Infinite Impulse Response Filter Design Research Articles

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.

Kalman filter/smoother-based design and implementation of digital IIR filters

Recently, a unified framework was proposed for forward-backward filtering and penalized least-squares optimization. It was shown that forward-backward filtering can be presented as instances of penalized least-squares optimization. In other words, the output of a zero-phase digital infinite-impulse response (IIR) filter can be computed by solving a constrained optimization problem, in which the weight controlling the constraint is directly related to cutoff frequencies with closed-form equations. It was also shown that a zero-phase digital IIR filter can be formed as an optimal smoothing Wiener filter for a random process obtained from an autoregressive (AR) or AR-moving average (ARMA) model driven by input (innovation) noise in presence of an observation noise.In this paper, the problem of zero-phase digital IIR filtering is re-examined using Kalman filter/smoother. The paper shows that every zero-phase digital IIR filter can be viewed as a special case of an optimal smoothing Wiener filter. Based on the fact that the formulations of the optimum filter by Wiener and Kalman are equivalent in steady state, we present a Kalman filter/smoother framework to the design and implementation of digital IIR filters. As an example, the zero-phase digital Butterworth filter is designed using Kalman smoother and compared with the traditional design (forward filtering and backward smoothing) method.

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

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

WITHDRAWN: High performance IIR filter design with MOD-GDI based array multiplier

Abstract Due to an upsurge in technology, there is a need for development resemblance in portable devices besides its high speed and low power capability. The most critical factors are area, total power dissipation, and propagation delay to estimate a device's performance. Signal processing modules viz. Finite Impulse Response (FIR) or Infinite Impulse Response (IIR) filters are fundamental elementary logics in DSP systems. Performance optimization of a digital IIR filter is always trendy for VLSI DESIGN Engineers. We can also achieve by improving sub-modules' efficiency (like. adder, multiplier, and delay elements) required to realize the filter architecture. This paper aims to extract a layout of the IIR filter implemented using a high-speed 4-bit Array Multiplier. The multiplier for this IIR designed with Modified Gate Diffusion Input (MOD-GDI) technique reduces the additional circuitry, which reduces the area and average power dissipation of overall filter logic. Extracted the layout by using Mentor Graphics EDA tools (with 130 nm technology). Compared to the performance characteristics like area, delay, the power consumption of the proposed and conventional IIR filters. The proposed IIR filter is space-efficient and consumes less power than the traditional IIR filters.

High performance IIR filter implementation on FPGA

This paper presents an improved design of reconfigurable infinite impulse response (IIR) filter that can be widely used in real-time applications. The proposed IIR design is realized by parallel–pipeline-based finite impulse response (FIR) filter. The FIR filters have excellent characteristics such as high stability, linear phase response and fewer finite precision errors. Hence, FIR-based IIR design is more attractive and selective in signal processing. In addition, the other two modern techniques such as look-ahead and two-level pipeline IIR filter designs are also discussed. All the said designs have been described in hardware description language and tested on Xilinx Virtex-5 field programmable gate array board. The implementation results show that the proposed FIR-based IIR design yields better performance in terms of hardware utilization, higher operating speed and lower power consumption compared to conventional IIR filter.

IIR Filter Design Using African Buffalo Optimization

In the modern world, the digital signal processing embeds more in real time applications. Several researchers focused on filtering process to identify the limitation in traditional methods. In this article, the meta-heuristic algorithm is deployed for optimizing infinite impulse response (IIR) filter design. The traditional IIR filter results create computational complexity and its performance is worse in the case of a noisy environment. In signal processing, IIR plays several roles in filtering and monitoring the signal amplitude. The African Buffalo Optimization (ABO) is quite easy for implementation and its performance outcomes solved many problems in various domains. Hence, it is selected for solving IIR filter problems for obtaining optimal filter coefficients. Initially, IIR filter is designed for different orders under ABO concept. The ABO based IIR filter’s performance is superior to those obtained by Genetic Algorithm and cuckoo search algorithm. The proposed method’s performance result proves that it has a smaller magnitude error and phase error with fast convergence rate.

