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High-Performance cameras are being used in an increasing number of applications. It requires designs that are more and more challenging. In order to validate these camera designs, a reusable automated test setup has been implemented to characterize the static performances of an image acquisition chain. This automated test setup features the characterization of the input referred noise, the integral non linarity (INL), the differential non linearity (DNL), the gain error, the offset error and the total unadjusted error (TUE) of the chain. By collecting samples out of the acquisition chains to build histograms, the static parameters can be computed. By using samples associated to a DC level applied to the acquisition chain and by computing the standard deviation of the built histogram, the input referred noise is computed. Based on coherent sampling condition, a sine signal is applied to the acquisition chain. The collected samples allow to build a sine histogram that can be compared with an ideal sine histogram in order to compute the TUE, the INL, the DNL, the gain and the offset error. In order to validate the measurement methods, a custom PCB has been designed. The PCB features the acquisition chains to test, as well as a FPGA and a USB interface to collect data on an external computer. After having programmed the FPGA to interface with the acquisition chain and the USB port, samples can be collected. A remote control of the signal sources (DC and sine generator) has also been implemented using a VISA interface to ease the acquisition and automate the test process. From the collected samples the static performances of the acquisition chain can be computed. The computation is done in post processing on a computer. From these computed performances, the test protocol can be validated. Most of the obtained results are already promising since they are coherent with the theoretical ones, even if improvements can still be brought to them.
ADC --- Characterization --- Input referred noise --- Differential Non Linearity --- Integral Non Linearity --- PCB --- Test bench --- VISA --- Electronics --- Ingénierie, informatique & technologie > Ingénierie électrique & électronique
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Although scientific computing is very often associated with numeric computations, the use of computer algebra methods in scientific computing has obtained considerable attention in the last two decades. Computer algebra methods are especially suitable for parametric analysis of the key properties of systems arising in scientific computing. The expression-based computational answers generally provided by these methods are very appealing as they directly relate properties to parameters and speed up testing and tuning of mathematical models through all their possible behaviors. This book contains 8 original research articles dealing with a broad range of topics, ranging from algorithms, data structures, and implementation techniques for high-performance sparse multivariate polynomial arithmetic over the integers and rational numbers over methods for certifying the isolated zeros of polynomial systems to computer algebra problems in quantum computing.
superposition --- SU(2) --- pseudo-remainder --- interval methods --- sparse polynomials --- element order --- Henneberg-type minimal surface --- timelike axis --- combinatorial decompositions --- sparse data structures --- mutually unbiased bases --- invariant surfaces --- projective special unitary group --- Minkowski 4-space --- free resolutions --- Dini-type helicoidal hypersurface --- linearity --- integrability --- Galois rings --- minimum point --- entanglement --- degree --- pseudo-division --- computational algebra --- polynomial arithmetic --- projective special linear group --- normal form --- Galois fields --- Gauss map --- implicit equation --- number of elements of the same order --- Weierstrass representation --- Lotka–Volterra system --- isolated zeros --- polynomial modules --- over-determined polynomial system --- simple Kn-group --- sum of squares --- four-dimensional space
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