Infinite Impulse Response Research Articles

EMG Signal Filtering Based on Variational Mode Decomposition and Sub-Band Thresholding.

Surface electromyography (EMG) signals are inevitably contaminated by various noise components, including powerline interference (PLI), baseline wandering (BW), and white Gaussian noise (WGN). These noises directly degrade the efficiency of EMG processing and affect the accuracy and robustness of further applications. Currently, most of the EMG filters only target one category of noise. Here, we propose a novel filter to remove all three types of noise. The noisy EMG signal is first decomposed into an ensemble of band-limited modes using variational mode decomposition (VMD). Each category of noise is located within specific modes and is separately removed in sub-bands. In particular, WGN is suppressed by soft thresholding with a noise level-dependent threshold. The denoising performance was assessed from simulated and experimental signals using three performance metrics: the root mean square error ([Formula: see text]), the improvement in signal-to-noise ratio ([Formula: see text]), and the percentage reduction in the correlation coefficient ( η). Other methods, including traditional infinite impulse response (IIR) filters, empirical mode decomposition (EMD) method, and ensemble empirical mode decomposition (EEMD) method, were examined for comparison. The proposed method achieved the best performance to remove BW or WGN. It also effectively reduced PLI noise when the signal-to-noise ratio (SNR) was low. The SNR was improved by 18.6, 19.2, and 8.0dB for EMG signals corrupted with PLI, BW, and WGN at -6dB SNR, respectively. The experimental results illustrated that noise was completely removed from resting states, and obvious spikes were distinguished from action states. For two of the ten subjects, the improved SNR reached 20dB. This study explores the special characteristics of VMD and demonstrates the feasibility of using the VMD-based filter to denoise EMG signals. The proposed filter is efficient at removing three categories of noise and can be used for any application that requires EMG signal filtering at the preprocessing stage, such as gesture recognition and EMG decomposition.

Height Adjustable Triangular (HAT) Window Function for Impulse Response Modification of Signal Processing Systems

A widow function, in signal processing and statistics, is a mathematical function that has zero values outside its chosen interval or limit of sequence, normally symmetric around the middle of the interval. Usually the middle of the window is either maximum or near maximum and tappers smoothly as it moves away to the sides. When another function or sequence of data is mathematically multiplied by the window function it forces the product to assume its nature of zero-value outside the interval and tapering from middle to the sides. Windows are finite functions and their main function is to modify an infinite impulse response sequence so as to make it finite within its chosen interval in system design. Several windows are in existence and they include Hamming, rectangular, Han, Kaiser, Triangular, Blackman, Sine, Blackman-Harris, Gaussian, Doph-Chebyshev and Lanczos, windows. Others are Parzen, Nuttall, flat top, Turkey, windows and many more. The window to apply in the design depends on the characteristics of the signal to be processed, types of system to be implemented and quality of output desired. In this paper, a new window called Height Adjustable triangular (HAT) window function is developed and added to the list of windows for signal processing system designs. The effectiveness of the window is tested by examining its characteristics. The adjustment parameter varies the amplitude or height of the window. Result shows that it is stable and linear.

Height Adjustable Triangular (HAT) Window Function for Impulse Response Modification of Signal Processing Systems

A widow function, in signal processing and statistics, is a mathematical function that has zero values outside its chosen interval or limit of sequence, normally symmetric around the middle of the interval. Usually the middle of the window is either maximum or near maximum and tappers smoothly as it moves away to the sides. When another function or sequence of data is mathematically multiplied by the window function it forces the product to assume its nature of zero-value outside the interval and tapering from middle to the sides. Windows are finite functions and their main function is to modify an infinite impulse response sequence so as to make it finite within its chosen interval in system design. Several windows are in existence and they include Hamming, rectangular, Han, Kaiser, Triangular, Blackman, Sine, Blackman-Harris, Gaussian, Doph-Chebyshev and Lanczos, windows. Others are Parzen, Nuttall, flat top, Turkey, windows and many more. The window to apply in the design depends on the characteristics of the signal to be processed, types of system to be implemented and quality of output desired. In this paper, a new window called Height Adjustable triangular (HAT) window function is developed and added to the list of windows for signal processing system designs. The effectiveness of the window is tested by examining its characteristics. The adjustment parameter varies the amplitude or height of the window. Result shows that it is stable and linear.

