Adaptive Filtering Technique Research Articles

Parallel Residual Stacked Bidirectional Long Short-Term Memory Network Optimized with Chameleon Swarm Optimization Algorithm for Time-Series Sensor Data

Internet of Things (IoT) allows the linking of disparate devices via wireless and mobile communication technology. The accuracy and integrity of data often determine IoT service quality. An acquired data, however, will be abnormal due to the harsh surroundings or device flaws. As a result, an efficient means of identifying anomalies is critical for ensuring excellent service. This article discusses Parallel Residual Stacked Bidirectional Long Short-Term Memory Network optimized with Chameleon Swarm Optimization Algorithm for Time-Series Sensor Data (PRSBi LSTM-CSOA-AD-TSSD) is proposed for anomaly identification for Time Sequence Sensor Data. Initially, the time sequence sensor data are acquired from the Yahoo Webs cope S5 dataset. Then the data are preprocessed using Sparsity Aware Robust Normalized Subb and Adaptive Filtering technique. Then the pre-processed data are given to the proposed PRSBiLSTM Network to detect the anomalous Time-Series Sensor Data. This PRSBiLSTM Network classifies the preprocessed Time-Series Sensor Data into normal and anomaly. Then the PRSBiLSTM Network is optimized using the Chameleon Swarm Optimization Algorithm (CSOA) which precisely classifies the anomalistic of the Time-Series Sensor Data. The proposed algorithm is implemented in Python and performance metrics, such as F1-score, precision, sensitivity, specificity, Error rate, accuracy, ROC, and computational time are analyzed to identify the performance of the proposed PRSBiLSTM-CSOA-AD-TSSD approach. The proposed approach provides 22.41%, 25.51%, and 21.65% higher accuracy, 24.56%, 23.36%, and 25.98% lower error rate, 22.59%, 22.29%, and 25.67% lower computational time analyzed with existing methods, such as LSTM-AD-TSSD, Unsupervised TCN-AE-based TCN-ADTSSD, and CNN-AD-TSSD.

Social media mining for business inequality: modified spotted hyena optimization based random forest

Businesses may advertise, communicate with customers and research industry trends on social media today. Due to the massive amount of data generated on social media platforms, organizations struggle to acquire valuable data and fix marketing inequalities. To combine Modified Spotted Hyena Optimization with Random Forest (MSHO-RF) for classification in social media mining for a business inequality study. Data-driven solutions to inequality in operations and customer interactions should increase prediction accuracy, lower feature dimensionality and extract actionable insights. We begin by collecting social media mining for business inequality data. Afterward, Adaptive mean filter (AMF) techniques were used to initialize the data preprocessing. With the use of Linear Discriminant Analysis (LDA), we examined the most suitable arrangement of feature extraction. This research offers a unique method MSHO-RF, to deal with the problem of business inequality using social media mining. The MSHO-RF algorithm had higher results than compared to a variety of performance parameters, featuring a 94% of F1-score, a precision rate of 97%, an accuracy rate of 98% and recall rates of 95%. Our findings suggest that firms might benefit from a framework for leveraging social media data to promote more equitable and sustainable practices if they combine MSHO-RF with social media mining for business inequality. This approach benefits companies and their stakeholders by fostering an open, data-driven ecosystem.

Blind source separation approach for multi-channel jamming suppression in cognitive radio networks using fastica and adaptive filtering techniques

Blind source separation approach for multi-channel jamming suppression in cognitive radio networks using fastica and adaptive filtering techniques

Performance Evaluation of 2D and 3D Beam and Channel Tracking Using Adaptive Filtering Techniques

Performance Evaluation of 2D and 3D Beam and Channel Tracking Using Adaptive Filtering Techniques

Estimation of wiener nonlinear systems with measurement noises utilizing correlation analysis and Kalman filter

