Pre-filtering Technique Research Articles

Vector space model, term frequency-inverse document frequency with linear search, and object-relational mapping Django on hadith data search

For Muslims, the Hadith ranks as the secondary legal authority following the Quran. This research leverages hadith data to streamline the search process within the nine imams’ compendium using the vector space model (VSM) approach. The primary objective of this research is to enhance the efficiency and effectiveness of the search process within Hadith collections by implementing pre-filtering techniques. This study aims to demonstrate the potential of linear search and Django object-relational mapping (ORM) filters in reducing search times and improving retrieval performance, thereby facilitating quicker and more accurate access to relevant Hadiths. Prior studies have indicated that VSM is efficient for large data sets because it assigns weights to every term across all documents, regardless of whether they include the search keywords. Consequently, the more documents there are, the more protracted the weighting phase becomes. To address this, the current research pre-filters documents prior to weighting, utilizing linear search and Django ORM as filters. Testing on 62,169 hadiths with 20 keywords revealed that the average VSM search duration was 51 seconds. However, with the implementation of linear and Django ORM filters, the times were reduced to 7.93 and 8.41 seconds, respectively. The recall@10 rates were 79% and 78.5%, with MAP scores of 0.819 and 0.814, accordingly.

Modified park transformation of three-phase signals for harmonic extraction with frequency-adaptive capability

To maintain high power quality in a polluted grid with power electronic devices and non-linear loads, the detection and extraction of harmonic components is inevitable, such as for active power filters, regenerative converters, and uninterruptible power supplies. In this paper, the three-phase current/voltage signal is converted to the dq-frame signal with a modification in the angle used for the Park transformation so that with the aid of pre-filtering techniques such as moving average filter and cascaded delayed signal cancellation method, the selected harmonic can be derived easily. With the implementation of the proposed technique, there is no more need for amplitude and phase correction for the extracted harmonic signal previously elaborated in the literature. The proposed scheme is also equipped with a frequency detection technique to be able to follow the frequency variations in the grid. Moreover, some modifications are applied to the detected frequency to restrict the large transients occur when the grid frequency changes suddenly. The simulation tests and results confirm the effectiveness of the proposed schemes.

Real-time image-based lighting of metallic and pearlescent car paints

The complex behavior of metallic and pearlescent paints is not accurately captured with simple (spatially varying) BRDF models. They exhibit goniochromatic effects and glints due to metallic flakes embedded in the paint. However, when viewed from a distance or when illuminated from a larger area light source, the high-frequency glints tend to vanish. Therefore, we do not consider glints in this work and focus on the remaining angular color variations of a homogeneous materials, based on the direction from which the paint is illuminated or viewed, and how to realize these effects in a real-time rendering engine. Many models have been proposed to capture these effects, but efficiently and accurately reproducing them in real-time rendering applications under image-based lighting is a challenging problem. Therefore, most of the analytical models are not suitable either because of their computational complexity or because they are not able to present these effects accurately enough. We propose a prefiltering technique to handle angular color variations with very little overhead in comparison to the widely used standard methods for real-time image-based lighting. We require that the BRDF can be factorized into a brightness BRDF which works well with real-time image-based lighting, and a function modeling the angular color variations. The color is then prefiltered with a kernel defined by the environment map and brightness BRDF. Marginalizing over the azimuthal rotation around the ideal reflection direction results in a 3D prefiltered color table, parameterized by the ideal reflection direction and the angle between viewing direction and surface normal, which can further be decomposed into two functions depending on one of the two parameters, respectively. The additional data that needs to be stored with our method consists of a low-resolution cubemap and 2D array per material and environment map combination.

Parameter estimation of a gyroless micro-satellite from telemetry data

This paper presents a new approach for the simultaneous inertia and actuator alignment parameter estimation. One of the strengths of the proposed estimation algorithm consists in its ability to work directly with the star tracker attitude measurements and it is therefore also suitable for small and medium size satellites, where the gyroscope measures are not reliable or not available. In the algorithm, the satellite inverse dynamics model is exploited for the estimation cost function to take advantage of the linearity with respect to the main satellite parameters. The overall proposed solution is based on the instrumental variable method which has been deeply adapted to take into account several practical issues. The main components of the disturbance torques are modeled and simultaneously estimated with the satellite parameters to compensate for their effect. Inspired by the prefiltering technique used in the optimal refined instrumental variable method, a tailor-made filter design is proposed and implemented to deal with noisy measurements and the unmodeled disturbance torques. Such prefilter considerably improves the algorithm performance by achieving low-variance parameter estimates, while still requiring little to no tuning. A final but important novelty of this work consists in proposing an effective solution for estimating and compensating synchronism errors in the sensor measurements. This latter aspect has never been considered in the literature, although the lack of synchronism has a non-negligible effect on the accuracy of the parameter estimates. The proposed method is analyzed and validated via Monte Carlo simulations using data coming from a high-fidelity satellite simulator designed by the French space agency, and the results show the effectiveness of the algorithm.

