This article discusses the use of a miniaturized wireless sensor module to obtain information on the presence of some undesirable gases in cork stoppers, such as chloroanisoles, principally 2,4,6-trichloroanisole (TCA). The proposed prototype has been developed as an instrumentation system with different digital commercial gas sensors (BME680 from Bosch Sensortech, CCS811 from Cambridge CMOS (ams), iAQ-Core from AppliedSensors (ams) and SGP30 from Sensirion). The architecture of the system is based mainly on the use of a microcontroller, a bluetooth module and UART serial port for communications, a power supply and a battery charger. The use of this system tries to improve the current TCA detection process in the cork industry, which is done by gas chromatography.

Algorithms based on RANSAC that estimate models using feature correspondences between images can slow down tremendously when the percentage of correct correspondences (inliers) is small. In this paper, we present a probabilistic parametric model that allows us to assign confidence values for each matching correspondence and therefore accelerates the generation of hypothesis models for RANSAC under these conditions. Our framework leverages Extreme Value Theory to accurately model the statistics of matching scores produced by a nearest-neighbor feature matcher. Using a new algorithm based on this model, we are able to estimate accurate hypotheses with RANSAC at low inlier ratios significantly faster than previous state-of-the-art approaches, while still performing comparably when the number of inliers is large. We present results of homography and fundamental matrix estimation experiments for both SIFT and SURF matches that demonstrate that our method leads to accurate and fast model estimations.