Infinite Impulse Response Filter Research Articles

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.

Hardware error correction for programmable photonics

Programmable photonic circuits of reconfigurable interferometers can be used to implement arbitrary operations on optical modes, providing a flexible platform for accelerating tasks in quantum simulation, signal processing, and artificial intelligence. A major obstacle to scaling up these systems is static fabrication error, where small component errors within each device accrue to produce significant errors within the circuit computation. Mitigating this error usually requires numerical optimization dependent on real-time feedback from the circuit, which can greatly limit the scalability of the hardware. Here we present a deterministic approach to correcting circuit errors by locally correcting hardware errors within individual optical gates. We apply our approach to simulations of large scale optical neural networks and infinite impulse response filters implemented in programmable photonics, finding that they remain resilient to component error well beyond modern day process tolerances. Our results highlight a potential way to scale up programmable photonics to hundreds of modes with current fabrication processes.

FPGA implementation of high‐speed tunable IIR band pass notch filter for identification of hot‐spots in protein

SummaryLocalization of hot spots in a protein sequence is an important problem for understanding complex 3D structure of amino acid sequence (due to which biological functionality of protein is performed by their mutual interactions). Several digital signal processing (DSP) techniques like digital filtering, wavelet, and Fourier transforms have been used for finding the hot‐spot locations. Fast processing of these longer protein sequences is desirable. Field Programmable Gate Array (FPGA) implementation of Infinite Impulse Response (IIR) band pass notch (BPN) digital filter (tuned automatically by characteristics frequency of protein family) is carried out in this paper for further speed‐up of this protein analysis process. Performance of proposed filter system (implemented on Zynq series Zybo board FPGA) is further improved using retiming, which has significantly increased the maximum clock frequency from 34.304 to 54.812 MHz. Simulation of both MATLAB and FPGA hardware implementation for BPN IIR filter is executed on 10 protein functional groups. The simulation results predict the same hot‐spots locations as mentioned in ASEdb data base, but the Hardware BPN IIR filter (proposed in this paper) is 190 to 278 times faster as compared to its MATLAB counterpart. Some additional new hot‐spot locations (not mentioned in ASEdb data base) have been determined by our proposed hardware filter, which can be the probable hot‐spots for newly identified proteins.

Design of Digital Infinite Impulse Response Filter Based on Artificial Fish Swarm Algorithm

Filter design is an indispensable part in the field of digital signal processing technology. With the continuous improvement of signal processing requirements, the calculations required for digital filter design are becoming more and more complex. Therefore, using biological intelligent optimization algorithms has very important practical significance in improving the quality of digital filters design. According to the characteristics of the infinite unit impulse response filter, taking the coefficients of the system function as the optimization variable, this paper constructs the mathematical optimization model of the digital filter design, which takes the minimum square error of the system function of the design filter and the ideal filter as the optimization the goal. The artificial fish swarm algorithm is used to solve the optimization model of the digital filter, and the realization process of the artificial fish swarm algorithm to solve the filter optimization model is given. We use MATLAB software to simulate, compare and analyse the influence of artificial fish swarm algorithm parameters and filter parameters on the design results. The experimental results show that the artificial fish swarm algorithm can effectively design the digital filter, and the amplitude-frequency response curve of the optimized filter is roughly the same as that of the ideal filter.

The Comparison Features of ECG Signal with Different Sampling Frequencies and Filter Methods for Real-Time Measurement

Electrocardiogram (ECG) signals have been used to monitor and diagnose signs of cardiovascular disease and abnormal signals about the human body. ECG signals are typically characterized by the PR, QRS, QT interval, ST-segment, and heart rate (HR) parameters. ECG devices are widely used for many applications, especially for the elderly. However, ECG signals are often affected by noises from the environment. There are mainly two types of noises that affect the ECG signals: low frequencies from muscle activity and 50/60 Hz from the electrical grid. Removing these noises is important for improving the quality of the ECG signal. A clear ECG signal makes it easy to diagnose cardiovascular problems. ECG signals with high sampling frequency are more accurate. However, the noises in the signal will be more obvious and it will be difficult to remove these noises with filters. We analyzed the symmetrical correlation between the sampling frequency of the signal and the parameters of the signal such as signal to noise ratio (SNR) and signal amplitude. This study will compare characterization of ECG signals performed at different sampling frequencies before and after applying infinite impulse response (IIR) and symmetric finite impulse response (FIR) filters. Therefore, it is critical that the sampling frequency is consistent at the same frequency of the ECG signal for accurate diagnosis. Furthermore, the approach can be also important for the device to help reduce the device’s computing power and hardware resources. Our results were tested with the MIT/ BIH database at 360 Hz sampling frequency with 11-bit resolution. We also experimented with the device operating in real-time with a sampling frequency from 100 Hz to 2133 Hz and a 24-bit resolution. The test results show the advantages of the symmetric FIR filter over IIR when applied to the filtering of ECG signals. The study’s conclusions can be applied to real-world devices to improve the quality of ECG signals.

