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One of the objectives of electrical distribution networks is to provide customers with access to high‐quality electricity. Because any disruptions in these systems result in voltage disorders, different devices are employed to offset these disruptions on consumers who are more susceptible. One of the most important and contemporary pieces of equipment that is connected in series with the network is dynamic voltage restoration (DVR), which shields delicate loads from network voltage issues by injecting the proper voltage. This article presents a DVR control scheme optimized with improved grey wolf optimization (IGWO) that uses a proportional derivative (PD) controller and adaptive notch filter (ANF). The output LC filter's resistance has been removed, and the control system has actively engaged in oscillation damping in order to accelerate dynamic responsiveness and lower system losses. The major component of the voltage, which comprises its frequency, amplitude, and phase, is extracted using ANF. The capacitor current of the output filter in this structure is fed back to the control system and from the current mode control in the inner loop to boost stability. Owing to the occasionally complex dynamic behavior in distribution networks, particularly during a fault, the system's frequency response has been altered and response speed has been accelerated using the PD controller. This kind of controller is distinguished by its accurate functioning in the presence of frequency deviations and its swifter dynamic reaction in the face of voltage swell and sag. In order to improve the THD and voltage sag indicators of the sensitive load, the PD coefficients were adjusted using the IGWO algorithm. As a consequence, the simulation results demonstrated that the suggested controller performed better than traditional controllers. [ABSTRACT FROM AUTHOR]

The texture is identifiable in optical and easy ways. Texture classification is an imperative region in texture analysis, where it gives descriptors for classifying the images. The categorization of normal and abnormal matter by magnetic resonance (MR), computed tomography (CT), and texture images has made noteworthy evolution in modern years. Recently, different novel robust classification techniques have been introduced to classify the different kinds of images for prediction. However, the accuracy of classification was not improved with lesser time. To address these issues, the edge‐preserved Tversky indexive Hellinger and deep perceptive Czekanowski classifier (ETIH‐DPCC) technique is introduced to segment and classify the images with more accuracy. The ETIH‐DPCC technique includes diverse processes namely preprocessing, segmentation, feature extraction, as well as classification. At first, different types of images, such as magnetic resonance imaging, CT, and texture, are used as input. With the acquired input, edge‐preserving normalized adaptive bilateral filtering is employed to carry the image preprocessing. In this stage, the noisy pixels are removed and edges are preserved. Then, the Tversky‐indexed quantile regression is applied to segment the images into diverse texture regions. After that, the feature extraction is performed on the segmented region using Hellinger kernel feature extraction, where a more informative feature for image prediction is extracted. During this process, the irrelevant features are avoided to decrease the dimensionality and feature extraction time. These extracted features are finally classified into positive and negative classes for disease prediction using DPCC. DPCC comprises multiple layers to deeply analyze the association between training and testing features. With this, the prediction accuracy is improved. Experimental outcomes show that the ETIH‐DPCC technique efficiently enhances prediction accuracy and less time compared to conventional methods. [ABSTRACT FROM AUTHOR]

The traditional Kalman filter assumes that all measurements can be obtained in real time, which is invalid in practical engineering. Therefore, a variational Bayesian- (VB-) based Gaussian sum cubature Kalman filter is proposed to solve the nonlinear tracking problem of multistep random measurement delay and loss (MRMDL) with unknown probability. First, the measurement model with MRMDL is modified by Bernoulli random variables. Then, the expression of the likelihood function is reformulated as a mixture of multiple Gaussian distributions, and the cubature rule is used to improve the estimation accuracy under the framework of Gaussian sum filter in the process of time update. Finally, by constructing a hierarchical Gaussian model, the unknown and time-varying measurement delay and loss probability are estimated in real time with the state jointly using the VB method in the measurement update stage. The algorithm does not need to calculate the equivalent noise covariance matrix so as to avoid the possible division by zero operation, which improves the stability of the algorithm. Simulation results for a target tracking problem show that the proposed algorithm has a better performance in the presence of MRMDL and can estimate the unknown measurement delay and loss probability accurately. [ABSTRACT FROM AUTHOR]

In this paper, an eddy current testing system equipped with low-performance processor is designed for rail defect detection. A digital lock-in amplifier (DLIA) based on adaptive Kalman filter (AKF) is presented to detect the weak voltage induced in two differential coils. The proposed method can fast demodulate the amplitude of a signal with a randomized phase using one cycle of the sinusoidal signal, and simultaneously improve signal-to-noise ratio. This DLIA has made use of the sinusoidal orthogonality and concurrently utilized AKF to track on the amplitude variation of voltage, which is numerically simulated and analyzed in MATLAB. The simulated results indicate that increasing excitation frequency or sampling points in one cycle has an effective suppression in low-frequency noise. Furthermore, the experiment is carried out to verify the performance of the system for rail defect detection. Finally, the experimental result shows that the proposed lock-in amplifier has a root mean squared error of 1.3 mV and performs well in terms of memory consumption and precision. Moreover, the amplitude variation in differential coils is linearly tracked, and the surface defects on rail specimens can be detected. [ABSTRACT FROM AUTHOR]

Vehicle trajectory data can reveal naturalistic driving behaviour trends. However, owing to measurement and processing errors, the trajectory data extracted from videos often contain obvious noise. In merging zones, vehicles tend to accelerate and decelerate frequently, leading to poor denoising performance of the linear Kalman filter (KF). To address this issue, this study proposes a new denoising method based on the adaptive Kalman filter, which automatically switches between KF and Unscented KF to accommodate car-following and merging behaviours, respectively. A merging behaviour detection method was designed based on the PELT method and normalized innovation squared (NIS). The F1 score of 92.9% shows the accuracy of behaviour detection. According to our results, the proposed method minimizes the range of jerk compared with other methods, reducing it from −4927.78 to 4960.72 of raw data to −44.92 to 47.14, indicating a significant improvement in denoising and trajectory smoothing. The goal of this study is to achieve high-precision trajectory data under complex real traffic scenarios. [ABSTRACT FROM AUTHOR]

In order to meet the requirements of UAV motion control and tracking control, an adaptive sensor-based technology is proposed. The main content of the technology is based on the mathematical model of the adaptive sensor, through the quaternion attitude update model, using nonlinear attitude SVDCK filtering and dynamic adaptive adjustment factors and other technologies, and finally through simulation experiments and analysis to build the research means of UAV motion control and tracking control system. The experimental results show that the roll Angle, pitch Angle, and yaw Angle of SVDCKF are 1.703, 1.972, and 1.928, respectively. By adjusting the dynamic adaptive factor, the attitude-filtering algorithm reduces the error of the attitude solution and improves the robustness of the attitude solution under high dynamic conditions. Conclusion. The technology research based on adaptive sensor can meet the requirements of UAV motion control and tracking control. [ABSTRACT FROM AUTHOR]

