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Minimum Total Quaternion Error Entropy filtering With Fiducial Points Against Asymmetric Noise

IEEE SIGNAL PROCESSING LETTERS 2025年 32卷 306-310页

作者： Lin, Dongyuan Cai, Peng Chen, Xiaofeng Zheng, Yunfei Wang, Shiyuan Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Peoples R China Chongqing Jiaotong Univ Dept Math Chongqing 400074 Peoples R China

Quaternion adaptive filters (QAFs) are extensively used in processing three- or four-dimensional signals effectively. However, their performance can significantly deteriorate or even diverge when system inputs and outputs are contaminated by complex noises. Therefore, this letter addresses the issue of parameter estimation in the quaternion errors-in-variables (QEIV) in asymmetric noise. First, a novel robust criterion, called improved quaternion minimum error entropy criterion with fiducial points (IQMEEF), is constructed. Then, a minimum total quaternion error entropy algorithm with fiducial points (MTQEEF) is proposed by integrating the IQMEEF criterion with the total least squares (TLS) method, leveraging stochastic gradient and quaternion generalized Hamilton-real (GHR) calculus theory. Finally, simulations validate the superior performance of MTQEEF in the QEIV model under asymmetric noise environments.

关键词： Quaternions Noise Entropy Signal processing algorithms Adaptation models Vectors Parameter estimation Kernel filtering algorithms Calculus Robust quaternion adaptive filters quaternion error entropy criterion with fiducial points quaternion errors-in-variables model asymmetric noise

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Application of Multidip Structure-Oriented filtering to Poststack Seismic Data

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2025年 22卷

作者： Guo, Mengxin Chen, Siyuan Liu, Zuoshi Wang, Weihong Northeast Petr Univ Sch Earth Sci Heilongjiang Prov Key Lab Oil & Gas Geophys Explor Daqing 163318 Heilongjiang Peoples R China Northeast Petr Univ Natl Engn Res Ctr Offshore Oil & Gas Explorat Daqing 163318 Heilongjiang Peoples R China

In geophysics, structure-oriented filtering (SOF) has been extensively utilized as an effective algorithm to eliminate random noise. This type of filtering is mainly restricted by the local dip of the data and smoothed along the structural direction. The algorithm assumes the strata of a single local dip, and even if the seismic data contain strata and faults, the SOF remains smoothed with a single dip angle. This condition leads to the smoothing of faults or strata in the wrong direction, which results in the effective removal of reflected signals. Given this, we herein propose a multidip SOF (Mdip-SOF) algorithm for complex structure data, including faults. This investigation employs a guided filtering theory to pre-extract structural features of faults. Guided by this attribute, we construct an edge-preserving filter, where faults may exist, aiming to preserve faults and other structures. Other positions with a single dip are also adopted for conventional SOF. A poststack data denoising test is also conducted, and the results obtained reveal that the proposed multidip constrained SOF algorithm outperforms the existing SOF algorithm in terms of fault handling.

关键词： filtering Three-dimensional displays Noise reduction filtering algorithms Prediction algorithms Smoothing methods filtering theory Transforms Heuristic algorithms Image edge detection Guided filtering preserving faults random noise structure-oriented filtering (SOF)

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MPFNet: A Multiscale Phase filtering Network for Interferometric SAR

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2025年 22卷

作者： Sun, Tao Wang, Zhen Ding, Zegang Zhao, Jian Zhu, Kaiwen Chen, Zhizhou Li, Han Beijing Inst Technol Sch Informat & Elect Radar Res Lab Beijing 100081 Peoples R China Beijing Inst Technol Minist Educ Key Lab Elect & Informat Technol Satellite Nav Beijing 100081 Peoples R China Beijing Inst Technol Chongqing Innovat Ctr Chongqing 401331 Peoples R China

Phase filtering is one of the core signal processing steps in interferometric synthetic aperture radar (InSAR). In recent years, InSAR phase filtering algorithms have evolved from traditional solutions to deep learning (DL) methods, significantly improving the processing efficiency. However, most DL-based phase filtering techniques originate from optical filtering methods, and these methods inevitably entail a tradeoff between noise suppression and detail preservation. To resolve this contradiction and fully take into account the characteristics of InSAR phase, a multiscale phase filtering network (MPFNet) based on multilook information fusion is proposed. First, the network adopts the multiscale structure to balance noise suppression and detail preservation, where the multiscale information is obtained through multilook interferograms of varying numbers of looks. Second, drawing on the mechanism of super-resolution, the network incorporates the residual feature distillation blocks (RFDBs) to restore the scale of interferograms. Finally, in response to the demand for complex phase filtering, a loss function based on cosine similarity is constructed, which avoids the discontinuity at $\pm \pi $ affecting the filtering results. Computer simulation and experiments based on real InSAR data verified the effectiveness of the proposed method.

