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Multiple Object Tracking (MOT) aims to employ computer vision techniques for real-time tracking and recognition of multiple objects within video sequences. It encompasses the tasks of detection, tracking, and Re-identification (ReID) of objects to achieve continuous tracking of targets over both temporal and spatial domains. MOT makes up a significant challenge within the domain of computer vision. This paper proposes Hyper DeepSORT, an advanced MOT model integrating three significant innovations: HyperNMS, Hyper Kalman Filter, and MTRNet. HyperNMS, a novel Non-Maximum Suppression (NMS) technique, leverages parallel matrix operations to perform NMS in a single iteration, enhancing object recognition accuracy and system efficiency. The Hyper Kalman Filter, an adaptive variant of the traditional Kalman filter, dynamically adjusts noise covariance based on detection confidence, improving the tracker's adaptability and robustness. Additionally, MTRNet incorporates ReID technology to refine feature representation within the DeepSORT framework, encompassing attributes like colour, texture, shape, and motion parameters, bolstering tracking performance. Experimental evaluations on multiple MOT datasets show Hyper DeepSORT outperforms existing models. Specifically, it shows average improvements of 12.75%, 5.37%, 7.20%, 9.94%, 4.90%, and 12.25% over current mainstream models in mAP, MOTA, IDF1, IDSW, FP, and FN metrics, respectively. These results underscore Hyper DeepSORT's superior accuracy and efficiency in complex tracking scenarios. [ABSTRACT FROM AUTHOR]

Summary: Problems like intermittent photovoltaic (PV) power, disconnected PV arrays, etc. are brought on by the extensive integration of PV generation. In this article, a single‐stage PV‐integrated grid‐tethered system is controlled using a reconfigurative multiple random features kernel mean p‐power (MRFKMP) control algorithm. The algorithm circumvents the dependence of kernel parameter in the classic random Fourier features mapping and lower the computing complexity in the traditional multiple kernel learning adaptive filter. MRFKMP algorithm with a linear filter structure can reduce both the computational and storage cost. This is the novel application of MRFKMP algorithm to extract fundamental component of nonlinear load currents in PV‐integrated grid‐tied system. Furthermore, to increase the reliability of the system, a reconfigurable control is developed, which adaptive regulates DC link voltage from PV mode (PVM) during PV hours to active power filtering mode (AFM) during PV nonavailability/PV disconnection. On a created laboratory testbed, the applied control's practical feasibility for numerous scenarios is evaluated. Comparison with existing control algorithms reveals that the MRFKMP controller outperforms the LMF based PV system by 79% less peak‐to‐peak load current ripple and 60% less than the best performing SRF control algorithm. The grid current THD is 2.60%, which is 30% low than that presented by SRF technique. [ABSTRACT FROM AUTHOR]

Nonlinear ocean waves have a significant impact on the functioning of several offshore activities. Predicting the internal ocean waves plays a crucial role on submarine and ship operations. Data assimilation is a mechanism in which data observed is interpreted, processed and adapted. The existing works for estimating the future atmospheric condition are highly dependent on the exact initial state, which mostly differ from the observation. This paper proposes modelling of internal ocean waves using automatic internal wave detection and data assimilation. Ensemble Kalman filtering method is used to model ocean waves. The proposed system is focused on satellite images. The images are pre-processed for speckle noise using adaptive filters. Enhanced residual network is used for edge detection. Unlike the existing edge detection methods that have high complexity, this enhanced residual network works with low complexity and makes a direct mapping between the input wave image and wave edge. Finally, the potential edges of the internal wave are detected and adapted using ensemble Kalman filter. Adaptive thresholding technique is used to determine the appropriate threshold to segregate objects from background. The proposed enhanced edge detection model is compared w.r.t to the parameters weighted cross-entropy loss function, accuracy and root mean squared error with canny edge detection and proved to be better. The detection of internal wave is demonstrated, and the accuracy of the approach is 91% with low RMSE when compared to existing works. [ABSTRACT FROM AUTHOR]

Temporal adaptive convolution has demonstrated superior performance over static convolution techniques in video understanding. However, it needs to be improved in long-time series modeling and multi-scale feature-map adaptation. To address these challenges, we introduce spatio-temporal hybrid adaptive convolution (STHAC), designed to enhance the spatio-temporal modeling capabilities of convolution. This is achieved by learning a set of spatio-temporal calibration filters to mitigate the spatial invariance intrinsic to static convolution methods. Specifically, STHAC learns a linear combination of N adaptive filters by parallelizing two lightweight attention branches. The resulting linearly mixed filters incorporate spatial multi-scale prior knowledge and long-range temporal dependencies. These spatio-temporal calibration filters modulate each frame's static convolutional weight parameters, thereby endowing static convolution with spatial multi-scale adaptability and long-range temporal modeling capabilities. Compared to other dynamic convolution methods, our proposed calibration filters require fewer parameters and incur lower computational complexity. Moreover, we introduce an Omni-dimensional aggregation module to augment the spatio-temporal modeling capacity of STHAC. When combined with STHAC, this aggregation module forms the spatio-temporal adaptive module (STAM) that can replace static convolution. We implement a spatio-temporal dynamic network based on STAM to validate our approach. Experimental results indicate that our model is competitive with state-of-the-art convolutional neural network architectures on action recognition benchmarks such as Kinetics-400(K400) and Something-Something V2(SSV2). [ABSTRACT FROM AUTHOR]

The filtered-x least mean square (F-xLMS) technique dependent distributed active noise control (DANC) system is less effective against outliers and disturbances that represent impulses. In such an environment, DANC algorithms based on Swish functions are being developed to reduce noise. The Swish function overcomes the vanishing gradient problem and accelerates convergence by utilising the smoothness and unboundedness properties. In light of this, the current manuscript presents a set of robust techniques for DANC systems that utilizes the Swish structure in lieu of diffusion strategy for filtered-x Blake Zisserman, filtered-x logarithmic hyperbolic cosine adaptive filter algorithms, filtered-x least mean fourth, and filtered-x generalized maximum correntropy criterion. In regard to the suggested methods, weight updating rules pertaining utilizing to the introduced algorithms are created and a convergence analysis is done. The proposed methods achieve quantitatively 1–5 dB enhancement in noise mitigation in case of varied impulsive noise models and input reference signals considered with a faster convergence compared to previous strategies. [ABSTRACT FROM AUTHOR]

Electronic equipment used on a daily basis now frequently includes active noise cancellation. The adaptive filters, which are positioned within, are essential for noise cancellation. An essential component to take into account for the overall performance is the structural and computational complexity of the filter. The filter's structure has an impact on this. The amount of taps determines the structure. Active noise cancellation filters often have set tap lengths and are lengthy, which causes sluggish convergence and delay. As a result, a trade-off between the filter's length and convergence is required. This is conceivable if there is a flexible filter with a tap length that adapts to the environment while still ensuring acceptable convergence. This study proposes a novel Minimum Mean M-estimate method with changeable tap length and uses a sub-band adaptive filtering technique to shorten the filter's length. In order to maximize the filter's efficiency, the advantages of three approaches are specifically merged in this work. They are the proposed algorithm, the proposed method's variable tap length variant, and the sub-band adaptive filtering. The simulation's findings and recommendations are supported. [ABSTRACT FROM AUTHOR]

