Félix F. González-Navarro

Surface recognition improvement in 3D medical laser scanner using Levenberg-Marquardt method

The 3D measurements of the human body surface or anatomical areas have gained importance in many medical applications. Three dimensional laser scanning systems can provide these measurements; however usually these scanners have non-linear variations in their measurement, and typically these variations depend on the position of the scanner with respect to the person. In this paper, the Levenberg-Marquardt method is used as a digital rectifier to adjust this non-linear variation and increases the measurement accuracy of our 3D Rotational Body Scanner. A comparative analysis with other methods such as Polak-Ribire and quasi-Newton method, and the overall system functioning is presented. Finally, computational experiments are conducted to verify the performance of the proposed system and its method uncertainty.

Feature and model selection with discriminatory visualization for diagnostic classification of brain tumors

Machine Learning (ML) and related methods have of late made significant contributions to solving multidisciplinary problems in the field of oncology diagnosis. Human brain tumor diagnosis, in particular, often relies on the use of non-invasive techniques such as Magnetic Resonance Imaging (MRI) and Spectroscopy (MRS). In this paper, MRS data of human brain tumors are analyzed in detail. The high dimensionality of the MR spectra makes difficult both their classification and the interpretation of the obtained results, thus limiting their usability in practical medical settings. The use of dimensionality reduction techniques is therefore advisable. In this work, we apply feature selection methods and several off-the-shelf classifiers on various ^1H-MRS modalities: long and short echo times and an ad hoc combination of both. The introduction of bootstrap resampling techniques permits the obtention of mean performance estimates and their variability. Our experimental findings indicate that the feature selection process enhances the classification performance compared to using the full set of features. We also show that the use of combined information from the different echo times is a better strategy for small numbers of spectral frequencies; however, the use of ever greater numbers of short echo time frequencies permits the obtention of many models with similar performance. The final induced models offer very attractive solutions both in terms of prediction accuracy and number of involved spectral frequencies, which are also amenable to metabolic interpretation. A linear dimensionality-reduction technique that preserves class discrimination capabilities is used for visualizing the data corresponding to the selected frequencies.

Combined application of Power Spectrum Centroid and Support Vector Machines for measurement improvement in Optical Scanning Systems

In this paper Support Vector Machine (SVM) Regression was applied to predict measurements errors for Accuracy Enhancement in Optical Scanning Systems, for position detection in real life application for Structural Health Monitoring (SHM) by a novel method, based on the Power Spectrum Centroid Calculation in determining the energy center of an optoelectronic signal in order to obtain accuracy enhancement in optical scanning system measurements. In the development of an Optical Scanning System based on a 45^o - sloping surface cylindrical mirror and an incoherent light emitting source, surged a novel method in optoelectronic scanning, it has been found that in order to find the position of a light source and to reduce errors in position measurements, the best solution is taking the measurement in the energy centre of the signal generated by the Optical Scanning System. The Energy Signal Centre is found in the Power Spectrum Centroid and the SVM Regression Method is used as a digital rectified to increase measurement accuracy for Optical Scanning System.

Outlier exploration and diagnostic classification of a multi-centre 1H-MRS brain tumour database

Non-invasive techniques such as magnetic resonance spectroscopy (MRS) are often required for assisting the diagnosis of tumours. Radiologists are not always accustomed to make sense of the biochemical information provided by MRS and they may benefit from computer-based support in their decision making. The high dimensionality of the MR spectra obscures atypical aspects of the data that may jeopardize their classification. In this study, we describe a method to overcome this problem that combines nonlinear dimensionality reduction, outlier detection, and expert opinion. MR spectra subsequently undergo a feature selection process followed by classification. The impact of outlier removal on classification performance is assessed.

Tracking the Evolution of the Internet of Things Concept Across Different Application Domains

Both the idea and technology for connecting sensors and actuators to a network to remotely monitor and control physical systems have been known for many years and developed accordingly. However, a little more than a decade ago the concept of the Internet of Things (IoT) was coined and used to integrate such approaches into a common framework. Technology has been constantly evolving and so has the concept of the Internet of Things, incorporating new terminology appropriate to technological advances and different application domains. This paper presents the changes that the IoT has undertaken since its conception and research on how technological advances have shaped it and fostered the arising of derived names suitable to specific domains. A two-step literature review through major publishers and indexing databases was conducted; first by searching for proposals on the Internet of Things concept and analyzing them to find similarities, differences, and technological features that allow us to create a timeline showing its development; in the second step the most mentioned names given to the IoT for specific domains, as well as closely related concepts were identified and briefly analyzed. The study confirms the claim that a consensus on the IoT definition has not yet been reached, as enabling technology keeps evolving and new application domains are being proposed. However, recent changes have been relatively moderated, and its variations on application domains are clearly differentiated, with data and data technologies playing an important role in the IoT landscape.

