Data fusion with computational intelligence techniques: a case study of fuzzy inference for terrain assessment

Dissertação apresentada para obtenção do Grau de Mestre em Engenharia Electrotécnica e de Computadores, pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia

Novel affinity pairs "tag-receptor" for the purification of fusion proteins

Fundação para a Ciência e Tecnologia - SFRH/BD/48804/2008 and the project PTDC/BI/65383/2006 assigned to Prof. Cecíla Roque and also to Associate Laboratory REQUIMTE (Pest-C/EQB/LA0006/2011)

Fatores de prognóstico para os resultados de sucesso da fisioterapia multimodal em utentes com dor cervical crónica

RESUMO: Introdução: O conhecimento acerca da influência das características dos indivíduos com dor cervical crónica (DCC) no prognóstico dos resultados alcançados com a Fisioterapia é ainda inconsistente, sendo escassos os estudos desenvolvidos neste âmbito. Objetivo: Este relatório pretende determinar se um modelo baseado em fatores de prognóstico é capaz de prever os resultados de sucesso da Fisioterapia, a curto prazo, em utentes com DCC, ao nível da incapacidade funcional, intensidade da dor e perceção global de melhoria. Metodologia: Trata-se de estudo de coorte prospetivo com 112 participantes. Os utentes foram avaliados na primeira semana de tratamento e sete semanas após o início da intervenção. Os instrumentos utilizados foram o Neck Disability Index–Versão Portuguesa (NDI-PT) e a Escala Numérica da Dor (END) nos dois momentos de avaliação, um Questionário de Caracterização Sociodemográfica e Clínica da Amostra na baseline e a Patient Global Impression Change Scale–Versão Portuguesa (PGIC-PT) no follow-up. As características sociodemográficas e clínicas foram incluídas como potenciais fatores de prognóstico e estes foram definidos com base nas diferenças mínimas clinicamente importantes (DMCI) dos instrumentos NDIPT (DMCI≥6) e END (DMCI≥2) e no critério de pontuação ≥5 na PGIC-PT. A análise dos dados foi realizada através do método de regressão logística (backward conditional procedure) para identificar as associações entre os indicadores e as variáveis de resultado (p<0.05). Resultados: Dos 112 participantes incluídos no estudo, 108 completaram o follow-up (média de idade: 51.76±10.19). No modelo multivariado relativo à incapacidade funcional, os resultados de sucesso encontram-se associados a elevados níveis de incapacidade na baseline (OR=1.123; 95% IC 1.056–1.194) e a duração da dor inferior a 12 meses (OR=2.704; 95% IC 1.138–6.424). Este modelo explica 30.0% da variância da melhoria da funcionalidade e classifica corretamente 74.1% dos utentes (sensibilidade: 75.9%; especificidade: 72.0%). O modelo relativo à intensidade da dor identificou apenas a associação do outcome com níveis elevados de intensidade da dor na baseline (OR=1.321; 95% IC 1.047–1.668), explicando 7.5% da variância da redução da mesma e classificando corretamente 68.2% dos utentes (sensibilidade: 94.4%; especificidade: 16.7%). O modelo final referente à perceção global de melhoria apresentou uma associação com a intensidade da dor na baseline (OR=0.621; 95% IC 0.465–0.829), com a presença de cefaleias e/ou tonturas (OR=2.538; 95% IC 0.987–6.526) e com a duração da dor superior a 12 meses (OR=0.279; 95% IC 0.109–0.719). Este modelo explica 27.5% da variância dos resultados de sucesso para este outcome e classifica corretamente 73.1% dos utentes (sensibilidade: 81.8%; especificidade: 59.5%). Conclusões: Utentes com DCC com elevada incapacidade na baseline e queixas de dor há menos de 12 meses apresentam maior probabilidade de obter melhorias ao nível da incapacidade funcional. Elevados níveis de intensidade da dor na baseline predizem resultados de sucesso na redução da dor após sete semanas de tratamento. Utentes com DCC com baixos níveis de dor na baseline, com cefaleias e/ou tonturas e com queixas de dor há mais de 12 meses apresentam maior probabilidade de obter uma melhor perceção de melhoria.--------------- ABSTRACT:Introduction: The influence of the characteristics of individuals with chronic neck pain (CNP) on the prognosis of physiotherapy outcomes is still inconsistent, there being few studies developed in this context. Aim: This study seeks to determine whether a model based on prognostic factors can predict the short-term physiotherapy successful outcomes in CNP patients, regarding functional disability, pain intensity and perceived recovery. Methodology: This is a prospective cohort study with 112 participants. Patients were assessed during the first week of treatment and seven weeks after the start of the intervention. The instruments used were the Neck Disability Index–Portuguese Version (NDI-PT) and the Numerical Rating Scale (NRS) at both moments of assessment, a Sample Sociodemographic and Clinical Characterization Questionnaire at baseline and Patient Global Impression Change Scale–Portuguese Version (PGIC-PT) at the follow-up. The sociodemographic and clinical characteristics were included as potential predictors of successful outcomes, and these were defined on the basis of minimal clinically important differences (MCID) of NDI-PT (MCID≥6) and END (MCID≥2) and the criteria score ≥5 on the PGIC-PT. Data analysis was performed using logistic regression (backward conditional procedure) to identify associations between predictors and outcomes (p<0.05). Results: Of the 112 participants included in the study, 108 completed the follow-up (mean age: 51.76±10.19). In the multivariate model of functional disability, the successful outcomes are associated with high levels of disability at baseline (OR = 1.123; 95% CI 1.056-1.194), and pain duration shorter than 12 months (OR=2.704; 95% CI 1.138–6.424). This model explains 30.0% of the variance of improved functional capacity and correctly classifies 74.1% of the patients (sensitivity: 75.9%, specificity: 72.0%). The model for pain intensity solely identified an outcome association with high pain intensity at baseline (OR=1.321; 95% CI 1.047-1.668), explaining 7.5% of the variance of pain reduction and correctly classifying 68.2% of the patients (sensitivity: 94.4%, specificity: 16.7%). The final model of perceived recovery showed an association with pain intensity at baseline (OR=0.621; 95% CI 0465-0829), with the presence of headache and/or dizziness (OR=2.538; 95% CI 0.987-6.526) and the duration of pain over 12 months (OR=0.279; 95% CI 0.109-0.719). This model explains 27.5% of the variance of successful outcomes and correctly classifies 73.1% of the patients (sensitivity: 81.8%, specificity: 59.5%). Conclusions: Patients with CNP with high disability at baseline and complaints of pain for less than 12 months are more likely to obtain improvements in functional disability. High levels of pain intensity at baseline predict successful outcomes in pain reduction after seven weeks of treatment. Patients with CNP with low levels of pain at baseline, with headache and/or dizziness and with pain complaints for more than 12 months are more likely to get a better perceived recovery.