Design of digital IIR filter: A research survey

This paper presents an overview on advancement made in designing of a digital infinite impulse response (IIR) filter. The design problem of an IIR filter was found to be challenging due to presence of poles in its transfer function. This makes the phase response of an IIR transfer function nonlinear and its magnitude response is also drifted due to quantization of coefficient values of denominator polynomials, which leads towards instability. Therefore, numerous efforts were made in order to acquire an optimal filter response using several optimization methods. Design problem of an IIR filter with various constraints was developed and solved using gradient based techniques, which resulted in optimal passband response with nearly linear phase or in some cases, absolute linearity was also achieved. However, the obtained solutions were sub-optimal in many cases due to transferring the multimodal design problem of an IIR filter into convex optimization. The solution was also affected due to the quantization of filter coefficients and in case of absolute linear phase response; a strong hick in magnitude response at beginning of transition edge frequency was obtained. To overcome the sub-optimality, researchers used evolutionary algorithms (EAs) for designing of an IIR filter. In time domain, system identification (SI) was adopted, whereas various error functions were developed in frequency domain for obtained magnitude responses close to desired response. This approach resulted in an optimal IIR filter response, however phase response linearity was not improved. Thus, EA approach was appropriated for lower order IIR filters. The design of various IIR filters like lowpass filter (LPF), highpass filter (HPF), bandpass filter (BPF) and bandstop filter (BSP) using an all-pass infinite impulse response (APF-IIR) was also reported. This approach is most appropriate, because the filter response is stable, nearly linear and magnitude response was also accurate. However, there is high error at the band edges of passband. Therefore, literature reveals that an APF based approach is most appropriate for various magnitude response filters.

A sigmoid attractiveness based improved firefly algorithm and its applications in IIR filter design

Recently, a novel population-based optimisation algorithm, namely firefly algorithm (FA), which mimics the flashing and attraction behaviour of fireflies, has shown promising performance in solving global optimisation problems. However, the preliminary studies have shown that FA often gets stuck in local optima. In this paper, we investigate the reasons why the FA suffers from getting stuck in local optima; and then propose an improved firefly algorithm (IFA). These improvements are twofold: first, a sigmoid-based attractiveness is employed to reformulate its definition and strengthen its local refinement ability; second, a dynamic step parameter tuning strategy is designed to adjust the random search intensity and narrow the search space iteratively to strengthen its global search ability. The empirical results indicate IFA can well balance between the global exploration and the local exploitation, and provides the best solutions, at least the competitive results, for most of 12 global optimisation problems over other FA variants. Besides, by employing IFA to solve well-known infinite impulse response filter design problems, we evaluate the effectiveness and efficiency of IFA. The experimental results and comparisons show that IFA performs better than, at least as competent again, other meta-heuristics in terms of the solution accuracy, solution robustness, and convergence rate.

Adaptive iir filter design using self-adaptive search equation based artificial beecolony algorithm

Infinite impulse response (IIR) system identification problem is defined as an IIR filter modeling to represent an unknown system. During a modeling task, unknown system parameters are estimated by metaheuristic algorithms through the IIR filter. This work deals with the self-adaptive search-equation-based artificial bee colony (SSEABC) algorithm that is adapted to optimal IIR filter design. SSEABC algorithm is a recent and improved variant of artificial bee colony (ABC) algorithm in which appropriate search equation is determined with a self-adaptive strategy. Moreover, the success of the SSEABC algorithm enhanced with a competitive local search selection strategy was proved on benchmark functions in our previous studies. The SSEABC algorithm is utilized in filter modelings which have different cases. In order to demonstrate the performance of the SSEABC algorithm on IIR filter design, we have also used canonical ABC, modified ABC (MABC), best neighbor-guided ABC, and an ABC with an adaptive population size (APABC) algorithms as well as other algorithms in the literature for comparison. The obtained results and the analysis on performance evolution of compared algorithms on several filter design cases indicate that SSEABC outperforms all considered ABC variants and other algorithms in the literature.

