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The objective of this paper is to first present some issues with impulse invariance filter (IIF) design during the design of digital infinite impulse response (IIR) filters. Engineers are often confused about some inconsistent observations. For instance, if the impulse response of a digital filter is designed using the impulse invariance procedure, then the analog and digital filters' frequency and step responses are very different. Two simple remedies are presented in this paper. One is a post-processing approach that scales the frequency and step responses of the digital filter by the sampling interval T. Another one is a pre-processing approach that scales the impulse response of the analog filter by T. However, even after these remedies, there is still a steady state bias in the step response of the digital filter for certain cases where there is discontinuity in the analog impulse response. A recommendation is to include a correction term in the digital filter. After that, the steady state bias in the digital filter is then suppressed. Moreover, the MATLAB R2021a command "impinvar" needs to also include a correction term so that the frequency and step responses can be more accurate in the digital filter. Two comparative studies were carried out to compare the improved IIF filter with three competing digital IIR filter design methods. Although the above issues and improvements have been proposed by researchers in the past, many researchers, engineers, and students are still not aware of them. This paper provides a fresh revisit of these issues and improvements by using figures, equations, and examples. Proper credits are also given to those researchers who first pointed out those issues and improvements. It is hoped that through an open access journal, future rediscovery of issues and improvements in IIF can be prevented. [ABSTRACT FROM AUTHOR]

This paper focuses on the numerical study of spherical particle sedimentation, taking into account hydrodynamic interactions. Infinite impulse response (IIR) digital filters, specially tailored to solve the sedimentation dynamics, were used in the present study to numerically solve the coupled ordinary differential equations with the time-dependent coefficients of the problem. Hydrodynamic interactions are modeled using the Rotne–Prager–Yamakawa (RPY) approximation, to which a correction is made to better account for short-range interactions. In order to validate both the proposed numerical resolution method and the RPY correction, this paper begins with the study of two interacting spherical particle sedimentation methods. Comparisons with previously published analytical or numerical results confirm the relevance of the present approach. [ABSTRACT FROM AUTHOR]

The goal of this study is to explore the effectiveness of applying multi-objective particle swarm optimization (MOPSO) algorithms in the design of infinite impulse response (IIR) filters. Given the widespread application of IIR filters in digital signal processing, the precision of their design plays a significant role in the system’s performance. Traditional design methods often encounter the problem of local optima, which limits further enhancement of the filter’s performance. This research proposes a method based on multi-objective particle swarm optimization algorithms, aiming not just to find the local optima but to identify the optimal global design parameters for the filters. The design methodology section will provide a detailed introduction to the application of multi-objective particle swarm optimization algorithms in the IIR filter design process, including particle initialization, velocity and position updates, and the definition of objective functions. Through multiple experiments using Butterworth and Chebyshev Type I filters as prototypes, as well as examining the differences in the performance among these filters in low-pass, high-pass, and band-pass configurations, this study compares their efficiencies. The minimum mean square error (MMSE) of this study reached 1.83, the mean error (ME) reached 2.34, and the standard deviation (SD) reached 0.03, which is better than the references. In summary, this research demonstrates that multi-objective particle swarm optimization algorithms are an effective and practical approach in the design of IIR filters.

The designs of first- and second-order digital low-pass filters with infinite impulse response (IIR) are presented in this letter, utilizing a meta-heuristic optimization technique. Firstly, the analog transfer functions of the first and second- order filters are considered, followed by the application of an L1-norm-based multi-verse optimization algorithm to directly emulate their magnitude-frequency response in the digital domain. The obtained magnitude-frequency response shows superior matching with the analog counterpart for different cut-off frequencies of the first- and second-order filters, as well as varying quality factors for the second-order filter. In comparison to the filter's magnitude-frequency response obtained through traditional bilinear transform and advanced operators, the proposed technique accurately manifests the analog magnitude-frequency response in the digital domain. [ABSTRACT FROM AUTHOR]

A new numerical method for solving Volterra linear convolution integral equations (CVIEs) of the second kind is presented in this work. This new approach uses first-order infinite impulse response digital filters method (IIRFM). Three convolutive kernels were analyzed, the unit kernel and two singular kernels: the logarithmic and generalized Abel kernels. The IIRFM is based on the combined use of the Laplace transformation, a first-order decomposition, and a bilinear transformation. This approach often leads to simple analytical expressions of the approximate solutions, enabling efficient numerical calculation, even using single-precision floating-point numbers. When compared with the method of homotopic perturbations with Laplace transformation (HPM-L), the IIRFM approach does not present, in linear cases, the convergence difficulties inherent to iterative approaches. Unlike most solution methods based on the Laplace transform, the IIRFM has the dual advantage of not requiring the calculation of the Laplace transform of the source function, and of not requiring the systematic calculation of inverse Laplace transforms. [ABSTRACT FROM AUTHOR]

The Electrocardiogram (ECG) records are crucial for predicting heart diseases and evaluating patient's health conditions. ECG signals provide essential peak values that reflect reliable health information. Analyzing ECG signals is a fundamental technique for computerized prediction with advancements in Very Large-Scale Integration (VLSI) technology and significantly impacts in biomedical signal processing. VLSI advancements focus on high-speed circuit functionality while minimizing power consumption and area occupancy. In ECG signal denoising, digital filters like Infinite Impulse Response (IIR) and Finite Impulse Response (FIR) are commonly used. The FIR filters are preferred for their higher-order performance and stability over IIR filters, especially in real-time applications. The Modified FIR (MFIR) blocks were reconstructed using the optimized adder-multiplier block for better noise reduction performance. The MIT-BIT database is used as reference where the noises are filtered by the MFIR based on Optimized Kogge Stone Adder (OKSA). Features are extracted and analyzed using Discrete wavelet transform (DWT) and Cross Correlation (CC). At this modern era, Hybrid methods of Machine Learning (HMLM) methods are preferred because of their combined performance which is better than non-fused methods. The accuracy of the Hybrid Neural Network (HNN) model reached 92.3%, surpassing other models such as Generalized Sequential Neural Networks (GSNN), Artificial Neural Networks (ANN), Support Vector Machine with linear kernel (SVM linear), and Support Vector Machine with Radial Basis Function kernel (SVM RBF) by margins of 3.3%, 5.3%, 23.3%, and 24.3%, respectively. While the precision of the HNN is 91.1%, it was slightly lower than GSNN and ANN but higher than both SVM linear and SVM -RBF. The HNN with various features are incorporated to improve the ECG classification. The accuracy of the HNN is switched to 95.99% when the DWT and CC are combined. Also, it improvises other parameters such as precision 93.88%, recall is 0.94, F1 score is 0.88, Kappa is 0.89, kurtosis is 1.54, skewness is 1.52 and error rate 0.076. These parameters are higher than recently developed models whose algorithms and methods accuracy is more than 90%. [ABSTRACT FROM AUTHOR]

