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33 2007Using LabVIEW for applying mathematical models in representing phenomena

33 2007Using LabVIEW for applying mathematical models in representing phenomena
33 2007Using LabVIEW for applying mathematical models in representing phenomena

Computers & Education 49 (2007) 856–872

https://www.doczj.com/doc/4c2219952.html,/locate/compedu

Using LabVIEW for applying mathematical models in representing

phenomena

G. Faraco a,*, L. Gabriele b

a

Dipartimento di Matematica, Universita` della Calabria, Via P. Bucci, Cubo 30/B, 87036 Rende, Italy

b Dipartimento di Linguistica, Universita` della Calabria, Via P. Bucci, Cubo 17/B, 87036 Rende, Italy

Received 4 January 2005; accepted 22 November 2005

Abstract

Simulations make it possible to explore physical and biological phenomena, where conducting the real experiment is impracticable or difficult. The implementation of a software program describing and simulating a given physical situation encourages the understanding of a phenomenon it self. Fifty-nine students, enrolled at the Mathematical Methods for Engineers used LabVIEW to develop software programs able to simulate physical and biological phenomena. They reported on the simulated phenomena and on the adopted strategies for its planning and the development. The works produced by the students have been analyzed taking into account the understanding of the simulated phenomenon, and the used strategy in the planning and development of the software. We found out that the more the program works, the more students understand the phenomena they simulated. The analysis of the programs and of the methodology each student used in the software development phase showed the different strategies and the cognitive styles they used and the skills that the use of LabVIEW enables learners to acquire. 2005 Elsevier Ltd. All rights reserved.

Keywords: LabVIEW; Learning/teaching models; Simulation; Learning strategies; Cognitive style

* Corresponding author.

E-mail addresses: gefa@mat.unical.it (G. Faraco), lgabriele@unical.it (L. Gabriele).

G. Faraco, L. Gabriele / Computers & Education 49 (2007) 856–872857

0360-1315/$ - see front matter 2005 Elsevier Ltd. All rights reserved.

doi:10.1016/https://www.doczj.com/doc/4c2219952.html,pedu.2005.11.025

1. Introduction

People engaged in scientific education recognize the need to combine theoretical studies with the use and/or the creation of suitable instruments for exploring natural phenomena. Human activity has always made use of artefacts to fulfil its goal and has always been bound by these artefacts (Leont’ev, 1981; Vygotskij, 1972). This practice has a long history; it has been utilized, for example, by Galileo who built his own telescope and by Boyle and Hooke who designed the airpump for experimentation with low pressure. Tools, such as the simulation, can be used to transmit and acquire knowledge in scientific disciplines as shown in many research (Eylon, Ronen, & Ganiel, 1996; Goldberg, 1997; Grayson & Mc Dermott, 1996; Hewson, 1985; Steinberg, Oberem, & Mc Dermott, 1996; Steinberg, 2000). Learning by doing, The theory formulate firstly by Piaget and Inhelder (1966), and by many others later (Harel & Papert, 1991; Papert, 1980), underline that learners are particularly likely to create knowledge when they are actively engaged in making something, a ‘‘product’’, an ‘‘object’’, an ‘‘artefact’’, that is also personally meaningful and that they can share with others. This theory emphasize that students acquire much more useful knowledge by applying procedures to ‘‘real-world’’ problems, such as constructing pulley systems, rather than through memorizing abstract algorithms and equations.

In solving a problem (Kapa, 2001), or in performing a laboratory experimentation (real or/and virtual), or in designing and in implementing a software program, people build their own model of solution. Starting from some hypothesis, they realize a conceptual model of the solution. Subsequently, implementing in a computer program this solution, they can confirm or invalidate their own conceptual model. At the same time, they acquire knowledge on the phenomenon under investigation, because they explore phenomena, intervening on it, trying to intuit and then to interiorize what effect a certain action produces.

Using a graphical language rather than a text-based one, a new type of human computer interaction comes out. In fact, the use of a graphical language allows user to develop its program in a natural way; on the contrary the use of a text-based programming language forces the user to express its program in terms which are obliged by the language (Jamal, 1994); a graphical language is based on a simpler and more intuitive mode of interaction, simplifying the programming process.