In the Internet of Things (IoT) era, the combination of mobile robots and IoT technology has realized the complementary advantages of the IoT and robots, which has brought new challenges and opportunities for the development and application of the IoT and mobile robots. In this study, a mobile robot positioning system based on the wireless positioning technology of the IoT is first designed, and an adaptive federated filtering algorithm based on the confidence of the sensor measurement data is given, and the data fusion processing is carried out for the positioning system to achieve the accurate positioning of the mobile robot. Then, we embed the intelligent control system of the ring lamp and propose an adaptive fuzzy PI double closed loop equalization control strategy, which can automatically identify the number of interlaced parallel and ensure its equalization effect. The experimental results show that the equalization strategy designed in this study has a good equalization effect in the loop interleaving parallel topology, strong adaptive parameter adjustment ability, good robustness, and anti-interference. [ABSTRACT FROM AUTHOR]

In this paper, we consider a two-way communication system using the unmanned aerial vehicle (UAV) as a relay (UAV-aided). This system eliminates impulse interference through an adaptive filter based on the least mean square (LMS) and uses the received signal transmitted by the UAVs to construct a parallel factor (PARAFAC) model. Based on the identifiability condition of the PARAFAC model, a pulse interference cancellation orthogonal pilot tensor (PIC-OPT) receiver without iteration is proposed. Our algorithm is also used in millimeter-wave to achieve the acquisition of channel information. Compared with the least squares method, the simulation results demonstrate the superiority of the proposed semiblind receiver in terms of the relative mean square error and bit error rate. [ABSTRACT FROM AUTHOR]

The high-efficiency video coding (HEVC) standard incorporates sample adaptive offset (SAO) as an in-loop filtering technique to reduce distortion. The fundamental tenet of SAO is to classify every reconstructed pixel and then simply add the offset to each group of pixels. The previous existing technique employs 32 bands in band offset (BO) calculation to determine the proper offset value to minimize the distortion in the SAO estimate algorithm for statistics collection and parametric determination procedure. The suggested method will reduce the necessary band count by 16 bands. It does not alter how edge offset is determined in any way. It offers enough and efficient room for creating superior band group predictions and selections, as well as an ideal decrease in the implemented area. Thus, reducing the number of bands to 16 also decreases the amount of storage needed to hold each pixel's information in binary files by up to 0.65%. [ABSTRACT FROM AUTHOR]

Summary: This paper proposes a new energy management strategy (EMS) for electric vehicles (EVs) with battery/supercapacitor hybrid energy storage systems (HESS). Firstly, the battery/supercapacitor HESS configuration and obtaining the load current from the driving cycle are comprehensively explained. Secondly, fixed and adaptive frequency‐based (AFB) EMS are discussed in detail. In the proposed method, the current demanded by the load is separated into low and high‐frequency components with the help of an adaptive low‐pass filter (A‐LPF). In addition, adaptive battery current (ABC) is generated according to the supercapacitor (SC) state of charge (SoC), and a double adaptive power allocation strategy is performed. The proposed method is compared with the AFB‐EMS for load currents, load powers, DC link voltages, filter cut‐off frequencies, battery, and SC SoCs on the UDDS driving cycle. When the obtained results were evaluated together, it was observed that the proposed method completed the driving cycle with a higher battery SoC and realized the adaptive cut‐off frequency in a narrower band. [ABSTRACT FROM AUTHOR]

Interrupted-sampling repeater jamming (ISRJ) is a new kind of coherent jamming for linear frequency modulation (LFM) signals. Based on digital radio frequency memory (DRFM), ISRJ can generate multiple false target groups by intercepting, storing, and retransmitting radar signal fragments, which significantly affects the postprocessing results of radar systems. Furthermore, due to the fragment interception of ISRJ, ISRJ false targets present a regular and discontinuous time-frequency (TF) distribution in contrast with real targets. Considering this intrinsic property and the coherent nature of ISRJ, this study proposes a new method based on TF analysis and target sparse reconstruction to address the ISRJ suppression issue. In this method, the echo signal is first sparsely represented to obtain both the real and false target positions. Then, according to the acquired target positions, information entropy features of targets are extracted in TF data for subsequent target identification. Finally, guided by the identification result, the real targets can be retained and reconstructed by adaptive filtering in the sparse domain to realize ISRJ suppression. Simulations have validated the effectiveness of the proposed method under various situations. [ABSTRACT FROM AUTHOR]

Real world data aggregation and delivery in Internet of Things (IoT) technology are essential to predict and retrieve target data in short time so that the end user feels no delay but ensures a high quality of information. In addition to habitat monitoring and disaster management, these networks have a wide range of other uses, including security and military operations. The processing capabilities of sensor nodes are restricted due to the fact that they have a limited battery life and hence a modest size and processing capacity. WSNs are also susceptible to failure as a result of the limited battery power available. In WSNs, data aggregation is practiced as an energy efficient strategy to reduce computing and transmission latency. It is because of sensor node distribution density that shares the same data at a time data redundancy comes to exist. It is possible to reduce redundancy by adopting a suitable machine learning algorithm while executing the data aggregation process. Researchers are still chasing behind algorithms and modeling strategies effectively to ease the process of developing an effective and acceptable data aggregation strategy from existing wireless sensor network (WSN) models. A three stage framework is proposed for an efficient data aggregation mechanism, and the stages are Modified LEACH, extreme learning machines (ELM), adaptive Kalman filter, and Bi-LSTM. This experiment result shows better performance than the existing methods. [ABSTRACT FROM AUTHOR]

With the progress of face manipulation techniques, synthesized faces are spreading on the Internet, which raises concerns about potential threats. To prevent these techniques' abuse, various detection algorithms have been proposed. In this paper, we consider the image's frequency information, then propose an adaptive filtering algorithm named spatial and adaptive filtering (SAF) Network. SAF is a dual-stream network that considers spatial and frequency domains. In the frequency domain, wavelet transform is used to divide the image into different frequency bands, then an adaptive filter is introduced, which aims to capture more decisive information by giving different weights to different frequencies. To fuse spatial and frequency features, spatial pyramid pooling fusion (SPPF) is proposed, which solves the mismatch of feature maps, and considers the relationship between different patches by attention mechanism. Experiment results show that the performance of SAF is better than the comparison algorithm. [ABSTRACT FROM AUTHOR]

