Implementação de uma Interface Homem-máquina baseada em Eletroculografia

A constante evolução da tecnologia permitiu ao ser humano a utilização de dispositivos electrónicos nas suas rotinas diárias. Estas podem ser afetadas quando os utilizadores sofrem de deficiências ou doenças que afetam as suas capacidades motoras. Com o intuito de minimizar este obstáculo surgiram as Interfaces Homem-Computador (HCI). É neste panorama que os sistemas HCI baseados em Eletroculografia (EOG) assumem um papel preponderante na melhoria da qualidade de vida destes indivíduos. A Eletroculografia é o resultado da aquisição do movimento ocular, que pode ser adquirido através de diversos métodos. Os métodos mais convencionais utilizam elétrodos de superfície para aquisição dos sinais elétricos, ou então, utilizam sistemas de gravação de vídeo, que gravam o movimento ocular. O objetivo desta tese é desenvolver um sistema HCI baseado em Eletroculografia, que adquire o sinal elétrico do movimento ocular através de elétrodos de superfície. Para tal desenvolveu-se um circuito eletrónico para a aquisição do sinal de EOG, bem como um algoritmo em Python para análise do mesmo. O circuito foi desenvolvido recorrendo a seis módulos diferentes, cada um deles com uma função específica. Para cada módulo foi necessário desenhar e implementar placas de circuito impresso, que quando conectadas entre si permitem filtrar, amplificar e digitalizar os sinais elétricos, adquiridos através de elétrodos de superfície, originados pelo movimento ocular. O algoritmo criado em Python permite analisar os dados provenientes do circuito e converte-os para coordenadas. Através destas foi possível determinar o sentido e a amplitude do movimento ocular.

Novel optical PVC probes for on-site detection/determination of fluoroquinolones in a solid/liquid interface: Application to the determination of Norfloxacin in aquaculture water

A novel optical disposable probe for screening fluoroquinolones in fish farming waters is presented, having Norfloxacin (NFX) as target compound. The colorimetric reaction takes place in the solid/liquid interface consisting of a plasticized PVC layer carrying the colorimetric reagent and the sample solution. NFX solutions dropped on top of this solid-sensory surface provided a colour change from light yellow to dark orange. Several metals were tested as colorimetric reagents and Fe(III) was selected. The main parameters affecting the obtained colour were assessed and optimised in both liquid and solid phases. The corresponding studies were conducted by visible spectrophotometry and digital image acquisition. The three coordinates of the HSL model system of the collected image (Hue, Saturation and Lightness) were obtained by simple image management (enabled in any computer). The analytical response of the optimised solid-state optical probe against concentration was tested for several mathematical transformations of the colour coordinates. Linear behaviour was observed for logarithm NFX concentration against Hue+Lightness. Under this condition, the sensor exhibited a limit of detection below 50 μM (corresponding to about 16 mg/mL). Visual inspection also enabled semi-quantitative information. The selectivity was ensured against drugs from other chemical groups than fluoroquinolones. Finally, similar procedure was used to prepare an array of sensors for NFX, consisting on different metal species. Cu(II), Mn(II) and aluminon were selected for this purpose. The sensor array was used to detect NFX in aquaculture water, without any prior sample manipulation.

Design a computer programming learning environment for Massive Open Online Courses

Teaching and learning computer programming is as challenging as difficult. Assessing the work of students and providing individualised feedback to all is time-consuming and error prone for teachers and frequently involves a time delay. The existent tools and specifications prove to be insufficient in complex evaluation domains where there is a greater need to practice. At the same time Massive Open Online Courses (MOOC) are appearing revealing a new way of learning, more dynamic and more accessible. However this new paradigm raises serious questions regarding the monitoring of student progress and its timely feedback. This paper provides a conceptual design model for a computer programming learning environment. This environment uses the portal interface design model gathering information from a network of services such as repositories and program evaluators. The design model includes also the integration with learning management systems, a central piece in the MOOC realm, endowing the model with characteristics such as scalability, collaboration and interoperability. This model is not limited to the domain of computer programming and can be adapted to any complex area that requires systematic evaluation with immediate feedback.

