Finite Impulse Response High Pass Research Articles

Fine-tuning digital FIR filters with gray wolf optimization for peak performance

The design of optimum filters constitutes a fundamental aspect within the realm of signal processing applications. The process entails the calculation of ideal coefficients for a filter in order to get a passband with a flat response and an unlimited level of attenuation in the stopband. The objective of this work is to solve the FIR filter design problem and to compare the optimal solutions obtained from evolutionary algorithms. The design of optimal FIR low pass (LP), high pass (HP), and band stop (BS) filters is achieved by the utilization of nature-inspired optimization approaches, namely gray wolf optimization ,cuckoo search, particle swarm optimization, and genetic algorithm. The filters are evaluated in terms of their stop band attenuation, pass band ripples, and departure from the anticipated response. In addition, this study compares the optimization strategies applied in the context of algorithm execution time which is achievement of global optimal outcomes for the design of digital finite impulse response (FIR) filters. The results indicate that when the Gray wolf algorithm is applied to the development of a finite impulse response (FIR) filter, it produces a higher level of performance than other approaches, as supported by enhanced design precision, decreased execution time, and achievement of an optimal solution.

A grayscale compression method to segment bone structures for 2D-3D registration of setup images in non-coplanar radiotherapy

Purpose. To evaluate the performance of an automated 2D-3D bone registration algorithm incorporating a grayscale compression method for quantifying patient position errors in non-coplanar radiotherapy. Methods. An automated 2D-3D registration incorporating a grayscale compression method to segment bone structures was proposed. Portal images containing only bone structures ( Portalbone ) and digitally reconstructed radiographs containing only bone structures ( DRRbone ) were used for registration. First, the portal image was filtered by a high-pass finite impulse response (FIR) filter. Then the grayscale range of the filtered portal image was compressed. Thresholds were determined based on the difference in gray values of bone structures in the filtered and compressed portal image to obtain Portalbone. Another threshold was applied to generate DRRbone when the CT image uses the ray-casting algorithm to generate DRR images. The compression performance was assessed by registering the DRRbone with the Portalbone obtained by compressing the portal image into various grayscale ranges. The proposed registration method was quantitatively and visually validated using (1) a CT image of an anthropomorphic head phantom and its portal images obtained in different poses and (2) CT images and pre-treatment portal images of 20 patients treated with non-coplanar radiotherapy. Results. Mean absolute registration errors for the best compression grayscale range test were 0.642 mm, 0.574 mm, and 0.643 mm, with calculation times of 50.6 min, 42.2 min, and 49.6 min for grayscale ranges of 0–127, 0–63 and 0–31, respectively. For the accuracy validation (1), the mean absolute registration errors for couch angles 0°, 45°, 90°, 270°, and 315° were 0.694 mm, 0.839 mm, 0.726 mm, 0.833 mm, and 0.873 mm, respectively. Among the six transformation parameters, the translation error in the vertical direction contributed the most to the registration errors. Visual inspection of the patient registration results revealed success in every instance. Conclusions. The implemented grayscale compression method successfully enhances and segments bone structures in portal images, allowing for accurate determination of patient setup errors in non-coplanar radiotherapy.

The Optimal Design of Finite Impulse Response High Pass Filter Using Bee Colony Algorithm

The Optimal Design of Finite Impulse Response High Pass Filter Using Bee Colony Algorithm

Optimal High Pass FIR Filter Based on Adaptive Systematic Cuckoo Search Algorithm

Abstract This paper presents the design of a desired linear phase digital Finite Impulse Response (FIR) High Pass (HP) filter based on Adaptive Systematic Cuckoo Search Algorithm (ACSA). The deviation, or error from the desired response, is assessed along with the stop-band and pass-band attenuation of the filter. The Cuckoo Search algorithm (CS) is used to avoid local minima because the error surface is typically non-differentiable, nonlinear, and multimodal. The ACSA is applied to the minimax criterion (L∞-norm) based error fitness function, which offers a better equiripple response for passband and stopband, high stopband attenuation, and rapid convergence for the developed optimal HP FIR filter algorithm. The simulation findings demonstrate that when compared to the Parks McClellan (PM), Particle Swarm Optimization (PSO), CRazy Particle Swarm Optimization (CRPSO), and Cuckoo Search algorithms, the proposed HP FIR filter employing ACSA leads to better solutions.

