlated white noise signals were then filtered to simulate binaural sounds that had a similar coherence structure between the left and right ear sounds to that observed in binaural measurements in the vehicle. The accuracy of the simulations was validated by comparing wind noise simulations to vehicle interior noise measured in the wind tunnel and on the road.

Quickest detection of bias injection attacks on the glucose sensor in the artificial pancreas under meal disturbances

Modern glucose sensors deployed in closed-loop insulin delivery systems, so-called artificial pancreas use wireless communication channels. While this allows a flexible system design, it also introduces vulnerability to cyberattacks. Timely detection and mitigation of attacks are imperative for device safety. However, large unknown meal disturbances are a crucial challenge in determining whether the sensor has been compromised or the sensor glucose trajectories are normal. We address this issue from a control-theoretic security perspective. In particular, a time-varying Kalman filter is employed to handle the sporadic meal intakes. The filter prediction error is then statistically evaluated to detect anomalies if present. We compare two state-of-the-art online anomaly detection algorithms, namely the χ2 and CUSUM tests. We establish a robust optimal detection rule for unknown bias injections. Even if the optimality holds only for the restrictive case of constant bias injections, we show that the proposed model-based anomaly detection scheme is also effective for generic non-stealthy sensor deception attacks through numerical simulations.

Generalized spectrum analysis of Chirp Cyclostationary signals associate with linear canonical transform

A chirp cyclostationary (CCS) process is a nonstationary stochastic process, whose statistics have been defined and utilized in radar and communication signal processing. However, spectrum analysis of CCS signals, which provides a spectral view to characterize a signal, has not been performed. Classical spectrum analysis is based on the Fourier transform, which is not suitable for CCS signals because of the multiplicative chirp signals in a CCS model. In this study, we propose a generalized spectral representation of chirp cyclostationary signals associated with a linear canonical transform. The proposed representation shows that a CCS signal can be represented by a linear combination of a series of jointly stationary and equal-bandwidth processes, which bridge the nonstationary and stationary processes. Furthermore, the relationships between the second-order statistics of the CCS process and its stationary representations are introduced. Finally, based on the above observations, a linear time-varying Wiener filter was designed for CCS signal denoising and time-varying channel estimation. Finally, the relationships between the nonstationarity of the input and output and time-varying properties of the systems are discussed, which are helpful for determining the nonstationarity of the outputs or the time-varying property of a system.

An enhanced spline-based time-varying filtering method and fast MKurtgram for bearing fault identification under random impulsive environment

In certain engineering problems, the presence of random impulsive components can heavily corrupt a signal. Thus, random impulsive components are also known as random impulsive noise or non-Gaussian noise. Commonly used signal processing tools in fault diagnosis may perform poorly under such operational conditions. To this end, an alternative approach integrating time-varying filtering with fast MKurtgram (FMK) is proposed as a solution. The FMK is developed based on the fast kurtogram (FK). The FMK addresses a major drawback of the FK, which is vulnerable to random impulsive noise, thus FMK exhibits good performance in impulsive environments. However, the FMK may produce inaccurate estimates of the bandwidth and center frequency when random impulsive noise is relatively intensive. To alleviate this, a spline-based time-varying filtering (TVF) method is designed in this work. This spline-based TVF method is highly effective in eliminating a large amount of Gaussian noise, as well as attenuating the energy of the impulsive components. After undergoing this pre-treatment, the FMK can estimate both bandwidth and center frequency more precisely. The proposed approach is demonstrated to be effective through the simulated and real bearing fault signals.

Fault diagnosis of offshore wind turbines based on component separable synchroextracting transform

The fault diagnosis of wind turbines is crucial for wind power generation. However, the violent variation in wind power and directions of offshore wind turbines often results in high nonstationary vibration signals, which poses a challenge for effective fault recognition. Time-frequency analysis (TFA) is a common method to reveal the time-varying frequency components caused by faults. In this paper, we propose a component separable synchroextracting transform (CSSET) by exploring the unique modulation characteristics of wind turbine vibration signals. For the method, a fundamental frequency is firstly estimated by a time-frequency ridge optimization method, and the potential fault characteristic frequency (FCF) set is constructed according to the modulation characteristics of the wind turbine vibration signal. Next, the instantaneous amplitudes (IAs) of these components in the set are estimated by a time-varying bandpass filter. Then, the instantaneous frequency (IF) of the reconstructed single component is extracted by the synchronous extraction operator (SEO), and a high-precision time-frequency representation (TFR) is obtained. The proposed method takes full advantage of the physical modulation characteristics of vibration signals and the frequency components which are most relevant to fault information are only preserved. The effectiveness of the proposed method is verified by analyzing simulation and experimental signals.

When FrFT meets quadratic frequency modulation functions—A novel tool for nonstationary signals and time-varying systems

The fractional Fourier transform (FrFT) is a general form of the Fourier transform, which has provided a theoretical framework for linear frequency modulation signal processing. The quadratic frequency modulation (QFM) function is a generalization of the linear frequency modulation function, which balances the precision and complexity of nonstationary signals in practical applications. However, there is no framework for analyzing and processing QFM signals, which limits the applications of QFM signal model. The QFMFrFT, a generalization of the FrFT, is developed in this study to tackle this issue. The impact of the QFMFrFT on the time-frequency distribution explains its usefulness in sampling, denoising, and filter design of QFM signals. Two one-place statistical functions are defined for QFM-type stochastic signals, of which the input-output relationships of a linear time-varying system are discussed. Using these relations, a linear time-varying matched filter is designed. Two types of generalized convolution operators are designed associated with the QFMFrFT, which allow a multiplicative realization of a time-varying system. The QFM signal is demonstrated as a singular function of this type of linear time-varying system, which inspires a multi-carrier technique in spread-spectrum communications. Simulations verify the applications of the QFMFrFT in parameter estimation, matched filter design, and communications.

