Thermal diagnostic of the Optical Window on board LISA Pathfinder

Vacuum conditions inside the LTP Gravitational Reference Sensor must comply with rather demanding requirements. The Optical Window (OW) is an interface which seals the vacuum enclosure and, at the same time, lets the laser beam go through for interferometric Metrology with the test masses. The OW is a plane-parallel plate clamped in a Titanium flange, and is considerably sensitive to thermal and stress fluctuations. It is critical for the required precision measurements, hence its temperature will be carefully monitored in flight. This paper reports on the results of a series of OW characterisation laboratory runs, intended to study its response to selected thermal signals, as well as their fit to numerical models, and the meaning of the latter. We find that a single pole ARMA transfer function provides a consistent approximation to the OW response to thermal excitations, and derive a relationship with the physical processes taking place in the OW. We also show how system noise reduction can be accomplished by means of that transfer function.Comment: 20 pages, 14 figures; accepted for publication in Class. Quantum Gra

Solea solea: landings data and LPUE standardization from official logbooks in Atlantic Iberian waters

Time series of abundance indices are the main source of information to calibrate stock assessment models. Standardized LPUEs (Landings per unit effort) derived from fishery-dependent data can be used as a proxy of the species abundance. In this study we present a first attempt of standardization of landings per unit of effort (LPUE) for soleid species. Soleid species, in particular the common sole (Solea solea), are important fisheries resources with high economic value, targeted by the Spanish fleet in Iberian Atlantic waters. Nevertheless, information on these resources is scarce. Time series data from 2009 to 2020 from the official logbooks of the Spanish fleet operating in the ICES subdivisions 8.c and 9.a. have been analysed in order to provide some insights into this fishery. Uncertainties in the accuracy of the identification of the species led to the aggregation of 6 taxa: Solea solea, Solea senegalensis, Solea elongata, Solea spp., Pegusa lascaris and Pegusa cadenati, as one single category, being the common sole, Solea solea, the most important taxon in terms of economic value and landings. Landings per unit of effort (LPUE) based on the estimated soleid species landed weight by fishing days (unit effort), for the most important métiers in terms of landings, were used as response variable. Generalised linear mixed models, fitted with a Gamma distribution, were employed, and several explanatory variables were tested to be included in the models: year, quarter, month, ICES division, statistical rectangle, landing port, vessel characteristics (LOA category, vessel power), depth, fishing time and number of fishing operations

Gait recognition and fall detection with inertial sensors

In contrast to visual information that is recorded by cameras placed somewhere, inertial information can be obtained from mobile phones that are commonly used in daily life. We present in this talk a general deep learning approach for gait and soft biometrics (age and gender) recognition. Moreover, we also study the use of gait information to detect actions during walking, specifically, fall detection. We perform a thorough experimental evaluation of the proposed approach on different datasets: OU-ISIR Biometric Database, DFNAPAS, SisFall, UniMiB-SHAR and ASLH. The experimental results show that inertial information can be used for gait recognition and fall detection with state-of-the-art results.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Gait recognition applying Incremental learning

when new knowledge needs to be included in a classifier, the model is retrained from scratch using a huge training set that contains all available information of both old and new knowledge. However, in this talk, we present a way to include new information in a previously trained model without training from scratch and using a small subset of old data. We perform a thorough experimental evaluation of the proposed approach on two image classification datasets: CIFAR-100 and ImageNet. The experiment results show that it is possible to include new knowledge in a model without forgetting the previous one, although, the performance is still lower than training from scratch with the complete training set.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech