Ideal Filter Research Articles

Unsupervised design for broadband multispectral and polarization filter array patterns.

Imaging multiple wavelength and polarization components is problematic due to the complexity of equipment and the increase in the number of imaging shots, so imaging using filter arrays with various patterns has been widely reported from elemental research to practical applications. Most of them use bandpass filters with different center wavelengths for each pixel. Recently, however, filter arrays with multimodal transmission characteristics have been proposed using photonic crystals or Fabry-Perot filters. In any of these methods, the design of the filter array arrangement pattern is important to improve the quality of the captured image, as well as the improvement of the demosaicking algorithm. One way to design a filter array pattern is to minimize the mean squared error (MSE) between the ideal image and the demosaicked image. However, the more multidimensional the imaging components, the more difficult it becomes to collect training data. In such cases, it is necessary to empirically determine candidate transmission characteristics and patterns of filter arrays. In this study, we propose a method for evaluating filter array patterns without using any training data in the design of filter arrays for multispectral and polarization imaging. The proposed method estimates the MSE by approximating the autocorrelation matrix without using image data by expressing the imaging model as a linear forward problem and the demosaicking as a linear inverse problem. Since this method can be applied not only to ideal bandpass filter arrangements, but also to multispectral filter arrays with multimodal spectral transmission characteristics and even multispectral polarization filter arrays with different extinction ratios at different wavelengths, we will show that image quality can be improved over empirical arrangements by evaluating these patterns and by testing examples of optimal designs using genetic algorithms.

Manipulation of quantum spin transport of TM (Mn, Fe, Co, and Ni) (II) phthalocyanines coupled to carbon nanotube leads applicable in spintronic devices

We conducted a theoretical scrutiny on the quantum spin transport characteristics of a class of open-shell 3d-transition metal (TM = Mn, Fe, Co, and Ni) (II) phthalocyanines (TMPcs) molecules sandwiched between two semi-infinite armchair single-walled carbon nanotube probes. Herein, the spin density functional formalism is utilized in conjunction with state-of-the-art non-equilibrium Green's function technique. Molecular junctions composed of MnPc and FePc have higher local magnetic spin moments than CoPc and NiPc because of the strong exchange spin-splitting between the 3d-states of the Mn or Fe cation atoms and the p-states of the N anion atom. This indicates that TMPc molecules (TM = Mn, and Fe) bridged between two single-walled carbon nanotube (SWCNT) electrodes with a π-type geometry demonstrate a pronounced transmission near the Fermi level. Furthermore, the sign and robustness of spin filter efficiency are tailored by manipulating the central atom of 3d-transition metal and the contact SWCNT electrodes. Subsequently, FePc and MnPc molecular junctions have the advantage of operating practically as ideal spin filter devices compared to CoPc and NiPc molecular structures. Moreover, the tunneling magnetoresistance (TMR) of these molecular devices is assessed. For MnPc and FePc molecular junctions, the estimated total conductance exhibited a robust trend at zero bias voltage, whereas the magnitudes of spin-up and spin-dn quantum conductances increased significantly in CoPc and NiPc molecular configurations with bias voltages range between ±0.45 and ±0.95 Volt. Spin-down carriers essentially govern the quantum conductance of TMPc (Mn, Fe, Co, and Ni) molecular junctions at bias voltage around ±0.75 Volt. By examining their I-V characteristics, it has been established that MnPc, and FePc molecular junctions exhibit metal-like behavior, whereas CoPc and NiPc molecular devices are subject to negative differential resistance. As a result of our theoretical findings, it is found that TMPcs (TM = Fe, Mn) coupled to SWCNT probes yield a robust spin filter efficiency that may be of interest for prospective technological realizations in organic-inorganic molecular spintronic devices.

Simulation and analysis of ideal low pass filter response using MATLAB

Filters are the crucial element of many modern electronic systems used in control engineering, wireless communication systems, and embedded systems designs. Filters in one form or another, are considered an important subsystem in developing communication systems. The ideal filter cannot be designed through any electronic circuit and the only way is to realize using simulations tools like MATLAB. In this paper, the simulation and analysis of the ideal low pass filter is presented. The MATLAB software is used to generate and perform analysis of the step response of ideal low-pass filter (Si function) and impulse response of ideal low-pass filter (Sinc function) in time and frequency domains. The aim of the work is to provide engineering students an easier understanding of the filter design process in an appropriate way with the help of simple programming. Where, it is easy to generate signals and analyze the ideal low pass filter response. To verify achievement of learning objectives, a survey is conducted from 45 engineering students of electronic and telecommunication which elicited students’ observations and interest in the learning process. Overall about 75% of students gave positive feedback, which indicates that such type of experimentation had a significant impact on the student academic outcomes.

