Adaptive Noise Filtering Research Articles

Closed-loop state of charge estimation of Li-ion batteries based on deep learning and robust adaptive Kalman filter

The state of charge (SOC) is among the most crucial monitoring states in battery management systems. To accurately and robustly estimate the battery SOC, a closed-loop SOC estimation method based on deep neural networks is proposed in this study. Initially, a hybrid deep learning model, TGMA, is established by combining a temporal convolutional network (TCN) with a gated recurrent unit (GRU) and incorporating a multi-head self-attention mechanism (MHA) to estimate SOC. Subsequently, to further enhance the accuracy of estimating the battery SOC and the ability to resist outliers, the pre-estimated SOC from the deep learning model TGMA is used as the noisy observation in the Kalman filter, and robust factor and noise adaptive filtering are introduced into the Kalman filter, proposing a closed-loop estimation method based on deep learning and robust adaptive Kalman filter (RAKF). Finally, through comparison with other similar methods, the proposed TGMA-RAKF method is validated to have higher accuracy, generalization, and robustness against outliers under different operating conditions and temperatures, with root mean square error, mean absolute error, and maximum absolute error all less than 1 %, 1 %, and 2.5 % respectively.

Digital neutron-gamma discrimination algorithm using adaptive noise filter

Pulse shape discrimination of a liquid scintillator-based detector (BC501A) has been studied using a digital approach. The objective of this work is to develop a new neutron-gamma discrimination algorithm to improve the discrimination efficiency. The new approach is a combination of two parts. The first part uses an adaptive Sallen-Key filter to reduce noise in the pulse followed by traditional approaches of either charge comparison or time over threshold. The approach of adaptive Sallen-Key combined with time over threshold was found to have a much better capability to discriminate neutron events from that of gamma. The pulse shape discrimination performance obtained for a 5 inches by 5 inches (length by diameter) liquid scintillator detector using the combination of the adaptive Sallen-Key filter with time over threshold is found to be significantly higher compared to that obtained using traditional methods. Moreover, this combination has the potential for real-time hardware implementation.

Vibration Analysis for Turning-Milling Process Condition Monitoring using Short-Time Fourier Transform Enhanced by Empirical Mode Decomposition

Turn-milling process is widely applied in industries that provides advantages for machining large-diameter mechanical parts with high speed, reducing cutting temperature, which in turn decreases tool wear. However, it needs to monitor the turn-milling process for preventing the onset of chatter vibration during operation and the chatter induces negative effects. Vibration analysis is one of the ways for monitoring it. However, acquired vibrations should be denoised from noises, wherein the conventional signal filter may have defiance to do that. This paper presents the utilization of the empirical mode decomposition (EMD) method as an efficient and adaptive noise filter. The Short-Time Fourier Transform (STFT) improvement using EMD is then used for monitoring turn-milling process conditions in the energy-time-frequency domain. The results showed that the reconstructed signal was quite impressive compared to the raw signal and the oscillation of the filtered signal was clearer than the raw signal. The improvement of the filtered signals was proved by the kurtosis index and spectral kurtosis. The improved STFT using EMD showed a significant spectrum with high resolution compared to conventional STFT. The energy density could be observed clearly in the machining characteristic frequencies with an improvement of about 10-100 times larger. The proposed method is therefore effectively applied to monitor the turn-milling condition.

Noise adaptive filtering model integrating spatio-temporal feature for soft sensor

Due to uncertain production conditions and external disturbances, there are a lot of noise in the industrial process dataset. Traditional denoising models can only solve a single noise problem. However, the noise in industrial process data has diversity and inhomogeneity. Therefore, the noise adaptive filtering (NAF) model integrating spatio-temporal feature (ST) (ST-NAF) is proposed for the soft sensor, which achieves a balance between noise and data features to reduce the influence of noise on soft sensor results. The gated recurrent unit extracts temporal features of the industrial process data. Then, the dynamically gated graph attention extracts the interaction relationship between monitoring points to obtain spatio-temporal features. Moreover, the NAF converts time-domain features into frequency-domain features, and adaptively selects frequency-domain components with important information through the noise filtering matrix. The selected frequency-domain components are converted to the temporal domain to obtain the denoised spatio-temporal features. Finally, the self-attention learns the dynamic relationships between denoised spatio-temporal features and output variables to predict the key indicator. Compared with other denoising soft sensor models in a public dataset and an actual industrial process dataset, the ST-NAF achieves state-of-the-art results. And the prediction accuracy of the proposed model improved by 4.5% and 10.0% in terms of R2, respectively. Furthermore, the experimental results show that the ST-NAF can adaptively select useful information from industrial noise data to achieve accurate prediction of key indicators.