Low-light image enhancement algorithms have been widely developed. Nevertheless, using long exposure under low-light conditions will lead to motion blurs of the captured images, which presents a challenge to address low-light enhancement and deblurring jointly. A recent effort called LEDNet addresses these issues by designing a encoder-decoder pipeline. However, LEDNet relies on paired data during training, but capturing low-blur and normal-sharp images of the same visual scene simultaneously is challenging. To overcome these challenges, we propose a self-supervised normalizing flow called SSFlow for jointing low-light enhancement and deblurring. SSFlow consists of two modules: an orthogonal channel attention U-Net (OAtt-UNet) module for extracting features, and a normalizing flow for correcting color and denoising (CCD flow). During the training of the SSFlow, the two modules are connected to each other by a color map. Concretely, OAtt-UNet module is a variant of U-Net consisting of an encoder and a decoder. OAtt-UNet module takes a low-light blurry image as input, and incorporates an orthogonal channel attention block into the encoder to improve the representation ability of the overall network. The filter adaptive convolutional layer is integrated into the decoder, applying a dynamic convolution filter to each element of the feature for effective deblurring. To extract color information and denoise, the CCD flow makes full use of the powerful learning ability of the normalizing flow. We construct an unsupervised loss function, continuously optimizing the network by using the consistent color map between the two modules in the color space. The effectiveness of our proposed network is demonstrated through both qualitative and quantitative experiments. Code is available at https://github.com/shibaoshun/SSFlow. [ABSTRACT FROM AUTHOR]

In realistic sound scenarios, cutting-edge active noise control (ANC) systems still suffer critical instabilities since these systems works under impulsive noise signals. To decrease the negative impact of this noise, several authors have made extraordinary efforts to develop efficient algorithms in terms of convergence speed and misadjustment. However, these algorithms still exhibits limited convergence capabilities. In this letter, we present for the first time, the development of a switching algorithm based on the maximum versoria criterion (MVC) algorithm. Our results demonstrate that the proposed switching algorithm exhibits good convergence properties while maintaining a lower-computational cost when compared with existing algorithms. [ABSTRACT FROM AUTHOR]

Conventional L1 adaptive controller utilizes high adaptation gain to quickly adapt time varying uncertainties and disturbances. However, it contaminates the control signal with high frequency components. To eliminate this high frequency signal, it uses a low pass filter with fixed structure. Yet, the main problem with this fixed structured filter is that it may cancel the estimated values of uncertainties and disturbances along with the high frequencies. To overcome this problem, in this research work, an adaptive fuzzy low pass filter is proposed in place of the fixed structured filter of the L1 adaptive controller. In this proposed research work, the filter changes its structure according to the time varying uncertainties and disturbances. Hence, the contributory part of this work is that the proposed adaptive fuzzy filter efficiently cancels out high frequency components without nullifying the estimated values of uncertainties and disturbances from the control signal. The stability of the proposed adaptive fuzzy filter-based L1 adaptive controller is guaranteed analytically. The proposed method is employed on an electromechanical actuator, viz., DC motor experimental setup to validate its effectiveness. Adequate cancellation of high frequency signal incurs smooth control signal which in turn provides lower control energy. [ABSTRACT FROM AUTHOR]

As image–text matching (a critical task in the field of computer vision) links cross-modal data, it has captured extensive attention. Most of the existing methods intended for matching images and texts explore the local similarity levels between images and sentences to align images with texts. Even though this fine-grained approach has remarkable gains, how to further mine the deep semantics between data pairs and focus on the essential semantics in data remains to be quested. In this work, a new semantic filtering and adaptive approach (FAAR) was proposed to ease the above problem. To be specific, the filtered attention (FA) module selectively focuses on typical alignments with the interference of meaningless comparisons eliminated. Next, the adaptive regulator (AR) further adjusts the attention weights of key segments for filtered regions and words. The superiority of our proposed method was validated by a number of qualitative experiments and analyses on the Flickr30K and MSCOCO data sets. [ABSTRACT FROM AUTHOR]

This paper considers an electric-hydrogen hybrid energy storage system composed of supercapacitors and hydrogen components (e.g., electrolyzers and fuel cells) in the context of a microgrid with photovoltaic generators. To manage the power and hydrogen flows within the microgrid and coordinate the coupling between the microgrid and a hydrogen refueling station, this paper proposes an energy management framework. The outer layer of the framework takes the hydrogen compressor power as part of the microgrid load and optimizes the hydrogen flow from the microgrid to the hydrogen refueling station. The inner layer develops a two-stage scheme to optimize the power allocation between the electric and hydrogen systems within the microgrid. A low pass filter with an adaptive cutoff frequency is utilized to split the net load power of the microgrid into high-frequency and low-frequency components, which are then assigned to the electric and hydrogen systems, respectively. The adaptive cutoff frequency is determined based on the power split factor tuned twice during the two stages of the power allocation scheme based on the supercapacitor state of charge. The effectiveness of the proposed framework is validated by performing real-time simulations using a dSPACE platform. • Proposes an energy management framework for electric-hydrogen systems. • Optimizes the hydrogen flow from the microgrid to the hydrogen refueling station. • Develops a two-stage power allocation scheme based on artificial potential field. • Validates the framework using a dSPACE DS1202 real-time simulation system. [ABSTRACT FROM AUTHOR]

• An adaptive filtering algorithm based on improved SPRT is proposed by combining fault detection function with Kalman filtering. • The fading weighting method is combined with SPRT. The influence of fault observation is reduced by calculating the weight coefficient. • The improved SPRT is more sensitive to faults and has a lower rate of missed detection. GNSS measurement noise faults can easily cause a drop or even divergence in the filter accuracy of integrated navigation system. Therefore, real-time fault detection and processing are necessary. With the aim of the defects of residual Chi-square test and sequential probability ratio test (SPRT) in detecting measurement noise faults, an adaptive filtering algorithm based on improved SPRT detection is proposed. On the one hand, the influence of the current time innovation on the statistics is strengthened by the method of fading weighting. On the other hand, the measurement update equation of the adaptive filter is constructed by calculating the weight factor of the statistics in real-time, which improves the accuracy and robustness of the state estimation. Simulation experiments show that the proposed algorithm is more sensitive and non-missing compared to the residual Chi-square test in the case of large value abrupt faults in the measurement noise. The proposed algorithm reduces the fault detection delay time by about 65.5% compared to SPRT in the case of slow change faults in measurement noise. In the case of slow change faults with small values, the proposed algorithm reduces the missing detection rate by 40% compared to the residual Chi-square test. In addition, the proposed algorithm is compared with the robust Kalman filter based on the residual Chi-square and Sage-Husa adaptive filtering. The results show that the proposed algorithm has higher navigation accuracy when the system has slow change faults. [ABSTRACT FROM AUTHOR]

Aiming at the characteristics that the signal noise ratio (SNR) gradually decreases from the near to far range of the swath, an adaptive phase filtering algorithm based on Goldstein filtering and combined with multiple quality-guided graphs was proposed. Firstly, the components used to determine the filtering parameters were obtained through residue density, pseudo-coherence coefficient and pseudo-SNR, the three quality-guided graphs. Then, the filter parameters were calculated by weighting the three components. Finally, the size of filtering window was determined according to the account of residues, and the interferometric phase noise was removed in frequency domain. Simulated data, TSX/TDX data and airborne interferometric imaging radar altimeter data were used to verify the performance of the new algorithm. Compared with the results of Goldstein filtering and its improved algorithms, the results showed that the proposed algorithm can effectively filter out phase noise while maintaining the edge characteristics of interferometric fringe. The section of filtering result can well match with the section of simulated pure interfeometric phase. Moreover, the algorithm proposed in this paper can effectively remove the noise in the interferogram of TSX/TDX sea ice data, and the residues' filtering rate was above 86%, which can effectively remove the phase residues of the sea ice surface while maintaining the characteristics of the sea ice edge. Experimental results showed that the new algorithm provides an effective phase noise filtering method for imaging radar altimeter data processing. [ABSTRACT FROM AUTHOR]