A Terra como Interface: o paradigma do Mapa para o Século XXI

Trabalho de Projecto de Mestrado em Ciências de Comunicação Variante Novos Media e Práticas Web

Gesture based interface for image annotation

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

Predictive control of a current-source inverter for solar photovoltaic grid interface

Solar photovoltaic systems are an increasing option for electricity production, since they produce electrical energy from a clean renewable energy resource, and over the years, as a result of the research, their efficiency has been increasing. For the interface between the dc photovoltaic solar array and the ac electrical grid is necessary the use of an inverter (dc-ac converter), which should be optimized to extract the maximum power from the photovoltaic solar array. In this paper is presented a solution based on a current-source inverter (CSI) using continuous control set model predictive control (CCS-MPC). All the power circuits and respective control systems are described in detail along the paper and were tested and validated performing computer simulations. The paper shows the simulation results and are drawn several conclusions.

Hand gesture recognition system based in computer vision and machine learning

"Lecture notes in computational vision and biomechanics series, ISSN 2212-9391, vol. 19"

Generic system for human-computer gesture interaction: applications on sign language recognition and robotic soccer refereeing

Hand gestures are a powerful way for human communication, with lots of potential applications in the area of human computer interaction. Vision-based hand gesture recognition techniques have many proven advantages compared with traditional devices, giving users a simpler and more natural way to communicate with electronic devices. This work proposes a generic system architecture based in computer vision and machine learning, able to be used with any interface for human-computer interaction. The proposed solution is mainly composed of three modules: a pre-processing and hand segmentation module, a static gesture interface module and a dynamic gesture interface module. The experiments showed that the core of visionbased interaction systems could be the same for all applications and thus facilitate the implementation. For hand posture recognition, a SVM (Support Vector Machine) model was trained and used, able to achieve a final accuracy of 99.4%. For dynamic gestures, an HMM (Hidden Markov Model) model was trained for each gesture that the system could recognize with a final average accuracy of 93.7%. The proposed solution as the advantage of being generic enough with the trained models able to work in real-time, allowing its application in a wide range of human-machine applications. To validate the proposed framework two applications were implemented. The first one is a real-time system able to interpret the Portuguese Sign Language. The second one is an online system able to help a robotic soccer game referee judge a game in real time.

Generic system for human-computer gesture interaction

Hand gestures are a powerful way for human communication, with lots of potential applications in the area of human computer interaction. Vision-based hand gesture recognition techniques have many proven advantages compared with traditional devices, giving users a simpler and more natural way to communicate with electronic devices. This work proposes a generic system architecture based in computer vision and machine learning, able to be used with any interface for humancomputer interaction. The proposed solution is mainly composed of three modules: a pre-processing and hand segmentation module, a static gesture interface module and a dynamic gesture interface module. The experiments showed that the core of vision-based interaction systems can be the same for all applications and thus facilitate the implementation. In order to test the proposed solutions, three prototypes were implemented. For hand posture recognition, a SVM model was trained and used, able to achieve a final accuracy of 99.4%. For dynamic gestures, an HMM model was trained for each gesture that the system could recognize with a final average accuracy of 93.7%. The proposed solution as the advantage of being generic enough with the trained models able to work in real-time, allowing its application in a wide range of human-machine applications.

