Optimal Filter Design Research Articles

Optimal prototype filter design in GFDM systems for self-interference elimination: A novel signal processing approach

We present an innovative conceptual framework and a comprehensive mathematical model to advance the understanding and mitigation of self-interference phenomena within generalized frequency division multiplexing (GFDM). By introducing a novel analytical perspective, we decompose the self-interference effects inherent to GFDM into two orthogonal constituents through a vectorized representation. Our elucidation of the self-interference components in terms of prototype filter parameters in the frequency domain is of particular significance. This theoretical characterization allows us to derive explicit analytical expressions, thereby paving the way for the proposition of an optimal filter design strategy that effectively mitigates self-interference distortions within GFDM systems. Our investigation reveals a noteworthy linkage between the required bandwidth allocation for individual subcarriers and the sub-symbol configuration within the proposed optimal prototype filter. This relationship underscores the filter’s adeptness in optimizing spectrum utilization across the system. Through an analytical examination of the bit error rate (BER) performance within the GFDM framework, we establish the superior efficacy of our proposed optimal filter design relative to contemporary approaches documented in extant literature. Validation of our analytical findings is conducted via meticulous computer simulations, where a strong concurrence between the analytical predictions and the observed simulation outcomes is manifest.

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

Optimal filter design using mountain gazelle optimizer driven by novel sparsity index and its application to fault diagnosis

The informative frequency band (IFB) plays a vital role in detecting defects in complex machinery through visible informative features. In the present work, a denoising filter has been designed to enhance the small non-stationarities present in the signal. Initially, the system impulse is computed to estimate the filter coefficients which are further optimized by the mountain gazelle optimization (MGO) based on the maximum value fitness function. The novel sparsity index based on kurtosis and negentropy (NE) is put forward as the fitness function. Then, optimized coefficients are convolved with the system impulse to design the denoising filter. The efficacy of the designed filter is verified through vibration and acoustic signals from the defective components of the belt conveyor system. The designed filter is better able to extract the impulsiveness from the signal, give improved values of kurtosis and signal-to-noise ratio (SNR), and reduce interferences from other machinery components and the environment simultaneously.

Optimal passive LCL filter design for grid-connected converters in weak grids

In an era characterized by the increasing integration of renewable energy sources into the power grid, the stability and resilience of the grid have become paramount, especially in areas with weak grid infrastructure. One effective approach to mitigate the adverse effects of grid disturbances and harmonics is passive LCL (inductor-capacitor-inductor) filters. However, designing these filters for optimal performance under varying conditions remains challenging. This research paper presents an effective approach to addressing this challenge by utilizing the power of evolutionary algorithms, specifically Multi-Objective Particle Swarm Optimization (MOPSO) and Multi-Objective Genetic Algorithm (MOGA), to design optimal passive LCL filters for weak grid environments. The proposed method aims to enhance grid resilience by optimizing the filter parameters to consider a weak grid's unique characteristics and uncertainties. Minimizing the resonance frequency has succeeded in achieving power quality and stability improvements related to changes in grid impedance. The proposed approach achieves power quality and stability improvements related to changes in grid impedance and dealing with fragile conditions, which is the main scope of this paper.© 2017 Elsevier Inc. All rights reserved.

Optimal design of multilayer optical thin film structure for smart energy saving applications using needle optimization approach

In this work, a novel design of a one dimensional photonic crystal (1D PC) is investigated. The 1DPC structure is composed of alternating layers of tantalum pentoxide (Ta2O5) and silicon dioxide (Sio2). The proposed 1D PC structure is designed to act as short wave pass (SWP) edge filter that selectively passes light of short wavelengths, while the infrared light is blocked. In this study, Essential Macleod software is used to create the optimal design with the computational support of the needle synthesis technique. By varying the incidence angle of the mean polarized light mode, we can determine the features of the optimal SWP edge filter design, which leads to an important application for this filter. It can shed light on the filter’s suitability as a smart energy saving window coating for hot climate regions. The study includes different hot regions in Saudi Arabia such as Mecca, Riyadh, Dammam, Arar and Alaqiq. They were used as case studies in this research. According to the study of the optimal design of SWP edge filter applied in Mecca, Riyadh, Dammam, Arar and Alaqiq provinces, the light transmittance in the visible region is more than 99% during the summer solstice and more than 96% during the winter solstice. The photonic band gab (PBG) is almost constant during the summer solstice without shifting or decreasing in size whereas in the winter solstice, the PBG shifts toward the short wavelengths and decreases in size by increasing the angle of incidence. This allows an amount of solar energy to enter in winter. Riyadh, Dammam, and Arar provinces experienced a significant increase in solar energy during the winter solstice, more than Mecca and Alaqiq provinces.

