Linear Bandpass Filter Research Articles

A novel approach for optimal design of digital FIR filter using grasshopper optimization algorithm

The idea behind designing digital filters is to compute the optimal filter coefficients such that the magnitude response of the designed matches the ideal frequency response using optimization algorithms. The proposed work employed a recently proposed swarm-based optimization technique, namely, a grasshopper optimization algorithm (GOA) to design a linear phase finite impulse response (FIR) low pass, high pass, band pass , and band stop filters. This proposed algorithm models the behaviour of grasshoppers while seeking food sources to solve optimization problems. For the designing of the FIR filter, an absolute error difference fitness function is used, which is minimized using GOA to obtain optimal filter coefficients. The performance comparison of the proposed work is done with already existing algorithms such as cuckoo search, particle swarm optimization, artificial bee colony to prove its superiority and consistency. It is found that GOA based filter meets the objective efficiently with reduced ripples in pass band and higher attenuation in stop band with least execution time.

Hybrid Integrator-Gain System for Active Vibration Isolation with Improved Transient Response

In this paper a linear bandpass filter is compared to a hybrid integrator-gain based bandpass filter regarding its usefulness in active vibration isolation. Vibration isolation refers to a form of skyhook damping in which a velocity output signal from a system having structural dynamics is fed back to a controller, the latter having bandpass characteristics. At those frequencies where the controller passes the input signal (after gain multiplication) a force proportional to velocity is obtained that can be used to provide active damping to the system, i.e., damping of one (or more) of its structural modes. Outside this frequency band the controller input signal is attenuated, which is often desirable in view of incorrect sensor information at low frequencies and/or to avoid amplification of high-frequency noise. In this context, the use of a hybrid integrator-gain system will be studied regarding its possible phase advantages compared to linear integrators. These advantages stem from the control design itself, in the sense that by design the control output force signal and the input velocity error signal have equal sign. In the context of vibration control, an enhanced transient (closed-loop) response is obtained, i.e., less overshoot and reduced settling times, but at the cost of increased rise times.

State Space Oscillator Models for Neural Data Analysis.

Neural oscillations reflect the coordinated activity of neuronal populations across a wide range of temporal and spatial scales, and are thought to play a significant role in mediating many aspects of brain function, including atten- tion, cognition, sensory processing, and consciousness. Brain oscillations are typically analyzed using frequency domain methods such as nonparametric spectral analysis, or time domain methods based on linear bandpass filtering. A typical analysis might seek to estimate the power within an oscillation sitting within a particular frequency band. A common approach to this problem is to estimate the signal power within that band, in frequency domain using the power spectrum, or in time domain by estimating the power or variance in a bandpass filtered signal. A major conceptual flaw in this approach is that neural systems, like many physiological or physical systems, have inherent broad-band 1/P' dynamics, whether or not an oscillation is present. Calculating power-in-band, or power in a bandpass filtered signal, can therefore be misleading, since such calculations do not distinguish between broadband power within the band of interest, and true underlying oscillations. In this paper, we present an approach for analyzing neural oscillations using a combination of linear oscillatory models. We estimate the parameters of these models using an expectation maximization (EM) algorithm, and employ AIC to select the appropriate model and identify the oscillations present in the data. We demonstrate the application of this method to univariate electroencephalogram (EEG) data recorded at quiet rest and during propofol-induced unconsciousness.