АНАЛИЗ УПРАВЛЯЕМОСТИ НЕКОТОРЫХ ЦИФРОВЫХ ФИЛЬТРОВ С КОНЕЧНОЙ ИМПУЛЬСНОЙ ХАРАКТЕРИСТИКОЙ

This overview article covers finite impulse response filters and filter banks. The use of these filtersfor hearing aids is considered. Ways to compensate for hearing loss and ways to increase loudnessusing broadband amplification are considered. A schematic diagram of a method for digitalsignal processing using a bank of filters, as well as a technique for synthesizing interpolation filterswith low computational complexity, is presented. Also, the application of the MATLAB system for thesynthesis of narrow-band non-recursive FIR filters, their design procedure, methodology and examplesare considered. Finite Impulse Response (FIR) filters and filter banks have specific propertiesthat guarantee stability. Therefore, they are popular in many applications such as communicationsystems, audio signal processing, biomedical instruments, and so on. Unfortunately, due to the longerwavelength, the cost of implementing an FIR filter is usually not higher than an infinite impulse response(IIR) filter that meets the same requirements. It is well known that the length of an FIR filter isinversely proportional to its transition bandwidth. Therefore, the disadvantage becomes acute when agiven filter has a narrow transition band. The main goal is to consider computationally efficientmethods for designing FIR filters and filter banks. The masking method (FRM) results in significantsavings in the number of multipliers. Next, a 16-band, low group delay, non-equal-spacing digitalFIR filter bank is considered. Overall latency is significantly reduced as a result of a new filter structurethat reduces the interpolation factor for prototype filters. Masking filter may be an interpolatedfinite impulse response (IFIR) filter that helps reduce complexity.

Design of Efficient Low Pass Infinite Impulse Response Filter Using Dynamic Regional Harmony Search Algorithm with Opposition and Local Learning and Comparison with Harmony Search Algorithm

Aim: The study aims to design an optimal digital IIR filter for the minimization of ripples in the frequency response of Low pass IIR filterMethods & Materials: Dynamic Regional Harmony Search Algorithm with opposition & local learning DRHS-OLL method is used for the optimal design of efficient infinite impulse response [IIR] due to presence of the premature convergence in Harmony Search algorithm.Results: DRHS-OLL algorithm has reduced the ripples in both passband & stop band regions of low pass IIR filter. The frequency response of the low pass filter also reached the ideal state when compared to the Harmony Search Algorithm. There is statistical significance difference between DRHS-OLL & HS algorithms where p<0.05 (Independent sample test). The mean gain of DRHS-OLL is 13.5873+/-1dB which is better than the mean gain of HS i.e., 5.7433+/-1dB.Conclusion: The performances conclude that the DRHS-OLL algorithm has better gain & reduced ripples when compared to HS algorithm.

Sparsity-Assisted Signal Denoising and Pattern Recognition in Time-Series Data

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

Wheelchair Movement Based on Convolution Neural Network

This paper intends to develop a methodology for helping amputees and crippled people old, by ongoing voice direction and association between patient and personal computer (PC) where these blends offer a promising response for helping the debilitated people. The major objective of this work is accurately detected audio orders via a microphone of an English language (go, stop, right and left) in a noisy environment by the proposed system. Thus, a patient that utilizes the proposed system can be controlling a wheelchair movement. The venture depends on preparing an off-line dataset of audio files are included 10000 orders and background noise. The proposed system has two important steps of preprocessing to get accurate of specific audio orders, accordingly, the accurate direction of wheelchair movement. Firstly, a dataset was preprocessed to reduce ambient noise by using Butterworth (cutoff 500-5000 Hz) and Wiener filter. Secondly, in the input (a microphone) of the proposed discriminative model put a procedure of infinite impulse response filter (Butterworth), passband filter for cutoff input microphone from 150-7000 Hz for back-off the loud and environment noise and local polynomial approximation (Savitzky-Golay) smoothing filter that plays out a polynomial regression on the signal values. Thus, a better for filtering from ambient noise and keeping on a waveform from distortion that makes the discriminative model accurate when voice orders were recognized. The proposed system can work with various situations and speeds for steering; forward, stop, left and right. All datasets are trained by using deep learning with specific parameters of...

