Linear Filter Research Articles

Wrinkle Detection in Carbon Fiber-Reinforced Polymers Using Linear Phase FIR-Filtered Ultrasonic Array Data

Carbon fiber-reinforced polymers (CFRP) are extensively used in aerospace applications. Out-of-plane wrinkles frequently occur in aerospace CFRP parts that are commonly large and complex. Wrinkles acting as failure initiators severely damage the mechanical performance of CFRP parts. Wrinkles have no significant acoustic impedance mismatch, reflecting weak echoes. The total focusing method (TFM) using weak reflection signals is vulnerable to noise, so our primary work is to design discrete-time filters to relieve the noise interference. Wrinkles in CFRP composites are geometric defects, and their direct detection requires high spatial precision. The TFM method is a time-domain delay-and-sum algorithm, and it requires that the time information of filtered signals has no change or can be corrected. A linear phase filter can avoid phase distortion, and its filtered signal can be corrected by shifting a constant time. We first propose a wrinkle detection method using linear phase FIR-filtered ultrasonic array data. Linear phase filters almost do not affect the wrinkle geometry of detection results and can relieve noise-induced dislocation. Four filters with different bandwidths have been designed and applied for wrinkle detection. The 2 MHz bandwidth filter is recommended as an optimum choice.

DNoiseNet: Deep learning-based feedback active noise control in various noisy environments

The use of active noise control/cancelation (ANC) has increased because of the availability of efficient circuits and computational power. However, most ANC systems are based on traditional linear filters with limited efficiency due to the highly nonlinear and nonstationary nature of various noises. This paper proposes an advanced deep learning–based feedback ANC named DNoiseNet that overcomes the limitations of traditional ANCs and addresses primary and secondary path effects, including acoustic delay. Mathematical operators (i.e., atrous convolution, pointwise convolution, nonlinear activation filters, and recurrent neural networks) learn multilevel temporal features under different noises in various environments, such as construction sites, vehicle interiors, and airplane cockpits. Due to the nature of feedback control using a single error sensor, an estimation of the reference noise signal must be regenerated. In this paper, a multilayer perceptron (MLP) neural network–based secondary path estimator is also proposed to improve the performance of DNoiseNet. In extensive parametric and comparative studies, the DNoiseNet with the MLP secondary path estimator exhibited the best performance in root mean square error and noise attenuation metrics.

Analysis and Comparison of Image Enhancement Technique for Improving PSNR of Lung Images by Linear Contrast Enhancement Technique over Histogram Equalization Technique

Aim: The aim of this study is to compare and analyze linear contrast enhancement algorithm for lung image enhancement over novel histogram equalization technique. Material and Methods: In this research, different sources of lung images collected from the kaggle website were used. Samples were considered as (N=30) for linear contrast filter and (N=30) for novel histogram equalization technique with total sample size calculated using clinical.com. As a result the total number of samples was calculated to be 60. Using SPSS Software and a standard data set, the PSNR was obtained. Both linear contrast filter and novel histogram equalization technique image enhancement were implemented on lung images through Matlab coding and also PSNR values of each image were extracted. Then through SPSS software comparison and analysis has been made. Result: In the final output of image enhancement, novel histogram equalization technique shows better performance in improving PSNR of lung images than linear contrast filter. Comparison of PSNR values are done by independent sample test using IBM-SPSS software. There is a statistical difference between histogram technique and linear contrast filter. The novel histogram equalization technique showed higher results of PSNR (65.9197dB) with (p=0.04) in comparison with linear contrast filter (36.1190dB). Conclusion: Within this research study the histogram equalization image enhancement technique has greater PSNR value of lung images than in linear contrast enhancement technique.

Analysis and Comparison of Image Enhancement Techniques for Improving PSNR of Liver Images by Linear Contrast Algorithm over Median Filtering

Aim: The goal of this research is to reduce noise present in the liver images in order to enhance it using linear contrast and median filters. And also to analyze output of both the filters based on its Peak Signal to Noise Ratio (PSNR). Materials and Methods: The research includes two groups; each group has a sample size of 20. Grayscale medical images collected from the kaggle website were used in this research. Samples were considered as (N=20) for guided filter and (N=20) for fast bilateral filter with total sample size 40 calculated using clinicalc.com. For this study, the affected and normal liver images were collected from the Kaggle website. Then the linear filtering, median filtering algorithms were executed using Matlab software. Sample size was calculated using clinicalc.com, and the comparison analysis has been carried out through SPSS software. This research contains two groups, with a Gpower of 80 percent. The performance of the novel median filter is evaluated and the performance measure PSNR is compared with the linear contrast filter. Result: Based on Matlab simulation results, the PSNR of novel median filters is 76.0355 and linear contrast filters have PSNR of 57.1785. From the statistical analysis, it is observed that the significant value of PSNR (Peak Signal to Noise Ratio) (0.409) and p>0.05. Conclusion: This study reveals that for the medical image enhancement purpose the novel median filter provides better PSNR than the linear contrast algorithm on ultrasound liver images.

