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The advent of millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems, coupled with reconfigurable intelligent surfaces (RISs), presents a significant opportunity for advancing wireless communication technologies. This integration enhances data transmission rates and broadens coverage areas, but challenges in channel estimation (CE) remain due to the limitations of the signal processing capabilities of RIS. To address this, we propose an adaptive channel estimation framework comprising two algorithms: log-sum normalized least mean squares (Log-Sum NLMS) and hybrid normalized least mean squares-normalized least mean fourth (Hybrid NLMS-NLMF). These algorithms leverage the sparse nature of mmWave channels to improve estimation accuracy. The Log-Sum NLMS algorithm incorporates a log-sum penalty in its cost function for faster convergence, while the Hybrid NLMS-NLMF employs a mixed error function for better performance across varying signal-to-noise ratio (SNR) conditions. Our analysis also reveals that both algorithms have lower computational complexity compared to existing methods. Extensive simulations validate our findings, with results illustrating the performance of the proposed algorithms under different parameters, demonstrating significant improvements in channel estimation accuracy and convergence speed over established methods, including NLMS, sparse exponential forgetting window least mean square (SEFWLMS), and sparse hybrid adaptive filtering algorithms (SHAFA). [ABSTRACT FROM AUTHOR]

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The accuracy of satellite positioning results depends on the number of available satellites in the sky. In complex environments such as urban canyons, the effectiveness of satellite positioning is often compromised. To enhance the positioning accuracy of low-cost sensors, this paper combines the visual odometer data output by Xtion with the GNSS/IMU integrated positioning data output by the satellite receiver and MEMS IMU both in the mobile phone through adaptive Kalman filtering to improve positioning accuracy. Studies conducted in different experimental scenarios have found that in unobstructed environments, the RMSE of GNSS/IMU/visual fusion positioning accuracy improves by 50.4% compared to satellite positioning and by 24.4% compared to GNSS/IMU integrated positioning. In obstructed environments, the RMSE of GNSS/IMU/visual fusion positioning accuracy improves by 57.8% compared to satellite positioning and by 36.8% compared to GNSS/IMU integrated positioning. [ABSTRACT FROM AUTHOR]

Current research focuses on improving electrocardiogram (ECG) monitoring systems to enable real-time and long-term usage, with a specific focus on facilitating remote monitoring of ECG data. This advancement is crucial for improving cardiovascular health by facilitating early detection and management of cardiovascular disease (CVD). To efficiently meet these demands, user-friendly and comfortable ECG sensors that surpass wet electrodes are essential. This has led to increased interest in ECG capacitive electrodes, which facilitate signal detection without requiring gel preparation or direct conductive contact with the body. This feature makes them suitable for wearables or integrated measurement devices. However, ongoing research is essential as the signals they measure often lack sufficient clinical accuracy due to susceptibility to interferences, particularly Motion Artifacts (MAs). While our primary focus is on studying MAs, we also address other limitations crucial for designing a high Signal-to-Noise Ratio (SNR) circuit and effectively mitigating MAs. The literature on the origins and models of MAs in capacitive electrodes is insufficient, which we aim to address alongside discussing mitigation methods. We bring attention to digital signal processing approaches, especially those using reference signals like Electrode-Tissue Impedance (ETI), as highly promising. Finally, we discuss its challenges, proposed solutions, and offer insights into future research directions. [ABSTRACT FROM AUTHOR]

One of the main challenges in using adaptive filtering algorithms is efficiently emulating a system subject to noisy disturbances. This can be facilitated in applications where the system response to impulse is sparse, which allows for acceleration of the convergence rate if appropriate strategies are used. As a result, methods that impose norm constraints on the estimates are widely used. However, in the case of non-stationary plants to be identified, there is a gap in terms of theoretical performance guarantees of these algorithms. This paper proposes a novel stochastic model capable of predicting the performance of the ℓ 0 -LMS algorithm in identifying a plant subjected to a first-order Markovian disturbance. Therefore, a tracking analysis is carried out, including both the average performance of the adaptive coefficients and second-order statistics of these coefficients. The theoretical model offers an analytical equation that predicts the asymptotic mean squared deviation in terms of the variance of the Markovian disturbance. Further, for most simulated scenarios, the theoretical model's error in mean squared deviation remains below 1 dB, even when the learning step varies across several orders of magnitude. It was possible to observe that the theoretical model can accurately predict the steady-state regime for a wide range of learning step values and calculate an optimal value for this parameter. The findings are confirmed through extensive simulations. [ABSTRACT FROM AUTHOR]