Systematic performance of the ASKAP fast radio burst search algorithm

ABSTRACT Detecting fast radio bursts (FRBs) requires software pipelines to search for dispersed single pulses of emission in radio telescope data. In order to enable an unbiased estimation of the underlying FRB population, it is important to understand the algorithm efficiency with respect to the search parameter space and thus the survey completeness. The Fast Real-time Engine for Dedispersing Amplitudes (fredda) search pipeline is a single pulse detection pipeline designed to identify radio pulses over a large range of dispersion measures (DM) with low latency. It is used on the Australian Square Kilometre Array Pathfinder (ASKAP) for the Commensal Real-time ASKAP Fast Transients (CRAFT) project. We utilize simulated single pulses in the low- and high-frequency observation bands of ASKAP to analyse the performance of the pipeline and infer the underlying FRB population. The simulation explores the signal-to-noise ratio (S/N) recovery as a function of DM and the temporal duration of FRB pulses in comparison to injected values. The effects of intrachannel broadening caused by dispersion are also carefully studied in this work using control data sets. Our results show that for Gaussian-like single pulses, >85 per cent of the injected signal is recovered by pipelines such as fredda at DM <3000 pc cm−3 using standard boxcar filters compared to an ideal incoherent dedispersion match filter. Further calculations with sensitivity implies at least ∼10 per cent of FRBs in a Euclidean universe at target sensitivity will be missed by fredda and heimdall, another common pipeline, in ideal radio environments at 1.1 GHz.

Investigating the Robustness of Filters for Integrated Processes in Business Cycles

Time series are frequently filtered to remove unwanted characteristics, such as trends and seasonal components, or to estimate components driven by stochastic cycles from a specific range of periods in a business cycle. A polynomial function of time is the most common deterministic time trend while an integrated process is the most common stochastic trend. The different filters implemented in this paper allow for different orders of deterministic time trends or integrated processes. The robustness of the filters is evaluated by plotting their gain function against the gain function of a simulated ideal filter. Implementing the filters on Nigerian gross domestic products (GDP), the results show that the gain of the Baxter-King (BK) filter deviates markedly from the square-wave gain of the ideal filter. The gain in Christiano–Fitzgerald (CF) filter is closer to the gain of the ideal filter than the BK filter. The gain in Hodrick–Prescott (HP) filter goes to one for those cycles at frequencies above six periods, whereas the other gain functions go to zero. The Butterworth (BW) filter does a reasonable job of filtering out the high-periodicity stochastic cycles but the low- periodicity stochastic cycles is not been completely removed.

Commodity price shocks: Order within chaos?

The prices of 27 internationally traded commodities are decomposed into transitory and permanent components by applying an ideal band-pass filter to monthly data during 1970–2022. The two components account roughly equally to price variations, but with wide heterogeneity. The permanent component accounts for two-thirds of the variability in agricultural prices but less than 30 percent in energy prices. The transitory component revealed three medium-term cycles. The first (from the early 1970s to the mid-1980s) and third (from the early 2000s to 2020) exhibit similar duration and involve almost all commodities, while the second (spanning the 1990s) is mostly applicable to metals, with the notable absence of energy. The permanent component differs across commodities, with an upward trend for most industrial commodities and downward trend for agriculture. Moreover, the permanent component of commodities in which investment is irreversible (including energy, metals, and tree crops) exhibits a high degree of nonlinearity, most likely reflecting less flexibility in investment allocation and input use of these commodities. Prices of commodities subjected to widespread policy interventions, such as internationally-coordinated supply management schemes, exhibit persistent deviations from linear trends.

An Experimental Method for Bio-Signal Denoising Using Unconventional Sensors.

