Exploring distributed computing tools through data mining tasks

Harnessing idle PCs CPU cycles, storage space and other resources of networked computers to collaborative are mainly fixated on for all major grid computing research projects. Most of the university computers labs are occupied with the high puissant desktop PC nowadays. It is plausible to notice that most of the time machines are lying idle or wasting their computing power without utilizing in felicitous ways. However, for intricate quandaries and for analyzing astronomically immense amounts of data, sizably voluminous computational resources are required. For such quandaries, one may run the analysis algorithms in very puissant and expensive computers, which reduces the number of users that can afford such data analysis tasks. Instead of utilizing single expensive machines, distributed computing systems, offers the possibility of utilizing a set of much less expensive machines to do the same task. BOINC and Condor projects have been prosperously utilized for solving authentic scientific research works around the world at a low cost. In this work the main goal is to explore both distributed computing to implement, Condor and BOINC, and utilize their potency to harness the ideal PCs resources for the academic researchers to utilize in their research work. In this thesis, Data mining tasks have been performed in implementation of several machine learning algorithms on the distributed computing environment.

The adoption of cloud computing by SMEs: identifying and coping with external factors.

Comunicação apresentada na CAPSI 2011 - 11ª Conferência da Associação Portuguesa de Sistemas de Informação – A Gestão de Informação na era da Cloud Computing, Lisboa, ISEG/IUL-ISCTE/, 19 a 21 de Outubro de 2011.

Neural mechanisms of stimulus generalization in auditory fear conditioning

Dissertation presented to obtain the Ph.D degree in Neuroscience Instituto de Tecnologia Química e Biológica, Universidade Nova de Lisboa

Study of behavioural and neural bases of visuo-spatial working memory with an fMRI paradigm based on an n-back task

The goal of this study was to propose a new functional magnetic resonance imaging (fMRI) paradigm using a language-free adaptation of a 2-back working memory task to avoid cultural and educational bias. We additionally provide an index of the validity of the proposed paradigm and test whether the experimental task discriminates the behavioural performances of healthy participants from those of individuals with working memory deficits. Ten healthy participants and nine patients presenting working memory (WM) deficits due to acquired brain injury (ABI) performed the developed task. To inspect whether the paradigm activates brain areas typically involved in visual working memory (VWM), brain activation of the healthy participants was assessed with fMRIs. To examine the task's capacity to discriminate behavioural data, performances of the healthy participants in the task were compared with those of ABI patients. Data were analysed with GLM-based random effects procedures and t-tests. We found an increase of the BOLD signal in the specialized areas of VWM. Concerning behavioural performances, healthy participants showed the predicted pattern of more hits, less omissions and a tendency for fewer false alarms, more self-corrected responses, and faster reaction times, when compared with subjects presenting WM impairments. The results suggest that this task activates brain areas involved in VWM and discriminates behavioural performances of clinical and non-clinical groups. It can thus be used as a research methodology for behavioural and neuroimaging studies of VWM in block-design paradigms.

De touch: extending service channels in an airport through touch technologies

This project aimed to create a communication and interaction channel between Madeira Airport and its passengers. We used the pre-existent touch enabled screens at the terminal since their potential was not being utilised to their full capacity. To achieve our goal, we have followed an agile strategy to create a testable prototype and take advantages of its results. The developed prototype is based on a plugin architecture turning it into a maintainable and highly customisable system. The collected usage data suggests that we have achieved the initially defined goals. There is no doubt that this new interaction channel is an improvement regarding the provided services and, supported by the usage data, there is an opportunity to explore additional developments to the channel.

An intelligent clustering scheme for distributed intrusion detection in vehicular cloud computing

In recent years, vehicular cloud computing (VCC) has emerged as a new technology which is being used in wide range of applications in the area of multimedia-based healthcare applications. In VCC, vehicles act as the intelligent machines which can be used to collect and transfer the healthcare data to the local, or global sites for storage, and computation purposes, as vehicles are having comparatively limited storage and computation power for handling the multimedia files. However, due to the dynamic changes in topology, and lack of centralized monitoring points, this information can be altered, or misused. These security breaches can result in disastrous consequences such as-loss of life or financial frauds. Therefore, to address these issues, a learning automata-assisted distributive intrusion detection system is designed based on clustering. Although there exist a number of applications where the proposed scheme can be applied but, we have taken multimedia-based healthcare application for illustration of the proposed scheme. In the proposed scheme, learning automata (LA) are assumed to be stationed on the vehicles which take clustering decisions intelligently and select one of the members of the group as a cluster-head. The cluster-heads then assist in efficient storage and dissemination of information through a cloud-based infrastructure. To secure the proposed scheme from malicious activities, standard cryptographic technique is used in which the auotmaton learns from the environment and takes adaptive decisions for identification of any malicious activity in the network. A reward and penalty is given by the stochastic environment where an automaton performs its actions so that it updates its action probability vector after getting the reinforcement signal from the environment. The proposed scheme was evaluated using extensive simulations on ns-2 with SUMO. The results obtained indicate that the proposed scheme yields an improvement of 10 % in detection rate of malicious nodes when compared with the existing schemes.

