Efficient Filter Research Articles

Retraction Note: Implementation of efficient reconfigurable FIR filter with control logic for 5G applications

Retraction Note: Implementation of efficient reconfigurable FIR filter with control logic for 5G applications

A Robust and Efficient Cubature Kalman filter based on the Variation Bayesian Method and Its Application in Target Tracking

Abstract This paper proposes a robust cubature Kalman filter (CKF) for nonlinear state-space models with unknown state and measurement noise covariance matrix (MNCM). This paper studies situations in which sensors are independent of each other. Therefore, the unknown measurement noise variance is modeled as an unknown inverse-Gamma (IG) distribution. The Gaussian-Student-t-inverse-Wishart mixture distribution (GSTIW) is used to model the one-step prediction distribution. Modeling generates a large number of unknown parameters. Therefore, this article adopts the statistical linearization method to linearize the observation function and then estimates the state and parameters separately to reduce the computational burden of estimating unknown parameters, significantly improving the algorithm's efficiency. Finally, using the variational Bayesian (VB) method, a novel and efficient robust cubature Kalman filter based on the IG distribution and GSTIW mixture distributions (IG-GSTIW-CKF) is obtained. Simulation and experimental results show that the proposed method has better estimation accuracy than several advanced algorithms when sensors are independent. In addition, the efficiency of the proposed algorithm is significantly higher than that of other CKF-based methods.

IMOABC: An efficient multi-objective filter–wrapper hybrid approach for high-dimensional feature selection

With the development of data science, the challenge of high-dimensional data has become increasingly prevalent. High-dimensional data contains a significant amount of redundant information, which can adversely affect the performance and effectiveness of machine learning algorithms. Therefore, it is necessary to select the most relevant features from the raw data and perform feature selection on high-dimensional data. In this paper, a novel filter–wrapper feature selection method based on an improved multi-objective artificial bee colony algorithm (IMOABC) is proposed to address the feature selection problem in high-dimensional data. This method simultaneously considers three objectives: feature error rate, feature subset ratio, and distance, effectively improving the efficiency of obtaining the optimal feature subset on high-dimensional data. Additionally, a novel Fisher Score-based initialization strategy is introduced, significantly enhancing the quality of solutions. Furthermore, a new dynamic adaptive strategy is designed, effectively improving the algorithm’s convergence speed and enhancing its global search capability. Comparative experimental results on microarray cancer datasets demonstrate that the proposed method significantly improves classification accuracy and effectively reduces the size of the feature subset when compared to various traditional and state-of-the-art multi-objective feature selection algorithms. IMOABC improves the classification accuracy by 2.27% on average compared to various multi-objective feature selection methods, while reducing the number of selected features by 88.76% on average. Meanwhile, IMOABC shows an average improvement of 4.24% in classification accuracy across all datasets, with an average reduction of 76.73% in the number of selected features compared to various traditional methods.

OpenSBLI v3.0: High-Fidelity Multi-Block Transonic Aerofoil CFD Simulations using Domain Specific Languages on GPUs

OpenSBLI is an automatic code-generation framework for compressible Computational Fluid Dynamics (CFD) simulations on heterogeneous computing architectures (previous release: Lusher et al, CPC (2021)). OpenSBLI is coupled to the Oxford Parallel Structured (OPS) Domain Specific Language (DSL), which uses source-to-source translation to enable parallel execution of the code on large-scale supercomputers, including multi-GPU clusters. To date, OpenSBLI has largely been applied to compressible turbulence and shock-wave/boundary-layer interactions on very simple geometries comprised of single mesh blocks with essentially orthogonal grid lines. OpenSBLI has been extended in this new release to target strongly curvilinear cases, including transonic aerofoils using multi-block grids. In addition to multi-block mesh support, more efficient numerical shock-capturing methods and filters have been added to the codebase. Improvements to post-processing, reduced-dimension data output, and coupling to a modal decomposition library are also included. A set of validation cases are presented to showcase the new code features. Furthermore, state-of-the-art wide-span transonic aerofoil simulations on up to N=2.5×109 grid points demonstrate that wider aspect ratios can alter buffet predictions and increase the regularity of the low-frequency shock oscillations by accommodating fully-developed trailing edge flow separation. Spectral Proper Orthogonal Decomposition (SPOD) analysis showed that overly-narrow aerofoil simulations contain additional domain-dependent energy content at a Strouhal number of St≈3 associated with wake modes.