Modulation classification is one of the essential requirements in the various cognitive radio applications where prior information about the incoming signal is unknown. The modulation classification using a pattern recognition approach can be achieved in 2 modules: first, parameters are extracted from the noisy signal, and then feature selection is carried out using a Gabor filter network (GFN). In the second module, features are exploited for classification purposes. The modulation formats considered for the purpose of classification are BPSK, QPSK, 8PSK, 16PSK, 64PSK, 4FSK, 8FSK, 16FSK, QAM, 8QAM, 16QAM, 32QAM, and 64QAM. The Gabor filter parameters and weights of the adaptive filter are attuned using the Delta rule and recursive least square (RLS) algorithm until the cost function is minimized. In the end, the artificial bee colony (ABC) algorithm is used to optimize the Gabor parameters as well as the classifier's performance. The simulation results show the supremacy of the proposed classifier structure. [ABSTRACT FROM AUTHOR]

The mobile geographic information service (GIS) is the key technology to realize mobile navigation. In this study, a transmission method of mobile geographic information services under the 4G terminal grounded on the channel Porter interval equalization algorithm is proposed. The multisensor information collection method of vehicle networking is adopted to realize data collection and data structure characteristics analysis of mobile GIS under the 4G terminal. Subsequently, the transmission channel model of mobile GIS under the 4G terminal is constructed. Furthermore, the interference suppression in the transmission process of mobile GIS under 4G terminals is realized by using an autocorrelation matching filter and an adaptive association rule mining algorithm. According to the symbol feature distribution of the mobile GIS channel under the 4G terminal, the Porter interval equalization approach is implemented to realize the segmented equalization adjustment of mobile GIS. The K-means clustering procedure is also implemented to coordinate the data packet adjustment in the process of mobile geographic information service transmission within the background of a 4G terminal. According to the dynamic distribution characteristics of the clustering center, the mobile geographic information service transmission under the 4G terminal is adaptively controlled to reduce the influence of channel fading and intersymbol interference caused by channel spreading. The simulation outcomes express that the suggested technique has strong anti-jamming ability, reduces the communication error rate, significantly diminishes the end-to-end delay performance, and improves the stability and accuracy of the mobile geographic information service transmission under the 4G terminal. [ABSTRACT FROM AUTHOR]

Digital twin technology assists physical entities better to achieve functional perfection through the interactive cointegration of physical entities and virtual spaces. In this view, this paper proposes a digital twin inspired intelligent diagnosis method for bearing faults based on adaptive correlation filtering and an improved stack autoencoder (SAE) classification model. First, the adaptive correlation filtering algorithm is designed to filter the engineering signal in physical space, in which the noise-free ideal bearing fault signal spectrum in the virtual space is used as the comparison. In this filtering, the optimal cut-off frequency is determined adaptively with the condition that the similarity between the simulated spectrum and the engineering spectrum is maximized. Second, the Tan activation function is used in the SAE classification model to enhance the signal differentiation and avoid the problem of large computation of the traditional sigmoid function. Finally, the accuracy of the method in this study is verified on the basis of engineering experiments and Case Western Reserve University bearing fault dataset. Experimental results show that the method can achieve 100% correct diagnosis rate of bearing faults. [ABSTRACT FROM AUTHOR]

As the main carrier of information transmission, plane images play an important role in the current network era. In order to enhance the color of the plane image, optimize the image effect, and solve the problem of image distortion with large color difference, a color enhancement processing optimization method is proposed based on the technical support of computer vision. According to computer vision theory, a computer vision model for adjusting perceived color and brightness is constructed. Combining it with the bilateral filtering algorithm, a color enhancement processing optimization model is obtained, which consists of three main stages: illumination information parameter estimation and image color correction, reflection coefficient parameter estimation and image color correction, and adaptive filtering. By estimating illumination information parameters and reflection coefficient parameters, the secondary gamma correction of image color is realized. After being processed by the bilateral filtering algorithm, the final optimized image is obtained. Verified by the subjective and objective evaluation results, the positive correlation index values of this method are all high, 8.0246, 16.4526, 0.9037, and 15.0246, respectively, and the negative correlation index value is low, 49.4169. It is proved that the proposed method can effectively suppress the halo and artifacts and obtain the image color quality that satisfies the visual perception. It not only enhances the image color but also retains the details to the greatest extent. [ABSTRACT FROM AUTHOR]

Aiming at the shortcomings of traditional bilateral filtering in suppressing speckle noise in SAR ship images, especially strong speckle noise and loss of image edge details, it proposes an improved bilateral filtering algorithm based on fast adaptive threshold and variable window in this paper. The algorithm was used to suppress speckle noise in SAR ship images. The traditional bilateral filtering cannot effectively filter out the strong speckle noise, but the SAR image has strong speckle noise because of the defects of its imaging principle. To solve these problems, an image sample truncation method based on fast adaptive truncation statistical characteristics is designed to adjust the photometric similarity weight characteristics to realize the adaptive adjustment of spatial standard deviation and gray standard deviation. After the local reference window is modified and truncated according to the local characteristics of the image, the adjusted combined similarity weight value greatly reduces the impact of strong speckle noise on the image. It is smoothed into speckle signal with strong impulse noise. In the traditional bilateral filtering, in order to enhance the effect of smoothing noise, it is necessary to specify a large value of geometric diffusion factor and gray similarity diffusion factor, resulting in the loss of image details. Based on the variable window size filtering method, when the extended local reference window is in the case of nonuniform edge, its window can be enlarged to make the speckle noise stronger. When an extended window contains details such as edges and textures, its size is not expanded to maintain image detail. This method can further smooth the speckle noise in the uniform region while preserving the edge details of the image. Finally, the adaptive truncated sample is used as the input of the bilateral filter. The image sample truncation method based on fast adaptive threshold can effectively eliminate the strong speckle noise information that affects the photometric similarity and weight accuracy of the image. The method based on variable window can greatly enhance the smoothness of the edge area of the image. The experimental results show that improved adaptive bilateral filtering algorithm improves the speckle noise removal ability by 16.06% compared with the traditional bilateral filtering algorithm in the speckle noise suppression of the SAR ship image, and the preservation performance of the image edge after filtering is improved by 5.41%. Compared with the original image, the filtered image has a 1.2% improvement in structural similarity. The algorithm can effectively suppress speckle noise and has a good ability to retain edge and texture information of the SAR ship image, which has strong practicability. [ABSTRACT FROM AUTHOR]