Estimating the sensitivity and linearity requirements of an OTH radar receiving system

A desirable goal for an over-the-horizon radar (OTHR) receiver chain is to avoid degradation of available signal to noise ratio over unused channels within a nominated band of interest. If there is a design requirement for the receiver to perform instantaneously wideband, this can present a significant challenge as the dynamic range of signals within the high frequency (HF) band in the form of large broadcast signals, by way of contrast to the very low spectral densities characterizing cosmic noise, can easily exceed the linear range of components in a typical receiver chain. In narrowband systems, this has been handled in various ways by constraining the bandwidth impinging on sensitive downstream components, with a highly linear bandpass filter, or custom mixer and filter. Even in narrowband systems care must be taken to ensure that the receive chain internal noise and nonlinear products do not significantly impact at operational frequencies of interest. More recently, with the advent of direct digital receivers (DDRx) based around high speed high dynamic range analogue to digital converters (ADCs), a desire to operate OTHR with wide open front ends (WOFE) has emerged. In a WOFE DDRx there is no bandpass filtering within the operational range of the receiver. The principle is to enable multiple simultaneous functions using the large radar receive array, e.g. the usual narrowband radar with a complementary wideband frequency management support (FMS) system [1]. This goal demands simultaneous access across the full spectrum and therefore constrains the filtering solution and increases the technical demand on the receiver components. As discussed in Barnes et. al. [2] the general application of Commercial-Off-The-Shelf (COTS) DDRx to attempt WOFE at HF will generally result in some level of system imposed performance limiting. The use of customized, dynamically adaptive front-end gain stages, including the use of equalization, where the gain is a tailored function of frequency, can allow the receiver to approach a satisfactory WOFE solution in some applications, e.g. the spectral environment evaluation and recording [2] receiver.

FILTERS RESEARCH FOR FREE MOTION EXTRACTION IN SELF-TUNING AUTOMATIC CONTROL SYSTEMS

Technological type control objects specific feature, which distinguish them among many mobile or electro technical types, is more low-frequency parametric disturbances spectral composition than spectral composition of coordinate disturbances. Most often parametric disturbances reveal themselves in changing control object transmission coefficient in the channel ―controller control action – control variable‖. On a number of occasions transmission coefficient can change in a wide range – in 2…10 times more than initial value. Coordinate disturbances change control variables but don’t change control object features. Besides spectral density of control variable change, under the impact of coordinate disturbances, has its peak in low-frequency range and characterize forced component of automatic control system motion. Parametric disturbances have influence on control variables and on control object properties, in particular on its transition coefficient. In this case, character of transient processes change, i.e. free motion component of the system. Spectral density of control variable changes, under the influence of transition coefficient change, has its peak, in general, in mid-frequency range. In the case, when using filters, even approximately, it is possible to separate out mid-frequency component from the overall process, which characterizes generally free motion of automatic control system, then it is possible to estimate current value of control object transmission coefficient using parameters change of this component. Such control variable components motions separation is used for designing selftuning ACS. In this case the informative parameter is dispersions difference of control object control variable and control variable of its model in the outputs of linear band pass filters. This dispersion difference is in proportion to current value of transition coefficient. In this article the results of simulation modeling computer experiments of self-tuning automatic control system with opened self-tuning loop are represented. The analysis of filtering efficiency, for three filter model options, are represented as well. The analysis was carried out with three different coordinate disturbances spectral compositions, with the same dynamics of control object and its model and with the different dynamics. Recommendations for filters structures alternatives selection are given. The possibility of optimal parametric tuning of filters is examined.

A two-stage framework for denoising electrooculography signals

Abstract Denoising of electrooculography (EOG) signals is a challenging task as the noise and signal share the same frequency band. This paper proposes a two-stage framework for denoising EOG signals. The first stage approach is based on preserving the nature of eye movements while the second stage is based on the nature of noise (Gaussian or not). In the first stage, denoising is carried out using one out of four filtering methods, each filter being optimal for a particular EOG pattern. The four methods used in the first stage are linear bandpass filtering, stationary wavelet transform (SWT), empirical mode decomposition (EMD) and median filtering. The Stage I framework selects the output that provides the highest estimated signal to noise ratio (SNR). In case, the Stage I filtering does not provide a significant SNR, the system uses Stage II filtering. In the second stage, we use two recursive state estimators, i.e. a Kalman filter and a particle filter for further denoising. The two-stage method is found to provide a better SNR as compared to a single stage method.