关键词： filtering Feature extraction Vectors Noise Training Optical filters Noise measurement Superresolution Indexes filtering algorithms Deep learning (DL) interferometric synthetic aperture radar (InSAR) multiscale phase noise filtering

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Interbeat Interval filtering

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IEEE SIGNAL PROCESSING LETTERS 2025年 32卷 481-485页

作者： Bayram, Ilker WHOOP Boston MA 02215 USA

Several inhibitory and excitatory factors regulate the beating of the heart. Consequently, the interbeat intervals (IBIs) vary around a mean value. Various statistics have been proposed to capture heart rate variability (HRV) to give a glimpse into this balance. However, these statistics require accurate estimation of IBIs as a first step, which can be challenging especially for signals recorded in ambulatory conditions. We propose a lightweight state-space filter that models the IBIs as samples of an inverse Gaussian distribution with time-varying parameters. We make the filter robust against outliers by adapting the probabilistic data association filter to the setup. We demonstrate that the resulting filter can accurately identify outliers and the parameters of the tracked distribution can be used to compute a specific HRV statistic (standard deviation of normal-to-normal intervals, SDNN) without further analysis.

关键词： Heart rate variability filtering algorithms Gaussian distribution Adaptive filters Standards Random variables Heart beat Computational modeling Adaptation models Accuracy Interbeat interval heart rate variability state-space robust filter probabilistic data association

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Robust Fractional-Order-Based Von Mises Subband Adaptive filtering for Feedback Cancellation in Hearing Aids

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2025年

作者： Devi, R. Vanitha Vasundhara Natl Inst Technol Warangal Dept Elect & Commun Engn Warangal 506004 India

The issue of acoustic feedback poses a consistent challenge in the context of hearing aids since it imposes restrictions on the attainable amplification levels and has the potential to significantly diminish the perceptual audio quality through the generation of whistling sounds. In recent studies, researchers have employed delay-less multiband structured band-wise acoustic feedback alleviation techniques for hearing aids. However, the resilience of this system in the existence of non-Gaussian noise has yet to be thoroughly addressed. To tackle this matter, a robust and new cost function has been introduced based on the modification of the von Mises distribution with a scaling parameter. In addition, fractional lower-order models provide resilience to heavy-tailed distributions, enhanced precision, heightened versatility and suitability for diverse data formats. In light of this viewpoint, a robust fractional order von Mises-based subband acoustic feedback compensation technique is introduced for hearing aids. The method's efficacy has been evaluated by computer simulations involving speech and music signals at various signal-to-noise ratio (SNR) levels. The evaluation results show 2-3 dB decrease in misalignment and 2-3 dB enhancement in added stable gain in contrast to prior methodologies. Moreover, perceptual evaluation of speech quality and hearing aid speech quality indexes have been improved by approximate to 2 % and approximate to 10 % respectively as employed with the proposed technique.

关键词： Noise Cost function Hearing aids Filter banks filtering Adaptive filters Acoustics Prediction algorithms Convergence filtering algorithms Hearing aid prediction error method fractional order adaptive filtering feedback compensation

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Imposing Correlation Structures for Deep Binaural Spatio-Temporal Wiener filtering

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IEEE TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2025年 33卷 1278-1292页

作者： Tammen, Marvin Doclo, Simon Carl von Ossietzky Univ Oldenburg Dept Med Phys & Acoust D-26129 Oldenburg Germany Carl von Ossietzky Univ Oldenburg Cluster Excellence Hearing4all D-26129 Oldenburg Germany