AbstractIn genomic research, identifying the exon regions in eukaryotes is the most cumbersome task. This article introduces a new promising model-independent method based on short-time discrete Fourier transform (ST-DFT) and fine-tuned variational mode decomposition (FTVMD) for identifying exon regions. The proposed method uses the N/3 periodicity property of the eukaryotic genes to detect the exon regions using the ST-DFT. However, background noise is present in the spectrum of ST-DFT since the sliding rectangular window produces spectral leakage. To overcome this, FTVMD is proposed in this work. VMD is more resilient to noise and sampling errors than other decomposition techniques because it utilizes the generalization of the Wiener filter into several adaptive bands. The performance of VMD is affected due to the improper selection of the penalty factor (α), and the number of modes (K). Therefore, in fine-tuned VMD, the parameters of VMD (K and α) are optimized by maximum kurtosis value. The main objective of this article is to enhance the accuracy in the identification of exon regions in a DNA sequence. At last, a comparative study demonstrates that the proposed technique is superior to its counterparts. [ABSTRACT FROM AUTHOR]

Multichannel Synthetic Aperture Radar (MC-SAR) systems, such as Azimuth Multi-Phase Centre (AMPC) SAR, provide an effective solution for achieving high-resolution wide-swath (HRWS) imaging by reducing the pulse repetition frequency (PRF) to increase the swath width. However, in an Electronic Countermeasures (ECM) environment, the image quality of multichannel SAR systems can be significantly degraded by electromagnetic interference. Previous research into interference and counter-interference techniques has predominantly focused on single-channel SAR systems, with relatively few studies addressing the specific challenges faced by MC-SAR systems. This paper uses the classical spatial filtering technique of adaptive digital beamforming (DBF). Considering the Doppler ambiguity present in the echoes, two schemes—Interference Reconstruction And Cancellation (IRC) and Channel Grouping Nulling (CGN)—are designed to effectively eliminate suppressive interference. The IRC method eliminates the effects of interference without losing spatial degrees of freedom, ensuring effective suppression of Doppler ambiguity in subsequent processing. This method shows significant advantages under conditions of strong Doppler ambiguity and low jammer-to-signal ratio. Conversely, the CGN method mitigates the effect of interference on multichannel imaging at the expense of degrees of freedom redundant to Doppler ambiguity suppression. It shows remarkable interference suppression performance under weak-Doppler-ambiguity conditions, allowing for better image recovery. Simulations performed on point and distributed targets have validated that the proposed methods can effectively remove interfering signals and achieve high-resolution wide-swath (HRWS) SAR images. [ABSTRACT FROM AUTHOR]

To address the issue of accuracy in Simultaneous Localization and Mapping (SLAM) for forested areas, a novel point cloud adaptive filtering algorithm is proposed in the paper, based on point cloud data obtained by backpack Light Detection and Ranging (LiDAR). The algorithm employs a K-D tree to construct the spatial position information of the 3D point cloud, deriving a linear model that is the guidance information based on both the original and filtered point cloud data. The parameters of the linear model are determined by minimizing the cost function using an optimization strategy, and a guidance point cloud filter is subsequently constructed based on these parameters. The results demonstrate that, comparing the diameter at breast height (DBH) and tree height before and after filtering with the measured true values, the accuracy of SLAM mapping is significantly improved after filtering. The Mean Absolute Error (MAE) of DBH before and after filtering are 2.20 cm and 1.16 cm; the Root Mean Square Error (RMSE) values are 4.78 cm and 1.40 cm; and the relative RMSE values are 29.30% and 8.59%. For tree height, the MAE before and after filtering are 0.76 m and 0.40 m; the RMSE values are 1.01 m and 0.50 m; the relative RMSE values are 7.33% and 3.65%. The experimental results validate that the proposed adaptive point cloud filtering method based on guided information is an effective point cloud preprocessing method for enhancing the accuracy of SLAM mapping in forested areas. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) are essential for a wide range of applications, including environmental monitoring and smart city developments, thanks to their ability to collect and transmit diverse physical and environmental data. The nature of WSNs, coupled with the variability and noise sensitivity of cost-effective sensors, presents significant challenges in achieving accurate data analysis and anomaly detection. To address these issues, this paper presents a new framework, called Online Adaptive Kalman Filtering (OAKF), specifically designed for real-time anomaly detection within WSNs. This framework stands out by dynamically adjusting the filtering parameters and anomaly detection threshold in response to live data, ensuring accurate and reliable anomaly identification amidst sensor noise and environmental changes. By highlighting computational efficiency and scalability, the OAKF framework is optimized for use in resource-constrained sensor nodes. Validation on different WSN dataset sizes confirmed its effectiveness, showing 95.4% accuracy in reducing false positives and negatives as well as achieving a processing time of 0.008 s per sample. [ABSTRACT FROM AUTHOR]

This work presents a variable step Champernowne Adaptive Filter (VS-CAF) based control architecture for a grid-integrated solar photovoltaic system. The control structure is implemented to successfully synchronize the two-stage solar photovoltaic at the point of common coupling where the source and domestic loads are connected. The control architecture ensures solar power injection and improvised power quality at the unity power factor. Moreover, the control structure provides only the balanced positive sequence fundamental sinusoidal source currents at solar irradiation alteration and load disturbances. The VS-CAF is exploited to estimate the peak of the fundamental component out of the harmonics-rich domestic load currents. To examine the efficacy of the suggested control technique, a 40 kW solar photovoltaic is simulated using MATLAB/Simulink. The performance assessment of the grid-tied solar generation system is carried out at solar irradiation alteration, zero solar irradiation, and detachment of one phase of the three-phase load. Various simulation results are carried out to verify the successful integration of solar photovoltaics into the three-phase grid following the IEEE-519 standard and satisfactory performance during various working conditions. Moreover, the simulation results are also verified with real-time results taken from the Opal-RT simulator. [ABSTRACT FROM AUTHOR]

Inexpensive cameras, different acquisition devices and mobile phones have resulted in the production of digital images in all spheres of life. There is common of these images for extracting useful information in many fields to perform image analysis and interpretation. One of the problems being faced in the use of this information is the presence of random valued impulse noise. Impulse noise removal is a crucial step in computer vision. Many articles have been published to remove or suppress impulse noise from grayscale digital images using different approaches. Edges and details are, however, often lost during the restoration of digital images. In this research, a two-stage approach for edge detection, preservation, and impulse noise removal is proposed. An effective statistics rank ordered absolute difference (ROAD) is used in the first stage to detect noisy pixels. Trimmed global mean (TGM) with adaptive window is employed later for the filtering process. With the combination of these processes in the proposed method, a higher quality image is restored and thus becomes more suitable for extracting the desired information. Comparison with many state-of-the-art methodologies indicates that the proposed method is effective in the detection of corrupted pixels and the restoration of images. [ABSTRACT FROM AUTHOR]

