Showing papers on "Filter (video) published in 2022"

Road-Map Aided GM-PHD Filter for Multivehicle Tracking With Automotive Radar

Abstract: Nowadays, accurate and real-time vehicle tracking is critical to ensure the safety of intelligent vehicles. However, tracking in the complex traffic environments still remains a challenging issue. In this article, we present a road-map aided Gaussian mixture probability hypothesis density (RA-GMPHD) filter for multivehicle tracking with automotive radar. Since the road-map is commonly available in traffic scenarios, we focus on leveraging road-map information to enhance the tracking performance. We first model the vehicle dynamics in a 2-D road coordinates, then approximatively map it onto ground coordinates considering map errors. Additionally, we integrate the variable structure interacting multiple model into the RA-GMPHD filter considering both the dynamic uncertainty of targets and the road geographic constraints. Furthermore, we perform extensive simulations and conduct physical testings to demonstrate the superiority of our approaches compared with state-of-the-art method. Experimental results show our methods enhance both the tracking quality and tracking continuity.

A Remote Estimation Method of Smart Meter Errors Based on Neural Network Filter and Generalized Damping Recursive Least Square

Abstract: To solve large-scale smart meter verification and periodic replacement problems, a remote estimation method based on neural network filter (NNF) and generalized damping recursive least square (GDRLS) is proposed. In this article, a smart meter error estimation model with a loss noise filter is built. A typical loss noise filter is designed with a neural network, so that the filtered loss noise meets the Gauss–Markov condition, which paves the way for the best linear unbiased estimation (BLUE). GDRLS algorithm is applied to solve the novel estimation model, which can effectively address the problem that large loss estimation errors merge small smart meter errors. Then, a complete process of the proposed method is constructed, which can estimate both the user smart meter errors, and the loss noises accurately. Finally, the effectiveness, superiority, and applicability of the proposed method are verified through simulation analysis and practical distribution network application.

A Mathematical Design Approach to Volumetric Optimization of EMI Filter and Modeling of CM Noise Sources in a Three-Phase PFC

Abstract: Designing an efficient, compact, and optimized electromagnetic interference (EMI) filter for the next generation high-frequency switched mode power converter while maintaining a small form factor with high power density, requires adequate research and development effort. This article presents a systematic as well as unified approach to design the EMI filter for any power electronic converter, particularly for three-phase ac–dc active boost rectifier systems. Since the differential mode (DM) filter stage consumes a major part of the EMI filter volume and weight, DM filter design optimization is a necessary yet challenging task to attain a higher power density. This article theoretically demonstrates the design steps for choosing the appropriate filter component values and number of filter stages to achieve the smallest volume of the DM EMI filter. Furthermore, to design an optimized common mode (CM) filter stage, a research effort has been made for estimation of the CM noise corner frequencies followed by multiconstraint volume optimization through a detailed mathematical noise modeling of the converter. While the validation of the proposed design methodology is done through MATLAB simulation, an experimental verification is also performed by designing the optimized EMI filter for a 2.3-kW proof of concept of a three-phase boost power factor correction converter to comply with the stringent EMI requirements of DO-160F standard.

A Fourier-Based Phasor Estimator With a Modified Moving Average Filter and Its Application in Distribution Networks

Abstract: Phasor estimation is fundamental for most real-time analysis, monitoring, and control tasks in power systems. New applications on microgrids and active distribution networks make such estimation increasingly important, leading to many research efforts focusing on known approaches, such as the Fourier and cosine filters, to abate the estimation errors; others rely on newly applied algorithms like the Taylor–Kalman–Fourier filter (TKF). This variety has led to discussions about the application cases of each technique, normally demoting the Fourier filter (FF). In this spirit, the FF was reworked under the Hilbert space power theory, showing its conceptual correctness and then deriving an accurate and fast alternative relying on a discrete, lead-compensated filter. Such FF is tested over an active distribution network system, showing advantages in relevant scenarios, namely impedance estimation, grid-monitoring, and fault location. The proposal exhibits better dynamic performance and overshoot than the conventional techniques, with no increased complexity, and “cleaner” results than the TKF, which is, however, faster to estimate the system's impedances.

