In this work, we propose a new method to re-identify the same individual among different people using RGB-D data. Each human signature is combination of soft biometric traits. particular, extract color-based descriptor and local feature through Monte Carlo-based algorithm taking into account uncertainty joints and, applied each descriptor, refines similarity match against spatiotemporal databas...

A crucial aspect in system identification is the assessment of accuracy identified matrices. Stochastic Subspace Identification (SSI) a widely used approach for linear systems from output-only data because it combines high computational robustness and efficiency with estimation accuracy. Practical approaches estimating (co)variance matrices that are using SSI exist case where obtained shift-inv...

This paper presents an algebraic wavenumber identification method to identify wavenumbers under stochastic conditions. Stochastic condition results from the introduction of small perturbation which is referred uncertainty measurements points’ coordinates caused by operation faults or problems with experimental errors. The proposed compared two popular alternatives, namely: inhomogeneous wave co...

In portfolio selection models, uncertainty plays an important role. The parameter’s uncertainty leads to getting away from optimal solution so it is needed to consider that in models. In this paper we presented a two-stage robust model that in first stage determines the desired percentage of investment in each industrial group by using return and risk measures from different industries. One rea...

This report attempts to document the broad scope of issues that must be satisfactorily resolved before one can expect to methodically obtain, with a reasonable confidence, a near-optimal robust closed loop performance in physical applications. These include elements of signal processing, noise identification, system identification, model validation, and uncertainty modeling. Based on a recently...

Entropic uncertainty relations are quantitative characterizations of Heisenberg’s uncertainty principle, which make use of an entropy measure to quantify uncertainty. In quantum cryptography, they are often used as convenient tools in security proofs. We propose a new entropic uncertainty relation. It is the first such uncertainty relation that lower bounds the uncertainty of all but one measur...

This article first systematically classified the uncertainty and provided the multi-scale fractional ordered association model in accordance with the multiple uncertainty time series. From the mathematical point of view, the model used in this thesis extended the integerorder correlation measurement to the fractional-order correlation measurement; elongate the information recognition from point...