Hilbert–Huang transform based instrumental assessment of intention tremor in multiple sclerosis

Objective. This paper describes a method to extract upper limb intention tremor from gyroscope data, through the Hilbert–Huang transform (HHT), a technique suitable for the study of nonlinear and non-stationary processes. The aims of the study were to: (i) evaluate the method’s ability to discriminate between healthy controls and MS subjects; (ii) validate the proposed procedure against clinical tremor scores assigned using Fahn’s tremor rating scale (FTRS); and (iii) compare the performance of the HHT-based method with that of linear band-pass filters. Approach. HHT was applied on gyroscope data collected on 20 MS subjects and 13 healthy controls (CO) during finger-to-nose tests (FNTs) instrumented with an inertial sensor placed on the hand. The results were compared to those obtained after traditional linear filtering. The tremor amplitude was quantified with instrumental indexes (TIs) and clinical FTRS ratings. Main results. The TIs computed after HHT-based filtering discriminated between CO and MS subjects with clinically-detected intention tremor (MS_T). In particular, TIs were significantly higher in the final part of the movement (TI2) with respect to the first part (TI1), and, for all components (X, Y, Z), MS_T showed a TI2 significantly higher than in CO subjects. Moreover, the HHT detected subtle alterations not visible from clinical ratings, as TI2 (Z-component) was significantly increased in MS subjects without clinically-detected tremor (MS_NT). The method’s validity was demonstrated by significant correlations between clinical FTRS scores and TI2 related to X (rs = 0.587, p = 0.006) and Y (rs = 0.682, p < 0.001) components. Contrarily, fewer differences among the groups and no correlation between instrumental and clinical indexes emerged after traditional filtering. Significance. The present results supported the use of the HHT-based procedure for a fully-automated quantitative and objective measure of intention tremor in MS, which can overcome the limitations of clinical scales and provide supplementary information about this sign.

The investigation of destabilization effect in India’s agriculture commodity futures market

Purpose– This paper aims to examine the destabilization effect in the case of India’s agricultural commodity market for the sample period of 01 January 2009 to 31 May 2013.Design/methodology/approach– The daily data of eight agricultural commodities traded on the National Commodity &amp; Derivatives Exchange, viz., barley, castor seed, chana (chickpea), chilli, potato, pepper, refined soya and soybean, have been used in this study. At the first stage of the empirical analysis, the study estimates the time-varying spot market volatility by using the exponential generalized autoregressive conditional heteroscedasticity model and applies three different high and band-pass filters, viz., the two-sided linear band-pass filter by Hodrick and Prescott (1997), the fixed-length symmetric band-pass filter by Baxter and King (1999) and the asymmetric band-pass filter by Christiano and Fitzgerald (2003), to calculate the unexpected liquidity of sample commodities. At the second stage of the empirical analysis, the study applies linear Granger causality and recently developed non-linear causality given by Diks and Panchenko (2006) to examine the cause and effect between time-varying volatility of spot market and futures market liquidity of sample commodities.Findings– The linear and non-linear causality results suggest the destabilizing effect of commodity futures on the underlying spot market for chana, chilli and pepper. The empirical findings are in contrast with the recommendations of Abhijit Sen’s committee and provide important direction for further policy research.Research limitations/implications– The study has a limitation in that it is based on the daily data. The use of intra-day data would have been more suitable for such type of analysis.Practical implications– The study has strong policy implications from a financial policy perspective, as there is already disagreement among researchers and policy makers with regard to the functioning of commodity derivatives markets in India. There have been many occasions when commodity market regulators have to undertake decisions of suspension of trading of many commodities. The study also provides new directions of policy research with regards to the restructuring of the commodity derivatives market in India.Social implications– The findings of this study may further help the regulators and policy makers to undertake decisions about how to provide an alternative platform for farmers to sell their agricultural produce more efficiently. This will certainly have some impact on the socioeconomic set-up of the country, as India is primarily an agriculture-dominated country.Originality/value– So far not many studies have investigated the destabilization hypothesis in the case of emerging markets. This study is a novel attempt to fill the gap. In the case of emerging markets and especially in the case of India’s commodity derivatives market, this is the first study that examines the destabilization hypothesis in the case of India by applying new methods of high and band-pass filters and non-linear causality.