In bio-signal denoising, current methods reported in the literature consider purely simulated environments, requiring high computational powers and signal processing algorithms that may introduce signal distortion. To achieve an efficient noise reduction, such methods require previous knowledge of the noise signals or to have certain periodicity and stability, making the noise estimation difficult to predict. In this paper, we solve these challenges through the development of an experimental method applied to bio-signal denoising using a combined approach. This is based on the implementation of unconventional electric field sensors used for creating a noise replica required to obtain the ideal Wiener filter transfer function and achieve further noise reduction. This work aims to investigate the suitability of the proposed approach for real-time noise reduction affecting bio-signal recordings. The experimental evaluation presented here considers two scenarios: (a) human bio-signals trials including electrocardiogram, electromyogram and electrooculogram; and (b) bio-signal recordings from the MIT-MIH arrhythmia database. The performance of the proposed method is evaluated using qualitative criteria (i.e., power spectral density) and quantitative criteria (i.e., signal-to-noise ratio and mean square error) followed by a comparison between the proposed methodology and state of the art denoising methods. The results indicate that the combined approach proposed in this paper can be used for noise reduction in electrocardiogram, electromyogram and electrooculogram signals, achieving noise attenuation levels of 26.4 dB, 21.2 dB and 40.8 dB, respectively.

Speed Measurement for Incremental Position Encoder Using Period-Based Method With Sinc3 Filtering

Precise measurement of the rotor speed is mandatory for machine tool spindle drives and other drives having the same demands for the accuracy of speed stabilization. These drives commonly use incremental position encoders for speed measurement as a derivative of the position in time. Currently, the best practice is the implementation of constant elapse time methods, the accuracy of which is limited by the accuracy of the position encoder itself and the discreteness of time. However, it was noted that the constant elapse time method ignores all the data in-between the measurements. An analysis of the periods between pulses from the encoder led to the discovery that these periods have the properties of delta–sigma modulated signals. Thus, it was suggested to process the results of the period-based method with a sinc filter of a high order to obtain extra information from the incoming data. The proposed period-based method with sinc filtering (PBSF) uses third-order filter as the most commonly used type in the measuring equipment. The simulation model was developed to show the error distribution for competing methods, and the experimental results were obtained in the steady state and during acceleration of the drive. The comparison of the methods was performed with identical data at the input and the same measurement time. Both the simulation and experimental results showed that at high speeds the proposed period-based method with sinc3 filtering (PBS3F) provides a much smaller deviation from the actual value than the conventional constant elapsed time (CET) methods. The computational complexity was analyzed as well showing the possibility of method implementation using modern microcontrollers.

Two-dimensional structural motion estimation via analytical signal enhancement of phase-based video processing

This paper concerns the use of phase information from image sequences for the extraction of structural two-directional motions. The phase-based motion estimation (PME) estimates local phase information via Gabor wavelet transform, and can only estimate the motion in single direction at a time, which will reduce the applicability and efficiency for many structures subject to more sophisticated bidirectional motions. This paper introduces a two-dimensional partial analytical signal to estimate the instantaneous phase and theoretically derives the relationship between the phase variations and the structure motion to achieve bidirectional motion estimations simultaneously. In addition to the analytical signal phase-based motion estimation (APME), this paper designs an ideal narrow-band filter (INF) addressing the limitation of the narrow frequency band in the analytical signals. In the proposed APME method, the acquisition of instantaneous phase improves the accuracy of structural bidirectional motion estimation. Moreover, the realization of the partial analytical signal is based on the fast Fourier transform (FFT) of the image and the dot product in the frequency domain, which bypasses the traditional PME convolution operation in the spatial domain. Therefore, the calculation efficiency of structural motion estimation is greatly improved. The proposed method is validated using a numerical simulation of a moving Gaussian surface with random motions. Results prove a good accuracy of the proposed APME method, and outperforming the traditional PME, the APME demonstrates an outstanding consistence to the true values (correlation coefficient 99.99%) four times better calculation efficiency. Further comparison with the experimental results of a cantilever beam also shows that the proposed method can better identify the structure bidirectional motion estimation.

