José Manuel Fonseca

Maciv a dai based resource management system

Managing resources in the framework of the civil construction sector is usually an extremely complex task. There are many factors contributing to this complexity: the variety and great number of existing resources, both human and material; the diversity of tasks that each working unit is able to execute; the performance of each working unit; the involved costs; and the spatial distribution of all resources over the different places, leading to the need for displacement from one site to another. All these important factors imply a high number of variables, resulting in a somewhat difficult optimization process. On the other hand, these factors are highly dynamic as a result of unpredictable situations responsible for the modification of the initial conditions, e.g., weather conditions, uncertainties attached to task duration, acquisition of new resources, technical problems related with those resources, and accidents. Such dynamics make it mandatory for the systems to have the capability to continuously ad...

FIF: A fuzzy information fusion algorithm based on multi-criteria decision making

The main goal of information fusion is to combine heterogeneous information to obtain a single composite of potential comparable alternative solutions that can be classified and ranked. The crux of information fusion, which is a type of data fusion, is threefold: (i) data must be comparable and numerical, using some normalization process; (ii) imprecision in data must be taken into consideration; (iii) an appropriate aggregation function to combine values into a single score must be selected. Recently, computational intelligence concepts and techniques to perform data/information fusion are emerging as suitable tools. Although with a different perspective, another field where much work has also been done for combining heterogeneous information is multi-criteria decision-making. In general, multi-criteria problems are modelled by choosing a set of relevant criteria - usually dealing with heterogeneous data - that have to be aggregated (i.e. fused) to obtain a single rating for each candidate alternative. In this paper we propose an algorithm for data/information fusion, which includes concepts from multi-criteria decision-making and computational intelligence, specifically, fuzzy multi-criteria decision-making and mixture aggregation operators with weighting functions. The application field of interest for this work is safe spacecraft landing with hazard avoidance; hence two existing hazard maps will be used to illustrate the versatility of the algorithm.

Automatic word naming recognition for an on-line aphasia treatment system

Abstract One of the most common effects among aphasia patients is the difficulty to recall names or words. Typically, word retrieval problems can be treated through word naming therapeutic exercises. In fact, the frequency and the intensity of speech therapy are key factors in the recovery of lost communication functionalities. In this sense, speech and language technology can have a relevant contribution in the development of automatic therapy methods. In this work, we present an on-line system designed to behave as a virtual therapist incorporating automatic speech recognition technology that permits aphasia patients to perform word naming training exercises. We focus on the study of the automatic word naming detector module and on its utility for both global evaluation and treatment. For that purpose, a database consisting of word naming therapy sessions of aphasic Portuguese native speakers has been collected. In spite of the different patient characteristics and speech quality conditions of the collected data, encouraging results have been obtained thanks to a calibration method that makes use of the patients’ word naming ability to automatically adapt to the patients’ speech particularities.

Missing Data Imputation in Longitudinal Cohort Studies: Application of PLANN-ARD in Breast Cancer Survival

Missing values are common in medical datasets and may be amenable to data imputation when modelling a given data set or validating on an external cohort. This paper discusses model averaging over samples of the imputed distribution and extends this approach to generic non-linear modelling with the Partial Logistic Artificial Neural Network (PLANN) regularised within the evidence-based framework with Automatic Relevance Determination (ARD). The study then applies the imputation to external validation over new patient cohorts, considering also the case of predictions made for individual patients. A prognostic index is defined for the non-linear model and validation results show that 4 statistically significant risk groups identified at the 95% level of confidence from the modelling data, from Christie Hospital (n=931), retain good separation during external validation with data from the British Columbia Cancer Agency (n=4,083).

Stratification of Severity of Illness Indices: A Case Study for Breast Cancer Prognosis

Prognostic modelling involves grouping patients by risk of adverse outcome, typically by stratifying a severity of illness index obtained from a classifier or survival model. The assignment of thresholds on the risk index depends of pairwise statistical significance tests, notably the log-rank test. This paper proposes a new methodology to substantially improve the robustness of the stratification algorithm, by reference to a statistical and neural network prognostic study of longitudinal data from patients with operable breast cancer.