In order to improve the digital transformation effect of enterprise accounting talents, this paper combines intelligent methods to carry out the digital transformation of enterprise accounting talents from the perspective of blockchain. Moreover, this paper studies the sliding window CS-SCHT algorithm in depth. Based on the theoretical derivation of the sliding window CS-SCHT based on the gray code kernel, the algorithm is implemented on the computer platform, and the test experiment of the operation time is carried out. In addition, this paper explores the application of the sliding window CS-SCHT algorithm in adaptive filtering. The experimental results show that the adaptive filter based on the CS-SCHT algorithm can obtain a higher signal-to-noise ratio than the sliding window DFT algorithm. Finally, this paper constructs an intelligent accounting digital information processing system. The research shows that the system proposed in this paper can play an important role in the digital transformation of enterprise accounting talents from the perspective of blockchain. [ABSTRACT FROM AUTHOR]

This paper aims to conduct an in-depth study on the effective application of virtual reality technology in TV program production. To begin, construct the three-dimensional animation system's overall structure, determine the animation system's user interface, and establish the three-dimensional animation system for TV program production. Second, software like YOLOv3 and ResNet-50 network is used for identification and facial recognition. Then, the pixel cross-support window is built using the adaptive stereo matching method. The adaptive filter window is built using the horizontal expansion of the cross-support window. For the successful application of virtual reality technology in TV program creation, the aggregation is completed by regional filtering, and the final parallax image is obtained by parallax selection and parallax optimization. This technology has the ability to improve the visual effect and quality of TV programs, increase the amount of information in programs' content, and shorten the production cycle of the program in the context of the gradual development of science and technology and the new technology period. In comparison to other approaches, simulation experiments demonstrate that the proposed method has the potential to increase the quality and attractiveness of TV programs and meet the psychological demand of viewers. [ABSTRACT FROM AUTHOR]

The carrier-based kinematic-to-kinematic relative positioning can obtain the precise baseline between two moving stations, which greatly expands the application field of dynamic relative positioning. However, the relative positioning performance is degraded greatly with low fixation rate of ambiguity with low-cost receivers. Especially, in the complex dynamic environment, ambiguity resolution effect is influenced by the satellite signal blocked, multipath outlier, and abnormal state prediction. Aiming at the problems, a novel inertial navigation system–aided robust adaptive filtering ambiguity resolution model is proposed. In addition, a hierarchical filtering strategy is developed to eliminate ambiguity parameters in BeiDou navigation satellite system/inertial navigation system tightly coupled integrated system. Finally, the precise relative position can be calculated with the "best" ambiguity solution. Both experiments with static data and field vehicle test were carried out to evaluate the algorithm efficiency in different data configurations. The results indicate that IRAFAR-TCRP method can effectively suppress the influence of observation outliers and model prediction abnormalities, which improves the success rate of ambiguity resolution, raises the accuracy as well as the continuity of relative positioning. The success rate of ambiguity resolution with single-frequency BeiDou navigation satellite system can reach 90% in the gross error and abnormal disturbance environments and centimeter-level accuracy can be achieved. [ABSTRACT FROM AUTHOR]

For the purpose of effectively improving the assessment accuracy of spoken English self-study quality of English learners, a speech scientific computing knowledge assessment algorithm is introduced into the assessment of the self-study system of spoken English. Through the combination of spoken English speech assessment and accuracy detection, a DSP-based self-study assessment system for spoken English is designed. This system is mainly divided into two parts: voice signal processing and hardware circuit design. The features of spoken English self-study speech are extracted based on the multilayer wavelet feature conversion method, and the English pronunciation is detected and analyzed by adaptive filtering based on the characteristic values obtained. The corresponding wavelet entropy features of spoken English self-study speech are automatically assessed and assessed for the self-study quality. Finally, a practical case is analyzed, and the results indicate that the system proposed in this paper has high accuracy and excellent stability in assessing the quality of self-study in spoken English. Hence, it is of practical value in self-study assessment of spoken English. [ABSTRACT FROM AUTHOR]

Summary: To accurately evaluate the state of charge (SOC) and state of health (SOH) of Li‐ion battery, the second‐order RC equivalent‐circuit model is used to characterize the battery performance, a novel dual adaptive Kalman filtering algorithm is presented by adding double cycles and noise adaptive steps to realize the joint estimation of the SOC and internal resistance. The state variables can be corrected with each other as go through the cycle under three operating conditions. The accuracy of the SOC estimation method proposed in this paper is significantly improved compared with the extended Kalman filtering and the unscented Kalman filtering algorithm. Under three operating conditions, the average error and the maximum error decreased obviously. An equation for calculating the SOH in terms of internal resistance increase was built. The estimation result of the SOH effectively simulated the actual situation, compared with the actual result, the maximum error under the three operating conditions are within a lower level than the improved unscented Kalman filtering algorithm. The convergence effect of the algorithm has obvious advantages over that of the algorithm used for comparison, which could effectively track the state change of the battery. [ABSTRACT FROM AUTHOR]

The block-sparse normalized least mean square (BS-NLMS) algorithm which takes advantage of sparsity, successfully shows fast convergence in adaptive block-sparse system identification, adaptive control, and other industrial informatics applications. It is also attractive in acoustic processing where long impulse response, highly correlated and sparse echo path are encountered. However, the major drawback of BS-NLMS is largely computational complexity. This paper proposes a novel selective partial-update block-sparse normalized least mean square (SPU-BS-NLMS) algorithm. Compared with conventional BS-NLMS for block-sparse system identification, the proposed elective partial-update block-sparse NLMS algorithm takes partial-update blocks scheme which is determined by the smallest squared Euclidean-norm at each iteration instead of entire block coefficients to save computations. Computational complexity analysis is conducted to help researchers select appropriate parameters for practical realizations and applications. Computer simulations on acoustic echo cancellation are conducted to verify the results and the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Phonocardiogram (PCG), the graphic recording of heart signals, is analyzed to determine the cardiac mechanical function. In the recording of PCG signals, the major problem encountered is the corruption by surrounding noise signals. The noise-corrupted signal cannot be analyzed and used for advanced processing. Therefore, there is a need to denoise these signals before being employed for further processing. Adaptive Noise Cancellers are best suited for signal denoising applications and can efficiently recover the corrupted PCG signal. This paper introduces an optimal adaptive filter structure using a Sign Error LMS algorithm to estimate a noise-free signal with high accuracy. In the proposed filter structure, a noisy signal is passed through a multistage cascaded adaptive filter structure. The number of stages to be cascaded and the step size for each stage are adjusted automatically. The proposed Variable Stage Cascaded Sign Error LMS (SELMS) adaptive filter model is tested for denoising the fetal PCG signal taken from the SUFHS database and corrupted by Gaussian and colored pink noise signals of different input SNR levels. The proposed filter model is also tested for pathological PCG signals in the presence of Gaussian noise. The simulation results prove that the proposed filter model performs remarkably well and provides 8–10 dB higher SNR values in a Gaussian noise environment and 2-3 dB higher SNR values in the presence of colored noise than the existing cascaded LMS filter models. The MSE values are improved by 75–80% in the case of Gaussian noise. Further, the correlation between the clean signal and its estimate after denoising is more than 0.99. The PSNR values are improved by 7 dB in a Gaussian noise environment and 1-2 dB in the presence of pink noise. The advantage of using the SELMS adaptive filter in the proposed filter model is that it offers a cost-effective hardware implementation of Adaptive Noise Canceller with high accuracy. [ABSTRACT FROM AUTHOR]

