Analysis of robot dynamics through complexity measures: an application in automatic design

L'applicazione di misure, derivanti dalla teoria dell'informazione, fornisce un valido strumento per quantificare alcune delle proprietà dei sistemi complessi. Le stesse misure possono essere utilizzate in robotica per favorire l'analisi e la sintesi di sistemi di controllo per robot. In questa tesi si è analizzata la correlazione tra alcune misure di complessità e la capacità dei robot di portare a termine, con successo, tre differenti task. I risultati ottenuti suggeriscono che tali misure di complessità rappresentano uno strumento promettente anche nel campo della robotica, ma che il loro utilizzo può diventare difficoltoso quando applicate a task compositi.

Enhancing quality of service in software-defined networks

Resource management is of paramount importance in network scenarios and it is a long-standing and still open issue. Unfortunately, while technology and innovation continue to evolve, our network infrastructure system has been maintained almost in the same shape for decades and this phenomenon is known as “Internet ossification”. Software-Defined Networking (SDN) is an emerging paradigm in computer networking that allows a logically centralized software program to control the behavior of an entire network. This is done by decoupling the network control logic from the underlying physical routers and switches that forward traffic to the selected destination. One mechanism that allows the control plane to communicate with the data plane is OpenFlow. The network operators could write high-level control programs that specify the behavior of an entire network. Moreover, the centralized control makes it possible to define more specific and complex tasks that could involve many network functionalities, e.g., security, resource management and control, into a single framework. Nowadays, the explosive growth of real time applications that require stringent Quality of Service (QoS) guarantees, brings the network programmers to design network protocols that deliver certain performance guarantees. This thesis exploits the use of SDN in conjunction with OpenFlow to manage differentiating network services with an high QoS. Initially, we define a QoS Management and Orchestration architecture that allows us to manage the network in a modular way. Then, we provide a seamless integration between the architecture and the standard SDN paradigm following the separation between the control and data planes. This work is a first step towards the deployment of our proposal in the University of California, Los Angeles (UCLA) campus network with differentiating services and stringent QoS requirements. We also plan to exploit our solution to manage the handoff between different network technologies, e.g., Wi-Fi and WiMAX. Indeed, the model can be run with different parameters, depending on the communication protocol and can provide optimal results to be implemented on the campus network.

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.

UWB radar sensor networks: Detection algorithms design and experimental analysis

In the last years radar sensor networks for localization and tracking in indoor environment have generated more and more interest, especially for anti-intrusion security systems. These networks often use Ultra Wide Band (UWB) technology, which consists in sending very short (few nanoseconds) impulse signals. This approach guarantees high resolution and accuracy and also other advantages such as low price, low power consumption and narrow-band interference (jamming) robustness. In this thesis the overall data processing (done in MATLAB environment) is discussed, starting from experimental measures from sensor devices, ending with the 2D visualization of targets movements over time and focusing mainly on detection and localization algorithms. Moreover, two different scenarios and both single and multiple target tracking are analyzed.