AbstractThis paper is concerned with parameter estimation of Wiener systems with measurement noises employing correlation analysis method and adaptive Kalman filter. The presented Wiener system consists of two series blocks, that is, a dynamic block represented by auto‐regressive moving average (ARMA) model, and static nonlinear block established by neural fuzzy model. Aim at estimating separately the two blocks, the separable signals are introduced. First, applying the separable signals to decouple the identification of linear dynamic block from that of static nonlinear block, then ARMA model parameters are estimated employing correlation function‐based least squares principle. Moreover, aiming at handle with error caused by colored measurement noise, adaptive Kalman filter technique and cluster method are introduced to estimate parameter of the nonlinear block and noises model, enhancing parameter estimation precision. The accuracy and applicability of estimated scheme presented are verified through numerical simulation and nonlinear process, the results demonstrate that it is feasible for estimating the Wiener systems in the presence of colored measurement noises.

DESIGN OF HYBRID ADAPTIVE MODEL FOR IMAGE DENOISING

This thesis work proposes a new denoising algorithm based on Particle filter and Wavelet (Curvelet) transform combination, particle filter generates weights through SIR algorithm to cancel the interference of noise present in the image, while curvelet transform is used to shrink the remaining segments of noise, so this method can both remove image blurr and maintain good texture as well. The PF+Clet Image Denoiser is successfully designed and implemented, which is a new approach in image enhancement and Interference cancellation. This thesis concludes that it is quite efficient algorithm among other adaptive filtering techniques. Experimental results also show that proposed algorithm performs extremely well when noise density is increased, as obtained image is completely visible. This approach comprises of generation of particles by performing weight normalization, resampling and update state. Therefore, for large number of particles execution time is more when compared to other adaptive filtering approach. Key Words: Particle Filter(PF), Curvelet(CLet), Wavelet(Wlet), Peak signal to noise Ratio(PSNR), Deblurring.

Development of Trio Optimal Feature Extraction Model for Attention-Based Adaptive Weighted RNN-Based Lung and Colon Cancer Detection Framework Using Histopathological Images

Due to the combination of genetic diseases as well as a variety of biomedical abnormalities, the fatal disease named cancer is caused. Colon and lung cancer are regarded as the two leading diseases for disability and death. The most significant component for demonstrating the best course of action is the histopathological identification of such malignancies. So, in order to minimize the mortality rate caused by cancer, there is a need for early detection of the aliment on both fronts accordingly. In this case, both the deep and machine learning techniques have been utilized to speed up the detection process of cancer which may also help the researchers to study a huge amount of patients over a short period and less loss. Hence, it is highly essential to design a new lung and colon detection model based on deep learning approaches. Initially, a different set of histopathological images is collected from benchmark resources to perform effective analysis. Then, to attain the first set of features, the collected image is offered to the dilated net for attaining deep image features with the help of the Visual Geometry Group (VGG16) and Residual Neural Network (ResNet). Further, the second set of features is attained by the below process. Here, the collected image is given to pre-processing phase and the image is pre–pre-processed with the help of Contrast-limited Adaptive Histogram Equalization (CLAHE) and filter technique. Then, the pre-processed image is offered to the segmentation phase with the help of adaptive binary thresholding and offered to a dilated network that holds VGG16 and ResNet and attained the second set of features. The parameters of adaptive binary thresholding are tuned with the help of a developed hybrid approach called Sand Cat swarm JAya Optimization (SC-JAO) via Sand Cat swarm Optimization (SCO) and JAYA (SC-JAO). Finally, the third set of features is attained by offering the image to pre-processing phase. Then, the pre-processed image is offered to the segmentation phase and the image is a segmented phase and features are tuned by developed SC-JAO. Further, the segmented features are offered to attain the textural features like Gray-Level Co-Occurrence Matrix (GLCM) and Local Weber Pattern (LWP) and attained the third set of features. Then, the attained three different sets of features are given to the optimal weighted feature phase, where the parameters are optimized by the SC-JAO algorithm and then given to the disease prediction phase. Here, disease prediction is made with the help of Attention-based Adaptive Weighted Recurrent Neural Networks (AAW-RNN), and their parameters are tuned by developed SC-JAO. Thus, the developed model achieved an effective lung and colon detection rate over conventional approaches over multiple experimental analyses.

