Technologies for Ambient intelligence: from Smart Objects to Sensor Networks

n the last few years, the vision of our connected and intelligent information society has evolved to embrace novel technological and research trends. The diffusion of ubiquitous mobile connectivity and advanced handheld portable devices, amplified the importance of the Internet as the communication backbone for the fruition of services and data. The diffusion of mobile and pervasive computing devices, featuring advanced sensing technologies and processing capabilities, triggered the adoption of innovative interaction paradigms: touch responsive surfaces, tangible interfaces and gesture or voice recognition are finally entering our homes and workplaces. We are experiencing the proliferation of smart objects and sensor networks, embedded in our daily living and interconnected through the Internet. This ubiquitous network of always available interconnected devices is enabling new applications and services, ranging from enhancements to home and office environments, to remote healthcare assistance and the birth of a smart environment. This work will present some evolutions in the hardware and software development of embedded systems and sensor networks. Different hardware solutions will be introduced, ranging from smart objects for interaction to advanced inertial sensor nodes for motion tracking, focusing on system-level design. They will be accompanied by the study of innovative data processing algorithms developed and optimized to run on-board of the embedded devices. Gesture recognition, orientation estimation and data reconstruction techniques for sensor networks will be introduced and implemented, with the goal to maximize the tradeoff between performance and energy efficiency. Experimental results will provide an evaluation of the accuracy of the presented methods and validate the efficiency of the proposed embedded systems.

ALMASat-1, ALMASat-EO and beyond: evolution of structural concepts and technologies towards multifunctional structures for microsatellites

Multifunctional Structures (MFS) represent one of the most promising disruptive technologies in the space industry. The possibility to merge spacecraft primary and secondary structures as well as attitude control, power management and onboard computing functions is expected to allow for mass, volume and integration effort savings. Additionally, this will bring the modular construction of spacecraft to a whole new level, by making the development and integration of spacecraft modules, or building blocks, leaner, reducing lead times from commissioning to launch from the current 3-6 years down to the order of 10 months, as foreseen by the latest Operationally Responsive Space (ORS) initiatives. Several basic functionalities have been integrated and tested in specimens of various natures over the last two decades. However, a more integrated, system-level approach was yet to be developed. The activity reported in this thesis was focused on the system-level approach to multifunctional structures for spacecraft, namely in the context of nano- and micro-satellites. This thesis documents the work undertaken in the context of the MFS program promoted by the European Space Agency under the Technology Readiness Program (TRP): a feasibility study, including specimens manufacturing and testing. The work sequence covered a state of the art review, with particular attention to traditional modular architectures implemented in ALMASat-1 and ALMASat-EO satellites, and requirements definition, followed by the development of a modular multi-purpose nano-spacecraft concept, and finally by the design, integration and testing of integrated MFS specimens. The approach for the integration of several critical functionalities into nano-spacecraft modules was validated and the overall performance of the system was verified through relevant functional and environmental testing at University of Bologna and University of Southampton laboratories.

Deep learning for computer vision: a comparison between convolutional neural networks and hierarchical temporal memories on object recognition tasks

In recent years, Deep Learning techniques have shown to perform well on a large variety of problems both in Computer Vision and Natural Language Processing, reaching and often surpassing the state of the art on many tasks. The rise of deep learning is also revolutionizing the entire field of Machine Learning and Pattern Recognition pushing forward the concepts of automatic feature extraction and unsupervised learning in general. However, despite the strong success both in science and business, deep learning has its own limitations. It is often questioned if such techniques are only some kind of brute-force statistical approaches and if they can only work in the context of High Performance Computing with tons of data. Another important question is whether they are really biologically inspired, as claimed in certain cases, and if they can scale well in terms of "intelligence". The dissertation is focused on trying to answer these key questions in the context of Computer Vision and, in particular, Object Recognition, a task that has been heavily revolutionized by recent advances in the field. Practically speaking, these answers are based on an exhaustive comparison between two, very different, deep learning techniques on the aforementioned task: Convolutional Neural Network (CNN) and Hierarchical Temporal memory (HTM). They stand for two different approaches and points of view within the big hat of deep learning and are the best choices to understand and point out strengths and weaknesses of each of them. CNN is considered one of the most classic and powerful supervised methods used today in machine learning and pattern recognition, especially in object recognition. CNNs are well received and accepted by the scientific community and are already deployed in large corporation like Google and Facebook for solving face recognition and image auto-tagging problems. HTM, on the other hand, is known as a new emerging paradigm and a new meanly-unsupervised method, that is more biologically inspired. It tries to gain more insights from the computational neuroscience community in order to incorporate concepts like time, context and attention during the learning process which are typical of the human brain. In the end, the thesis is supposed to prove that in certain cases, with a lower quantity of data, HTM can outperform CNN.

