Multi-modal Change Detection, Application to the Detection of Flooded Areas: Outcome of the 2009-2010 data fusion contest

The 2009-2010 Data Fusion Contest organized by the Data Fusion Technical Committee of the IEEE Geoscience and Remote Sensing Society was focused on the detection of flooded areas using multi-temporal and multi-modal images. Both high spatial resolution optical and synthetic aperture radar data were provided. The goal was not only to identify the best algorithms (in terms of accuracy), but also to investigate the further improvement derived from decision fusion. This paper presents the four awarded algorithms and the conclusions of the contest, investigating both supervised and unsupervised methods and the use of multi-modal data for flood detection. Interestingly, a simple unsupervised change detection method provided similar accuracy as supervised approaches, and a digital elevation model-based predictive method yielded a comparable projected change detection map without using post-event data.

Ampliació i millora d'un sistema avançat de compartició de vehicle

Actualment el cotxe és un dels mitjans de transport més utilitzats,comportant problemes de trànsit i aparcament, contaminació acústica i ambiental i una important despesa econòmica. Ja fa uns anys, i davant d'aquesta nova realitat, va sorgir la idea del carpooling, un sistema de transport privat compartit protagonitzat per particulars i que pot esdevenir una molt bona alternativa de mobilitat sostenible. Tot i això, els projectes de carpooling són actualment poc dinàmics I majoritàriament acotats a portals web. Molt pocs disposen de portals adaptats adispositius mòbils i, encara menys, disposen d’aplicacions natives. Amb tot, avui en dia elsaparells mòbils, ja siguin smartphones o PDAs, obren un ventall de possibilitats encara noexplotat en aquest camp.Per altra banda, compartir un viatge amb algú desconegut i de qui no se’n tenenreferències és quelcom que ens fa sentir insegurs, però això es pot resoldre gràciesal trust i reputation, un sistema basat en les valoracions dels usuaris que ja han viscutl’experiència, i que per tant, ens permet conèixer la reputació i confiança de que disposaun usuari dins el sistema. A tots aquests punts, cal afegir-hi la importància que té avui endia rebre en tot moment una informació clara i actualitzada.Aquest projecte vol exprimir aquestes oportunitats donant una plataforma d’accésusable per aquesta mena de dispositius, per tal de que no hi hagi cap barrera per estarconnectat al sistema en qualsevol lloc i moment. A més a més, aquests terminals donen lapossibilitat de saber la seva localització en temps real, cosa que pot resultar molt útil per agent que comparteix viatges i que s’està movent d’un lloc a un altre.Així doncs, partint de totes aquestes possibilitats de millora, es van idear una sèried’estratègies per a un projecte de carpooling ja existent anomenat e-hitchhiking, dotant aaquest sistema d’una major comunicació, protecció i seguretat per a que l’experiència del’usuari sigui molt més gratificant, on a més, hi ha la possibilitat de rebre informaciótotalment veraç i actualitzada en certs dispositius mòbils.Per tant la motivació principal per dur a terme aquest projecte és, a part de lesesmentades anteriorment, la de dotar un sistema de compartició de vehicle d’un majordinamisme, més realisme, més fiabilitat i fer-lo més sociable. El sistema s’implanta oficialment al setembre del 2011 amb la creació de lacomunitat UdG

Dynamic modelling of an underwater vehicle

Nessie is an Autonomous Underwater Vehicle (AUV) created by a team of students in the Heriot Watt University to compete in the Student Autonomous Underwater Competition, Europe (SAUC-E) in August 2006. The main objective of the project is to find the dynamic equation of the robot, dynamic model. With it, the behaviour of the robot will be easier to understand and movement tests will be available by computer without the need of the robot, what is a way to save time, batteries, money and the robot from water inside itself. The object of the second part in this project is setting a control system for Nessie by using the model

Protein phosphatase inhibition assays for okadaic acid detection in shellfish: matrix effects, applicability and comparison with LC-MS/MS analysis

The applicability of the protein phosphatase inhibition assay (PPIA) to the determination of okadaic acid (OA) and its acyl derivatives in shellfish samples has been investigated, using a recombinant PP2A and a commercial one. Mediterranean mussel, wedge clam, Pacific oyster and flat oyster have been chosen as model species. Shellfish matrix loading limits for the PPIA have been established, according to the shellfish species and the enzyme source. A synergistic inhibitory effect has been observed in the presence of OA and shellfish matrix, which has been overcome by the application of a correction factor (0.48). Finally, Mediterranean mussel samples obtained from Rı´a de Arousa during a DSP closure associated to Dinophysis acuminata, determined as positive by the mouse bioassay, have been analysed with the PPIAs. The OA equivalent contents provided by the PPIAs correlate satisfactorily with those obtained by liquid chromatography–tandem mass spectrometry (LC–MS/MS).

Risk Evaluation for Spoofing against a Sensor Supplied with Liveness Detection

The aim of this paper is to evaluate the risks associated with the use of fake fingerprints on a livescan supplied with a method of liveness detection. The method is based on optical properties of the skin. The sensor uses several polarizations and illuminations to capture the information of the different layers of the human skin. These experiments also allow for the determination under which conditions the system is deceived and if there is an influence respectively of the nature of the fake, the mould used for the production or the individuals involved in the attack. These experiments showed that current multispectral sensors can be deceived by the use of fake fingerprints created with or without the cooperation of the subject. Fakes created from direct casts perform better than those produced by fakes created from indirect casts. The results showed that the success of the attack is influenced by two main factors. The first is the quality of the fakes, and by extension the quality of the original fingerprint. The second is the combination of the general patterns involved in the attacks since an appropriate combination can strongly increase the rates of successful attacks.