This paper proposes fractional delay filter-based repetitive controllers (FDF-RCs) for grid-tied converters. Finite impulse response filters and infinite impulse response filters are used to estimate the fractional-order delay filter in a conventional repetitive controller. To do this, both the Lagrange filter-based repetitive controller (LF-RC) and Thiran filter-based repetitive controller (TF-RC) are utilized as FDF-RCs to compensate a fractional delay. The operating principle, the ladder structure, and the performance analysis of FDF-RCs are addressed. The simulation and the experimental results on a single-phase grid-tied converter verify the harmonic suppression ability and reference-tracking performance of the proposed FDF-RC under the grid frequency variation. Moreover, the advantages of the TF-RC over the LF-RC are discussed by comparing and analyzing the simulation and experimental results. [ABSTRACT FROM AUTHOR]

This study aimed to investigate the effect of soil–structure interaction (SSI) on the inelastic displacement ratio of bridge structures subjected to near-fault pulse-like earthquake ground motions. SSI was modeled using a discrete-time recursive filter approach and frequency-dependent foundation–soil impedance functions. A total of 105 near-fault earthquake ground motion records were used for the analysis. The study also compared the soil–foundation–superstructure (SFS) system with a fixed-base system to explore the effects of spectral period normalization, strength reduction factor, earthquake magnitude, site-to-source distance, and peak ground velocity on the inelastic displacement ratio. The findings indicate that both the SFS and fixed-base systems have significant inelastic displacement demand under near-fault earthquakes, with values that can be 2–3 times higher than those predicted by ASCE-41-13, particularly in the short period range. Furthermore, as the structural period approaches the earthquake pulse period, the SFS system generally shows larger inelastic displacement ratios compared to the fixed-base system. The analytical results provide insight into the factors affecting the inelastic displacement ratio for near-fault pulse-like earthquakes. [ABSTRACT FROM AUTHOR]

Pre-processing of the photoplethysmography (PPG) signal plays an important role in the analysis of the pulse wave signal. The task of pre-processing is to remove noise from the PPG signal, as well as to transmit the signal without any distortions for further analysis. The integrity of the pulse waveform is essential since many cardiovascular parameters are calculated from it using morphological analysis. Digital filters with infinite impulse response (IIR) are widely used in the processing of PPG signals. However, such filters tend to change the pulse waveform. The aim of this work is to quantify the PPG signal distortions that occur during IIR filtering in order to select a most suitable filter and its parameters. To do this, we collected raw finger PPG signals from 20 healthy volunteers and processed them by 5 main digital IIR filters (Butterworth, Bessel, Elliptic, Chebyshev type I and type II) with varying parameters. The upper cutoff frequency varied from 2 to 10 Hz and the filter order—from 2nd to 6th. To assess distortions of the pulse waveform, we used the following indices: skewness signal quality index (SSQI), reflection index (RI) and ejection time compensated (ETc). It was found that a decrease in the upper cutoff frequency leads to damping of the dicrotic notch and a phase shift of the pulse wave signal. The minimal distortions of a PPG signal are observed when using Butterworth, Bessel and Elliptic filters of the 2nd order. Therefore, we can recommend these filters for use in applications aimed at morphological analysis of finger PPG waveforms of healthy subjects. [ABSTRACT FROM AUTHOR]

The uncertainty in the secondary path of active noise control (ANC) headphones affects the waterbed effect and stability of the feedback system. This study focuses on the uncertainty of the secondary path when real users wear headphones and proposes a new uncertainty constraint based on the measured results of the secondary path transfer function under different wearing conditions of a dummy head and limited subjects. This constraint and a cascaded second-order infinite impulse response filter with fixed coefficients are used to formulate a control strategic function, which is optimized using the Improved Grey Wolf Optimizer (IGWO) algorithm to obtain the optimal controller with better noise reduction performance. The proposed method and simulation model are validated based on the experimental test results. The results demonstrate that the safety factor and waterbed suppressing factor contained in the proposed uncertainty constraint ensure more stable noise reduction and effective suppression of the waterbed effect for new subjects without a priori data. [ABSTRACT FROM AUTHOR]

In the attitude calculation process of quadrotor, the traditional pre-filtering method has degraded the attitude solving accuracy due to incomplete denoising of accelerometer and gyroscope measurements. Therefore, a complementary filtered attitude solution method based on double filter preprocessing is proposed to address this problem. First, the signal decomposition, hard threshold denoising, and signal reconstruction of the accelerometer and gyroscope acquired data are performed using the Haar real-time wavelet filtering algorithm. The reconstructed signal is then filtered by the infinite impulse response (IIR) low-pass filtering algorithm to remove the residual high-frequency noise and complete the dual filtering preprocessing. Next, the gyroscope data is corrected with accelerometer data according to the complementary filtering algorithm. Finally, the corrected data are used to solve the quaternion and thus the attitude angle by combining the Longacurta method. The results show that the dual filtering preprocessing method can further reduce the noise in the measurements of accelerometer and gyroscope. The attitude angle results calculated by the proposed method in the static and dynamic hovering states of the four-rotor aircraft have a small degree of dispersion, which can effectively improve the accuracy of attitude calculation. [ABSTRACT FROM AUTHOR]

In this paper, a behavioral model for the variable gain amplifiers working in the liner region is proposed. An iterative least‐square algorithm for complex curve fitting is proposed to model the variable gain amplifiers as an infinite impulse response filter. The polynomial fittings are used to obtain the coefficient matrix for the gain response and phase response to the frequency at different control voltages. The relationship between gain and control voltage is fitted by a hyperbolic tangent function. The modeling flow is given in this paper. Comparing the frequency response of the behavioral model and circuit model, the simulation results are similar at the operating frequency. Finally, an application is given, which is used to compare the effect of the nonlinear phase system with two different band signals modulated by 4QAM. This application proves that the model can be used in the simulation of large‐scale systems. [ABSTRACT FROM AUTHOR]

