Autonomous behaviors for an unmanned mini-aerial vehicle

This thesis aims at addressing the development of autonomous behaviors, for search and exploration with a mini-UAV (Unmanned Aerial Vehicle), or also called MAV (Mini Aerial Vehicle) prototype, in order to gather information in rescue scenarios. The platform used in this work is a four rotor helicopter, known as quad-rotor from the German company Ascending Technologies GmbH, which is later assembled with a on-board processing unit (i.e. a tiny light weight computer) and a on-board sensor suite (i.e. 2D-LIDAR and Ultrasonic Sonar). This work can be divided into two phases. In the first phase an Indoor Position Tracking system was settled in order to obtain the Cartesian coordinates (i.e. X, Y, Z) and orientation (i.e.heading) which provides the relative position and orientation of the platform. The second phase was the design and implementation of medium/high level controllers on each command input in order to autonomously control the aircraft position, which is the first step towards an autonomous hovering flight, and any autonomous behavior (e.g. Landing, Object avoidance, Follow the wall). The main work is carried out in the Laboratory ”Intelligent Systems for Emergencies and Civil Defense”, in collaboration with ”Dipartimento di Informatica e Sistemistica” of Sapienza Univ. of Rome and ”Istituto Superiore Antincendi” of the Italian Firemen Department.

Aceleração GPU da animação de superfícies deformáveis

Dissertação de Mestrado em Engenharia Informática

Acceleration of physics simulation engine through OpenCL

Dissertação para obtenção do Grau de Mestre em Engenharia Informática

Reconstrução/processamento de imagem médica com GPU em tomossíntese

Dissertação para obtenção do Grau de Mestre em Engenharia Biomédica

Reconstrução de imagem médica de mamografia por emissão de positrões (PEM) com GPU

Dissertação para obtenção do Grau de Mestre em Engenharia Biomédica

Algorithmic skeleton framework for the orchestration of GPU computations

Dissertação para obtenção do Grau de Mestre em Engenharia Informática

Adapter module for self-learning production systems

Dissertação para obtenção do Grau de Mestre em Engenharia Electrotécnica, Sistemas e Computadores

Conceção e implementação de um ambiente de baixo custo para processamento de imagens tomográficas

Dissertação para obtenção do Grau de Mestre em Engenharia Informática

Heterogeneous computing with an algorithmic skeleton framework

The Graphics Processing Unit (GPU) is present in almost every modern day personal computer. Despite its specific purpose design, they have been increasingly used for general computations with very good results. Hence, there is a growing effort from the community to seamlessly integrate this kind of devices in everyday computing. However, to fully exploit the potential of a system comprising GPUs and CPUs, these devices should be presented to the programmer as a single platform. The efficient combination of the power of CPU and GPU devices is highly dependent on each device’s characteristics, resulting in platform specific applications that cannot be ported to different systems. Also, the most efficient work balance among devices is highly dependable on the computations to be performed and respective data sizes. In this work, we propose a solution for heterogeneous environments based on the abstraction level provided by algorithmic skeletons. Our goal is to take full advantage of the power of all CPU and GPU devices present in a system, without the need for different kernel implementations nor explicit work-distribution.To that end, we extended Marrow, an algorithmic skeleton framework for multi-GPUs, to support CPU computations and efficiently balance the work-load between devices. Our approach is based on an offline training execution that identifies the ideal work balance and platform configurations for a given application and input data size. The evaluation of this work shows that the combination of CPU and GPU devices can significantly boost the performance of our benchmarks in the tested environments, when compared to GPU-only executions.

Scaling In-Memory databases on multicores

Current computer systems have evolved from featuring only a single processing unit and limited RAM, in the order of kilobytes or few megabytes, to include several multicore processors, o↵ering in the order of several tens of concurrent execution contexts, and have main memory in the order of several tens to hundreds of gigabytes. This allows to keep all data of many applications in the main memory, leading to the development of inmemory databases. Compared to disk-backed databases, in-memory databases (IMDBs) are expected to provide better performance by incurring in less I/O overhead. In this dissertation, we present a scalability study of two general purpose IMDBs on multicore systems. The results show that current general purpose IMDBs do not scale on multicores, due to contention among threads running concurrent transactions. In this work, we explore di↵erent direction to overcome the scalability issues of IMDBs in multicores, while enforcing strong isolation semantics. First, we present a solution that requires no modification to either database systems or to the applications, called MacroDB. MacroDB replicates the database among several engines, using a master-slave replication scheme, where update transactions execute on the master, while read-only transactions execute on slaves. This reduces contention, allowing MacroDB to o↵er scalable performance under read-only workloads, while updateintensive workloads su↵er from performance loss, when compared to the standalone engine. Second, we delve into the database engine and identify the concurrency control mechanism used by the storage sub-component as a scalability bottleneck. We then propose a new locking scheme that allows the removal of such mechanisms from the storage sub-component. This modification o↵ers performance improvement under all workloads, when compared to the standalone engine, while scalability is limited to read-only workloads. Next we addressed the scalability limitations for update-intensive workloads, and propose the reduction of locking granularity from the table level to the attribute level. This further improved performance for intensive and moderate update workloads, at a slight cost for read-only workloads. Scalability is limited to intensive-read and read-only workloads. Finally, we investigate the impact applications have on the performance of database systems, by studying how operation order inside transactions influences the database performance. We then propose a Read before Write (RbW) interaction pattern, under which transaction perform all read operations before executing write operations. The RbW pattern allowed TPC-C to achieve scalable performance on our modified engine for all workloads. Additionally, the RbW pattern allowed our modified engine to achieve scalable performance on multicores, almost up to the total number of cores, while enforcing strong isolation.

Multiresponsive supramolecular systems for information processing at the molecular level

Dissertação para obtenção do Grau de Doutor em Química Sustentável

Fractional discrete-time signal processing: scale conversion and linear prediction

Nonlinear Dynamics, Vol. 29

Fractional signal processing: scale conversion

In Proceedings of the “ECCTD '01 - European Conference on Circuit Theory and Design, Espoo, Finland, August 2001

A signal processing view of differintegration

Proceedings of the European Control Conference, ECC’01, Porto, Portugal, September 2001

Waste recovery into activated carbon for water purification from heavy metals

Dissertação apresentada para obtenção do Grau de Doutor em Engenharia Química Pela Universidade Nova de Lisboa,Faculdade de Ciências e Tecn