Indoor air quality improvement using developed polymeric membrane filter

Abstract Carbon dioxide (CO2) levels must be controlled to protect the environment and human health. Previous studies focused on improving indoor air quality (IAQ) by reducing these negative impacts with electrical equipment such as mechanical ventilators and air purifiers; however, passive techniques were lacking. This work develops a prototype membrane filters to improve IAQ by capturing and separating indoor CO2 levels. These filters use polyvinyl alcohol (PVA) membranes and activated carbon (AC) as innovative and effective passive materials at four concentrations. Scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FTIR), and X-Ray diffraction (XRD) were employed to characterize the materials. The effectiveness of CO2 absorption across the four filter types was then assessed through tests conducted in a controlled environment. Subsequently, site monitoring was undertaken to evaluate the most effective filter. The absorption rates of CO2 levels indoors of the developed filters are 17.5%, 8.1%, 4.5%, and 3.4%, respectively, for four filters. The most efficient filter increased absorption efficiency by 47.8% in an unoccupied space and 35.5% in an occupied one. This passive material can be applied in various indoor spaces, including office buildings, residential buildings, and educational facilities, due to its benefits in saving energy and enhancing indoor environmental quality.

Power and area efficient FIR filter using Radix- 2r multiplier for de-noise the electrooculography (EOG) signal

This article introduces a novel FIR filter using a Radix-2r multiplier combined with 4:2 and 3:2 compressors for denoising Electrooculography (EOG) signals. This approach replaces traditional ripple-carry adders with compressors to add the partial products generated by the Radix-2r multiplier, resulting in reduced delay and energy consumption. The FIR filter is implemented in gate-level Verilog HDL, verified using ModelSim and Altera DSP Builder, and synthesized with the Cadence RTL compiler. Compared to conventional designs, the proposed filter achieves a 71.48% reduction in area, 73.28% reduction in power, 51.84% reduction in delay, 86.22% reduction in the area-delay product (ADP), and 92.38% reduction in energy-per-operation (EPS), significantly outperforming the SOPOT filter.

Engineering spin-dependent catalysts: chiral covalent organic frameworks with tunable electroactivity for electrochemical oxygen evolution.

The chiral-induced spin selectivity (CISS) effect offers promising prospects for spintronics, yet designing chiral materials that enable efficient spin-polarized electron transport remains challenging. Here, we report the utility of covalent organic frameworks (COFs) in manipulating electron spin for spin-dependent catalysis via CISS. This enables us to design and synthesize three three-dimensional chiral COFs (CCOFs) with tunable electroactivity and spin-electron conductivity through imine condensations of enantiopure 1,1'-binaphthol-derived tetraaldehyde and tetraamines derived from 1,4-benzenediamine, pyrene, or tetrathiafulvalene skeletons. The CISS effect of CCOFs is verified by magnetic conductive atomic force microscopy. Compared with their achiral analogs, these CCOFs serve as efficient spin filters, reducing the overpotential of oxygen evolution and improving the Tafel slope. Particularly, the diarylamine-based CCOF showed a low overpotential of 430mV (vs reversible hydrogen electrode) at 10mAcm-2 with long-term stability comparable to the commercial RuO2. This enhanced spin-dependent OER activity stems from its excellent redox-activity, good electron conductivity and effective suppression effect on the formation of H2O2 byproducts.