An adaptive group sparse feature decomposition method in frequency domain for rolling bearing fault diagnosis

Group-sparse mode decomposition (GSMD) is a decomposition method designed based on the group sparse property of signals in frequency domain. It is proved to be highly efficient and robust against noise, showing bright prospects for bearing fault diagnosis. However, the following adverse factors may impede its application for incipient bearing fault feature extraction: Initially, the GSMD method did not consider the impulsiveness and periodicity of the bearing fault feature. As a result, the ideal filter bank generated by GSMD may not accurately cover the fault frequency band because it may produce over-coarse or over-narrow filter bank under the condition of strong interference harmonics, large random shocks and heavy noise. Moreover, the location of informative frequency band was obstructed since the bearing fault signal shows complicated distribution in frequency domain. To overcome the abovementioned limitations, an adaptive group sparse feature decomposition (AGSFD) method is proposed. Firstly, the harmonics, large-amplitude random shocks and periodic transient feature are modeled as limited bandwidth signals in the frequency domain. On this basis, an autocorrection of envelope derivation operator harmonic to noise ratio (AEDOHNR) indicator is proposed to guild the construction and optimization of the filter bank of AGSFD. Also, the regularization parameters of AGSFD are adaptively determined. With the optimized filter bank, the original bearing fault is decomposed into a serial of components with AGSFD method, where the sensitive fault-induced periodic transient component is maintained using the AEDOHNR indicator. Finally, the studies of the simulation and two experimental items are carried out to evaluate the feasibility and the superiority of AGSFD method. The results indicate the AGSFD method can identify the early failure in the presence of heavy noise, strong harmonics or random shocks and has better decomposition efficiency.

Near-flat top bandpass filter based on non-local resonance in a dielectric metasurface.

Localized light matter interaction at a resonant nanostructure facilitates spectrally selective transmission of light, which has led to demonstrations of ultrathin metasurface based optical filters. Unfortunately, due to the nature of Lorentzian spectral line shape in such resonances, it is inevitable to suffer significant spectral crosstalk. In this work, we demonstrate a conceptually new type of spectral filter which exhibits near flat-top bandpass with minimized spectral overlaps. To realize this, we leverage the recent development of non-local resonance in dielectric nanostructure to design a double-layered optical filter with performance comparable to the ideal spectral filters. The designed metasurface shows averaged transmission of more than 90% across the target spectral band and suppressed transmission of less than 10% out of the spectral band.

Operational forecasting of optimal operating frequencies for long­range radio communications under the influence of non­fluctuating interference

The analysis of the methods of moving average and moving median, which are widely used to build empirical models for forecasting optimal operating frequencies, is carried out. The reasons for the resulting distortions are shown. The methodological bases for processing the results of discrete observations developed for the operational forecasting of the values of optimal operating frequencies for a dedicated radio communication channel under the influence of non-fluctuating interference are described. The spectral analysis used to calculate the mathematical expectation shows that, due to averaging, such models do not make it possible to detect the influence of external sources of perturbation on the ionosphere. That is, in the case of such averaging, together with the random component, which is zero, the effect of ionospheric non-fluctuating disturbances on radio communications is excluded from consideration. To account for the influence of ionospheric disturbances in a standard ionosphere model, these non-fluctuational disturbances are added separately. In addition, in geophysics, much attention is paid to the calculation of various averages for days, months, years, etc. It is important to establish to what extent the results obtained can be free from insurmountable distortions due to discretization. A condition for the extrapolability of the process is its spectrum limitation, so to obtain a limited spectrum, the observation data must be subjected to digital low-pass filtering (instead of the operations of equal-weighted averaging or moving median). For the initial data processing, it is suggested to connect two filters in series: a continuous weighted averaging and an ideal low-pass filter. The Chebyshev filter is the best fit for the characteristics of the corrected continuous averaging filter. Using the method of digital extrapolation, a method for forecasting observation data is presented. The conditions of extrapolability of the real process are given. A set of programs for realizing the forecast of optimal operating frequencies in real time has been developed.

Graphene oxide enabled self-assembly of silver trimolybdate nanowires into robust membranes for nanosolid capture and molecular separation.