This research article details the design and implementation of a nonlinear adaptive filtering (NAF) technique using an exponential functional link network (EFLN) for a shunt hybrid active power filter (SHAPF) control to solve the current-associated power quality issues on the utility side at the distribution level of electrical power systems. Separation of the fundamental component from the harmonics, achieving unity power factor operation, reducing the reactive power drawn from the source, balancing the currents during transients, and reduction of total harmonic distortion (THD) of the source current are the issues considered to resolve. The proposed technique solves these issues by generating the sinusoidal reference current and separating the fundamental current from the harmonics. When compared to conventional and existing adaptive filtering techniques such as least mean square (LMS), least mean fourth (LMF), and variable step size LMS (VSS-LMS), the proposed EFLN-NAF method excels in terms of speedy convergence, adaptability in noise-specific environments and reduced steady-state coefficient error. MATLAB/Simulink software is utilized to perform the simulations to examine the suggested strategy for the chosen SHAPF topology both in static and dynamic scenarios. For a 15 kW and 3kVAr requirement of the nonlinear load, simulation results proved that the designed PPF for 2kVAr is able to share the reactive power with the APF, thereby reducing its rating and cost. The proposed method of filtering has been proven to be fast converging with 0.049 s, and the THD in steady state is brought to 1.32 % in steady-state and to 3.77 % during transient conditions, which are under standard limits. A hardware prototype of the experimental setup is constructed at the laboratory scale with OPAL-RT (OP4510) as the controller. With an active and reactive power demand of 1.1 kW and 210VAr, the designed PPF supplies 110 VAr, whereas the rest is supplied by the APF. The practical THD in source current is observed to be 2.081 %, which meets the standards. The results from both simulations and experiments are validated, and the efficacy of the proposed technique in mitigating the aforementioned power quality issues is proved. [Display omitted] • A passive power filter design to share the reactive power requirement in addition to mitigating the predominant harmonics of the 5th and 7th order. • A robust nonlinear adaptive filter implementation based on exponential functional link network (EFLN-NAF) algorithm to the shunt hybrid active power filter (SHAPF) control for reference current generation and fundamental component separation from harmonics. • Solution for current-associated power quality issues such as reducing the reactive power burden, achieving unity power factor operation, and mitigating the harmonics, thereby reducing the source current THD value. • Simulation of the proposed technique for the control of shunt hybrid active power filter and evaluation of the outcomes for resolving the power quality issues in both steady-state and dynamic conditions. • Development of a hardware prototype at a laboratory scale using OPAL-RT (OP4510) real-time simulator as a controller for the proposed technique for the control of SHAPF and evaluation of the outcomes. [ABSTRACT FROM AUTHOR]

This paper proposed a prediction algorithm for the degraded actuator taking into account the impact of estimation error of hidden index in the closed-loop system. To this end, a unified prediction framework is established to evaluate the hidden degradation information and recursively update the degradation model parameters simultaneously. The advantage is that the prediction framework can comprehensively compensate the estimation error of hidden degradation index caused by system uncertainty. To jointly estimate the degradation information in avoidance of the impact of system uncertainty, a modified adaptive Kalman filter is designed, and the proof of stability is provided. With the priori estimate from the filter, the degradation model parameters are updated by the inverse filtering probability based on Bayes' theorem. It is followed by the computation of the remaining useful life (RUL) prediction utilizing aforementioned hidden degradation information and the latest degradation model. The effectiveness of the proposed RUL prediction algorithm is demonstrated by the degraded actuator in the continuous casting process. • A novel prediction framework is designed for degraded actuator in closed-loop system. • Adaptive degradation model is established by modified adaptive Kalman filter. • The estimation error of hidden degradation index is integrated. • The stability of optimized adaptive Kalman filter is proven. • Recursive prediction algorithm is performed without full life data. [ABSTRACT FROM AUTHOR]

To achieve a rapid and stable dynamic response of the drive anti-slip system for distributed electric vehicles on low-friction surfaces, this paper proposes an adaptive acceleration slip regulation control strategy based on wheel slip rate. An attachment coefficient fusion estimation algorithm based on an improved singular value decomposition unscented Kalman filter is designed. This algorithm combines Sage–Husa with the unscented Kalman filter for adaptive improvement, allowing for the quick and accurate determination of the road friction coefficient and, subsequently, the optimal slip rate. Additionally, a slip rate control strategy based on dynamic adaptive compensation sliding mode control is designed, which introduces a dynamic weight integral function into the control rate to adaptively adjust the integral effect based on errors, with its stability proven. To verify the performance of the road estimator and slip rate controller, a model is built with vehicle simulation software, and simulations are conducted. The results show that under icy and snowy road conditions, the designed estimator can reduce estimation errors and respond rapidly to sudden changes. Compared to traditional equivalent controllers, the designed controller can effectively reduce chattering, decrease overshoot, and shorten response time. Especially during road transitions, the designed controller demonstrates better dynamic performance and stability. [ABSTRACT FROM AUTHOR]

Featured Application: TFBG sensors can be used to measure temperature, bending, rotation, twist and, in particular, refractive index. The value of the refractive index is useful in assessing the concentration of different substances in an aqueous solution. This finds applications in monitoring the environment and industrial processes, and in the field of medicine. Tilted fibre Bragg gratings are optical fibre structures used as sensors of various physical quantities. Their unique measurement capabilities result from the high complexity of the optical spectrum consisting of several dozen cladding mode resonances. TFBG spectra demodulation methods generate signal features that highlight changes in the spectrum due to changes in the interacting quantities. Such methods should enable the distinction between two slightly different values of the measured quantity. The paper presents an effective method of processing the TFBG spectrum for use in measuring the refractive index of liquids. The use of Gabor and log-Gabor filters and their adaptive version eliminates the problem of discontinuity in determining the SRI value related to the existence of the cladding mode comb. The Gabor filters used make visible the shifting and fading of spectral features related to the decrease in the intensity of leaking modes. Subsequent modifications of the proposed algorithm led to an increase in the quality factor of the processed spectrum. [ABSTRACT FROM AUTHOR]

With regards to the problem of multidimensional signal processing in the field of adaptive filtering, geometric algebra based higher-order statistics algorithms were proposed. For instance, to express a multidimensional signal as a multi-vector, the adaptive filtering algorithms described in this work leverage all of the benefits of GA theory in multidimensional signal processing. GA space is employed to extend the traditional least-mean absolute third (LMAT) and newly deduced least-mean mixed third-fourth (LMMTF) adaptive filtering methods for multi-dimensional signal processing. The objective of the presented GA-based least-mean absolute third (GA-LMAT) and GA-based least-mean mixed third-fourth (GA-LMMTF) algorithms is to minimize the cost functions by using higher-order statistics of the error signal e(n) in GA space. The simulation's results revealed that at significantly smaller step size, the given GA-LMAT algorithm is better than the others in terms of steady-state error and convergence rate. Besides, the defined GA-LMMTF algorithm mitigates for the instability of GA-LMAT as the step size increases and illustrates an improved performance relative to mean absolute error and convergence rate. [ABSTRACT FROM AUTHOR]

The Common-path Coherent-dispersion Spectrometer (CODES), an exoplanet detection instrument, executes high-precision Radial Velocity (RV) inversions by recording the phase shifts of interference fringes. Salt-and-pepper noise caused by factors such as improper operation of the CCD probe/analog-to-digital converter and strong dark currents may interfere with the phase information of the fringe. This lowers the quality of the interfering fringe image and significantly interferes with the RV's inversion. In this study, an adaptive median filtering algorithm (CODESmF) based on submaximum and subminimum values is designed to eliminate the interference fringe image's salt-and-pepper noise as well as to reduce RV error. This allows the interference fringe image's phase information to be retained more completely. The algorithm consists of two major modules. Pixel Sub-extreme-based Filtered Noise Monitoring Module: discriminates signal pixels and noise pixels based on the submaximum and subminimum values of the pixels in the filtering window. Adaptive Median Filter Noise Suppression Module: the signal pixel is kept at the original value output, the noise pixel serves as the filtering window's center pixel, and the adaptive median filtering procedure is repeated numerous times with various filtering window sizes. According to the experimental findings, the CODESmF outperforms comparable algorithms and works better at recovering interference fringes. More than 90% of the phase/RV error caused by salt-and-pepper noise is typically eliminated by the CODESmF algorithm, and in certain circumstances, it can even remove roughly 98% of the phase error. [ABSTRACT FROM AUTHOR]

