Performance Of Filter Research Articles

Attitude solving aided UWB/IMU integrated algorithm with real-time NLOS suppression

In response to the challenges posed by non-line-of-sight (NLOS) errors and inadequate attitude estimation accuracy in ultra-wideband and inertial measurement unit (UWB/IMU) integrated navigation algorithms in the complex environment, a robust UWB/IMU integrated positioning scheme is proposed. On one hand, the utilization of the robust local weighted regression algorithm (RLWR) is employed to mitigate the impact of NLOS errors on UWB data. RLWR incorporates information from nodes with known pseudo-range within local time intervals into the regression model, enhancing the identification of NLOS errors and improving positioning accuracy. On the other hand, the variational Bayesian filter algorithm based on adaptive conjugate gradient descent (ACGD) is proposed to improve the accuracy of IMU attitude calculation. The algorithm leverages an ACGD approach to optimize the attitude output of the accelerometer and magnetometer. The output is then incorporated into the variational Bayesian filtering system alongside the gyroscopic attitude output compensated by integrated positioning. Compared to conventional quaternion calculation and gradient descent linear filtering methods, the approach exhibits superior precision and stability. The experimental findings demonstrate that the amalgamation of the proposed NLSO identification suppression algorithm and the enhanced attitude computation algorithm confers significant advantages in terms of both localization accuracy and attitude estimation precision in complex environments. Moreover, the robust solution presented in the paper ensures the preservation of filter performance in the event of UWB measurement failure.

A Multi-Scale Fusion Strategy for Side Scan Sonar Image Correction to Improve Low Contrast and Noise Interference

Side scan sonar images have great application prospects in underwater surveys, target detection, and engineering activities. However, the acquired sonar images exhibit low illumination, scattered noise, distorted outlines, and unclear edge textures due to the complicated undersea environment and intrinsic device flaws. Hence, this paper proposes a multi-scale fusion strategy for side scan sonar (SSS) image correction to improve the low contrast and noise interference. Initially, an SSS image was decomposed into low and high frequency sub-bands via the non-subsampled shearlet transform (NSST). Then, modified multi-scale retinex (MMSR) was employed to enhance the contrast of the low frequency sub-band. Next, sparse dictionary learning (SDL) was utilized to eliminate high frequency noise. Finally, the process of NSST reconstruction was completed by fusing the emerging low and high frequency sub-band images to generate a new sonar image. The experimental results demonstrate that the target features, underwater terrain, and edge contours could be clearly displayed in the image corrected by the multi-scale fusion strategy when compared to eight correction techniques: BPDHE, MSRCR, NPE, ALTM, LIME, FE, WT, and TVRLRA. Effective control was achieved over the speckle noise of the sonar image. Furthermore, the AG, STD, and E values illustrated the delicacy and contrast of the corrected images processed by the proposed strategy. The PSNR value revealed that the proposed strategy outperformed the advanced TVRLRA technology in terms of filtering performance by at least 8.8%. It can provide sonar imagery that is appropriate for various circumstances.

Integrative BNN-LHS Surrogate Modeling and Thermo-Mechanical-EM Analysis for Enhanced Characterization of High-Frequency Low-Pass Filters in COMSOL

Integrative BNN-LHS Surrogate Modeling and Thermo-Mechanical-EM Analysis for Enhanced Characterization of High-Frequency Low-Pass Filters in COMSOL

Optimized Wide-Angle Metamaterial Edge Filters: Enhanced Performance with Multi-Layer Designs and Anti-Reflection Coatings

Optimized Wide-Angle Metamaterial Edge Filters: Enhanced Performance with Multi-Layer Designs and Anti-Reflection Coatings

Effect analysis on passive and active regeneration performance of catalytic diesel particulate filters at the equilibrium point in the presence of ash layer