Alzheimer's disease is characterized by the presence of abnormal protein bundles in the brain tissue, but experts are not yet sure what is causing the condition. To find a cure or aversion, researchers need to know more than just that there are protein differences from the usual; they also need to know how these brain nerves form so that a remedy may be discovered. Machine learning is the study of computational approaches for enhancing performance on a specific task through the process of learning. This article presents an Alzheimer's disease detection framework consisting of image denoising of an MRI input data set using an adaptive mean filter, preprocessing using histogram equalization, and feature extraction by Haar wavelet transform. Classification is performed using LS-SVM-RBF, SVM, KNN, and random forest classifier. An adaptive mean filter removes noise from the existing MRI images. Image quality is enhanced by histogram equalization. Experimental results are compared using parameters such as accuracy, sensitivity, specificity, precision, and recall. [ABSTRACT FROM AUTHOR]

Kernel adaptive filtering (KAF) algorithms derived from the second moment of error criterion perform very well in nonlinear system identification under assumption of the Gaussian observation noise; however, they inevitably suffer from severe performance degradation in the presence of non-Gaussian impulsive noise and interference. To resolve this dilemma, we propose a novel robust kernel least logarithmic absolute difference (KLLAD) algorithm based on logarithmic error cost function in reproducing kernel Hilbert spaces, taking into account of the non-Gaussian impulsive noise. The KLLAD algorithm shows considerable improvement over the existing KAF algorithms without restraining impulsive interference in terms of robustness and convergence speed. Moreover, the convergence condition of KLLAD algorithm with Gaussian kernel and fixed dictionary is presented in the mean sense. The superior performance of KLLAD algorithm is confirmed by the simulation results. [ABSTRACT FROM AUTHOR]

This paper introduces the application and classification of an adaptive filtering algorithm in the image enhancement algorithm. And the filtering noise reduction impact is compared using MATLAB software for programming, image processing, LMS algorithm, RLS algorithm, histogram equalisation algorithm, and Wiener filtering method filtering noise reduction effect. To optimize the intelligent graphic image interaction system, the proposed nonlinear adaptive algorithm of intelligent graphic image interaction system research is based on the digital filter and adaptive filtering algorithm for simulation experiment. The experimental results of several noise index data filtering algorithms show that the fuzzy coefficient k of LMS index is 0.86, RLS index is 0.91, the histogram equalization index is 0.53, and the Wiener filtering index is 0.62. LMS index of quality index Q is 0.90, RLS index is 0.95, histogram equalization index is 0.58, Wiener filtering index is 0.65. According to the above results, comparing LMS with the RLS method and according to SNR, k, and Q values in the simulation results in the process of processing, it is found that the convergence speed of the RLS algorithm is obviously better than that of the LMS algorithm, and the stability is also good. Additionally, the differential imaging data can provide a strong reference for the clinical diagnosis and qualitative differentiation of TBP and CP, and MSCT is worthy of extensive application in the clinical diagnosis of peritonitis. The processing effect of the image with high similarity to the original image is greatly improved compared with the histogram equalization and Wiener filtering methods used in the simulation. [ABSTRACT FROM AUTHOR]

A novel estimation-based and dropout-dependent control design for distributed control systems of aeroengines with packet dropout is proposed. The packet dropout is described as an independent identically distributed (i.i.d.) Bernoulli process with known probability. The objective of the control system is to effectively and stably impel aeroengines. An inverse system is proposed to reconstruct the missing measurements, based on which, a hybrid adaptive Kalman filter (HAKF) is proposed to provide the estimated states for control design. A new state-feedback control design is developed with the estimated states and the actual states. For guaranteeing the stability of aeroengines, both the stability of HAKF and the new controlled system are analyzed and proved. The new design is applied to an aeroengine, HAKF has superior estimation accuracy which ensures the effectiveness, and the desired stability performance is achieved by controlled system in the presence of packet dropout. [ABSTRACT FROM AUTHOR]

The idea of multivariate and online stock price prediction via the kernel adaptive filtering (KAF) paradigm is proposed in this article. The prediction of stock prices is traditionally done with regression and classification, thereby requiring a large set of batch-oriented and independent training samples. This is problematic considering the nonstationary nature of a financial time series. In this research, we propose an online kernel adaptive filtering-based approach for stock price prediction to overcome this challenge. To examine a stock's performance and demonstrate the work's superiority, we use ten different KAF family of algorithms. In this paper, we take on this challenge and propose an approach for predicting stock prices. To analyze a stock's performance and demonstrate the work's superiority, we use ten distinct KAF algorithms. Besides, the results are analyzed on nine-time windows such as one day, sixty minutes, thirty minutes, twenty five minutes, twenty minutes, fifteen minutes, ten minutes, five minutes, and one minute. We are the first to experiment with several time windows for all fifty stocks on the Indian National Stock Exchange, to the best of our knowledge. It should be noted here that the experiments are performed on stocks making up the main index: Nifty-50. In terms of performance and compared to existing methods, we have a 66% probability of correctly predicting a stock's next upward or downward movement. This number clearly shows the edge that the proposed method has in actual deployment. Furthermore, the experimental findings show that KAF is not only a better option for predicting stock prices but that it may also be used as an alternative in high-frequency trading due to its low latency. [ABSTRACT FROM AUTHOR]

