Infinite Impulse Response Filter Research Articles

Hybrid filter for lock-in amplifiers.

Lock-in amplifiers are instrumental in the precise measurement of extremely small AC signals within high-noise environments. Traditionally, noise reduction in these instruments relies on infinite impulse response (IIR) filters, which can necessitate prolonged settling times to ensure the acquisition of accurate, statistically independent data. While moving average filters offer faster settling times, their non-monotonic frequency response may not be optimal for noise reduction. Conversely, IIR filters frequently realized as N-pole RC filters exhibit a monotonic frequency response conducive to effective noise reduction. This study presents a hybrid filter architecture that combines a short IIR filter with a longer moving average finite impulse response filter. The objective is to enhance noise reduction as quantified by the filter's equivalent noise bandwidth (ENBW). Theoretical analysis is provided to derive the step response, settling time, frequency response, and ENBW of the hybrid filter configuration. Design methodologies are outlined for hybrid filters that either match the settling time of an N-pole RC filter while achieving a lower ENBW or maintain the ENBW of an N-pole RC filter but with significantly faster settling time. The performance of the hybrid filter is validated through noise measurements of low-value resistors and thermal noise of larger resistors, with results compared to theoretical predictions.

Comparative analysis of RNN versus IIR digital filtering to optimize resilience to dynamic perturbations in pH sensing for vertical farming

AbstractVertical farming (VF) refers to systems of agriculture where crops are grown in trays stacked vertically by exposing them to artificial light and using sensing technology to improve product quality and yield. In this work, we propose an advanced filtering scheme based on recurrent neural networks (RNNs) and deep learning to enable efficient control strategies for VF applications. We demonstrate that the best RNN model incorporates five neuron layers, with the first and second containing 90 long short‐term memory neurons. The third layer implements one gated recurrent units neuron. The fourth segment incorporates one RNN network, while the output layer is designed by using a single neuron exhibiting a rectified linear activation function. By utilizing this RNN digital filter, we introduce two variations: (1) a scaled RNN model to tune the filter to the signal of interest, and (2) a moving average filter to eliminate harmonic oscillations of the output waveforms. The RNN models are contrasted with conventional digital Butterworth, Chebyshev I, Chebyshev II, and elliptic infinite impulse response (IIR) configurations. The RNN digital filtering schemes avoid introducing unwanted oscillations, which makes them more suitable for VF than their IIR counterparts. Finally, by utilizing the advanced features of scaling of the RNN model, we demonstrate that the RNN digital filter can be pH selective, as opposed to conventional IIR filters.

An Enhanced Symmetric Sand Cat Swarm Optimization with Multiple Strategies for Adaptive Infinite Impulse Response System Identification

An infinite impulse response (IIR) system might comprise a multimodal error surface and accurately discovering the appropriate filter parameters for system modeling remains complicated. The swarm intelligence algorithms facilitate the IIR filter’s parameters by exploring parameter domains and exploiting acceptable filter sets. This paper presents an enhanced symmetric sand cat swarm optimization with multiple strategies (MSSCSO) to achieve adaptive IIR system identification. The principal objective is to recognize the most appropriate regulating coefficients and to minimize the mean square error (MSE) between an unidentified system’s input and the IIR filter’s output. The MSSCSO with symmetric cooperative swarms integrates the ranking-based mutation operator, elite opposition-based learning strategy, and simplex method to capture supplementary advantages, disrupt regional extreme solutions, and identify the finest potential solutions. The MSSCSO not only receives extensive exploration and exploitation to refrain from precocious convergence and foster computational efficiency; it also endures robustness and reliability to facilitate demographic variability and elevate estimation precision. The experimental results manifest that the practicality and feasibility of the MSSCSO are superior to those of other methods in terms of convergence speed, calculation precision, detection efficiency, regulating coefficients, and MSE fitness value.

A Fresh Revisit of the Issues and Improvements in Impulse Invariance Filter Design for Infinite Impulse Response Filters

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.

Design of Infinite Impulse Response Filters Based on Multi-Objective Particle Swarm Optimization

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.

Understanding How Filters Affect Data

There are many filters in digital data acquisition and analysis. These filters have both positive and negative effects on the data. While the positive effects (like removing signal in specified frequency bands) are obvious. The negative effects (like signal delays) may not be so obvious. This presentation will discuss in detail the negative effects on the data and how these effects can be mitigated. We will cover the Finite Impulse Response (FIR) and Infinite Impulse Response (IIR) filters.

