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Application of Multidip Structure-Oriented filtering to Poststack Seismic Data

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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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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Seizure Detection Algorithm Based on Hybrid Morphological filtering Optimization Multiscale Amplitude Integrated EEG

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IEEE SENSORS JOURNAL 2025年第8期25卷 13529-13540页

作者： Dong, Yongping Wu, Duanpo Jiang, Lurong Dong, Fang Liu, Junbiao Vidal, Pierre-Paul Cao, Jiuwen Wang, Danping Hangzhou Dianzi Univ Sch Commun Engn Hangzhou 310018 Peoples R China Hangzhou Dianzi Univ Artificial Intelligence Inst Hangzhou 310018 Zhejiang Peoples R China Zhejiang Sci Tech Univ Sch Informat Sci & Technol Hangzhou 310018 Peoples R China Hangzhou City Univ Sch Informat & Elect Engn Hangzhou 310015 Peoples R China French Natl Ctr Sci Res F-75016 Paris France Hangzhou Dianzi Univ Machine Learning & I Hlth Int Cooperat Base Zhejia Hangzhou 310018 Zhejiang Peoples R China Univ Paris Cite Plateforme Etud Sensorimotr PES BioMedTech Facil F-75270 Paris France

Automatic seizure detection algorithms have significant implications for improving patients' quality of life. However, the absence of multiscale analysis in these algorithms regarding electroencephalography (EEG) signals may lead to overlooking crucial seizure features. To address this limitation, our study aims to develop a novel seizure detection algorithm based on hybrid morphological filtering optimization multiscale amplitude integrated EEG (aEEG). First, multichannel EEG signals, collected by EEG sensors, are preprocessed with asymmetric filtering, rectification, envelope detection, multiscale time compression and signal smoothing to obtain multiscale aEEG. Second, morphological filter (MF), multiscale MF (MSMF), and average combination difference MF (ACDMF) are hybridized to filter, analyze, and reconstruct the signals, thereby emphasizing the seizures. Thereafter, optimal aEEG signal is selected using the channel and time compression scale (TCS) joint optimization algorithm based on the kappa (Ka), and this signal is utilized for seizure detection. Finally, it is evaluated on the CHB-MIT database using multiple validation methods, achieving satisfactory experimental results. The results indicate that the model attains accuracy (ACC), specificity (SPE), sensitivity (SEN), F1 score ( F1 ), and Ka of 97.60%, 96.56%, 98.63%, 97.60%, and 95.19%, respectively.

关键词： Electroencephalography filtering Feature extraction Sensors filtering algorithms Electronic mail Detection algorithms Optimization Smoothing methods filtering theory Electroencephalography (EEG) sensor signal hybrid morphology filter multiscale time compression optimal amplitude integrated EEG (aEEG) signal seizure detection

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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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Kullback-Leibler Divergence-Based Observer Design Against Sensor Bias Injection Attacks in Single-Output Systems

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IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2025年 20卷 2763-2777页

作者： Tosun, Fatih Emre Teixeira, Andre M. H. Dong, Jingwei Ahlen, Anders Dey, Subhrakanti Uppsala Univ Dept Elect Engn S-75103 Uppsala Sweden Uppsala Univ Dept Informat Technol S-75103 Uppsala Sweden

This paper considers observer-based detection of sensor bias injection attacks (BIAs) on linear cyber-physical systems with single output driven by white Gaussian noise. Despite their simplicity, BIAs pose a severe risk to systems with integrators, which we refer to as integrator vulnerability. Specifically, the residual generated by any linear observer is indistinguishable under attack and normal operation at steady state, making BIAs detectable only during transients. To address this, we propose a principled method based on Kullback-Leibler divergence to design a residual generator that significantly increases the signal-to-noise ratio against BIAs. For systems without integrator vulnerability, our method also enables a trade-off between transient and steady-state detectability. The effectiveness of the proposed method is demonstrated through numerical comparisons with three state-of-the-art residual generators.

