Fuzzy Adaptive Filter Research Articles

Frequency Decoupling-Based Energy Management Strategy for Fuel Cell/Battery/Ultracapacitor Hybrid Vehicle Using Fuzzy Control Method

In order to extend fuel cell lifespan and improve fuel economy of electrical hybrid vehicle with fuel cell/battery/ultracapacitor (FCHEV), a frequency decoupling-based energy management strategy (EMS) for FCHEV using fuzzy control method is proposed. In detail, firstly, according to different characteristics of energy sources, required power of FCHEV is decomposed into three frequency ranges based on Harr wavelet transform and an adaptive-fuzzy filter. Secondly, based on the proposed frequency decoupling, the obtained three frequency required power is supplied by fuel cell-battery and ultracapacitor, respectively, which can guarantee power performance of vehicle and reduce pressure and power fluctuation on fuel cell and battery. Thirdly, for improving fuel economy, one fuzzy controller is proposed to split the power between fuel cell and battery. Finally, the proposed strategy in this paper is verified by advisor-simulink and experimental bench. Simulation and experimental results show that the proposed EMS can effectively reduce impact of power fluctuations on fuel cell, extend its lifespan and reduce fuel consumption on 7.94% compared to equivalent consumption minimization strategy.

Removal of random valued impulse noise from grayscale images using quadrant based spatially adaptive fuzzy filter

An efficient image restoration technique based on spatially linked-directional adjoining pixels and fuzzy logic for addressing moderate and highly corrupted grayscale images with Random Valued Impulse Noise is presented. The proposed technique decomposes a larger sized impulsive region of an image into numerous overlapping small patches for low as well as high density impulse noise estimation with enhanced image restoration results. This subdivision highlights the details (edges, lines and textures) present in the neighborhood and the locations of impulsive pixels are relocated in multiple regions. Direction-based fuzzy rules give appropriate reasoning for edge and texture detection in an image. A switching technique based fuzzified degree identifies a certain pixel of an image as a noise-free, noisy or edge pixel in the filtering phase. Instead of using a static threshold, a directional non-parametric approach is introduced that determines and sets the threshold adaptively which empowers the proposed filter to handle different types of images automatically. Extensive simulations are conducted on a large set of monochrome images contaminated with varying degree of impulse noise densities. Objective analysis performed using popular quantitative measures and subjective evaluation of the results shows the efficacy of the proposed filter over most of the bench-marked denoising filters.

Grayscale Image Quality Analysis Result of Noises Reduction using Adaptive Fuzzy Filter (AFF) and Spatial Median Filter (SMF) Against Image Depth Variations

Removing salt and pepper noise in the image will have an impact on information in the image, with a greater percentage of noise, the changes in the results image will also be large, this is very likely different from the types of grayscale images with different depths such as 8, 16 and 24 bit. The percentage of noise that will be used starts from 45%, this is based on previous studies which reveal the ability of noise reduction algorithms to be able to work a maximum of below 20%, so that it needs to be studied more deeply the performance of the algorithm and its impact on images with greater noise. From the results of previous research studies, the Spatial Median Filter algorithm and Adaptive Fuzzy Filter algorithm can reduce noise with the maximum percentage of noise below 45% in the 8 Bit image, but leave some noise after being reduced. The test results on grayscale image obtained the average overall results quality Adaptive Fuzzy Filter algorithm is better than Spatial Median Filter in terms of image quality using PSNR but the diversity of information is even more reduced compared to the SMF algorithm referring to the entropy shannon value.

An Approach to Robust INS/UWB Integrated Positioning for Autonomous Indoor Mobile Robots.

The key to successful positioning of autonomous mobile robots in complicated indoor environments lies in the strong anti-interference of the positioning system and accurate measurements from sensors. Inertial navigation systems (INS) are widely used for indoor mobile robots because they are not susceptible to external interferences and work properly, but the positioning errors may be accumulated over time. Thus ultra wideband (UWB) is usually adopted to compensate the accumulated errors due to its high ranging precision. Unfortunately, UWB is easily affected by the multipath effects and non-line-of-sight (NLOS) factor in complex indoor environments, which may degrade the positioning performance. To solve above problems, this paper proposes an effective system framework of INS/UWB integrated positioning for autonomous indoor mobile robots, in which our modeling approach is simple to implement and a Sage–Husa fuzzy adaptive filter (SHFAF) is proposed. Due to the favorable property (i.e., self-adaptive adjustment) of SHFAF, the difficult problem of time-varying noise in complex indoor environments is considered and solved explicitly. Moreover, outliers can be detected and corrected by the proposed sliding window estimation with fading coefficients. This facilitates the positioning performance improvement for indoor mobile robots. The benefits of what we propose are illustrated by not only simulations but more importantly experimental results.

