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Impact of Right-Hand Polarized Signals in GNSS-R Water detection algorithms

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2025年 18卷 5646-5655页

作者： Peng, Jilun Cardellach, Estel Li, Weiqiang Ribo, Serni Rius, Antonio Spanish Natl Res Council Inst Space Sci Earth Observat Res Grp Barcelona 08193 Spain Inst Estudis Espacials Catalunya Barcelona 08034 Spain Univ Politecnicade Catalunya Fac Signal Theory & Commun Barcelona 08034 Spain

The water detection algorithm for the dual-polarized HydroGNSS mission was validated using spaceborne left-hand circular polarization (LHCP) data from the cyclone global navigation satellite system. With the public availability of dual-polarization data from Rongowai, an airborne global navigation satellite system-reflectometry mission, a unique opportunity arises to evaluate the contribution of right-hand circular polarization (RHCP) data to surface water detection. This analysis can offer a deeper understanding of RHCP data and yield predictive insights prior to the HydroGNSS launch. In this study, we initially analyzed coherence indicators in incoherently averaged dual-polarized signals, and subsequently, applied these indicators to a random forest classifier, similar to the HydroGNSS surface inundation algorithm. The findings have been compared with existing flooding products, showing promising results with over 91% agreement in water detection. The analysis revealed that, while the LHCP data exhibit a higher sensitivity to water, the incorporation of the RHCP data enhances the robustness and reliability of the classification. This reinforces the hypothesis that HydroGNSS, operating at dual polarization, might produce a more effective surface water detection product than single-polarization GNSS-R missions.

关键词： Coherence Floods Reflectivity Spaceborne radar detection algorithms Meteorology Doppler effect Polarization Monitoring Land surface Dual polarization global navigation satellite system-reflectometry (GNSS-R) random forest Rongowai water detection

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Evaluation of Drift detection algorithms in the Condition Monitoring Domain

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2025年第1期21卷 317-326页

作者： Estaji, Alireza Goetzinger, Maximilian Tutzer, Benedikt Kollmann, Stefan Sauter, Thilo Jantsch, Axel Danube Univ Krems Dept Integrated Sensor Syst A-2700 Wiener Neustadt Austria Vienna Univ Technol Inst Comp Technol A-1040 Vienna Austria

In condition monitoring, early detection of process signal drifts indicating, e.g., equipment degradation is crucial. exponentially weighted moving average (EWMA), cumulative sum (CUSUM), and discrete average block (DAB)-based drift detectors are statistical and commonly used methods. Each has benefits and limitations, suited to different data types. However, EWMA and CUSUM are fixed mean drift detectors, limiting their applicability and adaptability. This article explores adding dynamic behavior to drift detection methods. We use a wide range of synthetic data based on a real-world manufacturing process. The investigated parameter space includes standard deviation, drift rates, and outliers. Besides, each algorithm has some tuning parameters that define its behavior. Two metrics validate experiments against labeled data. Based on our observations, EWMA performs better for drift detection on average, but CUSUM is superior in detecting very small drifts. Furthermore, we derive guidelines for the choice and application of drift detection in practice.

关键词： Monitoring Condition monitoring Standards Detectors Informatics detection algorithms Delays Algorithm tuning and evaluation condition monitoring cumulative sum (CUSUM) drift detection exponentially weighted moving average (EWMA)

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A Systematic Review of Heart Sound detection algorithms: Experimental Results and Insights

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Bi, Xiuli Tang, Shizhan Xiao, Bin Li, Weisheng Gao, Xinbo Lio, Pietro Chongqing Univ Posts & Telecommun Dept Comp Sci & Technol Chongqing 400065 Peoples R China Jinan Inspur Data Technol Co Ltd Jinan 250101 Peoples R China Univ Cambridge Dept Comp Sci & Technol Cambridge CB3 0FD England

Automatic heart sound detection plays a vital role in the early detection of cardiovascular diseases (CVDs). In recent years, many heart sound detection algorithms have been proposed, becoming a popular research topic in medical diagnosis. In this article, our goal is to answer which heart sound detection algorithms and heart sound features perform best in which situations and to explore the problems in existing research. This work will provide ideas for subsequent research in this area. We achieve this aim through a standard process that includes preprocessing, segmentation, feature extraction, and classification. We discuss the length and overlap rate in segmentation and analyze the classification methods, especially the nine most salient heart sound features. The performance of the existing techniques is evaluated in different datasets and at the same level of comparison. The experiments show that the segmentation length includes at least one complete period, and we should use a large overlap rate in the segmentation phase. For feature extraction, time-domain feature (TIME) and fast Fourier transform (FFT) features based on single independent variables perform better in deep models and traditional classifiers. Short-time Fourier transform (STFT), continuous wavelet transform (CWT), and S-transform (ST) features based on double independent variables can perform well in all classification models;Mel spectrum/Mel frequency cepstrum coefficient (MFCC) is only better on deep neural networks (DNNs). The best performance is achieved by combining TIME or MFCC with DNNs.

