3D Hand Pose Estimation with Neural Networks

We propose the design of a real-time system to recognize and interprethand gestures. The acquisition devices are low cost 3D sensors. 3D hand pose will be segmented, characterized and track using growing neural gas (GNG) structure. The capacity of the system to obtain information with a high degree of freedom allows the encoding of many gestures and a very accurate motion capture. The use of hand pose models combined with motion information provide with GNG permits to deal with the problem of the hand motion representation. A natural interface applied to a virtual mirrorwriting system and to a system to estimate hand pose will be designed to demonstrate the validity of the system.

Human Behaviour Recognition based on Trajectory Analysis using Neural Networks

Automated human behaviour analysis has been, and still remains, a challenging problem. It has been dealt from different points of views: from primitive actions to human interaction recognition. This paper is focused on trajectory analysis which allows a simple high level understanding of complex human behaviour. It is proposed a novel representation method of trajectory data, called Activity Description Vector (ADV) based on the number of occurrences of a person is in a specific point of the scenario and the local movements that perform in it. The ADV is calculated for each cell of the scenario in which it is spatially sampled obtaining a cue for different clustering methods. The ADV representation has been tested as the input of several classic classifiers and compared to other approaches using CAVIAR dataset sequences obtaining great accuracy in the recognition of the behaviour of people in a Shopping Centre.

Improving drug discovery using a neural networks based parallel scoring function

Virtual Screening (VS) methods can considerably aid clinical research, predicting how ligands interact with drug targets. Most VS methods suppose a unique binding site for the target, but it has been demonstrated that diverse ligands interact with unrelated parts of the target and many VS methods do not take into account this relevant fact. This problem is circumvented by a novel VS methodology named BINDSURF that scans the whole protein surface to find new hotspots, where ligands might potentially interact with, and which is implemented in massively parallel Graphics Processing Units, allowing fast processing of large ligand databases. BINDSURF can thus be used in drug discovery, drug design, drug repurposing and therefore helps considerably in clinical research. However, the accuracy of most VS methods is constrained by limitations in the scoring function that describes biomolecular interactions, and even nowadays these uncertainties are not completely understood. In order to solve this problem, we propose a novel approach where neural networks are trained with databases of known active (drugs) and inactive compounds, and later used to improve VS predictions.

Biogeographical Patterns and Phenological Changes in Lapiedra martinezii Lag. Related to Its Alkaloid Diversity

The aim of this work was to investigate the alkaloid patterns of Lapiedra martinezii and their relation to biogeography and phenology focused in a phylogenetic comparison. Plants from 14 populations of L. martinezii, covering almost its entire distribution area, were subjected to morphological, ecological, and phytochemical analysis. Experiments for different alkaloid-type content are proposed as a new tool for analysis of plant distribution. Several plants were transplanted for weekly observation of their phenological changes, and alkaloids from different plant organs were extracted, listed, and compared. The alkaloid pattern of L. martinezii comprises 49 compounds of homolycorine, lycorine, tazettine, haemantamine, and narciclasine types. The populations located in the north and south margins of the distribution area displayed alkaloid patterns different from those of the central area. Changes in these patterns during their phenological cycle may be related to a better defence for plant reproduction. L. martinezii is an old relict plant, and it has maintained some of the more primitive morphological features and alkaloid profiles of the Mediterranean Amaryllidaceae. The variations in alkaloid content observed could be interpreted in a phylogenetic sense, and those found in their phenological changes, in an adaptive one.