Optimal loop filter design of a DC immunity single-phase PLL based single delay operator

Optimal loop filter design of a DC immunity single-phase PLL based single delay operator

An optimized filter design approach for enhancing imaging quality in industrial linear accelerator.

The polychromatic X-rays generated by a linear accelerator (Linac) often result in noticeable hardening artifacts in images, posing a significant challenge to accurate defect identification. To address this issue, a simple yet effective approach is to introduce filters at the radiation source outlet. However, current methods are often empirical, lacking scientifically sound metrics. This study introduces an innovative filter design method that optimizes filter performance by balancing the impact of ray intensity and energy on image quality. Firstly, different spectra under various materials and thicknesses of filters were obtained using GEometry ANd Tracking (Geant4) simulation. Subsequently, these spectra and their corresponding incident photon counts were used as input sources to generate different reconstructed images. By comprehensively comparing the intensity differences and noise in images of defective and non-defective regions, along with considering hardening indicators, the optimal filter was determined. The optimized filter was applied to a Linac-based X-ray computed tomography (CT) detection system designed for identifying defects in graphite materials within high-temperature gas-cooled reactor (HTR), with defect dimensions of 2 mm. After adding the filter, the hardening effect reduced by 22%, and the Defect Contrast Index (DCI) reached 3.226. The filter designed based on the parameters of Average Difference (AD) and Defect Contrast Index (DCI) can effectively improve the quality of defect images.

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.

Filter Design for Estimation of Stellar Metallicity: Insights from Experiments with Gaia XP Spectra

We search for an optimal filter design for the estimation of stellar metallicity, based on synthetic photometry from Gaia XP spectra convolved with a series of filter-transmission curves defined by different central wavelengths and bandwidths. Unlike previous designs based solely on maximizing metallicity sensitivity, we find that the optimal solution provides a balance between the sensitivity and uncertainty of the spectra. With this optimal filter design, the best precision of metallicity estimates for relatively bright (G ∼ 11.5) stars is excellent, σ [Fe/H] = 0.034 dex for FGK dwarf stars, superior to that obtained utilizing custom sensitivity-optimized filters (e.g., SkyMapper v). By selecting hundreds of high-probability member stars of the open cluster M67, our analysis reveals that the intrinsic photometric-metallicity scatter of these cluster members is only 0.036 dex, consistent with this level of precision. Our results clearly demonstrate that the internal precision of photometric-metallicity estimates can be extremely high, even providing the opportunity to perform chemical tagging for very large numbers of field stars in the Milky Way. This experiment shows that it is crucial to take into account uncertainty alongside the sensitivity when designing filters for measuring the stellar metallicity and other parameters.

Global and local breakthrough curves: A concept for filtration design through analysis of internal concentration distribution using CFD

Evaluating the filtration performance of a filter design traditionally relies on a global breakthrough curve, which represents changes in outlet concentration over time. This curve does not account for adsorption phenomena inside the filter, often necessitating trial-and-error experimentation for optimizing filter shape. To address this issue, we introduce an innovative approach that employs computational fluid dynamics (CFD) to define local breakthrough curves. These curves represent concentration changes over time at specific points within the filter. By comparing these local breakthrough curves with the conventional global breakthrough curve, we aim to enhance the precision of filter designs. This optimization strategy seeks to achieve shapes that meet predetermined performance targets or improve the efficiency of existing filters. Utilizing CFD-generated local breakthrough curves in the optimization process enables the prediction of essential filter elements required to meet specified performance criteria, such as height and thickness. Moreover, this method identifies underperforming areas in existing filters and suggests strategic avenues for improvement. Our results underscore the utility of CFD-informed local breakthrough curves as invaluable tools for streamlining the filter design optimization process.