The potential of filter algorithms in improving spectral model performance was evaluated. A total of 329 animal and vegetable oil samples were used to collect attenuated total reflectance – Fourier transform infrared (ATR-FTIR) spectra. Fisher discriminant analysis (FDA), support vector machine (SVM), decision tree (DT), K-nearest neighbor analysis (KNN), and multilayer perceptron neural network (MLP) were considered to build models. Three filter algorithms, finite length unit impulse response filter (FIR), infinite length impulse response filter (IIR) and wavelet transform (WT), were evaluated to enhance the performance of the models. The Morlet basis function was the most suitable wavelet transform, accurately classifying 90.3% of the training set and 94.4% of the test set. The MLP algorithms were demonstrated to be superior to the others. The best performance was obtained using low-pass or band-stop filters that provided 100% accuracy with the MLP model. The reported method is demonstrated to be affordable and easy-to-use in forensic analysis. [ABSTRACT FROM AUTHOR]

This paper aims to develop a high-performance method for designing Finite Impulse Response (FIR) filters that effectively control the over-frequency response parameters. The proposed approach involves designing an optimal FIR filter based on an Infinite Impulse Response (IIR) filter, considering various parameters such as pass and stop band ripples, stop band attenuation, transition bandwidth, and linear phase response. The advantages of the IIR filter's better frequency response are utilized in this design. The parameters of Butterworth and Chebyshev filters are optimized using Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO) algorithms, incorporating novel fitness functions based on different distance metrics. Visual examination of the filtered signals shows that the proposed algorithm reduces phase delay compared to traditional filters. Additionally, the performance of the designed filter is evaluated by decomposing a sinusoidal signal with four different frequencies into its sub-bands. The numerical results demonstrate that the proposed approach achieves more precise decomposition compared to commonly used traditional filters. [ABSTRACT FROM AUTHOR]

In the field of science and engineering, significant attention can be observed recently for system identification as a complex optimization problem. As the infinite impulse response (IIR) models can achieve more accurate models of physical plants for real-world applications, they are mostly preferred over finite impulse response models. Despite the latter advantage of the IIR structures, it is not straightforward to minimize the related cost functions as they tend to generate multimodal error surfaces. Metaheuristic algorithms have already been shown for their excellent promise to deal with such difficulties as they operate independent of the nature of the problem. In this regard, this work aims to demonstrate the excellent promise of a novel developed metaheuristic algorithm named pattern search ameliorated arithmetic optimization algorithm. The proposed algorithm integrates the original form of the arithmetic optimization algorithm (for exploration) with the pattern search algorithm (for exploitation) such that a better-performing metaheuristic structure is achieved. The excellent ability of the proposed pattern search ameliorated arithmetic optimization algorithm is demonstrated against the original arithmetic optimization algorithm by using well-known classical benchmark functions and welded beam design problem. A significant improvement is achieved for benchmark functions, and an improvement of up to 25% is obtained for the optimal cost of the welded beam design. Then, different IIR model identification problems are considered, and a comparative assessment is performed using different metaheuristic optimization techniques: particle swarm optimization algorithm, artificial bee colony algorithm, electromagnetism-like optimization algorithm, cuckoo search algorithm, and flower pollination algorithm. The obtained statistical results from different systems confirm that the pattern search ameliorated arithmetic optimization algorithm can achieve better accuracy and robustness in terms of IIR model identification. [ABSTRACT FROM AUTHOR]

One of the most common coal preparations is enrichment in a jig using a float regulation system. The latest solutions propose to compensate for significant measurement errors of the float by introducing a radiometric density meter operating on the principle of gamma radiation absorption into the bottom product discharge zone of the jig. The signal from the radiometric density meter detector is in the form of a sequence of pulses with a Poisson time distribution, which are counted by a counter, as a form of digital low-pass filter. The requirement to maintain accuracy at an appropriate level forces the measurement time to be extended, which worsens the dynamic properties. Changes in the density of the coal–water medium have an unsteady, cyclical course, resulting from the principle of operation of the jig. The research goal was to develop an algorithm for processing the signal from the radiation detector using an IIR filter in the measurement path in a way that ensures optimization of the dynamic properties of the radiometric density meter operating in the control system of the coal enrichment process in the jig. For this purpose, a low-pass IIR filter was introduced into the measurement path to process the signal from the pulse counter. The identified course of the medium density for one cycle (the first) served as a reference signal. A first-order IIR filter was proposed, with a constant parameter selected on the basis of the reference signal and a parameter depending on the time derivative of the identified density of the medium. The mean squared error MSE was adopted as an indicator for assessing the dynamic properties of the radiometric density meter. The results of simulation tests showed that introducing an IIR filter into the measurement path gives better results in terms of the adopted criterion than using a counter with a constant measurement time. The best results (MSE = 2.05 × 10−4) were obtained using an IIR filter with a parameter that is a linear function of the derivative of the medium density over time, determined for one air pulsation cycle and applied in four subsequent cycles. These results were obtained for an adaptive first-order filter with a variable parameter a from the designated range from 0.833 to 0.999, for a measurement time of 2 ms. [ABSTRACT FROM AUTHOR]

Many works in the state of the art are interested in the increase of the camera depth of field (DoF) via the joint optimization of an optical component (typically a phase mask) and a digital processing step with an infinite deconvolution support or a neural network. This can be used either to see sharp objects from a greater distance or to reduce manufacturing costs due to tolerance regarding the sensor position. Here, we study the case of an embedded processing with only one convolution with a finite kernel size. The finite impulse response (FIR) filter coefficients are learned or computed based on a Wiener filter paradigm. It involves an optical model typical of codesigned systems for DoF extension and a scene power spectral density, which is either learned or modeled. We compare different FIR filters and present a method for dimensioning their sizes prior to a joint optimization. We also show that, among the filters compared, the learning approach enables an easy adaptation to a database, but the other approaches are equally robust. [ABSTRACT FROM AUTHOR]