Determining harmonic component contribution for consumer and distribution systems based on adaptive fuzzy kalman filter

Determining harmonic component contribution for consumer and distribution systems based on adaptive fuzzy kalman filter

Adaptive Type-2 Fuzzy Approach for Filtering Salt and Pepper Noise in Grayscale Images

This paper proposes a novel adaptive Type-2 fuzzy filter for removing salt and pepper noise from the images. The filter removes noise in two steps. In the first step, the pixels are categorized as good or bad based on their primary membership function (MF) values in the respective filter window. In this paper, two approaches have been proposed for finding threshold between good or bad pixels by designing primary MFs. a) MFs with distinct Means and same Variance and b) MFs with distinct Means and distinct Variances. The primary MFs of the Type-2 fuzzy set is chosen as Gaussian membership functions. Whereas, in the second step, the pixels categorized as bad are denoised. For denoising, a novel Type-1 fuzzy approach based on a weighted mean of good pixels is presented in the paper. The proposed filter is validated for several standard images with the noise level as low as 20% to as high as 99%. The results show that the proposed filter performs better in terms of peak signal-noise-ratio values compared to other state-of-the-art algorithms.

Region Adaptive Fuzzy Filter: An Approach for Removal of Random-Valued Impulse Noise

This paper proposes region adaptive fuzzy filter for removal of random-valued impulse noise (RVIN) from color images. It is observed from existing literature that the filter performance increases with improved accuracy of noise detection and better adaption of the filter parameters. Improved minimum mean value detection mechanism is proposed for better classification of noisy and nonnoisy pixels in context to the removal of RVIN from color images. The modified fuzzy filter considers the correlation among the color channels and recursively adapts itself in accordance with the local noise densities. This filter incorporates an adaption technique to determine the maximum allowable window size used during fuzzification and filtering. Rather than one efficient filtering step that has a potential for losing information, region selective second iteration of the filter is applied on highly corrupted regions so as to preserve more image details. Experimental results suggest that the proposed filter provides improved performance in terms of peak signal to noise ratio, normalized color difference, structural similarity index, feature similarity index for color images, and perceptual similarity than most of the state-of-the-art filters.

Fuzzy SVM based fuzzy adaptive filter for denoising impulse noise from color images

Impulse noise is an “On-Off” noise that corrupts an image drastically. Classification of noisy and non-noisy pixels should be performed more accurately so as to restore the corrupted image with less blurring effect and more image details. In this paper, fuzzy c-means (FCM) clustering has been incorporated with fuzzy- support vector machine (FSVM) classifier for classification of noisy and non-noisy pixels in removal of impulse noise from color images. Here, feature vector comprises of newly introduced local binary pattern (LBP) with previously used feature vector prediction error, median value, absolute difference between median and pixel under operation. In this work, features have been extracted from the image corrupted with 10%, 50 and 90% impulse noise respectively and FCM clustering has been used for reduction of size of the feature vector set before processing through FSVM during training procedure. If the pixel is depicted as noisy in testing phase, fuzzy decision based adaptive vector median filtering is performed in accordance with available non-corrupted pixels within the processing window centring the noisy pixel under operation. It has been observed that proposed FSVM based fuzzy adaptive filter provides better performance than some of the established state-of-art filters in terms of PSNR, MSE, SSIM and FSIMC. It is seen that performance is increased by ~4 dB than baseline filters such as modified histogram fuzzy color filter (MHFC) and multiclass SVM based adaptive filter (MSVMAF).

