Infinite Impulse Response Filter Research Articles

Solution of Basset integro-differential equations by IIR digital filters

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.

Robust Periodic Adaptive Disturbance Observer Based Control Considering Long-Term Instability Problems for High-Load Motion Systems

This study investigates the long-term instability problem of periodic-feedback-loop-based control schemes such as repetitive control, periodic adaptive disturbance observer (PADOB), and iterative learning control. The long-term instability problem is the one wherein a closed-loop system becomes unstable because a tracking error diverges with the increase in the number of iterations. A novel robust PADOB is proposed in this article to guarantee the long-term stability of the closed-loop system. The overall system eventually becomes stable because the proposed control scheme using an infinite impulse response (IIR) filter with an optimized lead time can stabilize the error propagation in the iteration domain. Compared to other methods such as zero-phase and IIR filters, the proposed control scheme is very practical because it allows selecting the cutoff frequency of the filter intuitively to stabilize the control system. The optimized lead time in the IIR filter is calculated considering the phase response of the IIR filter; it can improve adaptation speed using a high adaptation gain of the control system. Various simulation and experimental tests based on high-load motion systems that require high-precision positioning are performed to verify the theoretical analysis of the long-term instability problem and the effectiveness of the proposed control scheme.

An enhanced sensorless control based on active disturbance rejection controller for a PMSM system: design and hardware implementation

PurposeThis 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/approachThis 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.FindingsThe 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/implicationsAn 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 implicationsThe use of sensorless can reduce costs and be more beneficial to actual industrial application.Originality/valueThe 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.

Very low frequency IEPE accelerometer calibration and application to a wind energy structure

Abstract. In this work, we present an experimental setup for very low frequency calibration measurements of low-noise integrated electronics piezoelectric (IEPE) accelerometers and a customised signal conditioner design for using IEPE sensors down to 0.05 Hz. AC-response IEPE accelerometers and signal conditioners have amplitude and phase deviations at low frequencies. As the standard calibration procedure in the low-frequency range is technically challenging, IEPE accelerometers with standard signal conditioners are usually used in frequency ranges above 1 Hz. Vibrations on structures with low eigenfrequencies like wind turbines are thus often monitored using DC-coupled micro-electro-mechanical system (MEMS) capacitive accelerometers. This sensor type suffers from higher noise levels compared to IEPE sensors. To apply IEPE sensors instead of MEMS sensors, in this work the calibration of the entire measurement chain of three different IEPE sensors with the customised signal conditioner is performed with a low-frequency centrifuge. The IEPE sensors are modelled using infinite impulse response (IIR) filters to apply the calibration to time-domain measurement data of a wind turbine support structure. This procedure enables an amplitude and phase-accurate vibration analysis with IEPE sensors in the low-frequency range down to 0.05 Hz.

Antisloshing Trajectories for High-Acceleration Motions in Automatic Machines

Abstract This paper studies the design of antisloshing trajectories for application in automatic machines for packaging liquid products, with specific reference to cylindrical containers and emphasis on prescribed motion durations. Different strategies, based on a discrete linear model of the sloshing phenomenon and applicable in real-time, are analyzed to perform antisloshing feedforward control of the container motion: finite impulse response (FIR) filters (input shapers and others), dynamic-model inversion, and infinite impulse response (IIR) filters. Unlike the previous literature, these strategies are applied to highly dynamical motion laws, with maximum accelerations from 4 m/s2 to 13 m/s2. The effectiveness of the proposed antisloshing trajectories is assessed by experiments.

Improving the Quality and Intelligibility of Electrolaryngeal Speech during Mobile Communication with Landline Analogous Bandpass Filtering

The normal functioning of the larynx or voicebox gives humans the ability to converse with others effortlessly. However, if the larynx is permanently removed due to injury or illness, speech cannot be produced. The electrolarynx, a noninvasive device used to produce speech for such persons, has a motor-induced hum which hinders telephone-based communication among electrolaryngeal speakers. This adversely affects the morale of electrolarynx users, leading to abandonment of technology, psychological issues and decreased quality of life. This paper examines a technique based on bandpass filtering of electrolaryngeal speech which has the potential of increasing the quality and intelligibility of speech produced by means of an electrolarynx. Finite impulse response and infinite impulse response filtering with bandpass frequencies analogous to telephone landline and wideband filtering were investigated. Objective testing and subjective testing of quality and intelligibility of electrolaryngeal speech have been performed in each phase of the work. Statistical analysis based on one-way analysis of variance (ANOVA) has been conducted after each round of subjective testing. Results indicate that the quality as well as intelligibility of electrolaryngeal speech can be improved with the help of bandpass filtering.

