Cost-effective and lightweight mobile units for MixAR: A comparative trial among different setups

Cultural heritage has arousing the interest of the general public (e.g. tourists), resulting in the increasing number of visitations to archaeological sites. However, many buildings and monuments are severely damaged or completely destroyed, which doesn’t allow to get a full experience of “travelling in time”. Over the years, several Augmented Reality (AR) approaches were proposed to overcome these issues by providing three-dimensional visualization of reconstructed ancient structures in situ. However, most of these systems were made available through heavy and expensive technological bundles. Alternatively, MixAR intends to be a lightweight and cost-effective Mixed Reality system which aims to provide the visualization of virtual ancient buildings reconstructions in situ, properly superimposed and aligned with real-world ruins. This paper proposes and compares different AR mobile units setups to be used in the MixAR system, with low-cost and lightweight requirements in mind, providing different levels of immersion. It was propounded four different mobile units, based on: a laptop computer, a single-board computer (SBC), a tablet and a smartphone, which underwent a set of tests to evaluate their performances. The results show that mobile units based on laptop computer and SBC reached a good overall performance while mobile units based on tablet and smartphone did not meet such a satisfactory result even though they are acceptable for the intended use.

MixAR mobile prototype: visualizing virtually reconstructed ancient structures in situ

Archeology and related areas have a special interest on cultural heritage sites since they provide valuable information about past civilizations. However, the ancient buildings present in these sites are commonly found in an advanced state of degradation which difficult the professional/expert analysis. Virtual reconstructions of such buildings aim to provide a digital insight of how these historical places could have been in ancient times. Moreover, the visualization of such models has been explored by some Augmented Reality (AR) systems capable of providing support to experts. Their compelling and appealing environments have also been applied to promote the social and cultural participation of general public. The existing AR solutions regarding this thematic rarely explore the potential of realism, due to the following lacks: the exploration of mixed environments is usually only supported for indoors or outdoors, not both in the same system; the adaptation of the illumination conditions to the reconstructed structures is rarely addressed causing a decrease of credibility. MixAR [1] is a system concerned with those challenges, aiming to provide the visualization of virtual buildings augmented upon real ruins, allowing soft transitions among its interiors and exteriors and using relighting techniques for a faithful interior illumination, while the user freely moves in a given cultural heritage site, carrying a mobile unit. Regarding the focus of this paper, we intend to report the current state of MixAR mobile unit prototype, which allows visualizing virtual buildings – properly aligned with real-world structures – based on user's location, during outdoor navigation. In order to evaluate the prototype performance, a set of tests were made using virtual models with different complexities.

Identifying finger postures when interacting with the touch screen of mobile devices

This study presents the results of preliminary test on the interaction between fingertip and touch screen. The objective of this study is to identify the fingertip posture when interacting with touch screen devices. Ten participants, 7 males and 3 females, participated in this study. The participants were asked to touch targets on the mobile devices screen by tapping them sequentially and connecting them. The participants performed the tasks in a sitting posture. A tablet with 10 inches screen and a mobile phone with 4 inches screen were used in the study. The results showed that all participants dominantly used their thumb to interact with the mobile phone in single and two hands postures. The common thumb posture adopted by the participants is the combination of the 60° pitch and 0° roll angles. While for interaction with tablet in various postures observed in the study, the participants commonly used their index fingers in the combination of 60° pitch and 0° roll angles. This study also observed the participant with long finger nails touched targets on the mobile devices screen by using her index or middle fingers very low pitch.

Improving quality of medical service with mobile health software

An increasing number of m-Health applications are being developed benefiting health service delivery. In this paper, a new methodology based on the principle of calm computing applied to diagnostic and therapeutic procedure reporting is proposed. A mobile application was designed for the physicians of one of the Portuguese major hospitals, which takes advantage of a multi-agent interoperability platform, the Agency for the Integration, Diffusion and Archive (AIDA). This application allows the visualization of inpatients and outpatients medical reports in a quicker and safer manner, in addition to offer a remote access to information. This project shows the advantages in the use of mobile software in a medical environment but the first step is always to build or use an interoperability platform, flexible, adaptable and pervasive. The platform offers a comprehensive set of services that restricts the development of mobile software almost exclusively to the mobile user interface design. The technology was tested and assessed in a real context by intensivists.