Spina bifida in the foetus is a neurological condition that develops when the spinal cord fails to close properly during pregnancy. A birth defect that causes permanent paralysis in affected babies. Spina bifida is a birth defect that can be treated well if identified and treated early. It is generally agreed that ultrasonic imaging is the gold standard for foetal monitoring. In this research, ultrasound pictures of the foetal spine were segmented and classified into normal and pathological categories using two distinct deep learning algorithms. Three hundred pregnant women were recruited between November 2015 and November 2020 and subjected to three-dimensional ultrasound exams at a hospital. Using an adaptive bilateral filter (ABF), any visible noise in the pictures is eliminated. Segmentation of foetal spina bifida images is performed using the dilated encoder-decoder (DED) technique, and the images are then classified using a Feature Map-based differential convolutional network (FMDDCN). Analysis of segmentation showed that the suggested model was 96% accurate in terms of pixels and 87% accurate in terms of mean intersection over union (MIoU). At the end of the classification evaluation, the suggested model had an accuracy of 96.5, whereas the state-of-the-art methods only managed 95.0. [ABSTRACT FROM AUTHOR]

Evaluating the computational complexity is critical for assessing the time-domain anti-jamming performance of GNSS receivers. The multiplier is the core component that contributes to the computational complexity in time-domain anti-jamming. However, current algorithms aimed at reducing the complexity of time-domain anti-jamming typically concentrate on shortening the filter length, which fails to address the high computational complexity introduced by the use of multipliers. This paper introduces a cascaded multiplier-free approach for implementing time-domain anti-jamming in navigation receivers. We propose a numerical power decomposition technique based on optimal Canonical Signed Digit coding and coefficient decomposition. By substituting the multiplier with minimal adder and shift operations, the computational complexity of the anti-jamming filter with a high quantization bit-width can be considerably decreased. An optimization strategy is presented, and the low-complexity multiplier-free technique is applied to the time-domain anti-jamming filter. Compared to the traditional Canonical Signed Digit multiplier-free technique, our method can reduce the components required for a 12-bit quantization anti-interference filter by one adder, 20 shift operations, and five coded word lengths, while maintaining a pseudo-range measurement deviation below 0.27 ns. [ABSTRACT FROM AUTHOR]

Radar sensors, leveraging the Doppler effect, enable the nonintrusive capture of kinetic and physiological motions while preserving privacy. Deep learning (DL) facilitates radar sensing for healthcare applications such as gait recognition and vital-sign measurement. However, band-dependent patterns, indicating variations in patterns and power scales associated with frequencies in time–frequency representation (TFR), challenge radar sensing applications using DL. Frequency-dependent characteristics and features with lower power scales may be overlooked during representation learning. This paper proposes an Enhanced Band-Dependent Learning framework (E-BDL) comprising an adaptive sub-band filtering module, a representation learning module, and a sub-view contrastive module to fully detect band-dependent features in sub-frequency bands and leverage them for classification. Experimental validation is conducted on two radar datasets, including gait abnormality recognition for Alzheimer's disease (AD) and AD-related dementia (ADRD) risk evaluation and vital-sign monitoring for hemodynamics scenario classification. For hemodynamics scenario classification, E-BDL-ResNet achieves competitive performance in overall accuracy and class-wise evaluations compared to recent methods. For ADRD risk evaluation, the results demonstrate E-BDL-ResNet's superior performance across all candidate models, highlighting its potential as a clinical tool. E-BDL effectively detects salient sub-bands in TFRs, enhancing representation learning and improving the performance and interpretability of DL-based models. [ABSTRACT FROM AUTHOR]

Aiming at tracking sharply maneuvering targets, this paper develops novel variational adaptive state estimators for joint target state and process noise parameter estimation for a class of linear state-space models with abruptly changing parameters. By combining variational inference with change-point detection in an online Bayesian fashion, two adaptive estimators—a change-point-based adaptive Kalman filter (CPAKF) and a change-point-based adaptive Kalman smoother (CPAKS)—are proposed in a recursive detection and estimation process. In each iteration, the run-length probability of the current maneuver mode is first calculated, and then the joint posterior of the target state and process noise parameter conditioned on the run length is approximated by variational inference. Compared with existing variational noise-adaptive Kalman filters, the proposed methods are robust to initial iterative value settings, improving their capability of tracking sharply maneuvering targets. Meanwhile, the change-point detection divides the non-stationary time sequence into several stationary segments, allowing for an adaptive sliding length in the CPAKS method. The tracking performance of the proposed methods is investigated using both synthetic and real-world datasets of maneuvering targets. [ABSTRACT FROM AUTHOR]

Rubidium atomic clocks have been used extensively in various fields, with applications such as a core component of Global Navigation Satellite Systems (GNSS). However, they exhibit inherently poor long-term stability. This paper presents the development of a control system for rubidium atomic clocks. It introduces an adaptive Kalman filtering algorithm for the disciplining of a rubidium atomic clock, utilizing autocovariance least squares (ALS) to estimate the clock's noise parameters. The experimental results demonstrate that the proposed algorithm achieves a high estimation accuracy. The standard deviation of the clock error between the steered rubidium atomic clock 1 Pulse Per Second (1PPS) and Coordinated Universal Time (UTC) provided by the National Time Service Center (NTSC) is better than 2.568 nanoseconds(ns), with peak-to-peak values improving to within 11.358 ns. Notably, its frequency stability is reduced to 3.06 × 10−13 @100,000 s. The results for the rubidium atomic clock demonstrate that the adaptive Kalman filtering algorithm proposed herein constitutes an accurate and effective control strategy for the rubidium atomic clock discipline. [ABSTRACT FROM AUTHOR]

This study addresses the state estimation problem of discrete‐time non‐linear stochastic systems with non‐Gaussian noises, particularly impulsive noises. Instead of minimizing the mean square error of the state estimate, which tends to excessively focus on outliers caused by non‐Gaussian noises, the ℓ1${\ell }_1$ norm‐based non‐linear recursive filter (L1KF) is put forward in this paper. Here, minimizing the ℓ1${\ell }_1$ norm of model errors is actually to pursue the minimum sum of absolute values of all errors, which is equitable to all model errors rather than paying much attention on outliers. To further improve estimation accuracy, a recursive nonlinear smoother (L1KS) is proposed, based on minimizing the ℓ1${\ell }_1$ norm of model errors. The proposed ℓ1${\ell }_1$ norm‐based filter and smoother are implemented using unscented transformation for statistical linear regression applied to nonlinear models. Additionally, the computational complexity of the proposed method is analysed. Simulation results of tracking a radar target with impulsive noises demonstrate the effectiveness and robustness of the proposed estimator. [ABSTRACT FROM AUTHOR]

