EE306 SIGNALS AND SYSTEMS II
| Course Code: | 5670306 |
| METU Credit (Theoretical-Laboratory hours/week): | 3 (3.00 - 0.00) |
| ECTS Credit: | 5.0 |
| Department: | Electrical and Electronics Engineering |
| Language of Instruction: | English |
| Level of Study: | Undergraduate |
| Course Coordinator: | Assist.Prof.Dr AHMED HAREEDY |
| Offered Semester: | Fall Semesters. |
Course Objectives
At this stage, students have already learned about probability and random variables in EE230. Moreover, they have learned about deterministic signals and systems in EE301. In EE306, EE230 and EE301 meet each other, and students learn about random/stochastic signals and processes, ones they encounter in real-life applications. Students will learn a variety of methods and tools that are adopted in a wide range of modern applications, including artificial intelligence, digital communications, and data storage. In particular,
- After probability and linear algebra reviews, students will learn about vectors and sums of random variables. They will learn parameter and random signal estimation. They will also learn basics of optimization as well as the gradient descent algorithm, which is used in machine learning.
- Students will learn the concept of random processes. They will learn autocorrelation and autocovariance. They will learn about stationarity and Gaussian processes, including white noise. They will also learn how to find and use the power spectral density of random processes.
- As examples of famous discrete stochastic processes, students will learn about Markov chains (transition probabilities, convergence and steady-state probabilities, ergodicity) and Poisson processes (counting processes, waiting time distribution).
Course Content
Correlation of signals. Energy and power spectral densities. Hilbert transform. Principles of modulation. Stochastic processes: Characterization, correlation functions, stationarity, ergodicity, power spectral density, transmission of random signals through linear systems. Special stochastic processes. Noise.
Course Learning Outcomes
EE306 is a course designed for students who are curious about the applied mathematical side of electrical engineering. The main learning outcome of EE306 is that students will learn many mathematical tools that lead them to better understanding of a wide range of modern applications, including digital communications, data storage, and machine learning. These tools include estimation, optimization, random processes, autocorrelation/spectrum, and Markov chains. A consequential learning outcome is that students will develop their analytical thinking and expand their analytical skill set. A third learning outcome is that students will visualize some of the important concepts about random signals/processes via short MATLAB projects.
Program Outcomes Matrix
| Contribution | |||||
| # | Program Outcomes | No | Yes | ||
| 1 | An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics | ✔ | |||
| 2 | An ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors | ✔ | |||
| 3 | An ability to communicate effectively with a range of audiences | ✔ | |||
| 4 | An ability to recognize ethical and professional responsibilities in engineering situations and make informed judgments, which must consider the impact of engineering solutions in global, economic, environmental, and societal contexts. | ✔ | |||
| 5 | An ability to function effectively on a team whose members together provide leadership, create a collaborative and inclusive environment, establish goals, plan tasks, and meet objectives | ✔ | |||
| 6 | An ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions | ✔ | |||
| 7 | An ability to acquire and apply new knowledge as needed, using appropriate learning strategies | ✔ | |||