CMP3010 Embedded Systems ProgrammingBahçeşehir UniversityDegree Programs ARTIFICIAL INTELLIGENCE ENGINEERINGGeneral Information For StudentsDiploma SupplementErasmus Policy StatementBologna CommissionNational Qualifications
ARTIFICIAL INTELLIGENCE ENGINEERING
Bachelor TR-NQF-HE: Level 6 QF-EHEA: First Cycle EQF-LLL: Level 6

Course Introduction and Application Information

Course Code Course Name Semester Theoretical Practical Credit ECTS
CMP3010 Embedded Systems Programming Spring 2 2 3 6

Basic information

Language of instruction: English
Type of course: Must Course
Course Level: Bachelor’s Degree (First Cycle)
Mode of Delivery: Face to face
Course Coordinator : Assist. Prof. TARKAN AYDIN
Recommended Optional Program Components: None
Course Objectives: This course is a hands-on course that requires writing software as well as board-level work. It sits at the intersection of fields such as microprocessors, digital design, operating systems, software design, and industrial automation. The students are exposed to topics such as meeting real-time constraints in embedded systems, generating delays and interrupts, using the serial interface, etc. They get theoretical as well as hands-on experience on embedded system design by using embedded software development environments and hardware emulators, as well as by working on actual hardware where they physically connect multiple building blocks.

Learning Outcomes

The students who have succeeded in this course;
A student completing this course will be able to
I. Develop embedded applications for consumer equipments,
II. Implement embedded solutions to solve automation systems,
III. Develop efficient code with C programming language for embedded target systems,
IV. Design and implement embedded systems with real time I/O requirements,
V. Implement embedded applications programming AtMega embedded microcontrollers,
VI. Determine the requirements of an embedded application and design/implement it on a selected target platform.

Course Content

Introduction to Course: Embedded Systems. Introducing embedded software development environment (Keil C Compiler and hardware simulator). Embedded microcontroller.
Hardware Fundamentals & Computer Architecture Review. (Embedded terminology, Gates, Clocks, Timing Diagrams, Buses, Registers, Memory, RISC, CISC, MIPS, CPU clock cycle etc.). Object Oriented Programming with C. Meeting real-time constraints, hardware delays and Interrupts.
GPIO: Digital Input, Output and Displays, ADC & DAC. Interrupts and Times. Creating an embedded operating system. Implementing Multi-state Systems. Communication: Serial RS232, SPI, I2C, CAN, Wireless etc.

Weekly Detailed Course Contents

Week Subject Related Preparation
1) Introduction to Course: Embedded Systems. Introducing embedded software development environment (Compiler and hardware simulator).
2) Embedded microcontroller architecture. Lab: Exercises for AtMega328 microcontroller.
3) Hardware Fundamentals & Computer Architecture Review. (Embedded terminology, Gates, Clocks, Timing Diagrams, Buses, Registers, Memory, RISC, CISC, MIPS, CPU clock cycle etc.)
4) Digital input/output
5) Analog Input/output
6) Meeting real-time constraints, hardware delays and Interrupts.
7) Interrupts and Timers and interrupt service routines
8) Driving actuators
9) Communication: Serial RS232, SPI, I2C, CAN, Wireless etc. I
10) Communication: Serial RS232, SPI, I2C, CAN - II
11) Sensors & actuators I
12) Sensors & actuators II
13) Real Time Operating Systems
14) Project Presentations.

Sources

Course Notes / Textbooks: Embedded C, Michael J. Pont, Addison Wesley 2005.
References:

Evaluation System

Semester Requirements Number of Activities Level of Contribution
Attendance 14 % 0
Laboratory 12 % 15
Quizzes 7 % 15
Project 1 % 20
Midterms 1 % 15
Final 1 % 35
Total % 100
PERCENTAGE OF SEMESTER WORK % 45
PERCENTAGE OF FINAL WORK % 55
Total % 100

ECTS / Workload Table

Activities Number of Activities Duration (Hours) Workload
Course Hours 14 2 28
Laboratory 13 2 26
Study Hours Out of Class 15 8 120
Project 1 34 34
Midterms 1 2 2
Final 1 2 2
Total Workload 212

Contribution of Learning Outcomes to Programme Outcomes

No Effect 1 Lowest 2 Low 3 Average 4 High 5 Highest
           
Program Outcomes Level of Contribution
1) Build up a body of knowledge in mathematics, science and Artificial Intelligence Engineering subjects; use theoretical and applied information in these areas to model and solve complex engineering problems. 5
2) Design complex Artificial Intelligence systems, platforms, processes, devices or products under realistic constraints and conditions, in such a way as to meet the desired result; apply modern design methods for this purpose. 5
3) Identify, formulate, and solve complex Artificial Intelligence Engineering problems; select and apply proper modeling and analysis methods for this purpose. 3
4) Devise, select, and use modern techniques and tools needed for solving complex problems in Artificial Intelligence Engineering practice; employ information technologies effectively. 3
5) Design and conduct numerical or physical experiments, collect data, analyze and interpret results for investigating the complex problems specific to Artificial Intelligence Engineering. 4
6) Ability to communicate effectively in English and Turkish (if he/she is a Turkish citizen), both orally and in writing. Write and understand reports, prepare design and production reports, deliver effective presentations, give and receive clear and understandable instructions. 4
7) Recognize the need for life-long learning; show ability to access information, to follow developments in science and technology, and to continuously educate oneself. 4
8) Develop an awareness of professional and ethical responsibility, and behave accordingly. Be informed about the standards used in Artificial Intelligence Engineering applications. 3
9) Learn about business life practices such as project management, risk management, and change management; develop an awareness of entrepreneurship, innovation, and sustainable development.
10) Acquire knowledge about the effects of practices of Artificial Intelligence Engineering on health, environment, security in universal and social scope, and the contemporary problems of Artificial Intelligence Engineering; is aware of the legal consequences of Mechatronics engineering solutions.
11) Cooperate efficiently in intra-disciplinary and multi-disciplinary teams; and show self-reliance when working on Artificial Intelligence-related problems.