Dipartimento d'Ingegneria

Measurements - Misure

Research activities in the Electronic Measurement Scientific Lab cover the following areas:

Indoor positioning
The need for services in the area of position measurements inside buildings and where global positioning systems are not available, has pushed our research group to investigate techniques and technologies useful to estimate the position of moving nodes in indoor environments.
This includes the usage of several enabling technologies (ultra-widebandwidth pulses, magnetic positioning, ultrasound, zigbee, bluetooth) to adapt systems to the environmental constraints. Devices and systems are developed from scratch and tested under real-world situations.

Test and measurement of data converters
The performance assessment of data converters (ADCs, DACs and TDCs) requires usage of state-of-the-art equipment and estimation procedures.
Our research group is a recognized leader in this area with results and contributions to the currently available IEEE standards on these topics.

Statistical signal processing
Development of original estimation procedures to address the practical needs arising from research activities in the former two research areas.
Both indoor positioning and testing of data converters require the usage of complex estimation procedures based on theoretical fundamentals.
Several new techniques and estimators are being developed and published for the parametric estimation in the areas of data converter testing and distance and position estimation.


In Downloads ,
Written by Monday, 28 July 2014 16:51
Advanced software tools for parametric identification based on quantized data (by Antonio Moschitta and Paolo Carbone)

Description: a quantile based estimation tool  for IMEKO 2014 Software Session,developed in Matlab by Paolo Carbone and Antonio Moschitta (see the "Download attachment" link below)

Abstract - Signal parametric estimation based on quantized data is often carried out by means of least mean square (LMS) or averaging techniques. Such an approach often leads to optimal performance, resulting in almost  unbiased estimators when quantization error can approximately be modeled as an additive white noise, or when other additive white noise sources dominate quantization error. When such hypotheses are not satisfied, however, averaging techniques may produce suboptimal biased estimators. In such a case, maximum likelihood or quantile based identification techniques can be shown to lead to more performing estimators, mostly unbiased and with a lower mean square error than that of a LMS estimator. A software tool is presented, capable of estimating a DC level, a DC level corrupted by AWGN, and sinewave parameters when the frequency is known frequency, using data quantized by a non uniform ADC.
In this paper, an accurate and computationally light technique for analyzing the performance of Photovoltaic (PV) arrays is introduced. The proposed approach can be used to quickly assess the achievable Maximum Power Point (MPP) of a PV array. A method for optimally allocating a set of available modules, used to create PV arrays with a given length, is proposed, that allows to maximize the achievable power output of the obtained PV arrays.


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