Artificial Intelligence and Cognitive Computing

Artificial intelligence (AI) is a subject garnering increasing attention in both academia and the industry today. The understanding is that AI-enhanced methods and techniques create a variety of opportunities related to improving basic and advanced business functions, including production processes, logistics, financial management and others. As this collection demonstrates, AI-enhanced tools and methods tend to offer more precise results in the fields of engineering, financial accounting, tourism, air-pollution management and many more. The objective of this collection is to bring these topics together to offer the reader a useful primer on how AI-enhanced tools and applications can be of use in today’s world. In the context of the frequently fearful, skeptical and emotion-laden debates on AI and its value added, this volume promotes a positive perspective on AI and its impact on society. AI is a part of a broader ecosystem of sophisticated tools, techniques and technologies, and therefore, it is not immune to developments in that ecosystem. It is thus imperative that inter- and multidisciplinary research on AI and its ecosystem is encouraged. This collection contributes to that

Spectrum sharing and management techniques in mobile networks

Το φάσμα συχνοτήτων αποδεικνύεται σπάνιο κομμάτι για τους πόρους ενός κινητού δικτύου το οποίο πρέπει να ληφθεί υπόψιν στη σχεδίαση τηλεπικοινωνιακών συστημάτων 5ης γενιάς. Επιπλέον οι πάροχοι κινητών δικτύων θα πρέπει να επαναπροσδιορίσουν επιχειρησιακά μοντέλα τα οποία μέχρι τώρα δεν θεωρούνταν αναγκαία (π.χ., γνωσιακά ραδιοδίκτυα), ή να εξετάσουν την υιοθέτηση νέων μοντέλων που αναδεικνύονται (π.χ., αδειοδοτούμενη από κοινού πρόσβαση) ώστε να καλύψουν τις ολοένα αυξανόμενες ανάγκες για εύρος ζώνης. Ο μερισμός φάσματος θεωρείται αναπόφευκτος για συστήματα 5G και η διατριβή παρέχει λύση για προσαρμοστικό μερισμό φάσματος με πολλαπλά καθεστώτα εξουσιοδότησης, βάσει ενός καινοτόμου αρχιτεκτονικού πλαισίου το οποίο επιτρέπει στα δικτυακά στοιχεία να λαμβάνουν αποφάσεις για απόκτηση φάσματος. Η προτεινόμενη διαδικασία λήψης αποφάσεων είναι μία καινοτόμα τεχνική προσαρμοστικού μερισμού φάσματος βασιζόμενη σε ελεγκτές ασαφούς λογικής που καθορίζονν το καταλληλότερο σχήμα μερισμού φάσματος και σε ενισχυμένη μάθηση που ρυθμίζει τους κανόνες ασαφούς λογικής, στοχεύοντας να βρει τη βέλτιστη πολιτική που πρέπει να ακολουθεί ο πάροχος ώστε να προσφέρει την επιθυμητή ποιότητα υπηρεσιών στους χρήστες, διατηρώντας πόρους (οικονομικούς ή ραδιοπόρους) όπου είναι εφικτό. Η τελευταία συνεισφορά της διατριβής είναι ένας μηχανισμός που εξασφαλίζει δίκαιη πρόσβαση σε φάσμα ανάμεσα σε χρήστες σε σενάρια στα οποία η εκχώρηση άδειας χρήσης φάσματος δεν είναι προαπαιτούμενη.Radio spectrum has loomed out to be a scarce resource that needs to be carefully considered when designing 5G communication systems and Mobile Network Operators (MNOs) will need to revisit business models that were not of their prior interest (e.g. Cognitive Radio) or consider adopting new business models that emerge (e.g. Licensed Shared Access) so as to cover the extended capacity needs. Spectrum sharing is considered unavoidable for 5G systems and this thesis provides a solution for adaptive spectrum sharing under multiple authorization regimes based on a novel architecture framework that enables network elements to proceed in decisions for spectrum acquisition. The decision making process for spectrum acquisition proposed is a novel Adaptive Spectrum Sharing technique that uses Fuzzy Logic controllers to determine the most suitable spectrum sharing option and reinforcement learning to tune the fuzzy logic rules, aiming to find an optimal policy that MNO should follow in order to offer the desirable Quality of Service to its users, while preserving resources (either economical, or radio) when possible. The final contribution of this thesis is a mechanism that ensures fair access to spectrum among the users in scenarios in which conveying spectrum license is not prerequisite