Increased cytoplasm viscosity hampers aggregate polar segregation in Escherichia coli

In Escherichia coli, under optimal conditions, protein aggregates associated with cellular aging are excluded from midcell by the nucleoid. We study the functionality of this process under sub‐optimal temperatures from population and time lapse images of individual cells and aggregates and nucleoids within. We show that, as temperature decreases, aggregates become homogeneously distributed and uncorrelated with nucleoid size and location. We present evidence that this is due to increased cytoplasm viscosity, which weakens the anisotropy in aggregate displacements at the nucleoid borders that is responsible for their preference for polar localisation. Next, we show that in plasmolysed cells, which have increased cytoplasm viscosity, aggregates are also not preferentially located at the poles. Finally, we show that the inability of cells with increased viscosity to exclude aggregates from midcell results in enhanced aggregate concentration in between the nucleoids in cells close to dividing. This weakens the asymmetries in aggregate numbers between sister cells of subsequent generations required for rejuvenating cell lineages. We conclude that the process of exclusion of protein aggregates from midcell is not immune to stress conditions affecting the cytoplasm viscosity. The findings contribute to our understanding of E. colis internal organisation and functioning, and its fragility to stressful conditions.

In-Plane Displacement and Strain Image Analysis

Measurements in civil engineering load tests usually require considerable time and complex procedures. Therefore, measurements are usually constrained by the number of sensors resulting in a restricted monitored area. Image processing analysis is an alternative way that enables the measurement of the complete area of interest with a simple and effective setup. In this article photo sequences taken during load displacement tests were captured by a digital camera and processed with image correlation algorithms. Three different image processing algorithms were used with real images taken from tests using specimens of PVC and Plexiglas. The data obtained from the image processing algorithms were also compared with the data from physical sensors. A complete displacement and strain map were obtained. Results show that the accuracy of the measurements obtained by photogrammetry is equivalent to that from the physical sensors but with much less equipment and fewer setup requirements.

Continuous Speech Classification Systems for Voice Pathologies Identification

Voice pathologies identification using speech processing methods can be used as a preliminary diagnostic. The aim of this study is to compare the performance of sustained vowel /a/ and continuous speech task in identification systems to diagnose voice pathologies. The system recognizes between three classes consisting of two different pathologies sets and healthy subjects. The signals are evaluated using MFCC (Mel Frequency Cepstral Coefficients) as speech signal features, applied to SVM (Support Vector Machines) and GMM (Gaussian Mixture Models) classifiers. For continuous speech, the GMM system reaches 74% accuracy rate while the SVM system obtains 72% accuracy rate. For the sustained vowel /a/, the accuracy achieved by the GMM and the SVM is 66% and 69% respectively, a lower result than with continuous speech.

Optical character recognition using automatically generated Fuzzy classifiers

Character recognition using Fuzzy classifiers has been showing very promising results. However, the definition of the membership functions together with the design of the classification rules is a challenging task even considering just the 10 digits and 23 characters of the Roman alphabet. In this paper we present a solution for the semi-automatic design of a Fuzzy classifier for letters and digits to be applied on the automatic recognition of cars license plates on unstructured conditions. Based on a training set of fuzzified examples of measures, taken from digital images of single characters, the CART algorithm learns the rules that regulate the design of the different characters and generates fuzzy rules that implement the fuzzy classifiers in a completely automatic way. After, a fuzzy inference engine executes the rules to obtain the characters classification. To take advantage of syntactical correction, a hierarchical classifier with two layers of classifiers is proposed: one classifier distinguishes between letters or digits; the second layer classifies either the letters or the digits. The performance achieved by the two-layer classifier is shown and discussed.

Language improvement one week after thrombolysis in acute stroke.

Language recovery following acute stroke is difficult to predict due to several evaluation factors and time constraints. We aimed to investigate the predictors of aphasia recovery and to identify the National Institute of Health and Stroke Scale (NIHSS) items that best reflect linguistic performance, 1 week after thrombolysis.