At the heart of every scientific discovery is a set of measurements enabled by precise and verifiable instrumentation. Through lectures, test case scenario, writing assignments, and design projects, this course draws on the history of scientific instrumentation and techniques for measurement using in-situ and remote sensing technologies to foster and inspire future instrumentation engineers to develop new and better space-based instruments facilitating future scientific discoveries. Topics include the physical basis for remote sensing, precise analog measurements, digital processing, spatial data analysis, verification of measurements, instrument life cycle, and science traceability matrices. The course is not intended to provide students with extensive training in a particular type of scientific instrument (e.g. particle, optical, magnetic). Working knowledge of computing package (e.g. coding in Python or Matlab) and completion of a design project are required. Although there are no specific prerequisites for this course, it will be helpful if students have completed courses 675.600 and 675.601 in this program prior to taking this class.
The course materials are divided into modules which can be accessed by clicking Modules on the course menu. A module will have several sections including the overview, content, readings, discussions, and assignments. You are encouraged to preview all sections of the module before starting. Most modules run for a period of seven (7) days, exceptions are noted in the Course Outline. You should regularly check the Calendar and Announcements for assignment due dates.
After working for nearly two decades on space scientific instrumentation, the content covered in this course is what i believe every successful engineer/applied physics should know should they choose the instrumentation career path. This course covers the cradle to grave lifecycle of a scientific instrument.
Morris, Alan, and Langari, Reza (2021). Measurement and Instrumentation: Theory and Application (3rd ed). Elsevier Inc. Academic Press.
Microsoft Office Suite
MATLAB (or Python)
There are 7 components to your final course grade:
EP uses a +/- grading system (see “Grading System”, Graduate Programs catalog, p. 10).
| Score Range | Letter Grade |
|---|---|
| 100-97 | = A+ |
| 96-93 | = A |
| 92-90 | = A− |
| 89-87 | = B+ |
| 86-83 | = B |
| 82-80 | = B− |
| 79-77 | = C+ |
| 76-73 | = C |
| 72-70 | = C− |
| 69-67 | = D+ |
| 66-63 | = D |
| <63 | = F |
This is an online asynchronous course; students are expected to self-pace themselves through the course content. The current module and a few following modules will always be available for those students who needs the flexibility to get ahead (for pre-planned trips or conflicts). Please make arrangement early for any special accommodation prior to the due date of the module or project. Late individual assignment may be accepted (with a 10% deduction). Late group assignment will not be accepted.
Artificial Intelligence (AI) has gained the ability to write fluently in a very short amount of time, and Large Language Models (LLMs) can now produce acceptable written products in almost every domain, including at this course level. Students should learn how to use this powerful new tool, and most can expect to use AI throughout their professional careers. However, over-reliance on AI can hinder learners’ ability to form and evaluate arguments, learn new ideas, articulate their thoughts, develop professional vocabulary, conduct effective research, and create novel syntheses of ideas.
I encourage the use of AI for information gathering and topic research. However, I strongly recommend that your assignments and projects be based on your own cognitive work so that you gain the greatest value from this course. Whenever you submit content that was generated by AI or by a website that generates content, you must clearly mark or cite it, such as AI-generated mechanical concept drawings for your project.
This course includes two projects, with content submitted across multiple modules. Although AI may be able to generate content for an individual component of a project, it often struggles to develop an entire cohesive instrument concept. Students will be graded on individual submissions; however, cohesiveness will also be evaluated in the final submission. Please note that when students submit projects in which most of the content is AI-generated, it is usually obvious and detectable.
Deadlines for Adding, Dropping, and Withdrawing from Courses
Students may add a course up to one week after the start of the term for that particular course. Students may drop courses according to the drop deadlines outlined in the EP academic calendar. Between the 6th week of the class and prior to the final withdrawal deadline, a student may withdraw from a course with a W on their academic record. A record of the course will remain on the academic record with a W appearing in the grade column to indicate that the student registered and withdrew from the course.
Academic Misconduct Policy
Students with Disabilities - Accommodations and Accessibility
Student Conduct Code
Classroom Climate
JHU is committed to creating a classroom environment that values the diversity of experiences and perspectives that all students bring. Everyone has the right to be treated with dignity and respect. Fostering an inclusive climate is important. Research and experience show that students who interact with peers who are different from themselves learn new things and experience tangible educational outcomes. At no time in this learning process should someone be singled out or treated unequally on the basis of any seen or unseen part of their identity. If you have concerns in this course about harassment, discrimination, or any unequal treatment, or if you seek accommodations or resources, please reach out to the course instructor directly. Reporting will never impact your course grade. You may also share concerns with your program chair, the Assistant Dean for Diversity and Inclusion, or the Office of Institutional Equity. In handling reports, people will protect your privacy as much as possible, but faculty and staff are required to officially report information for some cases (e.g. sexual harassment).
Course Auditing
When a student enrolls in an EP course with “audit” status, the student must reach an understanding with the instructor as to what is required to earn the “audit.” If the student does not meet those expectations, the instructor must notify the EP Registration Team (EP-Registration@exchange.johnshopkins.edu) in order for the student to be retroactively dropped or withdrawn from the course (depending on when the "audit" was requested and in accordance with EP registration deadlines). All lecture content will remain accessible to auditing students, but access to all other course material is left to the discretion of the instructor.