Design and simulation of FIR band pass and band stop filters using gravitational search algorithm

In this paper, a new optimization method named gravitational search algorithm (GSA) is adopted for designing optimal linear phase finite impulse response band pass (BP) and band stop (BS) digital filters. Other various population based evolutionary algorithms like real coded genetic algorithm, conventional particle swarm optimization, differential evolution (DE), bee swarm optimization have also been applied for the sake of comparative study of the same optimal designs. In GSA, particles are considered as objects and their performances are measured by their masses. All these objects attract each other by gravity forces, and these forces produce global movements of all objects towards the objects with heavier masses. GSA guarantees the exploitation step of the algorithm and it is apparently free from premature convergence. Extensive simulation results justify superior optimization capability of GSA over the afore-mentioned optimization techniques for the solution of the multimodal, non-differentiable, highly non-linear, and constrained filter design problems.

A Sound Processor for Cochlear Implant Using a Simple Dual Path Nonlinear Model of Basilar Membrane

We propose a new active nonlinear model of the frequency response of the basilar membrane in biological cochlea called the simple dual path nonlinear (SDPN) model and a novel sound processing strategy for cochlear implants (CIs) based upon this model. The SDPN model was developed to utilize the advantages of the level-dependent frequency response characteristics of the basilar membrane for robust formant representation under noisy conditions. In comparison to the dual resonance nonlinear model (DRNL) which was previously proposed as an active nonlinear model of the basilar membrane, the SDPN model can reproduce similar level-dependent frequency responses with a much simpler structure and is thus better suited for incorporation into CI sound processors. By the analysis of dominant frequency component, it was confirmed that the formants of speech are more robustly represented after frequency decomposition by the nonlinear filterbank using SDPN, compared to a linear bandpass filter array which is used in conventional strategies. Acoustic simulation and hearing experiments in subjects with normal hearing showed that the proposed strategy results in better syllable recognition under speech-shaped noise compared to the conventional strategy based on fixed linear bandpass filters.

Bacteria foraging optimisation algorithm for optimal FIR filter design

This paper presents a novel search algorithm, called bacteria foraging optimisation (BFO) for the design of linear phase positive symmetric FIR low pass, high pass, band pass and band stop filters, realising the respective ideal filter specifications. BFO is a population-based evolutionary optimisation concept used to solve nonlinear optimisation problem where each individual maintains the propagation of genes. BFO favours propagation of genes of those animals which have efficient foraging strategies and eliminate those animals that have weak foraging strategies i.e., method of finding, handling and taking in food. All animals with their own physiological and environmental constraints, try to maximise the consumption of energy per unit time interval. The performances of BFO-based FIR filter designs have proven to be superior as compared to those obtained by real coded genetic algorithm (RGA) and standard particle swarm optimisation (PSO) optimisation techniques. The simulation results justify that BFO is the best optimiser among the other optimisation techniques, not only in the convergence speed but also in the accuracy and the optimal performances of the designed filters.

Seeker optimisation algorithm: application to the design of linear phase finite impulse response filter

This study presents a novel seeker optimisation algorithm (SOA) for the design of linear phase finite impulse response low pass, high pass, band pass and band stop digital filters. A new fitness function has been adopted in order to improve the stop band attenuation, stop band ripple and to have an accurate control on the transition width. A comparison of simulation results reveals the optimisation efficacy of SOA in terms of error fitness value, stop band attenuation and stop band ripple over the prevailing optimisation techniques reported in recent literatures.

