Abstract
Naphthalene-based fluorophores have been widely used in different applications such as fluorescent sensors, biological/medical labels, in the development of lightharvesting systems and materials for light-emitting diodes. Typically, the naphthalene scaffold has low molecular weight and its properties depend strongly on the number, type and position of the substituent groups in the ring. Although the unsubstituted naphthalene is poorly fluorescent, when donor and acceptor groups are attached to positions 2 and 6 of the ring, the fluorescence increases significantly through an Intramolecular Charge Transfer (ICT) mechanism. Substantial changes in the fluorescence spectrum, quantum yield and lifetime are often observed with solvent changes or as a result of binding to a substrate. All aspects related with the design, synthesis and photophysical properties of the naphthalene-based fluorophores will be highlighted in this chapter. Also, naphthalene-based fluorescent chemosensors incorporating a specific binding site for sensing cations and anions will also be focused in this chapter.

Abstract
The current paper proposes a novel color correction approach for onboard multi-camera systems. It works by segmenting the given images into several regions. A probabilistic segmentation framework, using 3D Gaussian Mixture Models, is proposed. Regions are used to compute local color correction functions, which are then combined to obtain the final corrected image. An image data set of road scenarios is used to establish a performance comparison of the proposed method with other seven well known color correction algorithms. Results show that the proposed approach is the highest scoring color correction method. Also, the proposed single step 3D color space probabilistic segmentation reduces processing time over similar approaches.

Abstract
The AtlasCar is a prototype that is being developed at the University of Aveiro to research advanced driver assistance systems. The car is equipped with several sensors: 3D and 2D laser scanners, a stereo camera, inertial sensors and GPS. The combination of all these sensor data in useful representations is essential. Therefore, calibration is one of the first problems to tackle. This paper focuses on 3D/2D laser calibration. The proposed method uses a 3D Laser Range Finder (LRF) to produce a reference 3D point cloud containing a known calibration object. Manual input from the user and knowledge of the object geometry are used to register the 3D point cloud with the 2D Lasers. Experimental results with simulated and real data demonstrate the effectiveness of the proposed calibration method.

Abstract
The current paper proposes a novel color correction approach based on a probabilistic segmentation framework by using 3D Gaussian Mixture Models. Regions are used to compute local color correction functions, which are then combined to obtain the final corrected image. The proposed approach is evaluated using both a recently published metric and two large data sets composed of seventy images. The evaluation is performed by comparing our algorithm with eight well known color correction algorithms. Results show that the proposed approach is the highest scoring color correction method. Also, the proposed single step 3D color space probabilistic segmentation reduces processing time over similar approaches.

Abstract
This study is focused on the evaluation of the impact of Saccharomyces cerevisiae metabolism in the profile of compounds with antioxidant capacity in a synthetic wine during fermentation. A bioanalytical pipeline, which allows for biological systems fingerprinting and sample classification by combining electrochemical features with biochemical background, is proposed. To achieve this objective, alcoholic fermentations of a minimal medium supplemented with phenolic acids were evaluated daily during 11 days, for electrochemical profile, phenolic acids, and the volatile fermentation fraction, using cyclic voltametry, high-performance liquid chromatography-diode array detection, and headspace/solid-phase microextraction/gas chromatography-mass spectrometry (target and nontarget approaches), respectively. It was found that acetic acid, 2-phenylethanol, and isoamyl acetate are compounds with a significative contribution for samples metabolic variability, and the electrochemical features demonstrated redox-potential changes throughout the alcoholic fermentations, showing at the end a similar pattern to normal wines. Moreover, S. cerevisiae had the capacity of producing chlorogenic acid in the supplemented medium fermentation from simple precursors present in the minimal medium.

Abstract
The Darwinian Particle Swarm Optimization (DPSO) is an evolutionary algorithm that extends the Particle Swarm Optimization using natural selection to enhance the ability to escape from sub-optimal solutions. An extension of the DPSO to multi-robot applications has been recently proposed and denoted as Robotic Darwinian PSO (RDPSO), benefiting from the dynamical partitioning of the whole population of robots, hence decreasing the amount of required information exchange among robots. This paper further extends the previously proposed algorithm using fractional calculus concepts to control the convergence rate, while considering the robot dynamical characteristics. Moreover, to improve the convergence analysis of the RDPSO, an adjustment of the fractional coefficient based on mobile robot constraints is presented and experimentally assessed with 2 real platforms. Afterwards, this novel fractional-order RDPSO is evaluated in 12 physical robots being further explored using a larger population of 100 simulated mobile robots within a larger scenario. Experimental results show that changing the fractional coefficient does not significantly improve the final solution but presents a significant influence in the convergence time because of its inherent memory property.