关键词： Heart Feature extraction Standards Phonocardiography Electrocardiography detection algorithms Noise Classification algorithms Training Time-domain analysis Classification model comparative study feature extraction heart sound detection

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Automated Analysis of Teaching Models Based on Artificial Intelligence detection algorithms

Automated Analysis of Teaching Models Based on Artificial In...

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International Conference on Educational and Information Technology (ICEIT)

作者： Nie Shuai Wang Chongwen Jin Zening School of Computer Science and Technology Beijing Institute of Technology Beijing China

ISBN: (数字)9798331540883

ISBN: (纸本)9798331540890

This paper proposes an automated analysis of teaching models based on artificial intelligence detection algorithms. It aims to efficiently analyze actual classroom teaching models, providing insights that enable appropriate adjustments to teaching strategies, thereby improving the quality of education. We analyze classroom audio data streams using an improved Emphasized Channel Attention, Propagation and Aggregation-Time Delay Neural Network (ECAPA-TDNN) model and analyze video data streams using an enhanced You Only Look Once-v3 (YOLO-v3) model. Subsequently, the obtained information, such as head-raising rates and speaker identification, is automatically processed using an improved S-T analysis method to derive teaching models. Experimental results show that our analytical method, while ensuring that the results closely reflect reality, achieves an analysis speed 2.81 times faster than traditional methods. This demonstrates the advantages of applying automated teaching model analysis in the educational field.

关键词： Analytical models Accuracy Education Educational technology Streaming media Data models Reliability Artificial intelligence detection algorithms Streams

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detection algorithms for hyperspectral Imaging applications

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IEEE SIGNAL PROCESSING MAGAZINE 2002年第1期19卷 29-43页

作者： Manolakis, D Shaw, G MIT Lincoln Laboratory Lexington MA United States

Hyperspectral imaging (HSI) sensors in the reflective region of the spectrum acquire digital images in many contiguous and very narrow spectral bands that typically span the visible, near-infrared, and mid-infrared portions. This enables the construction of an essentially continuous radiance spectrum for every pixel in the scene. Irrespective of the design approach, however, performance evaluation of any detector should be done using statistical criteria. This paper focuses on the design and evaluation of detection algorithms using statistically hypothesis testing techniques.

关键词： detection algorithms Hyperspectral imaging Composite materials Signal processing algorithms Chemical sensors Reflectivity Laboratories Sensor phenomena and characterization Atmosphere Remote sensing

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detection algorithms in implantable cardioverter defibrillators

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PROCEEDINGS OF THE IEEE 1996年第3期84卷 428-445页

作者： Jenkins, JM Caswell, SA UNIV MICHIGAN BIOENGN PROGRAM ANN ARBOR MI 48109 USA

This paper presents a review of the evolution of tachycardia fibrillation detection algorithms designed for implantable cardioverter defibrillators (ICD) including those that have been incorporated into first, second, and third generation devices. The major emphasis of this review is an overview of the development of new and innovative means for improved detection in next-generation devices. Time-domain and frequency-domain methods of electrogram analyses are described, limitations are cited and promising new proposals Sol increased specificity which address the false shock incidence are presented.

关键词： detection algorithms Cardiology Electric shock Algorithm design and analysis Design engineering Implants Time domain analysis Frequency domain analysis Proposals Biological materials

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detection algorithms FOR IMAGE SEQUENCE-ANALYSIS

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IEEE TRANSACTIONS ON ACOUSTICS SPEECH AND SIGNAL PROCESSING 1989年第9期37卷 1454-1458页

作者： PATTERSON, TJ CHABRIES, DM CHRISTIANSEN, RW BRIGHAM YOUNG UNIV DEPT ELECT ENGNPROVOUT 84604

The development of five single-stage and four multistage algorithms is reported. A comparison of the performance, for moving target identification applications, of 13 algorithms on a simulated infrared (IR) image database is included. Several of the algorithms show significant improvement in performance over the well-known algorithms in the area.< >

关键词： detection algorithms Image sequence analysis Signal processing algorithms Finite impulse response filter Pixel Optical signal processing Motion detection Least squares approximation Adaptive filters Acoustics

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Graph-based low complexity detection algorithms in multiple-input-multiple-out systems: an edge selection approach

