Linear Filter Research Articles

Generalised performance of neural network controllers for feedforward active noise control of nonlinear systems

Advances in digital technologies have allowed for the development of complex active noise and vibration control solutions that have been utilised in a wide range of applications. Such control systems are commonly designed using linear filters, which cannot fully capture the dynamics of nonlinear systems. To overcome such issues, it has been shown that replacing linear controllers with Neural Networks (NNs) can improve control performance in the presence of nonlinearities. Many real systems are subject to non-stationary disturbances where the magnitude of the system excitation time dependent. However, within the literature, the performance of single NN controllers across different excitation levels has not been thoroughly explored. In this paper, a method of training Multilayer Perceptrons (MLPs) for single-input-single-output (SISO) feedforward acoustic noise control is presented. In a simple time-discrete simulation, the performance of the trained NNs is investigated for different excitation levels. The effects of the properties of the training data and NN controller on generalised performance are explored. It is demonstrated that the generalised control performance of the MLP controllers falls as the range of magnitudes included in the training data is increased, and that this performance can be recovered by increasing the number of hidden nodes within the controller.

Prescribed Fixed-Time Control for Constrained Uncertain Nonlinear Cyber-Physical Systems Against Deception Attacks.

In this note, a novel prescribed fixed-time adaptive tracking control scheme is developed to cope with the fixed-time tracking control issue for a category of constrained MIMO nonlinear cyber-physical systems (CPSs) with exogenous perturbations, which suffer from deception attacks started in controller-actuator (C-A) channel. Distinguished from the conservative dynamic surface control (DSC) schemes with a linear filter, a novel nonlinear filter is designed in our strategy, which can tackle the intrinsic issue of explosion of computational complexity and promote the system performance. Besides, a new barrier Lyapunov function (BLF) is designed to ulteriorly enhance the tracking performance on the basis of the prescribed performance function (PPF) approach. Prominently, the proposed control strategy could accommodate the exogenous interferences and deception attacks simultaneously. Furthermore, we have substantiated that the developed approach can not only make certain that all the tracking errors of the resulting closed-loop system, including output tracking errors and virtual tracking errors, enter a prespecified small region near equilibrium point with fixed-time convergence rate, but also guarantee them obey the corresponding constraints throughout the entire control operation, where the regulation time and the tracking accuracy level keep prior known and could be prespecified arbitrarily. Finally, the validity and effectiveness of the proposed control scheme are illustrated through a representative application instance.

Object Tracking with Sensor Fusion – An Interactive Learning Tool

Body tracking plays a key role in autonomous navigation applications. Behavior that resists inertia can be modelled as a dynamical system, wherein the kinematic component is constituted by the action of motion. Such a system may then be subjected to estimation algorithms and control laws formulated by systems theory, according to the specific problem domain for which it is modelled. This paper presents a detailed comparison of three main statistical algorithms for estimating dynamical system parameters: the linear, extended, and unscented Kalman filters. The body motion is intercepted by sensor fusion. To facilitate visual validation and concretization of the theoretical notions presented, a two-dimensional (2D) game-like graphical application has been developed to enhance user comprehension.

An efficient implementation for linear convolution with reduced latency in FPGA

AbstractA recently developed linear convolution filter based on Hirschman theory has shown its advantage in saving computations compared with other convolution filters. Here, the Hirschman convolution filter is improved with the use of the split‐radix algorithm and explore its latency‐reduced advantage for the first time. A comparison of hardware resources in Field Programmable Gate Array (FPGA) between the proposed Hirschman‐based filter and other convolution filters is presented. Simulation results indicate that the split‐radix Hirschman convolution filter achieves a promising reduction in latency by 18.15% on average with an acceptable power consumption rise by about 3.05%. In the case of a device capacity limited implementation, the proposed Hirschman convolution filter is still computationally attractive as it uses a small‐size originator function instead of larger Fourier transform.