Finite Impulse Response Filters for Stagger-Period Signals, their Designs and Applications

Those theories of conventional filters for uniform-period signals do not apply to the analysis and design of the finite impulse response (FIR) filters for stagger-period signals. In this paper, we defined the fundamental concepts related to the stagger-period signals, derived the calculating equations, and described the time-variant property of the stagger-period filter; we proposed the Fourier transform pair between the frequency and impulse responses of this type filter, and proved the inverse of each other. Then, we discussed the design methods of stagger-period frequency-selective FIR filters, including lowpass, bandpass, and high-pass, presented the staggered windowing philosophies, illustrated different windows’ effectiveness, and described the principles and designs of optimized stagger-period high-pass filters with the match algorithm. As applications, we introduced three staggered optimization algorithms: eigenvalue, match, and linear prediction; and discussed performances of the filters designed for a moving target indication (MTI) radar. The stagger-period MTI filters not only extended the blind speed of flying targets, but also had an optimized improvement factor. Finally, we proposed a mathematical programming to search the best period code, which makes this type filter’s velocity response flattened. Meanwhile, we compared properties of the stagger-period to uniform-period filters, and provided with some examples to illustrate the theories and designs.

Finite Impulse Response Filters for Stagger-Period Signals, their Designs and Applications

Those theories of conventional filters for uniform-period signals do not apply to the analysis and design of the finite impulse response (FIR) filters for stagger-period signals. In this paper, we defined the fundamental concepts related to the stagger-period signals, derived the calculating equations, and described the time-variant property of the stagger-period filter; we proposed the Fourier transform pair between the frequency and impulse responses of this type filter, and proved the inverse of each other. Then, we discussed the design methods of stagger-period frequency-selective FIR filters, including lowpass, bandpass, and high-pass, presented the staggered windowing philosophies, illustrated different windows’ effectiveness, and described the principles and designs of optimized stagger-period high-pass filters with the match algorithm. As applications, we introduced three staggered optimization algorithms: eigenvalue, match, and linear prediction; and discussed performances of the filters designed for a moving target indication (MTI) radar. The stagger-period MTI filters not only extended the blind speed of flying targets, but also had an optimized improvement factor. Finally, we proposed a mathematical programming to search the best period code, which makes this type filter’s velocity response flattened. Meanwhile, we compared properties of the stagger-period to uniform-period filters, and provided with some examples to illustrate the theories and designs.

Monolithically Integrated Silicon Photodiode-Based FIR Filter in 65 nm CMOS Technology

This letter presents a finite impulse response (FIR) filter utilizing segmented silicon (Si) photodiodes as a part of the filter for equalization. The filter utilizes an integrated Si photodiode divided into three segments which serve as current sources and capacitors, and realizes a high-pass FIR that partially compensates for the frequency-dependent gain loss due to the physical and the electrical bandwidths of Si photodiode. This technique is well-suited for low-power applications since it uses only passive components. The photodiode-based FIR filter is implemented in a standard 65-nm CMOS technology utilizing an n-well to p-substrate junction photodiode with 12 fingers. The photodiode has a measured physical bandwidth of 15 MHz and a simulated electrical bandwidth of 5.9 GHz. The filter compensates for the photodiode gain loss by 8.7 dB in the frequency range from 1 to 5 GHz.

Comparative analysis of a high pass Finite Impulse Response (FIR) filter designed and simulated by window methods

The design of digital filters is a deceptively complex topic. Although filters are easily understood and calculated, the practical challenges of their design and implementation are significant and are the subject of much advanced research. In this research paper, a high pass FIR filter is designed and simulated using Gaussian, Taylor and Hanning windows. The three windows are compared on the basis of their magnitude responses, step responses, impulse responses and phase responses and the results are presented. The comparative result shows that the proposed design of Taylor window is the most efficient of the three windows. Keywords: High Pass Filter, Window Method, Gaussian Method, Impulse response

Design of Minimum-Phase FIR Filter: An Evolutionary Approach

In this communication, one novel attempt has been made towards the design of minimum-phase finite impulse response (FIR) filter using one evolutionary computation technique, named differential evolution (DE) algorithm. Mean square error (MSE) between the frequency samples of the ideal filter and the designed filter in the pass-band and stop-band region and the minimum average error (MAE) between the frequency samples in the transition-band region had been considered for the formulation of the objective function in the optimization process. Performance of the optimization technique has been monitored in terms of convergence characteristics for four different orders of FIR filter under consideration. Robustness of our proposition has been firmly established in the sense that the proposed algorithm can be suitably tuned for the design of low-pass, high-pass or band-pass FIR filter of different specifications. Supremacy of the proposed algorithm over other existing techniques for the design of minimum-phase FIR filter has been substantiated by means of frequency response and pole-zero diagrams.