The application of active magnetic bearings (AMBs) in high-speed rotating machinery faces the challenge of micro-vibration. This research addresses the vibration control of a high-speed magnetically suspended turbo molecular pump (MSTMP) with rotor mass imbalance vibration and multi-stage-blade modal vibration. A novel integrated AMB controller consisting of parallel co-frequency adaptive notch filter (ANF) and cascaded multi-frequency improved double-T notch filters (DTNFs) is proposed. To suppress rotor mass imbalance vibration, a bandwidth factor rectification method of the ANF based on displacement stiffness perturbation is designed. To suppress multi-stage-blade modal vibration, a multi-objective constrained optimization method of cascaded improved DTNFs based on linear normalization is designed. Simulation and experimental results validate that the proposed structure improvement of the addition of an AMB controller and multi-parameter optimization of the algorithm can effectively improve not only the phase stability margin and the notch vibration performance of the magnetically suspended rotor (MSR) system but also the efficiency and practicability of the algorithm. At rotational speeds of 12,000 rpm , 15,000 rpm , 18,000 rpm , and 21,000 rpm , the suppression of co-frequency synchronous vibration is approximately maintained between −30.94 dB and −30.56 dB. At the rated speed of 24,000 rpm , compared with other algorithms, the value of the rotor displacement converges from 0.08 mm to 0.03 mm , a reduction of 62.50%. The convergence time decreases from 3.67 s to 2.85 s , a reduction of 22.34%. [ABSTRACT FROM AUTHOR]

This paper presents the design and implementation of a highly accurate smart temperature sensor designed in 65 nm CMOS technology. The sensor exhibits a ±0.15 °C (3σ) error across a wide temperature range from −40 °C to 125 °C, catering to diverse application needs. Leveraging advanced CMOS technology, the sensor employs an adaptive decimation filter that allows us to control the conversion time, ensuring that the accuracy of the conversion is maintained even in challenging conditions. The proposed sensor architecture integrates advanced techniques for temperature sensing for improved accuracy and reliability. Through meticulous circuit design and the usage of dynamic element matching, chopping, and calibration/trimming, the sensor demonstrates exceptional performance characteristics, making it suitable for various industrial, automotive, and consumer electronics applications demanding high precision temperature monitoring. [ABSTRACT FROM AUTHOR]

Electrocardiogram is the most essential diagnostic test for heart disease detection in this era, where it has low frequency and small amplitude, making it vulnerable to a variety of stimuli, including high/low-frequency noises resulting in diagnostic quality suffers. Implementation of adaptive filter removes noise from the signal in a better way. Adaptive filters in signal processing plays a major role in biomedical applications for denoising different types of noises such as Power Line Interference noise which is generated by the power line electromagnetic field and it exhibits its peak from 50 to 60 Hz, Baseline Wander noise which occurs due to the variation of electrode skin impedance, Motion Artifacts which is generated by the electrode motions away from the contact zone on the skins and Muscle Noise, this is due to the Electromyographic signals, originating from the skeletal muscle contractions. In order to eliminate the above-mentioned noises, the most common adaptive filters such as Least Mean Square (LMS) and Recursive Least Squares (RLS) are used. In existing method, they used Cascaded Least Mean Square adaptive filters to reduce noises at a much higher rate than the Least Mean Square Adaptive filter and to achieve the higher Signal to Noise Ratio (SNR). The Cascaded RLS filter can be used to obtain faster range of convergence than the Cascaded LMS. The filter is designed with adaptive variable step size which calculates the step size according to the input signal of the filter, this is used to increase the stability of the filter. The systolic architecture and associativity of the VLLMS filter is implemented to reduce the number of adders and multipliers along the critical path of the filter. This work comprises of designing the 4 tap, 8 tap and 16 tap cascaded systolic associative variable leaky least mean square adaptive filters using Simulink and the SNR comparisons have been made between normal LMS cascaded filter and cascaded VLLMS filter. The area and delay between normal filter and systolic associative filter are compared by converting the Simulink design into Verilog and by implementing in Xilinx. [ABSTRACT FROM AUTHOR]

Background: Residual image noise is substantial in positron emission tomography (PET) and one of the factors limiting lesion detection, quantification, and overall image quality. Thus, improving noise reduction remains of considerable interest. This is especially true for respiratory-gated PET investigations. The only broadly used approach for noise reduction in PET imaging has been the application of low-pass filters, usually Gaussians, which however leads to loss of spatial resolution and increased partial volume effects affecting detectability of small lesions and quantitative data evaluation. The bilateral filter (BF) — a locally adaptive image filter — allows to reduce image noise while preserving well defined object edges but manual optimization of the filter parameters for a given PET scan can be tedious and time-consuming, hampering its clinical use. In this work we have investigated to what extent a suitable deep learning based approach can resolve this issue by training a suitable network with the target of reproducing the results of manually adjusted case-specific bilateral filtering. Methods: Altogether, 69 respiratory-gated clinical PET/CT scans with three different tracers ( [ 18 F ] FDG, [ 18 F ] L-DOPA, [ 68 Ga ] DOTATATE) were used for the present investigation. Prior to data processing, the gated data sets were split, resulting in a total of 552 single-gate image volumes. For each of these image volumes, four 3D ROIs were delineated: one ROI for image noise assessment and three ROIs for focal uptake (e.g. tumor lesions) measurements at different target/background contrast levels. An automated procedure was used to perform a brute force search of the two-dimensional BF parameter space for each data set to identify the "optimal" filter parameters to generate user-approved ground truth input data consisting of pairs of original and optimally BF filtered images. For reproducing the optimal BF filtering, we employed a modified 3D U-Net CNN incorporating residual learning principle. The network training and evaluation was performed using a 5-fold cross-validation scheme. The influence of filtering on lesion SUV quantification and image noise level was assessed by calculating absolute and fractional differences between the CNN, manual BF, or original (STD) data sets in the previously defined ROIs. Results: The automated procedure used for filter parameter determination chose adequate filter parameters for the majority of the data sets with only 19 patient data sets requiring manual tuning. Evaluation of the focal uptake ROIs revealed that CNN as well as BF based filtering essentially maintain the focal SUV max values of the unfiltered images with a low mean ± SD difference of δ SUV max CNN , STD = (−3.9 ± 5.2)% and δ SUV max BF , STD = (−4.4 ± 5.3)%. Regarding relative performance of CNN versus BF, both methods lead to very similar SUV max values in the vast majority of cases with an overall average difference of δ SUV max CNN , BF = (0.5 ± 4.8)%. Evaluation of the noise properties showed that CNN filtering mostly satisfactorily reproduces the noise level and characteristics of BF with δ Noise CNN , BF = (5.6 ± 10.5)%. No significant tracer dependent differences between CNN and BF were observed. Conclusions: Our results show that a neural network based denoising can reproduce the results of a case by case optimized BF in a fully automated way. Apart from rare cases it led to images of practically identical quality regarding noise level, edge preservation, and signal recovery. We believe such a network might proof especially useful in the context of improved motion correction of respiratory-gated PET studies but could also help to establish BF-equivalent edge-preserving CNN filtering in clinical PET since it obviates time consuming manual BF parameter tuning. [ABSTRACT FROM AUTHOR]

