Filmes de óxido de zinco e nitreto de zinco depositados por magnetron sputtering com diferentes pressões de argônio, oxigênio e nitrogênio.

O óxido de zinco é um material semicondutor que apresenta alta transparência óptica no espectro visível, alta energia de ligação de éxcitons e piezoeletricidade. Por suas propriedades, ele é utilizado na área de sensores, eletrodos transparentes e dispositivos optoeletrônicos. No entanto, sua utilização ainda é limitada pela dificuldade de obtenção de condutividade tipo p, cujo principal dopante é o nitrogênio, devido à assimetria de dopagem ocasionada por defeitos intrínsecos do material, dopagem em valências diferentes das esperadas e formação de níveis de aceitadores profundos na banda proibida. A aplicação em dispositivos piezoelétricos também exige alta resistividade e ótimas propriedades cristalinas. Muitos processos de deposição estabelecidos hoje ainda utilizam altas temperaturas, o que impede sua deposição sobre superfícies ou substratos sensíveis a altas temperaturas. O objetivo deste trabalho é desenvolver técnicas de deposição de filmes de ZnO, principalmente em baixas temperaturas ( 100°C), pelo método de magnetron sputtering de rádio frequência, para avaliar a influência dos gases de processo nas características estruturais, estequiométricas, elétricas e ópticas dos filmes. Para isso, foram obtidos filmes utilizando pressão total de argônio, e pressões parciais de argônio e oxigênio e argônio e nitrogênio, utilizando alvo cerâmico de óxido de zinco ou alvo metálico de zinco. Para alvo de ZnO, filmes com condutividade tipo n foram obtidos em ambiente de argônio, em condições que geraram deficiências de oxigênio. Filmes altamente resistivos foram obtidos com a utilização de pressão parcial de oxigênio no gás de processo, em condições que resultaram em filmes estequiométricos, inclusive com condutividade tipo p. Condutividade tipo p mais alta foi observada, apenas por ponta quente, para uma amostra obtida em argônio logo após a utilização de nitrogênio na câmara de processo, que provavelmente sofreu influência da dopagem não intencional do cobre, que foi identificado como um contaminante do processo devido à estrutura da câmara. Para alvo de Zn, observou-se a formação de nitreto de zinco, que demonstrou alta capacidade de oxidação em ambiente atmosférico, e portanto, transforma-se naturalmente ao longo do tempo ou por processos de oxidação térmica em ZnO dopado com nitrogênio. Filmes de ZnO produzidos a partir de nitreto de zinco foram os únicos dos testados que apresentaram fotoluminescência característica do ZnO, mesmo para processos onde não houve aquecimento intencional.

Multifunctional cement composites strain and damage sensors applied on reinforced concrete (RC) structural elements

In this research, strain-sensing and damage-sensing functional properties of cement composites have been studied on a conventional reinforced concrete (RC) beam. Carbon nanofiber (CNFCC) and fiber (CFCC) cement composites were used as sensors on a 4 m long RC beam. Different casting conditions (in situ or attached), service location (under tension or compression) and electrical contacts (embedded or superficial) were compared. Both CNFCC and CFCC were suitable as strain sensors in reversible (elastic) sensing condition testing. CNFCC showed higher sensitivities (gage factor up to 191.8), while CFCC only reached gage factors values of 178.9 (tension) or 49.5 (compression). Furthermore, damage-sensing tests were run, increasing the applied load progressively up to the RC beam failure. In these conditions, CNFCC sensors were also strain sensitive, but no damage sensing mechanism was detected for the strain levels achieved during the tests. Hence, these cement composites could act as strain sensors, even for severe damaged structures near to their collapse.

Strain and damage sensing properties on multifunctional cement composites with CNF admixture

Both strain and damage sensing properties on carbon nanofiber cement composites (CNFCC) are reported in the present paper. Strain sensing tests were first made on the material’s elastic range. The applied loading levels have been previously calculated from mechanical strength tests. The effect of several variables on the strain-sensing function was studied, e.g. cement pastes curing age, current density, loading rate or maximum stress applied. All these parameters were discussed using the gage factor as reference. After this first set of elastic experiments, the same specimens were gradually loaded until material’s failure. At the same time both strain and resistivity were measured. The former was controlled using strain gages, and the latter using a multimeter on a four probe setup. The aim of these tests was to prove the sensitivity of these CNF composites to sense their own damage, i.e. check the possibility of fabricating structural damage sensors with CNFCC’s. All samples with different CNF dosages showed good strain-sensing capacities for curing periods of 28 days. Furthermore, a 2%CNF reinforced cement paste has been sensitive to its own structural damage.

Control framework for dexterous manipulation using dynamic visual servoing and tactile sensors’ feedback

Tactile sensors play an important role in robotics manipulation to perform dexterous and complex tasks. This paper presents a novel control framework to perform dexterous manipulation with multi-fingered robotic hands using feedback data from tactile and visual sensors. This control framework permits the definition of new visual controllers which allow the path tracking of the object motion taking into account both the dynamics model of the robot hand and the grasping force of the fingertips under a hybrid control scheme. In addition, the proposed general method employs optimal control to obtain the desired behaviour in the joint space of the fingers based on an indicated cost function which determines how the control effort is distributed over the joints of the robotic hand. Finally, authors show experimental verifications on a real robotic manipulation system for some of the controllers derived from the control framework.

