Master of Science (Intelligent Embedded Systems)

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Program Info (  Eligibility Criteria  )

Program Mission:
The purpose of the Master of Science Program in Intelligent Embedded Systems is to attain theoretical and practical depth in one of the areas of interest. The Master of Science (IES) program is structured in such a way as to enhance the student’s critical thinking and intuitive abilities using a combination of highly specialized courses and expert supervision. The program aims to produce graduates who will have the abilities and skills to be employed as practicing Engineers in fields such as design, research development, testing, and manufacturing, as well as assuming positions of leadership and responsibility within organizations.

Award of Degree:

For the award of MS (Intelligent Embedded System) degree, a student must have:

  • Passed courses totaling at least 30 credit hours, including Three core courses
  • Earned CGPA of at least 2.50
Tentative Study Plan
Sr. No Course Name Crdt Hrs.
Semester 1
1 Core Course - I 3+0
2 Core Course - II 3+0
3 Core Course - III 3+0
4 SS1021 Understanding of Holy Quran - I 1+0
5 SS1022 Understanding of Holy Quran - II 1+0
Total 11 + 0
Sr. No Course Name Crdt Hrs.
Semester 2
1 Core Course - IV 3+0
2 Elective - I 3+0
3 Elective - II 3+0
Total 9 + 0
Sr. No Course Name Crdt Hrs.
Semester 3
1 Elective - III 3+0
2 MS Thesis - I/Elective - IV 3+0
Total 6 + 0
Sr. No Course Name Crdt Hrs.
Semester 4
1 Elective - V 3+0
2 MS Thesis - II/MS Project/Elective - VI 3+0
Total 6 + 0

Proposed Core Courses:

  • Advanced Embedded Systems & Networks
  • Digital System Design
  • Computational Statistics
  • Research Methodology *

Elective Courses:   (12 Credit Hours – choose 4):

  • Embedded Systems & Internet of Things
  • Wireless Sensor Networks
  • Edge AI & Distributed Intelligence
  • Networked Control Systems
  • Cybersecurity for IoT & CPS
  • Smart Cities / Smart Grids
  • Network Planning & Optimization
  • Real-Time Systems & Scheduling
  • Modeling and Simulation for Cyber-Physical Systems
  • Advanced Robotics
  • Advanced Control Systems
  • Computer Vision for Embedded Devices
  • Robot Perception & Planning
  • SLAM and Navigation Systems
  • Human-Machine Interaction
  • Embedded Machine Learning
  • Autonomous Vehicle System
  • Deep Learning for Edge Devices
  • Signal Detection & Estimation
  • Advanced DSP & Embedded DSP
  • Speech, Vision & Sensor Fusion
  • Probabilistic Machine Learning
  • Reinforcement Learning for Embedded AI
  • Generative AI for Resource-Constrained Devices
  • Advanced Wireless Communications
  • Intelligent Systems Engineering

 * Research Methodology will be mandatory (core) for MS with thesis (research-based) option.

Note 1: Registration in "MS Thesis - I" is allowed provided the student has:

  • Earned at least 18 credits
  • Passed the "Research Methodology" course
  • CGPA is equal to or more than 2.50

Note 2: Subjected to the approval of Departmental Graduate Studies Committee (DGSC).

Program Educational Objectives (PEO)

  1. Provide students with advanced learning and application in a discipline or sub- discipline of Electrical Engineering. (Application to be added).
  2. Teach tools and techniques required for advanced learning, research and application in any discipline or sub-discipline of Electrical Engineering.
  3. Enhance skills such areas as problem – Solving, mathematical modelling, writing and oral presentation, leadership interrelation of business with technology and ethics applied to Electrical Engineering.