Design Of Digital Filters Research Articles

Automated design of digital filters using convolutional neural networks for extracting ringdown gravitational waves

Abstract The observation of gravitational waves is expected to allow new tests of general relativity to be performed. As the gravitational wave signal is hidden by detector noise in observed data, a method to reduce noise is required to analyze the ringdown phase of gravitational wave signals. Recently, some noise reduction methods based on a neural network have been proposed; however, the results of these methods must be considered with caution because the output can contain spurious components. To overcome this limitation, in this study, we developed a neural network– based method to design optimal digital filters for extracting ringdown gravitational wave signals. In this method, no spurious components appear in the output because the digital filters reduce the noise. We conducted simulations with waveforms of gravitational waves from binary black hole coalescence and confirmed that the proposed method designs appropriate filters that reduce detector noise.

A Fresh Revisit of the Issues and Improvements in Impulse Invariance Filter Design for Infinite Impulse Response Filters

The objective of this paper is to first present some issues with impulse invariance filter (IIF) design during the design of digital infinite impulse response (IIR) filters. Engineers are often confused about some inconsistent observations. For instance, if the impulse response of a digital filter is designed using the impulse invariance procedure, then the analog and digital filters’ frequency and step responses are very different. Two simple remedies are presented in this paper. One is a post-processing approach that scales the frequency and step responses of the digital filter by the sampling interval T. Another one is a pre-processing approach that scales the impulse response of the analog filter by T. However, even after these remedies, there is still a steady state bias in the step response of the digital filter for certain cases where there is discontinuity in the analog impulse response. A recommendation is to include a correction term in the digital filter. After that, the steady state bias in the digital filter is then suppressed. Moreover, the MATLAB R2021a command “impinvar” needs to also include a correction term so that the frequency and step responses can be more accurate in the digital filter. Two comparative studies were carried out to compare the improved IIF filter with three competing digital IIR filter design methods. Although the above issues and improvements have been proposed by researchers in the past, many researchers, engineers, and students are still not aware of them. This paper provides a fresh revisit of these issues and improvements by using figures, equations, and examples. Proper credits are also given to those researchers who first pointed out those issues and improvements. It is hoped that through an open access journal, future rediscovery of issues and improvements in IIF can be prevented.

Optimal design of digital FIR filters based on text convolutional neural network for aliasing errors reduction in FI-DAC

Optimal design of digital FIR filters based on text convolutional neural network for aliasing errors reduction in FI-DAC

The Evaluation of Explicit Parameters on Eulerian-Lagrangian Simulation of Wave Impact on Coastal Bridges

Simply supported bridges along the United States Gulf Coast are highly vulnerable to extreme wave conditions and water elevations. A better understanding of the loading demands on the bridges under these extreme events will lead to improved designs and enhanced resiliency. Nonetheless, analyses through numerical models pose difficulties in terms of reliability and computational burden. On the other hand, experimental models for large-scale bridge superstructures are subject to limited space allocation and related scaling challenges. Hence, optimized Finite Element (FE) models that can simulate Wave-Superstructure Interaction (WSI) are a preferred viable solution to estimate loads experienced by these bridge superstructures. Nonetheless, the results of the FE simulations are highly dependent on the numerical parameters and configurations implemented in the model; thus, the implications of parameter selection must be assessed to help scholars and engineers develop reliable FE models. In this paper, an experimental scaled model was used to calibrate numerical models following the Coupled Eulerian-Lagrangian (CEL) technique in ABAQUS. This explicit approach solves the Navier-Stokes (NS) equations to simulate fluid flow, and the dynamics response of the structure depends on analysis parameters (mass scaling factor, damping hourglass control, displacement hourglass scaling factor, and bulk viscosity scaling factors) and configurations (mesh size). The results provide a platform to compare numerical outputs and find the best parameters that suit WSI models. Moreover, the force signals applied to the superstructure experience high oscillations due to WSI. Different digital filter designs have been used to remove unwanted oscillations and provide researchers with recommendations on generating models that deliver optimal results.