Analysis and Comparison of Image Enhancement Techniques for Improving PSNR of Liver Image by Median Filtering over Wiener Filtering

Aim: The purpose of this study is to use median filters and wiener filters to minimize noise in liver images in order to improve them. In addition, the output of both filters was analyzed based on their Peak Signal to Noise Ratio (PSNR). Materials and Methods: The research includes two groups; each group has a sample size of 20. Grayscale medical images collected from the kaggle website were used in this research. Samples were considered as (N=20) for guided filter and (N=20) for fast bilateral filter with total sample size 40 calculated using clinicalc.com. Image enhancement is used to enhance the niceness of a picture for the visible notion of human beings. The kaggle website was used to collect data for this study. According to clinical.com, samples were considered as size 20 for PSNR ratio of image G power of 80%, and total sample size determined. Using matlab programming and a standard data set, the Linear filtering, Median filtering were computed. Results: According to Matlab simulation results, unique median filters have a PSNR of 48.1240, while wiener filters have a PSNR of 67.8360. Comparison of PSNR values are done by independent sample test using IBM-SPSS software. There is a statistical insignificant difference between both techniques. The significant value of PSNR (Peak Signal to Noise Ratio) (0.409) and p>0.05 was found in the statistical analysis. Conclusion: On ultrasound liver pictures, the innovative median filter gives greater PSNR than the wiener filter for medical image enhancing purposes, according to this study.

The Research on the Bandpass Filter for 5G N79 Band

With the continuous development of communication technology, 5G technology has gradually replaced the original 4G network and has been applied to all walks of life with its advantages of faster connection speed, lower delay, and larger capacity. The RF front-end market has also benefited from the prosperity of 5G, and filters are the most significant business segment in the RF front-end market. Filters are widely used in the 5G network technology hardware because it can significantly ensure the signal's integrity. The shield stripline bandpass filter and the high-resistance silicon-based bandpass filter using, which use Integrated passive devices (IPD) technology, are two ordinary filters on the market. This article will briefly outline the structure and performance of the two filters and use the comparison method to compare the advantages and disadvantages of the two filters. Finally, some reasonable solutions are proposed for improvement, such as adding a defective ground structure to the strip linear filter to reduce the insertion loss, replacing the material of the IPD filter with glass, and using active components instead of IPD to make the filter.

Shennong: A Python toolbox for audio speech features extraction.

We introduce Shennong, a Python toolbox and command-line utility for audio speech features extraction. It implements a wide range of well-established state-of-the-art algorithms: spectro-temporal filters such as Mel-Frequency Cepstral Filterbank or Predictive Linear Filters, pre-trained neural networks, pitch estimators, speaker normalization methods, and post-processing algorithms. Shennong is an open source, reliable and extensible framework built on top of the popular Kaldi speech processing library. The Python implementation makes it easy to use by non-technical users and integrates with third-party speech modeling and machine learning tools from the Python ecosystem. This paper describes the Shennong software architecture, its core components, and implemented algorithms. Then, three applications illustrate its use. We first present a benchmark of speech features extraction algorithms available in Shennong on a phone discrimination task. We then analyze the performances of a speaker normalization model as a function of the speech duration used for training. We finally compare pitch estimation algorithms on speech under various noise conditions.