A new hybrid algorithm for intelligent detection of sudden decline syndrome of date palm disease

Date palm is an important domestic cash crop in most countries. Sudden Decline Syndrome (SDS) causes a huge loss to the crop both in quality and quantity. The literature reports the significance of early detection of disease towards preventive measures to improve the quality of the crop. The number of prevailing detection methods limits to consideration of a certain aspect of disease identification. This study proposes a new hybrid fuzzy fast multi-Otsu K-Means (FFMKO) algorithm integrating the date palm image enhancement, robust thresholding, and optimal clustering for significant disease identification. The algorithm adopts a multi-operator image resizing cost function based on image energy and the dominant color descriptor, the adaptive Fuzzy noise filter, and Otsu image thresholding combined with K-Means clustering enhancements. Besides, we validate the process with histogram equalization and threshold transformation towards enhanced color feature extraction of date palm images. The algorithm authenticates findings on a local dataset of 3293 date palm images and, on a benchmarked data set as well. It achieves an accuracy of 94.175% for successful detection of SDS that outperforms the existing similar algorithms. The impactful findings of this study assure the fast and authentic detection of the disease at an earlier stage to uplift the quality and quantity of the date palm and boost the agriculture-based economy.

Recognizing Brain Tumors Using Adaptive Noise Filtering and Statistical Features

The human brain, primarily composed of white blood cells, is centered on the neurological system. Incorrectly positioned cells in the immune system, blood vessels, endocrine, glial, axon, and other cancer-causing tissues, can assemble to create a brain tumor. It is currently impossible to find cancer physically and make a diagnosis. The tumor can be found and recognized using the MRI-programmed division method. It takes a powerful segmentation technique to produce accurate output. This study examines a brain MRI scan and uses a technique to obtain a more precise image of the tumor-affected area. The critical aspects of the proposed method are the utilization of noisy MRI brain images, anisotropic noise removal filtering, segmentation with an SVM classifier, and isolation of the adjacent region from the normal morphological processes. Accurate brain MRI imaging is the primary goal of this strategy. The divided section of the cancer is placed on the actual image of a particular culture, but that is by no means the last step. The tumor is located by categorizing the pixel brightness in the filtered image. According to test findings, the SVM could partition data with 98% accuracy.

Self-training algorithm based on density peaks combining globally adaptive multi-local noise filter

Self-training algorithm highlights the speed of training a supervised classifier through small labeled samples and large unlabeled samples. Despite its long considerable success, self-training algorithm has suffered from mislabeled samples. Local noise filters are designed to detect mislabeled samples. However, two major problem with this kind of application are: (a) Current local noise filters have not treated the spatial distribution of the nearest neighbors in different classes in much detail. (b) They are being disadvantaged when mislabeled samples are located in overlapping areas of different classes. Here, we develop an integrated architecture – self-training algorithm based on density peaks combining globally adaptive multi-local noise filter (STDP-GAMLNF), to improve detecting efficiency. Firstly, the spatial structure of the data set is revealed by density peak clustering, and it is used for empowering self-training to label unlabeled samples. In the meantime, after each epoch of labeling, GAMLNF can comprehensively judge whether a sample is a mislabeled sample from multiple classes or not, and it will reduce the influence of edge samples effectively. The corresponding experimental results conducted on eighteen UCI data sets demonstrate that GAMLNF is not sensitive to the value of the neighbor parameter k, and it is capable of adaptively finding the appropriate number of neighbors of each class.

Extended Kalman Filter (EKF) Design for Vehicle Position Tracking Using Reliability Function of Radar and Lidar.