In our opinion, the use of a graphical programming language to build a simulation program promotes some specific curricular and cognitive skills:

1.the ability to formulate and to build up a specific conceptual model that describes the underlying

theory the phenomenon, (the problem is analyzed and decomposed in simple units) and that resolves the analyzed problem;

2.the ability of transforming own conceptual model into a logical solution, implementing it in a

computer program;

3.the ability to understand and to use LabVIEW objects (graphical symbols and icons that the

language translates into codes); to read, to re-read and to manipulate the data to input to the program; to know the modifiability of the data on the basis of specific ranges of values; to respect

a formal and procedural order;

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4.the ability of test its own computer program, to individuate some bugs, eventually to correct its

own conceptual model and its own computer program.

Fifty-nine undergraduate students of the Mathematical Methods for Engineers, were asked to realize a simulation program to represent a physical or biological phenomenon, using LabVIEW. The works realized by the students have been analyzed as ‘‘instances’’ of the cognition they used or as cognitive strategies they adopted to solve the problem (Bertacchini, Gabriele, Pantano, & Servidio, 2003). This analysis had the twofold aim of determining if the use of a graphical programming language supports the learning of natural phenomena, and of identifying the cognitive strategies that students use to solve the assignment, underlining at the end the skills the subjects acquired.

The results of this analysis show that all students have realized the simulation program, creating an interface and algorithms to make the system work. We found that the more the program works, the more students understand the phenomena they simulated, because it is necessary to take in consideration all aspects of the problem, such as inputs, desired outputs, special conditions; to define the problem in terms of the formal language in which the program would be implemented; to test and to debug, in order to verify if program requirements are validated.

The paper is organized as follows: in Section 2, we describe the research and in particular the objective, the participants, the assignment, the suggested software and the method we used to analyze the results. In Section 3, we present the results and in Section 4, the discussion. In Section 5, we present our remarks and conclusion. In Appendix A, we have showed some examples of SubVIs supplied to the students for developing their own simulation program.

2.The research

2.1.Objectives

The research is aimed to encourage the understanding of physical and/or biological phenomena through the planning and the development of software programs using LabVIEW. In addition, we assessed the strategies and the cognitive styles (Stemberg, Robert, & Zhang, 2001) used by the students by emphasizing the abilities they acquired. The research has been carried out within the Mathematical Methods for Engineering course, which is part of the program for a degree in Informatics Engineering. The goal of such a course is to provide to the students mathematical instruments and methods useful in applied contexts.

2.2.Participants

Fifty-nine students, enrolled at the Mathematical Methods for Engineers participated in this study. The necessary prerequisite to face our experimentation consisted just in some basic theoretical knowledge (differential equations and method of numeric integration) and ability in using software instruments. The necessary examinations for the registration to the course have widely checked all these prerequisites, distinctive for Engineering Students.

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2.3.Assignment

To each student, a mathematical model that describes a phenomenon was assigned. The task was to develop a software program to represent and to simulate the assigned phenomenon and to write a report about both the technical descriptions of the software program and the description of the simulated phenomenon. For the development of the simulation program we have suggested to the student a precise methodology that consists of the steps of Fig. 1. Nevertheless, the student has been invited to define its own strategy of software development in order to verify the used styles and the cognitive strategies. The students were supplied with exemplar LabVIEW procedures in an open modality: they could modify the source code adapting it to their needs. The necessity to supply such SubVIs is twofold: on one hand, to provide specific examples of realizations of the procedures, that is how to operate with icons and connectors and how to organize the Front Panels; on the other hand, to address the students towards the structured programming, which consists in subdividing the main program in subroutines, with specific functionality, opportunely recall in the main program. The structured programming offers some practical advantages, such as: increasing the readability of the program; avoiding repetition of the same lines of code; time-saving (Scholtz & Wiendenbeck, 1990), acquiring ‘‘detailed knowledge of problem domain, experience in the application area and creative insight’’ (Shneiderman, 1980). Exemplar SubVls are shown in Appendix A (Figs. A.1–A.10).