Reconfigurable photonic filters show great promise as a potential solution to meet the evolving needs of future microwave communication systems. By integrating high-performance filters into programmable microwave photonic processors, they can provide significant benefits for signal processing applications. The development of an algorithm that can automatically characterize and reconfigure the filter using a single optical input and output port is essential for this purpose. This paper presents an optimization technique for a fully tunable ring-assisted Mach–Zehnder interferometer filter. The proposed filter design eliminates the need for monitoring components and employs a novel algorithm that operates independently in each ring by switching between the two arms of the filter. In addition, the filter can be configured to implement different filter architectures, allowing for flexible filtering requirements. Measurements were performed using the device as an interleaver, implementing different types of infinite impulse response filters in the optical and radio frequency domains. Side-coupled integrated spaced sequence of resonator filters were also implemented by reconfiguring the same device. These results demonstrate the exceptional reconfigurability of the filter design proposed herein in terms of bandwidth and central frequency. [ABSTRACT FROM AUTHOR]

In this paper, a new mechanism for detecting population stagnation based on the analysis of the local improvement of the evaluation function and the infinite impulse response filter is proposed. The purpose of this mechanism is to improve the population stagnation detection capability for various optimization scenarios, and thus to improve multi-population-based algorithms (MPBAs) performance. In addition, various other approaches have been proposed to eliminate stagnation, including approaches aimed at both improving performance and reducing the complexity of the algorithms. The developed methods were tested, among the others, for various migration topologies and various MPBAs, including the MNIA algorithm, which allows the use of many different base algorithms and thus eliminates the need to select the population-based algorithm for a given simulation problem. The simulations were performed for typical benchmark functions and control problems. The obtained results confirm the validity of the developed method. [ABSTRACT FROM AUTHOR]

Digital filter design optimization counts as one of the most challenging problems in circuit development. Furthermore, the bit-width allocation has a crucial impact on the efficiency and accuracy of fixed-point digital filters. The conventional implementation procedure involves studying the bit-width allocation problem after determining the filter coefficients. This sequential procedure leads frequently to an excessive bit-width allocation, hence an extra implementation cost. The main contribution of this paper is to propose a new implementation procedure based on the simultaneous handling of both design issues and the bit-width optimization for the infinite impulse response filter. In this study, the problem is formalized by a multi-objective programming technique using the non-dominated sorting genetic algorithm II. The joint consideration of the two problems offers a better trade-off between design and bit-width allocation. The output of our approach is compared to the results of existing algorithms. Simulation results show that the multi-objective method significantly reduces the implementation cost while ensuring filter stability and an acceptable mean square error (MSE) compared to the existing algorithms. [ABSTRACT FROM AUTHOR]

The use of a filter bank of IIR filters for the spectral decomposition and analysis of signals has been popular for many years. As such, a new filter-bank resonator-based structure, representing an extremely hardware-efficient structure, has received a good deal of attention. Recently, multiple-resonator (MR)-based and general cascaded-resonator (CR)-based filters have been proposed. In comparison to single-resonator-based analyzers, analyzers with a higher multiplicity of resonators in the cascade provide lower side lobes and a higher attenuation in stopbands. In previous works, it was shown that the CR-based filter bank with infinite impulse response (IIR) filters, which is numerically more efficient than one with finite impulse response (FIR) filters, is suitable for dynamic harmonic analysis. This paper uses the same approach to design complex digital filter banks. In the previous case, the optimization task referred to the frequency responses of harmonic filters. In this work, the harmonic filters of the mother filter bank are reshaped so that the frequency response of the sum (or difference, depending on the parity of the number of resonators in the cascade) of two adjacent harmonic filters is optimized. This way, an online adaptive filter base can be obtained. The bandwidth of the filters in the designed filter bank can be simply changed online by adding or omitting the output signals of the corresponding harmonics of the mother filter. [ABSTRACT FROM AUTHOR]

A new O(n) time complexity numerical method for computing the solutions of Basset integro-differential equations is presented. The method is based on the use of Laplace and bilinear transformations, leading after a precise Chebyshev rational interpolation, to the construction of first order infinite impulse response (IIR) filters. The resulting recurrence relations allow for a very accurate O(n) resolution of Basset equations. Different numerical examples are presented in order to demonstrate the applicability of the method to the study of the transient creeping flow motion of a rigid spherical particle. The results are compared on the one hand to those provided by the Talbot inversion method and on the other hand to those provided by an iterative method specially elaborated here to solve the Basset problem.

In recent years, the development of microelectrode arrays and multichannel recordings has provided opportunities for high‐precision detection in signal processing. The study of neuronal frontal potentials has been rapidly emerging as an important component in brain‐computer interface and neuroscience research. Neuronal spike detection provides a basis for neuronal discharge analysis and nucleus cluster identification; its accuracy depends on feature extraction and classification, which affect neuronal decoding analysis. However, improving the detection accuracy of spike potentials in highly noisy signals remains a problem. IThe authors propose a heuristic adaptive threshold spike‐detection algorithm that removes noise and reduces the phase shift using a zero‐phase Butterworth infinite impulse response filter. Next, heuristic thresholding is applied to obtain spike points, remove repetitions, and achieve robust spike detection. The proposed algorithm achieved an average accuracy of 95.40% using extracellular spiked datasets and effectively detected spikes. [ABSTRACT FROM AUTHOR]

Low-power current integrating infinite impulse response filter having flat-passband for sensor read-out integrated circuit is proposed. In a current integrating filter, passband flatness degradation is inevitable due to sinc-like filtering characteristics. In this paper, by proposing a high order infinite impulse response filter architecture, flat-passband characteristic can be achieved. By implementing a filter architecture with a flat passband, the required sampling frequency can be lowered, which in turn can reduce power consumption. Moreover, the proposed high order infinite impulse response filter architecture has a high degree of freedom on adjusting input sample weights. The proposed integrated circuit is implemented in TSMC 65-nm CMOS process and operated on 1.2 V supply voltage. [ABSTRACT FROM AUTHOR]