On the existence of KKL observers with nonlinear contracting dynamics

KKL (Kazantzis-Kravaris/Luenberger) observers are based on the idea of immersing a given nonlinear system into a target system that is a linear stable filter of the measured output. In the present paper, we extend this theory by allowing this target system to be a nonlinear contracting filter of the output. We prove, under a differential observability condition, the existence of these new KKL observers. We motivate their introduction by showing numerically the possibility of combining convergence speed and robustness to noise, unlike what is known for linear filtering.

Frequency-domain distributed multichannel wiener filtering speech enhancement algorithm

A frequency-domain distributed microphone multi-channel Wiener filter speech enhancement algorithm is proposed in this paper. In this paper, the distributed microphone speech model is considered. First, the speech signal in the time domain is converted into the speech signal in the frequency domain by the discrete Fourier transform method. Then, the unconstrained minimization problem of the noise reduction and speech distortion of the complex linear filter in the frequency domain is established. Simulation results show that the proposed algorithm is superior to some existing multi-channel speech enhancement algorithms.

Research of adaptive algorithms of recurrent filtration at processing data from GNSS when radio signals lose from navigation satellites

Algorithms for linear and adaptive Kalman filtering of navigation data received from satellite navigation systems are considered. A comparison of the Kalman linear filter algorithms, the Yazvinsky adaptive filter, the adaptive filter with feedback on the updated sequence and the adaptive filter without a priori information about the noise covariance matrices is made. The algorithms are tested in a practical experiment — navigation information from GNSS is obtained by using a GNSS receiver installed on a car moving in an urban environment. The comparative analysis results of the algorithms usage for adaptive modifications of the Kalman filter are presented. The results showed that the Yazvinsky filter works worse when radio signals from navigation satellites disappear, and the adaptive filter algorithm without a priori information about the noise covariance matrices does not actually filter the data. Keywords GNSS, navigation information, Kalman filter, Yazvinsky filter, adaptive Kalman filter

Decentralized Dynamic Event-Triggered Output Feedback Adaptive Fixed-Time Funnel Control for Interconnection Nonlinear systems.

A decentralized dynamic event-triggered output feedback adaptive fixed-time (DDETOFAFxT) funnel controller is described for a class of interconnected nonlinear systems (INSs). A novel dynamic event-triggered mechanism is designed, which includes a triggering control input, fixed threshold, decreasing function of tracking error, and a dynamic variable. To obtain the unknown states, a decentralized linear filter is designed. By introducing a prescribed funnel and using an adding a power integrator technique and a neural network method, a DDETOFAFxT funnel controller is designed to obtain better tracking performance and effectively alleviate the computational burden. Furthermore, it is ensured that the tracking error falls into a preset performance funnel. A simulation example is presented to demonstrate the availability of the designed control scheme.

Toward Automated Gust Load Alleviation Control Design via Discrete Gust Impulse Filters

In the context of active gust- and turbulence-load alleviation function design, control designers optimize the response of the closed-loop system to satisfy the relevant certification specifications (e.g., European Union Aviation Safety Agency (EASA) CS-25.341 and Federal Aviation Regulations (FAR) Part 25, Sec. 341). These include time-domain criteria specifying the peak response to discrete gusts as well as frequency-domain continuous-turbulence criteria. Both types of criteria cannot be easily considered simultaneously with commonly available control design software. A frequency-domain formulation of 1−cosine discrete gust criteria based on a low-order, parameterized linear filter is proposed and demonstrated on a publicly available gust load alleviation benchmark. One example demonstrates its application in combination with continuous-turbulence specifications, and in principle, it can also be combined with other relevant frequency-domain specifications, such as passenger comfort metrics. This technique is shown to work well, producing the desired results with a simple, systematic design procedure, and should help reduce the time and effort required to obtain satisfactory control specifications.

Normalization by orientation-tuned surround in human V1-V3.