Control loop oscillation detection and quantification using PRONY method of IIR filter design and deep neural network

Generally, the process industry is affected by unwanted fluctuations in control loops arising due to external interference, components with inherent nonlinearities or aggressively tuned controllers. These oscillations lead to production of substandard products and thus affect the overall profitability of a plant. Hence, timely detection of oscillations is desired for ensuring safety and profitability of the plant. In order to achieve this, a control loop oscillation detection and quantification algorithm using Prony method of infinite impulse response (IIR) filter design and deep neural network (DNN) has been presented in this work. Denominator polynomial coefficients of the obtained IIR filter using Prony method were used as the feature vector for DNN. Further, DNN is used to confirm the existence of oscillations in the process control loop data. Furthermore, amplitude and frequency of oscillations are also estimated with the help of cross-correlation values, computed between the original signal and estimated error signal. Experimental results confirm that the presented algorithm is capable of detecting the presence of single or multiple oscillations in the control loop data. The proposed algorithm is also able to estimate the frequency and amplitude of detected oscillations with high accuracy. The Proposed method is also compared with support vector machine (SVM) and empirical mode decomposition (EMD) based approach and it is found that proposed method is faster and more accurate than the later.

An 11.8-fJ/Conversion-Step Noise Shaping SAR ADC with Embedded Passive Gain for Energy-Efficient IoT Sensors.

Herein, we present a noise shaping successive-approximation-register (SAR) analog-to-digital converter (ADC) with an embedded passive gain multiplication technique. The noise shaping moves the in-band quantization noise from the signal band to out-of-band for improved signal-to-noise ratio (SNR). The proposed approach tackles the drawback of the previous active noise shaping (increased power and extra noise) and passive noise shaping (limited noise suppression and signal loss). Both noise shaping and gain multiplication are realized on-chip in an energy-efficient manner without an opamp. This approach uses only capacitors and switches in the finite impulse response (FIR) and infinite impulse response (IIR) filters. A comparator suppressing kickback noise is presented to handle the tradeoff between noise suppression and the filter capacitor size. The energy-efficient merged-capacitor switching (MCS) technique is effectively combined with rail-to-rail swing comparator and thermometer-coded capacitor array, which reduces the settling error in the digital to analog converter (DAC). The process-induced mismatch effect in the capacitive DAC is investigated using a behavioral model of the ADC. Additionally, we propose dynamic element matching (DEM) for the thermometer-coded capacitor array. The ADC is fabricated using a 0.18 μm CMOS process in an area of 0.26 mm2. Consuming 4.1 μW, the ADC achieves a signal-to-noise and distortion ratio (SNDR) of 66.5 dB and a spurious-free dynamic range (SFDR) of 79.1 dB. The figure-of-merit (FoM) of the ADC is 11.8 fJ/conversion-step.

ADOPTION OF VIBRATION OF FUZZY PARTIAL FRACTIONAL-ORDER EQUATION IN MONITORING OF NOISE BIG DATA UNDER INFINITE IMPULSE RESPONSE DIGITAL FILTER ALGORITHM

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.

Bone‐Conducted Speech Synthesis Based on Least Squares Method

In this paper, we present a methodology to synthesize bone‐conduced (BC) speech. Without relying on commonly used techniques for speech synthesis, we consider to transform air‐conducted (AC) speech into BC speech. An infinite impulse response (IIR) filter is explored for the transformation. From the concept of system identification, the least squares (LS) method is employed and the IIR filter is designed through the recorded AC and BC speech data. By experiments, it is shown that the BC speech synthesis method is satisfactory. Filter model, order selection and stability in the methodology are discussed and noise‐robustness gained by the BC speech synthesis is also investigated. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

SPIIR online coherent pipeline to search for gravitational waves from compact binary coalescences

This paper presents the Summed Parallel Infinite Impulse Response (SPIIR) pipeline used for public alerts during the third advanced LIGO and Virgo observation run (O3 run). The SPIIR pipeline uses infinite impulse response (IIR) filters to perform extremely low-latency matched filtering and this process is further accelerated with graphics processing units (GPUs). It is the first online pipeline to select candidates from multiple detectors using a coherent statistic based on the maximum network likelihood ratio statistic principle. Here we simplify the derivation of this statistic using the singular-value-decomposition (SVD) technique and show that single-detector signal-to-noise ratios from matched filtering can be directly used to construct the statistic. Coherent searches are in general more computationally challenging than coincidence searches due to extra search over sky direction parameters. The search over sky directions follows an embarrassing parallelization paradigm and has been accelerated using GPUs. The detection performance is reported using a segment of public data from LIGO-Virgo's second observation run. We demonstrate that the median latency of the SPIIR pipeline is less than 9 seconds, and present an achievable road map to reduce the latency to less than 5 seconds. During the O3 online run, SPIIR registered triggers associated with 38 of the 56 nonretracted public alerts. The extreme low-latency nature makes it a competitive choice for joint time-domain observations, and offers the tantalizing possibility of making public alerts prior to the merger phase of binary coalescence systems involving at least one neutron star.