Design of Linear Phase High Pass FIR Filter using Weight Improved Particle Swarm Optimization

The design of Finite Impulse Response (FIR) digital filter involves multi-parameter optimization, while the traditional gradient-based methods are not effective enough for precise design. The aim of this paper is to present a method of designing 24th order high pass FIR filter using an evolutionary heuristic search technique called Weight Improved Particle Swarm Optimization (WIPSO). A new function of the weight parameters is constructed for obtaining a better optimal solution with faster computation. The performance of the proposed algorithm is compared with two other search optimization algorithms namely standard Genetic Algorithm (GA) and conventional Particle Swarm Optimization (PSO). The simulation results show that the proposed WIPSO algorithm is better than GA and PSO in terms of the magnitude response accuracy and the convergence speed for the design of 24th order high pass FIR filter.

An FIR Filter Using Segmented Photodiodes for Silicon Photodiode Equalization

This letter presents a finite-impulse response (FIR) filter implemented using segmented photodiodes, which provides a bandwidth enhancement or a partial frequency response equalization for a silicon photodiode. The FIR filter utilizes an $LC$ ladder network for its delay cells, where each segmented photodiode constitutes the ladder network. The measurement results show that the three-tap high-pass FIR filter incorporating segmented photodiodes designed for maximum bandwidth equalization achieves a bandwidth improvement of 7.4 times compared with that of a photodiode without segmentation and filtering. The upper bound of the equalization frequency of the three-tap high-pass FIR filter designed in this letter for maximum peaking frequency equalization is 14 times of the photodiode bandwidth.

Optimal design of FIR high pass filter based on L1 error approximation using real coded genetic algorithm

In this paper, an optimal design of linear phase digital finite impulse response (FIR) highpass (HP) filter using the L1-norm based real-coded genetic algorithm (RCGA) is investigated. A novel fitness function based on L1 norm is adopted to enhance the design accuracy. Optimized filter coefficients are obtained by defining the filter objective function in L1 sense using RCGA. Simulation analysis unveils that the performance of the RCGA adopting this fitness function is better in terms of signal attenuation ability of the filter, flatter passband and the convergence rate. Observations are made on the percentage improvement of this algorithm over the gradient-based L1 optimization approach on various factors by a large amount. It is concluded that RCGA leads to the best solution under specified parameters for the FIR filter design on account of slight unnoticeable higher transition width.

Extracting the Phase of Fault Currents: A New Approach for Identifying Arc Flash Faults

This paper proposes a new approach for detecting and identifying arc flash faults in power systems. The proposed approach is structured to extract the phases of transient frequency components present in arc flash fault currents. The desired phases can be extracted by processing fault currents using a modulated filter bank that is composed of high pass finite impulse response (FIR) filters. These filters are designed by using the Kaiser window method to achieve linear phase responses. Extracting the phases of transient frequency components, present in arc flash fault currents, can provide signature information for accurate and fast detection and distinguish of an arc flash fault. The proposed phase-based approach is implemented for off-line testing to evaluate its performance. Test cases of parallel and series arch flash faults are conducted for supplying linear, non-linear, and dynamic loads. Simulation and off-line results demonstrate the validity, accuracy, speed, and reliability of the phase-based approach to detect and distinguish arc flash faults with minor sensitivities to the load type and arch flash type.

Clutter Mitigation in Echocardiography Using Sparse Signal Separation.