Motion-robust anterior–posterior imaging ballistocardiography for non-contact heart rate measurements

Imaging ballistocardiography (iBCG) is a novel technique that utilizes video-based technology to measure heart rate (HR). This innovative method is based on the detection of subtle mechanical head movements that are caused by heartbeats and does not require direct physical contact with the body. However, the iBCG signals can be easily contaminated by rigid and nonrigid motion artifacts. In this paper, we propose a novel method for iBCG motion artifact removal (MAR). First, anterior–posterior (Z-axis) signals are reconstructed as the raw iBCG signals from two facial regions of interest (ROIs) according to the law of perspective. Next, the adaptive filtering technique is utilized to remove rigid motion artifacts from raw iBCG signals with respect to the motion reference of the X-axis traces. The rigid-motion-free iBCG signals in each ROI are further dimensionally compressed via principal component analysis (PCA). Then, the principle components (PCs) from the two regions of interest (ROIs) are subjected to canonical correlation analysis (CCA) to remove residual non-rigid motion artifacts that have low spatial correlations. Finally, the target HR value is identified by selecting the canonical variates (CVs) with the highest power spectrum amplitude. Experimental results on two publicly available databases and a in-house real-world database demonstrate that the proposed method achieves the best performance compared to existing iBCG methods during most cases, especially in the presence of motion artifacts. The study provides a feasible scheme for practical applications of iBCG HR measurements.

Sex estimation from Thai hand radiographs using convolutional neural networks

Manual analysis of hand radiographs for sex estimation is prone to biases and errors. This study addresses the need for automated methods by exploring the potential of Convolutional Neural Networks (CNNs) to accurately identify individual sex from Thai hand radiographs, overcoming limitations in data availability and variable quality. To improve dataset quality, we applied contrast limited adaptive histogram equalization (CLAHE) and Gaussian blur filter techniques to Thai hand radiographs from 385 male and 788 female individuals. We split these images into training, validation, and test sets. We also applied image augmentation techniques to increase the number of radiographs in the training dataset. Seven CNN models were trained, validated, and evaluated on 100 unseen male and female radiographs each. Among these models, the InceptionResNetV2 architecture demonstrated superior performance, achieving an accuracy of 87.50 % and an F1-Score of 86.91 %. Notably, this model utilized information from the 2nd to the 5th metacarpal bone and proximal phalanges in males, and from the 2nd metacarpal bone in females. Our findings provide a solid foundation for sex estimation from Thai hand radiographs, highlighting the power of CNNs in mitigating challenges associated with data quantity and quality. By automating the sex estimation process using CNNs, forensic analysis can benefit from enhanced accuracy and objectivity, enabling faster and more reliable sex assessment. We envisage that future research will build upon these findings to further improve the performance of sex estimation, contributing to advancements in forensic analysis and facilitating more effective utilization of Thai hand radiographs for sex estimation.

A Fast Variational Bayesian Adaptive Extended Kalman Filter for Robust Underwater Direction-of-Arrival Tracking

The underwater direction-of-arrival (DOA) tracking using hydrophone array is an important research subject in passive sonar signal processing. To deal with the unknown measurement noise results from underwater environmental noise, the existing variational Bayesian adaptive extended Kalman filter (VB-AEKF) technique for robust underwater DOA tracking jointly estimates the measurement noise covariance matrix and the bearing angle of a target. The iteration process and the nonlinear measurement model result in significant computational complexity of the VB-AEKF. By deriving computational process which is equivalent to VB-AEKF but more computationally efficient, a fast variational Bayesian adaptive extended Kalman filter (FVB-AEKF) for robust DOA tracking is proposed. The proposed method is tested via Monte Carlo simulations of an underwater DOA tracking scenario and an experiment in the South China Sea in July 2021. The results of the simulations and the experiment verified the accuracy and the computational efficiency of the FVB-AEKF.

