The human eye as human-machine interface

Eye tracking as an interface to operate a computer is under research for a while and new systems are still being developed nowadays that provide some encouragement to those bound to illnesses that incapacitates them to use any other form of interaction with a computer. Although using computer vision processing and a camera, these systems are usually based on head mount technology being considered a contact type system. This paper describes the implementation of a human-computer interface based on a fully non-contact eye tracking vision system in order to allow people with tetraplegia to interface with a computer. As an assistive technology, a graphical user interface with special features was developed including a virtual keyboard to allow user communication, fast access to pre-stored phrases and multimedia and even internet browsing. This system was developed with the focus on low cost, user friendly functionality and user independency and autonomy.

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.

Interaction paradigms versus age-related user profiles: an evaluation on content selection

Novel input modalities such as touch, tangibles or gestures try to exploit human's innate skills rather than imposing new learning processes. However, despite the recent boom of different natural interaction paradigms, it hasn't been systematically evaluated how these interfaces influence a user's performance or whether each interface could be more or less appropriate when it comes to: 1) different age groups; and 2) different basic operations, as data selection, insertion or manipulation. This work presents the first step of an exploratory evaluation about whether or not the users' performance is indeed influenced by the different interfaces. The key point is to understand how different interaction paradigms affect specific target-audiences (children, adults and older adults) when dealing with a selection task. 60 participants took part in this study to assess how different interfaces may influence the interaction of specific groups of users with regard to their age. Four input modalities were used to perform a selection task and the methodology was based on usability testing (speed, accuracy and user preference). The study suggests a statistically significant difference between mean selection times for each group of users, and also raises new issues regarding the “old” mouse input versus the “new” input modalities.

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

Multifunctional converter to interface renewable energy sources and electric vehicles with the power grid in smart grids context

This paper proposes a multifunctional converter to interface renewable energy sources (e.g., solar photovoltaic panels) and electric vehicles (EVs) with the power grid in smart grids context. This multifunctional converter allows deliver energy from the solar photovoltaic panels to an EV or to the power grid, and exchange energy in bidirectional mode between the EV and the power grid. Using this multifunctional converter are not required multiple conversion stages, as occurs with the traditional solutions, where are necessary two power converters to integrate the solar photovoltaic system in the power grid and also two power converters to integrate an off-board EV battery charger in the power grid (dc-dc and dc-ac power converters in both cases). Taking into account that the energy provided (or delivered) from the power grid in each moment is function of the EV operation mode and also of the energy produced from the solar photovoltaic system, it is possible to define operation strategies and control algorithms in order to increase the energy efficiency of the global system and to improve the power quality of the electrical system. The proposed multifunctional converter allows the operation in four distinct cases: (a) Transfer of energy from the solar photovoltaic system to the power grid; (b) Transfer of energy from the solar photovoltaic system and from the EV to the power grid; (c) Transfer of energy from the solar photovoltaic system to the EV or to the power grid; (d) Transfer of energy between the EV and the power grid. Along the paper are described the system architecture and the control algorithms, and are also presented some computational simulation results for the four aforementioned cases. It is also presented a comparative analysis between the traditional and the proposed solution in terms of operation efficiency and estimated cost of implementation.

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.

Three-phase current-source shunt active power filter with solar photovoltaic grid interface

This paper presents the proposal of a three phase current source shunt active power filter (CS-SAPF) with photovoltaic grid interface. The proposed system combines the compensation of reactive power and harmonics with the injection of energy from a solar photovoltaic array into the electrical power grid. The proposed equipment presents the advantage of giving good use to the current source inverter, even when the solar photovoltaic array is not producing energy. The paper describes the control system of the CS SAPF, the energy injection control strategy, and the current harmonics and power factor compensation strategy. Simulation results to assess the performance of the proposed system are also presented.

Comportamento de painéis de alvenaria reforçados com FRCM sujeitos a ações no plano: Estudo da interface

O presente trabalho dedica-se à caracterização do comportamento de modelos de alvenaria reforçada com FRCM (fiber reinforced cementitious matrix) quando sujeitos a ações que atuam no plano da parede. O sistema de reforço, composto por uma camada de argamassa cimentícia reforçada com uma malha de CFRP (carbon fiber reinforced polymer), foi aplicado a modelos de alvenaria de tijolo furado. Considerando a importância da interface entre a camada de reforço e o substrato para a eficiência do sistema de reforço, procedeu-se à caracterização do comportamento mecânico da interface por intermédio de ensaios de corte direto, com os quais foi possível definir as superfícies de cedência e de rotura da interface. Os resultados obtidos permitiram a quantificação dos parâmetros que caracterizam a interface entre o material cerâmico e uma argamassa do tipo PFRM (polypropylene fibre reinforced mortar) quando sujeita a cargas de corte combinadas com cargas normais à interface. Através de expressões analíticas e do critério de rotura de Mohr-Coloumb foi analisada a adequação de um critério baseado numa aproximação linear, aos dados obtidos experimentalmente.