To improve speech quality and intelligibility in environments with noise and interfering sounds, binaural speech enhancement algorithms use the microphone signals from both the left and the right hearing device to generate an enhanced output signal for each ear. As a multi-frame extension of the binaural multi-channel Wiener filter, in this paper we consider the binaural spatio-temporal Wiener filter (STWF) in the short-time Fourier transform domain, which requires estimates of the highly time-varying spatio-temporal correlations of the speech and interference components. To this end, the binaural STWF is embedded into an end-to-end supervised learning framework, where temporal convolutional networks estimate the required quantities, i.e., the inverse spatio-temporal correlation matrices of the interference component and the spatio-temporal correlation vectors and power spectral densities of the speech components. In this paper, we impose spatio-temporal correlation structure on these quantities and relate them between the left and the right hearing device, aiming to reduce computational complexity while maintaining speech enhancement and interaural cue preservation performance. Assuming that the spatial correlation of the speech component is stationary over a small number of frames, we propose to decompose the spatio-temporal correlation vectors as the Kronecker product of a relative transfer function vector and a temporal correlation vector, either considering a global reference microphone or a reference microphone for each hearing device. In addition, we consider a deep bilateral STWF by neglecting the spatio-temporal correlations of the speech and interference components between both devices. The imposed spatio-temporal correlation structures greatly differ in the number of parameters that need to be estimated. The performance of causal versions of the deep binaural and bilateral STWF algorithms is evaluated based on both simulated and measured binaural room imp

关键词： Correlation Microphones Speech enhancement Wiener filters Interference Auditory system Vectors filtering algorithms Noise Noise measurement Multi-channel wiener filter multi-frame filtering spatio-temporal correlation structure supervised learning binaural speech enhancement

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Data-Driven Dynamic State Estimation Framework Using a Koopman Operator-Based Linear Predictor

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IEEE ACCESS 2025年 13卷 31660-31670页

作者： Yang, Deyou Gao, Han Chen, Zhe Lv, Yanling Wang, Lixin Harbin Univ Sci & Technol Sch Elect & Elect Engn Harbin 150080 Peoples R China Aalborg Univ Dept Energy Technol DK-9220 Aalborg Denmark Northeast Elect Power Univ Sch Elect Engn Jilin 132012 Jilin Peoples R China

Dynamic state estimation (DSE) is a fundamental task in many fields, including control systems, robotics, and signal processing. Traditional DSE methods, which rely on mathematical models to describe system dynamics, are often limited in their applicability to real-world scenarios due to inaccuracies and assumptions. In this paper, we propose a purely data-driven DSE framework based on a Koopman operator-based linear predictor. The Koopman operator is a powerful tool in dynamical systems theory that allows us to analyze and predict the behavior of nonlinear systems. By leveraging the Koopman operator extracted solely from measured input-output data through the extended dynamic mode decomposition with control (EDMDc) method, a linear predictor that can accurately estimate the state variables of a dynamic system is developed. Introducing the extended Kalman filter (EKF) as an estimation method, the learned Koopman operator-based linear predictor is then used to estimate the current state of the system given only the past and present input-output measurements. To evaluate the effectiveness of the proposed framework, we conduct experiments on both simulated and real-world datasets of complex power systems. The results demonstrate that the proposed data-driven approach outperforms traditional model-based approaches in terms of accuracy and robustness. Moreover, the proposed framework is capable of handling nonlinear and time-varying systems, making it applicable to a wide range of practical cases.

关键词： Mathematical models Power system dynamics Heuristic algorithms Nonlinear dynamical systems Kalman filters Accuracy Estimation filtering algorithms filtering Covariance matrices Koopman operator data-driven linear predictor extended dynamic mode decomposition with control (EDMDc) extended Kalman filter (EKF) dynamic state estimation (DSE)

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Longest-First Search Using Bloom Filter: Algorithm and FPGA Implementation

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IEEE ACCESS 2025年 13卷 49354-49361页

作者： Lee, Jinsol Lim, Hyesook Ewha Womans Univ Dept Elect & Elect Engn Seoul 03760 South Korea

Due to the surge in Internet traffic and the rapid increase in forwarding table entries, achieving wire-speed packet forwarding in Internet routers demands both algorithmic enhancements and hardware innovations. In this paper, we propose the longest-first search algorithm with a Bloom filter that stores prefixes in a leaf-pushing trie. Our approach utilizes an on-chip Bloom filter to indicate the presence of prefixes within the trie, while an off-chip hash table stores the corresponding output port information for each prefix. For each incoming IP address, the Bloom filter query begins from the longest length, reducing the queried number of bits by one for each negative result. The off-chip hash table is only accessed when the query to the Bloom filter produces a positive result. Therefore, access to the slower off-chip memory is minimized to once, given a reasonable Bloom filter size. The proposed approach is simulated using C++ and constructed with Verilog for field programmable gate array (FPGA) implementation. The experimental results indicate that the proposed approach achieves the throughput of 0.8 million packets per second at a clock frequency of 100MHz.

