Course Syllabus

Course Information：

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Spring Semester Year 2025	345765	Adaptive Signal Processing	1	3.0	3	★	Ying-Ren Chien	ECE	22	1	新聘教師

Course Plan：

Instructor	Ying-Ren Chien　Office Hours
Email	yrchien@ntut.edu.tw
Last Updated	2025-02-05 18:06:58
Course Objective	本課程介紹自適應訊號處理的基本理論與應用，涵蓋維納濾波、最小均方（LMS）演算法、遞迴最小均方（RLS）演算法及仿射投影演算法（APA）。課程將探討自適應濾波器的收斂性、穩定性與效能分析，並透過 MATLAB 模擬與實作，讓學生理解其在噪聲消除、通道均衡、回音消除等領域的實際應用。此外，學生將研讀相關研究論文，培養分析與應用自適應演算法的能力，並透過期末專題整合所學內容，解決實際工程問題。 This course introduces the fundamental theories and applications of adaptive signal processing, covering Wiener filtering, Least Mean Squares (LMS) algorithms, Recursive Least Squares (RLS) algorithms, and the Affine Projection Algorithm (APA). It explores the convergence, stability, and performance analysis of adaptive filters. Through MATLAB simulations and implementations, students will gain hands-on experience in applying these techniques to noise cancellation, channel equalization, and echo cancellation. Additionally, students will study relevant research papers to develop analytical and practical skills in adaptive algorithms. The course culminates in a final project, where students integrate their knowledge to solve real-world engineering problems.
Course Schedule	第 1 週 自適應訊號處理導論 概述與應用 線性系統與隨機過程回顧 第 2 週（假期／停課） 第 3 週 維納濾波理論 數學背景 最佳濾波準則（最小均方誤差，MSE） 維納-霍夫方程 自相關矩陣求解方法 MATLAB 模擬：維納濾波器 第 4 週 最小均方（LMS）演算法 #1 LMS 演算法推導與實作 收斂性分析 穩定性考量 指派理論練習作業 第 5 週 最小均方（LMS）演算法 #2 學習率選擇 效能與收斂特性 MATLAB：LMS 演算法實作 第 6 週 LMS 演算法的變形 正規化 LMS（NLMS） 符號 LMS（Sign-LMS）及其他變形 個案研究：LMS 在噪聲消除中的應用 第 7 週（假期／停課） 第 8 週 期中考 開放回家作答考試 第 9 週 遞迴最小均方（RLS）演算法 RLS 演算法推導與矩陣反演引理 指數遺忘因子 計算複雜度分析 指派 RLS 系統識別作業 第 10 週 LMS 與 RLS 演算法比較 效能權衡分析 收斂速度與計算成本比較 MATLAB 模擬：LMS 與 RLS 比較 第 11 週 仿射投影演算法（APA） 高階自適應技術 計算複雜度分析 個案研究：自適應系統建模 第 12 週 論文研讀：自適應濾波的應用 （學生可自選主題） 噪聲消除 通道均衡 回音消除 指派真實應用案例研究專案 第 13 週 論文研讀：通訊領域中的自適應訊號處理 （學生可自選主題） 自適應均衡技術 自適應波束成形概念 MATLAB：自適應均衡器實作 第 14 週 LaTeX 論文排版系統 #1 第 15 週（假期／停課） 第 16 週 LaTeX 論文排版系統 #2 第 17 週 期末專題報告 學生專題研究成果展示 第 18 週 課程總結與回饋 課程回顧與未來發展討論 Week 1 Introduction to Adaptive Signal Processing - Overview and applications - Review of linear systems and stochastic processes Week 2 (Holladay/ No Class) Week 3 Wiener Filter Theory - Mathematical Background - Optimum filtering criterion (MSE) - Wiener-Hopf equations - Solution using autocorrelation matrices - MATLAB simulation: Wiener filter Week 4 Least Mean Squares (LMS) Algorithm #1 - Derivation and implementation - Convergence analysis - Stability considerations - Assign theoretical exercise Week 5 Least Mean Squares (LMS) Algorithm #2 - Learning rate selection - Performance and convergence properties - MATLAB: Implement LMS Week 6 Variants of LMS Algorithm - Normalized LMS (NLMS) - Sign-LMS and other modifications - Case study: Noise cancellation with LMS Week 7 (Holladay/ No Class) Week 8 Midterm - Take Home Exam Week 9 Recursive Least Squares (RLS) Algorithm - Derivation and matrix inversion lemma - Exponential forgetting factor - Computational complexity - Assign RLS-based system identification Week 10 Comparison of LMS and RLS Algorithms - Performance trade-offs - Convergence speed and computational cost - MATLAB simulation: LMS vs. RLS Week 11 Affine Projection Algorithm (APA) - Higher-order adaptation techniques - Computational complexity - Case study: Adaptive system modeling Week 12 Paper Study: Applications of Adaptive Filtering - (You may choose you own topics) - Noise cancellation - Channel equalization - Echo cancellation - Assign real-world application project Week 13 Paper Study: Adaptive Signal Processing in Communications - (You may choose you own topics) - Adaptive equalization techniques - Adaptive beamforming concepts - MATLAB: Adaptive equalizer implementation Week 14 Latex System #1 Week 15 (Holladay/ No Class) Week 16 Latex System #2 Week 17 Final Project Presentations - Students present project findings Week 18 Course Wrap-up and Feedback Session - Course evaluation and future directions
Evaluation and grading policy	作業 30% 期中考 30% 期末報告 30% 課堂表現 10% Assignments 30% Midterm Exam 30% Final Project 30% Class Participation 10%
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:Yes Adaptive filtering : algorithms and practical implementation / by Paulo S. R. Diniz.
The access to curricular consultation	請使用電子郵件跟老師預約諮詢時間 Please make appointments by e-mail
Expanding teaching and resources
The course corresponds to the SDGs	SDG4：優質教育（Quality Education） SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure）
Does the course incorporate AI
Note	英語授課 (Lecturing Language: English)