Detection and distance measurement using sensors is not always accurate. Sensor fusion makes up for this shortcoming by reducing inaccuracies. This study, therefore, proposes an extended Kalman filter (EKF) that reflects the distance characteristics of lidar and radar sensors. The sensor characteristics of the lidar and radar over distance were analyzed, and a reliability function was designed to extend the Kalman filter to reflect distance characteristics. The accuracy of position estimation was improved by identifying the sensor errors according to distance. Experiments were conducted using real vehicles, and a comparative experiment was done combining sensor fusion using a fuzzy, adaptive measure noise and Kalman filter. Experimental results showed that the study’s method produced accurate distance estimations.

Decision support system for breast lesions via dynamic contrast enhanced magnetic resonance imaging.

The presented study aims to design a computer-aided detection and diagnosis system for breast dynamic contrast enhanced magnetic resonance imaging. In the proposed system, the segmentation task is performed in two stages. The first stage is called breast region segmentation in which adaptive noise filtering, local adaptive thresholding, connected component analysis, integral of horizontal projection, and breast region of interest detection algorithms are applied to the breast images consecutively. The second stage of segmentation is breast lesion detection that consists of 32-class Otsu thresholding and Markov random field techniques. Histogram, gray level co-occurrence matrix and neighboring gray tone difference matrix based feature extraction, Fisher score based feature selection and, tenfold and leave-one-out cross-validation steps are carried out after segmentation to increase the reliability of the designed system while decreasing the computational time. Finally, support vector machines, k- nearest neighbor, and artificial neural network classifiers are performed to separate the breast lesions as benign and malignant. The average accuracy, sensitivity, specificity, and positive predictive values of each classifier are calculated and the best results are compared with the existing similar studies. According to the achieved results, the proposed decision support system for breast lesion segmentation distinguishes the breast lesions with 86%, 100%, 67%, and 85% accuracy, sensitivity, specificity, and positive predictive values, respectively. These results show that the proposed system can be used to support the radiologists during a breast cancer diagnosis.

High performance compensation using an adaptive strategy for real-time hybrid simulation

Abstract Real-time hybrid simulation (RTHS) is an innovative and versatile technique for evaluating the dynamic responses of structural and mechanical systems. This technique separates the emulated system into numerical and physical substructures, which are analyzed by computers and loaded in laboratories, respectively. Ensuring the boundary conditions between the two substructures through a transfer system plays a significant role in obtaining reliable and accurate testing results. However, measurement noise and the delay between commands and responses due to the dynamic performance of the transfer system are inevitable in RTHS. To address these issues and to achieve outstanding tracking performance and excellent robustness, this paper proposes an adaptive Kalman-based noise filter and an adaptive two-stage delay compensation method. In particular, in the novel noise filter strategy, adaptive inverse compensation with parameters updated by the least squares method is adopted to accommodate the amplitude and phase errors induced by a traditional Kalman filter. In the proposed delay compensation method, classic polynomial extrapolation and an adaptive inverse strategy are employed for coarse and fine compensation, respectively. Virtual RTHS on a benchmark problem reveals the satisfactory tracking performance and robustness of the proposed methods. Comparisons with polynomial extrapolation and single-stage adaptive compensation indicate the superiority of the proposed two-stage delay compensation method.

A New Method of Adaptive Filtering and Wavelet Transform to Filter Baseline Shift

This article describes how a baseline shift is a slow change in the orientation of the baseline over time. It often exists in the process of signals sampling, e g. ECG, TLC and so on. In order to filter the baseline shift, a combination method of wavelet transform and an adaptive filter is proposed. First, the wavelet transform method is used to decompose the original ECG signal and the high-frequency components are used to as Reference input data. Then, a new adaptive filtering algorithm, P-LMS, based on the power function, is proposed to conduct adaptive noise filtering. Finally, compared with the traditional normalized least mean square algorithm (NLMS), the proposed algorithm has the characteristics of faster convergence and the effect is better. Experiments on the ECG signal in MIT-BIH database, using the method of combining P-LMS and a wavelet transform is verified to effectively filter the baseline shift and maintain the geometric characteristics of the ECG signal.

