Results 1 to 10 of about 167,906 (119)
Bayesian Cramér-Rao Lower Bounds for Prediction and Smoothing of Nonlinear TASD Systems
Sensors, 2022 The performance evaluation of state estimators for nonlinear regular systems, in which the current measurement only depends on the current state directly, has been widely studied using the Bayesian Cramér-Rao lower bound (BCRLB).
Xianqing Li +3 more
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OS-PCA: Orthogonal Smoothed Principal Component Analysis Applied to Metabolome Data
Metabolites, 2021 Principal component analysis (PCA) has been widely used in metabolomics. However, it is not always possible to detect phenotype-associated principal component (PC) scores.
Hiroyuki Yamamoto +2 more
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Optical Flow Estimation Based on Curvelet Transform and Spatio-temporal Derivatives [PDF]
Engineering and Technology Journal, 2010 Optical flow estimation is still one of the key problems in computer vision.When estimating the displacement field between two images, it is applied as soonas correspondences between pixels are needed.
Atheer A. Sabri
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Time Series Smoothing Improving Forecasting
Applied Computer Systems, 2021 Both statistical and neural network methods may fail in forecasting time series even operating on a great amount of data. It is an open question of which amount fits best to make sufficiently accurate forecasts on it. This implies that the length or time
Romanuke Vadim
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Remote Sensing, 2023 Time series of optical remote sensing data are instrumental for monitoring vegetation dynamics, but are hampered by missing or noisy observations due to varying atmospheric conditions.
Pieter Kempeneers +2 more
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Reversible Image Processing for Color Images with Flexible Control
Applied Sciences, 2023 In this paper, we propose an image processing method for color images to reversibly achieve flexible functions. Most previous research has focused on reversible contrast enhancement (CE) for grayscale images. When we directly apply these methods to color
Yuki Sugimoto, Shoko Imaizumi
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Discrete and Fuzzy Models of Time Series in the Tasks of Forecasting and Diagnostics
Axioms, 2020 The development of the economy and the transition to industry 4.0 creates new challenges for artificial intelligence methods. Such challenges include the processing of large volumes of data, the analysis of various dynamic indicators, the discovery of ...
Anton Romanov +4 more
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Two approaches to solving the problem of smoothing digital signals based on a combined criterion [PDF]
Serbian Journal of Electrical Engineering, 2017 The paper presents a method for smoothing signals represented by a single realization of a finite-length random process, under conditions of a limited amount of a priori information about the signal function and statistical characteristics the ...
Semenishchev Evgenii +2 more
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Atmosphere, 2023 In this paper, we study the problem of extracting trends from time series data involving missing values. In particular, we investigate a general class of procedures that impute the missing data and then extract trends using seasonal-trend decomposition ...
Chun-Fung Kwok +2 more
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Rauch-Tung-Striebel Smoothing Linear Multi-Target Tracking in Clutter
IEEE Access, 2022 Most conventional data association based multi-target tracking (MTT) algorithms typically suffer from intractable computational complexities and could not perform in an environment where large number of closely spaced multiple targets move across each ...
Sufyan Ali Memon +3 more
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