MathML has been successful in improving the accessibility of mathematical notation on the web. All major screen readers support MathML to generate speech, allow navigation of the math, and generate braille. A troubles...
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Global Navigation Satellite Systems like the US Global Positioning System GPS and the Russian GLONASS system are currently going through a number of modernization steps. The first satellites of the type GPS-IIR-M with...
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Global Navigation Satellite Systems like the US Global Positioning System GPS and the Russian GLONASS system are currently going through a number of modernization steps. The first satellites of the type GPS-IIR-M with L2C support were launched and from now on all new GPS satellites will transmit this new civil L2 signal. The first launch of a GPS-IIF satellite with L5 support is announced for spring 2008. Russia has started to launch GLONASS-M satellites with an extended life-time and a civil L2 signal and has announced to build up a full 18 satellite system by 2007 and a 24 satellite system by 2009. Independently of that the European Union together with the European Space Agency and other partnering countries are going to launch the new European satellite system Galileo, which will also provide worldwide satellite navigation service at some time after 2011. As a consequence we can expect to have very heterogeneous receiver hardware in these reference station networks for a transition period which could last until 2015. Network server software computing network corrections will have to deal with an increased number of signals, satellites and heterogeneity of the available data. The complexity but also the CPU load for this server software will increase dramatically. With the increasing number of signals and satellites the demands for the network server software is growing rapidly. The progress on the satellite system side is going hand in hand with the tendency of the customers to operate growing numbers of reference station receivers resulting in higher demands for CPU power. The paper presents a new approach, which allows us to process data from a large number of reference stations and multiple signals via a new federated Kalman filter approach. With the newest improvements in the GLONASS satellite system, more and more Network RTK service providers have started to use GLONASS capable receivers in their networks. Today, practically all service providers, who are u
GRAPE (Graph Processing Environment) is an industrial distributed computer vision system currently in use in Orbotech's Automated Optical Inspection (AOI) machines. These machines are designed for the automatic de...
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ISBN:
(纸本)0769523129
GRAPE (Graph Processing Environment) is an industrial distributed computer vision system currently in use in Orbotech's Automated Optical Inspection (AOI) machines. These machines are designed for the automatic detection of defects in Flat Panel Displays (FPD), Printed Circuit Boards (PCB) and Ball Grid Arrays (BGA). The GRAPE system is designed to be easy to use for algorithm and systems engineers with little or no special training in parallel or distributed systems. algorithms are written in standard C++ and joined together in a visual dataflow graph. The user then partitions the graph into "contexts" which are used by the system to automatically parallelize the computation. The underlying execution model of GRAPE is based on a large-grained dynamic data-flow paradigm. In contrast to traditional dataflow engines GRAPE algorithms can hold "state" over multiple executions while also making use of data parallelism. This is useful for computer vision applications, which typically need to assemble and process data collected over many execution cycles. In this paper we present an overview of the GRAPE system with its context oriented parallelism and synchronization.
A large variety of countries all over the world have established GPS reference station networks in the last years and are using network software today to provide a correction stream to the user as a routine service. T...
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A large variety of countries all over the world have established GPS reference station networks in the last years and are using network software today to provide a correction stream to the user as a routine service. These data streams are corrected for regional atmospheric effects, orbit errors and other systematic effects derived from the network solution. Currently most of these installations are using the VRS (Virtual Reference Station) technique to transport the correction stream in form of RTCM 2.3, RTCM 3.0 or CMR from the server to the field user. The VRS technique requires bidirectional communication, which is available via GSM, GPRS and other cell phone based data transmission methods. While the VRS method is the most common technique used, the RTCM committee is currently discussing a network proposal for broadcast transmission of network corrections, which will be useful for radio systems and Internet based multicast solutions. The focus of the paper is on the characterization of the influence of different interpolation schemes and the ability to derive tropospheric and ionospheric models. In the VRS case these models and interpolation schemes are applied in the server while they are applied in the rover in the RTCM network format case. Advantages and disadvantages of both methods are presented. Other aspects discussed are the required bandwidth for the different formats, the influence of the network message update rates, the influence of the scheduling on the start up of the RTK rover and the effect of different network shapes on the rover performance. The paper presents results of performance analyses performed to compare the VRS solution with the RTCM network solution in different networks. Accuracy, initialization performance and reliability of RTK systems working with the formats are compared to quantify the performance differences.
GRAPE (graph processing environment) is an industrial distributed computer vision system currently in use in Orbotech's automated optical inspection (AOI) machines. These machines are designed for the automatic de...
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GRAPE (graph processing environment) is an industrial distributed computer vision system currently in use in Orbotech's automated optical inspection (AOI) machines. These machines are designed for the automatic detection of defects in flat panel displays (FPD), printed circuit boards (PCB) and ball grid arrays (BGA). The GRAPE system is designed to be easy to use for algorithm and systems engineers with little or no special training in parallel or distributed systems. algorithms are written in standard C++ and joined together in a visual dataflow graph. The user then partitions the graph into "contexts" which are used by the system to automatically parallelize the computation. The underlying execution model of GRAPE is based on a large-grained dynamic data-flow paradigm. In contrast to traditional dataflow engines GRAPE algorithms can hold "state" over multiple executions while also making use of data parallelism. This is useful for computer vision applications, which typically need to assemble and process data collected over many execution cycles. In this paper we present an overview of the GRAPE system with its context oriented parallelism and synchronization.
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