Towards Energy Consumption Measurement in a Cloud Computing Wireless Testbed

The evolution of the Next Generation Networks, especially the wireless broadband access technologies such as Long Term Evolution (LTE) and Worldwide Interoperability for Microwave Access (WiMAX), have increased the number of "all-IP" networks across the world. The enhanced capabilities of these access networks has spearheaded the cloud computing paradigm, where the end-users aim at having the services accessible anytime and anywhere. The services availability is also related with the end-user device, where one of the major constraints is the battery lifetime. Therefore, it is necessary to assess and minimize the energy consumed by the end-user devices, given its significance for the user perceived quality of the cloud computing services. In this paper, an empirical methodology to measure network interfaces energy consumption is proposed. By employing this methodology, an experimental evaluation of energy consumption in three different cloud computing access scenarios (including WiMAX) were performed. The empirical results obtained show the impact of accurate network interface states management and application network level design in the energy consumption. Additionally, the achieved outcomes can be used in further software-based models to optimized energy consumption, and increase the Quality of Experience (QoE) perceived by the end-users.

Evaluating the Impact of the Number of Access Points in Mobile Robots Localization Using Artificial Neural Networks

Localization is information of fundamental importance to carry out various tasks in the mobile robotic area. The exact degree of precision required in the localization depends on the nature of the task. The GPS provides global position estimation but is restricted to outdoor environments and has an inherent imprecision of a few meters. In indoor spaces, other sensors like lasers and cameras are commonly used for position estimation, but these require landmarks (or maps) in the environment and a fair amount of computation to process complex algorithms. These sensors also have a limited field of vision. Currently, Wireless Networks (WN) are widely available in indoor environments and can allow efficient global localization that requires relatively low computing resources. However, the inherent instability in the wireless signal prevents it from being used for very accurate position estimation. The growth in the number of Access Points (AP) increases the overlap signals areas and this could be a useful means of improving the precision of the localization. In this paper we evaluate the impact of the number of Access Points in mobile nodes localization using Artificial Neural Networks (ANN). We use three to eight APs as a source signal and show how the ANNs learn and generalize the data. Added to this, we evaluate the robustness of the ANNs and evaluate a heuristic to try to decrease the error in the localization. In order to validate our approach several ANNs topologies have been evaluated in experimental tests that were conducted with a mobile node in an indoor space.

The impact of new technologies on distance learning students

This is a European Commission Leonardo da Vinci Reference Material project on the impact of new technology on distance learning students. It is known that all the Ministries of Education of the 27 European Union countries pay millions of Euros annually in the provision of educational technology for their schools, colleges and universities. A review of the literature of the impact of technology, however, showed that the research in this field was unacceptably fragile. What research there was focused on the impact of technology on children in American schools. The project set out, therefore, to measure the impact of technology on adult education, lifelong learning and distance education, with a particular focus on adult distance learning.

International cooperation in ambient computing education

Funded by the US-EU Atlantis Program, the International Cooperation in Ambient Computing Education Project is establishing an international knowledge-building community for developing a broader computer science curriculum aimed at preparing students for real-world problems in a multidisciplinary, global world. The project is collaboration among Troy University (USA), University of Sunderland (UK), FernUniversität in Hagen (Germany), Universidade do Algarve (Portugal), University of Arkansas at Little Rock (USA) and San Diego State University (USA). The curriculum will include aspects of social science, cognitive science, human-computer interaction, organizational studies, global studies, and particular application areas as well as core computer science subjects. Programs offered at partner institutions will form trajectories through the curriculum. A degree will be defined in terms of combinations of trajectories which will satisfy degree requirements set by accreditation organizations. This is expected to lead to joint- or dual-degree programs among the partner institutions in the future. This paper describes the goals and activities of the project and discusses implementation issues.

Matching form with content

Successful computer-supported distance education requires that its enabling technologies are accessible and usable anywhere. They should work seamlessly inside and outside the information superhighway, wherever the target learners are located, without obtruding on the learning activity. It has long been recognised that the usability of interactive computer systems is inversely related to the visibility of the implementing technologies. Reducing the visibility of technology is especially challenging in the area of online language learning systems, which require high levels of interactivity and communication along multiple dimensions such as speaking, listening, reading and writing. In this article, the authors review the concept of invisibility as it applies to the design of interactive technologies and appliances. They describe a specialised appliance matched to the requirements for distance second language learning, and report on a successful multi-phase evaluation process, including initial field testing at a Thai open university.

Matching form with content

Successful computer-supported distance education requires that its enabling technologies are accessible and usable anywhere. They should work seamlessly inside and outside the information superhighway, wherever the target learners are located, without obtruding on the learning activity. It has long been recognised that the usability of interactive computer systems is inversely related to the visibility of the implementing technologies. Reducing the visibility of technology is especially challenging in the area of online language learning systems, which require high levels of interactivity and communication along multiple dimensions such as speaking, listening, reading and writing. In this article, the authors review the concept of invisibility as it applies to the design of interactive technologies and appliances. They describe a specialised appliance matched to the requirements for distance second language learning, and report on a successful multi-phase evaluation process, including initial field testing at a Thai open university.

DMG-Lib - The Digital Mechanism and Gear Library

Motion systems are important parts of technical products. Those are mostly composed of mechanisms and gears. Today mechanism and gear technology is essential for the whole industry and it will become even more important due to the introduction of new technologies and respective new fields of applications.