In recent years, hyperbolic functions including hyperbolic cosine, hyperbolic sine and inverse hyperbolic sine have been increasingly used in the design of adaptive filters. However, the performance of these algorithms may still be surpassed. This paper introduces a tanh-based logarithmic hyperbolic cosine adaptive filtering algorithm, aimed at enhancing both the accuracy and minimizing steady-state errors in adaptive filters. Through rigorous theoretical analysis, we derive the step-size criteria essential for ensuring algorithm convergence. Subsequently, we validate the algorithm's practical efficacy through simulation experiments, demonstrating its superior performance against comparable algorithms within the same category. The simulation outcomes conclusively show that our proposed algorithm outperforms its peers. [ABSTRACT FROM AUTHOR]

The Kalman filter is one of the best-known and most frequently used methods for dynamic state estimation. In addition to a measurement and state transition model, the Kalman filter requires knowledge about the covariance of the measurement and process noise. However, the noise covariances are mostly unknown and may vary during the application. Adaptive Kalman filters solve this problem by estimating the noise covariances online to improve the state estimation. Existing methods are often limited in their application because they are designed to adapt only the measurement noise or the process noise covariance and tend to diverge when both are unknown. Moreover, most methods provide no or only local convergence results, which implies that a poor initialization can adversely affect the estimation of the noise covariances, leading to a deteriorated state estimation. This paper introduces a novel adaptive Kalman filter based on additional Kalman smoothing and analytically derived covariance estimators. Firstly, the unbiased measurement and process noise covariance estimators are derived from the maximum a posteriori formulation of the Kalman smoother. Then, based on these estimators, which depend on the system formulation and the state estimates of the Kalman smoother, the adaptive Kalman filter algorithm is presented. The convergence of the derived estimators can be shown for time-invariant systems for one-dimensional measurement and process noise. For higher-dimensional problems, the convergence can be tested simulatively for the specific dynamical system. A detailed evaluation of various simulation scenarios is presented, demonstrating the accuracy and robustness of the proposed method.

In this work, the adaptation of the process noise covariance matrix for the nontraditional attitude filtering technique is discussed. The nontraditional attitude filtering technique integrates the unscented Kalman filter (UKF) and singular value decomposition (SVD) approaches to estimate the attitude of a nanosatellite. It is shown in this study that the process noise bias and process noise increment type system changes will cause a change in the statistical characteristics of the innovation sequence of UKF. The influence of these types of changes on the innovation of UKF is investigated. For differences between the process channels, the Q (process noise covariance) adaptation strategy with multiple scale factors is specifically recommended. We analyze the performance of the multiple scale factors-based adaptive SVD-aided UKF (ASaUKF) in the cases of process noise increment and bias that can be caused by variations in the satellite dynamics or space environment. The adaptive and nonadaptive variants of the nontraditional attitude filter are compared through simulations in order to estimate the attitude of a nanosatellite. [ABSTRACT FROM AUTHOR]

There is a growing need to facilitate the production of recorded music as independent musicians are now key in preserving the broader cultural roles of music. A critical component of the production of music is multitrack mixing, a time-consuming task aimed at, among other things, reducing spectral masking and enhancing clarity. Traditionally, this is achieved by skilled mixing engineers relying on their judgment. In this work, we present an adaptive filtering method based on a novel masking detection scheme capable of identifying masking contributions, including temporal interchangeability between the masker and maskee. This information is then systematically used to design and apply filters. We implement our methods on multitrack music to improve the quality of the raw mix.

