Faculty Syllabus

COURSE DESCRIPTION

A study of transducers and measurement techniques. Introduction to data conversion and computer data acquisition methods.

• Credit hours: 4
• Classroom contact hours per week: 2:20
• Laboratory contact hours per week: 2:40

COURSE RATIONALE

To introduce students to fundamental data acquisition principles, concepts and methods. In addition,
students will study and apply related software and hardware involved in acquiring data from sensors
for measurement purposes.

PREREQUISITES

CETT 1425 – Digital Fundamentals

Assignments and Examinations

The final course grade will be based on the following:

• 2 written exams worth 20% each
• 1 course project worth 20%
• 1 LabVIEW program exam worth 20%
• Lab assignments averaged worth 20% (Due dates and descriptions provided in Black Board)

1. Data Acquisition Overview
    a. Sensor Types Overview
    b. Application Areas and Trends
    c. LabVIEW Introduction 

2. Data Acquisition System Features
    a. System Components
    b. Signal Characteristics
    c. Signal Conditioning
    d. Signal Source and Measurement System Configuration

3. Analog to Digital Conversion elements
    a. Key analog to digital conversion parameters
    b. Measurement Error
    c. Triggers

4. LabVIEW – Sub VIs

5. Filters (signal conditioning)

6. Amplification (signal conditioning)

7. Analog to Digital Conversion characteristics part A
    a. Voltage resolution
    b. Quantization error
    c. Lab assignments

8. Analog to Digital Conversion characteristics part B
   a. Main characteristics
   b. Methods of representation
   c. Analog to Digital converter types

Student Learning Outcomes:

Upon completing this course, the student should:

a. Be able to identity a data acquisition system.
b. Be able to prescribe a sensor type to measure a specific environmental change.
c. Be able to determine what type of amplifier is needed for a specific sensor output.
d. Be familiar with different forms of signal conditioning.
e. Be familiar with different methods of Analog-to-Digital conversion.
f. Be able to identify the type of interface used to get a digital signal into a microprocessor.
g. Be familiar with at least one software package used to view data on a PC.
h. Be familiar with different forms of data transmission.

SCANS Competencies

In 1990, the U.S. Department of Labor established the Secretary’s Commission on
Achieving Necessary Skills (SCANS) to examine the demands of the workplace and
whether our nation’s students are capable of meeting those demands. The Commission
determined that today’s jobs generally require competencies in the following areas:

A. Resources: Identifies, organizes, plans and allocates resources
B. Interpersonal: Works with others
C. Information: Acquires and uses information
D. Systems: Understands complex interrelationships
E. Technology: Works with a variety of technologies

The Texas Higher Education Coordinating Board requires that all degree plans in
institutions of higher education incorporate these competencies and identify to the
student how these competencies are achieved in course objectives. This course
incorporates the SCANS competencies in the following ways:

A. Resources
B. Interpersonal
C. Information
D. Systems
E. Technology
F. Basic Skills
G. Thinking Skills
H. Personal Qualities

REQUIRED TEXTS/MATERIALS/SOFTWARE

DC/AC Parts kit (available at ACC Bookstore) – Quantity 1
USB Drive (at least 1 Gigabyte) – Quantity 1
Scientific Calculator – Quantity 1

LabView Student Edition, 1st. Edition 
by Robert H. Bishop
ISBN-13: 978-0134011332
ISBN-10: 0134011333  
 

Lecture and lab assignments will be the primary forms of instruction.

This grading criteria may be modified as needed during the semester.

The final course grade will be based on the following:

• 2 written exams worth 20% each
• 1 course project worth 20%
• Lab assignments averaged worth 40% (Due dates are listed in Black Board)

Exams will consist of multiple-choice, true-false, fill-in-the blank, problem-solving, and software tool
programming components. A calculator will be needed. There are no make up exams or extra credit
work for a missed exam.

Having 3 unexcused absences will result in 10 points being deducted from the final grade.

If you arrive late, inform your instructor that day so you are NOT accidentally counted as absent.

Lab assignments have a due date listed on Blackboard.

