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Establishing causal relations between random variables from observational data is perhaps the most important challenge in today's Science. In remote sensing and geosciences this is of special relevance to better understand the Earth's system and the complex and elusive interactions between processes. In this paper we explore a framework to derive cause-effect relations from pairs of variables via...
In this paper, the application of independent component analysis (ICA) to statistical process monitoring is studied. This paper mainly focuses on studying on the fault detection and isolation principle based on the data model of ICA. Contributions of this paper are: (1) for the purpose of fault detection, two monitoring statistics are designated by detailed analysis on the data model of ICA; (2) a...
In this paper, we discuss a function reconstruction problem by kernel regressors in which the autocorrelation of the unknown true function is given a priori. In general, a reconstructed function in the kernel regression problem, using a certain reproducing kernel Hilbert space, is represented by a linear combination of the corresponding kernel specified by each input point. We introduce a framework...
Single-image blind deblurring could be considered as an important preprocessing step in imaging information fusion. Its purpose is to simultaneously estimate blur kernel and latent sharp image from only one observed blurred image. Blind deblurring has been attracting increasing attention in the fields of image processing, computer vision, computational photography, etc. However, it is a typically...
For highly nonlinear problems, the linear minimum mean-square error (LMMSE) estimation using a nonlinearly converted measurement can outperform the one using the original measurement. For a function space of measurement conversions, every function in the space can be represented as a linear combination of a basis of the space. Then the LMMSE estimator using a vector with its entries forming a basis...
We present a novel deep learning framework for crowd counting by learning a perspective-embedded deconvolution network. Perspective is an inherent property of most surveillance scenes. Unlike the traditional approaches that exploit the perspective as a separate normalization, we propose to fuse the perspective into a deconvolution network, aiming to obtain a robust, accurate and consistent crowd density...
Blind image deblurring is one of the main phases in most media analysis tasks. Many existing works aim to simultaneously estimate the latent image and the blur kernel under a MAP framework. However, it has been demonstrated that such joint estimation strategies may lead to the undesired trivial solution. In this paper, we propose a learnable nonlinear dynamical system to formulate the image propagation...
In this paper we utilize the first large-scale "in-the-wild" (Aff-Wild) database, which is annotated in terms of the valence-arousal dimensions, to train and test an end-to-end deep neural architecture for the estimation of continuous emotion dimensions based on visual cues. The proposed architecture is based on jointly training convolutional (CNN) and recurrent neural network (RNN) layers,...
In this paper, we introduce a new sensor system to measure the vehicle body velocity using a CMOS camera and it's motion blur. When the camera faced on the ground and mounted on the vehicle body is shaken by a specific path to artificially form a motion blur, a Modulated Motion Blur is recorded on the image sensor. This Modulated Motion Blur implies the relative motion between the camera and the ground...
The use of dashboard-mounted video cameras is rapidly spreading in many countries around the world. Widespread usage of dash-cams brings new problems, for example, dash-cam videos are uploaded on public websites which contain footage of other cars with the number-plates visible. This can potentially compromise privacy. Further, dash-cam videos can be used as evidence in case of accidents. There have...
Blind deblurring consists a long studied task, however the outcomes of generic methods are not effective in real world blurred images. Domain-specific methods for deblurring targeted object categories, e.g. text or faces, frequently outperform their generic counterparts, hence they are attracting an increasing amount of attention. In this work, we develop such a domain-specific method to tackle deblurring...
Efficient test and diagnosis methods are required to ensure high levels of dependability of the electronic systems deployed to the market. These methods involve a trade-off in terms of accessibility to test nodes, test stimuli complexity, area overhead, and data processing that, altogether determine the impact that the involved operations have in the final cost, performance, and reliability presented...
In this paper a robust and flexible method is proposed that combines the strengths of detector as well as Floating Car (FC) data in order to provide short-term congestion front forecasts. Based on the high spatio-temporal resolution of FC data, congested regimes and according congestion fronts are identified accurately. Subsequently, the flow data provided by loop detectors are utilized in order to...
We derive the mean squared error convergence rates of kernel density-based plug-in estimators of mutual information measures between two multidimensional random variables X and Y for two cases: 1) X and Y are both continuous; 2) X is continuous and Y is discrete. Using the derived rates, we propose an ensemble estimator of these information measures for the second case by taking a weighted sum of...
The jackknife resampling procedure is a technique to reduce the bias of a statistic. As with other resampling techniques, the jackknife procedure is motivated by and is well understood in the i.i.d. regime. However, analysis of the procedure when samples have memory is limited, and is predominantly restricted to cases with strong mixing or memory constraints. In this paper, we analyze a natural jackknife...
Many graphical alternatives are useful to demonstrate critical characteristics of distribution systems such as voltage regulation or power flow. The visualization of electrical variables can also be an effective approach to analyze, compare and evaluate large-scale systems. However, visual analysis of large power distribution systems is increasing in complexity for operational and research purposes...
Optimum and heuristic sampling methods of a range of values of a two-dimensional random value are investigated. Conditions of their competence at recovery of the normal distribution law of two independent random values are defined.
In this paper, we use a restoration method that rapidly restores blurred images using local patches proposed by Senshiki et al. [1]. The computation time is significantly reduced by that method, but it is not yet a practical. Therefore, we propose to accelerate by implementing the image restoration processing on GPU. By measuring the processing time of the image restoration, we show the superiority...
A recent research trend is driven to increase the monitoring and control capabilities of low voltage networks. This paper describes a probabilistic forecasting methodology based on kernel density estimation and that makes use of distributed computing techniques to create a highly scalable forecasting system for LV networks. The results show that the proposed algorithm outperforms three benchmark models...
Density estimation is a fundamental part of statistical analysis and data mining. In high-dimensional domains, parametric methods and the commonly used non-parametric methods like histograms or Kernel estimators fail to perform properly. In this paper, we present computationally efficient data structures for efficient implementation of the Bayesian sequential partitioning (BSP), as a framework for...
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