Enhancing power quality in vehicle-to-grid (V2G) operations of FCEVs through the integration of real-time digital IIR filters in power calculations

Fuel cell electric vehicles (FCEVs) are estimated as the future's mobile distributed generators with vehicle-to-grid (V2G) functions. Besides, V2G topologies are required to be designed with an appropriate analog filter by accurately measuring the reference current and voltage values of inverters and converters. Thus, the highest accuracy is required in control structures for charging and grid connection of FCEVs to improve the power quality (PQ). In this study, PQ improvement of V2G operation of FCEVs is proposed by considering the developed real-time digital infinite impulse response (IIR) filters in generating reference signals in the power calculation stage. A band-pass digital IIR filter design is realized with the inverter using LabVIEW infrastructure, and reference signals are smoothed by using IIR filters. The performances of developed different IIR filters' results are examined in the study. Developed filters are designed using the order estimation method. Elliptic, Chebyshev, and Butterworth filters are applied to the voltage and current signals, and the best results are obtained in reference power signals with the Butterworth filter. The Butterworth filter maintains stability in current RMS, induces a 0.09 % decrease in voltage RMS, and shows a slight 0.30 % increase in active power. The proposed approach removes distortions in reference power signals by using a designed filter for a reliable V2G operation of FCEVs.

Reverse design and optimization of digital terahertz bandpass filters

In this paper, an ingenious reverse design method is applied to the design and optimization of terahertz bandpass filters in order to achieve standardized design of high-performance terahertz functional devices. An equivalent model of subwavelength metasurface mapped to digital space is established. Based on ideal objective functions and constraints, intelligent algorithms begin a bold journey to explore the vast potential structure in the solution space. Through iterative refinement, the algorithm reveals optimal structural patterns, unlocking areas of unparalleled performance. The direct binary search (DBS) algorithm and the binary particle swarm optimization (BPSO) algorithm are compared in optimization process. When using the DBS algorithm to optimize the design area, it takes a long time to poll the logic states of all pixel units point by point, and it is easy to get stuck in the local optimal value. However, BPSO algorithm has stronger global search capabilities, faster convergence speed, and higher accuracy. Through a comprehensive comparison of the device performance optimized by the two algorithms, the solution optimized by BPSO algorithm has better out-of-band suppression performance and a narrower full width at half peak, but slightly lower transmittance at the center frequency. The bandpass filter has a center frequency of 0.51 THz, a bandwidth of 41.5 GHz, and an insertion loss of -0.1071 dB. When considering computational efficiency, DBS algorithm lags behind, the simulation time is 11550 s, while BPSO algorithm only needs 9750 s. Compared with the traditional forward design, the reverse design method can achieve the narrower band, lower insertion loss, better out-of-band suppression and polarization stability. The fine structural changes of the optimal results have a significant influence on spectral performance, demonstrating the superiority and uniqueness of reverse design. This technology contributes to the design and optimization of high-performance and novel functional devices.

Fractional Kaiser Window With Application to Finite Impulse Response Digital Filter Design

Fractional Kaiser Window With Application to Finite Impulse Response Digital Filter Design

Energy efficient enhanced all pass transformation fostered variable digital filter design based on approximate adder and approximate multiplier for eradicating sensor nodes noise

Energy efficient enhanced all pass transformation fostered variable digital filter design based on approximate adder and approximate multiplier for eradicating sensor nodes noise

Optimized FIR Filter Using Genetic Algorithms: A Case Study of ECG Signals Filter Optimization

The advancement in technology and the availability of specialized digital signal processing chips have made digital filter design and implementation more feasible in a variety of fields, including biomedical engineering. This paper makes two key contributions. First, it uses a genetic algorithm to optimize the coefficients of finite impulse response (FIR) filters. Second, it conducts a case study on using genetic algorithms to optimize FIR filters for electrocardiogram (ECG) biomedical signal noise removal. The goal of the proposed filter design approach is to achieve the desired signal bandwidth while minimizing the side lobe level and eliminating unwanted signals using a genetic algorithm. The results of a comprehensive analysis show that the genetic algorithm-based filter is more effective than conventional filter designs in terms of noise removal efficiency.