Analyzing gravity anomaly variations before the 2016 Ms 6.4 earthquake in Menyuan, Qinghai with an interpolation/cutting potential field separation technique

We evaluated 2011–2015 mobile relative gravity data from the Hexi monitoring network that covers the epicenter of the 2016 Menyuan Ms6.4 earthquake, Qinghai Province, China and examined the spatiotemporal characteristics of the gravity field at the focal depth. In addition, we assessed the regional gravity field and its variation the half-year before the earthquake. We use first different interpolation algorithms to build a grid for the gravity data and then introduce potential field interpolation–cutting separation techniques and adaptive noise filtering. The results suggest that the gravity filed at the focal depth of 11.12 km separated from the total gravity field at about–400~150 × 10−8 m/s2 in the second half of 2015, which is larger than that in the same period in 2011 to 2014 (±30 × 10−8 m/s2). Moreover, at the same time, the gravity field changed fast from September 2014 to May 2015 and May 2015 to September 2015, reflecting to some extent material migration deep in the crust before the Menyuan earthquake.

Background Registration-Based Adaptive Noise Filtering of LWIR/MWIR Imaging Sensors for UAV Applications.

Unmanned aerial vehicles (UAVs) are equipped with optical systems including an infrared (IR) camera such as electro-optical IR (EO/IR), target acquisition and designation sights (TADS), or forward looking IR (FLIR). However, images obtained from IR cameras are subject to noise such as dead pixels, lines, and fixed pattern noise. Nonuniformity correction (NUC) is a widely employed method to reduce noise in IR images, but it has limitations in removing noise that occurs during operation. Methods have been proposed to overcome the limitations of the NUC method, such as two-point correction (TPC) and scene-based NUC (SBNUC). However, these methods still suffer from unfixed pattern noise. In this paper, a background registration-based adaptive noise filtering (BRANF) method is proposed to overcome the limitations of conventional methods. The proposed BRANF method utilizes background registration processing and robust principle component analysis (RPCA). In addition, image quality verification methods are proposed that can measure the noise filtering performance quantitatively without ground truth images. Experiments were performed for performance verification with middle wave infrared (MWIR) and long wave infrared (LWIR) images obtained from practical military optical systems. As a result, it is found that the image quality improvement rate of BRANF is 30% higher than that of conventional NUC.

Adaptive fuzzy inference system based directional median filter for impulse noise removal

Noise filtering in presence of important image detail information is considered as challenging task in imaging applications. Use of fuzzy logic based techniques is capturing more focus since last decade to deal with these challenges. In order to tackle conflicting issues of noise smoothing and detail preservation, this paper presents a novel approach using adaptive fuzzy inference system for random valued impulse noise detection and removal. The proposed filter uses the intensity based directional statistics to construct adaptive fuzzy membership functions which plays an important role in fuzzy inference system. Fuzzy inference system constructed in this way is used by the noise detector for accurate classification of noisy and noise-free pixels by differentiating them from edges and detailed information present in an image. After classification of pixels, noise adaptive filtering is performed based on median and directional median filter using the information provided by the noise detector. Simulation results based on well known quantitative measure i.e., peak-signal-to-noise ratio (PSNR) show the effectiveness of proposed filter.

EXTRACTION OF TEXTURE FEATURES BY USING GABOR FILTER IN WHEAT CROP DISEASE DETECTION

Like country India, there are so many people depending upon agriculture. In this area, many farmers don’t know about new diseases which are impacting on their farm. As the disease changes, the disease control policy also changes. So many farmers have very sharp observation on crop diseases, but whenever there is new diseases fall on crops then problems occur. Climate also changes instantly many of times, because of such reasons farmers unable to understand various diseases. If farmer unable to predict that diseases quickly then it will affect life of crops. Indirectly it gets affects on total productivity of farm. As we are well known about that world facing lot of problems due rapid growth in population. So our goal is to increase agricultural productivity using image processing technology which can help farmer in great extent [7]. In this research work, we are trying that crop disease using Artificial neural network (ANN) which work very effectively. First of all, we have provided an digital image which is taken by digital camera. That image given to Gaussian filter firstly then transferred to adaptive median filter to filter out noise present inside image. Gaussian filter removes Gaussian noise which is present inside image. Adaptive noise filter removes impulsive noise which is present inside image. Also it will reduce distortions which are present inside images. Then image transferred to segmentation part. In image segmentation we have choose CIELAB color space method to extract color components properly. For segmentation we have used Gabor filter. After this we distinguish crop diseases on the basis of texture features which are extracted by Gabor filter [6].