Performance Investigation of Digital Lowpass IIR Filter Based on Different Platforms

The work presented in this paper illuminates the design and simulation of a recursive or Infinite Impulse Response (IIR) filter. The proposed design algorithm employs the Genetic Algorithm to determine the filter coefficients to satisfy the required performance. The effectiveness of different platforms on filter design and performance has been studied in this paper. Three different platforms are considered to implement and verify the designed filter’s work through simulation. The first platform is the MATLAB/SIMULINK software package used to implement the Biquad form filter. This technique is the basis for the software implementation of the designed IIR filter. The HDL – Cosimulation technique is considered the second one; it inspired to take advantage of the existing tools in SIMULINK to convert the designed filter algorithm to the Very high-speed integrated circuit Hardware Description Language (VHDL) format. The System Generator is employed as the third technique, in which the designed filter is implemented as a hardware structure based on basic unit blocks provided by Xilinx System Generator. This technique facilitates the implementation of the designed filter in the FPGA target device. Simulation results show that the performance of the designed filter is remarkably reliable even with severe noise levels.

Experimental and numerical investigations of asymmetric chord-reference system regarding track geometry measurement

This paper studies the error theory of the asymmetric chord-reference system (ACR-system) for track geometry measurement. In contrast to mid-chord offset system (MCO-system), ACR-system shows a much better band-pass response even in very short wavelengths. The implementation of the ACR-system is challenging due to its nonlinear phase response. Based on z-transform, the inverse system of the ACR-system is realized by designing an infinite impulse response (IIR) filter. Moreover, the stability of ACR-system is explained according to the stability of the IIR filter. To quantify the error accumulation of the ACR-system, error amplification factor (EAF) is defined in spatial domain, and critical wavelength (CW) is defined in wavelength domain. To demonstrate the performance of ACR-system, a measurement trolley is developed and calibrated using a high precision marble platform. A field measurement is carried out on a 500-meter-long rail section. Finally, a comparison between the filtering and non-filtering implementations of the inverse system shows that the filtering method outperforms the non-filtering one.

EEG based visual cognitive workload analysis using multirate IIR filters

In this study, the visual cognitive workload is classified using electroencephalogram (EEG) signal acquired with dry electrodes. The visual cognitive workload of four levels is introduced using color objects and their counting in visual space. The EEG data of 44 subjects is acquired for this work. The classification accuracy along with computational complexity involved in subband decomposition technique is discussed in the proposed approach. The EEG signal is decomposed in ten subbands using multirate infinite impulse response (IIR) filter bank. Band energy, signal length, non-linear energy and band entropy of decomposed subbands are used as features. K-nearest neighbors (KNN) and ensemble subspace KNN classifiers are used for classification of cognitive workload. The classification accuracy and computational complexity are compared with two other subband decomposition methods: Morlet wavelet and analytic wavelet transform. The classification accuracy obtained using proposed method is 83.65%, which is greater than the Morlet wavelet (80.96%) and is less than the analytic wavelet transform (86.31%). The proposed method's computational complexity is 70% less than Morlet wavelet and 45% less than analytic wavelet transform.

Design and modeling of adaptive IIR filtering systems using a weighted sum - variable length particle swarm optimization

The use of optimization algorithms for designing Infinite Impulse Response (IIR) filters has been considered in many studies. The concern in this area is the multimodal error surface of such filters and their fitting with filter coefficients. The order of the modeled system has a direct effect on the number of coefficients, complexities of the error surface, and the filter’s stability. This paper proposes an efficient approach based on a variable length particle swarm optimization algorithm with a weighted sum fitness function (WS-VLPSO). The proposed WS-VLPSO is utilized as an effective adaptive algorithm for designing optimal IIR filters. The approach is based on the inclusion of the order as a discrete variable in the particle vector, which is done with the goal of intelligent minimizing of order and thereby reducing the design complexity of IIR filters. To ensure the optimality of the systems, the objective function is considered as a weighted sum. Also, a new criterion called Optimum Modeling Indicator (OMI) is introduced, a measure to determine the percentage reduction of order and the success rate of the proposed approach. The proposed algorithm is also applied for solving the sensor coverage problem as another real-world variable length engineering optimization application. Evaluation of simulation results, with Monte-Carlo simulation, indicates the acceptable improvement of identified structures and the significant performance of the proposed approaches. Note that the source codes of the paper will be publicly available at https://github.com/ali-ece.

Fiber based all-optical infinite impulse response filter tuned via stimulated Brillouin scattering

In this paper, we present a novel configuration for an all-fiber tunable infinite impulse response optical filter using stimulated Brillouin scattering. By applying the Brillouin acoustic waves within the coupling area of an optical coupler, we can control its transmissivity and so to affect the spectral response of the IIR optical filter. In this paper, we explain the principle of operation of the proposed device and experimentally demonstrate the ability to control the spectral response of an optical filter based on fiber ring resonator configuration.