Smart Wireless Sensor Networks

The recent development of communication and sensor technology results in the growth of a new attractive and challenging area - wireless sensor networks (WSNs). A wireless sensor network which consists of a large number of sensor nodes is deployed in environmental fields to serve various applications. Facilitated with the ability of wireless communication and intelligent computation, these nodes become smart sensors which do not only perceive ambient physical parameters but also be able to process information, cooperate with each other and self-organize into the network. These new features assist the sensor nodes as well as the network to operate more efficiently in terms of both data acquisition and energy consumption. Special purposes of the applications require design and operation of WSNs different from conventional networks such as the internet. The network design must take into account of the objectives of specific applications. The nature of deployed environment must be considered. The limited of sensor nodes� resources such as memory, computational ability, communication bandwidth and energy source are the challenges in network design. A smart wireless sensor network must be able to deal with these constraints as well as to guarantee the connectivity, coverage, reliability and security of network's operation for a maximized lifetime. This book discusses various aspects of designing such smart wireless sensor networks. Main topics includes: design methodologies, network protocols and algorithms, quality of service management, coverage optimization, time synchronization and security techniques for sensor networks

Agrupamiento, predicción y clasificación ordinal para series temporales utilizando técnicas de machine learning: aplicaciones

In the last years, there has been an increase in the number of fields improving their standard processes by using machine learning (ML) techniques. The main reason for this is that the vast amount of data generated by these processes is difficult to be processed by humans. Therefore, the development of automatic methods to process and extract relevant information from these data processes is of great necessity, giving that these approaches could lead to an increase in the economic benefit of enterprises or to a reduction in the workload of some current employments. Concretely, in this Thesis, ML approaches are applied to problems concerning time series data. Time series is a special kind of data in which data points are collected chronologically. Time series are present in a wide variety of fields, such as atmospheric events or engineering applications. Besides, according to the main objective to be satisfied, there are different tasks in the literature applied to time series. Some of them are those on which this Thesis is mainly focused: clustering, classification, prediction and, in general, analysis. Generally, the amount of data to be processed is huge, arising the need of methods able to reduce the dimensionality of time series without decreasing the amount of information. In this sense, the application of time series segmentation procedures dividing the time series into different subsequences is a good option, given that each segment defines a specific behaviour. Once the different segments are obtained, the use of statistical features to characterise them is an excellent way to maximise the information of the time series and simultaneously reducing considerably their dimensionality. In the case of time series clustering, the objective is to find groups of similar time series with the idea of discovering interesting patterns in time series datasets. In this Thesis, we have developed a novel time series clustering technique. The aim of this proposal is twofold: to reduce as much as possible the dimensionality and to develop a time series clustering approach able to outperform current state-of-the-art techniques. In this sense, for the first objective, the time series are segmented in order to divide the them identifying different behaviours. Then, these segments are projected into a vector of statistical features aiming to reduce the dimensionality of the time series. Once this preprocessing step is done, the clustering of the time series is carried out, with a significantly lower computational load. This novel approach has been tested on all the time series datasets available in the University of East Anglia and University of California Riverside (UEA/UCR) time series classification (TSC) repository. Regarding time series classification, two main paths could be differentiated: firstly, nominal TSC, which is a well-known field involving a wide variety of proposals and transformations applied to time series. Concretely, one of the most popular transformation is the shapelet transform (ST), which has been widely used in this field. The original method extracts shapelets from the original time series and uses them for classification purposes. Nevertheless, the full enumeration of all possible shapelets is very time consuming. Therefore, in this Thesis, we have developed a hybrid method that starts with the best shapelets extracted by using the original approach with a time constraint and then tunes these shapelets by using a convolutional neural network (CNN) model. Secondly, time series ordinal classification (TSOC) is an unexplored field beginning with this Thesis. In this way, we have adapted the original ST to the ordinal classification (OC) paradigm by proposing several shapelet quality measures taking advantage of the ordinal information of the time series. This methodology leads to better results than the state-of-the-art TSC techniques for those ordinal time series datasets. All these proposals have been tested on all the time series datasets available in the UEA/UCR TSC repository. With respect to time series prediction, it is based on estimating the next value or values of the time series by considering the previous ones. In this Thesis, several different approaches have been considered depending on the problem to be solved. Firstly, the prediction of low-visibility events produced by fog conditions is carried out by means of hybrid autoregressive models (ARs) combining fixed-size and dynamic windows, adapting itself to the dynamics of the time series. Secondly, the prediction of convective cloud formation (which is a highly imbalance problem given that the number of convective cloud events is much lower than that of non-convective situations) is performed in two completely different ways: 1) tackling the problem as a multi-objective classification task by the use of multi-objective evolutionary artificial neural networks (MOEANNs), in which the two conflictive objectives are accuracy of the minority class and the global accuracy, and 2) tackling the problem from the OC point of view, in which, in order to reduce the imbalance degree, an oversampling approach is proposed along with the use of OC techniques. Thirdly, the prediction of solar radiation is carried out by means of evolutionary artificial neural networks (EANNs) with different combinations of basis functions in the hidden and output layers. Finally, the last challenging problem is the prediction of energy flux from waves and tides. For this, a multitask EANN has been proposed aiming to predict the energy flux at several prediction time horizons (from 6h to 48h). All these proposals and techniques have been corroborated and discussed according to physical and atmospheric models. The work developed in this Thesis is supported by 11 JCR-indexed papers in international journals (7 Q1, 3 Q2, 1 Q3), 11 papers in international conferences, and 4 papers in national conferences