AbstractCatalytic diesel particulate filters (CDPF) form the main post‐processing devices for the diesel engines. Particulates trapped by CDPF can be oxidized by nitrogen dioxide (NO2) and oxygen (O2). The CDPF passive and active regeneration model on platinum‐based catalyst in the presence of ash layer, including the mass, momentum, and enthalpy balances of the gas and the solid phase, the gas diffusion, and the kinetic of the soot oxidation reaction is performed and validated by the bench test. The effects of different temperatures on CDPF passive and active regeneration at the equilibrium point are analyzed. Further, the effective analysis of ash loading and ash layer distribution on the equilibrium state is conducted. The research on CDPF passive and active regeneration at the equilibrium point in the presence of ash layer provides a theoretical basis for CDPF application.

Recovery of lead, iron, and copper from point-of-use-filters to examine performance

Over the last few decades, reliance on point-of-use (POU) treatment for removing actual or perceived contaminants in drinking water has increased within the United States. Understanding POU treatment removal performance, and accurately estimating metals exposure at the tap, is critical for understanding POU water treatment device effectiveness and potential reductions in contaminant exposure. Previous bench-scale efforts have documented significant removal of dissolved Pb using faucet-mounted POU filters; however, limited efforts have challenged these filters with extreme water quality conditions which are more common in homes reliant on private well water. Characterization of typical rates of metals uptake by POU filters would support: improved exposure estimates and predictions, a better understanding of long-term filter performance under different conditions, and identification of conditions where POU use is recommended. In the current study, standard faucet-mount activated carbon POU filters were tested in a laboratory setting in order to: 1) determine removal of Pb, Cu, and Fe under low and high concentration conditions designed to reflect previous observations of residential water quality; and 2) evaluate the effectiveness of an acid flow-through procedure in recovering metals from used POU filters exposed to varying concentrations of Pb, Cu, and Fe. Although the filters tested here successfully removed Pb and Cu from waters of both high and low-level concentrations (>91% removal), Fe removal varied considerably. The acid flow-through procedure yielded mixed results: while 25.1-70.4% of influent Pb mass was recovered, recovery of Cu and Fe from the dosed filters was unpredictable. This was attributed in part to leaching from the filter media itself; in addition to Cu and Fe, concentrations of several other elements (e.g., Ti, Si, Al) increased and appeared to leach from control filters during the acid flow-through procedure. Given these results, alternative methods for assessing uptake of metals to POU filters should be explored.

Removal of 6-methylquinoline from shale gas wastewater using electrochemical carbon nanotubes filter

6-Methylquinoline (6-MQ) is identified as a high-concentration organic compound pervasive in shale gas wastewater (SGW) and poses a significant risk of environmental pollution. In response, this study aimed to address these challenges by introducing an innovative electrochemical membrane constructed with multi-walled carbon nanotubes (CNTs) for the removal of 6-MQ. The investigation systematically explored the impact of voltage, initial pollutant concentration, and salinity on the performance of the electrochemical CNTs filter. It was found a positive correlation between removal efficiency and increasing voltage and salinity levels. Conversely, as the initial concentration of pollutants increased, the efficiency showed a diminishing trend. The electrochemical CNTs filter exhibited remarkable efficacy in both adsorption removal and electrochemical oxidation of 6-MQ. Notably, the CNTs membrane exhibited robust adsorption capabilities, evidenced by the sustained adsorption of 6-MQ for over 33 h. Furthermore, applying an electrochemical oxidation voltage of 3 V consistently maintained a removal rate exceeding 34.0% due to both direct and indirect oxidation, underscoring the sustained efficacy of the electrochemical membranes. Besides, real wastewater experiments, while displaying a reduction in removal efficiency compared to synthetic wastewater experiments, emphasized the substantial potential of the electrochemical CNTs filter for practical applications. This study underscores the significant promise of electrochemical membranes in addressing low molecular weight contaminants in SGW, contributing valuable insights for advancing SGW treatment strategies.

