Filter Design Approach Research Articles

A fast and effective approach for microstrip filter design using GA and TL-model

Microwaves and RF technology and their components like filters, antennas, etc. are commonly used in wireless networking and communication systems, wireless security systems, radar systems, and environmental remote sensing. In this paper, a fast and effective procedure has been proposed for microstrip filter design using a genetic algorithm (GA) with a transmission line model (TL). GA is modified to be highly efficient and accurate by encoding the topology. For the fixed filter topology, the electrical parameters of the filter are encoded in a single chromosome. To make the proposed procedure fast and effective, a transmission line model has been employed to compute the fitness value in GA. To demonstrate the effectiveness of the proposed procedure, a wideband second-order bandpass filter with a center frequency of 2.3 GHz is examined with a pair of short-circuited stubs and a pair of open-circuited stubs. The optimized design is validated using full-wave methods (MoM and EM simulator CST). The results show a low insertion loss of 0.1 dB and return loss better than 30 dB and a wide bandwidth of 46.95% with − 3 dB cutoff frequencies at 1.76 GHz and 2.84 GHz.

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.

Recursive Filtering Under Probabilistic Encoding-Decoding Schemes: Handling Randomly Occurring Measurement Outliers.

This article focuses on the recursive filtering problem for networked time-varying systems with randomly occurring measurement outliers (ROMOs), where the so-called ROMOs denote a set of large-amplitude perturbations on measurements. A new model is presented to describe the dynamical behaviors of ROMOs by using a set of independent and identically distributed stochastic scalars. A probabilistic encoding-decoding scheme is exploited to convert the measurement signal into the digital format. For the purpose of preserving the filtering process from the performance degradation induced by measurement outliers, a novel recursive filtering algorithm is developed by using the active detection-based method where the "problematic" measurements (i.e., the measurements contaminated by outliers) are removed from the filtering process. A recursive calculation approach is proposed to derive the time-varying filter parameter via minimizing such the upper bound on the filtering error covariance. The uniform boundedness of the resultant time-varying upper bound is analyzed for the filtering error covariance by using the stochastic analysis technique. Two numerical examples are presented to verify the effectiveness and correctness of our developed filter design approach.

A Novel Proximal Policy Optimization Approach for Filter Design

This paper proposes a proximal policy optimization (PPO) algorithm for coupling matrix synthesis of microwave filters. With the improvement of filter design requirement, the limitations of traditional methods such as limited applicability are becoming more and more obvious. In order to improve the filter synthesis efficiency, this paper constructs a reinforcement learning algorithm based on Actor-Critic network architecture, and designs a unique filter coupling matrix synthesis reward function and action function, which can solve combinatorial optimization problems stably.

Chirped Managed Laser for Multilevel Modulation Formats: A Semi-Analytical Approach for Efficient Filter Design

Chirped Managed Laser for Multilevel Modulation Formats: A Semi-Analytical Approach for Efficient Filter Design

Reconfigurable integrated photonic filters for optical signal processing using a silicon photonic platform.

We present a systematic photonic filter design approach by deploying pole-zero optimization. The filter transfer function is derived from its specifications by formulating closed-form optimization objective functions and subsequently translating them into optical design parameters. Two distinct filter examples, namely Chebyshev and elliptic filters, are considered for the design and validation. A compact reconfigurable three-pole photonic filter is fabricated on a silicon photonic platform to illustrate the proposed design technique including transmission tunability. Integrated thermal phase shifters coupled with micro-ring resonators are used to reconfigure filter responses. A well-matched experimental demonstration is presented to validate the proposed tuning method. We achieved a sharp out-of-band edge rejection of at least 20 and 40 dB for the elliptic and Chebyshev filter, respectively.

An efficient equalization filter design approach for the accurate sound reproduction in a room environment with wide-band content

In some sound reproduction applications, a desired sound field needs to be reproduced by remote loudspeakers in a room with sufficient accuracy over a wide frequency band. When low-frequency room modes exist and the low-frequency content is required to be reproduced accurately, an equalization filter with a long time-domain response may be necessary, resulting in a filter with a significantly high filter order, especially when the sampling rate is high for the high-frequency content. This imposes computational challenges on both filter design and implementation. In this article, a convex formulation is proposed for the wide-band sound reproduction filter design. To address issues due to the low-frequency room modes, a reduced-order technique is then proposed based on a sub-band filter structure where a low sampling rate is used to equalize the low-frequency room mode characteristics, and a high sampling rate is used for high-frequency features which usually has a short time span in response. The computational time can be reduced by several orders of magnitude and the numerical stability can be improved when the proposed order reduction method is used to eliminate redundant orders. Simulation and experimental results confirm the accuracy and efficiency advantages of the proposed method.