Estimation Models for the adoption and use of information technology: internet-related technologies at firm level in the Portuguese and European context

Dissertação apresentada como requisito parcial para obtenção do grau de Doutor em Gestão de Informação

A Novel Run-Time Monitoring Architecture for Safe and Efficient Inline Monitoring

20th International Conference on Reliable Software Technologies - Ada-Europe 2015 (Ada-Europe 2015), Madrid, Spain.

Metabolic flux analysis of neural cell metabolism in primary cultures

Dissertation presented to obtain the Ph.D degree in Biochemistry, Neuroscience

The removal of xenobiotic compounds from wastewater through the use of biological processes and advanced oxidation technologies

Dissertação apresentada para obtenção do Grau de Doutor em Engenharia Química e Bioquímica pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia

Neural control of an autonomous robot

This article aims to apply the concepts associated with artificial neural networks (ANN) in the control of an autonomous robot system that is intended to be used in competitions of robots. The robot was tested in several arbitrary paths in order to verify its effectiveness. The results show that the robot performed the tasks with success. Moreover, in the case of arbitrary paths the ANN control outperforms other methodologies, such as fuzzy logic control (FLC).

TURTLE: Systems and technologies for deep ocean long term presence

This paper describes the TURTLE project that aim to develop sub-systems with the capability of deep-sea long-term presence. Our motivation is to produce new robotic ascend and descend energy efficient technologies to be incorporated in robotic vehicles used by civil and military stakeholders for underwater operations. TURTLE contribute to the sustainable presence and operations in the sea bottom. Long term presence on sea bottom, increased awareness and operation capabilities in underwater sea and in particular on benthic deeps can only be achieved through the use of advanced technologies, leading to automation of operation, reducing operational costs and increasing efficiency of human activity.

Defeitos do Tubo Neural. Experiência de 10 Anos numa Maternidade Central

OBJECTIVES: 1) To determine trends in prevalence of neural tube defects and the impact of therapeutic abortion. 2) To review perinatal management of spina bifida. DESIGN: All spontaneous and therapeutic abortions, still births and live births affected by neural tube defects registered in Alfredo da Costa Maternity in Lisbon, from 1983 to 1992, were retrospectively analysed. RESULTS: Eighty-two cases with neural tube defects are reported and myelomeningocele and anencephaly++ were the most frequent ones. Total prevalence for all defects was 0.78:1000 births with a small upward trend during the last two years. Birth prevalence was 0.6:1000, with a clear downward trend, due to therapeutic abortion. Prenatal diagnosis improved significantly, from 9% of all defects detected in 1983-87 to 77.5% in 1988-92. Since 1989, all cases of anencephaly were detected before birth. Most cases of spina bifida were vaginally delivered, and elective cesarean section occurred in 4. Early closure of the defect was undertaken in 87.6% of the newborns with open spina bifida. CONCLUSION: While total prevalence of neural tube defects remained stable, with only a small upward trend, prenatal diagnosis and therapeutic abortion resulted in a 56.3% fall in birth prevalence. Optimal management of open spina bifida demands a multidisciplinary team with an individual program for each case.

Neural Mechanisms of Exercise: Anti-Depression, Neurogenesis, and Serotonin Signaling

Depression is associated with decreased serotonin metabolism and functioning in the central nervous system, evidenced by both animal models of depression and clinical patient studies. Depression is also accompanied by decreased hippocampal neurogenesis in diverse animal models. Neurogenesis is mainly defined in dentate gyrus of hippocampus as well as subventricular zone. Moreover, hypothalamus, amygdala, olfactory tubercle, and piriform cortex are reported with evidences of adult neurogenesis. Physical exercise is found to modulate adult neurogenesis significantly, and results in mood improvement. The cellular mechanism such as adult neurogenesis upregulation was considered as one major mood regulator following exercise. The recent advances in molecular mechanisms underlying exercise-regulated neurogenesis have widen our understanding in brain plasticity in physiological and pathological conditions, and therefore better management of different psychiatric disorders.