Integrated monitoring of mola mola behaviour in space and time

Over the last decade, ocean sunfish movements have been monitored worldwide using various satellite tracking methods. This study reports the near-real time monitoring of finescale (< 10 m) behaviour of sunfish. The study was conducted in southern Portugal in May 2014 and involved satellite tags and underwater and surface robotic vehicles to measure both the movements and the contextual environment of the fish. A total of four individuals were tracked using custom-made GPS satellite tags providing geolocation estimates of fine-scale resolution. These accurate positions further informed sunfish areas of restricted search (ARS), which were directly correlated to steep thermal frontal zones. Simultaneously, and for two different occasions, an Autonomous Underwater Vehicle (AUV) videorecorded the path of the tracked fish and detected buoyant particles in the water column. Importantly, the densities of these particles were also directly correlated to steep thermal gradients. Thus, both sunfish foraging behaviour (ARS) and possibly prey densities, were found to be influenced by analogous environmental conditions. In addition, the dynamic structure of the water transited by the tracked individuals was described by a Lagrangian modelling approach. The model informed the distribution of zooplankton in the region, both horizontally and in the water column, and the resultant simulated densities positively correlated with sunfish ARS behaviour estimator (r(s) = 0.184, p < 0.001). The model also revealed that tracked fish opportunistically displace with respect to subsurface current flow. Thus, we show how physical forcing and current structure provide a rationale for a predator's finescale behaviour observed over a two weeks in May 2014

Classification of alcohol abusers: An intelligent approach

this paper we propose a novel method to classify alcohol abusers. The method described efficiently estimated total power in gamma band spectral power (GBSP) of multi-channel visual evoked potential (VEP) signals in the time domain, circumventing power spectrum computation. Then, the total power extracted are used as features to classify alcohol abusers from control subjects using Multilayer Perceptron - Back Propogation (MLP-BP) neural network classifier. As a comparison study the total power using GBSP feature extraction is repeated for four types of Infinite Impluse Response (IIR) filters. Experimental study is conducted with 20 subjects totaling 800 VEP signals, which are extracted while subjects are seeing pictures from Snodgrass and Vanderwart set. Maximum classification of 91% is obtained for Elliptic filter for 20 hidden units. Also Elliptic filter shows the best performance for the averaged values of all the filters and it also has the lower order when compared to other filters