Abstract
The development of an underwater wireless communication systems is becoming a research and a technological priority due to the increasing demand for exploring the potential of oceans in fields such as pharmaceutics, oil, minerals, environmental and biodiversity. However, underwater wireless communications still fail to ensure high data-rate connections which support real time applications. In this work a low power high data-rate acoustic modem is presented, based on a piezoelectric poly (vinylidene fluoride) polymer as a transducer and a Xilinx Field Programmable Gate Array (FPGA) that can be programmed to work with different types of modulations. The system has been validated by the implementation of a full duplex point-to-point communication at 1 Mbps using On-Off Keying (OOK) modulation with a 1 MHz single carrier and it represents a major advance in the state of the art and a breakthrough in underwater acoustic communications, being the first to show the possibility to achieve data rates up to 1Mbps. It was successfully tested with a 1 Mbps rate, achieving a 3x10(-3) Bit Error Rate (BER) using just 1.4 mu W of power consumption per bit.

Abstract
This paper presents two new solution approaches capable of finding optimal solutions for the thermal unit commitment problem in power generation planning. The approaches explore the concept of "matheuristics", a term usually used to refer to an optimization algorithm that hybridizes (meta)heuristics with mixed integer programming solvers, in order to speed up convergence to optimality for large scale instances. Two algorithms are proposed: "local branching", and an hybridization of particle swarm optimization with a mixed integer programming solver. From extensive computational tests on a broad set of benchmarks, the algorithms were found to be able to solve large instances. Optimal solutions were obtained for several well-known situations with dramatic reductions in CPU time for the larger cases, when compared to previously proposed exact methods.

Abstract
The information infrastructure that pervades urban environments represents a major opportunity for collecting information about Human mobility. However, this huge potential has been undermined by the overwhelming privacy risks that are associated with such forms of large scale sensing. In this research, we are concerned with the problem of how to enable a set of autonomous sensing nodes, e.g. a Bluetooth scanner or a Wi-Fi hotspot, to collaborate in the observation of movement patterns of individuals without compromising their privacy. We describe a novel technique that generates Precedence Filters and allows probabilistic estimations of sequences of visits to monitored locations and we demonstrate how this technique can combine plausible deniability by an individual with valuable information about aggregate movement patterns.

Abstract

Abstract
Environmental issues raised by the use of fossil fuels lead to the search for alternatives that promote the reduction of emissions of greenhouse gases. CO2 has been identified as being the most important and urgent to control. Co-firing is a technique that allows the simultaneous combustion of different types of fuels, for example coal and biomass, combining the advantages of both. This study characterises the advantages of the system and the possibilities of using waste biomass as fuel in a coal-fired thermal power plant. For this, co-firing biomass waste, from forestry operations, with bituminous coal was simulated. Then reductions in CO2 emissions into the atmosphere from Sines Thermal Power Plant in Portugal were calculated, showing a reduction of more than 1,000,000 tons/year of CO2. Also it was verified that although environmentally advantageous, co-firing is still not economically viable due to the high cost of the residual biomass, combined with its low-energy density and high transportation costs.

Abstract
Recent advances in the area of computer vision has led to the development of various assisted diagnostics systems for the detection of melanoma in the patients. Texture and color are considered as two fundamental visual characteristics which are vital for the detection of melanoma. This paper proposes the use of a combination of texture and color features for the classification of dermoscopy images. The texture features consist of a variation of local binary pattern (LBP) in which the strength of the LBPs is used to extract scale adaptive patterns at each pixel, followed by the construction of a histogram. For color feature extraction, we used standard HSV histograms. The extracted features are concatenated to form a feature vector for an image, followed by classification using support vector machines. Experiments show that the proposed feature set exhibits good classification performance comparing favorably to other state-of-the-art alternatives.