Ricerc@Sapienza

Subjective tests for the assessment of the quality of experience (QoE) are typically run with a pool of subjects providing their opinion score using a 5-level scale. The subjects? Mean Opinion Score (MOS) is generally assumed as the best estimation of the average score in the target population. Indeed, for a large enough sample we can assume that the mean of the variations across the subjects approaches zero, but this is not the case for the limited number of subjects typically considered in subjective tests.

The paper deals with a sliding mode based sensorless control for permanent magnet synchronous motors (PMSMs), where an observer using only electrical signals, guarantees the robust asymptotical speed tracking without measuring rotor velocity and position. To compute online the motor parameters and the initial rotor angular position, a set of polynomial equations has been derived from the electrical subsystem. Performances are validated by realistic simulation results.

We study the problem of estimating the size of a matching when the graph is revealed in a streaming fashion. Our results are multifold:1.We give a tight structural result relating the size of a maximum matching to the arboricityα of a graph, which has been one of the most studied graph parameters for matching algorithms in data streams. One of the implications is an algorithm that estimates the matching size up to a factor of (α+ 2) (1 + ε) using O~ (αn2 / 3) space in insertion-only graph streams and O~ (αn4 / 5) space in dynamic streams, where n is the number of nodes in the graph.

The Frisch-Waugh-Lovell (FWL) Recursive Least Squares (RLS) algorithm has been recently proposed as an RLS algorithm with lower computational cost and better numerical properties. We propose a VHDL implementation that has been successfully implemented on a Xilinx Virtex-7 FPGA. The FWL RLS algorithm has a complexity of L2 + O(L) products, instead of 1.5L2 O(L) as in conventional RLS algorithms. Because it removes all matrix operations, separating an L input vector problem into L separate scalar problems, it is stable and often faster in fixed-point arithmetic than conventional RLS.

This paper presents a procedure for reconstruction of spatially localized images from compressively sampled measurements making use of Bayesian priors. The contribution of this paper is twofold: firstly, we analytically derive the expected value of wavelet domain signal structures conditional to a suitably defined noisy estimate; secondly, we exploit such conditional expectation within a nonlinear estimation stage that is added to an iterative reconstruction algorithm at a very low computational cost.