Practical feature filter strategy to machine learning for small datasets in chemistry

Many potential use cases for machine learning in chemistry and materials science suffer from small dataset sizes, which demands special care for the model design in order to deliver reliable predictions. Hence, feature selection as the key determinant for dataset design is essential here. We propose a practical and efficient feature filter strategy to determine the best input feature candidates. We illustrate this strategy for the prediction of adsorption energies based on a public dataset and sublimation enthalpies using an in-house training dataset. The input of adsorption energies reduces the feature space from 12 dimensions to two and still delivers accurate results. For the sublimation enthalpies, three input configurations are filtered from 14 possible configurations with different dimensions for further productive predictions as being most relevant by using our feature filter strategy. The best extreme gradient boosting regression model possesses a good performance and is evaluated from statistical and theoretical perspectives, reaching a level of accuracy comparable to density functional theory computations and allowing for physical interpretations of the predictions. Overall, the results indicate that the feature filter strategy can help interdisciplinary scientists without rich professional AI knowledge and limited computational resources to establish a reliable small training dataset first, which may make the final machine learning model training easier and more accurate, avoiding time-consuming hyperparameter explorations and improper feature selection.

Bipolar topological spin diode and topological spin transistor in finite size quantum spin Hall insulators

Abstract The miniaturization and stability of electronic devices are becoming increasingly important today. We attempt to provide the theoretical support for designing spintronic devices by numerically investigating spin transport in finite size quantum spin Hall insulators (QSHI) under a perpendicular weak magnetic field. By modifying magnetic field strength, we find the gapped spin up and spin down bands are split to realize a half-metal phase which is a promising candidate for designing an efficient spin filter. Moreover, one of the two energy gaps becomes larger and the other smaller due to the weakened or enhanced coupling between two edge states. Here we propose and demonstrate the spin filter based on a finite size QSHI junction under a magnetic field and the polarity can be inverted by a bias voltage or magnetic field. Interestingly, we find a bipolar spin diode effect that only one spin channel is opened and the other spin channel is closed at positive bias, and the opposite spin electron can be transmitted with negative bias. Two spin filters in series can be a spin transistor, the on and off states can be controlled by spin polarization of one spin filter. We show that the topological spin transistor can be controlled by the gate voltage, and it survives in moderate disorder.

Enhancing the performance of convolutional neural network image-based steganalysis in spatial domain using Spatial Rich Model and 2D Gabor filters

Image-based steganalysis problem has attracted many researchers, and several solutions have been proposed. Deep learning-based methods are the most promising as they provide superior performance. Convolutional Neural network(CNN) based steganalysis methods are designed to improve the detection rate. Unlike traditional CNN models, CNN-based steganalysis requires careful design of preprocessing layers with filter initialization to obtain a good performance. In this paper, we established a CNN model that consists of two convolution layers for preprocessing and feature extraction, and four fully connected layers for classification. The preprocessing layer uses a set of efficient filter banks consisting of SRM and 2D Gabor filters. We conducted experiments using grayscale cover images from a popular and publicly available BOSSbase_1.01 database and Alask_v2 database with consideration for two different image sizes. The results showed that the proposed CNN model outperforms many state-of-the-art studies in two out of three well-known adaptive spatial domain steganography algorithms (S-UNIWARD, HUGO) and provides a close result for (WOW) algorithm when using the database with 512 × 512 images. On the other hand, the proposed model outperforms many state-of-the-art studies in the three algorithms when using the database with the original image size (256 × 256). Using image size 256, and the S-UNIWARD algorithm, the proposed model improved the detection accuracy rate by 13%, and 4.25% payloads of 0.2 and 0.4 bpp respectively compared to the previously best-known model (GBRAS-Net). The proposed model achieved 7.4% and 6.27% improvement in the detection accuracy for both payloads 0.2 and 0.4 bpp respectively using the HUGO algorithm compared with the previously best-known model (GBRAS-Net). For the WOW algorithm, the proposed model is slightly behind the best model (GBRAS-Net) but was able to obtain a close result for both payloads of 0.2 and 0.4 bpp, respectively. Using an image size of 512, the proposed model achieved 31.26%, 21.51%, 6.84%, 4.22%, and 1.96% improvement in the detection rate for the five payloads 0.1, 0.2, 0.3, 0.4, and 0.5 bpp respectively over S-UNIWARD algorithm compared to the previously best-known model (H-CNN). In addition, the proposed model achieved 27.60%, 23.69%, 12.66%, 5.27%, and 6.23% improved detection accuracy for the five payloads 0.1, 0.2, 0.3, 0.4, and 0.5 bpp respectively over HUGO algorithm compared with the previously best-known model (H-CNN). Finally, the proposed model provided 57.81%, 46.84%, 28.29%, 20.34%, and 13.79% improvement in the detection rate for the five payloads 0.1, 0.2, 0.3, 0.4, and 0.5 bpp respectively over WOW algorithm compared to the previously best-known model (H-CNN).