Artificial intelligence assists medical practitioners with disease diagnosis from acute to chronic illnesses and has brought significant breakthrough in image-oriented analysis of diseases. Skin cancer is one of the deadliest diseases, posing a great threat to human health. Skin cancer warrants early and precise examination of skin lesions to prevent mortality. Skin lesions can be examined by biopsy, an invasive procedure to remove piece of tissue, tested in a laboratory. Biopsy is a painful procedure. The predictive models in artificial intelligence are found to be useful in analyzing skin lesions-based images. Hence, in this paper, we have analyzed 10,015 skin lesion-based images and classified seven types of skin diseases among which, one of them is melanoma, a form indicative of skin cancer. This work proposes, a novel APFB (Adaptive Percentage Filger for Binarization) to remove the hairline noise from the skin images. The performance of the APFB filter is compared with the state-of-art hairline noise removal methods such as Contrast Limited Adaptive Histogram Equalization (CLAHE) and Bilateral methods and proven to be best in terms of image quality analysis metrics such as PSNR, SSIM, BRISQUE and NIQE. The original images and hairline noise removed images are then passed into custom built CNN models for skin disease classification. The model examines the lesions features like pigment, texture, color, lines, shapes and symmetry to classify the different types of skin diseases. Among the four CNN models we created, the CNN model that takes images of APFB hair line noise removal method obtains 99.98% training accuracy and 100% testing accuracy than the other models. Thus, the model may also be used to predict skin cancer without a biopsy. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive maximum correntropy cubature Kalman filter based on multiple fading factors (MAMCKF) is proposed to address the problem of inaccurate process noise covariance and unknown measurement noise covariance together with outliers in target tracking. Although there are many adaptive filters and robust filters have been proposed to handle unknown measurement noise covariance or measurement outliers, most filters cannot deal with both unknown noise covariance and outliers simultaneously. In this article, we propose an adaptive and robust cubature Kalman filter. The modified measurement noise covariance matrix (MNCM) and innovation covariance matrix are used to construct multiple fading factors for correcting the prediction error covariance matrix (PECM), which can achieve adaptability. Then, the maximum correntropy criterion (MCC) is introduced to suppress outliers, which further enhances the robustness. Compared with the existing approaches, the proposed approach improves the performance by at least 5% in unknown time-varying noise, unknown time-varying heavy-tailed noise, and non-Gaussian heavy-tailed noise scenarios. The simulation results show that the proposed approach can effectively suppress inaccurate process noise covariance and unknown time-varying measurement noise together with outliers. Compared with the existing filtering approaches, the proposed approach exhibits both adaptability and robustness. [ABSTRACT FROM AUTHOR]

Currently, the use of acoustic echo cancellers (AECs) plays a crucial role in IoT applications, such as voice control appliances, hands-free telephony and intelligent voice control devices, among others. Therefore, these IoT devices are mostly controlled by voice commands. However, the performance of these devices is significantly affected by echo noise in real acoustic environments. Despite good results being achieved in terms of echo noise reductions using conventional adaptive filtering based on gradient optimization algorithms, recently, the use of bio-inspired algorithms has attracted significant attention in the science community, since these algorithms exhibit a faster convergence rate when compared with gradient optimization algorithms. To date, several authors have tried to develop high-performance AEC systems to offer high-quality and realistic sound. In this work, we present a new AEC system based on the grey wolf optimization (GWO) and particle swarm optimization (PSO) algorithms to guarantee a higher convergence speed compared with previously reported solutions. This improvement potentially allows for high tracking capabilities. This aspect has special relevance in real acoustic environments since it indicates the rate at which noise is reduced. [ABSTRACT FROM AUTHOR]

(1) Background: The electroencephalogram (EEG) is frequently corrupted by ocular artifacts such as saccades and blinks. Methods for correcting these artifacts include independent component analysis (ICA) and recursive-least-squares (RLS) adaptive filtering (-AF). Here, we introduce a new method, AFFiNE, that applies Bayesian adaptive regression spline (BARS) fitting to the adaptive filter's reference noise input to address the known limitations of both ICA and RLS-AF, and then compare the performance of all three methods. (2) Methods: Artifact-corrected P300 morphologies, topographies, and measurements were compared between the three methods, and to known truth conditions, where possible, using real and simulated blink-corrupted event-related potential (ERP) datasets. (3) Results: In both simulated and real datasets, AFFiNE was successful at removing the blink artifact while preserving the underlying P300 signal in all situations where RLS-AF failed. Compared to ICA, AFFiNE resulted in either a practically or an observably comparable error. (4) Conclusions: AFFiNE is an ocular artifact correction technique that is implementable in online analyses; it can adapt to being non-stationarity and is independent of channel density and recording duration. AFFiNE can be utilized for the removal of blink artifacts in situations where ICA may not be practically or theoretically useful. [ABSTRACT FROM AUTHOR]

In the task of classifying images of cracks in underwater dams, symmetry serves as a crucial geometric feature that aids in distinguishing cracks from other structural elements. Nevertheless, the asymmetry in the distribution of positive and negative samples within the underwater dam crack image dataset results in a long-tail problem. This asymmetry, coupled with the subtle nature of crack features, leads to inadequate feature extraction by existing convolutional neural networks, thereby reducing classification accuracy. To address these issues, this paper improves VanillaNet. First, the Seesaw Loss loss function is introduced to tackle the long-tail problem in classifying underwater dam crack images, enhancing the model's ability to recognize tail categories. Second, the Adaptive Frequency Filtering Token Mixer (AFF Token Mixer) is implemented to improve the model's capability to capture crack image features and enhance classification accuracy. Finally, label smoothing is applied to prevent overfitting to the training data and improve the model's generalization performance. The experimental results demonstrate that the proposed improvements significantly enhance the model's classification accuracy for underwater dam crack images. The optimized algorithm achieves superior average accuracy in classifying underwater dam crack images, showing improvements of 1.29% and 0.64% over the relatively more accurate models ConvNeXtV2 and RepVGG, respectively. Compared to VanillaNet, the proposed algorithm increases average accuracy by 2.66%. The improved model also achieves higher accuracy compared to the pre-improved model and other mainstream networks. [ABSTRACT FROM AUTHOR]

Constrained least mean square (CLMS) algorithm is the most popular constrained adaptive filtering algorithm due to its simple structure and easy implementation. However, its convergence slows down when the input signal is colored. To address this issue, this paper firstly introduces the normalized subband adaptive filter (NSAF) into the constrained filtering problem and derives a constrained NSAF (CNSAF) algorithm using the Lagrange multiplier method. Benefiting from the good decorrelation capability of the NSAF, the proposed CNSAF algorithm significantly improves the convergence performance of the CLMS algorithm under colored inputs. Then, the mean and mean-square stability of the CNSAF algorithm is analyzed, and the theoretical models to characterize the transient and steady-state mean square deviation (MSD) behaviors of the CNSAF algorithm are derived utilizing the Kronecker product property and vectorization method. Further to extend the CNSAF algorithm to the problem of sparse system identification, a sparse version of the CNSAF algorithm (S-CNSAF) is derived. Finally, the validity of the derived theoretical MSD prediction models and the superiority of the proposed algorithms are confirmed by extensive computer simulations on system identification with colored inputs. [ABSTRACT FROM AUTHOR]

One of the most relevant tasks of high-dimensional estimation is the computation of the parameters covariance matrix. This matrix offers valuable insights into the uncertainties associated with the estimation process. In the context of adaptive filtering, the weight error covariance matrix is a key parameter that determines how the filter adapts to input statistics and noise levels. Additional statistical information about the weight error vector allows one to depict a more precise description of the stochastic coupling between the adaptive weights. This paper concentrates on an in-depth study of the least mean squares asymptotic weight error covariance matrix, employing the exact expectation analysis. Through this examination, a key conclusion emerges: The symmetries commonly engendered by traditional analyses are not present in the actual covariance matrix. Instead, these symmetries are artifacts of the widespread assumption of independence. In summary, the advanced analysis performed in this work reveals a significantly more nuanced learning behavior exhibited by the least mean squares algorithm, challenging the conventional understanding put forth by traditional approaches. The theoretical predictions are confirmed by extensive simulations. [ABSTRACT FROM AUTHOR]