Low Area and Power-Efficient FPGA Implementation of Improved AM-CSA-IIR Filter Design for the DSP Application

Digital Signal Processing (DSP) grew enormously in the past few decades and it was extensively practiced for numerous engineering applications like biomedical signal processing, radar signal processing, adaptive antenna design, intelligent control, etc. In DSP, the Finite Impulse Response (FIR) filter and Infinite Impulse Response (IIR) filter plays a vital role in the design of a complex signal processing system. Generally, The FIR filters are stable, linear in phase, with fewer finite-precision errors and easy to implement. However, most of the IIR filters are used in signal processing applications due to the less computational complexity which is compared to the FIR filters. Moreover, the IIR filter requires only fewer filter coefficients and requires only less amount storage registers. In this research, an IIR filter is implemented with Array multiplier and Carry Skip Adder (CSA) to improve hardware utilization. The IIR filter input is generated randomly which is stored in Random Access Memory (RAM). Similarly, the coefficient is generated by utilizing the Parks-McClellan algorithm where the generated value is stored in Read Only Memory (ROM). The proposed AM-CSA IIR (Array Multiplier-Carry Skip Adder Infinite Impulse Response) filter performances are evaluated in terms of filter output, Input Output Block (IOB), Look Up Table (LUT), Flip Flops (FF), slices, delay, and fractional delay. Overall the proposed design attained LUT= 115 (0.0564%), FF=40 (0.01%) and IOB= 22 (3.77%) and reduced fractional delay of about 0.0057ns.

Multirate Adaptive Equalization

Finite Impulse Response (FIR) filter model emulates the Inter Symbol Interference (ISI) in a wireless communication channel. An equalizer, typically an Infinite Impulse Response (IIR) filter, behaves as an inverse filter to the FIR filter to remove the effects of the ISI. IIR filters are generally avoided due to tractability issues, and an FIR filter, with an adaptive signal processing algorithm to minimize the error due to the ISI, is deployed at the receiver. However, the filter is observed to quickly reach a steady state where further iterations do not yield a reduction in the error. This can be attributed to relatively slow variations in the steady state error which prevent further reduction of the errors. This work focuses on converting the low frequency error variations to high frequency variations by the use of multirate signal processing. As such, the steady state error can be damped as well, providing further reduction in the error and an enhanced adaptive filter performance.

Novel Fractional-Order Repetitive Controller Based on Thiran IIR Filter for Grid-Connected Inverters

The proportional-integral multi-resonance repetitive controller (PIMR-RC) can track the reference current signal of the grid-connected inverter without static error, suppress low-frequency harmonic disturbance signals, and have a fast dynamic response. However, when the grid frequency fluctuates, the performance of reference signal tracking and harmonic suppression will decrease. Based on PIMR-RC, this paper proposes a novel fractional-order repetitive controller (NFO-RC) based on the Thiran infinite impulse response (Thiran IIR) filter, which can automatically track the change of grid frequency. Firstly, the frequency domain analysis of PIMR-RC is given. Then, the realization method based on the Thiran IIR filter is designed, and the structure of NFO-RC system is presented. Moreover, the stability and harmonic suppression analysis of NFO-RC system based on Thiran IIR filter are given. Finally, experimental results on a single-phase <i>LCL</i> grid-connected inverter verify excellent harmonic suppression performance of the proposed NFO-RC system under grid frequency fluctuations.

Differentiable Artificial Reverberation

We propose differentiable artificial reverberation (DAR), a family of artificial reverberation (AR) models implemented in a deep learning framework. Combined with the modern deep neural networks (DNNs), the differentiable structure of DAR allows training loss gradients to be back-propagated in an end-to-end manner. Most of the AR models bottleneck training speed when implemented as is in the time domain and executed with a parallel processor like GPU due to their infinite impulse response (IIR) filter components. We tackle this by further developing a recently proposed acceleration technique, which borrows the frequency-sampling method (FSM). With the proposed DAR models, we aim to solve an artificial reverberation parameter (ARP) estimation task in a unified approach. We design an ARP estimation network applicable to both analysis-synthesis (RIR-to-ARP) and blind estimation (reverberant-speech-to-ARP) tasks. And using different DAR models only requires slightly a different decoder configuration. This way, the proposed DAR framework overcomes the previous methods' limitations of task-dependency and AR-model-dependency.

