Abstract
A new type of fibre-optic refractive index sensor based on a long period fibre grating (LPFG) coated with a titanium dioxide (TiO2) thin film was demonstrated. The wavelength shift of the attenuation bands of this LPFG sensor to changes in the refractive index of the external media from 1.30 to 1.64 RIU was investigated. In order to optimize the sensor the TiO2 thin film thickness deposited around the LPFGs was varied from 10 to 80 nm. It was found that the TiO2 thin film increases the wavelength sensitivity of the LPFG to changes in the surrounding refractive index for values lower and higher than the cladding refractive index. As opposed to the bare LPFG it was shown the possibility to monitor refractive indices lower and higher than cladding refractive index tailoring the TiO2 thickness. An average wavelength sensitivity of 5250 nm/RIU was achieved in the range 1.444 to 1.456 RIU for a TiO2 thickness of 50 nm. In the region between 1.46 and 1.48 RIU the average sensitivity of about 825 nm/RIU was measured for a 40 nm thick film.

Abstract
Existing adaptive educational hypermedia systems have been using learning resources sequencing approaches in order to enrich the learning experience. In this context, educational resources, either expository or evaluative, play a central role. However, there is a lack of tools that support sequencing essentially due to the fact that existing specifications are complex. This paper presents Seqins as a sequencing tool of digital educational resources. Seqins includes a simple and flexible sequencing model that will foster heterogeneous students to learn at different rhythms. The tool communicates through the IMS Learning Tools Interoperability specification with a plethora of e-learning systems such as learning management systems, repositories, authoring and automatic evaluation systems. In order to validate Seqins we integrate it in an e-learning Ensemble framework instance for the computer programming learning domain.

Abstract
This paper introduces a fast Traffic Sign Recognition system developed for a robot, participant in the Autonomous Driving Competition in the Portuguese Festival of Robotics. The Autonomous Driving Robot performs detection and classification of traffic signs and traffic lights based on the analysis of images acquired by a camera mounted on its chassis. The proposed algorithm is composed of three processing stages: detection, pictogram extraction and classification. After the two firsts processing stages, a binary pattern matrix is obtained by color segmentation. In the classification stage two different neural networks were trained to recognize the traffic signs or the traffic light sign. Experimental results show that the system precision is very close to 100% whereas recall presents values above 90% in most of the signs. The proposed system also proves to be reliable and suitable for real-time processing.

Abstract

Abstract
With the growing popularity of micro-task crowdsourcing platforms, a renewed interest in the resolution of complex tasks that require the cooperation of human and machine participants has emerged. This interest has led to workflow approaches that present new challenges at different dimensions of the human-machine computation process, namely in micro-task specification and human-computer interaction due to the unstructured nature of micro-tasks in terms of domain representation. In this sense, a semi-automatic generation environment for human-computer micro-task workflows from domain ontologies is proposed. The structure and semantics of the domain ontology provides a common ground for understanding and enhances human-computer cooperation.

Abstract
The growing influx of patients in healthcare providers is the result of an aging population and emerging self-consciousness about health. In order to guarantee the welfare of all the healthcare stakeholders, it is mandatory to implement methodologies that optimize the healthcare providers' efficiency while increasing patient throughput and reducing patient's total waiting time. This paper presents a case study of a conventional radiology workflow analysis in a Portuguese healthcare provider. Modeling tools were applied to define the existing workflow. Re-engineered workflows were analyzed using the developed simulation tool. The integration of modeling and simulation tools allowed the identification of system bottlenecks. The new workflow of an imaging department entails a reduction of 41 % of the total completion time.