Modification of Standard Kalman Filter Based on Augmented Input Estimation and Deadbeat Dissipative FIR Filtering

ABSTRACT This paper presents modification of standard Kalman filter (KF) based on augmented input estimation (AIE) and deadbeat dissipative FIR filtering (DDFF) for maneuvering target tracking. Although KF is a well-known tool in estimation and tracking but it has two weaknesses, disability in maneuvering motions and lack of robustness against temporary model uncertainty. For the first problem, the AIE is proposed to cover both the non-maneuvering and maneuvering parts of motion and so the maneuver detection procedure is eliminated. This model with an input estimation (IE) approach is a special augmentation in the state space model which considers both the state vector and the unknown input vector as a new augmented state vector. Also, KF is based on infinite impulse response (IIR) structure and cannot be robust against temporary model uncertainty. Unlike IIR, finite impulse response (FIR) filter is robust and stable against model uncertainty. So, for the second problem, an extra type of FIR filter named DDFF is introduced in this paper that not only has the intrinsic properties of FIR but also can improve these features by tuning some weighting parameters. In the last examples of the paper, the advantages of the proposed model against other models are shown.

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.

Direct Consideration of Eddy Current Losses in Laminated Magnetic Cores in Finite Element Method (FEM) Calculations Using the Laplace Transform

The following article presents a computation procedure that enables us to simulate the dynamic states of electric machines with a laminated magnetic core, with direct consideration of the eddy current losses. The presented approach enables a significant reduction of the simulation process computational complexity. The verification of the obtained data correctness is based on a detailed balance of energy and power in the investigated system. The correctness of the obtained results was also confirmed by comparing them with the results included in norms that describe the losses in laminated sheets. The presented approach is based on expressing the equivalent permeability of transformer metal sheets by using RC or RL circuits. The impedances of these circuits are treated as the transmittance of Infinite Impulse Response filters (IIR) of the Laplace s variable. In this form they are implemented in direct calculations of the dynamics of electric machines based on field-circuital models, using the Finite Element Method (FEM). In this way, we present the method of including eddy current losses in laminated metal circuits of chokes or transformers, during calculations using the finite element method, with the IIR filter in the domain of the variable s of the Laplace transform. Eddy current losses are directly included in the calculation process. Therefore, they have a direct impact on the transient state waveforms. However, the use of the Laplace variable s caused an excessive increase in the number of state variables, and the overall computational efficiency of the presented method is sufficiently low so as to be used in the simulation process of electrical machine dynamic states with a relatively large number of elements in the FE Model.

A new design approach for nearly linear phase stable IIR filter using fractional derivative

In this paper, a new design method for digital infinite impulse response &#x0028 IIR &#x0029 filters with nearly linear-phase response is presented using fractional derivative constraints &#x0028 FDCs &#x0029 . In the proposed method, design problem of an IIR filter is constructed as the minimization of phase error between the desired and designed phase response of an allpass filter &#x0028 APF &#x0029 such that the designed lowpass filter &#x0028 LPF &#x0029 or highpass filter &#x0028 HPF &#x0029 yields less passband &#x0028 ep &#x0029 , and stopband errors &#x0028 es &#x0029 with optimal stopband attenuation &#x0028 As &#x0029 . In order to have accurate passband &#x0028 pb &#x0029 response, FDCs are imposed on appropriate reference frequency, where the optimality of these FDCs are ensured by using a new greedy based sorting mechanism. The simulated results reflect the efficiency of the proposed method in term of improved passband response along with better transition width. However, small reduction in As is observed within the allowable limit, when compared to non-fractional design approach, but the designed filter remains immune to wordlength &#x0028 WL &#x0029 effect.

All-pass-based design of nearly-linear phase IIR low-pass differentiators

ABSTRACT A new approach to the design of the nearly linear phase infinite impulse response low-pass differentiators using a parallel all-pass structure is discussed in this paper. The magnitude and phase responses of the proposed low-pass differentiators are first formulated as functions of the phase responses of the corresponding all-pass filters, and a set of equations is derived such that the magnitude response approximates the ideal one in the weighted Chebyshev sense both in the passband and the stopband. The maximum passband phase response linearity error is shown to be related to the maximum passband magnitude error and the value of an additional design parameter. Comparison with the existing nearly-linear phase infinite impulse response low-pass differentiators shows that the low-pass differentiators designed using the proposed method usually require less multiplications, which comes at the cost of a somewhat higher filter order and consequently higher group delay. However, as the reduced number of multiplications lead to lower power consumption if hardware implementation is considered, the proposed low-pass differentiators are an attractive alternative in applications where low group delay is not of crucial importance.

A Simplified Realization of Zero Frequency Filter for Hardware Implementation

Zero frequency filtering (ZFF) is a well-explored technique for detecting glottal closure instants (GCIs) from the speech signal. The features extracted from the ZFF of the speech signal have also been used for many speech-based applications. Zero frequency resonators used in the infinite impulse response (IIR) realization of ZFF are unstable filters. Consequently, the filter output grows or decays in an unbounded manner. In the finite impulse response (FIR) realization of ZFF, the filter instability problem is address by using more adders and multipliers. In this paper, a simplified stable IIR realization of ZFF (SIIR-ZFF) is proposed for efficient hardware implementation. The SIIR-ZFF requires significantly less hardware than the earlier reported IIR-ZFF as well as FIR-ZFF. Performance of the proposed approach for the task of detecting GCIs from speech signal is found to be almost equivalent to that of the conventional IIR-ZFF. The hardware architecture for the proposed approach is designed and implemented on FPGA.