Improving Graph Collaborative Filtering with Directional Behavior Enhanced Contrastive Learning

Graph Collaborative Filtering is a widely adopted approach for recommendation, which captures similar behavior features through graph neural network. Recently, Contrastive Learning (CL) has been demonstrated as an effective method to enhance the performance of graph collaborative filtering. Typically, CL-based methods first perturb users’ history behavior data (e.g., drop clicked items), then construct a self-discriminating task for behavior representations under different random perturbations. However, for widely existing inactive users, random perturbation makes their sparse behavior information more incomplete, thereby harming the behavior feature extraction. To tackle the above issue, we design a novel directional perturbation-based CL method to improve the graph collaborative filtering performance. The idea is to perturb node representations through directionally enhancing behavior features. To do so, we propose a simple yet effective feedback mechanism, which fuses the representations of nodes based on behavior similarity. Then, to avoid irrelevant behavior preferences introduced by the feedback mechanism, we construct a behavior self-contrast task before and after feedback, to align the node representations between the final output and the first layer of GNN. Different from the widely-adopted self-discriminating task, the behavior self-contrast task avoids complex message propagation on different perturbed graphs, which is more efficient than previous methods. Extensive experiments on three public datasets demonstrate that the proposed method has distinct advantages over other contrastive learning methods on recommendation accuracy.

Implementation of filtration models for filter performance prediction in bioprocess dead-end membrane filtration

Mechanistic modeling is frequently applied during process development and scale-up in bioprocessing. For normal flow filtration (NFF, also called dead-end filtration) processes, such as aseptic filtration, harvest clarification, and virus removal etc., the modeling based on a theoretical fouling law has long been used to assess filter performance and filtration area requirements. This case study describes how an appropriate fouling model is selected and provides guidance for using the fouling model for bioprocess application. There is general agreement that the modeling and process simulation enables accurate filter size calculations for scale-up. Their predictions can then be used to identify optimal filter formats for each unit operation. However, because feed characteristics and conditions vary between processes, fouling models need to consider several filter fouling mechanisms for different bioprocess applications and unit operations. This study reviewed several fouling models and considered various bioprocess filter fouling scenarios, as well as the accuracy of filtration area predictions from the models. It was determined that filtration runs in small scale sizing studies should be run to at least 40% flux decay to minimize the risk of prediction error due to extrapolation.

Dynamic Mode Decomposition for Transient Cavitation Bubbles Imaging in Pulsed High-Intensity Focused Ultrasound Therapy.

Pulsed high-intensity focused ultrasound (pHIFU) can induce sparse de novo inertial cavitation without the introduction of exogenous contrast agents, promoting mild mechanical disruption in targeted tissue. Because the bubbles are small and rapidly dissolve after each HIFU pulse, mapping transient bubbles and obtaining real-time quantitative metrics correlated with tissue damage are challenging. Prior work introduced Bubble Doppler, an ultrafast power Doppler imaging method as a sensitive means to map cavitation bubbles. The main limitation of that method was its reliance on conventional wall filters used in Doppler imaging and its optimization for imaging blood flow rather than transient scatterers. This study explores Bubble Doppler enhancement using dynamic mode decomposition (DMD) of a matrix created from a Doppler ensemble for mapping and extracting the characteristics of transient cavitation bubbles. DMD was first tested in silico with a numerical dataset mimicking the spatiotemporal characteristics of backscattered signal from tissue and bubbles. The performance of DMD filter was compared to other widely used Doppler wall filter-singular value decomposition (SVD) and infinite impulse response (IIR) high-pass filter. DMD was then applied to an ex vivo tissue dataset where each HIFU pulse was immediately followed by a plane wave Doppler ensemble. In silico DMD outperformed SVD and IIR high-pass filter and ex vivo provided physically interpretable images of the modes associated with bubbles and their corresponding temporal decay rates. These DMD modes can be trackable over the duration of pHIFU treatment using k-means clustering method, resulting in quantitative indicators of treatment progression.

