625.620.8VL - Mathematical Methods for Signal Processing
Description
This course familiarizes the student with modern techniques of digital signal processing and spectral estimation of discrete-time or discrete-space sequences derived by the sampling of continuous-time or continuous-space signals. The class covers the mathematical foundation needed to understand the various signal processing techniques as well as the techniques themselves. Topics include the discrete Fourier transform, the discrete Hilbert transform, the singular-value decomposition, the wavelet transform, classical spectral estimates (periodogram and correlogram), autoregressive and autoregressivemoving average spectral estimates, and Burg maximum entropy method. Prerequisite(s): Mathematics through multivariate calculus, matrix theory, or linear algebra, and introductory probability theory and/or statistics. Students are encouraged to refer any questions to the instructor.
Instructor
Fred Fontaine
Course Structure
The course materials are divided into modules which can be accessed by clicking Course Modules
on the course menu. A module will have several sections including lecture notes and assignments.
You are encouraged to preview all sections of the module before starting. There are six modules,
each typically with 3 or 4 parts. Every assignment is tied to a part, and generally is due one week
after I complete the part, though I will announce specific due dates as we progress through the
course.
During each class session, we will complete about 1 ½ parts. You should regularly check the
Calendar and Announcements for assignment due dates.
Course Topics
TBD
Course Goals
applications, based on a solid mathematical foundation and focused on a unified approach that
emphasizes generalization and conceptual thinking, and fosters the ability to handle “any” type of
signal that arises in modern applications.
Textbooks
TBD
Student Coursework Requirements
during the lectures; another 5 hours would be spent doing the assignment and reviewing the notes,
or performing outside reading if you find it necessary. If you find you are making no progress on an
assignment after about 30 minutes or so of effort, CONTACT ME. A few minutes of explanation can
save you a LOT of time.
Keep in mind the assignments cannot span all the topics discussed in the course. Topics may
appear on the quizzes that are not covered directly in the assignments.
Assignments (66% of Final Grade Calculation)
The assignments for each of the 6 modules will have equal weight, i.e., 11%. The assignments for
each part within a module will have equal weight.
Each assignment is tied to a “Part” within a “Module.” The assignment is due exactly 7 days after
the corresponding Part is completed in class lecture. I will post the specific dates as they come
along, but this will be the general rule I will follow. Note there may not be assignments attached to
the some of the parts of the last Module, as the end of the semester and the final Quiz will be upon
us. Specifically, the last assignment for the course will be due not later than 7 days before the final
Quiz, so you can spend the last week preparing for the Quiz.
Some assignments involve the use of a scientific computing software package (MATLAB or Python,
with any exceptions following consultation with me first). I want the executable code (e.g., .m files
or .ipynb) that I can run on my computer. If there are any special instructions to run the code on my
computer you must provide them. If the code does not work I will automatically reduce your grade.
I expect good coding style, and clear presentation of results. You may submit published (MATLAB)
or annotated notebooks (Python). As part of grading I will provide feedback on your coding style: I
will be more lenient in the beginning, but will expect improvement as the course progresses. If I
have running your code on my installation, I may ask you for a comprehensive output (graphs, etc.)
so I can assess your work, but in any case I must have the CODE as well as the results.
If you write some of the assignments by hand, please make it legible. I can accept most file formats
but in general prefer PDF (for the written part).
Late assignments will be reduced by TWO letter grades for each week late. You are better off
submitting incomplete work rather than late work. In other words, hand in whatever you have
missing parts later, but we should DISCUSS this first. Similarly, if you want to resubmit work after it
is graded to receive a higher grade, or question the grade assigned, please contact me.
You should know that I grade all your work myself and I try to provide commentary on your graded
homework. If you do not understand some of my comments, please CONTACT ME.
Certain problems are labeled OPTIONAL. They really are optional. If you do submit them, I would
count them as extra credit, but my main purpose for putting them there is to provoke some thought
and challenge you.
Each assignment will be graded on a scale of 1-10.
10= A: All questions are addressed. Answers are precise, neither too verbose nor too terse.
Comments and explanations are clear, insightful and to the point. If the assignment requires using
a scientific computing software package, good coding techniques are used, the code works, graphs
are clear and labeled well, and good comments and analysis of results are provided.
9= A-: The work reflects a solid understanding of the material, but there are some minor errors, or
room for improvement e.g., in coding style or more precise explanations.
8= B: There are some errors but overall good effort and results are mostly correct. The code works,
and good coding style is used but there is room for improvement. The work reflects understanding
of the material.
7= B-: The work shows that some concepts are not understood. Some explanations are wrong or
missing. Either the code does not work, or poor coding style is used. All parts are at least
attempted.