A design method for the allpass-based infinite impulse response multi-notch filters with identical pole radiuses is derived in the paper. According to the standard procedure when design of the allpass-based filters is considered, the multi-notch filters' magnitude response specifications are first formulated as the phase response specifications of the corresponding allpass filter. Then, for the specified identical pole radiuses, it is shown that unknown allpass filter coefficients can be obtained determined from the square system of linear equations. On the other hand, the minimum value of the pole radius, such that specifications of the the magnitude response are satisfied in all passbands, can be determined using the bisection method. Results of comparison with some of the existing design methods lead to the conclusion that proposed filters have higher area under the squared passbands magnitude response compared to filters with the same maximum pole radius. Furthermore, utilization of the proposed method can result in transfer functions that have the lowest possible maximum pole radius. [ABSTRACT FROM AUTHOR]

The use of floating-point units (FPU) in digital signal processing has increased due to the range and the high precision numbers that can be represented. These units are required in image processing, digital filters with infinite impulse response (IIR), finite impulse response (FIR), and digital controllers, to obtain accurate results and avoid unstable responses, however, to implement them, some processors have built-in units, this implies a technological dependency of the manufacturers when prototypes are developed. To avoid such dependency, this article presents the design of modules for the most used operations in digital signal processing: multiplication and addition/subtraction. To be able to implement these operations in any Field Programmable Gate Array (FPGA), the steps and considerations to be aware of are presented, such as exceptions, rounding, and normalization of operands. Results are verified using the MODELSIM® test bench and the error rate was determined using MATLAB®. [ABSTRACT FROM AUTHOR]

In this study, a robust feedforward hybrid active noise control (ANC) system with online secondary‐path modelling (SPM) is proposed that is capable of not only effectively suppressing the broadband and narrowband noise components but also tracking the secondary path (SP) variations. An finite impulse response online SPM subsystem as well as an efficient decoupling filter are included in the proposed feedforward hybrid ANC (HANC) system. The decoupling filter is a parallel‐form bandpass filter bank that consists of multiple bandpass filters that are derived from the second‐order infinite impulse response notch filters. It takes the residual noise as its input and separates the broadband component from the narrowband component, with the former used as not only a desired signal for the SPM but also as an error signal for updating the broadband sub‐controller, whereas the latter adapted simultaneously to scale the auxiliary white Gaussian noise and to update the narrowband sub‐controller. Extensive simulations are conducted with both the synthetic and real SPs as well as the synthetic and real noise signals that are generated by a large‐scale factory cutting machine (strand‐cutter) to demonstrate the advantages and effectiveness of the proposed feedforward HANC system. Comparisons are also demonstrated with the original HANC system as well as its directly extended version with an online SPM subsystem. [ABSTRACT FROM AUTHOR]

High-level synthesis of data paths in digital filters and model identification tasks are a most complicated optimization problem. Generally, the use of infinite impulse response (IIR) models for identification is chosen over their equivalent finite impulse response (FIR) models, because the former yield more perfect models of physical plants for real-world optimization problems. Additionally, infinite impulse response (IIR) model structures tend to make various multimodal error surfaces whose objective cost of the problems is significantly more difficult to minimize. For the solution of these types of issues, we needed more powerful methods that could resolve it perfectly. In this study, a novel hybrid variant of chimp and cuckoo search method has been developed for IIR related large-scale optimization issues, and it is known as chimp–cuckoo search algorithm (ChCS). The strength of the proposed method has been tested on the 23 standard functions and three types of infinite impulse response (IIR) models. Meanwhile, the numerical and statistical tabulated solutions of the ChCS method are also presented, showing the advantages of the proposed method over the competitors. [ABSTRACT FROM AUTHOR]

A new O (n) time complexity numerical method for computing the solutions of Basset integro-differential equations is presented. The method is based on the use of Laplace and bilinear transformations, leading after a precise Chebyshev rational interpolation, to the construction of first order infinite impulse response (IIR) filters. The resulting recurrence relations allow for a very accurate O (n) resolution of Basset equations. Different numerical examples are presented in order to demonstrate the applicability of the method to the study of the transient creeping flow motion of a rigid spherical particle. The results are compared on the one hand to those provided by the Talbot inversion method and on the other hand to those provided by an iterative method specially elaborated here to solve the Basset problem. [ABSTRACT FROM AUTHOR]

We describe a custom and open source field-programmable gate array (FPGA)-based data acquisition (DAQ) system developed for electrophysiology and generally useful for closed-loop feedback experiments. FPGA acquisition and processing are combined with high-speed analog and digital converters to enable real-time feedback. The digital approach eases experimental setup and repeatability by allowing for system identification and in situ tuning of filter bandwidths. The FPGA system includes I2C and serial peripheral interface controllers, 1 GiB dynamic RAM for data buffering, and a USB3 interface to Python software. The DAQ system uses common HDMI connectors to support daughtercards that can be customized for a given experiment to make the system modular and expandable. The FPGA-based digital signal processing (DSP) is used to generate fourth-order digital infinite impulse response filters and feedback with microsecond latency. The FPGA-based DSP and an analog inner-loop are demonstrated via an experiment that rapidly steps the voltage of a capacitor isolated from the system by a considerable resistance using a feedback approach that adjusts the driving voltage based on the digitized capacitor current. [ABSTRACT FROM AUTHOR]

There exist several methods aimed at human–robot physical interaction (HRpI) to provide physical therapy in patients. The use of haptics has become an option to display forces along a given path so as to it guides the physiotherapist protocol. Critical in this regard is the motion control for haptic guidance to convey the specifications of the clinical protocol. Given the inherent patient variability, a conclusive demand of these HRpI methods is the need to modify online its response with neither rejecting nor neglecting interaction forces but to process them as patient interaction. In this paper, considering the nonlinear dynamics of the robot interacting bilaterally with a patient, we propose a novel adaptive control to guarantee stable haptic guidance by processing the causality of patient interaction forces, despite unknown robot dynamics and uncertainties. The controller implements radial basis neural network with daughter RASP1 wavelets activation function to identify the coupled interaction dynamics. For an efficient online implementation, an output infinite impulse response filter prunes negligible signals and nodes to deal with overparametrization. This contributes to adapt online the feedback gains of a globally stable discrete PID regulator to yield stiffness control, so the user is guided within a perceptual force field. Effectiveness of the proposed method is verified in real-time bimanual human-in-the-loop experiments. [ABSTRACT FROM AUTHOR]