An Improved Method to Compute the Mutual Capacitance between Interdigital Transducers in Radio Frequency Surface Acoustic Wave Filters

This paper proposes an improved method to calculate the mutual capacitance between interdigital transducer (IDT) electrodes to enhance the accuracy of the traditional coupling-of-modes (COM) model, which is commonly used to simulate surface acoustic wave (SAW) filters and duplexers. In this method, the boundary element method (BEM) is adopted to obtain the capacitance per unit length in a layered medium, while the partial capacitance (PC) method is used to derive the effective relative permittivity of the multi-layered IDT. Numerical results from commercially available software are provided for comparison with the results calculated using the proposed method. The consistent results verify the validity and accuracy of this method, which also demonstrates significantly faster calculation speed compared to commercially available software. Precise electrical response prediction of a dual-mode SAW (DMS) filter can be achieved by applying this method to the COM model, and this ultra-fast calculation method can also be included in filter design optimization.

Design Optimization and Performance Evaluation of Passive Filters for Acoustic Noise Mitigation in Locally Manufactured Small Wind Turbines

Design Optimization and Performance Evaluation of Passive Filters for Acoustic Noise Mitigation in Locally Manufactured Small Wind Turbines

Optimal design of single-tuned filters for industrial power systems to reduce harmonic distortion using forest algorithm

ABSTRACT This paper presents a novel optimization algorithm for designing single-tuned filters in industrial power systems to reduce harmonic distortion. Single-tuned filters are cost-efficient, simple, and easy to maintain. We formulate the filter design as an optimization problem, minimizing costs while incorporating harmonic limits and system constraints. Our solution algorithm simulates tree growth, proliferation, and death based on the forest algorithm to generate optimal solutions. This enables both local and global searches for fast convergence and feasible designs. Unlike traditional approaches, we account for variations in filter components and system parameters caused by factors like manufacturing tolerances, operating temperature, and frequency variations. Our study contributes by designing adaptive filters that match practical systems. We validate our method against previous publications and other techniques like simulated annealing and teaching-learning-based optimization. Simulation results demonstrate the effectiveness of our approach in minimizing costs, satisfying operation constraints, and accounting for component variations in this complex problem.

Harmonic mitigation using optimal active power filter for the improvement of power quality for a electric vehicle changing station

The idea of electric vehicles (EVs) is relatively new in the transportation industry. EVs are becoming increasingly popular due to many advantages. Apart from various advantages, some drawbacks of these vehicles are also a concern. One of the concerns for these vehicles is the substantial power requirement to charge the batteries. During charging, EVs deteriorate the power quality by injecting harmonic current into the system. As poor power quality is undesirable, the mitigation of this harmonic current is essential. Therefore, in this study, an optimal design of a shunt active power filter (SAPF) based EV charging system is suggested. The optimal design of the SAPF ensures that the DC-link voltage is efficiently regulated. To test the performance of the suggested scheme, a simulation system is developed and tested in MATLAB Simulink. The results show that the developed optimal control can efficiently regulate the DC-link voltage. The results, pre and post-compensation, show that the total harmonic distortion in the system is reduced to 4.33 % from 26.72 % using the suggested approach. This is even better by 12.93%, 7.39%, 14.32% and 20.32% in comparison to particle swarm optimization, whale optimization algorithms, bee colony optimization and ant colony optimization used for comparison.

Optimal sequential fusion estimation for sampled‐data systems with random delays and multiplicative noise

AbstractThis article is concerned with the optimal sequential fusion filter design issue for a class of sampled‐data systems subject to random transmission delays and multiplicative noise. The considered delay is modeled as a multistate Markov chain, while the multiplicative noise corrupting the observation measurements is assumed be a white noise sequence with known statistical properties. First, by reorganizing the original measurements, the solvability of the addressed problem is cast into the feasibility of an optimal estimation issue for the hybrid Markovian jump system without delays. Then, an optimal sequential fusion filter is constructed by utilizing a dynamic model with finite jumps on basis of the arrival order of measurements from different sensors, guaranteeing estimation performances both at sampling instants and within the sampling intervals. Moreover, the desired estimator gains can be computed via solving a series of coupled differential Riccati equations with jumps and coupled differential Lyapunov equations. Further, a stationary sequential fusion filter is achieved with the guarantee of the exponentially mean‐square stability. Finally, two simulation examples are provided to verify the validity of the proposed algorithms.