Metal Artifact Reduction With Tin Prefiltration in Computed Tomography: A Cadaver Study for Comparison With Other Novel Techniques.

With the aging population and thus rising numbers of orthopedic implants (OIs), metal artifacts (MAs) increasingly pose a problem for computed tomography (CT) examinations. In the study presented here, different MA reduction techniques (iterative metal artifact reduction software [iMAR], tin prefilter technique, and dual-energy CT [DECT]) were compared. Four human cadaver pelvises with OIs were scanned on a third-generation DECT scanner using tin prefilter (Sn), dual-energy (DE), and conventional protocols. Virtual monoenergetic CT images were generated from DE data sets. Postprocessing of CT images was performed using iMAR. Qualitative (bony structures, MA, image noise) image analysis using a 6-point Likert scale and quantitative image analysis (contrast-to-noise ratio, standard deviation of background noise) were performed by 2 observers. Statistical testing was performed using Friedman test with Nemenyi test as a post hoc test. The iMAR Sn 150 kV protocol provided the best overall assessability of bony structures and the lowest subjective image noise. The iMAR DE protocol and virtual monochromatic image (VMI) ± iMAR achieved the most effective metal artifact reduction (MAR) (P < 0.05 compared with conventional protocols). Bony structures were rated worse in VMI ± iMAR (P < 0.05) than in tin prefilter protocols ± iMAR. The DE protocol ± iMAR had the lowest contrast-to-noise ratio (P < 0.05 compared with iMAR standard) and the highest image noise (P < 0.05 compared with iMAR VMI). The iMAR reduced MA very efficiently. When considering MAR and image quality, the iMAR Sn 150 kV protocol performed best overall in CT images with OI. The iMAR generated new artifacts that impaired image quality. The DECT/VMI reduced MA best, but experienced from a lack of resolution of bony fine structures.

A novel U-shaped acoustic-manipulated design to enhance the performance of low-efficiency filters for sub-micron particles

Acoustic manipulation is a non-contact process that applies acoustic waves to immobilize particles into a specific region for a variety of potential applications. This provides an alternative way to address air ventilation requirements where building systems are becoming smarter and more efficient. Development of such a process within confined spaces can incorporate microscopic interactions to filter aerosol-based particulate matter (PM). In real-engineering conditions, it is hard to filter sub-micron particles (0.25–1.0 μm) than super-micron particles (> 2.5 μm) by using low-grade filters. The objectives of this work are twofold. First, we propose a new acoustic-driven pre-filtering device (i.e., a U-shaped resonant acoustic chamber) that can improve the working efficiency of low-grade filters for capturing such particles. Second, the device can optimize spatial homogeneity to enhance the removal efficiency of airborne particles under lower sound intensity requirements. The U-shaped acoustic-driven device in the form of a resonant chamber allows PM to reside at the pressure node of a standing wave. Experimental studies are conducted to verify the present design. The results show that an overall filtration efficiency of up to 89% for 1.0-μm airborne particles can be achieved when the acoustic-driven device is coupled together with a low-grade MERV-6 coarse filter. As a standalone device, the acoustic effect works well for the sub-micron particles with a filtration efficiency of up to 61% under a lower sound pressure level (116 dB) than as previously reported in the literature. In the analysis, we also discuss the performance dependence on frequency, sound pressure level and flow rate in terms of particle size distribution. The relevance of this research is a major step towards engineering an acoustic-based pre-filtering technique for developing future innovative ventilation solutions.