2.4.Suggested software

LabVIEW (Laboratory Virtual Instruments Engineering Workbench) is a Graphical Programming Language developed in 1986 by National Instruments. It has become a very powerful and flexible instrument for engineers and scientists in research throughout academia and industry. A LabVIEW program is called ‘‘Virtual Instruments’’ (VI) and it has three main parts:

the Front Panel is the interactive environment of a VI. It is used for operations and to specify the inputs (controls) and outputs (indicators) of the program. It can simulate the panel of a physical instrument; the Block Diagram which defines the actual data flow between the inputs and outputs. It is a source code of a VI that is constructed in G language. It is the actual executable program;

the icons/connectors that graphically represent the VI in the block diagrams. The connector terminals determine, where the inputs and outputs on the icon must be wired. They correspond to the controls and indicators on the Front Panel of the VI.

Fig. 1. Suggested steps.

Using LabVIEW the user can employ a variety of controls and indicators, such as buttons, charts, graphs, knobs, etc. to create an easily intelligible virtual operating environment.

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The execution of a LabVIEW program is governed by a principle called data flow. A node is executed only when data is available at all its input terminals. The node supplies all of its outputs terminals with data when it finishes and the data passes from source to destination terminals. The flow of data is controlled by structures, such as the Case structure, the While Loop, For Loop, and the Sequence Structure. The While Loop and the For Loop are structures that repeat the execution of a sub diagram. Particularly the While Loop executes as long as the value of its conditional terminal is true; the For Loop executes a given function for a specified number of times.

The Case structure is analogue to case If – Then – Else used in text-based programming languages. The Sequence Structure executes diagrams sequentially in the order in which they appear (Beyon, 2001).

2.5. Analysis results method

The work produced by the students have been analyzed as ‘‘cognitive artefacts’’ taking into account the following aspects:

the understanding of the simulated phenomena. An evaluation was made of the student’s description of the phenomena and of the student’s settlement of the range of values to supply to the program as initial data and parameters of control;

the used strategy in the planning and development of the software program. An evaluation was made on how students have implemented the software: the use of the proposed steps;

the use of the SubVIs proposed for data input, the SubVIs proposed for the resolution of the equations of the model and the SubVIs proposed for the representation of the solutions; on how the students have organized the interface of the simulation program: which tools and palettes were used to input data and which types of diagrams were used for the representation of the solutions; which labels, colours and graphs were utilized. We have evaluated also the level of interactivity offered by the program to the final user.

The analysis of used strategies allowed us to verify the style of learning adopted by the students and the ability that the use of LabVIEW allowed.

3. Results

The data for the analysis have been extracted both from the reports and the software program produced by the students. According to the methods of analysis, we have, firstly, analyzed their understanding of the simulated phenomenon. We found that all students have supplied a description of the theory underlying the phenomenon. Initial data and parameters of controls have been individuated in an accurate manner and inserted in the software program coherently with the typology of the phenomenon. Nevertheless, the students exhibited a non-uniform level of knowledge of the phenomenon:

1.Twenty per cent showed a deeper knowledge of the theory underlying the phenomenon; their

descriptions of theory was very clear and detailed;

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2.Sixty-nine per cent showed a good knowledge of the simulated phenomenon; their description of

theory was detailed;

3.Eleven per cent showed a reasonable mastery of the theoretical notions and they produced an

incomplete report.

Fig. 2 reports the students’ strategies when using the software.

The analysis of data demonstrated that the SubVIs suggested by teacher were used by 44% of the students. The remaining 56% of the students did not use the SubVIs. In particular (see Fig. 3):

1.Thirty-one per cent of the students missed steps (1) and (2) (see Fig. 1 by directly inserting the

differential equations of the model in the main program;

2.Twenty-five per cent of the students created alternative SubVIs.

The analysis of the ODE (LabVIEW SubVI for the numerical resolution of differential equations) that the students used for solving the differential equations of the mathematical models, demonstrated that:

Fig. 2. How the students use the supplied SubVIs.

Fig. 3. Software implementation.

