Implementation of adaptive active noise control (ANC) systems requires an estimate model of the secondary path to be uploaded onto digital control hardware. In practice, this model is not necessarily perfect; however, to avoid mathematical difficulties, theoretical analysis of these systems is usually conducted for a perfect secondary path model. This paper conducts a stochastic analysis on performance of Filtered-x LMS (FxLMS)-based ANC systems when the actual secondary path and its model are not identical. This analysis results in a number of mathematical expressions, describing effects of a general secondary path model on stability, steady-state performance and convergence speed of FxLMS-based ANC systems. As a surprising result, it is found that intentional misadjustment of secondary path models can enhance performance of ANC systems in practice. Theoretical results are found to be in a good agreement with the results obtained from numerical analysis. Also, experimental results confirm the validity and accuracy of the theoretical results.

This paper develops a new approach to fraud detection in honey. Specifically, we examine adulterating honey with sugar and use hyperspectral imaging and machine learning techniques to detect adulteration. The main contributions of this paper are introducing a new feature smoothing technique to conform to the classification model used to detect the adulterated samples and the perpetration of an adulterated honey data set using hyperspectral imaging, which has been made available online for the first time. Above $$95\%$$95 % accuracy was achieved for binary adulteration detection and multi-class classification between different adulterant concentrations. The system developed in this paper can be used to prevent honey fraud as a reliable, low cost, data-driven solution.