At present，the most widely used control algorithms in the field of active noise control（ANC）are the classical FxLMS algorithm and its improved algorithms，which are applied to noise control in large spaces and areas such as the turboprop aircraft cabin，the amount of algorithmic calculations will be rapidly expanded with the increase in the number of channels of the ANC system，and the real-time requirements of algorithms are difficult to meet. Sequential Partial Update FxLMS（SPU-FxLMS）algorithm effectively solves this problem，but its convergence performance is weaker than that of the FxLMS algorithm. In this paper，the improvements to the SPU-FxLMS algorithm for the problem of slow convergence are made，so that the algorithm can be converged with a faster speed in the early stage of the operation，and then converge with a lower speed after converging to a smooth state. After converging to a steady state，the algorithm continues to run with a low computational capacity. The theoretical derivation and simulation analysis of the improved algorithm are carried out. The results show that the algorithm has good noise reduction performance and robustness while further reducing the amount of computation，and has a good prospect for engineering applications.

As an adaptive finite impulse response filtering algorithm, the maximum total correntropy (MTC) algorithm plays an important role in parameter estimation of the errors-in-variables model where both input and output signals are contaminated with impulsive noises. However, the MTC algorithm is difficult to obtain a sufficiently high estimation accuracy under impulsive noises because the MTC cost function contains second-order moments of the error signal and its first-order gradient is susceptible to large outliers in the input noise. In this paper, a maximum total fractional-order correntropy (MTFOC) cost function is proposed and then a fractional-order gradient based MTFOC adaptive filtering algorithm is developed to improve the estimation accuracy of MTC. Moreover, the local stability and computational complexity of the proposed algorithm are analyzed. Simulation results indicate that the estimation accuracy and robustness of the MTFOC algorithm are superior to previous algorithms in both Gaussian mixture noise environments and α -stable distribution noise environments. [ABSTRACT FROM AUTHOR]

In this paper, the problem of robust distributed estimation in undirected networks is explored in depth. The Arctangent framework has garnered widespread adoption for adaptive estimation in prior research, with numerous adaptive estimation algorithms stemming from this foundation. However, the integration of the Arctangent framework with the maximum correntropy criterion, and its potential for enhanced estimation results, remains unexplored. In this paper, we have conducted a study in distributed networks and proposed a new robust distributed estimation algorithm using the Arctangent framework and the maximum correntropy criterion. Simulation experiments demonstrate that the proposed algorithm exhibits superior performance compared to the benchmark algorithm in both Gaussian and impulsive noise environments. Finally, a theoretical analysis of the proposed algorithm is conducted. [ABSTRACT FROM AUTHOR]

Computer-aided diagnosis (CAD) techniques, exemplified by chest x-ray (CXR)-based methods, offer a cost-effective alternative for early-stage COVID-19 diagnosis compared to expensive options such as polymerase chain reaction (PCR) and computed tomography (CT) scan. Despite efforts to diagnose COVID-19 with CXR-based methods, their performance could be improved by considering the spatial relationships between regions of interest (ROIs) in CXR images. This oversight hinders the ability to accurately identify areas of the human lung most vulnerable to COVID-19. This model implements a two-way classification system to differentiate between lung X-ray impressions, accurately determining whether they are affected or normal. The effectiveness of this system is assessed using metrics such as accuracy, recall, precision, and F1-score. We employed over 2409 samples of X-ray images in the COVID-19 diagnosis process. The results obtained from the VGG16 model showcase outstanding performance, with a recognition rate of 99.58% for X-ray images and 94.29% for CT-scan pictures within the given sample size and two-class categorization. This model surpasses all existing approaches documented in the literature. Medical professionals and healthcare workers can effectively utilize this proposed system, leveraging X-rays and CT scans of human lungs to identify COVID-19 cases accurately. [ABSTRACT FROM AUTHOR]