A graphene oxide (GO) assisted self-assembly strategy for growing a silver trimolybdate nanowire membrane with capabilities of nanosolid capture and small molecule separation is reported. Thanks to the GO bridges and the accurate self-assembly process, the resulting membrane exhibits outstanding mechanical properties (can withstand 4300 times its weight) and impressively high porosity (97%). On the basis of the robustness and high porosity of the membrane, column-shaped filter apparatus has been fabricated, in which the membrane served as a self-standing permeation barrier to assess its permeability and practical application as a nanosolid filter and molecule filter. The permeability test of the membrane with pure water uncovers that the membrane exhibits fast permeability while driven by hydrostatic pressure only because of its significantly high porosity. The separation test of the membrane with P25 TiO2 solution, 13 nm Au solution, and yellow-emitting CdTe QDs reveals that all the tiny nanosolids are completely removed from the solution, which suggests that the membrane is an efficient nanosolid filter. Its efficiency is increased by the induction of surface collision from numerous nanowire barriers and the deposition of nanosolids on the nanowire surface. The separation test of the membrane with a mixed-dye solution reveals that sulfur containing methylene blue (MB) molecules are highly efficiently extracted under various chemical conditions, evidencing that the membrane is an ideal molecule filter too. Its high selectivity and high efficiency originated from the Ag-S bonding between the interlayered silver ions of the silver trimolybdate nanowire and the sulfur atom of MB molecules. Based on the above results, the silver trimolybdate nanowire membrane has been applied to purify drugs, which successfully removed sulbactam sodium impurity F from sulbactam sodium, demonstrating a purity increment from 98.92% to 99.93%. The present work should provide a significant step forward to bringing macroscopic 1D nanomaterial architectures much closer to real-world applications involving isolation and enrichment of catalyst reclamation, high-value chemical recovery, drug purification, and environmental remediation.

Improved Moving Target Detection in Homogeneous and Heterogeneous Clutter With Limited Secondary Data Using Unit Circle Roots Constraints

This paper proves that the roots of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$z$</tex-math></inline-formula> -polynomial corresponding to the ideal adaptive matched filter (AMF) lie on the unit circle (UC). Motivated by the proof, we propose UC roots-constrained AMF (UCRC-AMF) in this work that enforces this fundamental UC roots property while maximizing the SINR to determine the detector polynomials. The UCRC algorithm exploits polynomial conjugate symmetry and applies it to the problem of radar moving target detection (MTD) in homogeneous and heterogeneous clutter. The algorithm splits the multidimensional AMF optimization problem into multiple 1-D AMF problems. Simulation studies show that the UCRC-based AMF for Gaussian clutter, as well as UCRC versions of NAMF and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\alpha$</tex-math></inline-formula> -AMF for compound Gaussian clutter significantly outperform existing state-of-the-art detection algorithms in the presence of limited secondary data although the performance gain is persistent for larger secondary data as well. This work will show that the combined enforcement of UC roots property on detector polynomials and complex-persymmetry of covariance matrices is most effective for superior detection performance. It is also shown that the proposed UCRC-AMF outperforms existing methods when there is a mismatch with respect to the true target velocity and ideal covariance matrix structure. Additionally, it will be demonstrated via simulation that the proposed UCRC-based detectors have approximate CFAR with respect to the unknown covariance matrix.

Design of Digital Low Pass FIR Filter Using Hybrid Particle Swarm – Grey Wolf Optimization Algorithm

In this study, digital low-pass FIR filter is designed using Hybrid Particle Swarm – Grey Wolf Optimization Algorithm (HPSGWO). The purpose of digital FIR filter design with HPSGWO is to optimized filter coefficients that are closest to the characteristics of the ideal filter. The obtained results are compared with PSO, and GWO which were previously used for FIR filters design in the literature. According to the obtained results, HPSGWO has better filter response and less stop band ripple than other methods.

Two-dimensional motion estimation using phase-based image processing with Riesz transform

As a kind of photogrammetry technique, phase-based motion estimation (PME) has proven to be a powerful image/video processing algorithm in the extraction of structural dynamics, due to its full-filed resolution and no requirement of surface preparation, compared to conventional structural testing. Although images and videos employed in PME are two-dimensional arrays, PME only extracts phase information in a single direction at a time. Therefore, extracting two-directional motions at the same time is needed to avoid two times of PME implementation. To address this issue, Riesz-transform is integrated to the phase-based motion estimation algorithm, and Riesz transform PME (RPME) is proposed in this work. The concept of plane phase is extended to spatial phase using Riesz transform to process images, because Riesz transform is able to extract phase information in two directions simultaneously. Then quaternion is utilized to derive the theoretical relationship between spatial phase and structure motions. In order to obtain the mono-component image sequence required in Riesz transform, an ideal narrow bandpass filter is constructed based on the set of Butterworth low-pass filters. Due to the capability of recognizing two-directional motions simultaneously using the proposed RPME, a much-improved efficiency of structural dynamics identification is observed. In addition, Riesz transform extracts instantaneous phase, thus the motion recognition accuracy is improved as well. To validate the benefit of RPME, both numerical simulation and laboratory-scale experiments are conducted, and motions in x and y directions are extracted simultaneously, with a drastically increased accuracy in both the simulation and experimental validations.

