代写ECET 35901 Computer Based Data Acquisition Applications Summer 2025代写C/C++编程

2025-05-19 代写ECET 35901 Computer Based Data Acquisition Applications Summer 2025代写C/C++编程

Summer 2025 Course Syllabus

ECET 35901 – 001 (CRN: 36294)

Computer Based Data Acquisition Applications

Meeting day(s) and time(s): Asynchronous Learning

Instructional Modality: Online  Learning

Course credit hours: 3 credits

Prerequisites (if any): Undergraduate level ECET 17700  Minimum Grade of D- and Undergraduate level ECET 17900  Minimum Grade of D-

Course Description

This course focuses on the study and application of computer based data acquisition (DAQ) systems. Concepts of high  resolution and high throughput data acquisition are introduced and applied. Students learn and  use programming and scripting languages to discover the interfacing, advantages and limitations of computer based (DAQ) systems.

Learning Resources, Technology & Texts

Informed Learning resources: Necessary contents will be provided

Software/web resources:

○    Thonny, Node-RED, Docker

Other Course Materials required:

○     Evaluation board for a MEMS microphone -  SPH0645LM4H

( https://www.digikey.com/en/products/detail/adafruit-industries-llc/3421/6691114)

○     Raspberry Pi 4 Model B

○    5V 3A USB-C Power Supply

○     Male-to-male micro-HDMI to HDMI cable .

○      Monitor with a HDMI port.

○     USB Type-A Keyboard & Mouse

○     >8 GB Micro-SD card & micro-SD to SD card adapter.

Windows PC w/ SD Card Slot

○     Breadboard

○    4 Single Pole Double Throw (SPDT) Switches

○     Breadboard Wires

○    Common Cathode Seven Segment Display (Common Anode version is also possible)

○     330 Ω Resistors

○     MCP3008 ADC

○    Two 10 kΩ Potentiometers

○    Two LEDs

Brightspace learning management system:

Access the course via Purdue’s Brightspace learning management system. Begin with the Start Here tab, which offers further insight to the course and how you can be successful in it. It is strongly suggested that you explore and become familiar  not  only with the site navigation, but also with content and resources available for this course . See the Student Services widget on the Brightspace homepage for resources such as Technology Help, Academic Help, Campus Resources, and Protect Purdue.

https://purdue.brightspace.com/d2l/home/1015185

Software/web resources: Internet, Google account

Hardware requirements: PC

Use of artificial intelligence (AI) or Large Language Models (LLM) in this course: The course does not cover AI or LLM-related topics, but the recording software (Camtasia) will be used to access students’ assessment by the instructor

Tutoring support:

○    The Academic Success Center, located in Wiley Hall, Room C215, provides a variety of proactive, practical and approachable academic support services for undergraduate students.

○    Visit Ask  a  Librarian to connect with helpful resources and services provided by the  Purdue Libraries and School of Information Studies for course assignments and projects.

○     Brightspace learning  management system (LMS)  Access the course via Purdue’s Brightspace learning management system. Begin with the Start Here tab, which offers further insight to the course and how you can be successful in it. It is strongly suggested that you explore and become familiar not only with the site navigation, but also with content and resources available for this course .  See the Student Services widget on the campus homepage for resources such as Technology Help, Academic Help, Campus Resources, and Protect Purdue.

Learning Outcomes

After completing this course, the successful student will be able to:

1.          Define, compare and contrast common data acquisition terms.

2.          Analyze the advantages and limitations  of DAQ systems.

3.          Demonstrate proficiency with software tools and computer based data acquisition.

4.          Develop user-friendly applications for the purpose of DAQ system control.

5.          Collect, analyze, and reduce data from a system for the purpose of written and oral reporting.

Assignments

Practical  Assignments: these assignments will be made throughout the semester. Due dates will be provided.

Q&A submissions: This assignment will be due at the end of the course  period, students will be asked to design questions and answers relevant to each practical assignments.

●    Grades will be assigned on the basis of the students overall understanding of the material.   This will include the two assignments.  The general breakdown of grading is as follows:

○    80%       Practical Assignments (PA): 8 PAs, each PA worth 10% .

○     10%      MCQ&A: Students designed Multiple Choice Questions and Answers relevant to each PA.

○     10%      Final project

Grading Scale

In this class, grades reflect the sum of your achievement of learning outcomes throughout the semester. You will accumulate points as described in the assignments portion above, with each assignment graded according to a rubric. At the end of the semester, final grades will be calculated  by adding the total points earned and translating those numbers (out of 100) into the following letters (there will be no partial points or rounding) .

●    A          90 and above

●    B            80-89

●    C            70-79

●    D          60-69

●    Fail       <59.9

± grades will be given within each letter grade range.

Please check the Add/Drop calendar for the finaldate to withdraw from a course with a W or WF for Summer 2024.