Functional Bayesian Filter

We present a general nonlinear Bayesian filter for high-dimensional state estimation using the theory of reproducing kernel Hilbert space (RKHS). By applying the kernel method and the representer theorem to perform linear quadratic estimation in a functional space, we derive a Bayesian recursive state estimator for a general nonlinear dynamical system in the original input space. Unlike existing nonlinear extensions of the Kalman filter where the system dynamics are assumed known, the state-space representation for the Functional Bayesian Filter (FBF) is completely learned online from measurement data in the form of an infinite impulse response (IIR) filter or recurrent network in the RKHS, with universal approximation property. Using a positive definite kernel function satisfying Mercer’s conditions to compute and evolve information quantities, the FBF exploits both the statistical and time-domain information about the signal, extracts higher-order moments, and preserves the properties of covariances without the ill effects due to conventional arithmetic operations. We apply this novel kernel adaptive filtering (KAF) to recurrent network training, chaotic time-series estimation and cooperative filtering using Gaussian and non-Gaussian noises, and inverse kinematics modeling. Simulation results show FBF outperforms existing Kalman-based algorithms.

Prediction of Blood Glucose Level Using Nonlinear System Identification Approach

Predicting the blood glucose level of type 1 diabetes mellitus of patients could prevent hypo/hyperglycemia incidents that are threats for the patients with this disease. A nonlinear system identification approach is proposed in this work to develop a mathematical model, which can be used to predict the blood glucose level over a given period with high accuracy. More specifically, the Hammerstein Box-Jenkins model is used to approximate the system, where two infinite impulse response filters represent the linear and noise processes, and a polynomial basis function represents the nonlinearity. The proposed identification method is based on the Steiglitz-McBride approach to predict the model parameters. Moreover, a simulation software licensed by the USA Food and Drug Administration that simulates the dynamics of the glucose-insulin system metabolism inside the body, called Type I Diabetes Metabolic Simulator (T1DMS), was used to generate the data. Thirty subjects of different age groups were considered, and the data was generated for a week with a sample per minute, i.e. 302430 data points. This data was then processed using a developed MATLAB code to predict the blood glucose level. Various scenarios were established to validate the proposed approach. The simulations showed very promising results with a very low average root mean square error of 1 mg/dl, which is seven times less compared to other prediction techniques. Other cost functions have also been used and they showed very good results. In the future, this approach can be embedded in closed-loop continuous blood glucose monitoring systems in order to give alerts to the patients and help in calculating the needed insulin dose.

Hardware-Aware Design of Multiplierless Second-Order IIR Filters With Minimum Adders

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.

A single-step recursive representation of foundation flexibility functions to soil-structure interaction using first-order IIR filters

The rational approximation of the dynamic impedance function of the foundation plays a vital role in the soil-structure interaction (SSI) analysis in the time domain. The filter representation of the rational approximation proposed by Safak provides a multi-step recursive parameter model facilitating incorporation into the time history analysis. However, it encounters both the uncertainty of the parameter identification and numerical instability. According to the linear time-invariant system theory in signal processing, this paper presents an improved single-step recursive representation of the foundation flexibility function using first-order infinite impulse response (IIR) filters. Based on the discrete responses of the foundation to the triangular unit impulse, the well-developed technologies of the digital filter can efficiently identify the stable filter parameters. It is easy to incorporate the filters into the motion equations of the structure in the forms of additional stiffness and single-step recursive variables. A computational scheme founded on the Newmark integration method is proposed to obtain the structural responses. According to the spectral radius theory, combining the filters with the Newmark integration method produces only a single operation matrix, thus effectively making the numerical stability guaranteed. Finally, a multi-degree-of-freedom model subjected to earthquake ground motion is employed to approve the proposed scheme.

FPGA Design of a High- Resolution FIR Band-Pass Filter by Using LabVIEW Environment

Designers regularly use Finite Impulse Response (FIR) filters to fulfil the need for current electronic design applications such as signal or image processing and digital communications because of the remarkable selectivity computational efficiency. Fast and efficient information processing requires a dedicated microprocessor or a digital signal processor that may not always be available or provide enough performance. In such scenarios, designers can configure FPGAs for processing digitized signals. One of the most popular signal processing applications is filtering. Unlike the Infinite Impulse Response (IIR) filters, FIR filters do not have analog equivalent circuits. For this purpose, continuous time-discrete time conversion is not possible with the help of transforms. Because analog filters cannot have a finite impulse response, the design methods of FIR filters can be made as windowing method, pulse response truncation, and optimal filter design method. Considering this information, it aims to digitally separate two signals with different frequencies (2.4 kHz and 4.2 kHz), which are given to the input as analog, to obtain the desired information signal and suppress other signals. We preferred to use LabVIEW graphical programming language to get the digital FIR filter coefficients. We selected rectangular windowing, set the digital filter's sampling frequency as 18720 Hz, and determined the filter's coefficient with high-frequency resolution as 24. Using filter coefficients in the real-time FPGA-VHDL environment, we showed the performance and resource consumption. LabVIEW is used for simulation as well as obtaining filter coefficients. In addition, we compared both simulation and real-time FPGA-VHDL application output waveforms and examined both platforms' advantages and disadvantages.