Infinite Impulse Response Filter Research Articles

The calibration device research for assessing the impact exposure level of rubber balls in architectural acoustics

In order to accurately measure the impact exposure level of rubber balls used in architectural acoustics, a calibration device is designed based on a force sensor, signal analyzer, programmable stepper motor and Infinite Impulse Response (IIR) filter. A weighing sensor with a range of 200 kg is selected to match the range of impact forces of rubber balls. The sensor is installed on a specially designed metal base plate of a rubber ball measuring fixture and connected to the signal analyzer. The rubber ball is dropped from a predetermined height in a free-fall manner to impact the sensor, generating an impact waveform. The force exposure level is then calculated by using software algorithms built into the device. The accuracy and reliability of the calibration device are evaluated by conducting repeated measurements on rubber balls and comparing the results with a known standard force exposure level curve. The experimental results demonstrate that the calibration device can consistently and stably measure the force exposure level of rubber balls, showing consistency with the known standard values. However, due to limitations such as a limited variety of measured objects and insufficient measurement repetitions, further research will be conducted to increase the number of tested rubber balls and enhance the device’s capabilities. This will provide technical support for the further development and application of the device.

Measuring stress of strand using magnetic barkhausen noise measured by solenoid-type sensor

ABSTRACT This paper presents a new approach on non-destructive stress estimation that involves measuring the magnetic Barkhausen noise (MBN) on prestressed strands using a solenoid-type sensor. Using a contact-type sensor on infrastructure is difficult because prestressed strands are wrapped with protective materials and cannot be directly touched. To test the feasibility of the new idea, a solenoid-type MBN sensor is developed that measures the stress of a seven-wire strand. The sensor consists of a magnetic-field inducing coil and a sensing coil, designed to generate a varying magnetic field and measure the change in magnetic flux, respectively. An infinite impulse response filter is recommended to obtain the MBN from the measured signal, and the root mean square (RMS) is introduced to determine the level of the MBN. An indoor test is performed on a seven-wire strand with a diameter of 15.2 mm. The test shows that the MBN’s RMS decreases as the tensile stress increases. The relationship between the MBN’s RMS and the strand’s stress exhibits a clear trend similar to those observed in previous studies using contact-type sensors. The test results indicate that it is possible to estimate the stress of a strand inside a duct using the solenoid-type MBN sensor.

Hysteresis modeling and analysis of piezoelectric actuators using a modified LuGre model at different preloads and frequencies

Despite a large of models proposed to characterize the nonlinear hysteresis of piezoelectric actuators, few of them can show the effects of different preloads on the nonlinear hysteresis. In this paper, a modified LuGre model (MLM) is presented for piezoelectric actuators under different preloads to accurately characterize its dynamic hysteresis nonlinearity at different frequencies. Based on the classical LuGre model (CLM), the presented model introduces infinite impulse response filters and smoothness coefficient to simplify it. The parameters of the models were identified by the non-linear least squares method. A series of experiments were conducted to verify the validity of the MLM under different preloads at different frequencies excitation signals.In the constant frequency experiments under different preloads, the MLM reduces the mean error by 68.1%−81.9% compared with the CLM; In the constant preload experiments at different frequencies, the MLM reduces the mean error by 72.2%−83.0% and 65.9%−83.2% compared with the classical Bouc–Wen model (CBM) and the CLM.

A Novel Approach for Realizing Loop-Filter in Low-Distortion Noise-Coupled ΣΔ Modulators

