ENEM 688: Independent Study

Instructor:

Liang Zhang, Assistant Professor

Prerequisites

N/A

Objectives

  1. To develop advanced knowledge and problem-solving skills in specialized areas of electrical engineering (e.g., communication systems, signal processing, machine learning for networks, etc.).
  2. To conduct independent research that applies theoretical foundations to practical problems in electrical engineering.
  3. To critically review and synthesize recent research literature in the chosen topic area.
  4. To design, model, and analyze innovative solutions, algorithms, or systems relevant to the study area.
  5. To evaluate the performance of proposed methods using simulations, experiments, or analytical techniques.
  6. To strengthen the ability to work independently on open-ended engineering problems.
  7. To improve technical writing and presentation skills through the preparation of reports, presentations, and the publication.

More information are available at: https://wwwcp.umes.edu/engineering/

Location

EASC 2041

Time

Wednesday 3:00 pm — 4:30 pm or TBA.

ENEM 688 Syllabus

ENEM 645 Lecture Notes

There is no lecture notes for the Independent study.

Here are some papers:

  1. [PDF] Radar Challenges, Current Solutions, and Future Advancements for the Counter Unmanned Aerial Systems Mission
  2. [PDF] UAS-Borne Radar for Autonomous Navigation and Surveillance Applications
  3. [PDF] Deep Reinforcement Learning Based Dynamic Trajectory Control for UAV-assisted Mobile Edge Computing

Course Schedule

Week Topic Notes
1 — 08/25 Learn basic skills of research, set up accounts, e.g., EDAS, ORCID, Overleaf, etc. We have a meeting with student 1&2. We have a 1-1 meeting with the student 3 and provide guidance for the research of vehicle communications.
2 — 09/01 Learn the format of a professional PPT. Learn how to formulate a problem. We have a meeting with all students. Two students (1&2) presented two papers and answered questions
3 — 09/08 Determin the research topic We have a meeting with all students. Student 1 presented the work of deep Reinforcement learning and student 3 present something related to vehicle communications.
4 — 09/15 Find state-of-art work
5 — 09/22 Find state-of-art work
6 — 09/29 Propose a new research problem
7 — 10/06 Formulate the problem
8 — 10/13 Find baseline solutions
9 — 10/20 Propose new solutions
10 — 10/27 Simulate the new solutions
11 — 11/03 Simulate the new solutions
12 — 11/10 Simulate the new solutions
13 — 11/17 Working on the project report
14 — 11/24 Extend the project report to a manuscript
15 — 12/01 Submit Manuscript
16 — 12/08 Final Exam Week

Last day of class is December 5, 2025.