Pre-Filtering Techniques for Spectrum Narrowing Caused by Optical Node Traversal in Ultra-Dense WDM Networks

To improve the spectral efficiency and increase network capacity, wavelength signals must be multiplexed densely, i.e., ultra-dense wavelength-division-multiplexing (WDM) networks. However, the spectrum of each WDM signal is narrowed by the wavelength-selective switches placed in optical nodes, so the transmissible distance and node hop count of the signal are strictly limited. To counter this spectrum narrowing problem caused by optical node traversal in ultra-dense WDM networks, pre-filtering techniques have been proposed. This paper comprehensively investigates the performance of four pre-filtering techniques: conventional Nyquist filtering, pre-emphasis filtering, partial-response filtering, and the combination of pre-emphasis and partial-response filtering. We conduct extensive computer simulations and discuss the optimality of pre-filtering techniques. The simulation results show that the adaptive use of pre-filtering can substantially extend the maximum attainable transmission distance and hop counts of 4/16/64-QAM signals.

Improving partial discharges location under dispersion and multi-path propagation

Dispersion and multi-path propagation distort partial discharge (PD) pulses that travel along power cables. Two new techniques to improve the location accuracy using double-ended methods are proposed: the inclusion of a pre-filter (PF), that increases the signal to noise ratio by properly selecting the filter bandwidth and the center frequency, and an echo suppression (ES) technique, that reduces the peak value of the interfering echoes.Two location algorithms (based on cross-correlation and the energy criterion function) are combined with the proposed techniques and their performances against noise, dispersion, and multi-path propagation are evaluated. Simulation results show that the proposed PF technique improves the location accuracy, even for high dispersion conditions, where the location error can be reduced down to half a meter. In addition, the proposed ES technique reduces the number of false detections in those cases where the first PD echo is close to the original PD. Experimental results confirm the benefits of the proposed PF technique, showing that, even under adverse conditions, the location error can be smaller than 1m in a 300m long cable.

Differentiable vector graphics rasterization for editing and learning

We introduce a differentiable rasterizer that bridges the vector graphics and raster image domains, enabling powerful raster-based loss functions, optimization procedures, and machine learning techniques to edit and generate vector content. We observe that vector graphics rasterization is differentiable after pixel prefiltering. Our differentiable rasterizer offers two prefiltering options: an analytical prefiltering technique and a multisampling anti-aliasing technique. The analytical variant is faster but can suffer from artifacts such as conflation. The multisampling variant is still efficient, and can render high-quality images while computing unbiased gradients for each pixel with respect to curve parameters. We demonstrate that our rasterizer enables new applications, including a vector graphics editor guided by image metrics, a painterly rendering algorithm that fits vector primitives to an image by minimizing a deep perceptual loss function, new vector graphics editing algorithms that exploit well-known image processing methods such as seam carving, and deep generative models that generate vector content from raster-only supervision under a VAE or GAN training objective.

Gene selection and classification of microarray data method based on mutual information and moth flame algorithm.

Several techniques or methods may help in detecting diseases and cancer. Creating an effective method for extracting disease information is one of the major challenges in the classification of gene expression data as long as there is (in the presence) a massive amount of redundant data and noise. Bio-inspired algorithms are among the most effective when used for solving gene selection. Moth Flame Optimization Algorithm (MFOA) is computationally less expensive and can converge faster than other methods.In this paper, we propose a new extension of the MFOA called the modified Moth Flame Algorithm (mMFA), the mMFA is combined with Mutual Information Maximization (MIM) to solve gene selection in microarray data classification. Our approach Called Mutual Information Maximization – modified Moth Flame Algorithm (MIM-mMFA), the MIM based pre-filtering technique is used to measure the relevance and the redundancy of the genes, and the mMFA is used to evolve gene subsets and evaluated by the fitness function, which uses a Support Vector Machine (SVM) with Leave One Out Cross Validation (LOOCV) classifier and the number of selected genes. In order to test the performance of the proposed MIM-mMFA algorithm, we compared the MIM-mMFA algorithm with other recently published algorithms in the literature. The experiment results which have been conducted on sixteen benchmark datasets either binary-class or multi-class, confirm that MIM-mMFA algorithm provides a greater classification accuracy.

Real-Time Estimation of Direction of Arrival of Speech Source Using Three Microphones.

In this paper, we present a real-time noise-robust direction of arrival (DOA) estimation technique using only the three built-in microphones of the modern Android-based smartphone. The proposed method eliminates the 'front-back' ambiguity caused by the symmetry of the two microphones reported previously and improves the performance of DOA estimation in noisy speech environments. Our method enhances the spatial awareness of hearing-impaired users by displaying the precise DOA angle of speech source on their smartphone screen. For increased efficiency, noise-robustness, and accuracy of the proposed DOA estimation method, a spectral pre-filtering technique and a Voice Activity Detector (VAD) based post-filtering are used along with a modified generalized cross-correlation (GCC) technique. Real recorded and simulated data under realistic noisy conditions are used in the evaluations of the proposed algorithm. Real-time implementation of the proposed system is carried out on an Android-based smartphone without any additional hardware or external microphone attachments. Experimental results show the performance of the proposed method versus those without pre or post-filtering under three different noisy conditions with 0dB to 10dB signal to noise ratios (SNRs).