Power Quality Improvement of Three-Phase Electrical Systems Using Active-Passive Hybrid Harmonic Filter

An overreliance on nonlinear load can result in electric current harmonics, which will ultimately result in a decrease in the quality of the electrical power. If a malfunction arises as a consequence of the internal integration of a power system in any of the internal networks, it will have an adverse effect on the overall operation of the power system. Because of this issue, there is a requirement for harmonic filters, which can minimize the levels of harmonics by referencing the IEEE-519 standards for enhancing power quality. This research offers an active-passive hybrid harmonic power filter to eliminate harmonic pollution in three-phase electrical systems. A MATLAB simulation and laboratory experiment proceeded for 240 Vac with nonlinear loads that represent by uncontrolled rectifier and series RLC load, and selected results were presented to verify the performance of the active-passive harmonic filter component. Upon completion of this research, an analysis of the conditions of a three-phase electrical system will be possible under four distinct circumstances: in the absence of a filter, with an active filter, with a passive filter, and with a hybrid filter. Furthermore, this model performs a comparative analysis of the percentage reduction in THD value achieved by the filter compared to the previous researcher. The result analysis reveals the proposed filter's good performance in eliminating harmonic pollution to 58.63 %, thereby significantly improving power quality. The findings presented in this study contribute to the ongoing efforts to ensure the reliable and efficient operation of modern power systems.

Miniaturized bandpass metamaterials filters using hybrid plasmonic waveguide and defected surface structures

In this paper, we present a novel approach for designing single-/multiple-band bandpass filters (BPFs) using a hybrid metamaterials approach, incorporating substrate integrated waveguide (SIW), spoof surface plasmon polariton (SSPP), and defected surface structures (DSSs) technologies. The substrate-integrated plasmonic waveguide (SIPW) is constructed by embedding a hybrid dual SSPP into a three-layer SIW. The SIPW demonstrates a reduced phase velocity and a lowered lower cutoff frequency, extracted from the slow-wave dispersion relationship. Initially, the SIPW-based single-band metamaterial BPF demonstrates miniaturized size and broadened bandwidth. Furthermore, by loading a pair of complementary electric-inductive capacitive (ELC) resonators consisting of DSSs and adjusting their symmetry properties and geometric parameters, multiple BPFs from 1 to 3 are realized. To validate the proposed designs, three prototypes are fabricated and measured. The results demonstrate good filtering performances of proposed metamaterial BPFs, featuring high reflection loss (S11 < −10.5 dB) and a high level of out-of-band rejection exceeding 25 dB between multiple passbands.

High throughput clogging free microfluidic particle filter by femtosecond laser micromachining.

In recent decades, driven by the needs of industry and medicine, researchers have been investigating how to remove carefully from the main flow microscopic particles or clusters of them. Among all the approaches proposed, crossflow filtration is one of the most attractive as it provides a non-destructive, label-free and in-flow sorting method. In general, the separation performance shows capture and separation efficiencies ranging from 70% up to 100%. However, the maximum flow rate achievable (µL/min) is still orders of magnitude away from those suitable for clinical or industrial applications mainly due to the low stiffness of the materials typically used. In this work, we propose an innovative hydrodynamic-crossflow hybrid filter geometry, buried in a fused silica substrate by means of the femtosecond laser irradiation followed by chemical etching technique. The material high stiffness combined with the accuracy of our manufacturing technique allows the 3D fabrication of non-deformable channels with higher aspect ratio posts, while keeping the overall device dimensions compact. The filter performance has been validated through experiments with both Newtonian (water-based solution of microbeads) and non-Newtonian fluids (blood), achieving separation efficiencies of up to 94% and large particles recovery rates of 100%, even at very high flow rates (mL/h).

