数据采集系统触发电路、随机采样短时间产生电路、存储器地址计数器电路等相关控制电路的设计、仿真和调试；
The circuits revelent to data acquisition system such as trigger circuit, the circuit of short time generator in random sampling etc, are designed, simulated and adjusted.
系统以ARM微处理器和FIFO存储器为核心，利用可编程逻辑器件实现对整个底层数据采集系统的逻辑控制，并给出了时序控制部分的仿真波形。
The system controls the logic of the data acquisition board by programmable logic device (PLD) with the center of the ARM microcontroller and FIFO memory and provides the simulate waveforms.
汽车加速工况仿真实验系统由发动机-测功机实验台架、模拟驾驶实验台和计算机数据采集及控制系统组成。
The vehicle acceleration simulation system consists of three parts: the engine dynamo test bench, the driving simulation test bench and the computer data acquisition and control system.
仿真与测试结果表明，该方法在数据采集系统通道间延时的评价中，具有很强的准确性和实用性。
Simulation and testing results prove that this arithmetic has a high veracity and practicability in estimating delays between channels in data acquisition system.
分析和仿真结果表明，通过在设计中运用信号完整性分析方法，本文设计的超高速数据采集系统能满足工作时序的要求。
The timing analysis and si simulation results show that the UHDA system can satisfy the system timing requirements with the help of the si analysis technique discussed in this thesis.
利用INV306型智能信号采集和处理分析系统对振动机构进行模态验证试验分析，以确定仿真模型和分析数据的正确性。
The modal experiment analysis on vibration mechanism will be completed by INV306 intellectualized signal collection and process analysis system for validating the finite element analysis model.
通过仿真实验，采集蠕滑速度和粘着系数的数据，利用RV -TS模型对粘着特性曲线进行模糊建模。
According to the simulation data of slip velocity and adhesion coefficient, a fuzzy model, RV-TS model, is built to describe the adhesion characteristic curve.
虚拟仪器技术具有强大的过程仿真、数据采集、显示和分析等功能，在工业生产、科研和教学中具有广泛的应用。
Virtual instrument technique has powerful functions of process simulation, data collection, display and analysis. It has been used widely in industrial product, research and teaching.
对采集数据进行仿真表明：与传统图像采集方法相比较，该系统的图像采集速度和图像采集质量都得到了极大的提高。
Through the simulation experiments from the acquired data, the simulation results demonstrate that this system provides image acquisition of higher speed and quality than traditional system.
通过现场采集数据对该模型进行仿真，其实验结果表明，该模型具有较好的学习能力和泛化能力，为烧结终点的预测提供了一种新的解决方法。
The simulation results show that the network has excellent learning capacities and generalization ability. Therefore the model is a new effective approach for BTP prediction.
通过现场采集数据对该模型进行仿真，其实验结果表明，该模型具有较好的学习能力和泛化能力，为烧结终点的预测提供了一种新的解决方法。
The simulation results show that the network has excellent learning capacities and generalization ability. Therefore the model is a new effective approach for BTP prediction.
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