On design of preprocessing and postprocessing in cascaded-resonator-based harmonic analyzers and filter banks

The cascaded-resonator (CR)-based filter structure provides high attenuation in the stopbands thanks to the serially coupled resonators, which is an advantage in many applications of harmonic analyses and/or filtering. In addition to that, it has good properties provided by its parallel structure. On the other side, there are applications which besides the high stopband attenuation need wider (or even flat-top) passbands, lower group delays, etc. These performances can be modified/improved by preprocessing and postprocessing through reshaping of the primary CR-based filter bank frequency responses. The numerical complexity is also an important aspect where infinite impulse response (IIR) filters provide implementation with a smaller number of numerical operations, but involving nonlinearity of the phase responses and stability control issue. Although most of these issues have been discussed in the previous papers dealing with the design of the CR-based harmonic filtering and/or filter banks, the aim of this paper is to highlight them all together. In addition, some improvements of the previously described design techniques are suggested too.

Certain investigation on hybrid neural network method for classification of ECG signal with the suitable a FIR filter

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%.

Design Polynomial IIR Digital Filters of the Integer Parameters Space Use to Compress Image Data

Polynomial IIR digital filters play a crucial role in the process of image data compression. The main purpose of designing polynomial IIR digital filters of the integer parameters space and introduce efficient filters to compress image data using a singular value decomposition algorithm. The proposed work is designed to break down the complex topic into bite-sized pieces of image data compression through the lens of compression image data using Infinite Impulse Response Filters. The frequency response of the filters is measured using a real signal with an automated panoramic measuring system developed in the virtual instrument environment. The analysis of the output signal showed that there are no limit cycles with a maximum radius of poles of 0.96 in the polynomial bandpass filters. Thus, all the functional requirements for the Integer Parameters Space of the proposed polynomial IIR digital filters were met. The results showed that the data compression and size reducing of an image file is processed without significantly impacting of visual quality. This is achieved by removing redundant or unnecessary information from the image while preserving the important details which removes unnecessary data to make the file smaller and more manageable.

Enhancing Sustainable Transportation with Advancements in Photonic Radar Technology with MIMO and IIR Filtering for Adverse Weather Conditions

Sustainable transportation is crucial in addressing global road safety and environmental challenges. This study introduces a novel photonic radar system, leveraging Linear Frequency-Modulated Continuous Wave (LFMCW) technology for high-speed data transmission. Operating in a homodyne configuration, this system uses a single oscillator to generate both signal and reference waveforms. It incorporates mode division multiplexing (MDM) to enable the detection and ranging of multiple targets, even under adverse atmospheric conditions. To counter atmospheric attenuation, the system is equipped with a 2 × 2 MIMO technique and an Infinite Impulse Response (IIR) filter. Numerical simulations demonstrate the system’s superior performance in range resolution and target detection, achieving significant power improvements. The IIR filter further enhances detection, achieving a power improvement of 200% for target 1 and 276% for target 2. With low power requirements and enhancement through IIR filter equalization, this system presents a viable option for battery-operated vehicles. This innovative approach offers a low-power high-efficiency solution suitable for battery-operated vehicles, promoting safer and more reliable sustainable transportation.

Torque Detection Using EMG Signals for Different Walking Speed of a Person

This paper analyses the different walking speed of a person for torque detection by using the Electromyography (EMG) signals. EMG measures electrical activity and the muscle response to a nerve stimulation. The reduced ability to move due to variation in tendon structured muscle in lower limb joints of legs affects the overall activity of the human lifecycle. This defect makes it difficult to move causing severe pain resulting in disability. In order to overcome this problem, exoskeleton technology is implemented where Electromyography (EMG) signals are collected from electric impulse generated during the muscle movements. From these electric impulses, the torque present in EMG signals are detected and classified accordingly. The first step consists of pre processing EMG signals using Infinite Impulse Response (IIR) filter. Secondly features are extracted from pre processed signals by using Continuous Wavelet Transform (CWT) methods and classified using Convolutional Neural Networks (CNN) classifier. From this classification, the rotational movement of muscles named torque in processed EMG signal is detected. Form detected torque, the normal muscle system and problematic muscle system are differentiated

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.