Optimal Design of IIR Filters via the Partial Fraction Decomposition Method

In this brief, we consider the minimax design of infinite impulse response (IIR) filter. First, we apply the partial fraction method to decompose the transfer function into a sum of low order fractions, as a result the minimax IIR filter design problem can be formulated with the stability condition as constraints. Second, among different possible decompositions of the transfer function, we prove that the second order decomposition can always achieve good approximation to the optimal solution. Based on the second order decomposition formulation, several numerical experiments have been conducted, and better IIR filters can be designed using the proposed method compared with existing methods in the literature.

Design of Pipelined IIR Filters Using Two-Stage Frequency-Response Masking Technique

A pipelined, high-speed infinite impulse response (IIR) filter based on the frequency-response masking (FRM) technique has been proposed by Johansson and Wanhammar. In their method, the bandedge shaping filters are a pair of IIR power complementary filters whose ${z}$ -transform functions are functions of ${z} ^{M}$ and the masking filters are linear phase finite impulse response (FIR) filters. The IIR bandedge shaping filters have $M$ delays in the feedback loop and can be pipelined by ${M}$ stages. In this brief, we present a two-stage FRM method for reducing the number of multipliers of the pipelined IIR filter proposed by Johansson and Wanhammar for a given magnitude response specification and a given number of pipeline stages in the feedback loop. In our design, all the subfilters are optimized jointly. We show that our two-stage masking design requires fewer multipliers if the number of pipeline stages in the feedback loop is larger than four.

Area- and Power-Efficient Nearly-Linear Phase Response IIR Filter by Iterative Convex Optimization

Low complexity infinite impulse response (IIR) filter design with nearly-linear phase response has attracted considerable attention in recent years due to the substantially high area and power consumption of linear phase finite impulse response (FIR) filter. Compared with the FIR filter, designing an IIR filter with the minimized group delay deviation and low power cost is a challenging topic. In this paper, the non-convex group delay deviation minimization problem for IIR filter design is reformulated into an iterative optimization problem to achieve lower group delay deviation. The hardware complexity of the solution is iteratively reduced by approximating the IIR filter coefficients to maximize the number of eliminable common subexpressions. The headroom for the coefficient adjustment is governed by the gradient of group delay deviation between iterations. Using our proposed design algorithm, a high-order lowpass filter with a minimum stopband attenuation of 60dB can be implemented by a 13-tap IIR filter with a group delay deviation of 0.002 only, as opposed to two linear-phase FIR filters designed by two recent and competitive FIR filter design algorithms with tap number of 51 and 57, respectively. Logic synthesis shows that the proposed IIR design saves 39.4% of the area and 41.8% of power consumption over the FIR solutions. Comparing with the latest nearly-linear phase IIR filter design algorithms, the group delay deviation of the solutions generated by our proposed algorithm are on average 25.5% lower, along with an average area and power savings of 20.5% and 18.4%, respectively, from the logic synthesis results.

Optimal design of IIR filters based on least ℓp‐norm using a novel meta‐heuristic algorithm

AbstractIn this work, an optimal infinite impulse response (IIR) filter is designed using least Pth norm and new meta‐heuristic optimization algorithm called Crow Search Algorithm (CSA). The two approaches try to get a resolution to the specified problem but use unlike approaches to perform so. Furthermore, the ℓp‐norm is used to design optimum IIR filters. The main motivation of using CSA is its user‐friendly optimization tool for both beginner and professional users. This Algorithm provides a good stability between variety and strength which are mainly controlled by the parameter of awareness probability. Some examples of IIR filter design cases are treated, and a comparison is done. It is revealed that the design objective is successfully attained, and the resultant filter parameters are better using the ℓp‐norm combined with the proposed CSA meta‐heuristic optimization algorithms compared with IIR filter designed using butter synthesis approach in terms of accuracy and robustness. Classical approaches are largely outperformed by CSA for the same filter order.