Root tracking using time-varying autoregressive moving average models and sigma-point Kalman filters

Root tracking is a powerful technique that provides insight into the mechanisms of various time-varying processes. The poles and the zeros of a signal-generating system determine the spectral characteristics of the signal under consideration. In this work, time-frequency analysis is achieved by tracking the roots of time-varying processes using autoregressive moving average (ARMA) models in cascade form. A cascade ARMA model is essentially a high-order infinite impulse response (IIR) filter decomposed into a series of first- and second-order sections. Each section is characterized by real or conjugate pole/zero pairs. This filter topology allows individual root tracking as well as immediate stability monitoring and correction. Also, it does not suffer from high round-off error sensitivity, as is the case with the filter coefficients of the direct-form ARMA structure. Instead of using conventional gradient-based recursive methods, we investigate the performance of derivative-free sigma-point Kalman filters for root trajectory tracking over time. Based on simulations, the sigma-point estimators provide more accurate estimates, especially in the case of tightly clustered poles and zeros. The proposed framework is applied to real data, and more specifically, it is used to examine the time-frequency characteristics of raw ultrasonic signals from medical ultrasound images.

Piezoelectric Dual-Axis Cantilever Force Measurement Probe

A piezoelectric cantilever force sensor consisting of a mechanical cantilever probe, an analog amplifier and an embedded digital signal processing unit is presented. The sensor is constructed from a lead zirconate titanate (PZT) piezo disc, which was cut into four segments and a 33-mm-long metal probe attached to it perpendicularly to the piezo disc plane. The signal of the piezoelectric elements was amplified with a four channel operational amplifier circuit having voltage gain of 10 (20 dB) in each channel and input resistance of 50 $\text{M}\Omega $ . The digital signal processing was implemented on an Arduino Due -based hardware. Signal pre-processing block consists of three cascaded digital infinite impulse response (IIR) filters were composed: A DC-blocking filter, a notch filter and a low pass filter. Additionally, a moving average filter was implemented to further enhance the system tolerance to power line interference and to reduce the sampling rate to 50 Hz. Finally, a LabVIEW software is used to provide an interface for the Arduino Due. The calibration of the system was carried out with a texture analyzer and it was capable of sensing biaxial forces in the range of 2 millinewton with a RMS error of less than 60 micro Newton and hysteresis in the order of 0.25 millinewton or 12.5% of the full scale reading. Cross talk ratio between axes was 25.3 or 28.0 in dB. The applicable force measurement frequency range was 0.5 – 10 Hz.

Appraising the Financial Sustainability of a Pension System with Signal Processing

One key issue of the Spanish pension system is its financial sustainability in regard to slumping fertility rate and rising longevity of the Spanish population. The paper presents a versatile and robust model that may help pension managers gain insight into the future Spanish pyramid of ages, from which they will appraise cash inflows and outflows of the pension system. The model forecasts ninety years of the Spanish population for each cohort of the pyramid of ages. Borrowed from the signal processing discipline, the model relies on the Burg method which fits a pth order autoregressive (AR) model to the input signal by minimizing (least squares) the forward and backward prediction errors while constrai-ning the AR parameters to satisfy the Levinson-Durbin recursión, then uses an infinite impulse response prediction error filter. Results add better perspective and insight to the Spanish population projection forecasted by the United Nations Population Division.

Development of equation-error adaptive IIR-filter-based active noise control system

An infinite impulse response (IIR) filter benefits to identify complex primary and secondary paths information with short filter length for active noise control (ANC) system. Conventional output-error (OE)-based system uses adaptive IIR filter with the filtered-U recursive least mean square (FURLMS) algorithm to cancel undesired noise. This paper develops an ANC system using equation-error (EE)-based IIR filter and analyzes its step size bound, global minimum and computation complexity. The EE-based ANC system can also solve measurement error problem for the error sensor. Several simulations show effectiveness and enhancement of the developed EE-based system.

OPTIMIZATION-BASED RECURSIVE FILTERING FOR VIBROSEIS HARMONIC NOISE ELIMINATION

The vibroseis sources always produce harmonics, which are considered here as noise to be eliminated. An algorithm for removing such noise has been developed based on the previously obtained mathematical model of a vibroseis wavelet distorted by the harmonics. It is shown that the ideal inversion operator is recursive, i.e. is a filter with infinite impulse response. However, due to the peculiarity of the problem, this operator is successfully approximated by a filter with a short impulse response, allowing linearization of the optimization scheme. An objective being the energy of the noise attenuation result is formed. It allows application of the geometrical divergence correction despite the fact that it introduces distortions in the signal shape and the harmonics.