Removal of Impulse Noise from Gray Images Using Fuzzy SVM Based Histogram Fuzzy Filter

Impulse noise is an image noise that degrades the quality of the image drastically. In this paper, k-means clustering has been incorporated with fuzzy-support vector machine (FSVM) classifier for classification of noisy and non-noisy pixels in removal of impulse noise from gray images. Here, local binary pattern (LBP) has been incorporated with previously used feature vector prediction error of the processing pixel, absolute difference between median value and processing pixel, median pixel, pixel under operation and mean value around the processing kernel. In this work, [Formula: see text]-means clustering has been used for reducing the feature vector set, where features have been extracted from the images corrupted with 10%, 50%, and 90% impulse noise. If the pixel is depicted as noisy in testing phase, histogram adaptive fuzzy filter is processed over the noisy pixel under operation. It is seen that the proposed filter offers improved performance over some of the state-of-the-art filter in terms of different image quality measures likely PSNR, SSIM, MSE, FSIM, etc. It is observed that performance is increased by [Formula: see text][Formula: see text]2–5[Formula: see text]dB than baseline filters likely SVM fuzzy filter, and artificial neural network based adaptive sized mean filter (ANNASMF) especially at high density noise.

Retracted: Raw tumor MRI edge detection using interval Type 2 Fuzzy inference system and adaptive filter

Brain tumor edge detection system has been of great aid to the human physicians. This paper presents a comparison study of two of the most effective edge detection methods. The two methods are Interval type-2 fuzzy system and adaptive filter. The study is conducted using MATLAB. These edge detectors are tested on images added with 0.001 white Gaussian noise and salt and pepper noise. Main feature of the algorithm is that it has been designed by using the adaptive filter with IT2FLS which produces clearer and brighter edges as compared to conventional methods results found in the literature.

An adaptive fuzzy filter for image denoising

This study considers the problem of fuzzy modeling of the images in pixel domain. A zero-order Takagi–Sugeno type fuzzy model provides fuzzy smoothing to the image intensities for removing the additive noise from an image. An adaptive fuzzy filtering algorithm is suggested for estimating the parameters of the fuzzy model with noisy image data. The mathematical analysis of the proposed filtering algorithm has been provided in both deterministic and stochastic framework. The deterministic robustness of the filtering algorithm was shown by deriving an upper bound on the magnitude of estimation errors. The fuzzy filtering algorithm doesn’t demand Gaussian assumption of the noise and is also optimal in the “sense” of variation Bayes towards Student-t distributed noises.

An adaptive particle filter based on PSO and fuzzy inference system for nonlinear state systems

ABSTRACTParticle filters have been widely used in nonlinear/non-Gaussian Bayesian state estimation problems. However, the particle filter (PF) is inconsistent over time. The inconsistency of PF mainly results from the particle depletion in resampling step and an incorrect priori knowledge of process and measurement noise. To cope with this problem and enhance the accuracy and consistency of the state estimation, an adaptive particle filter(APF) is proposed in this paper. In APF, an adaptive fuzzy square-root unscented Kalman filter (AFSRUKF) is used to generate the proposal distribution. This causes that beside the merit of reducing the computational cost, APF has some other advantages such as increasing consistency that leads to more numerical stability and better performance. Moreover,APF can work in unknown statistical noise behaviour and is more robust. This is why the fuzzy inference system (FIS) supervises the performance of square-root unscented particle filter (SRUPF) using tuning statistical noises. In APF, to increase the diversity of particles, the resampling process is done based on the particle swarm optimization (PSO). With this resampling strategy, the small-weight particles are modified to the large-weight ones without duplication and elimination of particles. The effectiveness of APF is demonstrated by using two experiment examples through Monte Carlo simulations. The simulation results demonstrate the effectiveness of the proposed method.

A new method based on pixel density in salt and pepper noise removal

In this paper, we deliver a new method to remove salt and pepper noise, which we refer to as based on pixel density filter (BPDF). The first step of the method is to determine whether or not a pixel is noisy, and then we decide on an adaptive window size that accepts the noisy pixel as the center. The most repetitive noiseless pixel value within the window is set as the new pixel value. By using 18 test images, we give the results of peak signal-to-noise ratio (PSNR), structural similarity (SSIM), image enhancement factor (IEF), standard median filter (SMF), adaptive median filter (AMF), adaptive fuzzy filter (AFM), progressive switching median filter (PSMF), decision-based algorithm (DBA), modified decision-based unsymmetrical trimmed median filter (MDBUTMF), noise adaptive fuzzy switching median filter (NAFSM), and BPDF. The results show that BPDF produces better results than the above-mentioned methods at low and medium noise density.