The Development of Snapshot Multispectral Imaging Technology Based on Artificial Compound Eyes

In the present study, the advantages of multispectral imaging over hyperspectral imaging in real-time spectral imaging are briefly analyzed, and the advantages and disadvantages of snapshot spectral imaging and other spectral imaging technologies are briefly described. The technical characteristics of artificial compound eyes and multi-aperture imaging and the research significance of snapshot artificial compound eye multispectral imaging are also introduced. The classification and working principle of the snapshot artificial compound eye multispectral imaging system are briefly described. According to the realization method of the optical imaging system, the ACE snapshot multi-aperture multispectral imaging system is divided into plane and curved types. In the planar compound eye spectral imaging system, the technical progress of the multispectral imaging system based on the thin observation module by bound optics (TOMBO) architecture and the multispectral imaging system based on the linear variable spectral filter are introduced. At the same time, three curved multispectral imaging systems are introduced. Snapshot artificial compound eye multispectral imaging technology is also briefly analyzed and compared. The research results are helpful to comprehensively understand the research status of snapshot multispectral multi-aperture imaging technology based on artificial compound eyes and to lay the foundation for improving its comprehensive performance even further.

Towards single atom computing via high harmonic generation

The development of alternative platforms for computing has been a longstanding goal for physics, and represents a particularly pressing concern as conventional transistors approach the limit of miniaturization. A potential alternative paradigm is that of reservoir computing, which leverages unknown, but highly nonlinear transformations of input-data to perform computations. This has the advantage that many physical systems exhibit precisely the type of nonlinear input-output relationships necessary for them to function as reservoirs. Consequently, the quantum effects which obstruct the further development of silicon electronics become an advantage for a reservoir computer. Here we demonstrate that even the most basic constituents of matter–atoms–can act as a reservoir for computing where all input-output processing is optical, thanks to the phenomenon of High Harmonic Generation. A prototype single-atom computer for classification problems is proposed, where a classification model is mapped to an all-optical setup, with linear filters chosen to correspond to the trained model’s parameters. We numerically demonstrate that this ‘all-optical’ computer can successfully perform classification tasks, and does so with an accuracy that is strongly dependent on dynamical nonlinearities. This may pave the way for the development of petahertz information processing platforms.

Theoretical modeling and simulation of fiber Bragg grating sensor interrogator based on linear variable filter.

With the increasing frequency of aviation accidents in recent years, aircraft safety has received increasing attention. Aircraft operating condition detection is an important part of aviation safety. Fiber Bragg grating (FBG) sensors, with their excellent characteristics, enable online monitoring of aircraft operating conditions. However, the application of FBG sensors in aviation is currently limited because it is difficult for FBG sensor interrogators to meet the requirements of small size, light weight, and good vibration resistance in the aviation field. Therefore, this paper proposes a linear variable filter (LVF)-based FBG sensor interrogator to meet the requirements. An optical model of the interrogator is established. The parameters which determine the performances of the interrogator are analyzed and the design criteria are discussed. According to the requirements in the aviation field, the optical system of the interrogator is designed. The simulation results show that the LVF-based FBG sensor interrogation system has a bandwidth range of 90 nm (1505 nm-1595 nm), a resolution of 2 pm, and a capacity of 15 FBG sensors.

A linear Bayesian filter for input and state estimation of structural systems

AbstractThis paper proposes a linear recursive Bayesian filter for minimum variance unbiased joint input and state estimation of structural systems. Unlike the augmented Kalman filter (AKF), the proposed filter falls within the category of Bayesian filters in which unknown inputs are estimated without attributing any fictitious input model or statistics. Also, in contrast with the existing algorithms in the latter category, such as the Gillijns and De Moor Filters (GDFs), the developed filter applies to systems with and without direct feedthrough, in particular, systems with a rank‐deficient feedforward matrix. Because of the latter features, the filter is referred to as universal filter (UF) for convenience. The numerical examples show that the UF performs better than the AKF. Due to its structure, the UF does not require the tuning of the hyperparameters for inputs, and therefore the problematic instability of the AKF is not encountered in the case of a large modeling error variance of the input. For systems with direct feedthrough, the error and covariance propagation terms differ due to the distinct state space. Consequently, the UF can enhance estimations due to the well‐conditionedness of the relevant inversion problem. Moreover, the UF can deal with systems with rank‐deficient feedforward matrix where these systems are not covered by GDFs.

A 2.2 nW Analog Electrocardiogram Processor based on Stochastic Resonance Achieving a 99.94% QRS Complex Detection Sensitivity.

