Material demands for storage technologies in a hydrogen economy

A hydrogen economy is needed, in order to resolve current environmental and energy-related problems. For the introduction of hydrogen as an important energy vector, sophisticated materials are required. This paper provides a brief overview of the subject, with a focus on hydrogen storage technologies for mobile applications. The unique properties of hydrogen are addressed, from which its advantages and challenges can be derived. Different hydrogen storage technologies are described and evaluated, including compression, liquefaction, and metal hydrides, as well as porous materials. This latter class of materials is outlined in more detail, explaining the physisorption interaction which leads to the adsorption of hydrogen molecules and discussing the material characteristics which are required for hydrogen storage application. Finally, a short survey of different porous materials is given which are currently investigated for hydrogen storage, including zeolites, metal organic frameworks (MOFs), covalent organic frameworks (COFs), porous polymers, aerogels, boron nitride materials, and activated carbon materials.

Modeling a mobile robot using a grammatical model

Virtual Worlds Generator is a grammatical model that is proposed to define virtual worlds. It integrates the diversity of sensors and interaction devices, multimodality and a virtual simulation system. Its grammar allows the definition and abstraction in symbols strings of the scenes of the virtual world, independently of the hardware that is used to represent the world or to interact with it. A case study is presented to explain how to use the proposed model to formalize a robot navigation system with multimodal perception and a hybrid control scheme of the robot.

Novel silica membrane material for molecular sieve applications

Development of new silica membranes properties, e.g., molecular sieving properties, has been increasingly gaining importance in the last few years. A novel unsupported silica membrane, referred to as hydrophobic metal-doped silica, was developed by cobalt-doping within the organic templated silica matrix. The novel material was prepared by the acid-catalyzed hydrolysis and condensation process of tetraethylorthosilicate (TEOS) and methyltriethoxysilane (MTES), which is the precursor for methyl ligand covalently bounded to the silica matrix. The synthesis and surface properties of the novel unsupported silica membrane as well as the unsupported blank silica and modified silica membranes were revealed by surface and microstructural techniques, such as water contact angle measurement, FTIR, X-ray, Solid-state 29Si MAS NMR, TGA and N2 and CO2 adsorption measurements. The results showed that the thermal stability of the organic templated silica matrix was enhanced by cobalt-doping process. A hydrophobic microporous silica membrane material with high thermal stability up to ∼560 °C in oxidizing atmosphere and a narrow pore size distribution centered at 1.1 nm was obtained. Therefore, a novel precursor material for molecular sieve silica membranes applications has been achieved and developed.

Flexible Framework for Real-Time Embedded Systems Based on Mobile Cloud Computing Paradigm

The development of applications as well as the services for mobile systems faces a varied range of devices with very heterogeneous capabilities whose response times are difficult to predict. The research described in this work aims to respond to this issue by developing a computational model that formalizes the problem and that defines adjusting computing methods. The described proposal combines imprecise computing strategies with cloud computing paradigms in order to provide flexible implementation frameworks for embedded or mobile devices. As a result, the imprecise computation scheduling method on the workload of the embedded system is the solution to move computing to the cloud according to the priority and response time of the tasks to be executed and hereby be able to meet productivity and quality of desired services. A technique to estimate network delays and to schedule more accurately tasks is illustrated in this paper. An application example in which this technique is experimented in running contexts with heterogeneous work loading for checking the validity of the proposed model is described.

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.

A Hardware-Software Approach for On-Line Soft Error Mitigation in Interrupt-Driven Applications

Integrity assurance of configuration data has a significant impact on microcontroller-based systems reliability. This is especially true when running applications driven by events which behavior is tightly coupled to this kind of data. This work proposes a new hybrid technique that combines hardware and software resources for detecting and recovering soft-errors in system configuration data. Our approach is based on the utilization of a common built-in microcontroller resource (timer) that works jointly with a software-based technique, which is responsible to periodically refresh the configuration data. The experiments demonstrate that non-destructive single event effects can be effectively mitigated with reduced overheads. Results show an important increase in fault coverage for SEUs and SETs, about one order of magnitude.