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IET COMMUNICATIONS 2013年第12期7卷 1202-1210页

作者： Lv, Tiejun Long, Feichi Beijing Univ Posts & Telecommun Sch Informat & Commun Engn Beijing 100876 Peoples R China

In this study, the problem of low complexity multiple-input-multiple-out signal detection based on belief propagation (BP) is addressed. The authors propose an edge selection approach that works on factor graph model to cut down the number of circles and high complexity of standard BP algorithm. The message passing from factor nodes to variable nodes is updated by only partial edges, and the mean feedback method is designed to compensate the information loss brought by the edge selection. Both binary and high-order modulations are considered, and the scheme of mapping between bit soft output and modulation symbols when computing the feedback information is discussed. In addition, a minimum mean-square error filter initialised algorithm is proposed, in which the initial message of BP detection is exploited. Both binary and high-order modulations are discussed as well when the authors design this initial message. Simulation results along with convergence and complexity analyses verify that the proposed edge selection approach can achieve good performance with low complexity, and significantly outperform the existing methods with comparative complexity. Moreover, our approach has asymptotic optimality and is a self-adapting scheme, which can achieve the trade-off between performance and complexity by varying the number of selected edges.

关键词： Circles information loss Complexity Edge of a figure or solid signal acquisition High order modulation feedback information detection algorithms

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Evaluation of Change detection algorithms using Difficulty Maps

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IEEE LATIN AMERICA TRANSACTIONS 2023年第6期21卷 700-706页

作者： Sanches, Silvio Ricardo Rodrigues Correa, Cleber Gimenez Brum, Beatriz Regina Bugatti, Pedro Henrique Saito, Priscila Tiemi Maeda da Silva, Claudinei Moreira Custodio Junior, Elton Univ Tecnol Fed Parana Cornelio Procopio Brazil Univ Fed Sao Carlos Sao Carlos Brazil

The evaluation of a change detection algorithm should show its superiority over state-of-the-art algorithms' performances. Evaluating an algorithm involves executing it to segment a set of videos and comparing the results with the ground truth. Here, we used the difficulty level to classify each pixel of each frame of the videos of a dataset as an algorithm performance measure. A structure called "difficulty map" stores information about the difficulty of classifying each pixel in a frame. Based on these maps, we developed a metric that aims to evaluate the performance of algorithms on the difficulty map. The results showed that there are algorithms with the characteristic of classifying pixels that most state-of-the-art algorithms cannot classify (promising algorithms). Identifying such algorithms is essential since improving their performance means facing challenges already overcome by existing approaches.

关键词： Classification algorithms Videos detection algorithms Change detection algorithms IEEE transactions Monitoring Meteorology Algorithm evaluation change detection difficulty map video segmentation

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Designing Heavy-Hitter detection algorithms for Programmable Switches

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IEEE-ACM TRANSACTIONS ON NETWORKING 2020年第3期28卷 1172-1185页

作者： Ben Basat, Ran Chen, Xiaoqi Einziger, Gil Rottenstreich, Ori Harvard Univ Sch Engn & Appl Sci Cambridge MA 02138 USA Princeton Univ Dept Comp Sci Princeton NJ 08544 USA Ben Gurion Univ Negev Dept Comp Sci IL-84105 Beer Sheva Israel Technion IL-3200003 Haifa Israel

Programmable network switches promise flexibility and high throughput, enabling applications such as load balancing and traffic engineering. Network measurement is a fundamental building block for such applications, including tasks such as the identification of heavy hitters (largest flows) or the detection of traffic changes. However, high-throughput packet processing architectures place certain limitations on the programming model, such as restricted branching, limited capability for memory access, and a limited number of processing stages. These limitations restrict the types of measurement algorithms that can run on programmable switches. In this paper, we focus on the Reconfigurable Match Tables (RMT) programmable high-throughput switch architecture, and carefully examine its constraints on designing measurement algorithms. We demonstrate our findings while solving the heavy hitter problem. We introduce PRECISION, an algorithm that uses Partial Recirculation to find top flows on a programmable switch. By recirculating a small fraction of packets, PRECISION simplifies the access to stateful memory to conform with RMT limitations and achieves higher accuracy than previous heavy hitter detection algorithms that avoid recirculation. We also evaluate each of the adaptations made by PRECISION and analyze its effect on the measurement accuracy. Finally, we suggest two algorithms for the hierarchical heavy hitters detection problem in which the goal is identifying the subnets that send excessive traffic and are potentially malicious. To the best of our knowledge, our work is the first to do so on RMT switches.

关键词： Pipelines Throughput Memory management detection algorithms Task analysis Approximation algorithms Software defined networking measurement

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