This paper presents an ultra-low power electrocardiogram (ECG) processor that can detect QRS-waves in real time as the data streams in. The processor performs out-of-band noise suppression via a linear filter, and in-band noise suppression via a nonlinear filter. The nonlinear filter also enhances the QRS-waves by facilitating stochastic resonance. The processor identifies the QRS-waves on noise-suppressed and enhanced recordings using a constant threshold detector. For energy-efficiency and compactness, the processor exploits current-mode analog signal processing techniques, which significantly reduces the design complexity when implementing the second-order dynamics of the nonlinear filter. The processor is designed and implemented in TSMC 65 nm CMOS technology. In terms of detection performance, the processor achieves an average F1 = 99.88% over the MIT-BIH Arrhythmia database and outperforms all previous ultra-low power ECG processors. The processor is the first that is validated against noisy ECG recordings of MIT-BIH NST and TELE databases, where it achieves better detection performances than most digital algorithms run on digital platforms. The design has a footprint of 0.08 mm2 and dissipates 2.2 nW when supplied by a single 1V supply, making it the first ultra-low power and real-time processor that facilitates stochastic resonance.

Event-triggered filter of positive semi-Markovian jump systems with Weibull distribution

This paper focuses on the event-triggered filter design of positive semi-Markovian jump systems with Weibull distribution. The considered systems are subject to random uncertainty and sensor fault. A linear programming-based filter design approach is proposed. First, an event-triggering condition is established by virtue of sensor output signals. Under the event-triggering condition, the filtering error system is transformed into an interval system. The positivity of the filtering error system is reached by considering the positivity of the lower bound of the interval system. By using a stochastic co-positive Lyapunov function, the filtering error system is stochastically stable with an ℓ1-gain performance by exploring the corresponding performance of the upper bound of the interval system. Then, additive and multiplicative models following the Bernoulli distribution are introduced to describe the random uncertainties of the systems. Consequently, the event-triggered filter is designed in terms of linear programming and a matrix decomposition approach. Moreover, the proposed filter design is developed for the systems subject to sensor faults. Finally, two examples are provided to illustrate the effectiveness of the proposed design.

The Geometry of Navigation Problems

While many works exploiting an existing Lie group structure have been proposed for state estimation, in particular the invariant extended Kalman filter (IEKF), few papers address the construction of a group structure that allows casting a given system into the framework of invariant filtering. In this article, we introduce a large class of systems encompassing most problems involving a navigating vehicle encountered in practice. For those systems we introduce a novel methodology that systematically provides a group structure for the state space, including vectors of the body frame such as biases. We use it to derive observers having properties akin to those of linear observers or filters. The proposed unifying and versatile framework encompasses all systems, where IEKF has proved successful, improves state-of-the art “imperfect” IEKF for inertial navigation with sensor biases, and allows addressing novel examples, like GNSS antenna lever arm estimation.

Superposition Signal Input Decoding for Lattice Reduction-Aided MIMO Receivers

This paper proposes a novel approach to low complexity soft input decoding for lattice reduction-aided MIMO receivers. The proposed approach feeds a soft input decoder with soft signals made from hard decision signals generated by using a lattice reduction-aided linear detector. The soft signal is a weighted-sum of some candidate vectors that are near by the hard decision signal coming out from the lattice reduction-aided linear detector. This paper proposes a technique to adjust the weight adapt to the channel for the higher transmission performance. Furthermore, we propose to introduce a coefficient that is used for the weights in order to enhance the transmission performance. The transmission performance is evaluated in a 4×4 MIMO channel. When a linear MMSE filter or a serial interference canceller is used as the linear detector, the proposed technique achieves about 1.0dB better transmission performance at the BER of 10-5 than the decoder fed with the hard decision signals. In addition, the low computational complexity of the proposed technique is quantitatively evaluated.

Limitations of FxLMS in Feedforward Active Vibration Control of a Nonlinear Two-Degree-of-Freedom System

Active control systems are often used to surmount the challenges associated with passive noise and vibration control measures to control low frequency disturbances, since they achieve control without the application of large or heavy control treatments. Historically, linear active control strategies have been used in feedforward control systems to drive the control source to minimise the signal measured at the error sensor. Amongst the various control algorithms available, the Filtered-reference Least Mean Squares (FxLMS) algorithm has become extremely popular in the last few decades due to its relatively good performance and high level of robustness, as well as simplicity in both design and application. However, when the system under control contains nonlinearities, either in the primary or secondary paths, the performance of the FxLMS algorithm can degrade dramatically. This paper explores the performance limitations of the FxLMS algorithm when applied to the control of a two degree of freedom mass-spring-damper system with linear and cubic nonlinear stiffness terms. The aim of this study is to improve understanding of and inspire better design of nonlinear control systems. The statistical uncertainty present in the linear plant model and control filter is discussed, as well as the effect of this on control performance and control effort. The effect of nonlinearity on the maximum convergence parameter of the algorithm is also discussed.