A bayesian approach for NDT data fusion: The Saint Torcato Church case study

This paper presents a methodology based on the Bayesian data fusion techniques applied to non-destructive and destructive tests for the structural assessment of historical constructions. The aim of the methodology is to reduce the uncertainties of the parameter estimation. The Young's modulus of granite stones was chosen as an example for the present paper. The methodology considers several levels of uncertainty since the parameters of interest are considered random variables with random moments. A new concept of Trust Factor was introduced to affect the uncertainty related to each test results, translated by their standard deviation, depending on the higher or lower reliability of each test to predict a certain parameter.

An immunity based configuration for multilayer single featured biometric authentication algorithms

Immune systems have been used in the last years to inspire approaches for several computational problems. This paper focus on behavioural biometric authentication algorithms’ accuracy enhancement by using them more than once and with different thresholds in order to first simulate the protection provided by the skin and then look for known outside entities, like lymphocytes do. The paper describes the principles that support the application of this approach to Keystroke Dynamics, an authentication biometric technology that decides on the legitimacy of a user based on his typing pattern captured on he enters the username and/or the password and, as a proof of concept, the accuracy levels of one keystroke dynamics algorithm when applied to five legitimate users of a system both in the traditional and in the immune inspired approaches are calculated and the obtained results are compared.

Enrollment time as a requirement for biometric hand recognition systems

Biometric systems are increasingly being used as a means for authentication to provide system security in modern technologies. The performance of a biometric system depends on the accuracy, the processing speed, the template size, and the time necessary for enrollment. While much research has focused on the first three factors, enrollment time has not received as much attention. In this work, we present the findings of our research focused upon studying user’s behavior when enrolling in a biometric system. Specifically, we collected information about the user’s availability for enrollment in respect to the hand recognition systems (e.g., hand geometry, palm geometry or any other requiring positioning the hand on an optical scanner). A sample of 19 participants, chosen randomly apart their age, gender, profession and nationality, were used as test subjects in an experiment to study the patience of users enrolling in a biometric hand recognition system.

Search for a Charged Higgs Boson Produced in the Vector-boson Fusion Mode with Decay H±→W±Z using pp Collisions at s√=8 TeV with the ATLAS Experiment

A search for a charged Higgs boson, H±, decaying to a W± boson and a Z boson is presented. The search is based on 20.3 fb−1 of proton-proton collision data at a center-of-mass energy of 8 TeV recorded with the ATLAS detector at the LHC. The H± boson is assumed to be produced via vector-boson fusion and the decays W±→qq′¯ and Z→e+e−/μ+μ− are considered. The search is performed in a range of charged Higgs boson masses from 200 to 1000 GeV. No evidence for the production of an H± boson is observed. Upper limits of 31--1020 fb at 95% CL are placed on the cross section for vector-boson fusion production of an H± boson times its branching fraction to W±Z. The limits are compared with predictions from the Georgi-Machacek Higgs Triplet Model.