A novel state of health estimation approach based on polynomial model for lithium-ion batteries

Energy management systems are one of the most critical components in a battery unit's safe and efficient operation. Many measurable or calculable parameters, such as voltage, current, capacity, and internal resistance, are considered when designing energy management systems. Parameters that cannot be measured or are costly to measure, such as battery state of charge (SOC) or battery health state (SOH), can be determined by estimation. SOH, which can be used as both an input and an output parameter for EMS, is an essential parameter for the long-life and high-efficiency use of the battery. Two different approaches, experimental-based and model-based, are used for SOH estimation. Experimental-based methods need all the real-time data of the batteries, which is caused the requirement of a very long time and data storage. The adaptive filtering technique, which is a model-based technique, requires a complete and accurate model of the battery. The nonlinear electrochemical structures of batteries make modeling difficult. On the other hand, optimization-based methods, a data-driven technique, need very high processing power to find the optimal result. In addition, separate data is needed for training and testing in machine learning-based methods and requires high processing power. Contrary to these disadvantages, high-accuracy predictions can be made using low processing power and very little data in the experimental curve fitting technique. In this study, experimental and curve-fitting-based SOH estimation is proposed by using polynomial-based system identification techniques. Polynomial-based identification methods can model the system with high accuracy, low cost, and low processing power. Battery degradation dataset belonging to Oxford University is used. In the aforementioned dataset, the general model inference is made using only %1 of the entire cycle life of the battery. Obtained proposed model's results were compared with different methods in the literature in terms of RMSE, and its superiority was demonstrated.

Partial Discharge Location Algorithm Based on Total Least-Squares With Matérn Kernel in Cable Systems

Partial discharge (PD) location techniques are a useful tool for condition monitoring of electrical apparatus in power systems. However, the noisy PD measurements may significantly degrade the performance of location algorithms. This article deals with the PD location problem by using adaptive filtering techniques. Heretofore, scarce literature focuses on addressing the PD location based on such method. A novel adaptive algorithm, termed as total least-squares (TLS)-Matérn kernel (TLS-MK), is proposed. Benefiting from the merits of the Matérn kernel, the TLS model can effectively suppress the noise from the direct and reflected waves of the PD source. Meanwhile, the TLS-MK algorithm is used to estimate the time difference, which is used in the PD location. Moreover, the convergence behavior of the TLS-MK algorithm is analyzed. Simulations and experiments show that the proposed algorithm can enhance the location accuracy as compared to state-of-the-art methods for various PD signals.

Automatic Adaptive Filtering Technique for Removal of Impulse Noise Using Gabor Filter

Tremendous development in Internet of Things (IoT) and mobile devices lead to several images pooled on social media websites and communicated through networking channels. These images are mostly corrupted with impulse noises due to hot pixels generated in the camera sensors and communication channels. Adaptive mean filter technique removes impulse noise at low density but is unsuccessful as noise density increases and computationally expensive. In this paper, automatic adaptive filtering technique for removal of impulse (salt and pepper) noise is demonstrated. The proposed algorithm consists of impulse noise detection and noise removal modules. An automatic impulse noise detection module is based on mean and variance technique that selects the noisy pixels among the entire image. The noise removal module is based on replacement of noisy pixel through mean and edge direction using Gabor filter. The proposed technique demonstrated better robustness compared with existing techniques.

Improved Edge Detection using Variable Thresholding Technique and Convolution of Gabor with Gaussian Filters

Medical Field, Robotic vision, Pattern recognition, Hurdle detection, and smart city are examples of areas that require image processing to achieve automation. Detecting an edge is an important stage in any computer vision application. The performance of the edge detecting algorithm is largely affected by the noise present in an image. An Image with a low signal-to-noise ratio (SNR), imposes a challenge to locate its edges. To improve the observable image boundaries, an adaptive filtering technique is proposed in this article. The proposed algorithm uses convolution of Gabor filter with Gaussian (GoG) operator to clean the noise before non-Maxima suppression. Furthermore, using variable hysteresis thresholding can further improve edge locating. The implementation of the algorithm was done by Python and Matlab. The obtained results were compared to a number of reviewed algorithms such as the Canny method, Laplacian of Gaussian, The Marr-Hildreth method, Sobel operator, and the Haar wavelet-based method. Three performance factors were used; PNSR, MSE, and processing time. The simulation result shows that the proposed method has higher PNSR, lower MSE, and shorter processing time when compared to the Canny detector, the Marr-Hildreth, Haar wavelet-based, Laplacian of Gaussian, and the Sobel operator methods. The higher PNSR, lower MSE, and shorter processing time mean improved edge details of the processed image.