Eighty-nine per cent of the students have utilized the Runge–Kutta numerical methods ( ODE Runge–Kutta 4th Order.vi);

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Eight per cent of the students have inserted in their program the possibility of choosing the ODE to use among Euler Metbods and Runge–Kutta Methods (ODE Euler Method.vi; ODE Runge–Kutta 4th Order.vi);

The remaining 3% did not use any ODE, but they implemented in the main program the necessary steps suitable for a numerical solution of the equations.

Analysis of how students have displayed data is shown in Fig. 4.

Ninety-two per cent of the students represented data only in the Phases Plane. They missed the step (6) of the methodological plane visualized in Fig. 1. The remaining 8% of the students represented the data both in the Phases Plane and in the Space Plane. Hence, the inserted both

Fig. 4. Graphical modality used by students to display the equation solution.

Fig. 5. Used tools and palettes.

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Fig. 6. Use of the buttons in the interface.

two- and three-dimensional graphs. The investigation of the interface of the simulation program has supplied the following data: the 78% of the simulation program presented a high readability. They were equipped by personal labels that indicated the functionality of each button present in the user’s interface. The Front Panels of these programs were equipped with figures that supplied information about the represented phenomena. In the remaining 22% of the examples both interfaces and Front Panels show the default labels of LabVIEW. The students have chosen both the tools and the palettes suitable to the functionality of the realized program.

Fig. 5 indicates how many and which Tools and Palettes had been used.

Finally, we analyzed the interface of the simulation program and particularly the level of interactivity that the program offered to the final user. The data show (see Fig. 6) that 32% of the students used buttons that allowed stopping the running of the simulation program; 2% of the students used buttons to reset the data showed on the display. The remaining 66% did not use specific buttons.

4. Discussion

As emphasized in the previous sections, an objective of our research is to encourage the understanding of physical and/or biological phenomena through the planning and the development of software programs. The methodologies suggested for planning the software program is the structured programming, and the environment of programming is LabVIEW. From the analysis of each software program, we assessed the strategies and the cognitive styles used by the students and pointed out the abilities that they acquired.

Notwithstanding both structured programming and LabVIEW have been used in other researches (Higa, Tawy, & Lord, 2002; Jamal, 1994; Jackson, 2000; Schwartz & Dunkin, 2000) these studies have not the same goal and the same typologies of participants of ours. For this reason we cannot directly compare our research with others.

As regard the methodologies, the data demonstrated that all students attending the research have used the principles of the structured programming, subdividing their main program in subroutines. Nevertheless, they have demonstrated to adopt a different cognitive strategy of programming and a different cognitive style. The 44% of the students who used the proposed methodology and the SubVIs

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supplied by the teacher, have demonstrated to prefer a convergent and resolver cognitive style. Starting from the available information, they arrived at a unique solution of the problem with the minimum waste of time and resources, taking advantage of all the information available in order to resolve the assignment.

The 31% of the students who omitted some of the suggested steps adopted a style of divergent type, starting from the available information they proceeded in a creative way, generating various answers and original solutions (insights) (Ko¨hler, 1923). The 25% of the students who created new SubVIs adopted a style of intuitive type, assuming various solutions that, in the program running phase, allowed them to corroborate or refuse the initial hypothesis.

The data demonstrated that all students were able to use the programming environment but that they exhibited a non-uniform level of knowledge and ability in the use of LabVIEW. The 25% of students who designed new SubVIs and the 78% of students who enriched their programs with labels had both demonstrated a thorough knowledge of the programming language.

The students who used buttons for running or stopping the simulation demonstrated their ability to develop an instrument adaptable to the representation of different mathematical models with the same characteristics: the number of differential equations, the number of control parameters, and the typology of phenomena. These students have discovered the mathematical abstraction and the fact that one model can represent different phenomena.

From the analysis of the Front Panels and the Block Diagrams (Figs. 7 and 8 show some realized examples) of the software program we inferred which abilities the students acquired. When the program works properly it denotes some abilities of the students, such as the ability of recognize and of use the graphical symbols and the icons that the programming language translates into source code; to distinguish between control palette and indicator palette of the programming language; to use the program’s functions. The tools to input numerical data have been thoroughly chosen and have been set at the right intervals of values. This denotes a good practice in reading and manipulating the data to be used as inputs, and their awareness of the modifiability of data.

