A novel reconfigurable hardware system which uses both muhi-DSP and FPGA to attain high performance and real-time imageprocessing are presented. The system structure and working principle of mainly processing multi-B...
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A novel reconfigurable hardware system which uses both muhi-DSP and FPGA to attain high performance and real-time imageprocessing are presented. The system structure and working principle of mainly processing multi-BSP board, extended multi-DSP board are analysed. The outstanding advantage is that the communication among different board components of this system is supported by high speed link ports ＆ serial ports for increasing the system performance and computational power. Then the implementation of embedded real-time operating systems （RTOS） by us is discussed in detail. In this system, we adopt two kinds of parallel structures controlled by RTOS for parallel processing of algorithms. The experimental results show that exploitive period of the system is short, and maintenance convenient. Thus it is suitable for real-time imageprocessing and can get satisfactory effect of imagerecognition.
The software systems which are related to national projects are always very crucial. This kind of systems always involves hi-tech factors and has to spend a large amount of money, so the quality and reliability of the...
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The software systems which are related to national projects are always very crucial. This kind of systems always involves hi-tech factors and has to spend a large amount of money, so the quality and reliability of the software deserve to be further studied. Hence, we propose to apply three classification techniques most used in data mining fields: Bayesian belief networks (BBN), nearest neighbor (NN) and decision tree (DT), to validate the usefulness of software metrics for risk prediction. Results show that comparing with metrics such as Lines of code (LOQ and Cyclomatic complexity (V(G)) which are traditionally used for risk prediction, Halstead program difficulty (D), Number of executable statements (EXEC) and Halstead program volume (V) are the more effective metrics as risk predictors. By analyzing we also found that BBN was more effective than the other two methods in risk prediction
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