Speech is a signal produced by humans to interact and communicate. Different information is gained from speech signals, such as the language being spoken, emotion, gender, speaker identification, and other information. Speech signals are exposed to different noises, which can be generated at the beginning of the speech or during the transmission. Due to this problem, noise reduction processes are an interesting field in different communication application systems that cultivate the intelligibility and quality of speech signals. It refers to removing or reducing the background noise in order to obtain an improved quality of the original speech signal without distorting the original (clean) signal. This paper reviews the state-of-the-art research, reviewing different speech enhancement filters and algorithms and comparing their performance to reach a conclusion about which is the best filter or the most effective one based on the kind of noise that was used and the most difficult noise to remove from the signal. [ABSTRACT FROM AUTHOR]

Context. Modern video conferencing systems work in different noise environments, so preservation of speech clarity and provision of quick adaptation to changes in this environment are relevant tasks. During the development of embedded systems, finding a balance between resource consumption, performance, and signal quality obtained after noise suppression is necessary. Systems on a chip allow us to use the power of both processor cores available on the hardware platform and FPGAs to perform complex calculations, which contributes to increasing the speed or reducing the load on the central SoC cores. Objective. To conduct a comparative analysis of the noise suppression quality in audio signals by an adaptive filtering algorithm and a filtering algorithm using machine learning based on the RNNoise neural network in noise suppression devices on the technological platform SoC. Method. Evaluation using objective metrics and spectrogram analysis using the Librosa library in Python. Neural network training and model design are performed on the basis of Python and Torch tools. The Vitis IDE package was used for the neural network implementation on the platform SoC. Results. The analysis of two noise suppression methods using the adaptive Wiener filter and the RNNoise neural network was performed. In the considered scenarios, it was determined that the neural network shows better noise suppression results according to the analysis of spectrograms and objective metrics. Conclusions. A comparative analysis of the effectiveness of noise suppression algorithms based on adaptive filters and a neural network was performed for scenarios with different noise environments. The results of objective SIGMOS metrics were obtained to evaluate the quality of the received audio signal. In addition, the possibility of running the RNNoise neural network on the technological platform SoC ZYNQ 7000 was verified [ABSTRACT FROM AUTHOR]

Background: Acoustic emission (AE) is a widely used non-destructive testing (NDT) technique for materials and structures, particularly for damage detection and health monitoring of structures. The interference of noise signals with the damage-related AE signals is one of the elements impacting the performance of the AE approach used for real-time monitoring of structures, leading to erroneous inspection findings. Objective: This article addresses the problem of noise reduction in AE signals by proposing a unique technique based on the bio-inspired Antlion Optimization (ALO) algorithm. Method: The ALO algorithm is based on the hunting instincts of antlions. In this research, ALO is used to reduce the adaptive filter's error fitness in an adaptive noise cancellation (ANC) setup, and the suggested method is used to filter AE signals generated during concrete compression and composite drilling tests. Results: The performance of the ALO-based ANC is assessed quantitatively using the signal to noise ratio (SNR), peak signal to noise ratio (PSNR), and mean square error (MSE), as well as qualitatively through visual inspection of spectrograms and waveforms. The results are compared to those obtained using the gradient-based recursive least squares technique (RLS). Conclusion: The ALO-based technique is of great significance to reduce the noise in AE signals by dramatically lowering the MSE and increasing the SNR when compared to the gradient-based approach RLS. [ABSTRACT FROM AUTHOR]

Aiming at the problem that the Cubature Kalman filter (CKF) algorithm has a non-positive definite of the covariance matrix in GNSS/SINS integrated navigation, which leads to the failure of Cholesky decomposition and the inability to estimate GNSS measurement noise, a backward smoothing adaptive CKF integrated navigation algorithm based on Singular Value Decomposition (SVD) is proposed. Backward smoothing adaptive CKF based on Singular Value Decomposition (BS-A-SVDCKF) algorithm replaces the Cholesky decomposition in standard CKF with SVD, constructs the test threshold for backward smoothing by using the relatively stable noise of the SINS system, and adaptively estimates based on different measurement characteristics when the GNSS measurement noise changes abruptly. Experimental results show that compared with adaptive SVDCKF (A-SVDCKF), backward smoothing SVDCKF (BS-SVDCKF) and SVDCKF, the average positioning accuracy of the proposed algorithm is improved by 32.12%, 57.07% and 68.66%, the average operating efficiency is reduced by 2.19% compared with SVDCKF, and 0.57% and 3.60% higher than that of BS-SVDCKF and A-SVDCKF, which improves the positioning accuracy of the integrated navigation system and ensures real-time. [ABSTRACT FROM AUTHOR]