An influential account of neuronal responses in primary visual cortex is the normalized energy model. This model is often implemented as a multi-stage computation. The first stage is linear filtering. The second stage is the extraction of contrast energy, whereby a complex cell computes the squared and summed outputs of a pair of the linear filters in quadrature phase. The third stage is normalization, in which a local population of complex cells mutually inhibit one another. Because the population includes cells tuned to a range of orientations and spatial frequencies, the result is that the responses are effectively normalized by the local stimulus contrast. Here, using evidence from human functional MRI, we show that the classical model fails to account for the relative responses to two classes of stimuli: straight, parallel, band-passed contours (gratings), and curved, band-passed contours (snakes). The snakes elicit fMRI responses that are about twice as large as the gratings, yet a traditional divisive normalization model predicts responses that are about the same. Motivated by these observations and others from the literature, we implement a divisive normalization model in which cells matched in orientation tuning ("tuned normalization") preferentially inhibit each other. We first show that this model accounts for differential responses to these two classes of stimuli. We then show that the model successfully generalizes to other band-pass textures, both in V1 and in extrastriate cortex (V2 and V3). We conclude that even in primary visual cortex, complex features of images such as the degree of heterogeneity, can have large effects on neural responses.

Increasing the energy efficiency of a fishing vessel by estimating the motion parameters of a towed object

The optimal mode of towing fishing gear has a direct impact on the energy efficiency of the fishing vessel. To build the combined traction characteristics of the vessel and trawl systems, data are needed on the nominal moments on the winch and engine drum, as well as the traction force in the warp. Estimation of the movement parameters of the towed object will make it possible to haul warps without overloading the trawl winch and the main engine. The paper proposes a block diagram of the algorithm for estimating the motion parameters of a trawl system. Processing of the current measurements of the warp tension in real time is carried out by digital signal processing methods based on the constructed optimal linear filter, which will be used to obtain the tension on the winch without taking into account the noise that occurs both as a result of the operation of the equipment and under the influence of the random nature of the warp tension process itself. and external hydrometeorological factors. Estimation and prediction of dynamic parameters is necessary for the development of an automated control system for a ship's winch, regardless of its purpose. Therefore, the results presented in this paper can be used in solving problems of automation of lifting equipment operating in non-stationary conditions.

Filtering-based gradient joint identification algorithms for nonlinear fractional-order models with colored noises

The main focus is to realize the on-line parameter estimation of the nonlinear fractional-order model with colored noises in this paper. Firstly, an auxiliary model gradient descent algorithm is derived to synchronously produce the estimates of the parameters, including the fractional-order. In order to decrease the noise interference, the filtering-based gradient descent estimation framework by constructing a linear filter provides a feasible method. Furthermore, the forgetting factor is introduced to the proposed filtering-based algorithm for the sake of improving the convergence rate. Comparative simulation results demonstrate the proposed effectiveness and high approximation accuracy of the proposed algorithms.

Strongly robust computational ghost imaging based on nearest neighbor filtering

Computational ghost imaging is a new optical imaging technique that obtains a reconstructed image of a target from a set of illumination patterns, but the quality of the reconstructed image is affected by ambient light noise. In this paper, two nearest neighbor filtered illumination patterns generated based on nonlinear noise filtering algorithm are proposed, which can effectively reduce the effect of ambient light in ghost imaging. When additional noise is added, compared with the reconstructed images of random illumination pattern, Hadamard illumination pattern and linear filter illumination pattern, the SNR of the reconstructed images of two different neighbor filtering illumination patterns is increased by 470 %, 1863 %, 75 % and 517 %, 2022 %, 90 %, respectively. In simulated noise environments, neighbor filtering illumination patterns reduce most of the effects of noise and results in high quality reconstructed images for a variety of target objects. In the experiments, these new illumination patterns has a strong robustness, and the reconstructed images obtained are sharper and contain more detailed information than random illumination pattern, Hadamard illumination pattern, and linearly filtered illumination pattern. Nearest neighbor filtering illumination patterns apply the nonlinear filtering method to computational ghost imaging and obtains excellent imaging results, which provides a new denoising idea for computational ghost imaging.