Autonomous aerial refueling (AAR) has generated great interest in recent years. However, much research has focused on the vision-based close docking stage; few studies have been conducted on the navigation algorithm for the rendezvous and following stages. High-precision relative navigation in following stage can provide favourable conditions for successful docking. Aiming at precise relative navigation in the complex high dynamic environment of aerial refueling rendezvous and following stages, a two-stage adaptive filtering architecture is exploited in this paper. An adaptive main Kalman filter (AKF) is realized for ambiguity eliminated GNSS/INS tightly coupled integrated system, and a robust adaptive subfilter is developed for GNSS individually. Particularly, aiming at the influence of pseudorange observation multipath outliers and state abnormal disturbances in unmanned air vehicle- (UAV-) tanker proximity, an INS-aided bifactor robust and classified factor adaptive filtering (IBRCAF) algorithm for single-frequency ambiguity resolution is proposed. Finally, the effectiveness of the algorithm is verified by the simulation experiments for UAV-tanker. The results indicate that the IBRCAF algorithm can efficiently suppress the influence of pseudorange multipath gross errors and abnormal state disturbances and greatly raise the success rate of ambiguity resolution, and the two-stage adaptive filtering algorithm of IBRCAF-AKF can significantly improve relative navigation performance and achieve centimeter-level accuracy. [ABSTRACT FROM AUTHOR]

In the literature, the fading factor was constructed to overcome the shortage of model uncertainties in the Kalman filter. However, the a priori covariance matrix might be inflated abnormally by the fading factor once the measurement is unreliable. Thus, the fading factor may become invalid, and this problem is rarely discussed and tested. In this paper, squares of the Mahalanobis distance are introduced as the judging index, and the fading factor or the covariance inflation factor is adopted conditionally according to the hypothesis testing result. Therefore, an adaptive filtering scheme based on the Mahalanobis distance is put forward for the systems with model uncertainties. The proposed algorithm is implemented with the actual data collected by the integration of the global navigation satellite system (GNSS) and the inertial navigation system and INS (inertial navigation system) integrated systems (INS). For the systems with model uncertainties, experimental results demonstrate that the influences of both the outlying measurements and model errors are controlled effectively with the proposed scheme. [ABSTRACT FROM AUTHOR]

In recent years, many low-orbit satellites have been widely used in the field of scientific research and national defense in China. In order to meet the demand of high-precision satellite orbit in China's space, surveying and mapping, and other related fields, navigation satellites are of great significance. The UKF (unscented Kalman filter) method is applied to space targets' spaceborne GPS autonomous orbit determination. In this paper, the UKF algorithm based on UT transformation is mainly introduced. In view of the situation that the system noise variance matrix is unknown or the dynamic model is not accurate, an adaptive UKF filtering algorithm is proposed. Simulation experiments are carried out with CHAMP satellite GPS data, and the results show that the filtering accuracy and stability are improved, which proves the algorithm's effectiveness. The experimental results show that the Helmert variance component estimation considering the dynamics model can solve the problem of reasonable weight determination of BDS/GPS observations and effectively weaken the influence of coarse error and improve the accuracy of orbit determination. The accuracy of autonomous orbit determination by spaceborne BDS/GPS is 1.19 m and 2.35 mm/s, respectively. [ABSTRACT FROM AUTHOR]

With the acceleration of the informatization process, but because of the late start of the informatization construction of logistics management, the current digital system construction of logistics management has not been popularized, and the intelligent logistics integrated management evaluation system is also extremely lacking. In order to solve the lack of existing intelligent logistics comprehensive management evaluation system, this paper introduces the research of intelligent logistics comprehensive management evaluation system based on hospital data fusion technology. This paper analyzes and utilizes the Kalman filter and adaptive weighted data fusion technology in data fusion technology and then analyzes the evaluation index and system design principles of the intelligent logistics comprehensive management evaluation system and then designs the application layer from the application layer. Design the application layer from the application layer. Then design the framework of the intelligent logistics comprehensive management evaluation system at the network layer and the data layer. The system is finally tested, and the test results show that the evaluation accuracy of the system reaches 80%. [ABSTRACT FROM AUTHOR]

Aiming at the low confidence of traditional spoken English automatic evaluation methods, this study designs an automatic evaluation method of spoken English based on multimodal discourse analysis theory. This evaluation method uses sound sensors to collect spoken English pronunciation signals, decomposes the spoken English speech signals by multilayer wavelet feature scale transform, and carries out adaptive filter detection and spectrum analysis on spoken English speech signals according to the results of feature decomposition. Based on multimodal discourse analysis theory, this evaluation method can extract the automatic evaluation features of spoken English and automatically recognize the speech quality according to the results. The experimental results show that, compared with the control group, the designed evaluation method has obvious advantages in confidence evaluation and can solve the problem of low confidence of traditional oral automatic evaluation methods. [ABSTRACT FROM AUTHOR]

This paper proposes a multivariate and online prediction of stock prices via the paradigm of kernel adaptive filtering (KAF). The prediction of stock prices in traditional classification and regression problems needs independent and batch-oriented nature of training. In this article, we challenge this existing notion of the literature and propose an online kernel adaptive filtering-based approach to predict stock prices. We experiment with ten different KAF algorithms to analyze stocks' performance and show the efficacy of the work presented here. In addition to this, and in contrast to the current literature, we look at granular level data. The experiments are performed with quotes gathered at the window of one minute, five minutes, ten minutes, fifteen minutes, twenty minutes, thirty minutes, one hour, and one day. These time windows represent some of the common windows frequently used by traders. The proposed framework is tested on 50 different stocks making up the Indian stock index: Nifty-50. The experimental results show that online learning and KAF is not only a good option, but practically speaking, they can be deployed in high-frequency trading as well. [ABSTRACT FROM AUTHOR]

To realize an automatic diagnosis of rotating machinery structure faults, this paper presents a novel fault diagnosis model based on adaptive multiband filter and stacked autoencoders (SAEs). First, to solve the problem where the actual rotating frequency and its harmonics cannot be accurately extracted in engineering applications, an improved adaptive multiband filtering method is designed. This method takes the theoretical rotating frequency as the search center, extracts the maximum within the positive and negative deviation as the actual rotating frequency, and sets a threshold according to the actual value to realize multiband filtering. This method can effectively remove background noise and accurately extract the actual rotating frequency and its harmonics. Second, an unsupervised SAE multiclassification model is established to realize an automatic diagnosis of fault types. This model can automatically extract the in-depth features of the filtered signal and improve the fault classification accuracy. Third, engineering and comparative experiments were carried out to verify the effectiveness and superiority of this model. Results show that the proposed automatic diagnosis model can extract the characteristic components abundantly and accurately recognize rotating machinery structural faults. [ABSTRACT FROM AUTHOR]