Monovarietal extra-virgin olive oil classification: a fusion of human sensory attributes and an electronic tongue

Olive oil quality grading is traditionally assessed by human sensory evaluation of positive and negative attributes (olfactory, gustatory, and final olfactorygustatory sensations). However, it is not guaranteed that trained panelist can correctly classify monovarietal extra-virgin olive oils according to olive cultivar. In this work, the potential application of human (sensory panelists) and artificial (electronic tongue) sensory evaluation of olive oils was studied aiming to discriminate eight single-cultivar extra-virgin olive oils. Linear discriminant, partial least square discriminant, and sparse partial least square discriminant analyses were evaluated. The best predictive classification was obtained using linear discriminant analysis with simulated annealing selection algorithm. A low-level data fusion approach (18 electronic tongue signals and nine sensory attributes) enabled 100 % leave-one-out cross-validation correct classification, improving the discrimination capability of the individual use of sensor profiles or sensory attributes (70 and 57 % leave-one-out correct classifications, respectively). So, human sensory evaluation and electronic tongue analysis may be used as complementary tools allowing successful monovarietal olive oil discrimination.

Interação multimodal de dispositivos robóticos em ambiente simulado

Dissertação de mestrado integrado em Engenharia e Gestão de Sistemas de Informação

Land cover classification of Lago Grande de Curuai floodplain (Amazon, Brazil) using multi-sensor and image fusion techniques

Given the limitations of different types of remote sensing images, automated land-cover classifications of the Amazon várzea may yield poor accuracy indexes. One way to improve accuracy is through the combination of images from different sensors, by either image fusion or multi-sensor classifications. Therefore, the objective of this study was to determine which classification method is more efficient in improving land cover classification accuracies for the Amazon várzea and similar wetland environments - (a) synthetically fused optical and SAR images or (b) multi-sensor classification of paired SAR and optical images. Land cover classifications based on images from a single sensor (Landsat TM or Radarsat-2) are compared with multi-sensor and image fusion classifications. Object-based image analyses (OBIA) and the J.48 data-mining algorithm were used for automated classification, and classification accuracies were assessed using the kappa index of agreement and the recently proposed allocation and quantity disagreement measures. Overall, optical-based classifications had better accuracy than SAR-based classifications. Once both datasets were combined using the multi-sensor approach, there was a 2% decrease in allocation disagreement, as the method was able to overcome part of the limitations present in both images. Accuracy decreased when image fusion methods were used, however. We therefore concluded that the multi-sensor classification method is more appropriate for classifying land cover in the Amazon várzea.

A network approach to brain aging through multimodal neuroimaging

Tese de Doutoramento em Ciências da Saúde.

KIAA1549: BRAF gene fusion and FGFR1 hotspot mutations are prognostic factors in pilocytic astrocytomas

Up to 20% of patients with pilocytic astrocytoma (PA) experience a poor outcome. BRAF alterations and Fibroblast growth factor receptor 1 (FGFR1) point mutations are key molecular alterations in Pas, but their clinical implications are not established. We aimed to determine the frequency and prognostic role of these alterations in a cohort of 69 patients with PAs. We assessed KIAA1549:BRAF fusion by fluorescence in situ hybridization and BRAF (exon 15) mutations by capillary sequencing. In addition, FGFR1 expression was analyzed using immunohistochemistry, and this was compared with gene amplification and hotspot mutations (exons 12 and 14) assessed by fluorescence in situ hybridization and capillary sequencing. KIAA1549:BRAF fusion was identified in almost 60% of cases. Two tumors harbored mutated BRAF. Despite high FGFR1 expression overall, no cases had FGFR1 amplifications. Three cases harbored a FGFR1 p.K656E point mutation. No correlation was observed between BRAF and FGFR1 alterations. The cases were predominantly pediatric (87%), and no statistical differences were observed in molecular alterations-related patient ages. In summary, we confirmed the high frequency of KIAA1549:BRAF fusion in PAs and its association with a better outcome. Oncogenic mutations of FGFR1, although rare, occurred in a subset of patients with worse outcome. These molecular alterations may constitute alternative targets for novel clinical approaches, when radical surgical resection is unachievable.

Multimodal automatic user disposition recognition in human-machine interaction

Magdeburg, Univ., Fak. für Elektrotechnik und Informationstechnik, Diss., 2013