Methodology to filter out outliers in high spatial density data to improve maps reliability

Abstract: The considerable volume of data generated by sensors in the field presents systematic errors; thus, it is extremely important to exclude these errors to ensure mapping quality. The objective of this research was to develop and test a methodology to identify and exclude outliers in high-density spatial data sets, determine whether the developed filter process could help decrease the nugget effect and improve the spatial variability characterization of high sampling data. We created a filter composed of a global, anisotropic, and an anisotropic local analysis of data, which considered the respective neighborhood values. For that purpose, we used the median to classify a given spatial point into the data set as the main statistical parameter and took into account its neighbors within a radius. The filter was tested using raw data sets of corn yield, soil electrical conductivity (ECa), and the sensor vegetation index (SVI) in sugarcane. The results showed an improvement in accuracy of spatial variability within the data sets. The methodology reduced RMSE by 85 %, 97 %, and 79 % in corn yield, soil ECa, and SVI respectively, compared to interpolation errors of raw data sets. The filter excluded the local outliers, which considerably reduced the nugget effects, reducing estimation error of the interpolated data. The methodology proposed in this work had a better performance in removing outlier data when compared to two other methodologies from the literature.

Tunable and wavelength interval precisely controlled erbium-doped fiber laser by employing the fused taper technology

Abstract: A tunable multi-wavelength erbium-doped fiber laser with precise wavelength interval control is reported theoretically and experimentally in this paper. It is made up of a Mach–Zehnder interferometer (MZI) filter and a Sagnac filter and supplemented by the four-wave-mixing effect. Compared with other filters, the proposed MZI filter based on the fused taper technology can change the wavelength interval more flexibly. The experiment result shows that wavelength tuning can be achieved, and the tuning range can reach ∼15 nm. Moreover, the variation in the number of wavelengths is also realized. The maximum side-mode suppression ratio can reach 39 dB.

Scaled Spherical Simplex Filter and State-Space Damage-Plasticity Finite-Element Model for Computationally Efficient System Identification

Abstract: This work presents an efficient online system identification approach by integrating a reduced sigma points–based filter with a high-fidelity mechanics-based state-space hysteretic finite-e...

Investigation on parallel auxiliary adjusting machine of spatial filter lens for a high-power laser facility

Abstract: As a key part in high-power laser facility, the spatial filter is used to determine the optical axis of the entire laser system, which consists of two conjugate lenses at both ends and filter hole in the middle. The lenses usually require precise adjustment, and the adjustment mechanism is integrated to both ends of the filter currently. To this end, a novel parallel adjusting machine for the filter lenses was proposed as an auxiliary tool in the spatial filter process. The machine was designed to realize the five-dimensional adjustment functions of the filter lenses with X-Y-Z axial translation, pitch, and yaw motion. The first-order ghost point of the filter lens was used as the feedback reference for the parallel machine to adjust Z-position of the lens. A more comprehensive optimization-oriented framework was established to improve the sensitivity of the lens to the posture error and to optimize the positioning speed based on the steepest descent method (SDM). The experimental results verified the superiority of the proposed adjustment machine and feedback reference point.

MIRFCS: An Ensemble of Filter Methods for Classification of Disease Data

Abstract: A facility for early disease diagnosis is very important in the medical field. Since most disease datasets are huge in dimension, an automatic diagnosis process through computing devices becomes complex and time-consuming. Feature selection methods can be used to eliminate unnecessary information from a dataset. Among the existing filter-based feature selection methods, one filter may end up eliminating important features. This is where our ensemble-based filter methods of Mutual Information, ReliefF, and Chi-Square come into play, where even if one filter eliminates an important feature, another filter may compensate for it. This has been done by forming a union of the reduced set produced by each filter. The three datasets on which the evaluation has been done are PID, DLBCL and Prostate cancer. From the union, accuracies are calculated using different classifiers and the classification accuracies of 97.18%, 97.32% and 91.90% are achieved in the three datasets, respectively.