Moda ética: a consciencialização através da comunicação

Dissertação de mestrado em Design e Comunicação de Moda

Designing next generation car washing machines with rotary brushes

Tese de Doutoramento - Leaders for Technical Industries (LTI) - MIT Portugal

Development of computational tools for the integrated analysis of DNA microarray data with applications in cancer research

The MAP-i Doctoral Program of the Universities of Minho, Aveiro and Porto

Metallic glass/PVDF magnetoelectric energy harvester working up to the radiofrequency range

Companies and researchers involved in developing miniaturized electronic devices face the basic problem of the needed batteries size, finite life of time and environmental pollution caused by their final deposition. The current trends to overcome this situation point towards Energy Harvesting technology. These harvesters (or scavengers) store the energy from sources present in the ambient (as wind, solar, electromagnetic, etc) and are costless for us. Piezoelectric devices are the ones that show a higher power density, and materials as ceramic PZT or polymeric PVDF have already demonstrated their ability to act as such energy harvester elements. Combinations between piezoelectric and electromagnetic mechanism have been also extensively investigated. Nevertheless, the power generated by these combinations is limited under the application of small magnetic fields, reducing the performance of the energy harvester [1]. In the last years the appearance of magnetoelectric (ME) devices, in which the piezoelectric deformation is driven by the magnetostrictive element, enables to extract the energy of very small electromagnetic signals through the generated magnetoelectric voltage at the piezoelectric element. However, very little work has been done testing PVDF polymer as piezoelectric constituent of the ME energy harvester device, and only to be proposed as a possibility of application [2]. Among the advantages of using piezopolymers for vibrational energy harvesting we can remember that they are ductile, resilient to shock, deformable and lightweight. In this work we demonstrate the feasibility of using magnetostrictive Fe-rich magnetic amorphous alloys/piezoelectric PVDF sandwich-type laminated ME devices as energy harvesters. A very simple experimental set-up will show how these laminates can extract energy, in amounts of μW, from an external AC field.

Discrimination of Brazilian propolis according to the seasoning using chemometrics and machine learning based on UV-Vis scanning data

Propolis is a chemically complex biomass produced by honeybees (Apis mellifera) from plant resins added of salivary enzymes, beeswax, and pollen. The biological activities described for propolis were also identified for donor plants resin, but a big challenge for the standardization of the chemical composition and biological effects of propolis remains on a better understanding of the influence of seasonality on the chemical constituents of that raw material. Since propolis quality depends, among other variables, on the local flora which is strongly influenced by (a)biotic factors over the seasons, to unravel the harvest season effect on the propolis chemical profile is an issue of recognized importance. For that, fast, cheap, and robust analytical techniques seem to be the best choice for large scale quality control processes in the most demanding markets, e.g., human health applications. For that, UV-Visible (UV-Vis) scanning spectrophotometry of hydroalcoholic extracts (HE) of seventy-three propolis samples, collected over the seasons in 2014 (summer, spring, autumn, and winter) and 2015 (summer and autumn) in Southern Brazil was adopted. Further machine learning and chemometrics techniques were applied to the UV-Vis dataset aiming to gain insights as to the seasonality effect on the claimed chemical heterogeneity of propolis samples determined by changes in the flora of the geographic region under study. Descriptive and classification models were built following a chemometric approach, i.e. principal component analysis (PCA) and hierarchical clustering analysis (HCA) supported by scripts written in the R language. The UV-Vis profiles associated with chemometric analysis allowed identifying a typical pattern in propolis samples collected in the summer. Importantly, the discrimination based on PCA could be improved by using the dataset of the fingerprint region of phenolic compounds ( = 280-400m), suggesting that besides the biological activities of those secondary metabolites, they also play a relevant role for the discrimination and classification of that complex matrix through bioinformatics tools. Finally, a series of machine learning approaches, e.g., partial least square-discriminant analysis (PLS-DA), k-Nearest Neighbors (kNN), and Decision Trees showed to be complementary to PCA and HCA, allowing to obtain relevant information as to the sample discrimination.

