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The long term goal of artificial intelligence and computer vision is to be able to build models of the world automatically and to use them for interpretation of new situations. It is natural that such models are efficiently organized in a hierarchical manner; a model is build by sub-models, these sub-models are again build of another models, and so on. These building blocks are usually shareable;...
Most target tracking algorithms work with data at the plot or detection level; that is, after an initial signal processing step of thresholding and centroiding that delivers point “hits” for data association and filtering. The GFMT (general frequency modulation tracker) and HPMHT (histogram probabilistic multi-hypothesis tracker), on the other hand, work directly with pixellated observation data,...
We propose a new real-time framework which efficiently reconstructs large-scale scenery by accumulating anisotropic point representations in combination with memory efficient representation of point attributes. The reduced memory footprint allows to store additional point properties that represent the accumulated anisotropic noise of the input range data in the reconstructed scene. We propose an efficient...
The change detection and diagnosis methods have gained considerable attention in scientif c research and appears to be the central issue in various application areas. These applications need some robust change detection schemes to work well and separate the changes in the experimental conditions from the real changes in the system, especially for systems with arbitrary and non-stationary known or...
t-Closeness was introduced as an improvement of the well-known k-anonymity privacy model for data release. On the other hand, e-differential privacy was originally proposed as a privacy property for answers to on-line database queries and it has been very welcome in academic circles. In spite of their quite diverse origins and motivations, we show in this paper that t-closeness and e-differential...
Noise is a prominent challenge found in many bioinformatics datasets and it refers to erroneous or missing data. The presence of noise in gene expression datasets has adverse effects on machine-learning techniques, such as supervised classification algorithms and feature selection techniques. Additionally, the identification of noise and its quantification are challenging tasks that require a proper...
This paper presents a hierarchical feature extraction technique for non-stationary time-series data that is considered to be a slow-time scale mixture of time-series segments which are quasi-stationary at a faster time-scale. The problem is to model an unknown number of unique stationary segments at the low level while capturing their switching characteristics at a higher level. Symbolic Dynamic Filtering...
A convex optimization based approach is presented to perform model-data assimilation of spatial temporal dynamical systems where sensor error characteristics are not available. The key idea of the proposed technique is that one should not make any assumption regarding the statistical properties of sensor data when they are not available. Recently developed quadrature scheme, Conjugate Unscented Transformation...
Accurate information about dynamic states is important for efficient control and operation of a power system. This paper compares the performance of four Bayesian-based filtering approaches in estimating dynamic states of a synchronous machine using PMU data. The four methods are Extended Kalman Filter, Unscented Kalman Filter, Ensemble Kalman Filter, and Particle Filter. The statistical performance...
This paper presents a solution to increase the accuracy of time-series processing of coarse-resolution Earth observation imagery (such as MODIS). It is based on two main points. First, the processing of imagery is based on a vector data model that enables more accurate representation of the actual observation footprints than the original raster model. Second, time-series composition is carried out...
Before releasing an anonymized data set, the data protector must know how safe the data set is, that is, how much disclosure risk is incurred by the release. If no privacy model is used to select specific privacy guarantees prior to anonymization, posterior disclosure risk assessment must be performed based on the anonymized data set and, if the result is not satisfactory, anonymization must be repeated...
The most common approach in Structural Health Monitoring (SHM) consists in performing accelerometric measures of the response of the monitored structures to natural or artificial stimuli (e.g. wind, urban traffic, seismic events etc.) and in modeling the dynamic behavior of the structure on the basis of these measures. The models can be used, in particular, to extract and compare the main modes i...
Simulation of higher order nonlinear device currents in RF integrated circuits becomes crucial when high linearity is a design goal. Prediction of the nonlinear behavior of passive circuits like mixers, attenuators or switches play an important role in low-distortion designs, but also causes trouble due to the inability of threshold voltage based compact models to inherently model higher order nonlinearities...
This paper presents a geometrical method for solving the non-negative Blind Source Separation (BSS) problem. The method is based on a weak sparsity condition: for each source, there should exist at least one observed vector where only this source is non-zero. The method does not require but allows the sum-to-one constraint for the mixing parameters or sources. Considering each observed vector as an...
The article is devoted to the instrumental means of the design of video data link, which are mounted on board the aircraft. The information concerning the possibilities of the program model use for the choice of the basic hosts and image translation options via physical "channel with gaps" is given in the article.
We consider the problem of clustering noisy finite-length observations of stationary ergodic random processes according to their nonparametric generative models without prior knowledge of the model statistics and the number of generative models. Two algorithms, both using the L1-distance between estimated power spectral densities (PSDs) as a measure of dissimilarity, are analyzed. The first algorithm,...
Registration is a fundamental problem in a myriad of applications ranging from heritage reconstruction to industrial applications. Descriptors are an important part of the registration pipeline as well as a very active research field. However, the sets used to illustrate descriptor performance have often undergone several preprocessing steps such as noise filtering, hole filling or outlier removal...
In order for robots to be useful in real world learning scenarios, non-expert human teachers must be able to interact with and teach robots in an intuitive manner. One essential robot capability is wide-area (mobile or nonstationary) pick-and-place tasks. Even in its simplest form, pick-and-place is a hard problem due to uncertainty arising from noisy input demonstrations and non-deterministic real...
Typically 2.5D maps provide a compact and efficient representation of the environment. When sensor data is obtained from multiple sets of noisy measurements at differing resolutions, the problem of compounding this information together to provide an effective and efficient means of mapping is not trivial, particularly as the size of the environment increases. In this paper, we propose a general framework...
Blast furnace gas (BFG) is regarded as a very important secondary energy in steel industry, and an effective model to describe the status of BFG system is fairly significant to maintain the system balance and stability. However, the high level noises in industrial data and the disturbances in training samples could lead to the overfitting phenomenon. A fuzzy subset fusion combined with a rule reduction...
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