In ultrasound imaging, clutter artifacts degrade images and may cause inaccurate diagnosis. In this paper, we apply a method called Morphological Component Analysis (MCA) for sparse signal separation with the objective of reducing such clutter artifacts. The MCA approach assumes that the two signals in the additive mix have each a sparse representation under some dictionary of atoms (a matrix), and separation is achieved by finding these sparse representations. In our work, an adaptive approach is used for learning the dictionary from the echo data. MCA is compared to Singular Value Filtering (SVF), a Principal Component Analysis- (PCA-) based filtering technique, and to a high-pass Finite Impulse Response (FIR) filter. Each filter is applied to a simulated hypoechoic lesion sequence, as well as experimental cardiac ultrasound data. MCA is demonstrated in both cases to outperform the FIR filter and obtain results comparable to the SVF method in terms of contrast-to-noise ratio (CNR). Furthermore, MCA shows a lower impact on tissue sections while removing the clutter artifacts. In experimental heart data, MCA obtains in our experiments clutter mitigation with an average CNR improvement of 1.33 dB.

Digital FIR filter design using fitness based hybrid adaptive differential evolution with particle swarm optimization

This paper presents an efficient way of designing linear phase finite impulse response (FIR) low pass and high pass filters using a novel algorithm ADEPSO. ADEPSO is hybrid of fitness based adaptive differential evolution (ADE) and particle swarm optimization (PSO). DE is a simple and robust evolutionary algorithm but sometimes causes instability problem; PSO is also a simple, population based robust evolutionary algorithm but has the problem of sub-optimality. ADEPSO has overcome the above individual disadvantages faced by both the algorithms and is used for the design of linear phase low pass and high pass FIR filters. The simulation results show that the ADEPSO outperforms PSO, ADE, and DE in combination with PSO not only in magnitude response but also in the convergence speed and thus proves itself to be a promising candidate for designing the FIR filters.

Perfect Decomposition Narrow-Band FIR Filter Banks

This brief introduces perfect decomposition filter banks based on narrow-band linear-phase finite impulse response (FIR) filters. They consist of inner and lateral FIR filters. The inner filters are optimal equiripple narrow-bandpass FIR filters based on isoextremal polynomials. The inner filters are supplemented by lateral narrow-band low- and high-pass FIR filters. The concept of such isoextremal polynomials of this kind enables flexibility in the resulting frequency response of the filter bank. The filter banks presented here are made under the constraint of the resulting frequency response with a constant value for all frequencies.

High-pass-filtered magnetocardiogram and cardiomyopathy in patients with type 1 diabetes mellitus

Prolonged and/or fractionated depolarization due to tissue degeneration of the ventricular myocardium is a feature of cardiomyopathy. Signal averaged electrocardiography uses high-pass filters of the Butterworth type to quantify, noninvasively, fractionated high-frequency components at the end of the QRS complex. In this study a finite impulse response high-pass filter of the 90th order (cutoff at 37 Hz) was applied to magnetocardiograms (MCGs) and high-resolution electrocardiograms (ECGs) order to quantify high-frequency components throughout the myocardial depolarization. Additionally, late-potential analysis on the signal-averaged ECG was performed. A prospective investigation was made of 23 cardiologically asymptomatic patients, 11 females and 12 males, with type I diabetes mellitus. Their mean age was 21.7 years (range, 13–34 years). The mean duration of diabetes was 14 years (range, 1–27 years). Data were compared with those of 22 control subjects (12 females, 10 males) of mean age, 23.2 years (range, 11–35 years). The ECGs and MCGs were simultaneously recorded and signal-averaged, digitally filtered, and quantified by a score obtained by multiplying the amplitude variation of the signal by the number of maximal/minimal in the QRS complex. Echocardiograms were used to calculate the left ventricular mass and to document the presence of cardiomyopathy. Scores were higher in the MCGs of with type I diabetes mellitus than in the control subjects ( P < .001). High scores correlated with an increased left ventricular muscular mass index ( P < .05) and duration of the diabetes ( P < .05). The high-resolution ECG, processed analogously, showed similar results in relation to left ventricular mass ( P = .06) and duration of diabetes ( P = .07), respectively (nonsignificant). No late potentials were found. These findings suggest that using a linear-phase high-pass finite impulse response filter may be useful for the noninvasive identification of patients with cardiomyopathy who exhibit possible disturbances of intraventricular depolarization. Our findings also suggest that analysis using the total QRS complex, rather than the final part of the QRS complex only, may improve identification of patients at risk.