An efficient volume-preserving binary filter for additive manufacturing in topology optimization

Topology optimization faces challenges concerning grey transition regions, manufacturability and computational efficiency. This article introduces an approach called the volume-preserving binary (VPB) filter. This novel sensitivity/density filter, based on a determinant-based matrix approach, efficiently detects grey elements and produces completely binary designs. The VPB filter addresses volume preservation by accurately expressing the volume-preserving condition, i.e. the volume before filtering equals the volume of solid materials after filtering. Comparison with other filtering methods, including the volume-preserving Heaviside filter and the Heaviside projection, illustrates the superior clarity and speed of the VPB filter. It reduces the number of iterations required for topology optimization, from hundreds or thousands to only tens of iterations, making it a promising solution for practical applications where manufacturability and computational efficiency are principal. Examples on compliance optimization, compliant mechanisms and heat conduction demonstrate the VPB filter's effectiveness in providing satisfactory solutions while perfectly preserving the length scale.

Efficient capture of particulate matter with composite air filters comprising bamboo-derived nanofiber and base paper

Particulate-matter (PM) air filters comprising chemically synthesized polymers and petroleum-based materials are not environmentally sustainable because they do not biodegrade, causing secondary environmental pollution. To overcome this problem, we synthesized and analyzed the PM-capturing performance of composite sandwich-structured air filters containing bamboo-derived lignocellulose nanofibers and lignocellulose base paper. Bamboo pulp was produced through an organosolv process, and lignocellulose nanofibers and lignocellulose base paper were manufactured via electrospinning and paper processes, respectively. The manufacturing process was not only optimized via a rheological analysis of the precursor solution with different mixed solvent composition (6:4, 5:5, and 4:6 of 1-ethyl-3-methylimidazolium acetate:dimethylformamide) and different lignocellulose weight percent (3.3, 4.0, and 4.6 wt%), but also via nanofiber characterization, including electrospinnability, diameter distribution, and basis weight. The base paper condition was optimized based on analyses of the air permeability, PM filtration efficiency, and strength according to the paper thickness of 0.38 ± 0.05 and 0.10 ± 0.05 mm. As the lignocellulose nanofibers and base paper effectively captured particles smaller and larger than 2.5 μm, respectively, the structure of their composite sandwich-structured air filter was optimized to improve its PM-capturing performance. The study results provide new insights into the manufacturing of highly efficient PM air filters suitable for practical applications with diverse PM size distributions.

IKern: Advanced Intrusion Detection and Prevention at the Kernel Level Using eBPF

The development of new technologies has significantly enhanced the monitoring and analysis of network traffic. Modern solutions like the Extended Berkeley Packet Filter (eBPF) demonstrate a clear advancement over traditional techniques, allowing for more customized and efficient filtering. These technologies are crucial for influencing system performance as they operate at the lowest layer of the operating system, such as the kernel. Network-based Intrusion Detection/Prevention Systems (IDPS), including Snort, Suricata, and Bro, passively monitor network traffic from terminal access points. However, most IDPS are signature-based and face challenges on large networks, where the drop rate increases due to limitations in capturing and processing packets. High throughput leads to overheads, causing IDPS buffers to drop packets, which can pose serious threats to network security. Typically, IDPS are targeted by volumetric and multi-vector attacks that overload the network beyond the reception and processing capacity of IDPS, resulting in packet loss due to buffer overflows. To address this issue, the proposed solution, iKern, utilizes eBPF and Virtual Network Functions (VNF) to examine and filter packets at the kernel level before forwarding them to user space. Packet stream inspection is performed within the iKern Engine at the kernel level to detect and mitigate volumetric floods and multi-vector attacks. The iKern detection engine, operating within the Linux kernel, is powered by eBPF bytecode injected from user space. This system effectively handles volumetric Distributed Denial of Service (DDoS) attacks. Real-time implementation of this scheme has been tested on a 1Gbps network and shows significant detection and reduction capabilities against volumetric and multi-vector floods.