Utilizing reliable and accurate positioning and navigation systems is crucial for saving the lives of rescue personnel and accelerating rescue operations. However, Global Navigation Satellite Systems (GNSSs), such as GPS, may not provide stable signals in dense forests. Therefore, integrating multiple sensors like GPS and Inertial Measurement Units (IMUs) becomes essential to enhance the availability and accuracy of positioning systems. To accurately estimate rescuers' positions, this paper employs the Adaptive Unscented Kalman Filter (AUKF) algorithm with measurement noise variance matrix adaptation, integrating IMU and GPS data alongside barometric altitude measurements for precise three-dimensional positioning in complex environments. The AUKF enhances estimation robustness through the adaptive adjustment of the measurement noise variance matrix, particularly excelling when GPS signals are interrupted. This study conducted tests on two-dimensional and three-dimensional road scenarios in forest environments, confirming that the AUKF-algorithm-based integrated navigation system outperforms the traditional Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), and Adaptive Extended Kalman Filter (AEKF) in emergency rescue applications. The tests further evaluated the system's navigation performance on rugged roads and during GPS signal interruptions. The results demonstrate that the system achieves higher positioning accuracy on rugged forest roads, notably reducing errors by 18.32% in the north direction, 8.51% in the up direction, and 3.85% in the east direction compared to the EKF. Furthermore, the system exhibits good adaptability during GPS signal interruptions, ensuring continuous and accurate personnel positioning during rescue operations. [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]

Copyright of Journal of Data Acquisition & Processing / Shu Ju Cai Ji Yu Chu Li is the property of Editorial Department of Journal of Nanjing University of Aeronautics & Astronautics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract The unconstrained partitioned-block frequency-domain adaptive filter (PBFDAF) offers superior computational efficiency over its constrained counterpart. However, the correlation matrix governing the natural modes of the unconstrained PBFDAF is not full rank. Consequently, the mean coefficient behavior of the algorithm depends on the initialization of adaptive coefficients and the Wiener solution is non-unique. To address the above problems, a new theoretical model for the deficient-length unconstrained PBFDAF is proposed by constructing a modified filter weight vector within a system identification framework. Specifically, we analyze the transient and steady-state convergence behavior. Our analysis reveals that modified weight vector is independent of its initialization in the steady state. The deficient-length unconstrained PBFDAF converges to a unique Wiener solution, which does not match the true impulse response of the unknown plant. However, the unconstrained PBFDAF can recover more coefficients of the parameter vector of the unknown system than the constrained PBFDAF in certain cases. Also, the modified filter coefficient yields better mean square deviation (MSD) performance than previously assumed. The presented alternative performance analysis provides new insight into convergence properties of the deficient-length unconstrained PBFDAF. Simulations validate the analysis based on the proposed theoretical model.

The unconstrained partitioned-block frequency-domain adaptive filter (PBFDAF) offers superior computational efficiency over its constrained counterpart. However, the correlation matrix governing the natural modes of the unconstrained PBFDAF is not full rank. Consequently, the mean coefficient behavior of the algorithm depends on the initialization of adaptive coefficients and the Wiener solution is non-unique. To address the above problems, a new theoretical model for the deficient-length unconstrained PBFDAF is proposed by constructing a modified filter weight vector within a system identification framework. Specifically, we analyze the transient and steady-state convergence behavior. Our analysis reveals that modified weight vector is independent of its initialization in the steady state. The deficient-length unconstrained PBFDAF converges to a unique Wiener solution, which does not match the true impulse response of the unknown plant. However, the unconstrained PBFDAF can recover more coefficients of the parameter vector of the unknown system than the constrained PBFDAF in certain cases. Also, the modified filter coefficient yields better mean square deviation (MSD) performance than previously assumed. The presented alternative performance analysis provides new insight into convergence properties of the deficient-length unconstrained PBFDAF. Simulations validate the analysis based on the proposed theoretical model. [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]