Modified non-adaptive bandpass filter based phase locked loop

In this Letter, a demodulation based pre-filtering technique is proposed for fast detection of the single-phase grid variables. The estimation of grid voltage parameters is achieved through a Type-I synchronous reference frame phase-locked loop (PLL). The modified bandpass filter in the pre-filtering stage ensure a better rejection of the DC-offset and harmonics. Thus, the grid variables are estimated rapidly with the help of a simple proportional controller. Consequently, in-loop filtering burden and the effort required to tune the PLL is minimal. However, steady errors are witnessed in the fundamental amplitude and the phase information which are mitigated by a curve fitting technique. Importantly, the proposed PLL scheme has improved the dynamic performance under off-nominal frequency conditions with immunity against grid disturbances. The robustness of the proposed algorithm is experimentally verified vis-a-vis existing literature.

Comparative analysis of image pre-filtering techniques for phase-shifted noise-affected interferograms.

Phase-shifting techniques are one of the most promising strategies to extract the phase information and retrieve the parameters of interest (e.g., refractive index, beam collimation, object shape, deformations, thickness, focal length, etc.) from interferograms. However, traditional phase-shifting techniques suffer from both internal and external noise, which reduce measurement accuracy. This paper reports a comparative analysis of the three commonly used filtering techniques, namely, Fourier, windowed Fourier, and wavelet filtering for noise reduction and accurate extraction of phase information from phase-shifted interferograms. Toward this, two basic types of noise (additive and multiplicative noise) are introduced in the simulated interferograms and processed using the pre-filtering strategies. The effect of second-order harmonics in the presence of noise is also examined. In addition, experimental demonstrations of the real-life applicability of the analyses are provided using the interferograms recorded on coherent (Talbot) and incoherent (Lau) grating shearing interferometers. High accuracy in the measurement of defocusing error of the lens is obtained using the filtering strategies. Further inferences and insights are drawn in favor of the pre-filtering techniques.

On the Accuracy and Efficiency of Sensing and Localization for Robotic

In recent robotic applications, a critical need is to simultaneously detect communication (emission state) and estimate its trajectory. Whilst wireless sensor observations are useful, they are often uncertain due to the stochastic communication bursts and robot mobility. Over-sampling the information environment can incur excessive radio interference and energy usage. Therefore, one challenge is how to improve the efficiency of sensing under sparse and dynamic information, and make accurate inference on the robot's location. Here, we design a novel mixed detection and estimation (MDE) scheme to enhance both the accuracy and the efficiency by exploiting the mobility pattern correlations. Relying on a Markov state-space model, dynamic behaviors of robot's communication state and movement are formulated. A two-stage sequential Bayesian scheme, premised on random finite set (RFS), is developed to detect and estimate the involved unknown states. Specifically, in order to counteract the probability likelihood disappearance (caused by no information emission) and improve robustness to ambient noise, a sequential pre-filtering technique is designed, which can refine local observations and thereby significantly improve the accuracy of the system. We validate the proposed MDE scheme via both theoretical analysis and numerical simulations, demonstrating it would improve both the detection and estimation accuracy and efficiency.

Fast Algorithms for Acoustic Impulse Response Shaping

Impulse response shaping (IRS) is a prefiltering technique for modifying the characteristics of a linear channel to achieve desirable characteristics. Acoustic impulse response shaping is used to apply partial equalization to reduce the effects of reverberation on audio signals propagating inside a room and is thus used for listening room compensation. To be capable of responding rapidly to time varying room acoustics, a shaping algorithm must be computationally fast; this is difficult to achieve since the filters to be designed may have thousands of coefficients. This paper derives variants of the Dual Augmented Lagrangian Method, presents their application to the design of the shaping filters, and validates their efficacy through experimental studies. In addition, the formulations have been modified to design shaping filters that are robust to change in microphone positions.