Efficiency comparison of optimum and adaptive methods for multichannel signal filtering

In this paper, two methods for estimating the efficiency of the multichannel reception of a noisy signal have been compared. The first method is based on sliding optimum estimates of both covariance matrices and response vectors of a useful signal in receiving channels. The second method is based on adaptive noise filtering according to the Widrow–Hoff stochastic gradient descent algorithm. The second method is only shown to provide the maximum reception efficiency if there is no useful signal in additional receiving channels.

A Noise Adaptive Approach to Impulse Noise Detection and Reduction

A noise adaptive filter has been proposed in this study aiming to estimate the original image pixel values in the presence of impulse noise in monochromatic images. The proposed filter approach is noise adaptive that as the percentage of noise density increases in the image, the size of neighborhood in filtering window is also increased. Proposed approach comprises of two stages, one is impulse noise detection and the other is impulse noise reduction or cancellation. First stage is based on median and mean distance and thresholding whereas the second stage is based on reconstruction of the image using the values of neighboring pixels of the pixel under consideration detected as contaminated pixel by first stage. Reconstruction is done by estimating reference values using uncorrupted pixels in the neighborhood of pixel under consideration. The proposed method has been compared to various existing methods by using peak signal to noise ratio (PSNR) for measuring the objective quality strength. To measure the impulse noise detection the method has also been compared with other existing methods using the ratio of mis etection (MD) and false detection (FD).DOI: http://dx.doi.org/10.3126/njst.v15i1.12016 Nepal Journal of Science and TechnologyVol. 15, No.1 (2014) 67-76

AutoGAD: An Improved ICA-Based Hyperspectral Anomaly Detection Algorithm

This work extends the emerging field of hyperspectral imagery (HSI) anomaly detectors and employs independent component (IC) analysis (ICA) to identify anomalous pixels. Using new techniques to fully automate feature extraction, feature selection, and anomaly pixel identification, an Autonomous Global Anomaly Detector has been developed for employment in an operational environment for real-time processing of HSI. Dimensionality reduction, which is the initial feature extraction prior to ICA, is effected through a geometric solution that estimates the number of retained principal components. The solution is based on the theory of the shape of the eigenvalue curve of the covariance matrix of spectral data containing noise. This research presents two new features, namely, potential anomaly signal-to-noise ratio and maximum pixel score, both of which are computed for each of the ICs to create a new 2-D feature space where the overlap between anomaly and nonanomaly classes is reduced. After anomaly feature selection, adaptive noise filtering is applied iteratively to suppress the background. Finally, a zero-detection histogram method is applied to the smoothed signals to identify anomaly locations to the user. After the algorithm is fully developed, a set of designed experiments are conducted to identify a reasonable set of input parameters.

Block-based adaptive noise filtering for H.264/AVC compression

Noise corruption degrades the R-D performance of H.264/AVC encoders because of a loss of image quality and an increase in bit-rate. Previous noise reduction techniques attempt to avoid the loss of image quality, but they do not take the effect of noise on compression efficiency into serious consideration. In this paper, the noise effect on an H.264/AVC encoder is analyzed and a blockbased adaptive denoising algorithm is proposed. The proposed algorithm applies the denoising operation adaptively to blocks which do not include image details that need to be preserved. Furthermore, the proposed noise filter is not applied when the quantization parameter is larger than a pre-defined threshold because noise is filtered by a quantization operation in the encoder. As a result, both residual and information bits are decreased by the proposed noise filtering operation. Edge blurring caused by the filtering operation is also reduced as a result of the adaptive application of the filtering operation. Compared to cases where filtering is not used, the proposed method shows a maximum PSNR enhancement of 1.98 dB and a maximum bitrate reduction of 41%.

PERFORMANCE ANALYSIS OF TDLMS FILTER FOR IMAGE ENHANCEMENT

In this study, the filtering performance of adaptive image noise filter based Two Dimensional Least Mean Squares (TDLMS) method was analyzed experimentally. For this purpose, an interface realizing the method was used to investigate the effect of the initial values, step size, and filter dimensions on performance. Comparative evaluations of the performance of the algorithm for test parameters were presented both visually and numerically. The experimental results show that the filtering performance is increased for high noise levels, while it is degraded for lower noise levels.