Pattern Recognition

A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition

Emerging Communications for Wireless Sensor Networks

Wireless sensor networks are deployed in a rapidly increasing number of arenas, with uses ranging from healthcare monitoring to industrial and environmental safety, as well as new ubiquitous computing devices that are becoming ever more pervasive in our interconnected society. This book presents a range of exciting developments in software communication technologies including some novel applications, such as in high altitude systems, ground heat exchangers and body sensor networks. Authors from leading institutions on four continents present their latest findings in the spirit of exchanging information and stimulating discussion in the WSN community worldwide

Recent Developments in Smart Healthcare

Medicine is undergoing a sector-wide transformation thanks to the advances in computing and networking technologies. Healthcare is changing from reactive and hospital-centered to preventive and personalized, from disease focused to well-being centered. In essence, the healthcare systems, as well as fundamental medicine research, are becoming smarter. We anticipate significant improvements in areas ranging from molecular genomics and proteomics to decision support for healthcare professionals through big data analytics, to support behavior changes through technology-enabled self-management, and social and motivational support. Furthermore, with smart technologies, healthcare delivery could also be made more efficient, higher quality, and lower cost. In this special issue, we received a total 45 submissions and accepted 19 outstanding papers that roughly span across several interesting topics on smart healthcare, including public health, health information technology (Health IT), and smart medicine

Data Mining in Smart Grids

Effective smart grid operation requires rapid decisions in a data-rich, but information-limited, environment. In this context, grid sensor data-streaming cannot provide the system operators with the necessary information to act on in the time frames necessary to minimize the impact of the disturbances. Even if there are fast models that can convert the data into information, the smart grid operator must deal with the challenge of not having a full understanding of the context of the information, and, therefore, the information content cannot be used with any high degree of confidence. To address this issue, data mining has been recognized as the most promising enabling technology for improving decision-making processes, providing the right information at the right moment to the right decision-maker. This Special Issue is focused on emerging methodologies for data mining in smart grids. In this area, it addresses many relevant topics, ranging from methods for uncertainty management, to advanced dispatching. This Special Issue not only focuses on methodological breakthroughs and roadmaps in implementing the methodology, but also presents the much-needed sharing of the best practices. Topics include, but are not limited to, the following: Fuzziness in smart grids computing Emerging techniques for renewable energy forecasting Robust and proactive solution of optimal smart grids operation Fuzzy-based smart grids monitoring and control frameworks Granular computing for uncertainty management in smart grids Self-organizing and decentralized paradigms for information processin