Optimal linear phase finite impulse response band pass filter design using craziness based particle swarm optimization algorithm

An efficient method is proposed for the design of finite impulse response (FIR) filter with arbitrary pass band edge, stop band edge frequencies and transition width. The proposed FIR band stop filter is designed using craziness based particle swarm optimization (CRPSO) approach. Given the filter specifications to be realized, the CRPSO algorithm generates a set of optimal filter coefficients and tries to meet the ideal frequency response characteristics. In this paper, for the given problem, the realizations of the optimal FIR band pass filters of different orders have been performed. The simulation results have been compared with those obtained by the well accepted evolutionary algorithms, such as Parks and McClellan algorithm (PMA), genetic algorithm (GA) and classical particle swarm optimization (PSO). Several numerical design examples justify that the proposed optimal filter design approach using CRPSO outperforms PMA and PSO, not only in the accuracy of the designed filter but also in the convergence speed and solution quality.

Separation of Nonlinear Ultrasound Signals Based on Second-Order Volterra System Identification

This paper introduces a system identification based on second Volterra filter (SVF) as a mathematical model to separate harmonic components of a radio frequency (RF) line from a simulated nonlinear pulse–echo system (NPS). Filter coefficients of both the linear (the first-order Volterra) and quadratic (the second-order Volterra) kernels of the SVF can be determined by processing the input and output data in the frequency domain and solving a system of linear equations. Results from the validation of the approach show that while the linear kernel correctly predicts the linear output in the fundamental energy band, the quadratic kernel appropriately captures energy primarily from the second harmonic and low frequency bands. In addition, it is shown that the system identification based on the SVF is capable of separating the second order nonlinearity embedded under the level of noise signal. This is the significant advantage of the SVF approach over other static models such as linear bandpass filters. In addition, the feasibility of regularization in decreasing the number of input–output sequences used in system identification is presented.

Basilar Membrane Responses to Noise at a Basal Site of the Chinchilla Cochlea: Quasi-Linear Filtering

Basilar membrane responses to clicks and to white noise were recorded using laser velocimetry at basal sites of the chinchilla cochlea with characteristic frequencies near 10 kHz. Responses to noise grew at compressive rates and their instantaneous frequencies decreased with increasing stimulus level. First-order Wiener kernels were computed by cross-correlation of the noise stimuli and the responses. For linear systems, first-order Wiener kernels are identical to unit impulse responses. In the case of basilar membrane responses, first-order Wiener kernels and responses to clicks measured at the same sites were similar but not identical. Both consisted of transient oscillations with onset frequencies which increased rapidly, over about 0.5 ms, from 4-5 kHz to the characteristic frequency. Both first-order Wiener kernels and responses to clicks were more highly damped, exhibited slower frequency modulation, and grew at compressive rates with increasing stimulus levels. Responses to clicks had longer durations than the Wiener kernels. The statistical distribution of basilar membrane responses to Gaussian white noise is also Gaussian and the envelopes of the responses are Rayleigh distributed, as they should be for Gaussian noise passing through a linear band-pass filter. Accordingly, basilar membrane responses were accurately predicted by linear filters specified by the first-order Wiener kernels of responses to noise presented at the same level. Overall, the results indicate that cochlear nonlinearity is not instantaneous and resembles automatic gain control.

Detecting phase synchronization in noisy data from coupled chaotic oscillators

Two schemes are proposed to detect phase synchronization from chaotic data contaminated by noise. The first is a neighborhood-based method which links time delay embedding with instantaneous phase estimation. The second adopts the local projection method as a preprocessing filter to noisy data. Both schemes utilize the state recurrence, an important feature of chaotic data. The proposed schemes are applied to data measured from two typical chaotic systems, i.e., the coupled Rössler systems and the coupled Lorenz systems, respectively. The results show that phase synchronization, which may be buried by noise, is detected even when the noise level is high. Moreover, the overestimation of the degree of phase synchronization, which may be introduced by the Hilbert transform combined with a traditional linear bandpass filter, can be avoided when the data are contaminated by only measurement noise.