This paper presents an innovative general structure for a single-loop Sigma-Delta ([Formula: see text]) modulator that combines low-distortion and noise-coupled techniques to achieve high-resolution for low-power applications. The low-distortion technique adjusts the signal transfer function of the proposed structure to unity, while the noise-coupled technique enhances the order of quantization noise-shaping at the modulator output. These techniques were incorporated into the design to increase the modulator order without requiring additional operational amplifiers during its circuit implementation, resulting in a low-power modulator compared to similar structures. To reduce the number of required amplifiers, a second-order infinite impulse response (IIR) filter was used in the modulator loop filter, in place of two integrators. To assess the proposed structure’s performance, a third-order modulator sample was implemented and simulated for speech recognition applications, specifically, digital hearing aids, using 180[Formula: see text]nm complementary metal oxide semiconductor (CMOS) technology. The results indicate that for a third structure with a sampling rate of 64, an input sine signal of −6[Formula: see text]dBFS and a sampling frequency of 2.56[Formula: see text]MHz, the signal to noise plus distortion (SNDR) was 81.9[Formula: see text]dB, and the dynamic range (DR) was 88[Formula: see text]dB for a 20[Formula: see text]KHz bandwidth. The modulator’s power consumption was 126.9[Formula: see text][Formula: see text]W. Circuit and system-level simulations demonstrate the effectiveness of the proposed method.

Digital IIR filters: Effective in edge preservation?

Infinite Impulse Response (IIR) filtering stands as a fundamental technique in various signal processing applications. Despite its widespread use, the scientific community widely acknowledges the suboptimal nature of IIR filters in preserving edges or abrupt transitions in piecewise signals. In situations where meticulous preservation of abrupt signal transitions is crucial, signal processing practitioners often explore alternative methodologies tailored to specific application demands. Consequently, the effectiveness of digital IIR filters in preserving signal edges while filtering out undesirable noise components continues to be a topic of active debate.In response to this limitation (or answering the question posed by the above title), the paper demonstrates that the suboptimal nature of IIR filters in preserving edges arises from the conventional methodology used to compute the output of zero-phase IIR filters. The standard method of output computation equates to solving an unconstrained optimization problem with an ℓ2-norm penalty term. Following this analysis, the paper introduces a refined approach to compute the output of digital IIR filters, aiming to overcome the challenge of preserving abrupt signal transitions. This innovative proposal replaces the ℓ2-norm with an ℓ1-norm, resulting in the development of an IIR filter that dynamically adjusts its coefficients based on the input signal. As a result, this novel approach to output computation effectively preserves essential signal features while simultaneously reducing noise, highlighting the efficacy of digital IIR filters in edge preservation. Therefore, the answer to the aforementioned question greatly depends on the chosen method for computing the output. By employing the suggested approach, the answer is unequivocally yes.

Multiplier Design Based on Booth and Sequential Algorithm

Research on cellular networks and low-power electronic devices has ramped up in recent years, thanks to the proliferation of portable and mobile systems. There are many portable applications in the present day that require low power (smaller and more efficient batteries) and more milli ampere hours than before. As a result, designing low-power devices has grown in importance as a performance criterion. Multipliers arrangement (it is an organized set of adders) and delay make up the basic construction of a infinite impulse response filter. This paper deals with analytic procedure for performance evaluation to reduce time and RAM usage throughout the computation process. The Booth and Sequential multipliers have been used to simulate, and implement in this article. Using the Sequential and Booth Algorithms, respectively. Comparative study is carried out utilising Xilinx 14.7 with the virtex 5 family, devices for bit lengths 4, 8, 16, and 32.

Estimation of phase distortions of the photoplethysmographic signal in digital IIR filtering

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.

An Efficient RTL Design for a Wearable Brain–Computer Interface

This article proposes an efficient and accurate embedded motor imagery-based brain–computer interface (MI-BCI) that meets the requirements for wearable and real-time applications. To achieve a suitable accuracy considering hardware constraints, we explore BCI transducer algorithms, among which Infinite impulse response (IIR) filter, common spatial pattern, and support vector machine are used to preprocess, extract features, and classify data, respectively. With our hardware implementation of these tasks, we have achieved an accuracy of 77%. Our system is designed at register transfer level (RTL) targeting an ASIC implementation, which significantly decreases power consumption, latency, and area compared to the state-of-the-art (SoA) architectures for embedded BCI systems. To this end, we fold IIR filters using time-shared and RAM-based techniques and use hardware-friendly algorithms for the implementation of other tasks. The RTL design is realized on 45 nm CMOS technology consuming 4 mW power and 0.25 mm2 area, which outperforms the SoA platforms for embedded BCI systems. To further illustrate the outperformance of our design, the proposed architecture is implemented on Virtex-7 field program gate array as a prototyping platform consuming 6 μJ energy with 1.52% area utilization.