Machine vision supported by artificial intelligence

A performance evaluation of different artificial intelligence methods for machine vision using a rotatory mirror scanner is presented. This assessment concludes importance results, in order to properly select a method for the development of an precise optical scanning system for machine vision, with application in Structural Health Monitoring and Robot Navigation task.

Parsimonious Selection of Useful Genes in Microarray Gene Expression Data

Machine learning methods have of late made significant efforts to solving multidisciplinary problems in the field of cancer classification in microarray gene expression data. These tasks are characterized by a large number of features and a few observations, making the modeling a nontrivial undertaking. In this study, we apply entropic filter methods for gene selection, in combination with several off-the-shelf classifiers. The introduction of bootstrap resampling techniques permits the achievement of more stable performance estimates. Our findings show that the proposed methodology permits a drastic reduction in dimension, offering attractive solutions in terms of both prediction accuracy and number of explanatory genes; a dimensionality reduction technique preserving discrimination capabilities is used for visualization of the selected genes.

Explicit and tacit knowledge conversion effects, in software engineering undergraduate students

This study evaluates the effect of conversion between tacit and explicit knowledge, and its influence on Software engineering and Software Process Improvement in the context of a small school software company in which undergraduate students participate as personnel. A survey measurement instrument was applied to the last three generations of students. The effect was measured from an interaction of the four modes of the SECI model knowledge conversion using regression analysis associated with four hypotheses study. The findings show that students are able to generate tacit and explicit knowledge in a similar way to software organizations. This study is considered a contribution of both academia and software industry that encourages this type of experiences in undergraduate students and prepares them as intellectual capital with an organizational culture that shares knowledge.

Glucose Oxidase Biosensor Modeling and Predictors Optimization by Machine Learning Methods

Biosensors are small analytical devices incorporating a biological recognition element and a physico-chemical transducer to convert a biological signal into an electrical reading. Nowadays, their technological appeal resides in their fast performance, high sensitivity and continuous measuring capabilities; however, a full understanding is still under research. This paper aims to contribute to this growing field of biotechnology, with a focus on Glucose-Oxidase Biosensor (GOB) modeling through statistical learning methods from a regression perspective. We model the amperometric response of a GOB with dependent variables under different conditions, such as temperature, benzoquinone, pH and glucose concentrations, by means of several machine learning algorithms. Since the sensitivity of a GOB response is strongly related to these dependent variables, their interactions should be optimized to maximize the output signal, for which a genetic algorithm and simulated annealing are used. We report a model that shows a good generalization error and is consistent with the optimization.

Effective Classification and Gene Expression Profiling for the Facioscapulohumeral Muscular Dystrophy

The Facioscapulohumeral Muscular Dystrophy (FSHD) is an autosomal dominant neuromuscular disorder whose incidence is estimated in about one in 400,000 to one in 20,000. No effective therapeutic strategies are known to halt progression or reverse muscle weakness and atrophy. It is known that the FSHD is caused by modifications located within a D4ZA repeat array in the chromosome 4q, while recent advances have linked these modifications to the DUX4 gene. Unfortunately, the complete mechanisms responsible for the molecular pathogenesis and progressive muscle weakness still remain unknown. Although there are many studies addressing cancer databases from a machine learning perspective, there is no such precedent in the analysis of the FSHD. This study aims to fill this gap by analyzing two specific FSHD databases. A feature selection algorithm is used as the main engine to select genes promoting the highest possible classification capacity. The combination of feature selection and classification aims at obtaining simple models (in terms of very low numbers of genes) capable of good generalization, that may be associated with the disease. We show that the reported method is highly efficient in finding genes to discern between healthy cases (not affected by the FSHD) and FSHD cases, allowing the discovery of very parsimonious models that yield negligible repeated cross-validation error. These models in turn give rise to very simple decision procedures in the form of a decision tree. Current biological evidence regarding these genes shows that they are linked to skeletal muscle processes concerning specific human conditions.