COMPACT OPTICAL CHARACTERIZATION PLATFORM FOR DETECTION OF BIO-MOLECULES IN FLUIDIC AND AEROSOL SAMPLES

An optical characterization unit based on fluorescence spectroscopy-on-a-chip is described. It comprises a compact fluidic platform that is integrated onto a chip-size spectrometer. The analyte is continuously excited within a novel waveguide. Fluorescence spectra are recorded as the analyte traverse the detection area. In order to achieve a strong interaction between excitation light and analyte we use an anti-resonant waveguide, in which the light is guided within the target-containing medium, thereby enabling a continuous excitation of a large volume. The excitation light is guided in the lower-refractive-index fluid when the light is coupled into the waveguide at an appropriate angle. Compact spectrometers can be integrated along the fluidic channel. The spectrometers are composed of a detector array which is coated with a linear variable band-pass filter. The filter converts the spectral fluorescence information into a spatially dependent signal that is analyzed by the detector array. These chip-size spectrometers are especially applicable for characterization of moving analytes.

Noise separation of underwater acoustic vocalization using auditory filter bank and Poisson rate estimation

Formant vocalization tracking has been achieved using a mammalian periphery model and a Poisson rate estimator. This approach used a set of linear bandpass filters to simulate the mechanical displacement of the basilar membrane. The auditory model simulated neural firing by producing a spike at the positive going zero crossing for each filter output. Poisson intensity of the neural firing rate is controlled by the dominant frequency components of the signal present in the filter. This approach is extended by incorporating neural synchronization information to separate the formant structure from that of noise. The filter structure is designed to overlap the frequency range of adjacent filters. The presence of a formant structure in adjacent filters controls the interspike intervals of neural firing for both filters, which results in the neural firing from both filters being synchronized. If a noise-only component is present in either filter, then the spiking outputs from the adjacent filters are unsynchronized. Experimental results have shown that incorporating neural synchronization information between adjacent filters has enabled separation of signal components from noise. This technique enables easier signal and noise separation than allowed by traditional methods.

Dendritic resonance in rat neocortical pyramidal cells.

Dendritic integration of synaptic signals is likely to be an important process by which nerve cells encode synaptic input into spike output. However, the response properties of dendrites to time-varying inputs are largely unknown. Here, I determine the transfer impedance of the apical dendrite in layer V pyramidal cells by dual whole cell patch-clamp recordings in slices of rat somatosensory cortex. Sinusoidal current waveforms of linearly changing frequencies (0.1-25 Hz) were alternately injected into the soma or apical dendrite and the resulting voltage oscillations recorded by the second electrode. Dendrosomatic and somatodendritic transfer impedances were calculated by Fourier analysis. At near physiological temperatures (T approximately 35 degrees C), the transfer impedance had a maximal magnitude at low frequencies (f(res) approximately 6 Hz). In addition, voltage led current up to approximately 3 Hz, followed by a current lead over voltage at higher frequencies. Thus the transfer impedance of the apical dendrite is characterized by a low-frequency resonance. The frequency of the resonance was voltage dependent, and its strength increased with dendritic distance. The resonance was completely abolished by the I(h) channel blocker ZD 7288. Dendrosomatic and somatodendritic transfer properties of the apical dendrite were independent of direction or amplitude of the input current, and the responses of individual versus distributed inputs were additive, thus implying linearity. For just threshold current injections, action potentials were generated preferentially at the resonating frequency. I conclude that due to the interplay of a sag current (I(h)) with the membrane capacitance, layer V pyramids can act as linear band-pass filters with a frequency preference in the theta frequency band.

Signal Preprocessing for Speech Recognition

Consideration was given to the transformations of speech in the frequency domain which precede extraction of the informative attributes of phonemes. A processing of the speech spectrum ensuring stability of recognition in the presence of frequency distortions and additive noise was proposed. It is based on linear bandpass filtering of the logarithmic amplitude spectrum and subsequent nonlinear transformation that models the effect of lateral inhibition in the auditory analyzer.