Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

ReSCon '09, Research Student Conference: Book of Abstracts

The second SED Research Student Conference (ReSCon2009) was hosted over three days, 22-24 June 2009, in the Lecture Centre at Brunel University. The conference consisted of technical presentations, a poster session and social events. The abstracts and presentations were the result of ongoing research by postgraduate research students from the School of Engineering and Design at Brunel University. The conference is held annually, and ReSCon plays a key role in contributing to research and innovations within the School

Convertisseurs multiniveaux à bus continu avec point milieu. Nouvelles topologies et stratégies de contrôles

Dans cette thèse, un nouveau concept de représentation, d'analyse et de synthèse des convertisseurs MN est introduit pour mieux comparer, comprendre et classifier les différentes topologies. La famille des convertisseurs, basée sur un bus d'alimentation DC à trois niveaux et des circuits de conversion multicellulaires, fait l'objet d'une analyse approfondie. Les structures de cette famille permettent de générer des niveaux de tension de sortie supplémentaires en gardant une bonne contrôlabilité et des contraintes acceptables au niveau des divers composants. Outre les structures SMC ("stacked multicell converter") et ANPC MN ("active neutral point clamped converter") déjà connues, de nouvelles topologies sont proposées et les limites de fonctionnement sont étudiées en détail. Des concepts de réglage pour le courant PN basés sur une variation de tension homopolaire et basés sur l'utilisation des états redondants sont proposés. Les performances peuvent être nettement améliorées. ABSTRACT :In this thesis, a new concept for the representation, the analysis and synthesis of ML (multi level) converters is introduced. This concept allows simple comprehension, comparison and classification of topologies. The family of ML converters with 3 level split DC link supply has been studied more indepth. The converter structures of this family allow for ML output generation while providing good controllability and reasonable components dimensioning. In addition to the known SMC (stacked multi cell converter) and ANPC (active neutral point clamped converter), new topologies are introduced and the functional limitations are studied. Control concepts based on CM (common mode) voltage injection and based on the use of redundant states are introduced. The performance can be significantly improved compered to state of the art control schemes

Estudios de sensibilidad para el Cherenkov Telescope Array

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Ciencias Físicas, Departamento de Física Atómica, Molecular y Nuclear, leída el 28-09-2015Since the creation of the first telescope in the 17th century, every major discovery in astrophysics has been the direct consequence of the development of novel observation techniques, opening new windows in the electromagnetic spectrum. After Karl Jansky discovered serendipitously the first radio source in 1933, Grote Reber built the first parabolic radio telescope in his backyard, planting the seed of a whole new field in astronomy. Similarly, new technologies in the 1950s allowed the establishment of other fields, such as the infrared, ultraviolet or the X-rays. The highest energy end of the electromagnetic spectrum, the gamma-ray range, represents the last unexplored window for astronomers and should reveal the most extreme phenomena that take place in the Universe. Given the technical complexity of gamma-ray detection and the extremely relative low fluxes, gamma-ray astronomy has undergone a slower development compared to other wavelengths. Nowadays, the great success of consecutive space missions together with the development and refinement of new detection techniques from the ground, has allowed outstanding scientific results and has brought gamma-ray astronomy to a worthy level in par with other astronomy fields. This work is devoted to the study and improvement of the future Cherenkov Telescope Array (CTA), the next generation of ground based gamma-ray detectors, designed to observe photons with the highest energies ever observed from cosmic sources. These results on the sensitivity studies performed for the CTA collaboration evaluate the observatory performance through the analysis of large-scale Monte Carlo (MC) simulations, along with an estimation of its future potential on specific physics cases. Together with the testing and development of the analysis tools employed, these results are critical to understand CTA's future capabilities, the efficiency of different telescope placement approaches and the effect on performance of the construction site, related to parameters such as the altitude or the geomagnetic field. The Northern Hemisphere proposed construction sites were analyzed and evaluated, providing an accurate estimation of their capabilities to host the observatory. As for the CTA layout candidates, an unbiased comparison of the different arrays proposed by the collaboration was performed, using Fermi-LAT catalogs to forecast the performance of each array over specific scientific cases. In addition, the application of machine learning algorithms on gamma-ray astronomy was studied, comparing alternative methods for energy reconstruction and background suppression and introducing new applications to these algorithms, such as the determination of gamma-ray source types through the training of their spectral features. The analysis presented here of both CTA-N and CTA-S candidates represents the most comprehensive study of CTA capabilities performed by the collaboration to date. Experience gained with the improvement of this software will guide the future \gls{cta} analysis pipelines by comparing the attained sensitivity by alternative analysis chains. From these results, both CTA-N and CTA-S candidates "2N" and "2Q" fulfill the sensitivity, angular and energy resolution, effective area and off-axis performance requirements. MC simulations provide an useful test-bench for the different designs within the CTA project, and these results demonstrate their correct implementation would attain the desired performance and potential scientific output.Depto. de Estructura de la Materia, Física Térmica y ElectrónicaFac. de Ciencias FísicasTRUEunpu