Brain-inspired GCN: Modularity-based Siamese simple graph convolutional networks

In graph representation learning, Graph Convolutional Networks (GCNs) and their variants have received much attention. However, GCNs encounter oversmoothing as the models get deeper, limiting their ability to aggregate node representations within high-order neighborhood. Inspired by the modular structure of the brain network, we propose Modularity-based Siamese Simple Graph Convolution (MS-SGC), a Siamese network architecture that incorporates the characteristics of brain modular structure into graph convolutional networks. Spectral clustering is leveraged to detect the modular structure in the graph, and then the weight of cross edges between modules is lowered. Siamese network is adopted to combine the modularity-preserved graph representation with the original graph representation, improving classification performance and reducing oversmoothing. Furthermore, a graph convolution method that functions as a linear low-pass graph filter is elaborated through spectral analysis to preserve the similarity of nodes within the same module and further alleviate the oversmoothing problem. We validate the effectiveness of MS-SGC on citation networks and extend our experimental analysis to various downstream tasks. Extensive experiments demonstrate that MS-SGC outperforms state-of-the-arts while reducing the time and computation complexity for node classification. Moreover, MS-SGC achieves competitive performance compared to other state-of-the-arts in node clustering, community prediction and text classification tasks.

Transverse mode distribution in multimode diode-pumped Raman fiber laser

Raman lasers based on multimode graded-index fibers may generate high-quality (M2∼2) Stokes beams when pumped by highly multimode (M2>30) laser diodes. Here we, examine, both experimentally and theoretically, the energy distribution of the output Stokes beam across the principal quantum mode number n in a bent multimode fiber operating well above the Raman threshold. In contrast to Kerr spatial beam cleaning, leading to a Rayleigh–Jeans mode power distribution, in a multimode Raman fiber laser, we find that the output mode powers approach an exponential distribution. We introduce a coupled-mode equations model, including random linear coupling between neighboring mode groups, and obtain a good agreement between numerical simulations and experimental results. The model shows that, for typical mode coupling coefficients, the randomization of the mode power distribution is compensated by both nonlinear (Raman and Kerr) effects and linear filtering from the fs-inscribed fiber Bragg grating, both acting on the Stokes beam over successive round trips. When random coupling becomes the dominating factor, the mode power distribution of the Stokes beam tends to equipartition, similar to what is observed with large-size highly multimode beams of low intensity (and nonlinearity) in the absence of any filtering.

Robust multi-scale weighting-based edge-smoothing filter for single image dehazing

The guided image filter (GIF) and weighted guided image filter (WGIF) are local linear model-based good edge-preserving filters. However, due to fixed regularization parameter, they suffer from halo artifacts (morphological artifacts) in the sharp regions. To overcome this issue, a robust multi-scale weighting-based edge-smoothing filter (RMWEF) for single image dehazing is proposed in this paper. It removes morphological artifacts and over-smoothness strongly and preserves edge information precisely in both flat and sharp regions. The proposed dehaze method has four-steps. First, initial transmission map and atmospheric map are estimated by using a novel dark channel prior (DCP) method. Then, the morphological artifacts of initial transmission map are reduced by using non-local haze line averaging (NL-HLA) method. In the third step, transmission map is refined by using the proposed RMWEF. Finally, the haze free image is restored. Theoretical and experimental analysis proves that the proposed algorithm produce effective dehaze results quicker than the existing methods.

Beamforming: a spatial de-noising approach for civil structural health monitoring

In the civil structural health monitoring fields, monitored data suffer from noise and sensor faults. In practice, redundant sensors are usually deployed to monitor structural condition to obtain more accurate and robust information. This paper proposes a beamforming-based spatial filtering method to improve the data quality by using the information redundancy within sensor networks. Data pre-processing is first implemented, including missing data imputation and thermal response separation. Subsequently, short-term Fourier transform is used to transform the measured time sequences into time–frequency domain to obtain more useful features. Finally, signals in the time and frequency domain are processed using the beamforming algorithm. In the beamformers, a linear filter is applied to suppress noise signals, which is formulated as a constrained optimization problem. Herein, interior point algorithm is used to optimize the allocation of the linear filter, wherein the objective function is to minimize the power of the noise component at the beamformer output. The effectiveness of the proposed method is verified by using signals from strain gauges installed on steel deck plates of the 3rd Nanjing Yangtze River Bridge. Results through the case study show that signals after spatial filtering have a satisfactory de-noising, which indicates the effectiveness of the proposed beamforming algorithm. We believe that the proposed beamforming algorithm has substantial potential applications, such as providing high quality data source for further investigations.

