Infinite Impulse Response Filter Research Articles

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.

Synchrophasor Measurement Method Based on Cascaded Infinite Impulse Response and Dual Finite Impulse Response Filters

Synchrophasor Measurement Method Based on Cascaded Infinite Impulse Response and Dual Finite Impulse Response Filters

Approaches to Synthesis of High-Frequency Filters of Finite Impulse Response Type Based on Convolution Algorithm

Digital filters are widely used in audio and video processing, telecommunications, and other data processing industries. They enable the removal of unwanted components from signals, improve signal quality, and solve various signal processing tasks. This article covers the basic principles of digital filtering, including the stages from design to implementation of a digital filter. The text provides a comprehensive overview of the filtering process, covering parameterization and analysis of real data at the filter output. The authors discuss various types of digital filters, such as infinite impulse response (IIR) and finite impulse response (FIR) filters, each with its own unique characteristics, advantages, and disadvantages. This information will assist in selecting the most appropriate filter for a given task. The article discusses the use of digital filters for signal processing tasks, including noise reduction, in various real-world scenarios. The language used is clear, concise, and objective, with a formal register and precise word choice. The article discusses the use of digital filters for signal processing tasks, including noise reduction, in various real-world scenarios. The sentences and paragraphs have a logical flow of information with causal connections between statements. The text is free from grammatical errors, spelling mistakes, and punctuation errors. It aims to help readers understand the functionality of digital filters, how to select the appropriate filter for a specific task, and how to use them to enhance signal quality. The text adheres to conventional academic structure and formatting, with consistent citation and footnote style. No changes in content have been made to the original text. It is an important resource for technicians, engineers, students, and anyone interested in signal processing.

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.

Performance Evaluation of LoRa Communications in Harsh Industrial Environments

LoRa technology is being integrated into industrial applications as part of Industry 4.0 owing to its longer range and low power consumption. However, noise, interference, and the fading effect all have a negative impact on LoRa performance in an industrial environment, necessitating solutions to ensure reliable communication. This paper evaluates and compares LoRa’s performance in terms of packet error rate (PER) with and without forward error correction (FEC) in an industrial environment. The impact of integrating an infinite impulse response (IIR) or finite impulse response (FIR) filter into the LoRa architecture is also evaluated. Simulations are carried out in MATLAB at 868 MHz with a bandwidth of 125 kHz and two spreading factors of 7 and 12. Many-to-one and one-to-many communication modes are considered, as are line of sight (LOS) and non-line of Sight (NLOS) conditions. Simulation results show that, compared to an environment with additive white Gaussian noise (AWGN), LoRa technology suffers a significant degradation of its PER performance in industrial environments. Nevertheless, the use of forward error correction (FEC) contributes positively to offsetting this decline. Depending on the configuration and architecture examined, the gain in signal-to-noise ratio (SNR) using a 4/8 coding ratio ranges from 7 dB to 11 dB. Integrating IIR or FIR filters also boosts performance, with additional SNR gains ranging from 2 dB to 6 dB, depending on the simulation parameters.

Comparison of Digital Filter Orders in Single Lead Electrocardiographic Design for Reducing Interference Motion Artifact

Developed a new method to overcome motion artifacts in Electrocardiogram (ECG) signals, which often interfere with accurate clinical analysis. Motion artifacts, such as body movements, can cause significant distortions in the ECG signal, resulting in incorrect interpretation and affecting medical diagnosis. The main objective of this research is to design and implement an infinite impulse response (IIR) filter with a predetermined sequence, namely orders 2, 4, 6, and 8 to reduce motion artifacts in the ECG signal. We aim to improve ECG signal quality by preserving important ECG signal information and reducing noise caused by motion artifacts. This research contributes to developing more precise and reliable ECG signal processing techniques. The proposed method provides an effective approach to handling motion artifacts, enabling more accurate and reliable ECG interpretation by medical professionals. We used an ECG simulator that provides body movement simulation as a basis for experiments. The detected ECG signal is processed with a predetermined order IIR filter. We compare the filtered signal to the original signal to measure the effectiveness of reducing motion artifacts. Experimental results show that the applied IIR filter efficiently reduces motion artifacts in the ECG signal. The SNR assessment showed a significant improvement, proving the success of this method in maintaining ECG signal quality. The result is that in the 2nd order, the SNR value is 22.25 dB, in the 4th order the SNR value is 22.75 dB, in the 6th order the SNR value is 22.99 dB, in the 6th order the SNR value is 22.99 dB. 8 obtained an SNR value of 23dB. This study successfully demonstrated that using IIR filters in a specified order effectively reduces motion artifacts in the ECG signal, increases SNR, and maintains the integrity of clinical information in the ECG signal. The implications of this research extend to medical technology development and clinical applications, providing a strong foundation for continued research in more efficient and reliable ECG signal processing.