Design and implementation of low power FPGA-based optimal multiband filter with Spline function for denoising ECG signals

This article presents the design and implementation of a low power FPGA-based optimal multiband filter with Spline function for denoising ECG signals. The proposed multiband filter design utilizes least mean square algorithm to determine the optimal filter coefficients for multiple frequency bands, while the Spline function is used to interpolate the filter coefficients within each band to achieve a smooth transition between adjacent bands. The experimental work is carried out with ECGID database and it shows that the proposed filter outperforms in terms of benchmark performance matrices SNR, MSE, CC and PRD. The filter is implemented on a low power FPGA platform, which allows for real-time processing of ECG signals with low power consumption. The experimental results are analyzed and compared for different architecture using the MATLAB and XILINX VIVADO tools. The serial architecture of proposed filter design is implemented on Artix 7 (XC7A35T) EDGE Board, utilizing 1932 LUT, 5299 FF, 1 DSP block and 0.158 W on-chip power. The experimental results indicate that the proposed filter design approach is efficient, effective, and suitable for implementation in compact biomedical devices.

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.

A constrained multi-channel hear-through filter design approach using active control formulations

In many daily communication environments, especially during a pandemic era, partitioning structures are placed between people for security or virus concerns, for example, partitions in classrooms, banks, and retail stores. Distortion occurs when sound propagates through these partitions, which can cause distractions in communication and degrade the service quality. In the current work, a constrained multi-channel active hear-through system is proposed to provide a solution to provide more natural sound communication environments and prevent sound distortions due to partitioning structures. The proposed method can preserve both the spectrum and the spatial distribution of the original sound field (the sound field when the partition is absent). This research opens the potential for future development of multi-channel two-way communication systems and improvement in existing augmented reality techniques. Experimental results show that, compared with the traditional direct inverse filter design method, the proposed method can reduce the comb-filtering effect effectively. The constraints applied to the filter design procedure can also prevent instability caused by the acoustic feedback loop.

Finite-time ℋ∞ filtering for Markov jump systems with uniform quantization

This paper is concerned with finite-time ℋ ∞ filtering for Markov jump systems with uniform quantization. The objective is to design quantized mode-dependent filters to ensure that the filtering error system is not only mean-square finite-time bounded but also has a prescribed finite-time ℋ ∞ performance. First, the case where the switching modes of the filter align with those of the MJS is considered. A numerically tractable filter design approach is proposed utilizing a mode-dependent Lyapunov function, Schur’s complement, and Dynkin’s formula. Then, the study is extended to a scenario where the switching modes of the filter can differ from those of the MJS. To address this situation, a mode-mismatched filter design approach is developed by leveraging a hidden Markov model to describe the asynchronous mode switching and the double expectation formula. Finally, a spring system model subject to a Markov chain is employed to validate the effectiveness of the quantized filter design approaches.

A Stable IIR Filter Design Approach for High-Order Active Noise Control Applications

In commercial non-adaptive active noise control (ANC) applications, an IIR filter structure is often used to reduce real-time computations. On the contrary, an FIR filter structure is usually preferred in the filter design phase because the FIR filter design formulation can be convex and is simple to solve. To combine the benefits of both FIR and IIR filter structures, one common approach in ANC applications is to use an IIR filter structure to fit a pre-designed FIR filter. However, to ensure stability, most of the common IIR filter fitting approaches involve the computation and relocation of poles which can be difficult for high-order cases. In this current work, a stable IIR filter design approach that does not need the computation and relocation of poles is improved to be applicable in ANC applications. The results demonstrate that the proposed method can achieve better fitting accuracy and steady-state noise control performance in high-order non-adaptive applications when the pre-designed noise control FIR filter is fitted. Besides fitting the noise control filter, the proposed method can also be used to fit the secondary path and acoustic feedback path to reduce the required real-time computations if adaptive controllers are applied.

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.

Variance-Constrained Filter Design With Sensor Resolution Under Round-Robin Communication Protocol: An Outlier-Resistant Mechanism

In this article, a new outlier-resistant mechanism is proposed to deal with the variance-constrained filtering problem for a class of networked systems subject to sensor resolution under the round-robin protocol (RRP). Sensor resolution, which serves as an important index in determining measurement accuracy, is taken into account in the addressed filtering problem, and the sensor-resolution-induced uncertainty is tackled by using an upper-bounding technique. The RRP is employed to regulate the order of signal transmission in order to relieve communication overhead. In the case of measurement outliers, a tailored saturation function is dedicatedly introduced to the filter structure for the purpose of suppressing the outlier-corrupted innovations, thereby maintaining satisfactory filtering performance. By solving a matrix difference equation, an upper bound is first acquired on the error covariance of the devised filter, and the associated filter parameters are subsequently determined through minimizing the acquired bound. The validity of the developed variance-constrained filter design approach is thoroughly demonstrated via two simulation examples.