关键词： filtering algorithms IP networks Information filters filtering theory Field programmable gate arrays Matched filters Lenses Internet System-on-chip Hash functions Binary trie Bloom filter FPGA IP address lookup leaf-pushing Verilog HDL

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Research on An Improved Time Scale Algorithm of Time-keeping Atomic Clocks

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IEEE INSTRUMENTATION & MEASUREMENT MAGAZINE 2025年第1期28卷 64-70页

作者： Jiang, Meng Zhang, Yu Xian Polytech Univ Sch Elect & Informat Xian Peoples R China Xian Univ Sci & Technol Coll Mech Engn Xian Peoples R China Zhejiang Weiying Intelligent Technol Co Ltd Zhuji Peoples R China Chinese Acad Sci Natl Time Serv Ctr Xian Peoples R China

In this article, quadratic exponential smoothing combined with VC filter, a new time scale data fusion algorithm is proposed. The frequency drifts of hydrogen masers are predicted by the use of the minimum deviation method, so that the time difference prediction becomes more accurate. The time scale is constructed by the modified quadratic exponential smoothing method based on the data of hydrogen masers, and the cesium atomic clocks time scale is established by the weighted mean method, and the VC filter is designed to combine the advantages of long-short-term stability of the two time scales to improve the comprehensive time scale performance. The experimental results demonstrate that the proposed approach produces a comprehensive time scale has a stability of 1.60e-15 for one hour and 3e-15 for one day, and it is superior to the performance of time scale generated by three classic methods: ALGOS, AT1, and Kalman filtering.

关键词： Time-frequency analysis Smoothing methods filtering Hydrogen Stability criteria Masers filtering algorithms Prediction algorithms Kalman filters Atomic clocks

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Network-Based Video Recommendation Using Viewing Patterns and Modularity Analysis: An Integrated Framework

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IEEE ACCESS 2025年 13卷 5660-5678页

作者： Maghsoudi, Mehrdad Valikhani, Mohammad Hossein Zohdi, Mohammad Hossein Shahid Beheshti Univ Fac Management & Accounting Dept Ind & Informat Management Tehran *** Iran Iran Univ Sci & technol Dept Management Econ & Progress Engn Tehran *** Iran

The proliferation of video-on-demand (VOD) services has led to a paradox of choice, overwhelming users with vast content libraries and revealing limitations in current recommender systems. This research introduces a novel approach by combining implicit user data, such as viewing percentages, with social network analysis to enhance personalization in VOD platforms. The methodology constructs user-item interaction graphs based on viewing patterns and applies centrality measures (degree, closeness, and betweenness) to identify important videos. Modularity-based clustering groups related content, enabling personalized recommendations. The system was evaluated on a documentary-focused VOD platform with 328 users over four months. Results showed significant improvements: a 63% increase in click-through rate (CTR), a 24% increase in view completion rate, and a 17% improvement in user satisfaction. The approach outperformed traditional methods like Naive Bayes and SVM. Future research should explore advanced techniques, such as matrix factorization models, graph neural networks, and hybrid approaches combining content-based and collaborative filtering. Additionally, incorporating temporal models and addressing scalability challenges for large-scale platforms are essential next steps. This study contributes to the state of the art by introducing modularity-based clustering and ego-centric ranking methods to enhance personalization in video recommendations. The findings suggest that integrating network-based features and implicit feedback can significantly improve user engagement, offering a cost-effective solution for VOD platforms to enhance recommendation quality.

关键词： Social networking (online) Recommender systems Streaming media Measurement filtering Collaborative filtering Accuracy filtering algorithms Entertainment industry Prediction algorithms Video recommendation social network analysis implicit feedback modularity clustering ego-centric ranking

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