A Fuzzy Gravitational Search Algorithm to Design Optimal IIR Filters

The goodness of Infinite Impulse Response (IIR) digital filters design depends on pass band ripple, stop band ripple and transition band values. The main problem is defining a suitable error fitness function that depends on these parameters. This fitness function can be optimized by search algorithms such as evolutionary algorithms. This paper proposes an intelligent algorithm for the design of optimal 8th order IIR filters. The main contribution is the design of Fuzzy Inference Systems able to tune key parameters of a revisited version of the Gravitational Search Algorithm (GSA). In this way, a Fuzzy Gravitational Search Algorithm (FGSA) is designed. The optimization performances of FGSA are compared with those of Differential Evolution (DE) and GSA. The results show that FGSA is the algorithm that gives the best compromise between goodness, robustness and convergence rate for the design of 8th order IIR filters. Moreover, FGSA assures a good stability of the designed filters.

A Continuous-Time Digital IIR Filter With Signal-Derived Timing and Fully Agile Power Consumption

Presented is the first continuous-time (CT) digital infinite impulse response (IIR) filter working on signal-derived timing in lieu of a clock. We introduce a novel design method which enables the design of high-order IIR filters using only two tap delays. An event-grouping technique is also introduced to prevent parasitic oscillations in the presence of tap delay mismatches. The 1.2-V, 65-nm CMOS prototype implements a sixth-order IIR filter, with a maximum input rate of 20 Msample/s and a stop-band rejection of more than 80 dB. Without using any power-down circuitry, the chip’s power consumption tracks the input activity in a fully agile manner, and varies by more than $50\times $ , from 0.04 to 2.32 mW. The filter achieves an figure of merit (FoM) which competes with that of discrete-time (DT) filters, while avoiding the use of a clock and an antialiasing filter. Compared to prior art in CT digital signal processings, the prototype achieves 45-dB improvement in stop-band rejection and 9 $\times $ smaller delay line area. For the first time, the filtered CT digital signal is converted to synchronous mode at the end of signal chain, allowing integration with DT digital systems.

Design of IIR Filter Using PSO Algorithm and its Implementation in FPGA

In this paper, Particle Swarm Optimization (PSO) algorithm based Adaptive Infinite Impulse Response (IIR) filter is designed. PSO algorithm is inspired by bird or fish swarm movement. PSO as global optimization technique provides advantages of simple implementation, quickly convergence providing better results and robustness for avoiding local minima problem. The designed PSO based Adaptive filter is then implemented to work as a noise canceller. Simulation results shows that the designed filter is effective with good noise suppression. The PSO based filter is also synthesized in FPGA and run fittings in small commercial devices Stratix-II and cyclone-II Keywords: Adaptive Infinite Impulse Response (IIR) Filter, FPGA implementation, Particle Swarm Optimization, VHDL

High order stable infinite impulse response filter design using cuckoo search algorithm

In this paper, an efficient technique for optimal design of digital infinite impulse response (IIR) filter with minimum passband error (e p ), minimum stopband error (e s ), high stopband attenuation (A s ), and also free from limit cycle effect is proposed using cuckoo search (CS) algorithm. In the proposed method, error function, which is multi-model and non-differentiable in the heuristic surface, is constructed as the mean squared difference between the designed and desired response in frequency domain, and is optimized using CS algorithm. Computational efficiency of the proposed technique for exploration in search space is examined, and during exploration, stability of filter is maintained by considering lattice representation of the denominator polynomials, which requires less computational complexity as well as it improves the exploration ability in search space for designing higher filter taps. A comparative study of the proposed method with other algorithms is made, and the obtained results show that 90% reduction in errors is achieved using the proposed method. However, computational complexity in term of CPU time is increased as compared to other existing algorithms.

