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This paper proposes a diagnostics tool for a Discrete-Event System (DES) under uncertain activation conditions. This diagnosis tool, the diagnoser (as it is called), detects, identifies, and locates system faults in relation to a set of states of which the system under diagnosis could possibly be located, upon the diagnoser's instance of activation. This diagnoser is designed to diagnose system faults...
In multimedia distribution platforms, one of the main challenges is to provide an efficient and accurate tracing process despite the lack of information about the colluders' strategy. Indeed, the original Tardos tracing performance is considered as suboptimal because of its agnostic behavior and conservative accusation regardless the collusion strategy. The Expectation Maximization algorithm has shown...
Many occupations require both physical exertion and the ability to navigate in an environment, simultaneously. This study investigated how intensity of physical activity influences direction determination and distance estimation. Thirty high fit young males participated in a lab study. Results showed that while high fit young males were accurate in determining direction across levels of physical exertion,...
The increasing presence of renewable energy sources and the novel consumption types will obviously cause the increase of the fluctuation of electrical power in households. In order to better manage the electrical power consumption and production the integration of information and communication technologies and power grid is necessary, which is obviously a recent research topic. The availability of...
Accurate and flexible probability density estimation is fundamental in machine learning tasks, in classification and routine data analyses applications. In this paper we propose an adaptive version of the Histogram Trend Filtering (HTF), which is a relatively new method used for non-parametric density estimation. This technique enjoys low computational complexity, while being able to automatically...
Side Channel Analysis is a powerful attack to recover the secret key by exploiting an extra source of information such as timing, power consumption and electromagnetic leakage. Pre-processing scheme is used to improve the attack performance in the restricted traces. One of the pre-processing schemes is Singular Spectrum Analysis, which makes the original trace can be split into main signal and noise...
This paper proposes a new method to detect static caption regions using intensity range maps and a statistical model of motion vectors. The method relies on the probability of block-based intensity ranges and magnitudes of motion vectors. Intensity range is used to find candidate locations of static captions and the statistical model of motion vector is used to refine accurate caption regions. The...
Haze is mainly occurred by atmospheric phenomena. Recently, many researchers in haze removal algorithm area are using single image. At the single image, we can't use depth information. To estimate the thickness of haze without depth information is not easy. As a result, single image haze removal method includes halo effect. In this paper, we propose halo effect suppression for single image haze removal...
This paper presents novel video feature-based favorite video estimation method. In the proposed method, we use three features, videos, users' viewing behavior and users' evaluation scores for these videos. In order to calculate the novel video features, Multiset Canonical Correlations Analysis (MCCA) is applied to these features to integrate the different types of features. Specifically, MCCA maximizes...
We studied the feasibility of the use of iBeacons for position detection. We compared the precision achieved by two well-known algorithms, i.e. nearest neighbor and k-nearest beacon. The nearest neighbor algorithm showed lower average error distance, by 1.0 m, than the k-nearest beacon algorithm. We also found that both indoor position methods are not influenced by the difference in the measured rooms...
In this paper, the operation cycle for Motion Vector Search is evaluated between the proposed search method and the conventional Full Search algorithm. As a result, the operation cycle of the proposed method is 74% smaller than Full Search algorithm but the circuit size is 0.8% larger than that of Full Search algorithm and the vector matching accuracy of the proposed search method deteriorate to 66...
This study proposes a practical method for noise reduction using a Kinect microphone array. The method initially estimates the direction of arrival (DOA) and waveform of a noise signal, then subtracts the estimated noise from the output of a reference microphone to restore a target signal. A previous study proposed the complex spectrum circle centroid (CSCC) method, and the proposed method has all...
In this paper, we propose a tourism category classification method based on estimation of reliable decision. The proposed method performs tourism category classification using location, visual, and textual tag features obtained from tourism images in image sharing services. As the biggest contribution of this paper, the proposed method performs successful classification based on two classification...
The present paper describes a low-cost algorithm for video stabilization. Like other feature based algorithms, it is robust to motion blur, noise and illumination changes. Moreover, maintaining real time processing, it is not negatively affected by moving objects in the scene, works fine even in conditions of low details in the background and it is robust to scene changes.
Super-resolution theory aims to estimate the discrete components lying in a continuous space that constitute a sparse signal with optimal precision. This work investigates the potential of recent super-resolution techniques for spectral estimation in multi-rate sampling systems. It shows that, under the existence of a common supporting grid, and under a minimal separation constraint, the frequencies...
This paper considers the minimum mean p-th error (MMPE) estimation problem: estimating a random vector in the presence of additive white Gaussian noise (AWGN) in order to minimize an Lp norm of the estimation error. The MMPE generalizes the classical minimum mean square error (MMSE) estimation problem. This paper derives basic properties of the optimal MMPE estimator and MMPE functional. Optimal estimators...
This paper proposes a new algorithm, which uses the second order information of a Least Absolute Shrinkage and Selection Operator (LASSO) to achieve an active sensing approach driven by minimizing the entropy of sparse unknown environments, for the multi agent case. For this, a signal model, which restricts the agent's measurements according to its sensor's view, is introduced into the Distributed...
We present a time delay estimation method for sparse translation-invariant signals, off the grid. We enforce sparsity by imposing penalties based on an atomic norm to consider signals in a continuous domain. Our formulation leads to an atomic norm minimization problem. A number of recent theoretical results demonstrate that an atomic norm minimization can be solved using a semidefinite programming...
In this paper we study a wireless passive sensor network. The sensors are deployed to estimate the true values of multiple active target signals. The sensors forward their observation to a fusion center, which processes the observation of each sensor by a set of fusion rules. One achievement of this paper is proposing an unbiased estimator with minimized variance of errors. To do so, we optimize both...
An image was taken by the XMM telescope to better understand galaxy clusters. We model the data as a high dimensional Poisson inverse problem and develop a lasso type estimator with a composite penalty (with two thresholds) to handle both estimation of the gas density in the cluster and detection of point sources.
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