Implementation of Digital Filter Using Verilog HDL

Abstract: In the context of Very Large Scale Integration technology, where addition is essential, digital filters are essential elements. The performance of the Finite Impulse Response Filter is highly dependent on the speed of its multiplier unit, making itstand out among the others. In order to improve the effectiveness of the FIR filter, we suggest using a Wallace tree multiplier. This novel method offers improvements over conventional multipliers, with a decrease in latency being one of the main advantages. Significant improvements in latency are obtained by using Xilinx tools and implementing this filter design in Verilog HDL. The Wallace tree multiplier is perfect for FIR filter construction in low-voltage and low-power VLSI applications since it has benefits like higher operating frequency and reduced power consumption. This development might improve the effectiveness and performance of digital filters, especially in settings with limited resources, opening the way for more robust VLSI systems

Increasing Isolation Efficiency Using a Segmented Quadrupole Mass Filter Operated with Rectangular Waveforms.

The performance of a segmented quadrupole mass filter operated with rectangular waveforms and capacitively coupled rectangular waveforms applied to the prefilters was examined on a home-built quadrupole-Orbitrap platform. For peak widths of 50 m/z, 100% isolation efficiency was achieved, which fell to approximately 20% for 5 m/z peak width for a rectangular waveform of 150 V0-p. Due to a small exit aperture following the mass filter, peak structure was observed in both experimental peak shapes and those simulated using SIMION. A larger radius quadrupole was examined and achieved similar performance. While the segmented quadrupole does remove the defocusing effects of the fringing fields, the ion beam is only slightly refocused due to the low RF voltage which limits achievable gains in isolation efficiency.

Fabrication of high-permeance SiC filters reinforced with in situ-grown mullite whiskers for gas/solid filtration

The pursuit of cost-efficiency, superior strength, and excellent gas permeance has driven the development of silicon carbide (SiC) membranes or filters for gas/solid filtration. In this study, SiC filters with high gas permeance reinforced with in situ-grown mullite whiskers were successfully prepared through reaction bonding. Low-cost kaolin and bauxite were used as sintering aids and raw materials for mullite whisker formation, and MoO3 was used as an additive to promote mullite whisker growth. The effects of different sintering temperatures and the addition of kaolin, bauxite, and MoO3 on filter performance were systematically investigated. When the sintering aid and MoO3 content additions were 6 and 1.5 wt%, respectively, and the sintering temperature was 1450 °C, the resulting ceramic filter exhibited a porosity of 45.5 %, a bending strength of 17.9 MPa, and an average pore size of 47.7 µm. Moreover, the filter exhibited a high Darcy’s permeability coefficient of 2.42 × 10−11 m2, satisfactory chemical stability, and thermal shock resistance. The addition of MoO3 facilitated the consumption of the molten phases and increased the anisotropic growth rate of the mullite whiskers, which contributed to the fabrication of the high-permeance filters. Finally, the ceramic filters demonstrated good performance for the filtration of dusty gases loaded with fly ash particles with a mean size of 15.3 µm at room temperature and 300 °C, and the removal rate reached > 99.9 %. The pressure drop was a little lower than that of our commercially available SiC filters. The SiC filters showed good prospects in the field of gas/solid filtration.

Automated SEM analysis of particles in Hanford tank waste

Multiple bench-scale filtration campaigns of Hanford tank waste supernatant on a backpulseable dead-end filtration skid have provided greater insight into the solids that cause fouling and reduce filter performance. The solids collected during each campaign were concentrated from the backpulse solutions and examined using automated particle analysis (APA) methods with scanning electron microscopy and X-ray energy dispersive spectroscopy to categorize particle types and their morphological characteristics. We show that with APA, thousands of particles can be analyzed to provide accurate insight into the phases that may be impacting filter performance.