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Fig. 7. Front Panel of the software program that allows implementing a link.

Fig. 8. Block diagram of the software program that allows to implement a Chaotic Model of Rossler.

Sixty-nine per cent of the created programs, showed clear and perfectly readable diagrams; this denotes the ability of the students to respect a formal and procedural order. As regards the formulation of alternative hypothesis, the analysis demonstrated that the 25% of the students had created new SubVIs, denoting specific abilities in the formulation, in the realization and the verification of the hypothesis. 5. Conclusion

In evaluating the works (software programs and reports) produced by the students we found that the design and the development of the software program have encouraged the understanding of physical and/or biological phenomena. The more the program works, the more students understand the phenomenon they simulated. In order to design a program, it is necessary to describe in a clear and detailed manner the underlying theory; to consider all aspects of the problem ( inputs, desired outputs, special conditions); to define the problem in terms of the formal language to use for the implementation; to test and to debug, validating the requirements of the program. Using the produced program students acquired knowledge by direct experience, they explore phenomena, intervening on it, trying to visualize and then to interiorize what effect a certain action produces. This confirms the importance of extending the use of computer simulation to the learning of natural phenomena. The validity of such instruments is already well established for learning in topics, such as Cinematics, Optics, Modern Physics and New Paradigms of Applied Mathematics (Eylon et al., 1996; Faraco, Pantano, & Servidio, 2004; Goldberg, 1997; Grayson & Mc Dermott, 1996; Hewson, 1985; Steinberg et al., 1996).

The results of our research indicate that LabVIEW is a useful tool to represent the phenomena described by mathematical models. It offers to the students the possibility to model, to manage and to represent in an easy way specific natural phenomena with various levels of complexity. It has been

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interesting to notice how to a progressive structuring of the task and to a greater ability of the students to modify or to create the SubVI, a greater structuring and enrichment of the Front Panel corresponds.

We conclude that the graphical nature of the used instruments, allows the students to construct a useful conceptual model to de-structure and to decompose the assigned problem and therefore to resolve it. LabVIEW can effectively support the user, for sometimes to replace the realization of the flow-chart, usually used during the activity of implementation of an algorithm. We can in addition infer that during the programming activity (analysis of the task, choice of the commands, correct disposition of the blocks of code), this graphical programming language furthers the formation of a conceptual model of the problem to implement.

Acknowledgments

The authors thank Pietro Pantano, full Professor of the course of Mathematical Methods for Engineers, for allowing them to carry out the research using the materials developed during the course. Appendix A

Some examples of SubVIs supplied to the students for developing own simulation programs

Fig. A.1. Differ.vi and Differ2.vi: SubVIs useful for data acquisition.

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Fig. A.2. Differ.vi and Differ2.vi: SubVI useful for data acquisition.

Fig. A.3. Differenziale2.vi: SubVI useful for data acquisition and using two-dimensional graph to represent the solution.

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Fig. A.4. Molla2.vi: An example of main program, useful for data acquisition, numerical resolution, representation of the solution with two-dimensional and three-dimensional graphs.

Fig. A.5. Combat.vi BAT: SubVIs useful for data acquisition and data indexing.

Fig. A.6. Combattimento.vi: SubVIs useful for control and monitoring processes.

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Fig. A.7. Comp.vi COM: SubVI useful for data acquisition and data indexing, using a FOR LOOP structure.

Fig. A.8. Competizione.vi: An example of a main program.

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Fig. A.9. Eq-logistica.vi and LOG.vi: An example of a main program and a SubVI.

