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A. Cruz Serra

Researcher at Technical University of Lisbon

Publications -  52
Citations -  736

A. Cruz Serra is an academic researcher from Technical University of Lisbon. The author has contributed to research in topics: Waveform & Histogram. The author has an hindex of 13, co-authored 52 publications receiving 680 citations. Previous affiliations of A. Cruz Serra include University of Lisbon & Instituto Superior Técnico.

Papers
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Automatic calibration of analog and digital measuring instruments using computer vision

TL;DR: An automatic calibration system capable of calibrating measuring instruments that do not have a digital interface and can be used with analog and with digital displays is presented.
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Low frequency impedance measurement using sine-fitting

TL;DR: An impedance measurement technique based on the use of a personal computer, two digitizing channels and the application of four-parameter sine-fitting algorithms to estimate amplitude, phase, offset and frequency of the voltages across the impedance under measurement and of a reference impedance is described.
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Automated ADC characterisation using the histogram test stimulated by Gaussian noise

TL;DR: A broadband variant of the histogram test where Gaussian noise is used as a stimulus signal is presented and tolerance and confidence intervals are determined both for the integral nonlinearity (INL) and differential non linearity (DNL) vectors, related to the number of samples acquired.
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A one-dimensional, self-consistent numerical solution of Schrödinger and poisson equations

TL;DR: In this paper, a self-consistent, one-dimensional numerical solution of Schrodinger and Poisson equations has been obtained by dividing the space in intervals of constant potential energy, in which the solution type is well known.
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A new four parameter sine fitting technique

TL;DR: In this paper, a new procedure to perform four-parameter sine fitting is presented in a closed form, ready for standardization, which grants convergence of the algorithm, even in those cases where the traditional techniques tend to converge to local minimums of the error function.