Analysis and Treatment of a male portrait in oil and a study of the size layer with reconstructions from Vibert’s recipe (1892)

This thesis project concentrated on both the study and treatment of an early 20th century male portrait in oil from Colecção Caixa Geral de Depósitos, Lisbon, Portugal. The portrait of Januário Correia de Almeida, exhibits a tear (approximately 4.0 cm by 2.3 cm) associated with paint loss on the right upper side, where it is possible to observe an unusually thick size layer (approximately 50 microns) and an open weave mesh canvas. Size layers made from animal glue remain subject to severe dimensional changes due to changes in relative humidity (RH), thereby affecting the stability of the painting. In this case, the response to moisture of the size layer is minimal and the painting is largely uncracked with very little active flaking. This suggests that the size layer has undergone pre-treatment to render it unresponsive to moisture or water. Reconstructions based on late nineteenth century recipes using historically appropriate materials are used to explore various options for modifying the characteristics of gelatine, some of which may relate to the Portrait’s size layer. The thesis is separated into two parts: Part 1: Describes the history, condition, materials and techniques of the painting. It also details the treatment of Januário Correia de Almeida as well as the choices made and problems encountered during the treatment. Part 2: Discusses the history of commercial gelatine production, the choice of the appropriate animal source to extract the collagen to produce reconstructions of the portrait’s size layer as well as the characterization of selected reconstructions. The execution of a shallow textured infill led to one publication and one presentation: Abstract accepted for presentation and publication, International Meeting on Retouching of Cultural Heritage (RECH3), Francisco Brites, Leslie Carlyle and Raquel Marques, ‘’Hand building a Low Profile Textured Fill for a Large Loss’’.

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.

Botnet cluster identification

Botnets are a group of computers infected with a specific sub-set of a malware family and controlled by one individual, called botmaster. This kind of networks are used not only, but also for virtual extorsion, spam campaigns and identity theft. They implement different types of evasion techniques that make it harder for one to group and detect botnet traffic. This thesis introduces one methodology, called CONDENSER, that outputs clusters through a self-organizing map and that identify domain names generated by an unknown pseudo-random seed that is known by the botnet herder(s). Aditionally DNS Crawler is proposed, this system saves historic DNS data for fast-flux and double fastflux detection, and is used to identify live C&Cs IPs used by real botnets. A program, called CHEWER, was developed to automate the calculation of the SVM parameters and features that better perform against the available domain names associated with DGAs. CONDENSER and DNS Crawler were developed with scalability in mind so the detection of fast-flux and double fast-flux networks become faster. We used a SVM for the DGA classififer, selecting a total of 11 attributes and achieving a Precision of 77,9% and a F-Measure of 83,2%. The feature selection method identified the 3 most significant attributes of the total set of attributes. For clustering, a Self-Organizing Map was used on a total of 81 attributes. The conclusions of this thesis were accepted in Botconf through a submited article. Botconf is known conferênce for research, mitigation and discovery of botnets tailled for the industry, where is presented current work and research. This conference is known for having security and anti-virus companies, law enforcement agencies and researchers.

Digital microfluidic devices: the role of the dielectric layer

Digital microfluidics (DMF) is a field which has emerged in the last decade as a re-liable and versatile tool for sensing applications based on liquid reactions. DMF allows the discrete displacement of droplets, over an array of electrodes, by the application of voltage, and also the dispensing from a reservoir, mixing, merging and splitting fluidic operations. The main drawback of these devices is due to the need of high driving volt-ages for droplet operations. In this work, alternative dielectric layers combinations were studied aiming the reduction of these driving voltages. DMF chips were designed, pro-duced and optimized according to the theory of electrowetting-on-dielectric, adopting different combinations of parylene-C and tantalum pentoxide (Ta2O5) as dielectric ma-terials, and Teflon as hydrophobic layer. With both devices’ configurations, i.e., Parylene as single dielectric, and multilayer chips combining Parylene and Ta2O5, it was possible to perform all the fluidic opera-tions in the microliter down to hundreds of nanoliters range. Multilayer chips presented significant reduction on driving voltages for droplet op-erations in silicone oil filler medium: from 70 V (parylene only) down to 30 V (parylene/Ta2O5) for dispensing; and from 50 V (parylene only) down to 15 V (parylene/Ta2O5) for movement. Peroxidase colorimetric reactions were successfully performed as proof-of-concept, using multilayer configuration devices.

Medium access control design for distributed opportunistic radio networks

Existing wireless networks are characterized by a fixed spectrum assignment policy. However, the scarcity of available spectrum and its inefficient usage demands for a new communication paradigm to exploit the existing spectrum opportunistically. Future Cognitive Radio (CR) devices should be able to sense unoccupied spectrum and will allow the deployment of real opportunistic networks. Still, traditional Physical (PHY) and Medium Access Control (MAC) protocols are not suitable for this new type of networks because they are optimized to operate over fixed assigned frequency bands. Therefore, novel PHY-MAC cross-layer protocols should be developed to cope with the specific features of opportunistic networks. This thesis is mainly focused on the design and evaluation of MAC protocols for Decentralized Cognitive Radio Networks (DCRNs). It starts with a characterization of the spectrum sensing framework based on the Energy-Based Sensing (EBS) technique considering multiple scenarios. Then, guided by the sensing results obtained by the aforementioned technique, we present two novel decentralized CR MAC schemes: the first one designed to operate in single-channel scenarios and the second one to be used in multichannel scenarios. Analytical models for the network goodput, packet service time and individual transmission probability are derived and used to compute the performance of both protocols. Simulation results assess the accuracy of the analytical models as well as the benefits of the proposed CR MAC schemes.