Autocalibration signal is acquired in the k-space-based parallel MRI reconstruction for estimating interpolation coefficients and reconstructing missing unacquired data. Many ACS lines can suppress aliasing artifacts and noise by covering the low-frequency signal region. However, more ACS lines will delay the data acquisition process and therefore elongate the scan time. Furthermore, a single interpolator is often used for recovering missing k-space data, and model error may exist if the single interpolator size is not selected appropriately. In this work, based on the idea of the disagreement-based semi-supervised learning, a dual-interpolator strategy is proposed to collaboratively reconstruct missing k-space data. Two interpolators with different sizes are alternatively applied to estimate and re-estimate missing data in k-space. The disagreement between two interpolators is converged and real missing values are co-estimated from two views. The experimental results show that the proposed method outperforms GRAPPA, SPIRiT, and Nonlinear GRAPPA methods using relatively low number of ACS data, and reduces aliasing artifacts and noise in reconstructed images. [ABSTRACT FROM AUTHOR]

Infinite Impulse Response (IIR) systems identification is complicated by traditional learning approaches. When reduced-order adaptive models are utilised for such identification, the performance suffers dramatically. The IIR system is identified as an optimization issue in this study. For system identification challenges, a novel population-based technique known as Elitist teacher learner-based optimization (ETLBO) is used to calculate the best coefficients of unknown infinite impulse response (IIR) systems. The MSE function is minimised and the optimal coefficients of an unknown IIR system are found in the system identification problem. The MSE is the difference between an adaptive IIR system's outputs and an unknown IIR system's outputs. For the unknown system coefficients of the same order and decreased order cases, exhaustive simulations have been performed. In terms of mean square error, convergence speed, and coefficient estimation, the results of actual and reduced-order identification for the standard system using the novel method outperform state-of-the-art techniques. For approximating the same-order and reduced-order IIR systems, four benchmark functions are examined utilizing GA, PSO, CSO, and BA. To demonstrate the improvements, the approach is evaluated on three conventional IIR systems of 2nd, 3rd, and 4th order models. On the basis of computing the mean square error (MSE) and fitness function, the suggested ETLBO approach for system identification is proven to be the best among others. Furthermore, it is confirmed that the suggested ETLBO method outperforms some of the other known system identification strategies. Finally, the efficiency of the dynamic nature of the control parameters of DE, TLBO, and BA in finding near parameter values of unknown systems is demonstrated through comparison data. The simulation results show that the suggested system identification approach outperforms the current methods for system identification. [ABSTRACT FROM AUTHOR]

This work presents a cellular neural network (CNN) learning tool based on the Center of Mass Algorithm (CMA), which provides a friendly graphical user interface. As original contributions of this work, the learning tool here developed for CNN features a few adaptations in CMA to improve the training process for grayscale image filtering, such as decreasing learning rates and training with multiple image sets, and comprises many graphical user interface facilities for CNN designers. The training capability of the developed tool is validated through several filter applications, among which lowpass and highpass filters with finite or infinite impulse responses. A comparative analysis is performed between the theoretical responses of the filters and the results obtained from the simulation of a CMOS analog CNN configured by the parameters determined through the learning tool. The training process was very fast for the lowpass filters and acceptable RMS pixel errors have been obtained for all examples, confirming the reliability of the learning tool. [ABSTRACT FROM AUTHOR]

In order to improve the effect of engineering safety management, this paper combines intelligent monitoring technology to construct a monitoring robot system. Moreover, this paper analyzes the characteristics of the digital impulse signal and analyzes the least square filtering and infinite impulse response filtering. At the same time, this paper detects the sample material on the detection test platform, obtains the digital pulse signal, and selects different filtering algorithms and filtering parameters. In addition, this paper uses a signal analysis and processing platform to establish a gamma energy spectrum and analyze the resolution of hydrogen peaks in the gamma energy spectrum. Finally, this paper compares and verifies the filtering algorithms and constructs an engineering safety management system based on robot intelligent monitoring. From the simulation monitoring test results, it can be seen that the engineering safety management system based on robot intelligent detection proposed in this paper has a good effect. [ABSTRACT FROM AUTHOR]

Purpose: This paper mainly aims to deal with the problems of uncertainties including modelling errors, unknown dynamics and disturbances caused by load mutation in control of permanent magnet synchronous motor (PMSM). Design/methodology/approach: This paper proposes an enhanced speed sensorless vector control method based on an active disturbance rejection controller (ADRC) for a PMSM. First, a state space model of the PMSM is obtained for the field orientation control strategy. Second, a sliding mode observer (SMO) based on back electromotive force (EMF) is introduced to replace the encode to estimate the rotor flux position angle and speed. Third, an infinite impulse response (IIR) filter is introduced to eliminate high frequency noise mixed in the output of the sliding mode observer. In addition, a speed control method based on an extended state observer (ESO) is proposed to estimate and compensate for the total disturbances. Finally, an experimental set-up is built to verify the effectiveness and superiority of the proposed ADRC-based control method. Findings: The comparative experimental results show that the proposed speed sensorless control method with the IIR filter can achieve excellent robustness and speed tracking performance for PMSM system. Research limitations/implications: An enhanced sensorless control method based on active disturbance rejection controller is designed to realize high precision control of the PMSM; the IIR filter is used to attenuate the chattering problem of traditional SMO; this method simplifies the system and saves the total cost due to the speed sensorless technology. Practical implications: The use of sensorless can reduce costs and be more beneficial to actual industrial application. Originality/value: The proposed enhanced speed sensorless vector control method based on an ADRC with the IIR filter enriches the control method of PMSM. It can ameliorate system robustness and achieve excellent speed tracking performance. [ABSTRACT FROM AUTHOR]

As a result, the estimated equivalent input disturbance HT ht is directly applied to the input for disturbance compensation. The lumped dynamic TCP deviations HT ht are modeled with a virtual rotational spring-mass-damper system described in detail in [[31]]. Therefore, the optimal control problem is simplified by removing the third and fourth term in the cost function (22a) and solved with a small prediction horizon HT ht . The parameter HT ht represents the equivalent translational mass of the rotational motor and HT ht is the combined mass of the sledge including the load. [Extracted from the article]