Terahertz metamaterials for spectrum modulation: structural design, materials and applications

Metamaterials, an artificial electromagnetic (EM) material, have exhibited unique characteristics, enabling innovations in terahertz (THz) wavefront shaping, polarization modulation, surface wave manipulation, and spectrum modulation. In this review, we focus on the advancements in THz metamaterials for spectrum modulation, emphasizing their structural design, special materials, and applications. The review discusses various structural designs, including metal-dielectric composite structures, all-metal structures, and all-dielectric structures, each offering distinct advantages for THz applications. Key special materials such as graphene, vanadium dioxide (VO2), molybdenum disulfide (MoS2), and flexible materials are highlighted for their significant contributions to enhancing the performance and functionality of THz metamaterials. The applications of THz metamaterials in EM stealth and sensing are thoroughly introduced, with a particular focus on biosensing, pesticide detection, and other sensing applications. The review emphasizes the potential of THz metamaterials in developing highly sensitive and selective sensors, as well as efficient THz absorbers and filters. Future perspectives include the continuous development of novel materials, advanced fabrication technologies, and the integration of multifunctional capabilities to further expand the applications and efficiency of THz metamaterials. These advancements are expected to drive significant progress in THz technology, impacting fields such as medical diagnostics, environmental monitoring, food safety, wireless communication, and security.

Efficient trust region filter modeling strategies for computationally expensive black-box optimization

The study and application of contemporary optimization techniques considerably enhance the efficiency of chemical research and manufacturing. With the dynamic progression of modern manufacturing technologies, the emergence of numerous black-box models characterized by inaccessible mathematical formulations and high evaluation costs poses new challenges to traditional optimization methods, leading to difficulty in programming and solving. Hence, based on the trust region filter (TRF) method, we define a new framework to elevate optimization efficiency in this study for chemical systems involving computationally expensive black-box functions. Sampling size per iteration is reduced, and sampling efficiency is improved by incorporating the known data beyond the trust region to assist in constructing reduced models, which is achieved by the application of the Gaussian process. Through comparison and validation of benchmark tests and case studies, this approach demonstrates that using the Gaussian process as the reduced model can lower the number of calls to black-box functions by more than half compared to common linear and quadratic models, and convergence to first-order critical points can still be guaranteed.

Polarity-controllable magnetic skyrmion filter

Abstract The skyrmion generator is one of the indispensable components for the future functional skyrmion devices, but the process of generating skyrmion cannot avoid mixing with other magnetic textures, such as skyrmionium and nested skyrmion bags. These mixed magnetic textures will inevitably lead to the blockage of skyrmion transport and even the distortion of data information. Therefore, the design of an efficient skyrmion filter is of great significance for the development of skyrmion-based spintronic devices. In this work, a skyrmion filter scheme is proposed, and the high-efficiency filtering function is demonstrated by micromagnetic simulations. The results show that the filtering effect of the scheme depends on the structure geometry and the spin current density that drives the skyrmion. Based on this scheme, the polarity of the filtered skyrmion can be controlled by switching the magnetization state at the output end, and the “cloning” of the skyrmion can be realized by geometric optimization of the structure. We believe that in the near future, the skyrmion filter will become one of the important components of skyrmion-based spintronic devices in the future.