Processing Nomex Nanofibers by Ionic Solution Blow-Spinning for Efficient High-Temperature Exhausts Treatment.

Hard-to-dissolve nanofibers provide alternatives for high-efficiency and low-resistant air filtration but are limited by the universality and economics of fabrication methods. A scalable and efficient ionic solution blow-spinning strategy has herein been proposed in preparing hard-to-dissolve nanofibrous filters. The online version contains supplementary material available at 10.1007/s42765-022-00231-x.

Lightning attachment procedure optimization algorithm for optimal design of digital FIR band stop filter

This study uses the Lightning Attachment Procedure Optimization (LAPO) method to show how to design a digital finite impulse response (FIR) 20th order band stop filter (BSF). LAPO is a physical-based metaheuristic optimization algorithm. One of the main goals of filter design is the simultaneous reduction of pass band ripples and stop band ripples. The aim is to identify such ideal filter coefficients that reduce the magnitude and phase errors. Comparing the LAPO method to the Cuckoo Search Algorithm (CSA) and Particle Swarm Optimization (PSO), the comprehensive simulation results and Wilcoxon rank-sum test at 99% level of confidence demonstrate how effectively the LAPO algorithm works in determining the optimal coefficients of the digital FIR BSF. For this design, the LAPO method achieves its global best solution in 11.94 seconds which is 25.84%, 25.19% and 0.83% better as compared to time taken by PSO, CSA and Integrated Optimization Technique (IOT), respectively since LAPO technique has no parameter adjustment.

The application of inferior vena cava filters in orthopaedics and current research advances.

Deep vein thrombosis is a common clinical peripheral vascular disease that occurs frequently in orthopaedic patients and may lead to pulmonary embolism (PE) if the thrombus is dislodged. pulmonary embolism can be prevented by placing an inferior vena cava filter (IVCF) to intercept the dislodged thrombus. Thus, IVCFs play an important role in orthopaedics. However, the occurrence of complications after inferior vena cava filter placement, particularly recurrent thromboembolism, makes it necessary to carefully assess the risk-benefit of filter placement. There is no accepted statement as to whether IVCF should be placed in orthopaedic patients. Based on the problems currently displayed in the use of IVCFs, an ideal IVCF is proposed that does not affect the vessel wall and haemodynamics and intercepts thrombi well. The biodegradable filters that currently exist come close to the description of an ideal filter that can reduce the occurrence of various complications. Currently available biodegradable IVCFs consist of various organic polymeric materials. Biodegradable metals have shown good performance in making biodegradable IVCFs. However, among the available experimental studies on degradable filters, there are no experimental studies on filters made of degradable metals. This article reviews the use of IVCFs in orthopaedics, the current status of filters and the progress of research into biodegradable vena cava filters and suggests possible future developments based on the published literature by an electronic search of PubMed and Medline databases for articles related to IVCFs searchable by October 2022 and a manual search for citations to relevant studies.

Identification of exon locations in DNA sequences using a fractional digital anti-notch filter

Identification of protein coding region (exon) locations in DNA sequences is a fundamental initial step in genomic signal processing (GSP). Several techniques have already been applied to achieve this challenging task. However, improvements are still needed. Transforms-based methods and digital filtering are among those techniques that have been widely used. These techniques exploit the period-3 property of protein coding regions. This paper proposes the application of a narrowband bandpass fractional digital filter to extract more selectively the single frequency component corresponding to the frequency f=1/3 from DNA sequences. The ideal fractional digital anti-notch filter has an infinite amplitude at the central frequency and two tuning parameters which may be used to independently adjust the central frequency and the amplitude frequency response. The ideal filter has been approximated and implemented efficiently as an infinite impulse response (IIR) filter. The effectiveness of the proposed method has been assessed in terms of common performance evaluation metrics computed from the results obtained using DNA sequences taken from the National Center for Biotechnology Information (NCBI) and HMR195 datasets using different numerical transformations including Voss mapping and electron–ion potential (EIIP) representation. In addition to overcome the problem of sliding window size encountered in transform-based methods, comparison with existing state-of-the-art methods for exon location identification has demonstrated superiority of the proposed method on benchmark datasets.