6= C: Some work is not completed. The work reflects partial understanding. There are some
substantial errors.
≤5= F: Work reflects minimal effort to complete the assignment or understand the material.
Significant portions of the work are missing.
Exams (34% of Final Grade Calculation)
There will be two exams of equal weight. You will have 48 hours to complete each exam. I will post
the first one on Wednesday July 5 to be due Tuesday July 11, and the second will be posted on
Wednesday August 9 to be due Tuesday August 15. The first will cover the first 3 modules, the
second the last 3.
I plan to give sample questions two weeks in advance of the exams. I am not requiring that you
complete them, but they will serve as a guide and I am happy to answer questions. I do not plan to
do formal review sessions during the lectures.
The exams will tend to be mostly short answer type questions, covering the key concepts in the
course. For example, I may ask for one or two sentence responses to certain prompts. The
assignments are there for longer, more in-depth work. I am less concerned about your memorizing
formulas than in understanding concepts. One model I like to use is: can you have a professional
discussion on the subject with someone you are working with, say on a project or research task?
That requires a certain level of fluency, and hence the purpose of the exams is to assess that type
of fluency in the subject.
As such you will not be permitted to communicate with any person other than myself regarding the
test questions. Violations of these rules will be dealt with seriously, according to JHU guidelines on
Academic Integrity, summarized later in this document.
The point values for each question will be clearly posted on the exams. I will normalize the grades
to a 100 scale to convert to what would ultimately lead to a letter grade for the course. For
example, the cutoff for an A grade may correspond to a raw score of 80, not 90. I will make the
mapping from the raw score to the normalized score available after the exam is completed. I do
NOT grade on a curve, meaning I do not pre-determine that a certain percentage of the class gets a
certain grade. The purpose of the mapping from the raw score to the final score is to allow me to
align the level of difficulty of the exam to my expectations for the course.
Participation
Ideally, you should be present for each lecture. I will take attendance. Video recordings of the
lectures will be available for you to review later. If you know in advance you are unable to attend a
lecture, please let me know in advance.
If you miss a lecture, you must review the video AND then send me an email where you tell me you
have seen the video in its entirety, and where you pose a substantive question related to the topics
discussed. I want to see some evidence that you have not only seen the video but also reflected on
its content. I will NOT excuse your absence unless I get this communication from you within 7 days
after the lecture took place. Any exceptions fall under the “Accommodations” portion discussed
later in this document.
The due time for each exam is strict. Exceptions would be granted only under extenuating
circumstances.
“Participation” as such is not a direct percentage of your grade. However, if you miss a class and do
not follow up as described above, I will first issue a warning. Continued absence would lead to a
reduced grade, including possible F: you will be given a clear warning from me in advance in either
case.
Grading Policy
Grading Scale
90- 100 =A
80-89 = B
70-79 = C
60- 69 = D
< 59 = F
Academic Policies
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 (https://ep.jhu.edu/student-services/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
All students are required to read, know, and comply with the Johns Hopkins University Krieger School of Arts and Sciences (KSAS) / Whiting School of Engineering (WSE) Procedures for Handling Allegations of Misconduct by Full-Time and Part-Time Graduate Students.
This policy prohibits academic misconduct, including but not limited to the following: cheating or facilitating cheating; plagiarism; reuse of assignments; unauthorized collaboration; alteration of graded assignments; and unfair competition. Course materials (old assignments, texts, or examinations, etc.) should not be shared unless authorized by the course instructor. Any questions related to this policy should be directed to EP’s academic integrity officer at ep-academic-integrity@jhu.edu.
Students with Disabilities - Accommodations and Accessibility
Johns Hopkins University values diversity and inclusion. We are committed to providing welcoming, equitable, and accessible educational experiences for all students. Students with disabilities (including those with psychological conditions, medical conditions and temporary disabilities) can request accommodations for this course by providing an Accommodation Letter issued by Student Disability Services (SDS). Please request accommodations for this course as early as possible to provide time for effective communication and arrangements.
For further information or to start the process of requesting accommodations, please contact Student Disability Services at Engineering for Professionals, ep-disability-svcs@jhu.edu.
Student Conduct Code
The fundamental purpose of the JHU regulation of student conduct is to promote and to protect the health, safety, welfare, property, and rights of all members of the University community as well as to promote the orderly operation of the University and to safeguard its property and facilities. As members of the University community, students accept certain responsibilities which support the educational mission and create an environment in which all students are afforded the same opportunity to succeed academically.
For a full description of the code please visit the following website: https://studentaffairs.jhu.edu/policies-guidelines/student-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.