LQG control for hydrodynamic compensation on large floating wind turbines

This work proposes a novel Linear Quadratic Gaussian (LQG)-based blade pitch control method for floating offshore wind turbines, in which a state-space model of the turbine and water hydrodynamics is included in the LQG design. The actuation considered is collective blade pitch control with the objective of generator power stabilisation and platform motion reduction. A linear Kalman filter is used to estimate un-measurable states relating to wave excitation and radiation through measurements of generator speed, platform pitch, and wind disturbance. Controller design models were validated with the full order nonlinear model under various testing conditions. The new controller design is tested on a nonlinear high-fidelity simulation model of the 15 Mega-Watt (MW) floating semi-submersible wind turbine. In simulations with realistic stochastic wind and wave disturbances, the new controller achieves 32% lower generator speed Root Mean Square Error (RMSE) and 16% lower platform pitch RMSE compared to a standard LQG controller that does not include hydrodynamic states, for equivalent levels of pitch actuation and with a 2°/sec rate limit on pitch. The inclusion of hydrodynamics in the controller design not only reduced platform pitching fluctuation, but also had a strong effect of hub-height factors such as the generator speed.

Parameter Optimization for an Accurate Swept-Sine Identification Procedure of Nonlinear Systems

Modeling the nonlinearity of a system is of primary importance both for optimizing its design and for controlling the behavior of physical systems operating with a wide dynamic range of input values, for which the linearity hypothesis may not be sufficient. To become of practical use, the identification of nonlinear models must be accurate and computationally efficient. For these reasons, in recent years, among the numerous models of nonlinear systems that have been proposed in the technical literature, the Hammerstein model has been widely applied as a consequence of the proposal of a new pattern identification technique based on pulse compression, which makes the identification of the model very accurate in numerous applications for which it has been adopted. Hammerstein model identification of a nonlinear system requires characterization of the linear filters present on the different branches of the model. These linear filters, which constitute the parameters of the model to be identified, must be considered with respect to their trends over time or, equivalently, in their frequency trends, as amplitude and phase responses. The identification can be considered accurate if the trends obtained for each filter adequately characterize it for the entire frequency range to which that specific filter is subjected in the normal operation of the system to be identified. This work focuses on this aspect, i.e., on the adequacy of the frequency range for which the filter is identified and on how to obtain correct identification in the entire frequency range of interest. The identification procedure based on exponential swept-sine signals defines these filters in the time domain by making use of intermediate functions that are related to the impulse responses of the model filters through a linear transformation. In this paper, we analyze, in detail, the roles of the bandwidths of both the excitation signal and the matched filter, which are the basis of the procedure, we verify the assumptions made about the amplitudes of their frequency bands, and we propose criteria for defining the bandwidths in order to maximize accuracy in model identification. The experiment performed makes it possible to verify that the proposed procedure avoids possible limitations and significantly improves the quality of the identification results, both if the description is made in the time domain and in the frequency domain.

The continuous fractional Bessel wavelet transform and its applications

In this paper, the fractional Bessel wavelet transform is introduced by exploiting the theory of the fractional Hankel transform and the boundedness of the fractional Bessel wavelet transform obtained. Time invariant linear filter associated with the fractional Hankel transform is investigated and its various properties are obtained. In the present paper, authors also expressed time-invariant linear filters in the form of the fractional Bessel wavelet transform and applications of the aforesaid filters in the integral equation are given.

Combining the global trends of DFA or CDFA of different orders

When dealing with the detrended fluctuation analysis (DFA) and its variants such as the higher-order DFA and the continuous DFA (CDFA), removing the mean, integrating the signal and detrending it amount to applying an equivalent linear filter characterized by its frequency response when processing a wide-sense stationary random process. By studying the frequency response of each variant, one can better understand its behavior. Thus, in this paper, we first compare the frequency responses of two DFAs whose orders are different. Then, by deriving the higher-order CDFA using Lagrange multipliers, we can show how the frequency responses of the equivalent filters of the CDFA and the DFA are related to each other. Finally, we propose to combine the global trends obtained with the DFA or the CDFA of different orders to derive a new variant. Once again, the behavior is studied using the frequency response of the equivalent filter. Illustrations and comments on ARFIMA processes and Weierstrass functions are also given to evaluate their long range dependence.