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.

A new paradigm: cloud agile manufacturing

Cloud Agile Manufacturing is a new paradigm proposed in this article. The main objective of Cloud Agile Manufacturing is to offer industrial production systems as a service. Thus users can access any functionality available in the cloud of manufacturing (process design, production, management, business integration, factories virtualization, etc.) without knowledge — or at least without having to be experts — in managing the required resources. The proposal takes advantage of many of the benefits that can offer technologies and models like: Business Process Management (BPM), Cloud Computing, Service Oriented Architectures (SOA) and Ontologies. To develop the proposal has been taken as a starting point the Semantic Industrial Machinery as a Service (SIMaaS) proposed in previous work. This proposal facilitates the effective integration of industrial machinery in a computing environment, offering it as a network service. The work also includes an analysis of the benefits and disadvantages of the proposal.

Cloud agile manufacturing

This paper proposes a new manufacturing paradigm, we call Cloud Agile Manufacturing, and whose principal objective is to offer industrial production systems as a service. Thus users can access any functionality available in the cloud of manufacturing (process design, production, management, business integration, factories virtualization, etc.) without knowledge — or at least without having to be experts — in managing the required resources. The proposal takes advantage of many of the benefits that can offer technologies and models like: Business Process Management (BPM), Cloud Computing, Service Oriented Architectures (SOA) and Ontologies. To develop the proposal has been taken as a starting point the Semantic Industrial Machinery as a Service (SIMaaS) proposed in previous work. This proposal facilitates the effective integration of industrial machinery in a computing environment, offering it as a network service. The work also includes an analysis of the benefits and disadvantages of the proposal.

Flexible processing architecture for maintaining QoS in embedded systems applications

Comunicación presentada en las V Jornadas de Computación Empotrada, Valladolid, 17-19 Septiembre 2014

Hacia la seguridad criptográfica en sistemas DaaS

El nuevo paradigma de computación en la nube posibilita la prestación de servicios por terceros. Entre ellos, se encuentra el de las bases de datos como servicio (DaaS) que permite externalizar la gestión y alojamiento del sistema de gestión de base de datos. Si bien esto puede resultar muy beneficioso (reducción de costes, gestión simplificada, etc.), plantea algunas dificultades respecto a la funcionalidad, el rendimiento y, en especial, la seguridad de dichos servicios. En este trabajo se describen algunas de las propuestas de seguridad en sistemas DaaS existentes y se realiza un análisis de sus características principales, introduciendo un nuevo enfoque basado en tecnologías no exclusivamente relacionales (NoSQL) que presenta ventajas respecto a la escalabilidad y el rendimiento.

Guidance of robot arms using depth data from RGB-D camera

Image Based Visual Servoing (IBVS) is a robotic control scheme based on vision. This scheme uses only the visual information obtained from a camera to guide a robot from any robot pose to a desired one. However, IBVS requires the estimation of different parameters that cannot be obtained directly from the image. These parameters range from the intrinsic camera parameters (which can be obtained from a previous camera calibration), to the measured distance on the optical axis between the camera and visual features, it is the depth. This paper presents a comparative study of the performance of D-IBVS estimating the depth from three different ways using a low cost RGB-D sensor like Kinect. The visual servoing system has been developed over ROS (Robot Operating System), which is a meta-operating system for robots. The experiments prove that the computation of the depth value for each visual feature improves the system performance.

3DCOMET: 3D compression methods test dataset

The use of 3D data in mobile robotics applications provides valuable information about the robot’s environment. However usually the huge amount of 3D information is difficult to manage due to the fact that the robot storage system and computing capabilities are insufficient. Therefore, a data compression method is necessary to store and process this information while preserving as much information as possible. A few methods have been proposed to compress 3D information. Nevertheless, there does not exist a consistent public benchmark for comparing the results (compression level, distance reconstructed error, etc.) obtained with different methods. In this paper, we propose a dataset composed of a set of 3D point clouds with different structure and texture variability to evaluate the results obtained from 3D data compression methods. We also provide useful tools for comparing compression methods, using as a baseline the results obtained by existing relevant compression methods.

Geometric 3D point cloud compression

The use of 3D data in mobile robotics applications provides valuable information about the robot’s environment but usually the huge amount of 3D information is unmanageable by the robot storage and computing capabilities. A data compression is necessary to store and manage this information but preserving as much information as possible. In this paper, we propose a 3D lossy compression system based on plane extraction which represent the points of each scene plane as a Delaunay triangulation and a set of points/area information. The compression system can be customized to achieve different data compression or accuracy ratios. It also supports a color segmentation stage to preserve original scene color information and provides a realistic scene reconstruction. The design of the method provides a fast scene reconstruction useful for further visualization or processing tasks.