The uncertainty propagation of composite structures is investigated considering descriptive statistical measures of the response variability. A study based on sensitivity to uncertainty that allows selecting the important parameters using sensitivity indices is presented. The uncertainty propagation and the importance measure of input parameters are analysed using four different approaches: a f...

We study the variance of the solution of a periodic randomly perturbed one-dimensional Schrödinger operator after propagation through N periods. It is shown that if the frequency of propagation lies inside the band, then the total variance is proportional to Nσ 2, where σ is the intensity of the white noise. However, if the wave frequency is close to the band edge (where the transfer matrix has...

We develop a new receiver for joint symbol, channel characteristics, and code delay estimation for DS spread spectrum systems under conditions of multipath fading. The approach is based on particle filtering techniques and combines sequential importance sampling, a selection scheme, and a variance reduction technique. Several algorithms involving both deterministic and randomized schemes are co...

The Multipath effect in Time-of-Flight(ToF) cameras still remains to be a challenging problem that hinders further processing of 3D data information. Based on the evidence from previous literature, we explored the possibility of using machine learning techniques to correct this effect. Firstly, we created two new datasets of of ToF images rendered via ToF simulator of LuxRender. These two datas...

Synchronous activation of neural networks is an important physiological mechanism, and dysregulation of synchrony forms the basis of epilepsy. We analyzed the propagation of synchronous activity through chronically epileptic neural networks. Electrocorticographic recordings from epileptic patients demonstrate remarkable variance in the pathways of propagation between sequential interictal spike...

We address the problem of estimating statistics of hidden units in a neural network using a method of analytic moment propagation. These statistics are useful for approximate whitening of the inputs in front of saturating non-linearities such as a sigmoid function. This is important for initialization of training and for reducing the accumulated scale and bias dependencies (compensating covaria...

An experimental technique for investigation of the behavior of acoustic wave propagation through a turbulent medium is discussed. The present study utilizes the ultrasonic travel-time technique to diagnose a grid-generated turbulence. The statistics of the travel-time variations of ultrasonic wave propagation along a path are used to determine some metrics of the turbulence. Experimental data o...

We present a novel method for approximate inference in Bayesian models and regularized risk functionals. It is based on the propagation of mean and variance derived from the Laplace approximation of conditional probabilities in factorizing distributions, much akin to Minka’s Expectation Propagation. In the jointly normal case, it coincides with the latter and belief propagation, whereas in the ...

Signal statistics are modelled in an environment of shadowing and ground wave path loss. This propagation model is used to investigate channel performance of a slotted ALOHA network. The results are presented as receiver capture probability versus packet propagation distance, with the log-normal variance and the offered traffic as parameters.