Uncertainty Constraint on Headphone Secondary Path Function for Designing Cascade Biquad Feedback Controller with Improved Noise Reduction Performance

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.

A behavioral model for the linear variable gain amplifier

AbstractIn 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.

Design and Implementation of Efficient IIR Low Pass Filter Based On Vedic Multiplier Algorithm

Abstract: This article presents a state-of-the-art design for a limitless impulse response (IIR) filter that can be easily reconfigured for use in real-time software. Using a vedic multiplication strategy, this work demonstrates the excellent general performance of a recursive or Infinite Impulse Response (IIR) filter. The number of repeats may be kept to a minimum and the processor's overall performance is improved by the reduction of computational delay and hardware. This research investigates the impact of different filter architectures on the development process and overall performance. To enforce and evaluate the planned filter's functionality, simulation is used across three independent platforms. The first system is MATLAB/ SIMULINK, the software package utilised to implement the IIR filter. The second strategy is called "HDL - Cosimulation," and it involves using SIMULINK's already-present tools in order to translate the formulated filter-out method into VHDL, the language used to describe very fast integrated circuits. The third approach uses Xilinx System Generator's pre-existing building components to physically realise the filter design. The method shown here allows the proposed filter to be implemented locally inside the FPGA device of interest

Arrhythmia classification for non-experts using infinite impulse response (IIR)-filter-based machine learning and deep learning models of the electrocardiogram.

Arrhythmias are a leading cause of cardiovascular morbidity and mortality. Portable electrocardiogram (ECG) monitors have been used for decades to monitor patients with arrhythmias. These monitors provide real-time data on cardiac activity to identify irregular heartbeats. However, rhythm monitoring and wave detection, especially in the 12-lead ECG, make it difficult to interpret the ECG analysis by correlating it with the condition of the patient. Moreover, even experienced practitioners find ECG analysis challenging. All of this is due to the noise in ECG readings and the frequencies at which the noise occurs. The primary objective of this research is to remove noise and extract features from ECG signals using the proposed infinite impulse response (IIR) filter to improve ECG quality, which can be better understood by non-experts. For this purpose, this study used ECG signal data from the Massachusetts Institute of Technology Beth Israel Hospital (MIT-BIH) database. This allows the acquired data to be easily evaluated using machine learning (ML) and deep learning (DL) models and classified as rhythms. To achieve accurate results, we applied hyperparameter (HP)-tuning for ML classifiers and fine-tuning (FT) for DL models. This study also examined the categorization of arrhythmias using different filters and the changes in accuracy. As a result, when all models were evaluated, DenseNet-121 without FT achieved 99% accuracy, while FT showed better results with 99.97% accuracy.

Signal Processing from the Radiation Detector of the Radiometric Density Meter Using the Low-Pass Infinite Impulse Response Filter in the Measurement Path in the Coal Enrichment Process Control System

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.

Enhancing power quality in vehicle-to-grid (V2G) operations of FCEVs through the integration of real-time digital IIR filters in power calculations

Fuel cell electric vehicles (FCEVs) are estimated as the future's mobile distributed generators with vehicle-to-grid (V2G) functions. Besides, V2G topologies are required to be designed with an appropriate analog filter by accurately measuring the reference current and voltage values of inverters and converters. Thus, the highest accuracy is required in control structures for charging and grid connection of FCEVs to improve the power quality (PQ). In this study, PQ improvement of V2G operation of FCEVs is proposed by considering the developed real-time digital infinite impulse response (IIR) filters in generating reference signals in the power calculation stage. A band-pass digital IIR filter design is realized with the inverter using LabVIEW infrastructure, and reference signals are smoothed by using IIR filters. The performances of developed different IIR filters' results are examined in the study. Developed filters are designed using the order estimation method. Elliptic, Chebyshev, and Butterworth filters are applied to the voltage and current signals, and the best results are obtained in reference power signals with the Butterworth filter. The Butterworth filter maintains stability in current RMS, induces a 0.09 % decrease in voltage RMS, and shows a slight 0.30 % increase in active power. The proposed approach removes distortions in reference power signals by using a designed filter for a reliable V2G operation of FCEVs.

