Abstract
The detection of dissolved carbon dioxide (dCO(2)) is made possible through a colorimetric effect that occurs in a sensitive membrane. The reaction with dCO(2) changes the pH of the membrane causing a small difference in its colour which results in a characteristic absorbance spectrum band near 435 nm. A sensing platform based on this effect was developed and tested in gaseous and in aqueous environments. It is a combination of a bundle of large core fibre optics (with diameters above 200 mu m) with light emission diodes (LEDs) in the visible range of the spectrum, a silicon photodetector and a polymer membrane sensitive to CO2. A variation in the absorption of 3 / %VV was obtained in the range from 0 to 1.6 % of gaseous CO2 with an estimated response time below 60 seconds.

Abstract
It is in the nature of chaotic atmospheric processes that weather forecasts will never be perfectly accurate. This natural fact poses challenges not only for private life, public safety, and traffic but also for electrical power systems with high shares of weather-dependent wind and solar power production. © 2012 IEEE.

Abstract
Assessment of retinal vessels is fundamental for the diagnosis of many disorders such as heart diseases, diabetes and hypertension. The imaging of retina using advanced fundus camera has become a standard in computer-assisted diagnosis of opthalmic disorders. Modern cameras produce high quality color digital images, but during the acquisition process the light reflected by the retinal surface generates a luminosity and contrast variation. Irregular illumination can introduce severe distortions in the resulting images, decreasing the visibility of anatomical structures and consequently demoting the performance of the automated segmentation of these structures. In this paper, a novel approach for illumination correction of color fundus images is proposed and applied as preprocessing step for retinal vessel segmentation. Our method builds on the connection between two different phenomena, shadows and haze, and works by removing the haze from the image in the inverted intensity domain. This is shown to be equivalent to correct the nonuniform illumination in the original intensity domain. We tested the proposed method as preprocessing stage of two vessel segmentation methods, one unsupervised based on mathematical morphology, and one supervised based on deep learning Convolutional Neural Networks (CNN). Experiments were performed on the publicly available retinal image database DRIVE. Statistically significantly better vessel segmentation performance was achieved in both test cases when illumination correction was applied.

Abstract
A Fabry-Perot air bubble microcavity fabricated between a section of single mode fiber and a multimode fiber that requires only the use of a commercial fusion splicer is proposed. The study of the microcavities growth with the number of applied arcs is performed and several sensors are tested. The sensors are tested for lateral load measurements, and it is observed that there is dependence between the sensor dimensions and its sensitivity. The maximum sensitivity of 2.11 nm/N was obtained for the 161-mu m-long cavity. Moreover, given the low temperature sensitivity (<1 pm/degrees C), the proposed cavity should be adequate to perform temperature-independent measurements. The accurate technique control leads to the fabrication of reproducible cavities with the sensitivity required for the application. The way of manufacturing using a standard fusion splicer, given that no oils or etching solutions are involved, emerges as an alternative to the previously developed air bubble-based sensors.

Abstract
Pose invariant object detection and classification plays a critical role in robust image recognition systems and can be applied in a multitude of applications, ranging from simple monitoring to advanced tracking. This paper analyzes the usage of the Bag of Words model for recognizing objects in different scales, orientations and perspective views within cluttered environments. The recognition system relies on image analysis techniques, such as feature detection, description and clustering along with machine learning classifiers. For pinpointing the location of the target object, it is proposed a multiscale sliding window approach followed by a dynamic thresholding segmentation. The recognition system was tested with several configurations of feature detectors, descriptors and classifiers and achieved an accuracy of 87% when recognizing cars from an annotated dataset with 177 training images and 177 testing images. © Springer International Publishing AG 2018.

Abstract
Peripheral nerve injury and regeneration is a challenging scientific field with relevant clinical implications. Most peripheral nerve regeneration studies have been mainly carried out on rodents. However, it is important to note that the validity of the rodent as a model to study nerve injury and regeneration and translate these results into clinical practice has been questioned by several researchers. To overcome this problem, some investigators have used companion animals and large animal species as models for experimental peripheral nerve regeneration studies. Live sheep are often used in biomedical research because of availability, simplicity of care and housing, cost and body weight similar to humans and acceptance by society as a research animal. Despite these advantages, studies on nerve regeneration and repair in sheep have only been undertaken a few decades ago and compared to rat and mice experimental studies, there are much fewer investigations. The authors have compiled and sorted the available literature on experimental ovine nerve studies in order to guide the peripheral nerve investigator in choosing clinically relevant and interpretable models for studies on neural regeneration that are much needed in order to make progress towards new surgical and medical treatment of peripheral nerves.