A biological inspired fuzzy adaptive window median filter (FAWMF) for enhancing DNA signal processing

Background and ObjectiveDigital signal processing techniques commonly employ fixed length window filters to process the signal contents. DNA signals differ in characteristics from common digital signals since they carry nucleotides as contents. The nucleotides own genetic code context and fuzzy behaviors due to their special structure and order in DNA strand. Employing conventional fixed length window filters for DNA signal processing produce spectral leakage and hence results in signal noise. A biological context aware adaptive window filter is required to process the DNA signals. MethodsThis paper introduces a biological inspired fuzzy adaptive window median filter (FAWMF) which computes the fuzzy membership strength of nucleotides in each slide of window and filters nucleotides based on median filtering with a combination of s-shaped and z-shaped filters. Since coding regions cause 3-base periodicity by an unbalanced nucleotides' distribution producing a relatively high bias for nucleotides' usage, such fundamental characteristic of nucleotides has been exploited in FAWMF to suppress the signal noise. ResultsAlong with adaptive response of FAWMF, a strong correlation between median nucleotides and the Π shaped filter was observed which produced enhanced discrimination between coding and non-coding regions contrary to fixed length conventional window filters. The proposed FAWMF attains a significant enhancement in coding regions identification i.e. 40% to 125% as compared to other conventional window filters tested over more than 250 benchmarked and randomly taken DNA datasets of different organisms. ConclusionThis study proves that conventional fixed length window filters applied to DNA signals do not achieve significant results since the nucleotides carry genetic code context. The proposed FAWMF algorithm is adaptive and outperforms significantly to process DNA signal contents. The algorithm applied to variety of DNA datasets produced noteworthy discrimination between coding and non-coding regions contrary to fixed window length conventional filters.

Space debris tracking based on fuzzy running Gaussian average adaptive particle filter track-before-detect algorithm

Although tracking with a passive optical telescope is a powerful technique for space debris observation, it is limited by its sensitivity to dynamic background noise. Traditionally, in the field of astronomy, static background subtraction based on a median image technique has been used to extract moving space objects prior to the tracking operation, as this is computationally efficient. The main disadvantage of this technique is that it is not robust to variable illumination conditions. In this article, we propose an approach for tracking small and dim space debris in the context of a dynamic background via one of the optical telescopes that is part of the space surveillance network project, named the Asia-Pacific ground-based Optical Space Observation System or APOSOS. The approach combines a fuzzy running Gaussian average for robust moving-object extraction with dim-target tracking using a particle-filter-based track-before-detect method. The performance of the proposed algorithm is experimentally evaluated, and the results show that the scheme achieves a satisfactory level of accuracy for space debris tracking.

Improved fault detection employing hybrid memetic fuzzy modeling and adaptive filters

We propose an improved fault detection (FD) scheme based on residual signals extracted on-line from system models identified from high-dimensional measurement data recorded in multi-sensor networks. The system models are designed for an all-coverage approach and comprise linear and non-linear approximation functions representing the interrelations and dependencies among the measurement variables. The residuals obtained by comparing observed versus predicted values (i.e., the predictions achieved by the system models) are normalized subject to the uncertainty of the models and are supervised by an incrementally adaptive statistical tolerance band. Upon violation of this tolerance band, a fault alarm is triggered. The improved FD methods comes with two the main novelty aspects: (1) the development of an enhanced optimization scheme for fuzzy systems training which builds upon the SparseFIS (Sparse Fuzzy Inference Systems) approach and enhances it by embedding genetic operators for escaping local minima⟶a hybrid memetic (sparse) fuzzy modeling approach, termed as GenSparseFIS. (2) The design and application of adaptive filters on the residual signals, over time, in a sliding-window based incremental/decremental manner to smoothen the signals and to reduce the false positive rates. This gives us the freedom to tighten the tolerance band and thus to increase fault detection rates by holding the same level of false positives. In the results section, we verify that this increase is statistically significant in the case of adaptive filters when applying the proposed concepts onto four real-world scenarios (three different ones from rolling mills, one from engine test benches). The hybridization of sparse fuzzy inference systems with genetic algorithms led to the generation of more high quality models that can in turn be used in the FD process as residual generators. The new hybrid sparse memetic modeling approach also achieved fuzzy systems leading to higher fault detection rates for some scenarios.