The final course letter grade will be as follows:

90 – 100:       A
80 – 89.99:    B
70 – 79.99:    C
60 – 69.99:    D
Below 60:      F

Please note that content outline and schedule changes may occur during the semester. Any changes will be
announced in class and posted as a Blackboard Announcement (or other resource faculty is using to communicate).

Topics/Lab Assignments covered by week. (See Black Board for Lab Assignment details.)

(Items in parenthesis indicate the name of the slide set)

1. Data Acquisition (DAQ) Overview
    a. Sensor Types Overview
    b. Application Areas and Trends
    c. LabVIEW Introduction – (LabVIEW Part 1)
    d. Lab assignments

2. DAQ System Features (DAQ Section 1)
    a. System Components
    b. Signal Characteristics
    c. Signal Conditioning
    d. Signal Source and Measurement System Configuration
    e. Lab assignments

3. Analog to Digital Conversion elements (DAQ Section 2)
    a. Key analog to digital conversion parameters
    b. Measurement Error
    c. Triggers
    d. Lab assignments

4. LabVIEW – Sub Vis (LabVIEW Sub Vis)
    a. Lab assignments

5. Exam 1 – (DAQ Section 1 and 2)
    a. Lab assignments

6. Filters (DAQ Section 3)
    a. Lab assignments

7. LabVIEW Test - (LabVIEW Part 1 and LabVIEW Sub Vis slides) + LabVIEW lab assignments
    a. Lab assignments

8. Amplification (DAQ Section 4)
    a. Lab assignments

9. Analog to Digital Conversion characteristics part A (DAQ Section 5A)
    a. Voltage resolution
    b. Quantization error
    c. Lab assignments

10. Analog to Digital Conversion characteristics part B (DAQ Section 5B)
      a. 3 main characteristics
      b. Methods of representation
      c. Analog to Digital converter types
      d. Lab assignments

11. Exam 2 (DAQ Section 3, 4, 5A, 5B)
      a. Lab assignments

12. Lab assignments

13. Lab assignments

14. Lab assignments

15. Lab assignments

16. Lab Project (Completed & submitted by the last class meeting)

• Engineering Technology & Advanced Manufacturing Students who are seeking a degree or certificate should visit
  with their assigned Area-Of-Study advisor. To find out who your assigned advisor is, go to
  Find My Advisor and follow the steps using MyACC.
   
• If you have not been assigned an advisor, then please visit the Advising Office on your campus or fill out
  the Contact ACC Advising form and someone will get back to you.

For online advising, please go to the following site:

https://students.austincc.edu/advising/online-advising/

High School students taking classes in dual enrollment or as part of an academy or institute will have different
advisors and counselors assigned to them. For help finding advising support for High School students,
contact the ACC Office of College & High School Relations.

• All students are expected to check their ACC gmail regularly throughout the semester.
  We will be sending pertinent information about scholarships, the course schedule, job & internship opportunities,
  Microsoft Azure free student software program, career fairs, special events, and etc. 
  Your instructor will communicate with you through Blackboard Announcement and Email.

Please use the link below to access this information.

Policies & Student Support Services

1. Introduction: The use of generative AI (GAI) is permitted in this course under certain conditions and with instructor approval for the purposes of enhancing learning while maintaining academic integrity.
    
2. Rationale: GAI is permitted to foster technological fluency and to leverage advanced tools for research, projects and other relevant assignments, as long as it does not substitute for the students’ original work, critical thinking and learning.
    
3. Definition of GAI: Generative AI encompasses technologies that create content through learned patterns and data without direct human input.
    
4. Usage Permissions: Permitted: GAI can be used for initial research, idea generation, and learning coding practices. It is not to be used for final submissions unless explicitly cited and discussed. Students should check with their instructors for approval before using AI in their assignments.
    
5. Resources: The ACC Library provides guidance on the ethical and effective use of GAI . Additional resources may be provided by your instructor.
    
6. Assessment: Contributions of GAI must be clearly cited and will be assessed on the student’s ability to critically analyze and integrate the AI-generated content.
    
7. Penalties: Misuse of GAI, including a failure to cite, will be considered a breach of academic integrity, with consequences including a failing grade for the assignment and academic review.
    
8. Exceptions: Should the technology be required as an accommodation, exceptions will be made on a case-by-case basis.