MScMS-II: an innovative IR-based indoor coordinate measuring system for large-scale metrology applications

According to the current great interest concerning large-scale metrology applications in many different fields of manufacturing industry, technologies and techniques for dimensional measurement have recently shown a substantial improvement. Ease-of-use, logistic and economic issues, as well as metrological performance are assuming a more and more important role among system requirements. This paper describes the architecture and the working principles of a novel infrared (IR) optical-based system, designed to perform low-cost and easy indoor coordinate measurements of large-size objects. The system consists of a distributed network-based layout, whose modularity allows fitting differently sized and shaped working volumes by adequately increasing the number of sensing units. Differently from existing spatially distributed metrological instruments, the remote sensor devices are intended to provide embedded data elaboration capabilities, in order to share the overall computational load. The overall system functionalities, including distributed layout configuration, network self-calibration, 3D point localization, and measurement data elaboration, are discussed. A preliminary metrological characterization of system performance, based on experimental testing, is also presente

Vision Based Calibration and Localization Technique for Video Sensor Networks

The recent evolutions in embedded systems have now made the video sensor networks a reality. A video sensor network consists of a large number of low cost camera-sensors that are deployed in random manner. It pervades both the civilian and military fields with huge number of applications in various areas like health-care, environmental monitoring, surveillance and tracking. As most of the applications demand the knowledge of the sensor-locations and the network topology before proceeding with their tasks, especially those based on detecting events and reporting, the problem of localization and calibration assumes a significance far greater than most others in video sensor network. The literature is replete with many localization and calibration algorithms that basically rely on some a-priori chosen nodes, called seeds, with known coordinates to help determine the network topology. Some of these algorithms require additional hardware, like arrays of antenna, while others require having to regularly reacquire synchronization among the seeds so as to calculate the time difference of the received signals. Very few of these localization algorithms use vision based technique. In this work, a vision based technique is proposed for localizing and configuring the camera nodes in video wireless sensor networks. The camera network is assumed randomly deployed. One a-priori selected node chooses to act as the core of the network and starts to locate some other two reference nodes. These three nodes, in turn, participate in locating the entire network using tri-lateration method with some appropriate vision characteristics. In this work, the vision characteristics that are used the relationship between the height of the image in the image plane and the real distance between the sensor node and the camera. Many experiments have been simulated to demonstrate the feasibility of the proposed technique. Apart from this work, experiments are also carried out to locate any other new object in the video sensor network. The experimental results showcase the accuracy of building up one-plane network topology in relative coordinate system and also the robustness of the technique against the accumulated error in configuring the whole network

The H.E.S.S. central data acquisition system

The High Energy Stereoscopic System (H.E.S.S.) is a system of Imaging Atmospheric Cherenkov Telescopes (IACTs) located in the Khomas Highland in Namibia. It measures cosmic gamma rays of very high energies (VHE; >100 GeV) using the Earth's atmosphere as a calorimeter. The H.E.S.S. Array entered Phase II in September 2012 with the inauguration of a fifth telescope that is larger and more complex than the other four. This paper will give an overview of the current H.E.S.S. central data acquisition (DAQ) system with particular emphasis on the upgrades made to integrate the fifth telescope into the array. At first, the various requirements for the central DAQ are discussed then the general design principles employed to fulfil these requirements are described. Finally, the performance, stability and reliability of the H.E.S.S. central DAQ are presented. One of the major accomplishments is that less than 0.8% of observation time has been lost due to central DAQ problems since 2009.Comment: 17 pages, 8 figures, published in Astroparticle Physic

The future of camera networks: staying smart in a chaotic world

Camera networks become smart when they can interpret video data on board, in order to carry out tasks as a collective, such as target tracking and (re-)identi cation of objects of interest. Unlike today’s deployments, which are mainly restricted to lab settings and highly controlled high-value applications, future smart camera networks will be messy and unpredictable. They will operate on a vast scale, drawing on mobile resources connected in networks structured in complex and changing ways. They will comprise heterogeneous and decentralised aggregations of visual sensors, which will come together in temporary alliances, in unforeseen and rapidly unfolding scenarios. The potential to include and harness citizen-contributed mobile streaming, body-worn video, and robot- mounted cameras, alongside more traditional xed or PTZ cameras, and supported by other non-visual sensors, leads to a number of di cult and important challenges. In this position paper, we discuss a variety of potential uses for such complex smart camera networks, and some of the challenges that arise when staying smart in the presence of such complexity. We present a general discussion on the challenges of heterogeneity, coordination, self-recon gurability, mobility, and collaboration in camera networks

Towards Adaptive, Self-Configuring Networked Unmanned Aerial Vehicles

Networked drones have the potential to transform various applications domains; yet their adoption particularly in indoor and forest environments has been stymied by the lack of accurate maps and autonomous navigation abilities in the absence of GPS, the lack of highly reliable, energy-efficient wireless communications, and the challenges of visually inferring and understanding an environment with resource-limited individual drones. We advocate a novel vision for the research community in the development of distributed, localized algorithms that enable the networked drones to dynamically coordinate to perform adaptive beam forming to achieve high capacity directional aerial communications, and collaborative machine learning to simultaneously localize, map and visually infer the challenging environment, even when individual drones are resource-limited in terms of computation and communication due to payload restrictions