In this paper, we derive an EM algorithm for nonlinear state space models. We use it to estimate jointly the neural network weights, the model uncertainty and the noise in the data. In the E-step we apply a forwardbackward Rauch-Tung-Striebel smoother to compute the network weights. For the M-step, we derive expressions to compute the model uncertainty and the measurement noise. We find that th...

This paper proposes a robust spectrum sensing scheme for cognitive radio networks. The proposal performs spectrum sensing simultaneously over the total channels rather than a single channel at a time. It first estimates the number of occupied channels via the exponentially embedded family criterion, and then determines the occupancy status for each channel by sample power. The proposed method i...

An important challenge in robotics is to achieve robust performance in object grasping and manipulation, dealing with noise and uncertainty. This paper presents an approach for addressing the performance of dexterous grasping under shape uncertainty. In our approach, the uncertainty in object shape is parameterized and incorporated as a constraint into grasp planning. The proposed approach is u...

Most conventional techniques for noise adaptation assume a clean initial speech model which is adapted to a specific noise condition using adaptation data accumulated from the condition. In this paper, a different problem is considered, i.e. adapting a noisy speech model to a specific noise condition. For example, the initial noisy model may be a multi-condition model which is used to provide m...

In this paper, we derive an EM algorithm for nonlinear state space models. We use it to estimate jointly the neural network weights, the model uncertainty and the noise in the data. In the E-step we apply a forward-backward Rauch-Tung-Striebel smoother to compute the network weights. For the M-step, we derive expressions to compute the model uncertainty and the measurement noise. We nd that the...

In this paper, we derive an EM algorithm for nonlinear state space models. We use it to estimate jointly the neural network weights, the model uncertainty and the noise in the data. In the E-step we apply a forward-backward Rauch-Tung-Striebel smoother to compute the network weights. For the M-step, we derive expressions to compute the model uncertainty and the measurement noise. We nd that the...

In basic genetic algorithm (GA) applications, the fitness of a solution takes a value that is certain and unchanging. There are two classes of problem for which this formulation is insufficient. The first consists of ongoing searches for better solutions in a nonstationary environment, where the expected fitness of a solution changes with time in unpredictable ways. The second class consists of...

A complete uncertainty analysis on a given fringe projection set-up has been performed using Monte-Carlo approach. In particular the calibration procedure is taken into account. Two applications are given: at a macroscopic scale, phase noise is predominant whilst at microscopic scale, both phase noise and calibration errors are important. Finally, uncertainty found at macroscopic scale is close...