Neural-Adaptive Stochastic Attitude Filter on SO(3)

Abstract: This letter proposes a novel stochastic nonlinear neural-adaptive-based filter on $SO(3)$ for the attitude estimation problem. The proposed filter produces good results given measurements extracted from low-cost sensing units (e.g., IMU or MARG sensor modules). The filter is guaranteed to be almost semi-globally uniformly ultimately bounded in the mean square. In addition to Lie Group formulation, quaternion representation of the proposed filter is provided. The effectiveness of the proposed neural-adaptive filter is tested and evaluated in its discrete form under the conditions of large initialization error and high measurement uncertainties.

Improved forward and backward adaptive smoothing algorithm

Abstract: Kalman smoothing algorithms are widely used in offline data processing in target tracking systems to improve filter calculations accuracy. The essence is weight averaging in forward and backward Kalman filters. When there is an abnormal dynamic model in the system, the adaptive Kalman filter algorithm can reduce its impact on the filter results to a certain extent. Nevertheless, because there are various methods for selecting adaptive factors and all of them are complicated, it is difficult to select the optimal adaptive factors. Therefore, the forward filter and backward filter results are suboptimal when a dynamic model abnormality occurs, which, in turn, causes the smoothing accuracy to decrease after the weighted average before and after this abnormality. We propose an improved forward and backward adaptive smoothing (IFBAS) algorithm. During the smoothing process, adaptive factors of the forward adaptive Kalman filter and the backward adaptive Kalman filter are used to modify the covariance information twice to reduce the influence of suboptimal filter information on smoothing accuracy. We apply the IFBAS algorithm to the GPS/INS integrated navigation system and data postprocessing of the GNSS network. The results of simulation experiments and time series of IGS station analysis examples show that the IFBAS algorithm can effectively suppress the influence of abnormal dynamic models and improve smoothing accuracy.

Guided Filter Bank

Abstract: Guided filter can perform edge-preserving smoothing by utilizing the structures of a guidance image. However, it is difficult to obtain two images with different contents from the same scenario. Therefore, we focus on the case that the input image and the guidance image are identical. In this case, the direction of the gradient of the output image is the same as the guidance image. Based on this discovery, we change the regularization term of guided filter and develop a more general model which can generate a bank of guided filters. To take examples, we pick up three filters from this bank, where \(L_1\) guided filter and \(L_{0.5}\) guided filter are newly proposed filters. Mathematical and experimental analysis are performed to demonstrate that the new filters have totally different properties from the guided filter. \(L_1\) guided filter is very suitable for edge-preserving and texture-removing tasks, while \(L_{0.5}\) guided filter can do enhancement automatically. We applied them to a variety of image processing applications, including image smoothing, image denoising, edge detection, image detail enhancement and X-ray image enhancement. The experimental results reveals the effectiveness of the newly proposed filters and their state of the art performance. We also believe that more interesting filters can be developed from our model.

MOSFET-C current mode filter for secure communication applications

Abstract: In this paper, a dual output current-mode biquad filter with band-pass and low-pass outputs using MOS transistors and capacitors is presented. In these type of filters MOSFETs operating in saturation mode are used instead of standard active elements and transconductances of these transistors are utilized instead of resistors. In the study a voltage controlled current source (VCCS) based core filter circuit is presented. This circuit is used as a starting point for the complete filter design. After VCCSs are replaced by MOSFETs biased for operating in saturation region, a resistorless MOSFET-C type filter circuit is obtained with only seven transistors and two grounded capacitors. Also, an agile filter application for secure communication is given to illustrate the functionality.