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历、身体指数等的管理。 1.1编写目的: 本需求分析旨在对病房监护系统的阐释,使人们可以对病房监护系统更轻松的使用和更容易的维护。 1.2项目背景: 在现代社会,病人管理通常要投入大量的人力资源,用于查房,看护等方面,方便于医院随时获取病人病情,和处理病人应急情况。而本项目可以减少这些不必要的人力资源输出,降低医院在此方面的经济投入。 1.3定义: 本系统可以定义为一个主要为处理病人危急情况而设计的病房监护管理系统。 1.4预期读者: 本项目的预期读者为项目软件使用者与项目软件维护者。 1.5参考文献: 《软件工程》,浙江大学出版社,王慧芳、毕建权编著,齐志昌、陈越主审。 2任务概述 本项目以简单的硬件接口,实现对病房的电子管理。

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基于labview的温度采集系统

目录

1 绪论 现代计算机技术和信息技术的迅猛发展,冲击着国民经济的各个领域,也引起了测量仪器和测试技术的巨大变革。人们曾为测量仪器从模拟化、数字化到智能化的进步而欣喜,也为自动测试技术的日新月异的发展所鼓舞,当今虚拟仪器技术的出现又使得测量仪器进步入了高科技的殿堂。 与传统的仪器不同,虚拟仪器(virtual instrument)是基于计算机和标准总线技术的模块化系统,通常它是由控制模块、仪器模块和软件组成,在虚拟仪器中软件是至关重要的,仪器的功能都要通过它来实现,因此软件是虚拟仪器的核心,“软件就是仪器”,从本质上反映了虚拟仪器的特征。 从构成方式上讲,虚拟仪器可分为四大类:GPIB体系结构、PC-DAQ体系结构、VXI体系结构和PXI体系结构。 GPIB体系结构是通过GPIB总线将具有GPIB接口的计算机和仪器集成的测试系统。组建方便灵活、操作简单。 VXI体系结构综合了。pib和vem总线的优点,它集成的系统硬件集成度高、数据传输率快、便携性好,是当今倍受业界关注的体系结构。 PXI体系结构是以PCI总线为基础的体系结构,由于其总线吞吐率高、硬件的价格较低被业内人士认为是符合国情的一种体系结构。 虚拟仪器应用程序的开发环境主要有两种。一种是基于传统的文本语言的软件开发环境,常用的有lab windows/cvi、.visual basidc=vc++等:一种是基于图形化语言的软件开发环境,常用的有LabVIEW和hp vee。其中图形化软件开发系统是用工程人员所熟悉的术语和图形化符号代替常规的文本语言编程,界面友好,操作简便,可大大缩短系统开发周期,深受专业人员的青睐。 1.1 课题背景 随着世界经济的发展,工业的迅速扩张,政府和企业家们花在设备上的投入越来越多,这笔巨大的开销,极大地限制了企业的资金,从而制约着企业的发展。而虚拟仪器技术凭借着其开发容易、开发成本低、开发周期短等明显的优点,渐渐地在工业测控领域崭露头角。 它的出现使企业家们看到了降低成本的希望。本设计将就虚拟仪器怎样用在工业测控中进行一番简单的探讨。 1.2 虚拟仪器简介

基于虚拟仪器的温度采集系统

电控学院 课程设计(论文) 课程名称:虚拟仪器 题目:基于虚拟仪器的温度采集控制 院(系):电气与控制工程学院 专业班级:测控技术与仪器专业 姓名:*** 学号:*********** 指导教师:

目录 1.绪论--------------------------------------------------------------------------------------------1 1.1设计意义--------------------------------------------------------------------------------1 1.2国内外的研究状况及发展趋势----------------------------------------------------1 1.3主要研究内容--------------------------------------------------------------------------2 2.系统整体设计----------------------------------------------------------------------------------2 2.1整体设计--------------------------------------------------------------------------------2 2.2设计任务--------------------------------------------------------------------------------2 2.3系统方案选择--------------------------------------------------------------------------2 3.下位机设计-------------------------------------------------------------------------------------5 3.1硬件设计-------------------------------------------------------------------------------5 3.2软件设计-------------------------------------------------------------------------------6 4.上位机设计-------------------------------------------------------------------------------------6 4.1前面板设计----------------------------------------------------------------------------6 4.2后面板设计-----------------------------------------------------------------------------7 5.系统调试----------------------------------------------------------------------------------------9 6.结论----------------------------------------------------------------------------------------------9 7.参考文献----------------------------------------------------------------------------------------9 8.附录--------------------------------------------------------------------------------------------10 程序清单-----------------------------------------------------------------------------------10

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