Abstract
Reject option is a technique used to improve classifier’s reliability in decision support systems. It consists in withholding the automatic classification of an item, if the decision is considered not sufficiently reliable. The rejected item is then handled by a different classifier or by a human expert. The vast majority of the works on this issue has been concerned with the development of reject option mechanisms to be used by supervised learning architectures (e.g., MLP, LVQ or SVM). In this paper, however, we aim at proposing alternatives to this view, which are based on the self-organizing map (SOM), originally an unsupervised learning scheme, but that has also been successfully used in the design of prototype-based classifiers. The basic hypothesis we defend is that it is possible to design SOM-based classifiers endowed with reject option mechanisms whose performances are comparable to or better than those achieved by standard supervised classifiers. For this purpose, we carried out a comprehensively evaluation of the proposed SOM-based classifiers on two synthetic and three real-world datasets. The obtained results suggest that the proposed SOM-based classifiers consistently outperform standard supervised classifiers. © 2015 The Natural Computing Applications Forum

Abstract
Long-period gratings is class of fiber gratings, in which the period of the refractive index modulation is such that it satisfies a phase matching condition between the fundamental core mode and forward propagating cladding modes of an optical fiber. Many applications in optical communications and optical fiber sensing have been reported along the last years. In this work we present a simulation tool for determining LPG transmission spectrum and the coupling curve. The simulation tool uses MatLab code. The software produces also estimations of the resonant wavelength displacement due to changes in external parameters like temperature or external refractive index. A simple comparison between the simulation and the experimental result show that exist a good agreement with results obtain.

Abstract
Over 250 million people, worldwide, are affected by chronic lung conditions such as Asthma and COPD. These can cause breathlessness, a harsh decrease in quality of life and, if left undetected or not properly managed, even death. In this paper, we approached part of the lines of development suggested upon earlier work. This concerned the development of a system design for a smartphone lung function classification app, which would only use recordings from the built-in microphone. A more systematic method to evaluate the relevant combinations of methods was devised and an additional set of 44 recordings was used for testing purposes. The previous 101 were kept for training the models. The results enabled to further reduce the signal processing pipeline leading to the use of 6 envelopes, per recording, half of the previous amount. An analysis of the classification performances is provided for both previous tasks: differentiation into Normal from Abnormal lung function, and between multiple lung function patterns. The results from this project encourage further development of the system.

Abstract
Worldwide, over 250 million people are affected by chronic lung conditions such as Asthma and COPD. These can cause breathlessness, a harsh decrease in quality of life and, if not detected and duly managed, even death. In this paper, we aim to find the best and most efficient combination of signal processing and machine learning approaches to produce a smartphone application that could accurately classify lung function, using microphone recordings as the only input. A total of 61 patients performed the forced expiration maneuver providing a dataset of 101 recordings. The signal processing comparison experiments were conducted in a backward selection approach, reducing from 54 to 12 final envelopes, per recording. The classification experiments focused first on differentiating Normal from Abnormal lung function, and second in multiple lung function patterns. The results from this project encourage further development of the system.

Abstract
Nowadays, there are several communication gaps that isolate deaf people in several social activities. This work studies the expressiveness of gestures in Portuguese Sign Language (PSL) speakers and their differences between deaf and hearing people. It is a first effort towards the ultimate goal of understanding emotional and behaviour patterns among such populations. In particular, our work designs solutions for the following problems: (i) differentiation between deaf and hearing people, (ii) identification of different conversational topics based on body expressiveness, (iii) identification of different levels of mastery of PSL speakers through feature analysis. With these aims, we build up a complete and novel dataset that reveals the duo-interaction between deaf and hearing people under several conversational topics. Results show high recognition and classification rates.

Abstract
This paper presents the results of an experimental study of the popular Bag-of-Features (BoF) model for the application of unsupervised learning of images, or image clustering. Although this method has been extensively applied for image classification and scene recognition, there has been few works which employ it in an unsupervised way. Also, due to the fact that the BoF model requires a great amount of steps, algorithms and parameter settings, we felt like there was a lack of detailed studies about the subject. We implemented testing routines in Python which we made publicly available in GitHub. In order to assess the performance of the model, three image datasets were used, namely, Coil-20 dataset, Natural and Urban dataset and Event dataset. The results obtained indicate that the BoF method provides a good representation of simple image collections for the purpose of clustering. However, it requires fine tunning of the parameters and algorithms for each dataset and obtains poor results for more complex scene datasets. We can therefore conclude that more advanced techniques are required in order to be able to effectively extract information from large image collections.