Improved Filter Ranking Incorporated Binary Black Hole Algorithm for Feature Selection

Abstract: Feature selection is a very important preprocessing step in all machine learning tasks. Selection of the most informative attributes increases algorithm performance, provides valuable domain information, reduces noise and reduces computational requirements. Recently, the Black Hole metaheuristic algorithm inspired by the real-life behavior of stars and the black hole has been developed in the literature. This algorithm is increasingly employed for solving several real-life problems. We have modified the existing binary Black Hole algorithm by incorporating filter ranking for improving algorithm performance. We have introduced two different combinations of filters: these are Pearson correlation and mean decrease in Gini combination and, Pearson correlation and mutual information combination. In our implementation we probabilistically switched between the standalone BH algorithm fitness criteria and the filter ranking enabled BH algorithm fitness criteria during the progress of the algorithm. We ran simulations with benchmark datasets covering different fields of science and engineering. We have compared our results with the standalone Black Hole algorithm. Our new algorithms exhibit improved performance in terms of selected subset size and accuracy. We also compared our algorithm with the filter ranking-incorporated ant colony algorithm available in the literature. We found that our algorithm compares very well with this algorithm. Our results indicate that the probabilistic incorporation of filter combinations enhances performance considerably. Various other synergistic filter combinations can also be incorporated in future for performance improvement.

Wideband Bandpass Substrate Integrated Waveguide (SIW) Filter for C Band Application

Abstract: This paper presents a substrate integrated waveguide (SIW) filter using electromagnetic band gap (EBG) structure. The periodic EBG structure is etched on the top metal surface of SIW cavity. These periodic structures create a slow wave effect on the filter performance to achieve wide pass band at lower frequency in a small compact size. In the proposed design, Rogers 4350 is used as a dielectric material with the permittivity(er) of 3.48 and thickness 1.524 mm. The simulated results obtained by HFSS 19.1 has a broadband from 3.25 to 6.94 GHz with the bandwidth of 3.38 GHz in C band used for satellite communication. The insertion loss is less than 0.5 dB and return loss is better than 18 dB. The size of filter is 48 × 10 mm2. The fractional bandwidth (FBW) of proposed filter is 68%.

Dual bandpass filter based on spoof surface plasmon polaritons and half-mode substrate integrated waveguide

Abstract: In this work, a dual-bandpass filter based on spoof surface plasmon polaritons (SSPPs) and half-mode substrate integrated waveguide (HMSIW) is designed. This filter uses etched metal layers with periodic rectangular groove unit to support SSPPs. Meanwhile, the substrate integrated waveguide (SIW) unit is combined to produce a strong coupling along the propagation direction of the waveguide. The transition section of the gradient of the rectangular groove is supplemented with double rows of metal through holes to limit the propagation of the signal, thus achieving the effect of the dual-bandpass filter. This filter is fabricated and measured. The results verify the excellent ability of the filter and the great band selection ability of the stopband. The measured results show that the insertion loss of the dual-bandpass filter is less than 3 dB in the frequency from 8.7 to 16.4 GHz and 28.3 to 34.6 GHz, and the stopband rejection level is lower than − 20 dB from 17.2 to 26.6 GHz. Compared with the traditional dual-bandpass filter, the proposed filter has lower loss in the passband and stronger rejection ability of the stopband between two passbands.

Design and Simulation of a Photonic Crystal-Based 2-D Octagonal-Shaped Optical Drop Filter

Abstract: A 2-D photonic crystal (PhC) based octagonal-shaped optical channel drop filter (CDF) resonance structure is suggested and simulated. The structure comprises of four ports. Port ‘A’ acts as an input and ‘B’, ‘C’ and ‘D’ are taken out as output ports. The plane wave expansion (PWE) method is used to evaluate the Photonic bandgap (PBG) as well as the distributions of electric fields. When the applied optical signal lies in the PBG range and also equal to the resonance wavelength of the structure then it enabled the structure to behave as a filter; otherwise, it would be performed as a normal waveguide. In the proposed structure for the wavelengths 1200–1600 nm, it behaves as a normal waveguide and for 1650–1700 nm. Wavelengths behave like a drop filter. By using scatterer and coupling rods in the proposed structure, we obtained 98–99% of drop efficiency for the wavelengths 1650 and 1700 nm.