Large-area ultracompact pixelated aluminum-wire-grid-based metamaterials for Vis-NIR full-Stokes polarization imaging

The study of pixelated metamaterials that integrate both the functions of linear and circular polarization filters is rapidly growing due to the need for full-Stokes polarization imaging. However, there is a lack of large-area, ultracompact pixelated full-Stokes metamaterials with excellent performance, especially circular polarization filters with a high extinction ratio, a broad operating bandwidth, and a low-cost, high-quality, efficient manufacturing process, which limits the practical applications of pixelated full-Stokes metamaterials. In this study, we propose a universal design and fabrication scheme for large-area, ultracompact pixelated aluminum wire-grid-based metamaterials used in Vis-NIR full-Stokes polarization imaging. The aluminum wire-grid was designed as a linear polarization filter with an average linear polarization extinction ratio of 36,000 and a circular polarization filter with an average circular polarization extinction ratio of 110 in Vis-NIR. A large-area, ultracompact 320×320 pixelated aluminum wire-grid-based full-Stokes metamaterial was fabricated using nanoimprint lithography and nano transfer printing with the advantages of low cost and high efficiency. This metamaterial was used to achieve full-Stokes polarization imaging with errors within 8.77%, 12.58%, 14.04%, and 25.96% for Stokes parameters S0, S1, S2, and S3, respectively. The inversion errors of the compensated Stokes parameters can be reduced to 0.21%, 0.21%, 0.42%, and 1.96%, respectively.

Fuzzy EKF-Based Intrusion Detection and Accurate State Estimation of Interconnected DC MGs With CPLs

With the advancement of digital technologies in complex and large-scale power systems, the issue of cyber-attack detection has become of paramount importance to maintain system reliability and performance. In this regard, accurate state estimation plays a key role and leads to proper supervisory decision-making. To detect cyber-attacks in direct current microgrids (DC MGs), a new centralized attack detection system based on the Kalman filter and clustering approach is proposed. On the other hand, appearing nonlinear loads such as constant power loads (CPLs) make the overall system behavior nonlinear and conventional linear Kalman filters ineffective. So, in this paper, an extended Kalman filter (EKF) is utilized. By constructing several output vectors and parallel EKFs, local state estimations are achieved. They are then used to detect attacks based on the residuals and localize them based on the fuzzy clustering approach. The local healthy estimations are aggregated to compute global estimations with low noise. The simulation results on a five-area interconnected DC MG subjected to data integrity and denial-of-service (DoS) attacks show that the proposed approach can detect attacks, evaluate how many attacks occur in the case of multiple attacks, localize the attacks, and obtain accurate state estimations.

A Low-Cost Relative Positioning Method for UAV/UGV Coordinated Heterogeneous System Based on Visual-Lidar Fusion

Unmanned Ground Vehicles (UGVs) and Unmanned Aerial Vehicles (UAVs) are commonly used for various purposes, and their cooperative systems have been developed to enhance their capabilities. However, tracking and interacting with dynamic UAVs poses several challenges, including limitations of traditional radar and visual systems, and the need for the real-time monitoring of UAV positions. To address these challenges, a low-cost method that uses LiDAR (Light Detection and Ranging) and RGB-D cameras to detect and track UAVs in real time has been proposed. This method relies on a learning model and a linear Kalman filter, and has demonstrated satisfactory estimation accuracy using only CPU (Central Processing Unit)- in GPS (Global Positioning System)-denied environments without any prior information.