Implementation of a Finite Impulse Response Filter using PUFs to Avoid Trojans

In the modern era of signal processing, digital filters play an important role in real-time applications such as communication, consumer electronics, digital signal processing, audio, etc. In digital filter design, Finite Impulse Response (FIR) filters are highly preferable due to their linear phase and inherent stability. These filters benefit from being time-invariant and simple to implement with minimal computational requirements. Therefore, the hardware security of FIR filters is essential for good performance and reliable results. On the other hand, there is the possibility of hardware threats, such as tampering, reverse engineering, hardware Trojans, etc., as the design of an FIR filter involves many stages. Such hardware attacks on FIR filters can cause several problems, including performance degradation, leakage of confidential information, lack of stability, etc. This study presents the design and implementation of a Trojan-aware FIR filter using Physical Unclonable Functions (PUFs). The key feature of PUFs is that they generate a unique and unpredictable response for each given challenge. In the proposed design, PUFs were used to generate the FIR filter coefficients that are unique and unpredictable by attackers/trojans to improve security. The security of FIR with PUF was tested using ML-based challenges, and the results showed approximately 30% more reliability and consistency compared to the FIR without PUFs.

Exploring the use of Genetic Algorithms Toolbox in Engineering Education: Did it Provide an Interesting Learning Experience for Students?

Teaching in engineering education using Genetic Algorithms (GAs) toolbox as a teaching tool in engineering education can be an effective approach, particularly in Signal Processing subjects, as it encourages students to learn how to find optimal values required in designing digital filters. This research investigates the use of GAs for teaching digital filter design, where the evaluation function of the problem is optimized using Gas. The GAs toolbox simulation is designed to yield a stable, lowest-order H[z] that meets the tolerance parameters and satisfies the design criteria. Teaching GAs involves representing the filter design problem in a way that can be accepted by genetic programming.

Recursive digital filters with optimum equiripple passband magnitude characteristic

The paper investigates, for the first time, the design of recursive digital filters that exhibit optimum equiripple passband magnitude characteristics. After a brief description of the optimum equiripple filters in the analog domain, digital optimum C (Chebyshev) tan and sine filters are derived directly in the digital domain through the minimization of the area under the passband characteristic function. The influence of the truncated coefficients is also considered and its effects are demonstrated through an example proving the robustness of the magnitude function. Furthermore, a design procedure is proposed for the optimum C tan half-band filter. To highlight the effectiveness of the proposed approximation, a comparative analysis is conducted with the staircase digital filter, as both filters optimize passband responses by minimizing the area under the characteristic function. The results of comparison demonstrate that the optimum equiripple approximation surpasses the staircase approximation, not only in the analog but also in the digital domain.

L1-Norm and LMS Based Digital FIR Filters Design Using Evolutionary Algorithms

L1-Norm and LMS Based Digital FIR Filters Design Using Evolutionary Algorithms

Enhancing IIR system identification: Harnessing the synergy of gazelle optimization and simulated annealing algorithms

In the field of digital filter design and system identification, accurately modeling Infinite Impulse Response (IIR) systems is of utmost importance. This paper introduces a new adaptive algorithm that combines the gazelle optimization algorithm with simulated annealing to achieve superior optimization performance for digital IIR filters used in system identification. To evaluate the algorithm's effectiveness, extensive experiments were conducted on two fronts: the challenging CEC2020 benchmark functions and a fifth order IIR system identification problem. The proposed algorithm proves its effectiveness by achieving optimal solutions on the CEC2020 benchmark functions when compared to other well-known optimization algorithms such as arithmetic optimization, particle swarm optimization, differential evolution, sine cosine algorithm, gray wolf optimizer, biogeography-based optimization, salp swarm algorithm, and the original gazelle optimization algorithm. The algorithm's performance is measured by its ability to find the best solutions for these benchmark functions. For the IIR system identification problem, the study considers both the same order and reduced order cases. The system to be identified is represented by a fifth order plant. The algorithm's statistical analysis and convergence behavior are compared to the original gazelle optimization algorithm, artificial hummingbird algorithm, mountain gazelle optimization algorithm, and the widely used artificial bee colony algorithm. These comparisons demonstrate the proposed algorithm's superior capability and efficiency in accurately identifying the parameters of IIR systems.