In this work we optimally solve the problem of multiplierless design of second-order Infinite Impulse Response filters with minimum number of adders. Given a frequency specification, we design a stable direct form filter with hardware-aware fixed-point coefficients where all multiplications are replaced by bit shifts and additions. The coefficient design, quantization and implementation, typically conducted independently, are now gathered into one global optimization problem, modeled through integer linear programming and efficiently solved using generic solvers. The optimal filters are implemented within the FloPoCo IP core generator and synthesized for field programmable gate arrays (FPGAs) and application specific integrated circuits (ASICs). With respect to state-of-the-art three-step filter design methods, our one-step design approach achieves, on average, 48% reduction in number of lookup tables, 27% delay reduction and 57% reduction in power on FPGAs. ASICs experiment illustrate similar 48% reduction in circuit area, 27% delay reduction and 65% power reduction for a 14 nm ASIC. [ABSTRACT FROM AUTHOR]

It is discussed in many studies and demonstrated in many pieces of research that based on certain applications, analog design of filter has several issues including complex design, re-use limitations, and accuracy of generating the output at various frequencies. Therefore, instead of analog filter design, the digital design of the filter is preferred for both Finite and Infinite Impulse Response Filter. This paper demonstrates the design of the digital Finite Impulse Response (FIR) filter designed is demonstrated using Ultra-Scale Field Programming Gate Array (FPGA) having chip XCKU3P. The filter is designed using a coefficient multiplier via Canonic Signed Digit (CSD) Technique. The optimized design of the digital filter is conducted via real-time implementation is performed using Ultra-Scale FPGA. The filter is designed and evaluated with an ordinary filter at 10 MHz and 10 GHz frequencies. The performance analysis of the system is illustrated using the response rate at the bitstream of 16-bit. In the results, it is demonstrated that for 10 MHz frequency design FIR filter in FPGA the 30% faster response filter is achieved at for 10 GHz, the 15% faster response is achieved at the I/O standard of Low Voltage Complementary Metal Oxide Semiconductor (LVCOMS). The optimization of 30% in terms of the response time of the filter is attained using the proposed work. The proposed improved FIR filter design using Ultra-Scale FPGA helps in increasing design performance to increase the speed of overall response of FIR filter that is lacking in ordinary Filters. [ABSTRACT FROM AUTHOR]

Smart structure vibration reduction based on adaptive active vibration control has become a hot research spot in recent years. A filtered-U least mean square algorithm based on an infinite impulse response filter structure is used to solve the interference of controller output to reference signal. The filtered-U least mean square algorithm is very suitable for the nonlinear vibration control of the flexible structure. This study focuses on the analysis and implementation of an adaptive active vibration control system for smart structure with a surface-bonded piezoelectric actuator. The piezoelectric actuator contained in the secondary path has nonlinear hysteresis property. The nonlinear hysteresis property will cause a nonlinear relationship between the structural vibration response and the control voltage, which deteriorates the robustness and control effect of the adaptive control. This study designs an improved version of the filtered-U least mean square algorithm with online hysteresis identification and compensation (filtered-U least mean square–online hysteresis identification and compensation) based on a discrete Prandtl–Ishlinskii model. The Prandtl–Ishlinskii model parameters of the nonlinear hysteresis property are identified online based on the least mean square algorithm. Based on the identified Prandtl–Ishlinskii model parameters, an inverse hysteresis compensator is established for feedforward compensation in the secondary path. Simulation results show that the proposed method can dynamically compensate the hysteresis nonlinearity of the secondary path, linearizing the nonlinear hysteresis. The vibration reduction effect of the proposed method is obviously better than that of other competing methods. A piezoelectric smart cantilever plate with PZT (or lead zirconate titanate, Pb (Zr, Ti)) actuators and sensors is designed to demonstrate the validity and efficiency of the proposed method by experiments. Experiment results demonstrate that the adverse effect of nonlinear hysteresis is eliminated well after feedforward hysteresis compensation is introduced; the unexpected frequency vibration caused by the hysteresis property is suppressed. The proposed methodology possesses an important advantage in application of the adaptive active vibration control of the piezoelectric smart structure. [ABSTRACT FROM AUTHOR]

In order to study the application of fuzzy partial fractional-order equation based on infinite impulse response (IIR) digital filter in noise big data monitoring, in this study, first, the noise level is calculated through IIR filter to obtain the sound signal sequence after C weight and A weight; the weight equivalent continuous sound level of C, A, and Z is calculated. Then, the vibration condition of fuzzy partial fractional-order equation is studied; the vibration acceleration signal is converted into the vibration velocity and displacement signal; the corresponding effective values are calculated according to the root mean square (RMS), and the noise monitoring platform is designed from hardware and software aspects. Finally, the calibrated sound level value is compared with the standard signal value generated by the digital signal generator; the sequence of sound signal after weight of the original signal, the actual acceleration signal, velocity signal, and displacement signal are calculated; the acceleration signal, velocity signal, and theoretical signal values are compared at different frequencies. The results show that the error between the calculated and calibrated sound level and the standard signal value is very small, and the results of weight counting of C and A are good and meet the requirement of calculation; the error between the measured value of actual acceleration, velocity, and displacement signal and the theoretical value is small, which can realize the accurate acquisition of vibration signal; the error between the actual measured value and the theoretical value of acceleration and velocity at different frequencies is small, about 1% and 10%, respectively. Therefore, the fuzzy partial fractional-order equation based on IIR digital filter can effectively monitor the big noise data. [ABSTRACT FROM AUTHOR]

We address the problem of signal denoising and pattern recognition in processing batch-mode time-series data by combining linear time-invariant filters, orthogonal multiresolution representations, and sparsity-based methods. We propose a novel approach to designing higher-order zero-phase low-pass, high-pass, and band-pass infinite impulse response filters as matrices, using spectral transformation of the state-space representation of digital filters. We also propose a proximal gradient-based technique to factorize a special class of zero-phase high-pass and band-pass digital filters so that the factorization product preserves the zero-phase property of the filter and incorporates a sparse-derivative component of the input in the signal model. To demonstrate applications of our novel filter designs, we validate and propose new signal models to simultaneously denoise and identify patterns of interest. To denoise or detect a pattern of interest in the signal, our proposed signal models simultaneously combine linear time-invariant (LTI) filters and sparsity-based methods with orthogonal multiresolution representations, such as wavelets and short-time Fourier transform. We illustrate the capabilities of our proposed signal models using sleep-electroencephalography (EEG) data to detect K-complexes and sleep spindles. Reproducible research is available at https://github.com/prateekgv/sasdpr. [ABSTRACT FROM AUTHOR]

