Fog paradigm for local energy management systems

Cloud Computing infrastructures have been extensively deployed to support energy computation within built environments. This has ranged from predicting potential energy demand for a building (or a group of buildings), undertaking heat profile/energy distribution simulations, to understanding the impact of climate and weather on building operation. Cloud computing usage in these scenarios have benefited from resource elasticity, where the number and types of resources can change based on the complexity of the simulation being considered. While there are numerous advantages of using a cloud based energy management system, there are also significant limitations. For instance, many such systems assume that the data has been pre-staged at a cloud platform prior to simulation, and do not take account of data transfer times from the building to the simulation platform. The need for supporting computation at edge resources, which can be hosted within the building itself or shared within a building complex, has become important over recent year. Additionally, network connectivity between the sensing infrastructure within a built environment and a data centre where analysis is to be carried out can be intermittent or may fail. There is therefore also a need to better understand how computation/analysis can be carried out closer to the data capture site to complement analysis that would be undertaken at the data centre. We describe how the Fog computing paradigm can be used to support some of these requirements, extending the capability of a data centre to support energy simulation within built environments

Computationally efficient implementation of video rectification in an FPGA for stereo vision applications

Abstract—In order to obtain depth perception in computer vision, it is needed to process pairs of stereo images. This process is computationally challenging to be carried out in real-time, because it requires the search for matches between objects in both images. Such process is significantly simplified if the images are rectified. Stereo image rectification involves a matrix transformation which when done in software will not produce real-time results although it is very demanding. Therefore, the video streaming and matrix transformation are not usually implemented in the same system. Our product is a stereo camera pair which produces a rectified real time image output with a resolution of 320x240 at a frame rate of 15FPS and delivers them via a 100-Ethernet interface. We use a Spartan 3E FPGA for real-time processing within which we implement an image rectification algorithm

Applications of computational intelligence in industrial and environmental scenarios

Computational Intelligence (CI) techniques are receiving increasing attention by the industrial and academic communities involved in the design of automatic systems for industrial and environmental monitoring and control applications. CI techniques are able to aggregate inputs from several heterogeneous sensors, adapt themselves to wide ranges of operational and environmental conditions, and cope with incomplete or noise-affected data. With current computing architectures evolving towards smaller size, higher computational power, and more affordable cost, a great number of devices can embed CI techniques to support different kinds of applications. In this paper, we present a survey of the recent CI methods designed for the main processing steps of industrial and environmental monitoring systems