| Weekly Schedule and Class Notes |
- Lecture notes : all notes will be posted in this section.
- It is your responsibility to download, print and bring the notes to the class. Notes will be available 24 hours before each class.
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Week |
Date |
Topic |
Reading |
Assignments |
Notices and Dues |
Notes |
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1 |
09/06 |
1. Course Organization /Introduction |
Lecture Note |
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2 |
09/13 |
2. Introduction to Edge computing 3. 3-tier architecture 4. Architecture of Embedded System |
Lecture Note |
[Homework #1: Homepage]
[Homework #2: Edge setting] |
Homework #1 & #2's dues are on Sep. 18th Midnight |
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3 |
09/20 |
5.Power and resistance control 6. Passive resistance and active resistance 7. Diaod control 8. Schematic and embedded code |
Lecture Note |
[Homework #3: Diaod control] |
Homework #3's due is on Sep. 25th Midnight |
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4 |
09/27 |
9. ADC control 10. I2C protocol, SCL, SDA 11. Edge/Mist/ Fog / Cloud computing |
Lecture Note |
[Homework #4: ADC control] |
Homework #4's due is on Oct. 3rd Midnight |
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5 |
10/04 |
12. Shift-register control 13. Matrix display (I) |
Lecture Note |
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6 |
10/06 |
14. Martrix display (II) |
Lecture Note |
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7 |
10/11 |
15. Motro control (without relay) 16. MQTT (MQTT library) 17. Node-red (I) 18. Json file format 19. Rest API and real time data |
Lecture Note |
[Homework #5: Motor & Button & Potentiometer control] |
Homework #5's due is on Oct. 20th Midnight |
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8 |
10/13 |
20. Fundamentals of Electronics 21.Fundamentals of Communications |
Lecture Note |
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| 9 |
10/25 |
Mid term |
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10 |
11/01 |
22.MQTT Publishing 23. Node-red Dashboard |
Lecture Note |
[Homework #6: Real-time data publishing] |
Homework #6's due is on Nov. 7th Midnight |
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11 |
11/08 |
24. Deep neural net & Deep Learning 25. Architecture of Neuron 26. Type of activation function : Sigmoid, tanh |
Lecture Note |
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12 |
11/15 |
27. Steepest Gradient Method 28. Backpropagation (I) 29. Correlation Plot 30. Deep learning layer |
Lecture Note |
[Homework #7: Data analysis and deep layer design] |
Homework #7's due is on Nov. 20th Midnight |
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13 |
11/21 |
31. Deep Learning Implementation |
Lecture Note |
[Homework #8: Ddeep learning analysis] |
Homework #8's due is on Nov. 21st Midnight |
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14 |
11/22 |
32. Backpropagation (II) 33. Bias and AND/XOR node issues 34. Moment method and Adaptive methods (Moment, Adagrad,Adadelta, RMSProp, Adam) |
Lecture Note |
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15 |
11/29 |
35. Signal Processing : Hz data analysis 36. Spectrum analysis : FFT, IFFT 37. MQTT publisher / Subscriber with Node-red |
Lecture Note |
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16 |
12/06 |
Final exam |
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