Maximum Complex Correntropy Criterion Adaptive IIR Filtering Based on Gauss-Newton Approach

Adaptive infinite impulse response (IIR) filters have received noticeable consideration for its lower computational complexity compared with adaptive finite impulse response (FIR) filters recently. At present, the adaptive IIR algorithms are almost using the well-established stochastic gradient descent method, but the convergence rate is less than satisfactory. In this paper, to pursue the fast convergence speed of Gauss-Newton (GN) method, we present a GN algorithm for adaptive IIR filters with complex coefficients based on the complex correntropy measure. Besides, the steady-state excess mean square error is provided. Simulations are performed to show the eximious performance of this algorithm.

Self-configuring programmable silicon photonic filter for integrated microwave photonic processors

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.

Tricks for Designing a Cascade of Infinite Impulse Response Filters With an Almost Linear Phase Response [Tips & Tricks

Tricks for Designing a Cascade of Infinite Impulse Response Filters With an Almost Linear Phase Response [Tips & Tricks

A Reconfigurable 4th Order IIR Filter For The Low Frequency Applications

Huge numbers of advanced electrical engineering applications employ infinite impulse response (IIR) filters very frequently in order to meet market’s demands. Especially, some applications such as video processing, digital signal processing and high-speed digital communication necessitate computational efficiency and low latency. In this point of view, high-speed processing of digital data require a digital signal processor or an FPGA instead of a conventional microprocessor. In this respect, in the last decades, FPGAs are commonly used in order to bring new opportunities to the designers. This work gives a FPGA hardware design of a 4th order IIR reconfigurable filter structure. The proposed filter’s maximum clock frequency is around 32MHz which covers different low frequency applications from biomedical signal processing up to speech applications. To verify the performance of the filter, 4th order Butterworth and Chebyshev filters are realized in the basis of Matlab results and FPGA behavioral model. Also, consuming logic gates and blocks are given in order to evaluate chip area occupation. It should be considered that the proposed filter scheme presents promising results to meet low frequency applications.

Filtering Eye-Tracking Data From an EyeLink 1000: Comparing Heuristic, Savitzky-Golay, IIR and FIR Digital Filters.

In a prior report (Raju et al., 2023) we concluded that, if the goal was to preserve events such as saccades, microsaccades, and smooth pursuit in eye-tracking recordings, data with sine wave frequencies less than 75 Hz were the signal and data above 75 Hz were noise. Here, we compare five filters in their ability to preserve signal and remove noise. We compared the proprietary STD and EXTRA heuristic filters provided by our EyeLink 1000 (SR-Research, Ottawa, Canada), a Savitzky- Golay (SG) filter, an infinite impulse response (IIR) filter (low-pass Butterworth), and a finite impulse filter (FIR). For each of the non-heuristic filters, we systematically searched for optimal parameters. Both the IIR and the FIR filters were zero-phase filters. All filters were evaluated on 216 fixation segments (256 samples), from nine subjects. Mean frequency response profiles and amplitude spectra for all five filters are provided. Also, we examined the effect of our filters on a noisy recording. Our FIR filter had the sharpest roll-off of any filter. Therefore, it maintained the signal and removed noise more effectively than any other filter. On this basis, we recommend the use of our FIR filter. We also report on the effect of these filters on temporal autocorrelation.

A New Approach to Detecting and Preventing Populations Stagnation Through Dynamic Changes in Multi-Population-Based Algorithms

Abstract 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.