On Using a Genetic Algorithm in the Designing of Linear Digital Filters

Digital filters, either as filters with moving average (Finite Impulse Response) or autoregressive filters (Infinite Impulse Response), are widely used in noise suppression, signal processing or extracting information from data streams. Although well‑known theory allows for optimal parameter selection, there still exist such real applications where requirements limit the use of digital filters. One of the most important limitations is the response time delay caused by too many used lagged input signals. The method proposed in the article allows us to estimate filter parameters with a genetic algorithm, decreasing its delay but keeping the requirements important for the user (e.g.: attenuation). Transfer functions of such filters were compared with transfer functions of the most known classical filters.

Identification Using Binary Measurements for IIR Systems

This paper studies the identification of infinite impulse response (IIR) systems with binary-valued measurements. The key to our approach is to recast the initial identification problem into an identification problem in the set-membership framework. This enables us to identify the system using an identification algorithm devoted to IIR systems in such a framework. Although the proposed algorithm is designed for noise free data, an analysis is provided in the presence of noise, and illustrative examples are provided to demonstrate the effectiveness of the algorithm.

Lossless Wideband RF Compression via Lifting-Based IIR Subband Decomposition

This correspondence presents a scheme for the lossless compression of wideband spectrally and/or temporally sparse radio-frequency intercepts. It decomposes intercepts into time/frequency partitions using an adaptive invertible channelizer based on infinite impulse response orthogonal wavelet filter banks. The resulting subband decomposition is compressed via arithmetic coding.

Optimised Design and Implementation of Band-Pass Filter for Neural Recording System

In this paper, an eight order efficient digital infinite impulse response filter is designed to improve the signal to noise ratio (SNR) and minimise the hardware and power consumption. For this task, an optimisation method has been adapted to reduce the root mean square error and hardware usage. The filter has been designed and analysed using Matlab and Modelsim, the implementation has been synthesis on Xilinx Spartan 3E-100 (xc3s100e) field-programmable gate array board. Moreover, an optimisation process using parallel algorithm has bee adapted for further reduction in the hardware area and power consumption. The results show the Band Pass Filter effectively functions in real time recording application with significant improvement in the SNR which could achieve high-velocity selective resolution. The present work offers a structure of implementing a band-pass filter on FPGAs using a nonlinear digital filter shows a significant saving of 25.4% in power consumption and 29.9% of the hardware size comparing with the latest algorithm of IIR filter design. Consequently, this is an essential development to enhance the neural signals to be adopted as reference or control signals in artificial limbs devices.

A comparison between the sampling Kantorovich algorithm for digital image processing with some interpolation and quasi-interpolation methods

In this paper we study the performance of the sampling Kantorovich (S–K) algorithm for image processing with other well-known interpolation and quasi-interpolation methods. The S-K algorithm has been implemented with three different families of kernels: central B-splines, Jackson type and Bochner–Riesz. The above method is compared, in term of PSNR (Peak Signal-to-Noise Ratio) and CPU time, with the bilinear and bicubic interpolation, the quasi FIR (Finite Impulse Response) and quasi IIR (Infinite Impulse Response) approximation. Experimental results show better performance of S-K algorithm than the considered other ones.

Control of Grid Fed PV Generation Using Infinite Impulse Response Peak Filter in Distribution Network

An upsurge in the integration of renewable sources to the utility grid has resulted into escalating concerns regarding power quality (PQ) improvement. In order to provide efficient operation under weak grid conditions, the necessity of a competent control technique is dominant. Consequently, the implementation of an infinite impulse response (IIR) peak filter is used here for harmonics mitigation, power factor correction, and alleviating other PQ issues by providing distribution static compensator (DSTATCOM) capabilities. In addition, this control technique also works during conditions when solar photovoltaic (PV) power is not procurable. During day, the requirement of load is fed from the solar PV array and the stored grid power. On the other hand, at night when the power from solar PV array is unavailable, the requirements of load are fulfilled from the grid and DSTATCOM operation ensures power quality improvement. In order to validate this system, it is maintained according to the IEEE-519 standard. The considered weak grid conditions include load unbalancing, intermittent solar insolation, voltage sag, voltage swell, voltage distortion, and voltage unbalance conditions. The benefits of IIR peak filter include adaptive computation during filtering, thereby alleviating the computational complexity. Moreover, less memory space is required during execution guaranteeing fast performance during weak grid conditions. Simulated results are utilized to depict the behavior of the system during abnormal grid conditions. Moreover, its comparative performance with the existing control techniques, is also presented. For further substantiation and in order to observe reliable operation, a laboratory prototype is developed and tested during weak grid conditions such as voltage distortion, voltage unbalance, voltage sag, voltage swell, and load unbalancing.