Exploring the filtration performance and usage cost of commercial fiber air filters for capturing moxa smoke aerosols

Traditional moxibustion treatments plays a role in preventing and treating COVID-19 while aggravating indoor air pollution. Therefore, commercial fiber air filters (CFAF) are widely used to improve indoor air quality to reduce the risk of people being exposed to moxa smoke aerosols (MSA). Considering that there were few reports on the performance of CFAF for capturing MSA, we explored in detail the effects of face velocity and MSA particle size on the filtration performance of CFAF to fill this gap. Then, we analyzed the deposition characteristics of MSA on fibers in combination with SEM images. In addition, the long-term stability, cleaning performance and MSA holding capacity of the CFAF were further characterized by tests of continuous operation, clean air delivery rate (CADR) and MSA loading. The results indicated that the CFAF had the potential to efficiently capture MSA in indoor air at an acceptable annual usage cost (AUC). Specifically, the pristine CFAF had high classification counting efficiency (CCE) (99.1 %-100 %), medium pressure drop (150.3 Pa), and low quality factor (0.04 Pa−1) at the face velocity of 0.3 m·s−1. Note that increasing face velocity not only decreased CCE and quality factor but also significantly increased pressure drop. Furthermore, the CFAF maintained excellent CCE (99.5 %-100 %) after 24-h of continuous operation at high concentrations of MSA (100 mg·m−3), while the pressure drop increased to 203.7 Pa. Obviously, the deposited MSA severely clogged the inter-fiber pores, which explained the fact of the rapid increase in the pressure drop of the CFAF. Of course, the high CADR value (66.6 m3·h−1) also demonstrated that the CFAF had the ability to efficiently remove MSA from confined place. Moreover, a total of 216.3 g MSA can be captured by the CFAF when the final pressure drop reached 300 Pa, which meant that further increasing the MSA holding capacity would be the focus of future research by filter media manufacturers. Finally, through the established mathematical model, we successfully predicted the service life (86 days) and the total AUC (276 $·year−1) of using the CFAF to capture MSA. This work is expected to provide a useful reference for routine maintenance and design optimization of existing CFAF.

Impact Analysis of Time Synchronization Error in Airborne Target Tracking Using a Heterogeneous Sensor Network

This paper investigates the influence of time synchronization on sensor fusion and target tracking. As a benchmark, we design a target tracking system based on track-to-track fusion architecture. Heterogeneous sensors detect targets and transmit measurements through a communication network, while local tracking and track fusion are performed in the fusion center to integrate measurements from these sensors into a fused track. The time synchronization error is mathematically modeled, and local time is biased from the reference clock during the holdover phase. The influence of the time synchronization error on target tracking system components such as local association, filtering, and track fusion is discussed. The results demonstrate that an increase in the time synchronization error leads to deteriorating association and filtering performance. In addition, the results of the simulation study validate the impact of the time synchronization error on the sensor network.

Fault-tolerant shunt active power filter with synchronous reference frame control and self-tuning filter

This research proposes a novel method that combines Proportional-Integral (PI) control, Artificial Neural Networks (ANNs), and Synchronous Reference Frame (SRF) theory to improve the performance of a three-phase shunt Active Power Filter (APF) under fault situations. The main goal is to reduce power quality problems in electrical grids that are having problems, such harmonics and voltage sags. To precisely manage the APF, the reference frame in which the grid voltages and currents are synchronized is identified using the SRF theory. In order to provide quick and precise correction of voltage and current distortions, the PI controller is integrated to control the APF's compensatory action. The PI controller offers trustworthy control while running normally, but during errors or disruptions, its functionality may suffer. In order to overcome this difficulty, a self-tuning filter in the form of an Artificial Neural Network (ANN) is presented, which may adaptively modify the PI controller's settings under fault situations. To maintain ideal filter performance, the ANN constantly learns from the system's reaction and modifies in real-time. This self-adjusting feature makes sure that even in the event of grid failures, the APF maintains its ability to mitigate problems with power quality. The suggested method effectively reduces harmonics, voltage sags, and other power quality disturbances under both normal and fault circumstances, as shown by the simulation results. In complicated electrical grid systems, the combination of SRF theory, PI control, and ANN-based self-tuning provides a strong way to improve the dependability and effectiveness of three-phase shunt Active Power Filters.