Based on the criterion of minimum error entropy, this paper proposes a novel conjugate gradient algorithm, called MEE-CG. This algorithm has robust performance under non-Gaussian interference. Theoretical analysis and experimental results demonstrate that the proposed algorithm displays more robust performance than the conventional conjugate gradient methods on the basis of the mean square error and the maximum correntropy criterion. Compared with the stochastic gradient minimum error entropy algorithm and the recursive minimum error entropy algorithm, the proposed algorithm provides a trade-off between computational complexity and convergence speed. [ABSTRACT FROM AUTHOR]

Gaussian filter (GF) is a commonly used linear filter in signal and image noise reduction. However, its limitation is that it cannot adapt parameters to deal with non-stationary noise that varies over time. To address this problem and improve the filtering effectiveness of GF in the face of non-stationary non-Gaussian (NSNG) noise, this paper proposes a new approach called adaptive Gaussian filter based on improved complete ensemble empirical mode decomposition (ICEEMDAN-AGF). The ICEEMDAN-AGF firstly uses the fusion information of the dispersion entropy (DE) and the power spectral entropy (PSE) to divide the intrinsic mode functions (IMFs) into two groups. One group is called guiding IMFs, which contains the high-frequency components of the NSNG noise, and the other group is called hybrid IMFs, which contains the low-frequency components of the NSNG noise and all the noise-free signals. Next, a method called multi-resolution local similarity (MRLS) is proposed to identify the mixed modes presented in the guiding IMFs. Then, the variance of the guiding IMFs is used to adjust the window width w and kernel parameter σ of GF. Finally, the adaptive Gaussian filter (AGF) obtained above is used to filter the hybrid IMFs. The experiments shows that ICEEMDAN-AGF performs better than other conventional algorithms on known signals. [ABSTRACT FROM AUTHOR]

In this article, the problem of adaptive finite‐time tracking control for a class of nonlinear systems subject to backlash‐like input hysteresis and multiple objective constraints is investigated. For the purpose of realizing multiple objective constraints, a new time‐varying barrier function (TVBF) is introduced to ensure that all objective constraint functions are always within the defined range. Meanwhile, based on the combination of the command filter approach and adaptive backstepping control, an error compensation system (ECS) is supplied to reduce the impact of filter error on control performance. Additionally, the problem of “singularity” caused by hysteresis is avoided by linearizing the backlash‐like hysteresis model, and Nussbaum‐type function is also applied to reduce the influence of hysteresis on the stability of the system. Then, by combining multi‐dimensional Taylor network (MTN) technology and command filter backstepping approach, an adaptive finite‐time control strategy is designed. The proposed control strategy ensures that all the signals in the closed‐loop system realize finite‐time semi‐globally uniformly ultimately bounded (SGUUB), and the output signal of the system can track the reference signal greatly while adhering to multiple objective constraints. Finally, the effectiveness of the proposed control strategy is verified by a practical simulation example. [ABSTRACT FROM AUTHOR]

The focus of this article lies in the adaptive command filter tracking control problems for a class of switched time‐delay systems with an asymmetric dead‐zone and external disturbance under arbitrary switching. First, the external disturbance is estimated via an exact disturbance observer. Then, an echo state network (ESN) was adopted to approximate unknown function in system without tuning the weights between a reservoir and an input layer. Second, the time‐varying input of the dead‐zone model is considered as uncertain physical quantity of the system based on the dead‐zone slope boundary information. Third, an adaptive command filtered controller is employed to conquer the matter of “explosion of complexity” encountered in the traditional backstepping method. The filtering errors present in a dynamic surface control (DSC) approach are effectively eliminated by means of error compensation signals. A prescribed performance control (PPC) is resorted to confine the system output to meet prescribed steady‐state and transient tracking performances. The time delay terms are compensated by a Lyapunov–Krasovskii functional. The semi‐globally ultimately uniformly boundedness of all states in the closed‐loop system is ensured by the Liapunov's stability criterion. Finally, the validity of the developed control scheme is further verified through a simulation example. [ABSTRACT FROM AUTHOR]

A new model-free and data-adaptive feature screening procedure referred to as the concordance filter is developed for ultrahigh-dimensional data. The proposed method is based on the concordance filter which measures concordance between random vectors and can work adaptively with several types of predictors and response variables. We apply the concordance filter to deal with feature screening problems emerging from a wide range of real applications, such as nonparametric regression and survival analysis, among others. It is shown that the concordance filter enjoys the sure screening and rank consistency properties under weak regularity conditions. In particular, the concordance filter can still be powerful in the presence of censoring and heavy tails. We further demonstrate the superior performance of the concordance filter over existing screening methods by numerical examples and medical applications. [ABSTRACT FROM AUTHOR]

Electro-hydraulic systems are extensively utilized to generate desired acceleration waveforms to provide a vibration environment for testing the performance and reliability of objects in various industrial applications. However, as electro-hydraulic systems are often affected by some inevitable drawbacks resulted from hydraulic nonlinearities, unwanted dynamic variations and disturbances, the generated acceleration waveform is generally far behind the expectation. In this paper, a convex combined adaptive controller with input shaping technique is proposed for enhancing the transient acceleration waveform replication accuracy of electro-hydraulic systems. The proposed controller is comprised of a three variable controller at the bottom level, an input shaping technique controller at the middle level, and a convex combined adaptive controller at the upper level. The three variable controller is firstly utilized for the establishment of a fundamental closed-loop acceleration control system, and then the input shaping technique controller is constructed by introducing an offline designed inverse prefilter utilizing the multi-innovation recursive least squares algorithm and the zero magnitude error tracking algorithm. The convex combined adaptive controller at the upper level is comprised of two individual adaptive filters with high and low step sizes, which provides the merits of fast convergence rate and high tracking accuracy, and it is further exploited to address for system's dynamic variations, model uncertainties and unexpected perturbations. Comparative experiments of the proposed controller with a manually generated random waveform and a recorded earthquake waveform as the testing inputs are conducted on a typical electro-hydraulic test bench, and the corresponding results demonstrate the feasibility and superiority of the proposed controller in improving the transient acceleration waveform replication performance of electro-hydraulic systems. • A new transient acceleration waveform replication strategy using convex combined adaptive controller with input shaping technique is proposed. • Input shaping technique controller acting as a prefilter is adopted to enhance tracking frequency bandwidth and reduce phase delay error. • Convex combined adaptive controller is exploited to address for system's dynamic variations, model uncertainties and unexpected disturbances. • Experimental results validate feasibility and superiority of the proposed controller than the other comparative methods. [ABSTRACT FROM AUTHOR]

In this study, a novel estimation scheme is proposed for identifying extended Wiener–Hammerstein systems with hysteresis nonlinearity subject to quantised measurements. The proposed scheme is established in a self-error learning framework to achieve high-performance parameter estimation compared with classic error feedback learning estimation algorithms. Initially, the useful identification data can be extracted from contaminated system data by introducing an adaptive filter. Then, with the help of the filtered data, the identification error expression used to establish the estimator is derived. Subsequently, an online compensation estimation error variable is proposed to eliminate the effect of the regression vector on the convergence performance. A new adaptive law is designed with adaptive recursive gain, considering the compensation estimation error data and parameter initial error data. Under general persistent excitation (PE) condition, the PE condition of the regressor information is verified online, and the estimator convergence is strictly proven. Finally, the statistical results of two illustrated examples and a real-word example are provided to validate the positive features and effectiveness of the proposed estimation scheme. • Adaptive law is updated by using the self-error information rather than non-self-error data. • Cost function is established by right of the compensated error and initial error data. • The effect of regressor on estimator is remedied by considering a compensation matrix. • Introduced filter takes into account system prior knowledge and the performance is improved. [ABSTRACT FROM AUTHOR]

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