Role of the filter functions in noise spectroscopy

The success of quantum noise sensing methods depends on the optimal interplay between properly designed control pulses and statistically informative measurement data on a specific quantum-probe observable. To enhance the information content of the data and reduce as much as possible the number of measurements on the probe, the filter orthogonalization method has been recently introduced. The latter is able to transform the control filter functions on an orthogonal basis allowing for the optimal reconstruction of the noise power spectral density. In this paper, we formalize this method within the standard formalism of minimum mean squared error estimation and we show the equivalence between the solutions of the two approaches. Then, we introduce a nonnegative least squares formulation that ensures the nonnegativeness of the estimated noise spectral density. Moreover, we also propose a novel protocol for the design in the frequency domain of the set of filter functions. The frequency-designed filter functions and the nonnegative least squares reconstruction are numerically tested on noise spectra with multiple components and as a function of the estimation parameters.

Automatic classification of tobacco leaves based on near infrared spectroscopy and nonnegative least squares

A nonnegative least squares classifier was proposed in this paper to classify near infrared spectral data. The method used near infrared spectral data of training samples to make up a data dictionary of the sparse representation. By adopting the nonnegative least squares sparse coding algorithm, the near infrared spectral data of test samples would be expressed via the sparsest linear combinations of the dictionary. The regression residual of the test sample of each class was computed, and finally it was assigned to the class with the minimum residual. The method was compared with the other classifying approaches, including the well-performing principal component analysis–linear discriminant analysis and principal component analysis–particle swarm optimization–support vector machine. Experimental results showed that the approach was faster and generally achieved a better prediction performance over compared methods. The method can accurately recognize different classes of tobacco leaves and it provides a new technology for quality evaluation of tobacco leaf in its purchasing activities.