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We consider tensor factorizations using a generative model and a Bayesian approach. We compute rigorously the mutual information, the Minimal Mean Square Error (MMSE), and unveil information-theoretic phase transitions. In addition, we study the performance of Approximate Message Passing (AMP) and show that it achieves the MMSE for a large set of parameters, and that factorization is algorithmically...
In this paper, non-linear estimators and feed-forward actions are designed to solve a vehicular platoon control problem. In particular, these estimators are useful in implementation of a Multi-Layer Consensus Seeking approach to the platooning problem. In the Multi-Layer approach the feed-forward part is built separately from the feedback part, based on desired trajectories. This opens the way to...
Formation flying missions require the knowledge of the relative positions of the satellites for formation, maintenance and scientific purposes. In Low Earth Orbit, this task is typically performed by GPS-based navigation filters. However, the final accuracy, especially over long baselines, is strongly affected by the capability of correctly estimating the ionospheric delays. In this paper the performance...
Precise radius estimation is of high interest for rebar and pipe characterization but very challenging. In this work, we present a novel 3D frequency-domain full-waveform inversion (FWI) approach with which the geometrical information of subsurface cylindrical objects and the dielectric properties of the penetrating medium are simultaneously extracted from ground penetrating radar (GPR) data. The...
We consider the problem of reconstructing a signal from multi-layered (possibly) non-linear measurements. Using non-rigorous but standard methods from statistical physics we present the Multi-Layer Approximate Message Passing (ML-AMP) algorithm for computing marginal probabilities of the corresponding estimation problem and derive the associated state evolution equations to analyze its performance...
This research proposes a comparative analysis of the performance of various random utility models (RUM) — namely Multinomial Logit, Nested Logit, Cross Nested Logit, FinMix and CoNL-estimated on a synthetic dataset with variable sample size and correlation patterns. This experimental framework allows comparing model estimates in a fair, controlled environment wherein all relevant characteristics (coefficients,...
To enable an intelligent traffic light system (ITLS) to consider the interactions between the signal controls and the traffic flow distribution resulting from the selfish-routing behaviors of travelers, a dynamic origin-destination (O-D) demand estimation model and a dynamic combined traffic assignment and signal control (CTA-SC) model are needed. However, the ITLS may collect inaccurate and incomplete...
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.
The optimization of a model that expresses time series data for a given period is a problem associated with the development of a regression model that estimates future data on the extension of the past data time series. This is a two-step optimization problem where the order of past data used in the regression model (number of orders of the solution space) is decided, and weighted coefficients for...
The classification of dynamical data streams is among the most complex problems encountered in classification. This is, firstly, because the distribution of the data streams is non-stationary, and it changes without any prior “warning”. Secondly, the manner in which it changes is also unknown. Thirdly, and more interestingly, the model operates with the assumption that the correct classes of previously-classified...
In this paper we study single image haze removal techniques on outdoor images for visibility enhancement in foggy weather conditions. Haze removal techniques based on dark channel prior model have used different filters for estimating the transmission. We have studied effect of using different filters along with the fundamental mean and gaussian filters in the visibility enhancement in foggy conditions...
Condition monitoring data have been widely used to evaluate the health state and reliability, as well as estimate the remaining useful life (RUL) for degrading systems. Among various degradation modeling and RUL estimating methods, Wiener process based models is recognized by both scholars and engineers as the one of the most effect tools, and thus becomes very popular nowadays. In this paper, a prognostic...
This paper proposes a novel estimator for active and reactive power control of single-phase power electronic applications. The proposed estimator includes: 1) an instantaneous grid power regression model; 2) a fast upper-triangular and diagonal recursive least square (FUDRLS) algorithm for parameter identification of the regression model; and 3) a new self-tuning variable forgetting factor included...
In this paper, a hybrid measurement and model-based method is proposed which can estimate the dynamic state Jacobian matrix in near real-time. The proposed method is computationally efficient and robust to the variation of network topology. A numerical example is given to show that the proposed method is able to provide good estimation for the dynamic state Jacobian matrix and is superior to the model-based...
Photoplethysmographic signals are synthesized using a Fourier representation with a fundamental and 2 harmonic components, and an accurate method for parameter estimation using this synthesis model is discussed. The estimated parameters enable to determine physiologically meaningful features related to arterial stiffness, heart rate variability, blood pressure variations, etc. The method allows signal...
In this paper, we propose a graph-based model for pupil localization, which is a step towards gaze detection. The proposed model can differentiate the key points located at the eyelashes, eyebrows and eye white regions. We first crop the eye region with an ellipse and then estimate the pupil center within the ellipse, thus reducing the computational complexity. We also consider the light reflections...
In the presence of environmental noise, speaker verification systems inevitably see a decrease in performance. This paper proposes the (1) use of two parallel classifiers, (2) feature enhancement based on blind signal-to-noise ratio (SNR) estimation and (3) fusion, to improve the performance of speaker verification systems. The two classifiers are based on Gaussian mixture models and the partial least-squares...
Three methods for extracting the behavioral modeling coefficients of the memory polynomial model are compared herein. The first one is the ordinary least square regression, which is widely used for adjusting model parameters; the second is the order recursive least squares, which is suitable for exploring the optimal nonlinearity order and memory depth by comparing subsequent errors while increasing...
The problem of preference functions model development for multiple criteria decision-making is considered based on machine-learning approach. It is assumed that the training sample for a plurality of objects, for which decisions are made, is formed from a set of measured features or the particular criteria and the matrix of pairwise comparisons. The problem of constructing a linear preference function...
The paper considers the problem of decision support systems constructing for solving the problems of modeling and estimating selected types of risks with the possibility for application of alternative data processing techniques, modeling and estimation of parameters and states for the processes under study. The system proposed has a modular architecture that provides a possibility for easy extension...
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