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]

The primary diagnostic tool for monitoring problems with the heart or related diseases is the electrocardiogram (ECG). The rising mortality rate caused by cardiac abnormalities leads to the development of more advanced techniques to detect heart anomalies. An ECG is used for multiple diagnostic purposes since it produces continuous tracings of the electrophysiological activity generated from the heart. This paper developed a Bidirectional recurrent chimp search (Bi-RCS) method for diagnosing abnormalities by collecting ECG signals via various phases such as the data acquisition phase, signal processing phase, feature extraction phase, abnormality detection phase, real-time FPGA integration phase, monitoring and reporting phase as well as feedback phase. Signal pre-processing removes contaminants like noise using the Adaptive filtering methodology which severely limits the utility of the recorded ECG for better clinical evaluation. Bi-RNN is utilized for extracting related features and detecting abnormalities and the Chimp search optimization is employed to tune the parameters of Bi-RNN. After detecting the abnormalities, the reports of patients are monitored and recorded to provide an alert signal to healthcare providers. Finally, the potency of the methodology is analyzed with the ECG signal dataset and metrics like precision, F1-score accuracy, sensitivity, and specificity values. The obtained outputs equalized with the existing methods like KNN, EBT, and SVM. The analysis showed convincing performance with an accuracy of 99% and less error rate of 0.05. [ABSTRACT FROM AUTHOR]

Convenient and helpful defect information within the magnetic field signals of an energy pipeline is often disrupted by external random noises due to its weak nature. Non-destructive testing methods must be developed to accurately and robustly denoise the multi-dimensional magnetic field data of a buried pipeline. Complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is an innovative technique for decomposing signals, showcasing excellent noise reduction capabilities. The efficacy of its filtration process depends on two variables, namely the level of additional noise and the number of ensemble trials. To address this issue, this paper introduces an adaptive geomagnetic signal filtering approach by leveraging the capabilities of both CEEMDAN and the salp swarm algorithm (SSA). CEEMDAN generates a sequence of intrinsic mode functions (IMFs) from the measured geomagnetic signal based on its initial parameters. The Hurst exponent is then applied to distinguish signal IMFs and reproduce the primary filtered signal. SSA fitness, representing its peak value (excluding the zero point) in the normalized autocorrelation function, is utilized. Ultimately, optimal parameters that maximize fitness are determined, leading to the acquisition of their corresponding filtered signal. Comparative tests conducted on multiple simulated signal variants, incorporating varied levels of background noise, indicate that the efficacy of the proposed technique surpasses both EMD denoising strategies and conventional CEEMDAN approaches in terms of signal-to-noise ratio (SNR) and root mean square error (RMSE) assessments. Field testing on the buried energy pipeline is performed to showcase the proposed method's ability to filter geomagnetic signals, evaluated using the detrended fluctuation analysis (DFA). [ABSTRACT FROM AUTHOR]

A thorough examination of current developments in adaptive filtering for coherent dual-polarization optical communication systems is provided in this work. The emphasis is on high-capacity networks made possible by dual-polarization, coherent detection, variable bit-rate transceivers. The review explores the effectiveness of different adaptive algorithms in coherent receivers, the importance of dual polarization, and the function of adaptive filters in reducing channel impairments. The research also sheds light on the trade-offs and difficulties related to flexible bitrate optical transceivers. The paper comprises an extensive assessment of dynamic optical networks, categorized by network granularity and generation, in addition to the study of adaptive filtering. A comprehensive understanding of the developments in dynamic optical networking technologies is provided by the discussion of the evolution and traits of each generation. The poll also covers optical access networks, emphasizing the acceptance and advantages of optical access protocols as IEEE EPON and ITU-T GPON. The study also examines how digital filtering might be used to manage transmission constraints, highlighting the significance of segmenting digital filtering into distinct blocks. An overview of the types and applications of optical filters utilized in optical communication systems is included in the discussion. The study wraps up with a review of the literature that summarizes current research on optical communication systems, such as studies on adaptive algorithms' convergence properties, coherent dual-polarization receivers, and machine learning's use in optical fiber communication. The contributions of this paper include a thorough analysis of adaptive algorithm performance, a comparative study of dynamic optical networks, and a comprehensive overview of recent research in optical communication systems. [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]