In this article, a new metaheuristic algorithm named average differential evolution with local search (ADE-LS) has been developed and implemented to find the optimal coefficients of unknown infinite impulse response (IIR) system as a system identifier. The developed method minimizes the error between unknown system output and the adaptive IIR filter output. Rapid convergence is aimed for the global solution in system identification problem using the ADE-LS based adaptive IIR filter modelling with local search. In this way, more precise prediction of filter coefficients is ensured in the filter design with multimodal error surface. ADE-LS algorithm is applied to four benchmarked IIR systems commonly studies in literature to show its performance. Results found by using ADE-LS are compared to other methods reported in terms of convergence rate and the mean square error value. The attained results approve the efficiency of the suggested method. [ABSTRACT FROM AUTHOR]

The article presents a study that explores the contribution of low contrast–preferring neurons to information representation in the primary visual cortex after learning. It explores the neuronal mechanism using multiple single-unit recordings from deep layers of the primary visual cortex (V1) of trained rats during orientation discrimination.

The paper presents a method of optimizing frequency characteristics of filter banks in terms of their implementation in digital CMOS technologies in nanoscale. Usability of such filters is demonstrated by frequency-interleaved (FI) analog-to-digital converters (ADC). An analysis filter present in these converters was designed in switched-current technique. However, due to huge technological pitch of standard digital CMOS process in nanoscale, its characteristics substantially deviate from the required ones. NANO-studio environment presented in the paper allows adjustment, with transistor channel sizes as optimization parameters. The same environment is used at designing a digital synthesis filter, whereas optimization parameters are input and output conductances, gyration transconductances and capacitances of a prototype circuit. Transition between analog s and digital z domains is done by means of bilinear transformation. Assuming a lossless gyrator-capacitor (gC) multiport network as a prototype circuit, both for analysis and synthesis filter banks in FI ADC, is an implementation of the strategy to design filters with low sensitivity to parameter changes. An additional advantage is designing the synthesis filter as stable infinite impulse response (IIR) instead of commonly used finite impulse response (FIR) filters. It provides several dozen-fold saving in the number of applied multipliers.. The analysis and synthesis filters in FI ADC are implemented as filter pairs. An additional example of three-filter bank demonstrates versatility of NANO-studio software. [ABSTRACT FROM AUTHOR]

For the first time, a method is considered that makes it possible to synthesize stable filters with magnitude responses approximating the atomic functions . The atomic filters constructed can be used both independently and as prototypes of digital filters with an infinite impulse response. Three theorems are proven that underlie the new method for approximating the squares of atomic functions by rational fractions satisfying the nonnegativity condition. These fractions are used further in the synthesis of a filter with a magnitude response approximating function An example of signal recovery using an atomic smoothing filter based on the function is presented. A method is described for constructing filters with magnitude responses that approximate arbitrary compactly supported nonnegative functions with a rapidly converging Fourier series. [ABSTRACT FROM AUTHOR]

This letter proposes an online extraction method of common-mode (CM) impedance of induction motor based on all-phase spectrum calculation. All-phase fast Fourier transform with intermediate-frequency filtering (ApFFTIF) is proposed based on infinite impulse response filter, zero-phase filtering technique, and all-phase fast Fourier transform. By using ApFFTIF, the accurate amplitude spectrum together with accurate phase spectrum of CM impedance of induction motor are extracted. Then genetic algorithm is used to establish the model of CM impedance of inductor motor. Finally, through comparing CM impedances extracted by using different spectrum calculation methods, and through comparing measured and calculated CM currents, the accuracy of the proposed method is validated. [ABSTRACT FROM AUTHOR]

The repetitive control (RC) scheme, which can achieve zero steady-state error tracking of any periodic signal with a known integer period, is widely employed in grid-connected inverters to mitigate feed-in current harmonics. However, the conventional RC (CRC) scheme cannot exactly compensate harmonics with fractional period caused by grid frequency variations. Furthermore, the small RC gain of the CRC scheme causes slow transient response. In this article, a proportional-repetitive control (PRC) scheme, which can accommodate a larger RC gain than the CRC scheme, is proposed to provide fast transient response for RC systems. In order to enhance the frequency adaptability of the PRC scheme with a fixed sampling rate, the Thiran all-pass filter, which is a type of infinite impulse response filter for approximating the fractional delay (FD), is used as the FD filter to achieve fast online tuning of the FD and the fast update of the coefficients. The proposed frequency adaptive PRC (FA-PRC) scheme provides grid-connected inverters with a control solution with excellent dynamic performance and accurate frequency adaptability to eliminate harmonic distortion even in the presence of grid frequency variations. A comprehensive series of experiments have demonstrated the effectiveness of the proposed FA-PRC scheme. [ABSTRACT FROM AUTHOR]

Fault detection and identification (FDI) systems are responsible for detecting and identifying errors as fast as possible with high reliability. These systems should be robust against noise and avoid false warnings. Herein, the perspective of using wavelet filters for noise reduction in FDI systems has been investigated. To achieve that, a wavelet filter and a wavelet‐hybrid filter are presented and compared in noise reduction with conventional filters, such as linear filters (finite impulse response (FIR) and infinite impulse response), median filter, and FIR‐median hybrid filter (SWFMH). The comparison is conducted in two steps: (a) noise reduction of a noisy sample signal from a gas turbine and (b) increasing the fault detection accuracy of a gas turbine FDI system in the presence of noisy data. In step one, a conventional noisy sample signal of a gas turbine is presented, and the performances of different filters in noise reduction of the signal have been studied. In step two, considering that excessive filtering can result in the loss of useful information for an FDI system's diagnostics, the performances of an FDI system coupled with different filters have been evaluated. For this purpose, an FDI system utilising an adaptive neuro‐fuzzy inference system and gas path analysis has been designed. It is demonstrated that, in some cases, the wavelet filters have a lower denoising capability for a noisy sample signal, but when used together with the FDI system, they have better performance. Therefore, wavelet filters are better suited for use in FDI systems. [ABSTRACT FROM AUTHOR]