Enhancing acoustic contrast while tracking acoustic transfer functions: A simultaneous control approach

In the context of sound zone control (SZC) systems, the ability to effectively track variations in acoustic transfer functions is essential due to the inevitable variation of acoustic environments. This paper addresses the non-uniqueness problem encountered in time domain SZC, arising from the coupling observed in multichannel transfer function modeling. To address this issue, the utilization of time-varying control filters is proposed, which offers a viable solution. In order to ensure a stable acoustic contrast performance regardless of environmental changes, as well as to mitigate extreme distortion resulting from maximal acoustic contrast (AC) in the time domain, the paper focuses on the objective of maintaining a desired level of AC while tracking the acoustic transfer functions. Simulations are carried out to evaluate the proposed method, demonstrating its capability to effectively track environmental changes and continually update the control filters, consequently providing a stable acoustic contrast performance.

Design and analysis of nonlinear numerical algorithm for seismic response of structures based on HVSR algorithm.

Earthquake is one of the main factors causing structural disasters in current buildings. Under earthquake action, adjacent building structures are generally in different vibration stages, and collisions may occur in the structures, which may cause serious damage to the structure. In order to prevent certain earthquakes from damaging the designed buildings, this article mainly introduced the design and analysis of a numerical algorithm for seismic nonlinear structural dynamic response based on the HVSR algorithm. This article evenly divided the acceleration response time series into 15 time periods and then selected the position corresponding to the peak point of instantaneous amplitude within each period as the selected data point position. The same seismic load can be applied at the bottom of the established nonlinear model to extract the dynamic response data of the top layer of the structure, and then, the instantaneous amplitude and corresponding instantaneous phases and frequencies of the main components of the structural dynamic response can be extracted through the time-varying filter and Hilbert transform based on the discrete analytic mode decomposition. Under the influence of these four ground motions, the collision force within the range of 0-50 kN accounted for over 87% of the total number of collisions. In the comparison results of collision response to peak displacement, the four ground motions all led to structural collision, and the collision inhibited the positive peak displacement response of node 1512. Compared with noncollision, the peak displacement was reduced by 27.273, 33.675, 27.727, and 37.248%, respectively. The peak displacement of 1512 nodes was suppressed and reduced by 18.856%. The results indicate that the HVSR algorithm can obtain the instantaneous characteristic parameters of nonlinear structural dynamic response and achieve model correction.

Adaptive-Neuro-Learning Tracking Control for the Permanent Magnet Synchronous Motor with Full-State Prescribed Performances and Time Delays

High-performance tracking control is essential for permanent magnet synchronous motors in the perturbed environment. Given this, a new hybrid controller is proposed in this study for a permanent magnet synchronous motor with load disturbances as well as time delays. First, a new prescribed performance method is proposed to achieve the full-state performance constraints with load disturbances. Second, a time-varying filter is proposed for the first time to avoid the “complexity explosion” problem of the backstepping method while guaranteeing the convergence of the filtering error. Third, by combining Lyapunov–Krasovskii functionals with adaptive neural networks, the time-delay disturbance and unknown nonlinear dynamics of the control system have been solved. The stability analysis proves that all signals in the closed-loop system are bounded. To show the effectiveness of the intelligent controller, the comparison simulations are given to confirm the advantages of the proposed adaptive neural control scheme.

Post stack seismic data processing for enhancing subsurface reservoir mapping in the Shushan basin, Western Desert, Egypt

ABSTRACT Reprocessing of seismic reflection data over Nader field in the Egyptian Western Desert, improved the images of the surface structural framework. The appearance of seismic data can be affected by various factors apart from subsurface structure, including data acquisition problems and processing artefacts. Different post-stack processing steps were tested and compared to the original seismic data determine the enhancement of data quality and improvements on imaging the subsurface. The applied post-stack processing sequence increased the seismic data resolution through increasing the signal-to-noise ratio, enhancing faults detection, and reflectors sharpness and continuity. The Time Variant Frequency Filter (TVF) application is found to be better than applying the filter in one time window and provided more detailed frequency contents and in turn better reflectors identification. The Frequency/Wave Number (F/K) filter removed lot of the existing linear noises resulted in more clear images. The Frequency/Distance (F/X) Deconvolution enhanced the data coherency by removing the existing random noises. The Spiking Deconvolution showed remarkable enhancement in compressing the reflectors and increasing their continuity. The reprocessed data interpretation of Abu Roash C member in Nader Filed, Shushan basin, North Western Desert, Egypt, comparing to the original, showed better mapping and faults detection, which highlighted new potential structures that could act as hydrocarbon traps.

Autoregressive Based Vocal Tract Shape Modelling of Vowels in Speech Processing

In the present research article, which concentrates on the estimation of the vocal tract shape for the five vowels of Indian English in the current growing interest in the field of speech processing, with practical limitation of the data that can be collected and analysed. Speakers without the degree of control on articulators cannot produce the desired data. Here, we aim to design an approximate phonatory model by improving the present approach for acoustic calculation with a fully aerodynamic simulation, explicitly accounting for the propagation of sound along the tract, generating patterns of movement of simulated vocal tract articulators, and specifying the temporal relations among dynamically defined gestures that lead to a time-varying vocal tract filter function, and an acoustic waveform. The principle of phonetic distinctiveness and formants frequency spread is one of the crucial parameters that are measured and compared on intra, inter-speaker in actual speaker identification & forensic applications.