Interrupted-sampling repeater jamming (ISRJ) is a new type of DRFM-based jamming designed for linear frequency modulation (LFM) signals. By intercepting the radar signal slice and retransmitting it many times, ISRJ can obtain radar coherent processing gain so that multiple false target groups can be formed after pulse compression (PC). According to the distribution characteristic of the echo signal and the coherence of ISRJ to radar signal, a new method for ISRJ suppression is proposed in this study. In this method, the position of the real target is determined using a gated recurrent unit neural network (GRU-Net), and the real target can be, therefore, reconstructed by adaptive filtering in the sparse representation of the echo signal based on the target locating result. The reconstruction result contains only the real target, and the false target groups formed by ISRJ are suppressed completely. The target locating accuracy of the proposed GRU-Net can reach 92.75%. Simulations have proved the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper presents the dual‐mode control to improve the power quality (PQ) of three‐phase four‐wire grid interfaced photovoltaic (PV) battery system in a single‐stage configuration, where the current control is used at normal circumstances for the grid‐interactive mode and for the grid interruption, the system operation is through the voltage control. The transient free switching between grid interactive and off‐grid modes is executed by a power electronics switch, thus at disconnection, the system gives reliable and incessant power to the loads. The weight of the active current constituent from the load current is acquired using Mathew's algorithm‐based adaptive filter. The voltage control regulates the frequency and voltage across the loads in off‐grid mode. The current control used for a voltage source converter mends the PQ namely current harmonics extenuations and unity power factor operation and it utilizes the solar PV's array power feed‐forward unit for the enhancement of the system's dynamic performance. The incremental conductance technique is applied for obtaining the maximum PV array's power. The addition of a battery across the DC link is realised using a DC–DC buck‐boost converter. The battery is discharged and charged in accord with the load's variation. This converter's control sustains the maximum power point tracking voltage. The system's performances for the steady‐state and dynamic situations, such as solar insolation change, load perturbation, load unbalancing and neutral current compensation, are authenticated on a developed prototype through test results. [ABSTRACT FROM AUTHOR]

Speech enhancement in a vehicle environment remains a challenging task for the complex noise. The paper presents a feature extraction method that we use interchannel attention mechanism frame by frame for learning spatial features directly from the multichannel speech waveforms. The spatial features of the individual signals learned through the proposed method are provided as an input so that the two-stage BiLSTM network is trained to perform adaptive spatial filtering as time-domain filters spanning signal channels. The two-stage BiLSTM network is capable of local and global features extracting and reaches competitive results. Using scenarios and data based on car cockpit simulations, in contrast to other methods that extract the feature from multichannel data, the results show the proposed method has a significant performance in terms of all SDR, SI-SNR, PESQ, and STOI. [ABSTRACT FROM AUTHOR]

Electromyography (EMG) signals can be used for clinical diagnosis and biomedical applications. It is very important to reduce noise and to acquire accurate signals for the usage of the EMG signals in biomedical engineering. Since EMG signal noise has the time-varying and random characteristics, the present study proposes an adaptive Kalman filter (AKF) denoising method based on an autoregressive (AR) model. The AR model is built by applying the EMG signal, and the relevant parameters are integrated to find the state space model required to optimally estimate AKF, eliminate the noise in the EMG signal, and restore the damaged EMG signal. To be specific, AR autoregressive dynamic modeling and repair for distorted signals are affected by noise, and AKF adaptively can filter time-varying noise. The denoising method based on the self-learning mechanism of AKF exhibits certain capabilities to achieve signal tracking and adaptive filtering. It is capable of adaptively regulating the model parameters in the absence of any prior statistical knowledge regarding the signal and noise, which is aimed at achieving a stable denoising effect. By comparatively analyzing the denoising effects exerted by different methods, the EMG signal denoising method based on the AR-AKF model is demonstrated to exhibit obvious advantages. [ABSTRACT FROM AUTHOR]

In this paper, a fault identification method combining adaptive local iterative filtering and permutation entropy is proposed. The adaptive local iterative filtering can decompose the nonstationary signal into a finite number of stationary intrinsic mode functions. And the experiment gear fault data are decomposed into several intrinsic mode functions by this method. Then, using the permutation entropy to calculate each intrinsic mode function, it is found that the permutation entropy of the first several intrinsic mode functions can represent the characteristics of different fault types, and the permutation entropy of the intrinsic mode function corresponding to the rotating frequency signal of the gear system could be the boundary. Finally, the fault type of gear is identified by calculating the gray correlation degree of permutation entropy of essential mode function of vibration signal decomposition under different working conditions. The example analysis results show that the proposed method can be effectively applied to the fault diagnosis of the gear system. [ABSTRACT FROM AUTHOR]

Summary: A non‐linear cubic Bezier‐functional expansion‐based adaptive filter (CB‐FEBAF) has been designed for achieving shunt compensation in this article. The algorithm is developed for mitigating current‐based power quality problems such as harmonics in supply current, reactive VAR compensation, active power compensation, power factor improvement, load balancing, and so on. The CB‐FEBAF is developed using Bezier curve expansion of the input signal. The functional expansion‐based real time and online training converges fast and shows fast response over offline techniques such as neural network and neuro‐fuzzy‐based algorithms. The designed CB‐FEBAF controller is trained online using gradient descent least mean square algorithm to extract the fundamental component of the load current. Moreover, the feedforward active power term corresponding to PV power contribution has been added to the developed controller. This helps to overcome the challenges in the integration of renewable energy‐based distribution systems. The proposed controller is compared with non‐adaptive synchronous reference frame theory, backpropagation neural network, and legendre‐based functional neural network. Hardware results prove the multifunctional capabilities of the developed approach. [ABSTRACT FROM AUTHOR]

Summary: This paper describes the performance of a dynamic voltage restorer (DVR) using various adaptive controls. The implemented control algorithms are; the amplitude adaptive notch filter (AANF), the quasi newton (QN), and the sliding mode observer (SMO) based control algorithms equipped with optimized PI tuning for three leg voltage sources converter‐based topology. These control algorithms are assessed and compared using key factors including computational parameters complexity, system response, dynamic and steady state performance etc. The control of a DVR is mainly dependent on the error minimization of the DC voltage and the load terminal voltages, respectively, which directly affects the generation reference load voltage for a DVR. Accordingly, optimization algorithms have been implemented for estimating the PI controllers' gains along with the control algorithms under investigation. The optimization method Harris Hawks Optimization algorithm (HOA) has been implemented for PI tuning for all aforementioned control algorithms. The estimation technique of the PI controller gains using the aforementioned optimization algorithm is elaborated mathematically and pictorially described. A comparative analysis of the performance of the HOA optimization method in estimating the gains of PI controllers of the adaptive control algorithms for three phase DVR are simulated using MATLAB. Moreover, all control algorithms have been developed and tested on the Micro Lab Box based experimental setup of DVR in the laboratory and their performance were compared while considering unbalance for compensation and satisfactory results were obtained. [ABSTRACT FROM AUTHOR]

