Abstract
In the last few decades, optical trapping has played an unique role concerning contactless trapping and manipulation of biological specimens. More recently, optical fiber tweezers (OFTs) are emerging as a desirable alternative to bulk optical systems. In this paper, an overview of the state of the art of OFTs is presented, focusing on the main fabrication methods, their features and main achievements. In addition, new OFTs fabricated by guided wave photo polymerization are reported. Their theoretical and experimental characterization is given and results demonstrating its application in the manipulation of yeast cells and the organelles of plant cells are presented.

Abstract
Over the past years, inverse perspective mapping has been successfully applied to several problems in the field of Intelligent Transportation Systems. In brief, the method consists of mapping images to a new coordinate system where perspective effects are removed. The removal of perspective associated effects facilitates road and obstacle detection and also assists in free space estimation. There is, however, a significant limitation in the inverse perspective mapping: the presence of obstacles on the road disrupts the effectiveness of the mapping. The current paper proposes a robust solution based on the use of multimodal sensor fusion. Data from a laser range finder is fused with images from the cameras, so that the mapping is not computed in the regions where obstacles are present. As shown in the results, this considerably improves the effectiveness of the algorithm and reduces computation time when compared with the classical inverse perspective mapping. Furthermore, the proposed approach is also able to cope with several cameras with different lenses or image resolutions, as well as dynamic viewpoints.

Abstract
Optimization in current decision support systems has a highly interdisciplinary nature related with the need to integrate different techniques and paradigms for solving real-world complex problems. Computing optimal solutions in many of these problems are unmanageable. Heuristic search methods are known to obtain good results in an acceptable time interval. However, parameters need to be adjusted to allow good results. In this sense, learning strategies can enhance the performance of a system, providing it with the ability to learn, for instance, the most suitable optimization technique for solving a particular class of problems, or the most suitable parameterization of a given algorithm on a given scenario. Hyper-heuristics arise in this context as efficient methodologies for selecting or generating (meta) heuristics to solve NP-hard optimization problems. This paper presents the specification of a hyper-heuristic for selecting techniques inspired in nature, for solving the problem of scheduling in manufacturing systems, based on previous experience. The proposed hyper-heuristic module uses a reinforcement learning algorithm, which enables the system with the ability to autonomously select the meta-heuristic to use in optimization process as well as the respective parameters. A computational study was carried out to evaluate the influence of the hyper-heuristics on the performance of a scheduling system. The obtained results allow to conclude about the effectiveness of the proposed approach.

Abstract
Manufacturing companies are being pushed to their limits due to an increase of production complexity guided by a growing standards demand by the costumers. To respond properly to this, manufacturing companies must adopt innovative control architectures that are able to handle better the occurrence of disturbances at shop-floor level (e.g. workstation breakdown, orders cancellation or modification). Additionally, the selection of a proper scheduling algorithms assumes a crucial point, in the sense that the increase of optimization levels depend on this. This paper presents a Genetic Algorithm (GA) based technique to be embedded into the supervisor entity present at the ADACOR(2) aiming to improve the existing fast and non-optimal scheduling technique, improving the overall system processing execution. The main requirements of the GA is to be fast enough to be usable in demanding environments improving the optimization output. The proposed algorithm is tested using a Flexible Manufacturing System using different configurations of transportation and batch sizes. Results show that despite the presented GA technique increased the optimization calculation time it performs better considering the sum of this time with the gain in the optimization output.

Abstract
Ambient intelligence (AmI) paradigm has been the catalyst for the creation of several types of smart spaces, and the smart meeting rooms (SMR) is one of them. One of the main aims of AmI spaces is the development of middleware able to provide context aware computing specifically in SMR face-to-face, and distributed/asynchronous meeting settings are envisaged. This paper describes OLAVAmIa context aware middleware system which was tested in LAID environment, a smart meeting room. OLAVAmI allows video production focusing on the speaker, an audio-to-text conversion service, and a multimedia database of meetings produced in an autonomous way. To experiment OLAVAmI usage and functionalities, two tools present in LAID test bed were used. These AmI intelligent layer tools cover two application scenarios: a ubiquitous group decision-making and a service reposition in power systems. The results and insights are presented in this article.

Abstract