Variable Conversion Approach for Design Optimization of Low-Voltage Low-Pass Filters

Variable Conversion Approach for Design Optimization of Low-Voltage Low-Pass Filters

Optimal linear filter design for process state and packet loss estimation in networked control systems

Considering the effect of packet losses on the behavior of networked systems, this work is concerned with estimation of the packet loss occurrences in the input channels possibly together with the system state. For this purpose, the commonly used Markov chain model of the successive packet loss occurrences is transformed to a linear recursive model in which the packet loss occurrence variables appear as new state variables. Two methods are proposed for combining the recursive packet loss model with the plant model to obtain an overall model for the whole networked control system (NCS). In the first method, a state space model of the plant is used which allows for simultaneous estimation of the packet loss occurrences and the plant state. In the second method, an input–output model of the plant is employed which allows for estimating only the packet loss occurrences. Both the zero and the hold packet loss handling strategies are considered and stability of the filters is analyzed. The proposed methods are compared with some existing results during an example to show their advantages.

Advanced Single-Phase PLL-Based Transfer Delay Operators: A Comprehensive Review and Optimal Loop Filter Design

In recent years, several research works have addressed and developed the phase-locked loop (PLL) in single-phase grid-connected converters with different structures and properties. Each has merits and demerits, such as a complex structure, high computational burden, and slow transient response. This paper aims to comprehensively review advanced single-phase PLLs based on transport delay operators to realize signal orthogonality. A deep insight into the PLLs’ small-signal modeling, main characteristics, stability analysis, and loop filter design are provided in this paper. The main advantages and drawbacks are explained for each type of PLL in terms of different performance indexes, such as settling time, estimation error, and ripples in the estimated grid information. This paper also aims to provide optimal tuning and design of the loop filter gains from the large-signal model point of view, including all the nonlinearities, adopting the stochastic optimization method. All simulations are implemented using the MATLAB/Simulink 2018b environment to validate all theoretical analyses of this paper. The sampling and nominal frequencies are set to be 100 kHz and 50 Hz throughout all the simulation studies.

Optical Filter Design for Daylight Outdoor Electroluminescence Imaging of PV Modules

This paper presents an advanced outdoor electroluminescence (EL) imaging system for inspecting solar photovoltaic (PV) modules under varying daylight conditions. EL imaging, known for its effectiveness in non-destructively detecting PV module defects, is enhanced through specialized optical filters. These filters, including a bandpass filter targeting EL emissions and a neutral density filter to reduce background light, significantly improve the system’s signal-to-noise ratio (SNR). The experimental results demonstrate the system’s enhanced performance, with superior clarity and detail in EL emissions, enabling precise defect localization and characterization at the cellular level. Notably, the system achieves an SNR improvement, with values consistently above two, outperforming previous systems and confirming its suitability for efficient solar PV maintenance and diagnostics. This research offers a flexible approach to optimizing EL imaging quality across various solar irradiance levels and angles, essential for improved PV module performance and reliability. The system effectively handles different PV module configurations, orientations, and types, including monofacial and bifacial arrays. It showcases robust imaging capabilities under high solar irradiance and different sun illumination levels, maintaining high-quality imaging due to its optimized filter design. Additionally, the system’s adaptability in detecting EL emissions from series-connected PV modules is highlighted, demonstrating its comprehensive evaluation capabilities for PV array performance.

1-D multi-channel CNN with transfer functions for inverse electromagnetic behaviors modeling and design optimization of high-dimensional filters

1-D multi-channel CNN with transfer functions for inverse electromagnetic behaviors modeling and design optimization of high-dimensional filters