When the strapdown inertial navigation system does not perform coarse alignment, the misalignment angle is generally a large angle, and a nonlinear error model and a nonlinear filtering method are required. For large azimuth misalignment, the initial alignment technology with a large azimuth misalignment angle is researched in this paper. The initial alignment technology with a large azimuth misalignment angle is researched in this paper. First, the SINS/GPS nonlinear error model is established. Secondly, in the view of observation gross errors and inaccurate noise statistical characteristics, an adaptive robust CKF algorithm is proposed. Finally, according to the simulation analysis and experiment, the adaptive robust CKF algorithm can augment the stability and improve the filter estimation precision and convergence rate. [ABSTRACT FROM AUTHOR]

Fetal movement (FM) is an essential physiological parameter to determine the health status of the fetus. To address the problems of harrowing FM signal extraction and the low recognition rate of traditional machine learning classifiers in FM signal detection, this paper develops a passive FM signal detection system based on intelligent sensing technology. FM signals are obtained from the abdomen of the pregnant woman by using accelerometers. The FM signals are extracted and identified according to the clinical nature of the features hidden in the amplitude and waveform of the FM signals that fluctuate in duration. The system consists of four main stages: (i) FM signal preprocessing, (ii) maternal artifact signal preidentification, (iii) FM signal identification, and (iv) FM classification. Firstly, Kalman filtering is used to reconstruct the FM signal in a continuous low-amplitude noise background. Secondly, the maternal artifact signal is identified using an amplitude threshold algorithm. Then, an innovative dictionary learning algorithm is used to construct a dictionary of FM features, and orthogonal matching pursuit and adaptive filtering algorithms are used to identify the FM signals, respectively. Finally, mask fusion classification is performed based on the multiaxis recognition results. Experiments are conducted to evaluate the performance of the proposed FM detection system using publicly available and self-built accelerated FM datasets. The classification results showed that the orthogonal matching pursuit algorithm was more effective than the adaptive filtering algorithm in identifying FM signals, with a positive prediction value of 89.74%. The proposed FM detection system has great potential and promise for wearable FM health monitoring. [ABSTRACT FROM AUTHOR]

Based on the improved adaptive filtering method, this paper conducts in-depth discussion and research on embedded graphics and video coding and chooses to improve the adaptive filtering algorithm from three aspects: starting point prediction, search template, and window partitioning. The algorithm is imported into the encoder for video capture and encoding. By capturing videos of different formats, resolutions, and times, the memory size of the video files collected before and after the algorithm optimization is compared, and the optimized algorithm occupies the memory space of the video file in the actual system. The conclusion of less and higher coding rates. The collected video information is stored on a personal computer equipped with a freeness, and external electronic devices only need to download and install the browser, and the collected video information can be accessed in the local area network through the protocol. The improved coding algorithm has higher coding efficiency and can reduce the storage space occupied by the video. [ABSTRACT FROM AUTHOR]

In this article, a mathematical analysis model of economics of prefabricated building structure based on improved neural network algorithm is proposed in order to solve the low analysis accuracy in traditional methods. Firstly, by means of analyzing the costs of materials, labor, and equipment, the economic characteristics of the cost of fabricated building structures are determined. Secondly, the single neuron is analyzed and the weight coefficient is adjusted in accordance with the multilayer neural network structure, so as to minimize the construction error of the economic analysis model of the assembled building structure. Meanwhile, the weight vector is obtained, error-weighted square sum is calculated through choosing an adaptive filter and obtained, and the weight vector is updated by the least squares algorithm. Thirdly, the neural network algorithm training and learning process is designed and improved, the dependent variable is selected, the number of input points is determined, and then, the training and learning process of the improved neural network algorithm is completed. Finally, a fitness function is set to measure the authenticity of dataset, which is further defined as a combination of different weights to construct an economic mathematical analysis model. The experimental results indicate that the analysis results of this method can reach an accuracy up to 96%, so it has a broader application prospect in low-rise buildings. [ABSTRACT FROM AUTHOR]

Summary: Accurate estimation of the state‐of‐energy (SOE) of lithium batteries is of great significance for the stable operation of electric vehicles. However, the time‐varying parameters of the equivalent model and the difficulty of describing the nonlinear characteristics between the open circuit voltage (OCV) and SOE limit the further improvement of SOE estimation accuracy. In this study, the fitting method of OCV‐SOE curves is improved and dual adaptive particle filters are used to improve the estimation accuracy of SOE. To improve the accuracy of the OCV‐SOE correspondence expression at the beginning and end of the discharge process, the curve fitting is accomplished using Gaussian polynomials, and the model equations based on the Gaussian function system are re‐established. The identification of the equivalent model parameters and SOE estimation are achieved simultaneously using two interrelated particle filters, which partially weaken the influence of the time‐varying equivalent parameters on the SOE estimation accuracy. To further improve the convergence capability of the algorithm, a noise adaptation method based on the change of state estimation is added to the dual particle filters so that the noise variance meets the requirements of the algorithm throughout the discharge process. The test results of two different discharge conditions, dynamic stress test, and supplemental federal test procedure, verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The proportionate affine projection sign subband adaptive filter (PAP-SSAF) has a better performance than the affine projection sign subband adaptive filter (AP-SSAF) when we eliminate the echoes. Still, the robustness of the PAP-SSAF algorithm is insufficient under unknown environmental conditions. Besides, the best balance remains to be found between low steady-state misalignment and fast convergence rate. In order to solve this problem, we propose a normalized combination of PAP-SSAF (NCPAP-SSAF) based on the normalized adaption schema. In this paper, a power normalization adaptive rule for mixing parameters is proposed to further improve the performance of the NCPAP-SSAF algorithm. By using Nesterov's accelerated gradient (NAG) method, the mixing parameter of the control combination can be obtained with less time consumed when we take the l1-norm of the subband error as the cost function. We also test the algorithmic complexity and memory requirements to illustrate the rationality of our method. In brief, our study contributes a novel adaptive filter algorithm, accelerating the convergence speed, reducing the steady-state error, and improving the robustness. Thus, the proposed method can be utilized to improve the performance of echo cancellation. We will optimize the combination structure and simplify unnecessary calculations to reduce the algorithm's computational complexity in future research. [ABSTRACT FROM AUTHOR]