关键词： Security Generators Symmetric matrices Transient analysis Steady-state Linear systems Vectors Control theory filtering algorithms Anomaly detection Cyber-physical systems sensor deception attacks bias injection attacks observer-based anomaly detection

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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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Pseudolite Self-Interference Cancellation Without Additional GNSS Signal Distortion

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IEEE ACCESS 2025年 13卷 55927-55938页

作者： Gao, Lichao He, Jin Wu, Yuanxin Yu, Wenxian Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai Key Lab Intelligent Sensing & Recognit Shanghai 200240 Peoples R China

The goal of this paper is to solve the low-orbit pseudolite self-interference problem. The low-orbit pseudolite navigation signals will interference satellite navigation as they broadcast at the same frequency and same time. However, the low-orbit pseudolite also needs satellite navigation to get continuous and effective high-precision positioning. So, while effectively filtering the self-interference, the GNSS navigation signals need low distortion. In this paper, we propose an anti-self-interference method which ensures that GNSS navigation signals are not distorted. Specifically, the method uses the known characteristics of self-interference signals to establish an optimization problem. By solving this problem, self-interference filtering can be achieved without estimating the direction of satellite navigation signal and without the assistance of the receiver channel amplitude-frequency and phase-frequency characteristics. The simulation test results show that the algorithm can filter self-interference without additional distortion of the satellite navigation signals. The application test results show that, the algorithm can filter self-interference with high-precision carrier observation of the satellite navigation signal which statistical standard deviation is only 0.01 times of the carrier wavelength. The method could help in achieving high-precision satellite navigation and positioning of pseudolite terminals and providing high-precision position information for low-orbit pseudolites.

关键词： Satellite broadcasting Satellites Interference cancellation Global navigation satellite system Receiving antennas Aircraft navigation Accuracy filtering algorithms Calibration Full-duplex system Pseudolite navigation self-interference cancellation GNSS zero-distortion low-orbit

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Enhanced Knowledge Tracing With Learnable Filter

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2025年第1期12卷 198-209页

作者： Qian, Fulan Hu, Yetong Li, Guangyao Chen, Jie Wang, Shijin Zhao, Shu Anhui Univ Artificial Intelligence Inst Hefei 230601 Peoples R China Anhui Univ Key Lab Intelligent Comp & Signal Proc Minist Educ Hefei 230601 Peoples R China Anhui Univ Informat Mat & Intelligent Sensing Lab Anhui Prov Hefei 230601 Peoples R China IFLYTEK AI Res Hefei 230088 Peoples R China

The primary objective of knowledge tracing (KT) is to evaluate students' understanding and mastery of knowledge through their responses to exercises, which aids in predicting their future performance. Deep neural networks have been widely applied in the area of knowledge tracing and have demonstrated encouraging results. Nevertheless, in real-world scenarios, there is a substantial amount of noise in students' response records. These noises may amplify the inherent risk of overfitting in deep neural networks, leading to a decrease in model performance. To address these issues, we introduce a new model called filter knowledge tracing (FKT). This innovative model incorporates a learnable filter into KT to filter out noise information from students' exercise sequences. We redefine the input paradigm of the data, using learnable filters to perform filtering operations in its frequency domain representation space, effectively removing noise. Additionally, an attention module has been introduced in the FKT model to evaluate the impact of students' historical interactions on their current knowledge state. To validate our model, we conduct extensive experiments utilizing four publicly available datasets. The results demonstrate that FKT outperforms existing benchmarks, particularly on larger datasets, signifying an improvement in KT performance while effectively reducing the risk of overfitting.

关键词： Data models Noise Noise reduction Signal processing algorithms Knowledge engineering Prediction algorithms Noise measurement Heuristic algorithms Frequency-domain analysis filtering algorithms Attention mechanism denoising filtering algorithm knowledge tracing (KT)

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