Novel classification method to predict the accuracy of UWB ranging estimates

Real time location systems (RTLSs) are becoming more relevant in a more data driven economy and society due to their wide range of application cases. When the location of an object needs to be tracked with high accuracy, ultra wideband (UWB) technology is usually the best option. Nevertheless, UWB ranging estimates are not completely immune to some sources of error such as non line of sight (NLOS) or multipath conditions. Thus, this paper proposes a real-time classification model based on machine learning (ML) to predict if received ranging estimates are in line of sight (LOS) or NLOS conditions and discard those in NLOS. However, it is also shown that classifying measurements as LOS or NLOS does not guarantee detecting inaccurate ranging estimates, since LOS measurements can also yield large errors. As an example, the ranging root mean square error (RMSE) of the data labelled as LOS in a UWB based localization system database in the literature is of 0.714 m, significantly higher than the theoretical accuracy of a UWB system. Thus, a novel ML-based classification model is proposed to predict the magnitude of the ranging error. After applying the proposed classification model in the same data, the ranging RMSE of those ranging samples classified as most accurate is of only 0.183 m, significantly lower than the best RMSE we can obtain on the classical LOS/NLOS classification approach

Biological control of postharvest brown rot (Monilinia spp.) of peaches by field applications of Epicoccum nigrum

Seven field experiments were carried out in peach orchards located in Spain, Italy, and France in 2001 and 2002 to develop an effective and practical method of controlling brown rot disease caused by Monilinia spp. by pre-harvest applications of Epicoccum nigrum treatments. Three trees (100 fruits), randomly selected in each orchard, were used as the sample unit and every treatment was repeated four times. Factors considered in each orchard and year to compare E. nigrum and/or fungicide pre-harvest application were the time of application, fresh or formulated cells, and dose. Fresh or formulated cells (10 6-7 conidia ml -1) of E. nigrum need to be applied twice both at bloom and preharvest to reduce postharvest brown rot. Chemical fungicides reduced disease in French and Italian trials but not in a Spanish trial. Integrated control (biological and chemical) was efficient in controlling the pathogens. E. nigrum application, alone (applied 4 times) or in combination with fungicides can be considered in a disease control strategy for reducing fungicide treatments and residues. A further reduction of brown rot may be possible by a better formulation of the biological product and postharvest combined treatments. © 2004 Elsevier Inc. All rights reserved