A continuous-state cellular automata algorithm for global optimization

Cellular automata are capable of developing complex behaviors based on simple local interactions between their elements. Some of these characteristics have been used to propose and improve metaheuristics for global optimization; however, the properties offered by the evolution rules in cellular automata have not yet been used directly in optimization tasks. Inspired by the complexity that various evolution rules of cellular automata can offer, the continuous-state cellular automata algorithm is proposed. In this way, the algorithm takes advantage of different evolution rules to maintain a balance that maximizes the exploration and exploitation properties in each iteration. The efficiency of the algorithm is proven with 48 test problems widely used in the literature, 4 engineering applications that were also used in recent literature, and the design of adaptive infinite-impulse response filters, with the reference functions of 10 full-order filters being tested. The numerical results prove its competitiveness in comparison with state-of-the-art algorithms. The source codes of the proposed algorithm are publicly available at https://github.com/juanseck/CCAA.git.

Transformations for FIR and IIR Filters’ Design

In this paper, the transfer functions related to one-dimensional (1-D) and two-dimensional (2-D) filters have been theoretically and numerically investigated. The finite impulse response (FIR), as well as the infinite impulse response (IIR) are the main 2-D filters which have been investigated. More specifically, methods like the Windows method, the bilinear transformation method, the design of 2-D filters from appropriate 1-D functions and the design of 2-D filters using optimization techniques have been presented.

Variable Admittance Control Based on Human-Robot Collaboration Observer Using Frequency Analysis for Sensitive and Safe Interaction.

A collaborative robot should be sensitive to the user intention while maintaining safe interaction during tasks such as hand guiding. Observers based on the discrete Fourier transform have been studied to distinguish between the low-frequency motion elicited by the operator and high-frequency behavior resulting from system instability and disturbances. However, the discrete Fourier transform requires an excessively long sampling time. We propose a human–robot collaboration observer based on an infinite impulse response filter to increase the intention recognition speed. By using this observer, we also propose a variable admittance controller to ensure safe collaboration. The recognition speed of the human–robot collaboration observer is 0.29 s, being 3.5 times faster than frequency analysis based on the discrete Fourier transform. The performance of the variable admittance controller and its improved recognition speed are experimentally verified on a two-degrees-of-freedom manipulator. We confirm that the improved recognition speed of the proposed human–robot collaboration observer allows us to timely recover from unsafe to safe collaboration.

Design of a nearly linear-phase IIR filter and JPEG compression ECG signal in real-time system

This paper presents the design of an Electrocardiogram (ECG) signal acquisition system based on 32-bit microprocessor platform. In hardware design, we present a complete design similar to the wearable devices used to monitor heart rates over the long term. The firmware is applied by an adaptive data compression method to increase storage space and reduces data transmission time between hardware devices and personal computer (PC). This adaptive algorithm is a combination of Joint Photographic Experts Group (JPEG) compression. The ECG signal was applied to the nearly-linear infinite impulse response (IIR) filter to eliminate noise from ECG signals. There are advantages of nearly-linear IIR filter over traditional IIR that the signal pass through filter is not distorted. Improvements in the JPEG compression algorithm make them suitable for one dimensional signals with a resolution greater than 8 bits and optimize the operation of the Huffman compression as well as the microcontroller unit (MCU) system with low processing speed. Placing the IIR filter at the end of the system promotes the ability to filter while reducing signal distortion after the decompression process. We tested the system with 8 records from the MIT/BIH cardiac arrhythmic database before and after we applied the compression algorithm to the firmware and software in the system that shows the effectiveness of these algorithms. The test results show that the system reached a maximum compression ratio of 8,8. The system deployed on the 32bit MCU (PIC32MZ2048), ECG analog front-end ADS1293 and operation time of ECG device up to 110 h (4.5 days).

Weighted Least Square Design Technique for Hilbert Transformer using Fractional Derivative

In this paper, a new design method for realization of Hilbert transformer (HT) by an all-pass infinite impulse response (IIR) filter is proposed. The design problem is devised as minimization of the weighted least squared errors of the phase response in the frequency domain, where the phase response of denominator polynomial of HT acts as a weighting function. The least squared design techniques usually suffer from large errors at the band edge frequencies. To resolve this issue, our aim is to have the phase response of the all-pass IIR filter closely matched with the desired phase response of HT. The approximation with desired phase response of HT has been achieved by using fractional derivatives constraints (FDCs). In our experiments, it is observed that the design problem using single fractional derivative (FD) has a multimodal behavior in nature. Moreover, highly precise value of order of FD is required, whose exploration task is computationally expensive. Therefore, recently developed heuristic search techniques, also known as swarm-based optimization techniques (SOTs), such as particle swarm optimization (PSO), and its variants, cuckoo search (CS) algorithm, and artificial bee colony (ABC) algorithm, are used for finding the required values. However, these methods are capable of solving non-differentiable and multimodal problems due to their multi-dimensional randomly guided search mechanism. The proposed methodology has gained 59% improvement in phase approximation error as compared to conventional reported techniques.