Evaluation of the determinism of the actuator sensor interface (ASI) on programmable logic controllers (PLC), personal computers (PC) and real-time operating systems (RTOS)

The purpose of this study was to evaluate the determinism of the AS-lnterface network and the 3 main families of control systems, which may use it, namely PLC, PC and RTOS. During the course of this study the PROFIBUS and Ethernet field level networks were also considered in order to ensure that they would not introduce unacceptable latencies into the overall control system. This research demonstrated that an incorrectly configured Ethernet network introduces unacceptable variable duration latencies into the control system, thus care must be exercised if the determinism of a control system is not to be compromised. This study introduces a new concept of using statistics and process capability metrics in the form of CPk values, to specify how suitable a control system is for a given control task. The PLC systems, which were tested, demonstrated extremely deterministic responses, but when a large number of iterations were introduced in the user program, the mean control system latency was much too great for an AS-I network. Thus the PLC was found to be unsuitable for an AS-I network if a large, complex user program Is required. The PC systems, which were tested were non-deterministic and had latencies of variable duration. These latencies became extremely exaggerated when a graphing ActiveX was included in the control application. These PC systems also exhibited a non-normal frequency distribution of control system latencies, and as such are unsuitable for implementation with an AS-I network. The RTOS system, which was tested, overcame the problems identified with the PLC systems and produced an extremely deterministic response, even when a large number of iterations were introduced in the user program. The RTOS system, which was tested, is capable of providing a suitable deterministic control system response, even when an extremely large, complex user program is required.

EEG-based functional brain networks: does the network size matter?

Functional connectivity in human brain can be represented as a network using electroencephalography (EEG) signals. These networks--whose nodes can vary from tens to hundreds--are characterized by neurobiologically meaningful graph theory metrics. This study investigates the degree to which various graph metrics depend upon the network size. To this end, EEGs from 32 normal subjects were recorded and functional networks of three different sizes were extracted. A state-space based method was used to calculate cross-correlation matrices between different brain regions. These correlation matrices were used to construct binary adjacency connectomes, which were assessed with regards to a number of graph metrics such as clustering coefficient, modularity, efficiency, economic efficiency, and assortativity. We showed that the estimates of these metrics significantly differ depending on the network size. Larger networks had higher efficiency, higher assortativity and lower modularity compared to those with smaller size and the same density. These findings indicate that the network size should be considered in any comparison of networks across studies.

Mutagenesis and modelling of the alpha(1b)-adrenergic receptor highlight the role of the helix 3/helix 6 interface in receptor activation.

Computer simulations on a new model of the alpha1b-adrenergic receptor based on the crystal structure of rhodopsin have been combined with experimental mutagenesis to investigate the role of residues in the cytosolic half of helix 6 in receptor activation. Our results support the hypothesis that a salt bridge between the highly conserved arginine (R143(3.50)) of the E/DRY motif of helix 3 and a conserved glutamate (E289(6.30)) on helix 6 constrains the alpha1b-AR in the inactive state. In fact, mutations of E289(6.30) that weakened the R143(3.50)-E289(6.30) interaction constitutively activated the receptor. The functional effect of mutating other amino acids on helix 6 (F286(6.27), A292(6.33), L296(6.37), V299(6.40,) V300(6.41), and F303(6.44)) correlates with the extent of their interaction with helix 3 and in particular with R143(3.50) of the E/DRY sequence.

Segmentation of brain structures in presence of a space-occupying lesion.

Brain deformations induced by space-occupying lesions may result in unpredictable position and shape of functionally important brain structures. The aim of this study is to propose a method for segmentation of brain structures by deformation of a segmented brain atlas in presence of a space-occupying lesion. Our approach is based on an a priori model of lesion growth (MLG) that assumes radial expansion from a seeding point and involves three steps: first, an affine registration bringing the atlas and the patient into global correspondence; then, the seeding of a synthetic tumor into the brain atlas providing a template for the lesion; finally, the deformation of the seeded atlas, combining a method derived from optical flow principles and a model of lesion growth. The method was applied on two meningiomas inducing a pure displacement of the underlying brain structures, and segmentation accuracy of ventricles and basal ganglia was assessed. Results show that the segmented structures were consistent with the patient's anatomy and that the deformation accuracy of surrounding brain structures was highly dependent on the accurate placement of the tumor seeding point. Further improvements of the method will optimize the segmentation accuracy. Visualization of brain structures provides useful information for therapeutic consideration of space-occupying lesions, including surgical, radiosurgical, and radiotherapeutic planning, in order to increase treatment efficiency and prevent neurological damage.

