WWW++ - Adding why to what, when and where

RFID technology can be used to its fullest potential only with software to supplement the hardware with powerful capabilities for data capture, filtering, counting and storage. The EPCglobal Network architecture encourages minimizing the amount of business logic embedded in the tags, readers and middleware. This creates the need for a Business Logic Layer above the event filtering layer that enhances basic observation events with business context - i.e. in addition to the (what, when, where) information about an observation, it adds context information about why the object was there. The purpose of this project is to develop an implementation of the Business Logic Layer. This application accepts observation event data (e.g. from the Application Level Events (ALE) standard interface), enriches them with business context and provides these enriched events to a repository of business-level events (e.g. via the EPC Information Services (EPCIS) capture interface). The strength of the application lies in the automatic addition of business context. It is quick and easy to adapt any business process to the framework suggested and equally easy to reconfigure it if the business process is changed. A sample application has been developed for a business scenario in the retail sector.

Automated Detection of Concrete Columns from Visual Data

After earthquakes, licensed inspectors use the established codes to assess the impact of damage on structural elements. It always takes them days to weeks. However, emergency responders (e.g. firefighters) must act within hours of a disaster event to enter damaged structures to save lives, and therefore cannot wait till an official assessment completes. This is a risk that firefighters have to take. Although Search and Rescue Organizations offer training seminars to familiarize firefighters with structural damage assessment, its effectiveness is hard to guarantee when firefighters perform life rescue and damage assessment operations together. Also, the training is not available to every firefighter. The authors therefore proposed a novel framework that can provide firefighters with a quick but crude assessment of damaged buildings through evaluating the visible damage on their critical structural elements (i.e. concrete columns in the study). This paper presents the first step of the framework. It aims to automate the detection of concrete columns from visual data. To achieve this, the typical shape of columns (long vertical lines) is recognized using edge detection and the Hough transform. The bounding rectangle for each pair of long vertical lines is then formed. When the resulting rectangle resembles a column and the material contained in the region of two long vertical lines is recognized as concrete, the region is marked as a concrete column surface. Real video/image data are used to test the method. The preliminary results indicate that concrete columns can be detected when they are not distant and have at least one surface visible.

Sensor data scheduling for linear quadratic Gaussian control with full state feedback

Networked control systems (NCSs) have attracted much attention in the past decade due to their many advantages and growing number of applications. Different than classic control systems, resources in NCSs, such as network bandwidth and communication energy, are often limited, which degrade the closed-loop system performance and may even cause the system to become unstable. Seeking a desired trade-off between the closed-loop system performance and the limited resources is thus one heated area of research. In this paper, we analyze the trade-off between the sensor-to-controller communication rate and the closed-loop system performance indexed by the conventional LQG control cost. We present and compare several sensor data schedules, and demonstrate that two event-based sensor data schedules provide better trade-off than an optimal offline schedule. Simulation examples are provided to illustrate the theories developed in the paper. © 2012 AACC American Automatic Control Council).