In this paper, denoising of seismic signals based on Maximal Overlap Discrete Wavelet Transform and Adaptive Filtering (MODWT-AF) is discussed. In this method, first the seismic signals are converted into a set of coefficients called approximation and detailed coefficients by applying MODWT-based Multi Resolution Analysis (MODWT-MRA) technique. Then, adaptive filtering is applied to these coefficients, which removes noise present in the frequency sub bands of the signals. Finally, the denoised signal is reconstructed by performing the inverse MODWT to the modified set of coefficients. As a statistical analysis, the performance measures such as input signal-to-noise $ (SN{R_{in}}) $ (SN R in ) , output signal-to-noise ratio $ (SN{R_{out}}) $ (SN R out ) , SNR improvement $ (\Delta SNR) $ (ΔSNR) , Normalized Correction Coefficient (NCC), Mean Square Error (MSE), and RMS error are evaluated for different real earthquake records and synthetic seismic signals. The performance results of the proposed methodology are compared with other conventional thresholding methods such as MODWT-based thresholding (MODWT-TH) and Discrete Wavelet Transform based thresholding techniques namely Heursure (DWT-HE), Sqtwolog (DWT-SG), Minimaxi (DWT-MI), and Rigrsure (DWT-RE). The experimental results effectively demonstrated that the proposed methodology produced better outcomes compared to the conventional methods. [ABSTRACT FROM AUTHOR]

Train length status reflects whether the carriage is uncoupled or thrown away, it directly affects the safety and efficiency of train operations. At present, satellite positioning technology is used within a train length monitoring system. Such a system can ensure positioning accuracy while reducing the need for trackside equipment. For train length monitoring using GNSS technology, multi-constellation can utilize more visible satellites and achieve better spatial geometric distribution. A robust train length calculation method using multi-constellation GNSS moving-baseline positioning resolution is proposed. The time and space coordinate systems of the Global Positioning System (GPS) and BeiDou Navigation Satellite System (BDS) are unified as the foundation to realize multi-constellation positioning. Double-differenced carrier phase measurements are used to mitigate or eliminate the propagation errors and the satellite and receiver clock errors. Then, the moving-baseline length can be estimated using a Kalman filtering algorithm, and the carrier phase ambiguity terms are fixed using an online ambiguity fix algorithm to further improve the system performance. More measurements are utilized in the multi-constellation train length calculation method, the probability of fault measurement would be increased thereafter, and thus, the fault identification and adaptive filtering algorithm is introduced in the multi-constellation train length calculation to reduce the influence of fault measurement on the accuracy of moving-baseline solution and enhance the robustness of the system. To evaluate the performance of the proposed system, an experiment was conducted on the Beijing-Shenyang high-speed railway line. The results obtained on GPS-FLOAT, GPS-FIX, GPS/BDS-FLOAT, and GPS/BDS-FIX modes were compared. Both the FLOAT and FIX solutions can achieve the train length computation with sub-meter level accuracy, which can prove that the system is able to be adapt to the different GNSS signal conditions. The GPS/BDS-FIX solution can achieve train length accuracy with RMS of 0.155 m, which is better than the other three solutions. In addition, the moving-baseline accuracy is further improved after the adaptive filtering algorithm is added to smooth the noise interference, which had the best performance with RMS of 0.077 m compared with the non-adaptive filtering solutions. The superiority of the adaptive filtering algorithm is verified via using the dataset including two types of random fault measurements. It is proved that the random faults can be tolerated using adaptive filtering algorithm. The effectiveness and superiority of proposed robust train monitoring system are confirmed via comparison of results and simulation of different scenes. [ABSTRACT FROM AUTHOR]