Neural Correlates of the Severity of Cocaine, Heroin, Alcohol, MDMA and Cannabis Use in Polysubstance Abusers: A Resting-PET Brain Metabolism Study

INTRODUCTION Functional imaging studies of addiction following protracted abstinence have not been systematically conducted to look at the associations between severity of use of different drugs and brain dysfunction. Findings from such studies may be relevant to implement specific interventions for treatment. The aim of this study was to examine the association between resting-state regional brain metabolism (measured with 18F-fluorodeoxyglucose Positron Emission Tomography (FDG-PET) and the severity of use of cocaine, heroin, alcohol, MDMA and cannabis in a sample of polysubstance users with prolonged abstinence from all drugs used. METHODS Our sample consisted of 49 polysubstance users enrolled in residential treatment. We conducted correlation analyses between estimates of use of cocaine, heroin, alcohol, MDMA and cannabis and brain metabolism (BM) (using Statistical Parametric Mapping voxel-based (VB) whole-brain analyses). In all correlation analyses conducted for each of the drugs we controlled for the co-abuse of the other drugs used. RESULTS The analysis showed significant negative correlations between severity of heroin, alcohol, MDMA and cannabis use and BM in the dorsolateral prefrontal cortex (DLPFC) and temporal cortex. Alcohol use was further associated with lower metabolism in frontal premotor cortex and putamen, and stimulants use with parietal cortex. CONCLUSIONS Duration of use of different drugs negatively correlated with overlapping regions in the DLPFC, whereas severity of cocaine, heroin and alcohol use selectively impact parietal, temporal, and frontal-premotor/basal ganglia regions respectively. The knowledge of these associations could be useful in the clinical practice since different brain alterations have been associated with different patterns of execution that may affect the rehabilitation of these patients.

Multispectral brain morphometry in Tourette syndrome persisting into adulthood.

Tourette syndrome is a childhood-onset neuropsychiatric disorder with a high prevalence of attention deficit hyperactivity and obsessive-compulsive disorder co-morbidities. Structural changes have been found in frontal cortex and striatum in children and adolescents. A limited number of morphometric studies in Tourette syndrome persisting into adulthood suggest ongoing structural alterations affecting frontostriatal circuits. Using cortical thickness estimation and voxel-based analysis of T1- and diffusion-weighted structural magnetic resonance images, we examined 40 adults with Tourette syndrome in comparison with 40 age- and gender-matched healthy controls. Patients with Tourette syndrome showed relative grey matter volume reduction in orbitofrontal, anterior cingulate and ventrolateral prefrontal cortices bilaterally. Cortical thinning extended into the limbic mesial temporal lobe. The grey matter changes were modulated additionally by the presence of co-morbidities and symptom severity. Prefrontal cortical thickness reduction correlated negatively with tic severity, while volume increase in primary somatosensory cortex depended on the intensity of premonitory sensations. Orbitofrontal cortex volume changes were further associated with abnormal water diffusivity within grey matter. White matter analysis revealed changes in fibre coherence in patients with Tourette syndrome within anterior parts of the corpus callosum. The severity of motor tics and premonitory urges had an impact on the integrity of tracts corresponding to cortico-cortical and cortico-subcortical connections. Our results provide empirical support for a patho-aetiological model of Tourette syndrome based on developmental abnormalities, with perturbation of compensatory systems marking persistence of symptoms into adulthood. We interpret the symptom severity related grey matter volume increase in distinct functional brain areas as evidence of ongoing structural plasticity. The convergence of evidence from volume and water diffusivity imaging strengthens the validity of our findings and attests to the value of a novel multimodal combination of volume and cortical thickness estimations that provides unique and complementary information by exploiting their differential sensitivity to structural change.