A constrained multi-channel hear-through filter design approach using active control formulations

In many daily communication environments, especially during the pandemic era, partitioning structures are placed between people for security or virus concerns, for example, partitions in classrooms, banks, retail stores, etc. . Distortion occurs when sound propagates through these partitions can cause distractions in communication and degrade the service quality. In the current work, a constrained multi-channel active hear-through system is proposed to provide a solution to provide more natural sound communication environments and prevent sound distortions due to portioning structures. The proposed method can preserve both the spectrum and the spatial distribution of the original sound field (the sound field when the partition is absent). This research opens the potential for future development of multi-channel two-way communication systems and improvement in existing augmented reality techniques. Experimental results show that, compared with the traditional direct inverse filter design method, the proposed method can reduce the comb-filtering effect effectively. The constraints applied to the filter design procedure can also prevent instability caused by the acoustic feedback loop.

An adaptive constrained multi-channel active noise control filter design approach using convex cone optimization

In current practical active noise control (ANC) applications, solutions to three main technical challenges need to be sought: the requirement of a stable controller when acoustic feedback paths exist, a larger quiet zone, and adaptation to a time-varying environment. The imposed controller stability constraints can significantly increase the computational requirements and negatively impact the adaptation of ANC filters in real time. Multi-channel systems are usually needed if a larger quiet zone is required while the coupling between different channels can greatly lower the convergence rate of adaptive algorithms. In the current work, a multi-channel adaptive constrained ANC filter design method is proposed. The recently proposed convex cone formulation for ANC design is adopted to reduce the computational time of solving the constrained ANC filter design problem. The optimal filter coefficients are then redesigned continuously using convex optimization when the environment is time-varying. The experimental result shows that, compared with the traditional leaky FxLMS method, the proposed method has a faster convergence rate and a better steady-state noise control performance.

A miniaturized filter design approach using GMDH neural networks

AbstractA miniaturized microstrip low‐pass filter (LPF) has been designed and fabricated using semicircular‐shaped resonators. The designing procedures as well as the LC equivalent circuit of each step have been reported. Due to the small size of the presented filter, flat group delay in the pass‐band, low insertion loss (IL), and high return loss (RL) in operational frequency. By producing adjusted transmission zeros (TZs), the second to fourth unwanted harmonics were attenuated using the proposed LPF. The machine learning approach has been utilized to adjustment of the filter parameters. The 3 dB cut‐off frequency (Fc), TZs, IL, and RL were predicted using the group method of data handling neural network (GMDHNN). Ninety simulations were performed with different inductor and capacitor values. The collected data were used for neural network training. The GMDHNN can diagnose the efficient inputs, so this feature was applied to determine the efficient L and C elements on TZs, Fc, IL, and RL. Finally, the fabricated filter results are well‐matched with the simulation ones. IL and RL of the suggested filter in operational frequency are −0.09 and −17.26 dB, respectively. The total filter size is only 14.65 mm × 3.5 mm.

Multi-objective Approach for IIR Filter Design and Bit-Width Optimization

Multi-objective Approach for IIR Filter Design and Bit-Width Optimization

Design of Optimal Multiplierless FIR Filters With Minimal Number of Adders

This work presents two novel methods that simultaneously optimize both the design of a finite impulse response (FIR) filter and its multiplierless hardware implementation. We use integer linear programming (ILP) to minimize the number of adders used to implement a direct/transposed FIR filter adhering to a given frequency specification. The proposed algorithms work by either fixing the number of adders used to implement the products (multiplier block adders) or by bounding the adder depth (AD) used for these products. The latter can be used to design filters with minimal AD for low-power applications. In contrast to previous multiplierless FIR filter approaches, the methods introduced here ensure adder count optimality. We perform extensive numerical experiments, which demonstrate that our simultaneous filter design approach yields results that are in many cases on par or better than those in the literature.

Event-triggered filter of positive semi-Markovian jump systems with Weibull distribution

This paper focuses on the event-triggered filter design of positive semi-Markovian jump systems with Weibull distribution. The considered systems are subject to random uncertainty and sensor fault. A linear programming-based filter design approach is proposed. First, an event-triggering condition is established by virtue of sensor output signals. Under the event-triggering condition, the filtering error system is transformed into an interval system. The positivity of the filtering error system is reached by considering the positivity of the lower bound of the interval system. By using a stochastic co-positive Lyapunov function, the filtering error system is stochastically stable with an ℓ1-gain performance by exploring the corresponding performance of the upper bound of the interval system. Then, additive and multiplicative models following the Bernoulli distribution are introduced to describe the random uncertainties of the systems. Consequently, the event-triggered filter is designed in terms of linear programming and a matrix decomposition approach. Moreover, the proposed filter design is developed for the systems subject to sensor faults. Finally, two examples are provided to illustrate the effectiveness of the proposed design.