Algorithms for Imaging Atmospheric Cherenkov Telescopes

Imaging Atmospheric Cherenkov Telescopes (IACTs) are complex instruments for ground-based -ray astronomy and require sophisticated software for the handling of the measured data. In part one of this work, a modular and efficient software framework is presented that allows to run the complete chain from reading the raw data from the telescopes, over calibration, background reduction and reconstruction, to the sky maps. Several new methods and fast algorithms have been developed and are presented. Furthermore, it was found that the currently used file formats in IACT experiments are not optimal in terms of flexibility and I/O speed. Therefore, in part two a new file format was developed, which allows to store the camera and subsystem data in all its complexity. It offers fast lossy and lossless compression optimized for the high data rates of IACT experiments. Since many other scientific experiments also struggle with enormous data rates, the compression algorithm was further optimized and generalized, and is now able to efficiently compress the data of other experiments as well. Finally, for those who prefer to store their data as ASCII text, a fast I/O scheme is presented, including the necessary compression and conversion routines. Although the second part of this thesis is very technical, it might still be interesting for scientists designing an experiment with high data rates

Tracking the Temporal-Evolution of Supernova Bubbles in Numerical Simulations

The study of low-dimensional, noisy manifolds embedded in a higher dimensional space has been extremely useful in many applications, from the chemical analysis of multi-phase flows to simulations of galactic mergers. Building a probabilistic model of the manifolds has helped in describing their essential properties and how they vary in space. However, when the manifold is evolving through time, a joint spatio-temporal modelling is needed, in order to fully comprehend its nature. We propose a first-order Markovian process that propagates the spatial probabilistic model of a manifold at fixed time, to its adjacent temporal stages. The proposed methodology is demonstrated using a particle simulation of an interacting dwarf galaxy to describe the evolution of a cavity generated by a Supernov

Intelligent Biosignal Processing in Wearable and Implantable Sensors

This reprint provides a collection of papers illustrating the state-of-the-art of smart processing of data coming from wearable, implantable or portable sensors. Each paper presents the design, databases used, methodological background, obtained results, and their interpretation for biomedical applications. Revealing examples are brain–machine interfaces for medical rehabilitation, the evaluation of sympathetic nerve activity, a novel automated diagnostic tool based on ECG data to diagnose COVID-19, machine learning-based hypertension risk assessment by means of photoplethysmography and electrocardiography signals, Parkinsonian gait assessment using machine learning tools, thorough analysis of compressive sensing of ECG signals, development of a nanotechnology application for decoding vagus-nerve activity, detection of liver dysfunction using a wearable electronic nose system, prosthetic hand control using surface electromyography, epileptic seizure detection using a CNN, and premature ventricular contraction detection using deep metric learning. Thus, this reprint presents significant clinical applications as well as valuable new research issues, providing current illustrations of this new field of research by addressing the promises, challenges, and hurdles associated with the synergy of biosignal processing and AI through 16 different pertinent studies. Covering a wide range of research and application areas, this book is an excellent resource for researchers, physicians, academics, and PhD or master students working on (bio)signal and image processing, AI, biomaterials, biomechanics, and biotechnology with applications in medicine