1000× faster than PLINK: Combined FPGA and GPU accelerators for logistic regression-based detection of epistasis

Logistic regression as implemented in PLINK is a powerful and commonly used framework for assessing gene-gene interactions. However, fitting regression models for each pair of markers in a genome-wide dataset is a computationally intensive task, for which reason pre-filtering techniques and fast epistasis screenings are applied to reduce the computational burden.We demonstrate that employing a combination of a Xilinx UltraScale FPGA with an Nvidia Tesla GPU leads to runtimes of only minutes for logistic regression tests on a genome-wide scale, resulting in a speedup of more than 1000 up to 1600 when compared to multi-threaded PLINK on a server-grade computing platform.This article is an extended version of our conference paper [1].

A Novel Distribution-Level Phasor Estimation Algorithm Using Empirical Wavelet Transform

Distribution-level signals are often contaminated with harmonics and noise, along with dynamic conditions like frequency deviations. This paper proposes an empirical wavelet transform (EWT) based M-class distribution-level phasor estimation technique under such polluted and dynamic conditions. The capability of the EWT of extracting different frequency components present in the signal makes it suitable for distribution-level phasor estimation. However, the empirical wavelet filters are designed on the basis of the Fourier spectrum of the signal, which, inturn suffers from a spectral leakage problem at off-nominal frequencies. To avoid the errors arising due to spectral leakage, a sample value adjustment based prefiltering technique is employed. The effectiveness of the proposed estimator is demonstrated on various simulated signals, field-data, and real-time signals obtained from the hardware implementation setup. The proposed algorithm is found to be least affected by the presence of dc components, harmonics, and noise. The use of a fixed window size and fixed sampling frequency makes it suitable for synchronization with GPS clocks. The proposed scheme is also able to provide an accurate harmonic phasor estimation.

Band gap synthesis in elastic monatomic lattices via input shaping

This work describes control-theoretic methods for inducing a band gap-like behavior in elastic monatomic lattices. The dynamics of the system under consideration are derived in detail. Open-loop pre-filtering techniques, in the form of Posicast and user-selected time delay filters, are then utilized to eliminate specific frequency contents from the response of the multi-degree of freedom spring-mass chain. The input shaping approach is used to synthesize one or more band gaps in a homogenous lattice that can be designed to resemble Bragg-scattering in phononic crystals, as well as local resonance effects in acoustic metamaterials. The presence of the synthesized band gaps in the lattice’s dispersion behavior is validated using a spatiotemporal Fourier transform of the system’s impulse response as well as the frequency response of the end-to-end transfer function. Finally, an analysis of the control effort required to cancel the targeted system poles is carried out.

Efficient projection onto a low-dimensional ball

ABSTRACTProjection onto convex sets is a technique well known in optimization for its versatility and wide range of applications. This article presents an efficient projection onto a low-dimensional ball. The technique is based on analytically solving a quartic equation, and is exceptionally computationally efficient. An example application of this projection technique relates to acoustic impulse response shaping. Acoustic impulse response shaping is a pre-filtering technique to reduce reverberation of sound reproduction inside a room. If the focus is solely on the time domain, peaks and troughs in the frequency domain can occur. The projection approach presented in this article has been combined with the shaping algorithm to flatten the frequency response, thus providing control of both time domain and frequency domain characteristics.

Gene selection using hybrid binary black hole algorithm and modified binary particle swarm optimization

In cancer classification, gene selection is an important data preprocessing technique, but it is a difficult task due to the large search space. Accordingly, the objective of this study is to develop a hybrid meta-heuristic Binary Black Hole Algorithm (BBHA) and Binary Particle Swarm Optimization (BPSO) (4-2) model that emphasizes gene selection. In this model, the BBHA is embedded in the BPSO (4-2) algorithm to make the BPSO (4-2) more effective and to facilitate the exploration and exploitation of the BPSO (4-2) algorithm to further improve the performance. This model has been associated with Random Forest Recursive Feature Elimination (RF-RFE) pre-filtering technique. The classifiers which are evaluated in the proposed framework are Sparse Partial Least Squares Discriminant Analysis (SPLSDA); k-nearest neighbor and Naive Bayes. The performance of the proposed method was evaluated on two benchmark and three clinical microarrays. The experimental results and statistical analysis confirm the better performance of the BPSO (4-2)-BBHA compared with the BBHA, the BPSO (4-2) and several state-of-the-art methods in terms of avoiding local minima, convergence rate, accuracy and number of selected genes. The results also show that the BPSO (4-2)-BBHA model can successfully identify known biologically and statistically significant genes from the clinical datasets.