István Kollár | Searcharxiv

arXiv2018

Measuring the Noise Cumulative Distribution Function Using Quantized Data

This paper considers the problem of estimating the cumulative distribution function and probability density function of a random variable using data quantized by uniform and non-uniform quantizers. A simple estimator is proposed based on the empirical distribution function that also takes the values of the quantizer transition levels into account. The properties of this estimator are discussed and analyzed at first by simulations. Then by removing all assumptions that are difficult to apply, a new procedure is described that does not require neither the transition levels nor the input sequence used to source the quantizer to be known. The experimental results obtained using a commercial 12-b data acquisition system show the applicability of this estimator to real-world type of problems.

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arXiv2018

Accurate Sine-Wave Amplitude Measurements Using Nonlinearly Quantized Data

The estimation of the amplitude of a sine wave from the sequence of its quantized samples is a typical problem in instrumentation and measurement. A standard approach for its solution makes use of a least squares estimator (LSE) that, however, does not perform optimally in the presence of quantization errors. In fact, if the quantization error cannot be modeled as an additive noise source, as it often happens in practice, the LSE returns biased estimates. In this paper, we consider the estimation of the amplitude of a noisy sine wave after quantization. The proposed technique is based on a uniform distribution of signal phases and it does not require that the quantizer has equally spaced transition levels. The experimental results show that this technique removes the estimation bias associated with the usage of the LSE and that it is sufficiently robust with respect to small uncertainties in the known values of transition levels.

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