大數據時代的來臨，所處理的數據就是本課程所稱的訊號 (signal,信號)，一般而言分成離散(表格式)的數據以及時間序列兩種，本課程針對時間序列型的數據，進行分析處理，有助於有幫助同學朝向數據分析方向發展。
訊號與影像處理為應用在電機、通訊、網路與機電系統設計領域中的一門必要課程，同時也作為 AI 深度學習的基礎。本課程以連續時間訊號與系統為主要對象，介紹對應的時域、頻域表示法，時域與頻域之分析技巧(Fourier series and transform)，以及取樣定理等主題。
本課程教材內容將搭配 MATLAB 範例與模擬作業，俾提供學生透過 MATLAB模擬實驗，加深對學理之認知。除此之外，本課程以醫療數據為主要分析與處理對象，因此會包含 ECG、EEG、MRI 等訊號與影像的討論。
學生修讀本課程，將學到訊號處理的基本理論與分析方法，作為其日後的學習深度學習進行跨領域的發展與研究的基礎，能夠熟稔地利用訊號與系統架構及其分析技巧，達到解決及分析問題的目的，並進一步應用類神經網路學習模型建立與數值模擬的目的。With the advent of the big data era, the data processed is what this course calls signals. Generally speaking, it is divided into two types: discrete (table format) data and time series. This course focuses on time series data. Analysis and processing can help students develop in the direction of data analysis.
Signal and image processing is a necessary course applied in the fields of motors, communications, networks and electromechanical system design. It also serves as the basis for AI deep learning. This course takes continuous-time signals and systems as its main objects, and introduces corresponding time domain and frequency domain representations, time and frequency domain analysis techniques (Fourier series and transform), and sampling theorem and other topics.
The teaching materials of this course will be paired with MATLAB examples and simulation assignments to provide students with a deeper understanding of academic theories through MATLAB simulation experiments. In addition, this course focuses on the analysis and processing of medical data, so it will include discussion of signals and images such as ECG, EEG, and MRI.
Students who take this course will learn the basic theories and analysis methods of signal processing, which will serve as the basis for their future study of deep learning and cross-field development and research. They will be able to skillfully use signal and system architecture and analysis techniques to achieve solutions. and analyze the problem, and further apply neural network learning model establishment and numerical simulation.
Kayvan Najarian and Robert Splinter, Biomedical Signal and Image Processing, 2nd Edition, CRC Press, 2012.
以及本人編寫的課程講義。
Kayvan Najarian and Robert Splinter, Biomedical Signal and Image Processing, 2nd Edition, CRC Press, 2012.
and course handouts written by me.
| 評分項目 Grading Method | 配分比例 Grading percentage | 說明 Description |
|---|---|---|
| 出席作業、隨堂測驗出席作業、隨堂測驗 Attend homework and quizzes |
30 | 課堂點名 |
| 期中考試期中考試 midterm exam |
30 | |
| 期末考試或報告期末考試或報告 Final exam or report |
40 | 上課討論 |