Transcranial direct current stimulation based metaplasticity protocols in working memory

It has been already shown that delivering tDCS that are spaced by an interval alters its impact on motor plasticity. These effects can be explained, based on metaplasticity in which a previous modification of activity in a neuronal network can change the effects of subsequent interventions in the same network. But to date there is limited data assessing metaplasticity effects in cognitive functioning.

Metabolic profiling and classification of propolis samples from Southern Brazil: an NMR-based platform coupled with machine learning

The chemical composition of propolis is affected by environmental factors and harvest season, making it difficult to standardize its extracts for medicinal usage. By detecting a typical chemical profile associated with propolis from a specific production region or season, certain types of propolis may be used to obtain a specific pharmacological activity. In this study, propolis from three agroecological regions (plain, plateau, and highlands) from southern Brazil, collected over the four seasons of 2010, were investigated through a novel NMR-based metabolomics data analysis workflow. Chemometrics and machine learning algorithms (PLS-DA and RF), including methods to estimate variable importance in classification, were used in this study. The machine learning and feature selection methods permitted construction of models for propolis sample classification with high accuracy (>75%, reaching 90% in the best case), better discriminating samples regarding their collection seasons comparatively to the harvest regions. PLS-DA and RF allowed the identification of biomarkers for sample discrimination, expanding the set of discriminating features and adding relevant information for the identification of the class-determining metabolites. The NMR-based metabolomics analytical platform, coupled to bioinformatic tools, allowed characterization and classification of Brazilian propolis samples regarding the metabolite signature of important compounds, i.e., chemical fingerprint, harvest seasons, and production regions.

Hand gesture recognition system based in computer vision and machine learning: Applications on human-machine interaction

Tese de Doutoramento em Engenharia de Eletrónica e de Computadores

Forensic neuropsychological assessment: clinical case of depression and working incapacity

Issues related to mental health in relation to court matters have increasingly required the participation of the psychologist. We present the use of forensic neuropsychological assessment in a case of retirement reversal. Incapacity was attested due to disability resulting from depression of a 35-year-old attorney, and the case was forwarded from the courts to the Forensic Psychiatry and Psychology Unit at the USP Clinical Hospital. A clinical interview and application of cognitive tests was conducted. Despite the depression, significant cognitive losses that would prevent return to his professional assignments were not detected. The neuropsychological assessment has been shown to be an important tool in the forensic context, as it assists with diagnostic value for clarification of functional aspects in the various psychopathological areas in terms of disabilities or potentialities.

Investigation of cognition in schizophrenia: psychometric properties of instruments for assessing working memory updating

Objective This study describes the development of two updating measures of working memory (WM): Letter Updating Test (LUT) and Word Updating Test (WUT). Methods In stage 1, items were created and the instruments were assessed by experts and laymen. In stage 2, tests were given to 15 patients with schizophrenia and 15 paired controls. All were able to understand and respond to the instruments. In stage 3, 141 patients with schizophrenia and 119 healthy controls aged 18 to 60 took part; they were assessed on WM, processing speed (PS) and functional outcome. Results The results showed adequate rates of internal consistency for both measures developed, for both the total sample and each group separately, as well as evidence of convergent validity, discriminant validity and sensitivity to differentiate performance among the groups. Principal component analysis yielded two components, one for updating tests and other for PS measures, indicating factorial validity. Positive and significant, yet low, correlations were found with functionality measures. Conclusion These results provide adequate psychometric parameters for the measures developed, applicable to cognitive research settings in schizophrenia.

Investigação das dificuldades na memória de trabalho em alunos do 2º ano do Ensino Fundamental em Manaus: um estudo com a Working Memory Rating Scale

Dissertação de mestrado em Educação Especial (área de especialização em Dificuldades de Aprendizagem Específicas)