Distributed inference tasks could be performed by adaptive filtering techniques. Several enhancement strategies for such techniques were proposed, such as sparsity-aware algorithms, coefficients reuse and correntropy-based cost functions in the case of impulsive noise. In this paper, a general framework based on Lagrange multipliers for the derivation of sophisticated algorithms that incorporate most of these improvements is described. A new general identification algorithm is derived as an example of the proposed approach and its performance is assessed in a distributed setting. [ABSTRACT FROM AUTHOR]

Atrial fibrillation (AF) is a common arrhythmia, and out-of-hospital, wearable, long-term electrocardiogram (ECG) monitoring can help with the early detection of AF. The presence of a motion artifact (MA) in ECG can significantly affect the characteristics of the ECG signal and hinder early detection of AF. Studies have shown that (a) using reference signals with a strong correlation with MAs in adaptive filtering (ADF) can eliminate MAs from the ECG, and (b) artificial intelligence (AI) algorithms can recognize AF when there is no presence of MAs. However, no literature has been reported on whether ADF can improve the accuracy of AI for recognizing AF in the presence of MAs. Therefore, this paper investigates the accuracy of AI recognition for AF when ECGs are artificially introduced with MAs and processed by ADF. In this study, 13 types of MA signals with different signal-to-noise ratios ranging from +8 dB to −16 dB were artificially added to the AF ECG dataset. Firstly, the accuracy of AF recognition using AI was obtained for a signal with MAs. Secondly, after removing the MAs by ADF, the signal was further identified using AI to obtain the accuracy of the AF recognition. We found that after undergoing ADF, the accuracy of AI recognition for AF improved under all MA intensities, with a maximum improvement of 60%. [ABSTRACT FROM AUTHOR]

Recently, there has been a strong interest in the minimum error entropy (MEE) criterion derived from information theoretic learning, which is effective in dealing with the multimodal non-Gaussian noise case. However, the kernel function is shift invariant resulting in the MEE criterion being insensitive to the error location. An existing solution is to combine the maximum correntropy (MC) with MEE criteria, leading to the MEE criterion with fiducial points (MEEF). Nevertheless, the algorithms based on the MEEF criterion usually require higher computational complexity. To remedy this problem, an improved MEEF (IMEEF) criterion is devised, aiming to avoid repetitive calculations of the a p o s t e r i o r i error, and an adaptive filtering algorithm based on gradient descent (GD) method is proposed, namely, GD-based IMEEF (IMEEF-GD) algorithm. In addition, we provide the convergence condition in terms of mean sense, along with an analysis of the steady-state and transient behaviors of IMEEF-GD in the mean-square sense. Its computational complexity is also analyzed. Simulation results demonstrate that the computational requirement of our algorithm does not vary significantly with the error sample number and the derived theoretical model is highly consistent with the learning curve. Ultimately, we employ the IMEEF-GD algorithm in tasks such as system identification, wind signal magnitude prediction, temperature prediction, and acoustic echo cancellation (AEC) to validate the effectiveness of the IMEEF-GD algorithm. • We propose an IMEEF criterion for the high computational complexity of the MEEF. • Based on the IMEEF criterion, we derive a gradient-based algorithm. • We analyze the performance of the proposed algorithm based on statistical properties. • The experimental results show that the proposed algorithm has excellent performance. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Geodesy & Geodynamics (1671-5942) is the property of Editorial Board Journal of Geodesy & Geodynamics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The accuracy of satellite positioning results depends on the number of available satellites in the sky. In complex environments such as urban canyons, the effectiveness of satellite positioning is often compromised. To enhance the positioning accuracy of low-cost sensors, this paper combines the visual odometer data output by Xtion with the GNSS/IMU integrated positioning data output by the satellite receiver and MEMS IMU both in the mobile phone through adaptive Kalman filtering to improve positioning accuracy. Studies conducted in different experimental scenarios have found that in unobstructed environments, the RMSE of GNSS/IMU/visual fusion positioning accuracy improves by 50.4% compared to satellite positioning and by 24.4% compared to GNSS/IMU integrated positioning. In obstructed environments, the RMSE of GNSS/IMU/visual fusion positioning accuracy improves by 57.8% compared to satellite positioning and by 36.8% compared to GNSS/IMU integrated positioning.

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)