Abstract
A theoretical and experimental study of self-referencing fiber optic intensity sensors based on Michelson and on Mach-Zehnder configurations is conducted. Via the definition of the measurement parameter R, sensor linearity and sensitivity are analyzed. Theoretical and experimental results are compared, considering the problem of sensor design and optimization. The choice between the hive configurations is addressed; the Michelson one is recommended for practical reasons. (C) 2000 Society of Photo-Optical instrumentation Engineers. [S0091-3286(00)03406-1].

Abstract
A theoretical and experimental characterisation of a self-referenced fibre optic intensity sensor based on a multiple beam Sagnac configuration is conducted. Via the definition of the measurement parameter (R parameter) sensor linearity and sensitivity are analysed. Theoretical and experimental results are compared, being considered the problem of sensor design and optimisation.

Abstract

Abstract
This paper presents an on-chip silicon bulk-micromachined Soil Moisture Sensor (SMS) for irrigation control in greenhouses. The system-on-a-chip includes the SMS, readout electronics, self-test, calibration facilities and a digital bus interface for external data transmission. Moreover, this low-cost device can be applied in several locations (e. g. per each plant root), and networked together with the 1-wire bus. The heat-pulse technique is used (for measuring the maximum temperature on a distant point) to determine the volumetric heat capacity and hence the water content of a porous media, such as soil. In order to validate this method, experimental results were achieved with a macrosensor based on the heat-pulse and capacitance methods. The capacitance method relates the volumetric water content with the capacitance change (dielectric properties) of a probe, inserted in the soil. Several tests were performed for different type of soils and water contents. These results are compared with the measurements achieved using the conventional thermogravimetric method.

Abstract

Abstract
We investigate the problem of using past performance information to select an algorithm for a given classification problem. We present three ranking methods for that purpose: average ranks, success rate ratios and significant wins. We also analyze the problem of evaluating and comparing these methods. The evaluation technique used is based on a leave-one-out procedure. On each iteration, the method generates a ranking using the results obtained by the algorithms on the training datasets. This ranking is then evaluated by calculating its distance from the ideal ranking built using the performance information on the test dataset. The distance measure adopted here, average correlation, is based on Spearman's rank correlation coefficient. To compare ranking methods, a combination of Friedman's test and Dunn's multiple comparison procedure is adopted. When applied to the methods presented here, these tests indicate that the success rate ratios and average ranks methods perform better than significant wins.