Moschitta Antonio
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Additional Details
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Phone075 585 3933
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Mobile Phone348 151 6468
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RoleRicercatore - Research fellow
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AreaMisure - Measurements
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Monday, 28 July 2014 16:51
imeko2014MoschittaCarbone
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.
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.
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Thursday, 20 December 2012 11:11
A Simple and Accurate Model for Predicting Mismatch Effects in Photovoltaic Arrays
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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