Autorenwerkzeuge für digitale, multimediale und interaktive Lernbausteine im Web 2.0

Die vorliegende Forschungsarbeit siedelt sich im Dreieck der Erziehungswissenschaften, der Informatik und der Schulpraxis an und besitzt somit einen starken interdisziplinären Charakter. Aus Sicht der Erziehungswissenschaften handelt es sich um ein Forschungsprojekt aus den Bereichen E-Learning und Multimedia Learning und der Fragestellung nach geeigneten Informatiksystemen für die Herstellung und den Austausch von digitalen, multimedialen und interaktiven Lernbausteinen. Dazu wurden zunächst methodisch-didaktische Vorteile digitaler Lerninhalte gegenüber klassischen Medien wie Buch und Papier zusammengetragen und mögliche Potentiale im Zusammenhang mit neuen Web 2.0-Technologien aufgezeigt. Darauf aufbauend wurde für existierende Autorenwerkzeuge zur Herstellung digitaler Lernbausteine und bestehende Austauschplattformen analysiert, inwieweit diese bereits Web 2.0-Technologien unterstützen und nutzen. Aus Sicht der Informatik ergab sich aus der Analyse bestehender Systeme ein Anforderungsprofil für ein neues Autorenwerkzeug und eine neue Austauschplattform für digitale Lernbausteine. Das neue System wurde nach dem Ansatz des Design Science Research in einem iterativen Entwicklungsprozess in Form der Webapplikation LearningApps.org realisiert und stetig mit Lehrpersonen aus der Schulpraxis evaluiert. Bei der Entwicklung kamen aktuelle Web-Technologien zur Anwendung. Das Ergebnis der Forschungsarbeit ist ein produktives Informatiksystem, welches bereits von tausenden Nutzern in verschiedenen Ländern sowohl in Schulen als auch in der Wirtschaft eingesetzt wird. In einer empirischen Studie konnte das mit der Systementwicklung angestrebte Ziel, die Herstellung und den Austausch von digitalen Lernbausteinen zu vereinfachen, bestätigt werden. Aus Sicht der Schulpraxis liefert LearningApps.org einen Beitrag zur Methodenvielfalt und zur Nutzung von ICT im Unterricht. Die Ausrichtung des Werkzeugs auf mobile Endgeräte und 1:1-Computing entspricht dem allgemeinen Trend im Bildungswesen. Durch die Verknüpfung des Werkzeugs mit aktuellen Software-Entwicklungen zur Herstellung von digitalen Schulbüchern werden auch Lehrmittelverlage als Zielgruppe angesprochen.

New Trends in Networking, Computing, E-learning, Systems Sciences, and Engineering

This book provides the latest in a series of books growing out of the International Joint Conferences on Computer, Information and Systems Sciences and Engineering. It includes chapters in the most advanced areas of Computing, Informatics, Systems Sciences and Engineering. It has accessible to a wide range of readership, including professors, researchers, practitioners and students. This book includes a set of rigorously reviewed world-class manuscripts addressing and detailing state-of-the-art research projects in the areas of Computer Science, Informatics, and Systems Sciences, and Engineering. It includes selected papers form the conference proceedings of the Ninth International Joint Conferences on Computer, Information, and Systems Sciences, and Engineering (CISSE 2013). Coverage includes topics in: Industrial Electronics, Technology & Automation, Telecommunications and Networking, Systems, Computing Sciences and Software Engineering, Engineering Education, Instructional Technology, Assessment, and E-learning.

E-Learning Systems, Environments and Approaches

The volume consists of twenty-five chapters selected from among peer-reviewed papers presented at the CELDA (Cognition and Exploratory Learning in the Digital Age) 2013 Conference held in Fort Worth, Texas, USA, in October 2013 and also from world class scholars in e-learning systems, environments and approaches. The following sub-topics are included: Exploratory Learning Technologies (Part I), e-Learning social web design (Part II), Learner communities through e-Learning implementations (Part III), Collaborative and student-centered e-Learning design (Part IV). E-Learning has been, since its initial stages, a synonym for flexibility. While this dynamic nature has mainly been associated with time and space it is safe to argue that currently it embraces other aspects such as the learners’ profile, the scope of subjects that can be taught electronically and the technology it employs. New technologies also widen the range of activities and skills developed in e-Learning. Electronic learning environments have evolved past the exclusive delivery of knowledge. Technology has endowed e-Learning with the possibility of remotely fomenting problem solving skills, critical thinking and team work, by investing in information exchange, collaboration, personalisation and community building.

Artificial Neural Nets with Interaction of Afferents

The aim is to obtain computationally more powerful, neuro physiologically founded, artificial neurons and neural nets. Artificial Neural Nets (ANN) of the Perceptron type evolved from the original proposal by McCulloch an Pitts classical paper [1]. Essentially, they keep the computing structure of a linear machine followed by a non linear operation. The McCulloch-Pitts formal neuron (which was never considered by the author’s to be models of real neurons) consists of the simplest case of a linear computation of the inputs followed by a threshold. Networks of one layer cannot compute anylogical function of the inputs, but only those which are linearly separable. Thus, the simple exclusive OR (contrast detector) function of two inputs requires two layers of formal neurons