The Hidden Links in Communication Mathematics and Their Applications
課程代碼
4303049_01
授課教師:
翁健家
學分數
3
必/選修
選修
開課年級
大學部三年級(含以上)
先修科目或先備能力:
Programming, Signals and Systems, Linear Algebra, Probability Theory, Principles of Communication Systems, Digital Communications (Optional)
課程概述:
This is an EMI course, which explores the hidden links between different communication mathematics. Students will revisit core concepts from linear algebra, probability theory, differential equations, and signals and systems, not as isolated topics, but to learn their connections in a more general setting. Based on a modular teaching approach, students will combine different materials to implement signal compression, channel modeling, predictive coding, signal detection, etc. Emphasis is placed on active learning, interdisciplinary thinking, and computational implementation using Python or MATLAB. The course will end with an integrated project simulating key components of a communication system, enabling students to bridge theory and practice in a hands-on, applied setting.
2. Apply a modular problem-solving framework to communication tasks
3. Implement and validate an end-to-end simulation project
教科書:
課程大綱
分配時數
核心能力
備註
單元主題
內容綱要
講授
示範
隨堂作業
其他
Signals, Vectors, and Systems
1. Signal representation via vector spaces
2. Convolution as a linear transformation
3. Signals generated from a linear constant coefficient difference equation
4. Random signals
8
1
1.11.21.32.12.23.13.24.14.24.34.4
Quantization and Predictive Coding
1. Scalar and vector quantization
2. Lloyd-Max algorithm
3. Differential pulse code modulation (DPCM):
Open-loop and closed loop design
4. Autoregressive (AR) Model
5. Differential encoding via linear prediction
8
1
1.11.21.32.12.23.13.24.14.24.34.4
Transformations and Compression
1. Singular value decomposition (SVD) for data representation
2. Image compression discrete Fourier transform (DCT) and discrete wavelet transform
3. Karhunen-Loeve Expansion
4. Principal component analysis (PCA) for dimension reduction
1. Signal detection from the viewpoint of systems of linear equations
2. Detection method: Zero forcing (ZF), minimum mean-square error (MMSE), and maximum-a-posteriori (MAP)
3. Bayesian and Kalman filtering
8
1
1.11.21.32.12.23.13.24.14.24.34.4
Final Project
Design and evaluate a full mini communication system