A quadratic polynomial signal model and fuzzy adaptive filter for frequency and parameter estimation of nonstationary power signals

Accurate estimation of amplitude, phase and frequency of a sinusoid in the presence of harmonics/inter harmonics and noise plays an important role in a wide variety of power system applications, like protection, control and state monitoring. With this objective, the paper presents a novel hybrid approach for the accurate estimation of dynamic power system frequency, phasor and in addition to suppressing the effect of harmonics/interharmonics and noise in the voltage and current signals. The algorithm assumes that the current during a fault occurring on a power system consists of a decaying dc component, and time variant fundamental and harmonic phasors. For accurate estimation of fundamental frequency, phasor, decaying dc and ac components in the fault current or voltage signal, the algorithm uses a quadratic polynomial signal model and a fuzzy adaptive ADALINE filter with a modified Gauss–Newton algorithm. Extensive study has been carried out to demonstrate the performance analysis and fast convergence characteristic of the proposed algorithm. The proposed method can also be implemented for accurate estimation of dynamic variations in the amplitude and phase angles of the harmonics and inter harmonics mixed with high noise conditions.

A short-delay video stabilizer using an adptive fuzzy non-causal filter

Digital video stabilization (DVS) allows acquiring video sequences without disturbing jerkiness, removing unwanted camera movements. A good DVS should remove the unwanted camera movements while maintains the intentional camera movements. In this article, we propose a novel real-time DVS algorithm with a short-delay. The proposed DVS compensates the camera jitters applying an adaptive fuzzy filter on the global motion of video frames. The adaptive filter is an infinite impulse response (IIR) non-causal filter which is tuned by a fuzzy system adaptively to the camera motion characteristics. The fuzzy system itself is also tuned during operation according to the amount of camera jitters. The fuzzy system uses two inputs which are quantitative representations of the unwanted and the intentional camera movements. The global motion of video frames is estimated based on the block motion vectors which resulted by video encoder during motion estimation operation. Experimental results indicate a good performance for the proposed algorithm.

Superior foetal electrocardiogram signal elicitation using a novel artificial intelligent Bayesian methodology

Innumerable casualties due to intrauterine hypoxia are a major worry during prenatal phase besides advanced patient monitoring with latest science and technology. Hence, the analysis of foetal electrocardiogram (fECG) signals is very vital in order to evaluate the foetal heart status for timely recognition of cardiac abnormalities. Regrettably, the latest technology in the cutting edge field of biomedical signal processing does not seem to yield the desired quality of fECG signals required by physicians, which is the major cause for the pathetic condition. The focus of this work is to extort non-invasive fECG signal with highest possible quality with a motive to support physicians in utilizing the methodology for the latest intrapartum monitoring technique called STAN (ST analysis) for forecasting intrapartum foetal hypoxia. However, the critical quandary is that the non-invasive fECG signals recorded from the maternal abdomen are affected by several interferences like power line interference, baseline drift interference, electrode motion interference, muscle movement interference and the maternal electrocardiogram (mECG) being the dominant interference. A novel hybrid methodology called BANFIS (Bayesian adaptive neuro fuzzy inference system) is proposed. The BANFIS includes a Bayesian filter and an adaptive neuro fuzzy filter for mECG elimination and non-linear artefacts removal to yield high quality fECG signal. Kalman filtering frame work has been utilized to estimate the nonlinear transformed mECG component in the abdominal electrocardiogram (aECG). The adaptive neuro fuzzy filter is employed to discover the nonlinearity of the nonlinear transformed version of mECG and to align the estimated mECG signal with the maternal component in the aECG signal for annulment. The outcomes of the investigation by the proposed BANFIS system proved valuable for STAN system for efficient prediction of foetal hypoxia.

An Efficient Approach for Removal of Impulse Noise from Highly Corrupted Images

In this paper, a method based on adaptive fuzzy filter is used to denoise image corrupted by impulse noise. The algorithm is proposed for restoration of grayscale as well as color images which are highly corrupted by salt and pepper noise. The algorithm detects level of corruption for each pixel using triangular fuzzy membership function to ensure the replacement of noisy pixels based on degree of corruption. Besides providing ease of computation here we use fully adaptive and automatically adjustable threshold value. Final result shows better performance in the impulse noise reduction over standard images relative to the conventional median filtering approach in terms of both noise reduction and fine detail preservation.