Reduced order infinite impulse response system identification using manta ray foraging optimization

This article presents a useful application of the Manta Ray Foraging Optimization (MRFO) algorithm for solving the adaptive infinite impulse response (IIR) system identification problem. The effectiveness of the proposed technique is validated on four benchmark IIR models for reduced order system identification. The stability of the proposed estimated IIR system is assured by incorporating a pole-finding and initialization routine in the search procedure of the MRFO algorithm and this algorithmic modification contributes to the MRFO algorithm when seeking stable IIR filter solutions. The absence of such a scheme, which is primarily the case with the majority of the recently published literature, may lead to the generation of an unstable IIR filter for unknown real-world instances (particularly when the estimation order increases). Experiments conducted in this study highlight that the proposed technique helps to achieve a stable filter even though large bounds for the design variables are considered. The convergence rate, robustness, and computational speed of MRFO for all the considered problems are investigated. The influence of the control parameters of MRFO on the design performances is evaluated to gain insight into the interaction between the three foraging strategies of the algorithm. Extensive statistical performance analyses employing various non-parametric hypothesis tests concerning the design consistency and convergence are conducted for comparison of the proposed MRFO-based approach with six other metaheuristic search procedures to investigate the efficiency. The results on the mean square error metric also highlight the improved solution quality of the proposed approach compared to the various techniques published in the literature.

A Detailed Study on IIR-FIR Filters and Design of a Graphical User Interface for Simulation of EEG Signals

Electroencephalography (EEG) plays a pivotal role in accepting brain activity and identifying neurological disorders. The precise analysis and explanation of EEG signals require the solicitation of digital signal processing techniques, such as Infinite Impulse Response (IIR) as well as Finite IJmpulse Response (FIR) filters. This paper investigates into a complete exploration of IIR-FIR filters and their application in EEG signal processing. The first part of this research paper involves a detailed examination of IIR and FIR filter types, their mathematical foundations, pros, and cons. Various filter design methods, such as Chebyshev, Butterworth and Elliptic filters, are discussed as well as compared through literature survey. Speculative aspects, including filter design, transfer functions, and frequency responses, are presented in a clear and much accessible manner. The second phase of the study introduces the design of a Graphical User Interface (GUI) on mathematical modelling tool aimed at enabling EEG signal simulation and analysis. This GUI is designed to enable users, including researchers and clinicians, to generate synthetic EEG signals with controllable parameters, apply IIR-FIR filters in real-time, and thereby visualize the filtered signals. The interface offers user-friendly controls for customizing filter characteristics, such as filter order, cutoff frequencies, and filter type. To validate the efficiency of the designed GUI and the selected IIR-FIR filters, general simulations are conducted using EEG datasets. The results showcase the GUI's efficacy in real-time EEG signal processing, demonstrating its prospective in research, clinical diagnostics, and educational settings and many areas. In summary, this paper presents a full investigation into IIR-FIR filters, proposing insights into their theory and practical application for EEG signal processing. The development of a intelligible GUI for EEG signal simulation and study further enhances the approachability of these mathematical modelling tools to a wider audience, eventually contributing to progressions in the field of neuroscience and brain signal analysis.