Design of digital IIR filter with low quantization error using hybrid optimization technique

In this paper, a hybrid optimization technique based on particle swarm optimization (PSO) and artificial bee colony (ABC) algorithm is presented for the optimal design of infinite impulse response (IIR) filter with low quantization effect. In this method, different variants of PSO have been exhaustively tested, and the time varying coefficients-PSO (TVC-PSO) is used to formulate a new hybrid technique for better exploitation and exploration, which is further modified by sorting and replacement mechanism of Scout Bee from ABC algorithm. For designing IIR filter, an objective function is constructed that satisfies the absolute error including peak ripples in passband and stopband regions in frequency domain, while stability of designed filter is confirmed by exploiting the lattice form structure during iterative computation that also reduces computation complexity. Several attributes such as passband error $$(e_{\mathrm{p}})$$ , stopband error $$(e_{\mathrm{s}})$$ , and stopband attenuation $$(A_{\mathrm{s}})$$ are used to measure the performance of proposed algorithm. The simulation results presented in this paper evidence that this technique can be effectively used for designing digital IIR filter with higher filter taps, and low quantization effect for fixed number of bits.

Finite-difference time-domain analysis in porous materials by using Z-transform theory and infinite impulse response filter design

Porous materials are wildly used to control noise in various places. When simulating sound field containing porous materials by Finite-difference time-domain (FDTD) method, it is necessary to develop FDTD formulations in the porous materials. When the frame of the porous material is motionless, the porous material can be replaced on the macroscopic scale by an equivalent fluid. Based on the equivalent fluid model, FDTD formulations in the porous material are developed. The method combines IIR (Infinite impulse response) filter design and Z transform theory. The effective bulk modulus and the effective density of the porous material are frequency dependant, which is designed as IIR filter. Wave equations with IIR filter modeled parameters are solved in Z domain to avoid troublesome convolution integrals in the time domain. At the Z domain, new parameters are assumed to simplify the wave equations. Finally, wave equations and assumed parameters are returned back to the time domain. The accuracy of the proposed method is verified with the measurement. As two application examples, porous material coved with permeable membrane or non-permeable membrane are simulated and compared with the theory.

Integrated Cat Swarm Optimization and Differential Evolution Algorithm for Optimal IIR Filter Design in Multi-Objective Framework

This paper proposes an integrated optimization technique which combines the features of the cat swarm optimization (CSO) algorithm with the traditional differential evolution (DE) algorithm and applies it for the optimal design of digital infinite impulse response (IIR) filters. Traditional design methods treat the digital IIR filter design as a single-objective optimization problem by taking into account the minimization of magnitude response error only and lack in considering the linear phase response error and the order of the filter. The aim of this paper was to design an IIR filter in multi-objective framework by equally considering the minimization of magnitude response error, the linear phase response error and the order of the filter. Firstly, the CSO algorithm is applied for digital IIR filter design. In order to start with a better solution set, the opposition-based learning strategy is then incorporated. To further improve the performance of CSO for designing stable digital IIR filters, the DE optimization algorithm is combined with CSO hence producing an integrated algorithm called multi-objective cat swarm and differential evolution algorithm (MOCSO-DE) which has the capability to explore and exploit the solution search space locally as well as globally. The developed integrated algorithm is effectively applied for the designing of the digital IIR low-pass (LP), high-pass (HP), band-pass (BP) and band-stop (BS) filters. To evaluate the effectiveness of the developed integrated algorithm, the computational results are compared with some well-established algorithms and it is observed that the developed algorithm is superior or at least comparable to other algorithms in getting better magnitude response and linear phase response together with lowest filter order and can also be implemented for the higher-order filter designs.