A Survey on FPGA-Based Sensor Systems: Towards Intelligent and Reconfigurable Low-Power Sensors for Computer Vision, Control and Signal Processing

The current trend in the evolution of sensor systems seeks ways to provide more accuracy and resolution, while at the same time decreasing the size and power consumption. The use of Field Programmable Gate Arrays (FPGAs) provides specific reprogrammable hardware technology that can be properly exploited to obtain a reconfigurable sensor system. This adaptation capability enables the implementation of complex applications using the partial reconfigurability at a very low-power consumption. For highly demanding tasks FPGAs have been favored due to the high efficiency provided by their architectural flexibility (parallelism, on-chip memory, etc.), reconfigurability and superb performance in the development of algorithms. FPGAs have improved the performance of sensor systems and have triggered a clear increase in their use in new fields of application. A new generation of smarter, reconfigurable and lower power consumption sensors is being developed in Spain based on FPGAs. In this paper, a review of these developments is presented, describing as well the FPGA technologies employed by the different research groups and providing an overview of future research within this field.

Learning Probabilistic Features for Robotic Navigation Using Laser Sensors

SLAM is a popular task used by robots and autonomous vehicles to build a map of an unknown environment and, at the same time, to determine their location within the map. This paper describes a SLAM-based, probabilistic robotic system able to learn the essential features of different parts of its environment. Some previous SLAM implementations had computational complexities ranging from O(Nlog(N)) to O(N2), where N is the number of map features. Unlike these methods, our approach reduces the computational complexity to O(N) by using a model to fuse the information from the sensors after applying the Bayesian paradigm. Once the training process is completed, the robot identifies and locates those areas that potentially match the sections that have been previously learned. After the training, the robot navigates and extracts a three-dimensional map of the environment using a single laser sensor. Thus, it perceives different sections of its world. In addition, in order to make our system able to be used in a low-cost robot, low-complexity algorithms that can be easily implemented on embedded processors or microcontrollers are used.

A Computational Architecture Based on RFID Sensors for Traceability in Smart Cities

Information Technology and Communications (ICT) is presented as the main element in order to achieve more efficient and sustainable city resource management, while making sure that the needs of the citizens to improve their quality of life are satisfied. A key element will be the creation of new systems that allow the acquisition of context information, automatically and transparently, in order to provide it to decision support systems. In this paper, we present a novel distributed system for obtaining, representing and providing the flow and movement of people in densely populated geographical areas. In order to accomplish these tasks, we propose the design of a smart sensor network based on RFID communication technologies, reliability patterns and integration techniques. Contrary to other proposals, this system represents a comprehensive solution that permits the acquisition of user information in a transparent and reliable way in a non-controlled and heterogeneous environment. This knowledge will be useful in moving towards the design of smart cities in which decision support on transport strategies, business evaluation or initiatives in the tourism sector will be supported by real relevant information. As a final result, a case study will be presented which will allow the validation of the proposal.

Towards a real-time 3D object recognition pipeline on mobile GPGPU computing platforms using low-cost RGB-D sensors

In this project, we propose the implementation of a 3D object recognition system which will be optimized to operate under demanding time constraints. The system must be robust so that objects can be recognized properly in poor light conditions and cluttered scenes with significant levels of occlusion. An important requirement must be met: the system must exhibit a reasonable performance running on a low power consumption mobile GPU computing platform (NVIDIA Jetson TK1) so that it can be integrated in mobile robotics systems, ambient intelligence or ambient assisted living applications. The acquisition system is based on the use of color and depth (RGB-D) data streams provided by low-cost 3D sensors like Microsoft Kinect or PrimeSense Carmine. The range of algorithms and applications to be implemented and integrated will be quite broad, ranging from the acquisition, outlier removal or filtering of the input data and the segmentation or characterization of regions of interest in the scene to the very object recognition and pose estimation. Furthermore, in order to validate the proposed system, we will create a 3D object dataset. It will be composed by a set of 3D models, reconstructed from common household objects, as well as a handful of test scenes in which those objects appear. The scenes will be characterized by different levels of occlusion, diverse distances from the elements to the sensor and variations on the pose of the target objects. The creation of this dataset implies the additional development of 3D data acquisition and 3D object reconstruction applications. The resulting system has many possible applications, ranging from mobile robot navigation and semantic scene labeling to human-computer interaction (HCI) systems based on visual information.

Methodology based on registration techniques for representing subjects and their deformations acquired from general purpose 3D sensors

In this thesis a methodology for representing 3D subjects and their deformations in adverse situations is studied. The study is focused in providing methods based on registration techniques to improve the data in situations where the sensor is working in the limit of its sensitivity. In order to do this, it is proposed two methods to overcome the problems which can difficult the process in these conditions. First a rigid registration based on model registration is presented, where the model of 3D planar markers is used. This model is estimated using a proposed method which improves its quality by taking into account prior knowledge of the marker. To study the deformations, it is proposed a framework to combine multiple spaces in a non-rigid registration technique. This proposal improves the quality of the alignment with a more robust matching process that makes use of all available input data. Moreover, this framework allows the registration of multiple spaces simultaneously providing a more general technique. Concretely, it is instantiated using colour and location in the matching process for 3D location registration.