An improved repetitive controller with fractional time-delay for discrete-time linear systems: Synthesis and comparison study

This paper develops a discrete-time repetitive control (RC) system with a fractional-delay internal model. Unlike the conventional RC, the time delay for constructing the internal model is not necessarily an integer, implying that the time delay is allowed to be fractional. In this work, a fractional delay-based repetitive control system is presented. Firstly, the fractional time delay is calculated based on the information of the reference or disturbance along with the associated sampling period. Secondly, the fractional time delay is divided into two parts, i.e., the large integer and small fractional delays. Finally, the fractional part is realized by a stable and causal infinite impulse response (IIR) filter, whose coefficients are calculated using the Thiran formula. The controller structure and stability analysis are developed for three different RCs, namely a fractional-delay general RC (FD-GRC), a fractional delay-odd harmonics RC (FD-OHRC), and a fractional delay-(6l±1) RC (FD-(6l±1)RC). Experiments and comparison studies of the integer delay-based RCs are conducted to verify the effectiveness and improved tracking accuracy of the proposed method.

An atom search optimization approach for IIR system identification

ABSTRACT Filtering, or digital signal processing, is a significant and fundamental requirement in fields such as signal systems and computers. The process of designing optimal digital filters is difficult, which has led researchers to design filters using emerging evolutionary computations. Metaheuristics have emerged as the most promising tool for solving optimization problems, with excellent development and improvement. However, it has not been clear how to select the best performing metaheuristic to design an optimal digital filter. In this paper, a digital infinite impulse response (IIR) filter is constructed using the atom search optimization (ASO) algorithm impressed by the physical motion of atoms in nature based on molecular dynamics. The simulation results obtained are extensively compared with the results of other optimization algorithms such as moth flame optimization, gravitational search algorithm and artificial bee colony optimization. ASO was found to have the highest percentage of improvement. Furthermore, eight cases are analyzed across four numerical filter instances with the same degree and four with reduced degree, and the results are validated by outperforming several different algorithm-based approaches in the literature. The stability analysis on the basis of pole zero diagrams further cements the efficacy of the ASO for IIR system identification problem.

Fractional-Order Phase Lead Compensation Multirate Repetitive Control for Grid-Tied Inverters

To reduce computational load and memory consumption, multirate repetitive control (MRC) with downsampling rates provides a flexible and efficient design for proportional-integral multi-resonant repetitive control (PIMR-RC) systems for grid-tied inverters. However, in MRC systems, repetitive controllers with low sampling rates produce low delay periods, and integer-order phase lead compensation may cause undercompensation or overcompensation. These imprecise linear phase lead compensations may result in deteriorated control performance. To address these problems, based on an infinite impulse response (IIR) filter, a fractional-order phase lead proportional-integral multi-resonant multirate repetitive control (FPL-PIMR-MRC) is proposed for grid-tied inverters in this paper. The proposed method can provide a suitable fractional phase lead step to achieve a wide stability region, minor tracking errors, and low hardware costs. The IIR fractional-order lead filter design, stability analysis, and the step-by-step parameter tuning of the FPL-PIMR-MRC system are derived in detail. Finally, simulation performed confirms the feasibility and effectiveness of the proposed scheme.

Simulation of background neuronal activity and noise in human intracranial microwire recordings

BackgroundHuman intracranial microwire recordings allow measurement of neuronal activity in human subjects at a fine temporal and spatial scale. The recorded extracellular potentials represent a mixture of action potentials from nearby neurons, local field potentials, and other noise sources. Signal processing of these recordings is used to separate the activity of putative single neurons from other background and noise. To better understand the separation of single neuron activity, one approach is to simulate the signals produced by neurons firing action potentials combined with background activity and noise. New methodThis paper characterizes the background activity and noise in human intracranial microwire recordings and presents an accurate and efficient method of simulation using an infinite impulse response filter to color white noise. Results and comparisonThis method reproduces the power spectral density of the background activity and noise over a frequency range of 1–5000 Hz and is over 200 times faster than previously used methods. It thus facilitates large scale studies of variation of noise sources, field potentials, and processing parameters. It performs equivalently in terms of spike sorting to simulation using white noise. Another advantage is that the simulated signals are known to arise from a pseudorandom number generator and cannot be the result of detecting simulated background spiking activity. ConclusionsThis approach provides a rapid and accurate method of simulating background noise and neural activity in human intracranial microwire recordings. It is suitable for use in large scale simulations to study spike sorting in this type of recording.