Capricious Digital Filter Design and Implementation Using Baugh–Wooley Multiplier and Error Reduced Carry Prediction Approximate Adder for ECG Noise Removal Application

Capricious Digital Filter Design and Implementation Using Baugh–Wooley Multiplier and Error Reduced Carry Prediction Approximate Adder for ECG Noise Removal Application

A Novel Guided Box Filter Based on Hybrid Optimization for Medical Image Denoising

Medical image denoising is a crucial pre-processing task in the medical field to ensure accurate analysis of anomalies or sicknesses in the human body. Digital filters are popular for reducing undesired noise as they provide reliability, high accuracy, and reduced sensitivity to component tolerances compared to analog filters. However, conventional digital filter design approaches lack efficiency in achieving global optimization robustness. To overcome these incapabilities, this paper adopted bio-inspired optimization algorithms to offer viable digital filter designing tools because of their simple implementation and requirement of a few parameters to control their convergence. This research article explores a hybrid strategy that combines a novel guided decimation box filter (GDBF) with a hybrid cuckoo particle swarm optimization (HCPSO) algorithm to design a denoising filter for medical images. It is the first time a decimation box filter has been used for denoising, leading to novelty. The HCPSO algorithm is applied to obtain the filter parameters optimally. Medical images mostly suffer from four types of noises. The performance of the proposed filter is analyzed for these types of noise. To highlight the importance of parameter selection, the results of the proposed method are compared with other recently utilized bio-inspired genetic algorithms, such as PSO (particle swarm optimization), CS (cuckoo search), and FF (firefly). The superiority (potency) of the proposed method has been established by calculating the improvement in quality parameters such as the peak signal-to-noise ratio (PSNR), structure similarity index (SSIM), and feature similarity index (FSIM). The proposed filter achieved the highest PSNR (~35.7 dB), SSIM (~0.95), and FSIM (~0.92) and proved its numerical and visual quality efficacy over state-of-the-art models.

Applying MATLAB in the design of digital filters for selecting Pc5 geomagnetic pulsations

Applying MATLAB in the design of digital filters for selecting Pc5 geomagnetic pulsations

Fixed-Frequency PWM Control and Stability of Series-Stacked Buffer for 2ω-Power Decoupling

Active second-harmonic ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2\omega $ </tex-math></inline-formula> ) filters have become an integral technology for single-phase power converters in high-power density designs. The series-stacked buffer (SSB) has emerged as an attractive topology among the existing solutions due to its low power, high efficiency, and compact design. However, one of the challenges in adopting SSB lies in its control, where hysteresis current control has been adopted conventionally. This results in a wide variation in the switching frequency, making the digital control implementation and filter design complex. On the other hand, fixed-frequency pulsewidth modulation (PWM) control necessitates developing a model for the systematic controller design to ensure stability and desired filtering performance. The assumption of SSB modeled as a second-harmonic current source, independent of the dc bus circuit components, fails to capture the dc bus loading effect on the SSB. In this work, a dynamic model of SSB is developed where the main dc bus and its passive elements are considered. The derived model enables a systematic approach for the controller design while ensuring the desired <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2\omega $ </tex-math></inline-formula> filtering. The stability limits and parameter sensitivities of the model are studied analytically and in simulations. The analytical model is validated, and the proposed controller performance and stability limit are verified experimentally on a hardware prototype. A video demonstrating the transition from stable to unstable operation is provided.

Design and applications of adjustable 2D digital filters with elliptical and circular symmetry

Design and applications of adjustable 2D digital filters with elliptical and circular symmetry

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

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