Design Polynomial IIR Digital Filters of the Integer Parameters Space Use to Compress Image Data

Polynomial IIR digital filters play a crucial role in the process of image data compression. The main purpose of designing polynomial IIR digital filters of the integer parameters space and introduce efficient filters to compress image data using a singular value decomposition algorithm. The proposed work is designed to break down the complex topic into bite-sized pieces of image data compression through the lens of compression image data using Infinite Impulse Response Filters. The frequency response of the filters is measured using a real signal with an automated panoramic measuring system developed in the virtual instrument environment. The analysis of the output signal showed that there are no limit cycles with a maximum radius of poles of 0.96 in the polynomial bandpass filters. Thus, all the functional requirements for the Integer Parameters Space of the proposed polynomial IIR digital filters were met. The results showed that the data compression and size reducing of an image file is processed without significantly impacting of visual quality. This is achieved by removing redundant or unnecessary information from the image while preserving the important details which removes unnecessary data to make the file smaller and more manageable.

Spin Filtering and Amplification in Self-Assembled Nanofibers Based on Chiral Asymmetric Building Blocks.

The cooperativity in artificial self-assembling systems can be enhanced to expand their applications and redesign their properties. Recently, chiral molecules have garnered renewed attention due to their potential as highly efficient spin filters through the chiral-induced spin selectivity (CISS) effect. However, the potential of asymmetric building blocks based on chiral perylene diimides (PDIs) self-assembled materials to generate a spin-polarized current is still not widely acknowledged. In this work, we have demonstrated that nanofibers derived from "asymmetric PDIs" molecules have been found to exhibit promising spin-filtering property and the amplification of spin polarization at room temperature. Also, the exploration of chiral amplification and correlating it with the amplification of spin polarization have been reported for the first time through this work. These findings underscore the significance of self-assembled materials in the realm of spintronics, as they offer fascinating platforms with evolving structure-property relationship. It also provides the feasible possibility of enhancing the CISS-based spintronic devices that can accomplish controllability and high spin-filtering efficiency simultaneously.

Microplastics Uptake by Four Filter Feeders

Microplastics (MPs) are insidious plastic particles with sizes ranging from 1 to 5000 µm. Their presence has been reported all over the world. Recently, bioremediation to remove MPs from water columns using filter feeders as biofilters has been proposed. In a previous lab experiment, the MP bioremediation potential of four fouling organisms from a mariculture facility (Mytilus galloprovincialis, Sabella spallanzanii, Phallusia mammillata, Paraleucilla magna) was separately assessed in single-species experiment. Herein, a follow-up of the work is presented using a multi-species approach. The four organisms were placed together in the same 5 L beaker and fed with a concentration of 250 p/L 6 µm red polystyrene discernible particles. After digesting the organisms and counting the MPs in both the water and the organisms, the results of the two experiments were compared. In the previous experiment, S. spallanzanii had the highest particle retention (PR) value (PR = 88.01%), while in this experiment, P. mammillata has the lowest PR value (PR = 31%). The multi-species approach resulted in a higher number of plastics being removed from the water (88%) compared to the single-species experiments. These fouling organisms naturally exist as a community, acting as an efficient filter with complex morphologies and hydrodynamic features. Here, this simple marine animal forest is re-evaluated by exploiting the ecosystem services provided by these organisms as a solution to MP pollution problem in a mariculture environment.

Design of Channel Drop Filters Based on Photonic Crystal with a Dielectric Column with Large Radius inside Ring Resonator

Photonic crystal channel drop filters (CDFs) play a vital role in optical communication owing to their ability to drop the desired channel. However, it remains challenging to achieve high-efficiency CDFs. Here, we demonstrate a highly efficient three-channel CDF with both high transmission and high quality (Q) factor based on a novel ring resonator that is in the middle of two waveguides. A dielectric column with a large radius replaces the homogeneously distributed dielectric columns inside the ring cavity to modulate the coupling ratio with a straight waveguide, thereby enhancing the transmission and Q factor. The transmission and Q factor of the single-cavity filter are 99.7% and 12,798.4, respectively. The mean value of the three-channel filter based on the basic unit can reach up to 94.6% and 10,617, respectively, and a crosstalk between −30.16 and −50.61 dB is obtained. The proposed CDFs provide efficient filter capability, which reveals great potential in integrated optoelectronics and optical communication.