Scalable extension of HEVC with adaptive de-blocking filter for bandwidth-limited applications

A primary latest video coding standard H.265/SHVC (Scalable High Efficiency Video Coding), an extension of High Efficiency Video Coding (HEVC) is dealt in this paper. To transfer high quality video over any network that usually occupies large bandwidth, suffers bandwidth limitation. It can be controlled by trading-off between the quality and the coding efficiency. The trade-off between these two parameters is considered in this paper to keep the difference at the minimum level. A novel Neural Network based Adaptive De-blocking Filter (NNADF) technique is proposed to maintain good PSNR with minimum bit rate in SHVC encoder. The adaptive de-blocking filter (ADF) performs block matching for nonlinear predictions to all frames in the video. Moreover, the ADF performs frame de-noising hence good quality of the video is achieved and trains a neural network (NN) model. The NN model restrains their outputs to achieve bit rate reduction, thereby coding efficiency is achieved. The simulation results show that the proposed NNADF technique delivers an average increment of 0.62 dB in BD-PSNR and an average decrement of 24 % in BD-BR for combined quality and spatial ratios respectively compared with the previously proposed algorithm.

Single channel approach for filtering electroencephalographic signals strongly contaminated with facial electromyography.

Eliminating facial electromyographic (EMG) signal from the electroencephalogram (EEG) is crucial for the accuracy of applications such as brain computer interfaces (BCIs) and brain functionality measurement. Facial electromyography typically corrupts the electroencephalogram. Although it is possible to find in the literature a number of multi-channel approaches for filtering corrupted EEG, studies employing single-channel approaches are scarce. In this context, this study proposed a single-channel method for attenuating facial EMG noise from contaminated EEG. The architecture of the method allows for the evaluation and incorporation of multiple decomposition and adaptive filtering techniques. The decomposition method was responsible for generating EEG or EMG reference signals for the adaptive filtering stage. In this study, the decomposition techniques CiSSA, EMD, EEMD, EMD-PCA, SSA, and Wavelet were evaluated. The adaptive filtering methods RLS, Wiener, LMS, and NLMS were investigated. A time and frequency domain set of features were estimated from experimental signals to evaluate the performance of the single channel method. This set of characteristics permitted the characterization of the contamination of distinct facial muscles, namely Masseter, Frontalis, Zygomatic, Orbicularis Oris, and Orbicularis Oculi. Data were collected from ten healthy subjects executing an experimental protocol that introduced the necessary variability to evaluate the filtering performance. The largest level of contamination was produced by the Masseter muscle, as determined by statistical analysis of the set of features and visualization of topological maps. Regarding the decomposition method, the SSA method allowed for the generation of more suitable reference signals, whereas the RLS and NLMS methods were more suitable when the reference signal was derived from the EEG. In addition, the LMS and RLS methods were more appropriate when the reference signal was the EMG. This study has a number of practical implications, including the use of filtering techniques to reduce EEG contamination caused by the activation of facial muscles required by distinct types of studies. All the developed code, including examples, is available to facilitate a more accurate reproduction and improvement of the results of this study.

QR Code Detection

Due to the incorrect image collecting approach, QR code identification frequently confronts obstacles such as uneven backdrop fluctuations, inadequate illuminations, and distortions. As a result, identifying QR codes is difficult, and artificial intelligence-based methods were developed to address this challenge. This article uses an improved adaptive median filter technique and a QR code distortion correction method based on backpropagation (BP) neural networks to increase the identification rate of QR image codes. The distorted QR image can be fitted into the geometric deformation pattern using this combination of artificial intelligence methods, and QR code identification is possible. This paper addresses two-dimensional code distortion, which has been a critical research topic in present software systems. The research findings, which focused on the picture preprocessing step, demonstrated a significant improvement of 14 percent in the reading rate of QR image codes following processing using the system algorithm described in this article. The artificial intelligence technique used improves the recognition rate of the two-dimensional code image to some extent.

