Abstract: The present disclosure relates to a number of inventions directed, generally, to the application of image processing techniques to traffic data acquisition using video images. The inventions reside in a traffic monitoring system, the basic function of which is for traffic data acquisition and incident detection. More specifically, the application of image processing techniques for the detection of vehicle, from sequence of video images, as well as the acquisition of traffic data and detection of traffic incident. In one aspect, the present invention provides a method of processing images received from a video based traffic monitoring system. In another aspect, the present invention is directed to a Region Of Interest (ROI) for detection of a moving vehicle and a further aspect is directed to a method of detecting day or night status in a traffic monitoring system. It's The application of various algorithms to a video based traffic monitoring system is also considered inventive.

Abstract: An antenna suitable for use in a Radio Frequency Identification (RFID) tag is disclosed. The antenna includes a plurality of discrete loop antennas (130, 140, 150) disposed concentrically on a substrate. Each one of said plurality of loop antennas is electrically isolated from each other one of said plurality of loop antennas.

Abstract: A dual mode ISDN S/U interface converter and a network arrangement are provided. The dual mode ISDN S/U interface converter is capable of selectively operating in either line terminator (LT) or work terminator (NT) modes by an adjustment of jumpers or other electronic switches. The ISDN S/U interface converter also provides a selectable and reversible direction of sync flow over a common path, with a common sync circuit, common control, and common data bus. With the dual mode capability of the ISDN S/U converter, a system integrator can use a single type of equipment for insertion between an ISDN switch and a multiplexer.

Abstract: Design of a neural network for automatic detection of incidents on a freeway is described. A neural network is trained using a combination of both back-propagation and genetic algorithm-based methods for optimizing the design of the neural network. The back-propagation and genetic algorithm work together in a collaborative manner in the neural network design. The training starts with incremental learning based on the instantaneous error and the global total error is accumulated for batch updating at the end of the training data being presented to the neural network. The genetic algorithm directly evaluates the performance of multiple sets of neural networks in parallel and then use the analyzed results to breed new neural networks that tend to be better suited to the problems at hand.