Study of the interaction between asphalt and plastic wastes in new polymer modified binders (PMB)

The aim of this study is evaluating the interaction between several base pen grade asphalt binders (35/50, 50/70, 70/100, 160/220) and two different plastic wastes (EVA and HDPE), for a set of new polymer modified binders produced with different amounts of both plastic wastes. After analysing the results obtained for the several polymer modified binders evaluated in this study, including a commercial modified binder, it can be concluded that the new PMBs produced with the base bitumen 70/100 and 5% of each plastic waste (HDPE or EVA) results in binders with very good performance, similar to that of the commercial modified binder.

Hand gesture recognition for human computer interaction : a comparative study of different image features

Hand gesture recognition for human computer interaction, being a natural way of human computer interaction, is an area of active research in computer vision and machine learning. This is an area with many different possible applications, giving users a simpler and more natural way to communicate with robots/systems interfaces, without the need for extra devices. So, the primary goal of gesture recognition research is to create systems, which can identify specific human gestures and use them to convey information or for device control. For that, vision-based hand gesture interfaces require fast and extremely robust hand detection, and gesture recognition in real time. In this study we try to identify hand features that, isolated, respond better in various situations in human-computer interaction. The extracted features are used to train a set of classifiers with the help of RapidMiner in order to find the best learner. A dataset with our own gesture vocabulary consisted of 10 gestures, recorded from 20 users was created for later processing. Experimental results show that the radial signature and the centroid distance are the features that when used separately obtain better results, with an accuracy of 91% and 90,1% respectively obtained with a Neural Network classifier. These to methods have also the advantage of being simple in terms of computational complexity, which make them good candidates for real-time hand gesture recognition.

Hand gesture recognition system based in computer vision and machine learning

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

Vision-based portuguese sign language recognition system

Vision-based hand gesture recognition is an area of active current research in computer vision and machine learning. Being a natural way of human interaction, it is an area where many researchers are working on, with the goal of making human computer interaction (HCI) easier and natural, without the need for any extra devices. So, the primary goal of gesture recognition research is to create systems, which can identify specific human gestures and use them, for example, to convey information. For that, vision-based hand gesture interfaces require fast and extremely robust hand detection, and gesture recognition in real time. Hand gestures are a powerful human communication modality with lots of potential applications and in this context we have sign language recognition, the communication method of deaf people. Sign lan- guages are not standard and universal and the grammars differ from country to coun- try. In this paper, a real-time system able to interpret the Portuguese Sign Language is presented and described. Experiments showed that the system was able to reliably recognize the vowels in real-time, with an accuracy of 99.4% with one dataset of fea- tures and an accuracy of 99.6% with a second dataset of features. Although the im- plemented solution was only trained to recognize the vowels, it is easily extended to recognize the rest of the alphabet, being a solid foundation for the development of any vision-based sign language recognition user interface system.

Para cá e para lá dos muros: relações familiares na interface entre o interior e o exterior da prisão

Tese de Doutoramento em Sociologia

Assessment of the properties to characterize the interface between clay brick substrate and strengthening mortar

The behaviour of masonry elements under in-plane and out-of-plane loads can be improved through the application of strengthening systems based on reinforcing overlays. After strengthening, the transition region between the original substrate and the strengthening layer is especially stressed, and premature failure of the strengthened masonry is reached if insufficient interfacial capacity is assured. Therefore, the assessment of the mechanical behaviour of the interface is critical to the development of the masonry strengthening system based on the application of strengthening overlays. In this research a method for the characterization of the interface behaviour between two different materials, a polypropylene fibre reinforced mortar (PFRM) and a ceramic brick used for masonry construction is presented. Direct shear tests were carried out in couplet specimens. Due to the orthotropic nature of the bricks surface, the shear load was applied along three different directions in order to perform an overall estimation of the interface behaviour. The peak and residual shear stresses, as well as the failure modes, were obtained at different levels of the normal stress. Based on these experimental results constitutive laws were assessed for the simulation of the interface mechanical behaviour based on the Mohr and Mohr-Coulomb failure criteria.