A New Approach to Improve the Performance of OFDM Signal for 6G Communication

The orthogonal frequency division multiplexing is a very efficient modulation technique that can achieve very high throughput by transmitting many carriers simultaneously and it is spectrally efficient because of the proximity of the subcarriers. OFDM is used in 5G communication for higher data transmission. 6th generation communication also demands OFDM, since it is more spectrally efficient and suitable for high data transmission. The drawback of the OFDM includes peak to average power ratio and sensitivity to carrier offsets and drifts. The usage of a non-linear power amplifier causes the signal spreading and leads to inter-modulation and signal constellation distortion. These two distortions have an impact on the signalto-noise ratio and hence reduce the efficiency. The methods used to reduce PAPR are clipping and filtering, selective mapping, partial transmit sequence, tone reservation, and injection and non-linear commanding. The drawbacks of the above methods are computational complexity, spectrum inefficiency, increase in bit error rate and PAPR rate. In this work, three effective methods are discussed and compared to improve the performance parameters. These are adaptive peak window method based on harmonize clipping, harmonics kernel adaptive filter and Slepian-based flat-top window techniques are presented to reduce the BER, PAPR, and CCDF to improve the signal-to-noise of the system. This window technique averages out the noise spread out in the spectrum and thus reduces the signal loss by minimizing peak to average power ratio. The results are analyzed and compared with the existing conventional methods. Finally, the reductions in PAPR, BER and CCDF obtained are discussed in the results and comparison section. The proposed work has a higher signal-to-noise ratio than the conventional methods.

A Novel Noise Removal Technique for ECG Enhancement: A Comparative Analysis

The Electro Cardiogram (ECG) is most generally used for the identification and conclusion of heart illnesses. Great quality ECGs are used by doctors for understanding and identifying physiological and neurotic peculiarities. Nonetheless, in real circumstances, ECG signals are undermined by the corruption of artifcats. Though the LMS algorithm is mostly used for the removal of noise to enhance ECG, some issues persist such as the higher input data vector. So, to overcome such limitations, a logarithmic adaptive filtering technique is been used for ECG enhancement. Logarithmic algorithms incorporate the lower and higher order values of the error into a single continuous updating equation since they are dependent on the error amount. This result in the surge of convergence rate in logarithmic adaptive algorithms similar to the LMLS algorithm and reduces the complexity of computations in a conventional LMS algorithm. Experiment results are performed over various state-of-art models in the literature review under various measures and the proposed model outperforms effectively.

Experimental verification of STATCOM based on quasi impedance source inverter for power quality enhancement in the hybrid distributed generation system

PurposeThis paper aims to introduce the quasi impedance source inverter (qZSI)-based static compensator (STATCOM), which is incorporated into the hybrid distributed power generation system for enhancement of power quality. The distributed power generation system includes the photovoltaic (PV), wind energy conversion system (WECS) and battery energy storage system.Design/methodology/approachThe WECS is used by the self-excited induction generator (SEIG) and the flywheel energy storage system (FESS). To regulate its terminal voltage and frequency, the SEIG requires adjustable volt-ampere reactive (VAR). A combination of a STATCOM and a fixed condenser bank usually serves to satisfy the VAR demand. The maximum correntropy criterion-based adaptive filter technique (AFT) is proposed to control the qZSI-STATCOM and to guarantee that the voltage at the SEIG terminal is harmonic-free while providing non-linear three-phase and single-phase loads.FindingsThe coordinated operation of the suggested voltage control and flywheel control systems ensures that load voltage and frequency are retained in their respective values at very low harmonic distortions regardless of wind speed and load variation. The simulation and experimental studies are carried out under different load conditions to validate the efficiencies of the PV-assisted STATCOM.Originality/valueTo improve system stability and minimize total costs, extra load current sensors can also be avoided. This paper proposes to control the SEIG terminal voltage and harmonic elimination in the standalone WECS systems using maximum correntropy criterion-based AFT with a fuzzy logic controller.