Objective

The emerging intelligence of automotive vehicles requires increasingly powerful computing capability embedded on vehicle, which motivates the eager demand for automotive Integrated Circuit (IC). Because of such specifics as high reliability, the automotive IC is quite distinct from consumer IC. This course intends to provide for the enrolled students with the knowledge of automotive IC, including the characteristics, fabrication process, development flow, testing, packaging, related software and application. The students can therefore be with fundamental knowledge and development capability of automotive IC.

The content of this course is composed of six parts: 1) Overview and particulars of automotive IC; 2) Fabrication process and devices of automotive IC; 3) Design of analog automotive IC; 4) Design of digital automotive IC; 5) Testing and packaging of automotive IC; 6) Software related to automotive IC. The students to be enrolled in this course are required to be with basic knowledge in physics, software and integrated circuits.

汽車的智能化需要日益強大的車載算力，從而激發了對汽車芯片的強烈需求。車載領域高可靠等的獨特性使得汽車芯片與消費電子芯片有很大的不同。本課程為參加此課程的學生提供汽車芯片的相關知識，包括汽車芯片的特點、製造工藝、開發流程、測試封裝、軟硬件結合以及應用。通過本課程學生將具有汽車芯片的基礎知識和基本的開發能力。

本課程的內容分為六部分：1）汽車芯片總覽與特性；2）汽車芯片製造工藝與器件；3）汽車芯片模擬電路開發；4）汽車芯片數字電路開發；5）汽車芯片測試與封裝；6）汽車芯片相關的軟件。參加本課程的學生需要具有基本的物理知識、軟件知識和芯片知識。

Syllabus

Introduction to automotive IC
Types and particulars of automotive IC
Fabrication process of automotive IC
Devices of automotive IC
Design of analog automotive IC
Design of digital automotive IC 
Testing and packaging of automotive IC 
Software related to automotive IC

Learning Outcome

Upon successful completion of this course, students will be able to:
1. Understand the basic knowledge and particulars of automotive IC. 
2. Understand the fabrication techniques and devices for automotive IC.
3. Understand the fundamental design techniques for analog and digital automotive IC.
4. Understand the knowledge about the testing and packaging of automotive IC.
5. Understand the software related to automotive IC.
6. Obtain practical experience of designing automotive IC. 

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Objective
A series of lectures on current research in solid state technology.

Syllabus

Learning Outcome

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Objective

Medical imaging has been an integral part in modern healthcare procedures. Advances in deep learning have revolutionized the analysis of biomedical data, clinical diagnosis, and prognosis. In this course, students will learn fundamental image processing techniques, characteristics of different types of medical images, and how to apply different classical image processing techniques to different types of medical images. Topics covered in this course include but are not limited to:

- An overview of medical imaging modalities and their clinical use,
- Introduction to medical image computing, including registration, segmentation, classification, reconstruction, super-resolution, and visualization,
- Traditional image processing techniques for medical image analysis,
- Machine learning/deep learning for medical image analysis, and
- Frontline of AI in medical imaging and case studies.

醫學成像已成為現代醫療保健中不可或缺的一部分。深度學習的進步改變了生物醫學資料分析、臨床診斷和預後。在本課程中，學生將學習基本的影像處理技術，不同類型醫學圖像的特點，以及如何將不同的經典影像處理技術應用於不同類型的醫學圖像。本課程涵蓋的主題包括但不限於:

-醫學成像模式及其臨床應用概述，
-醫學圖像計算介紹，包括配准，分割，分類，重建，超解析度和視覺化，
-醫學圖像分析的傳統影像處理技術，
-醫學圖像分析的機器學習/深度學習，以及
-人工智能在醫學成像和案例研究中的前沿。

Syllabus

Introduction to the course and requirements. An overview of medical imaging modalities, e.g., MRI, CT, ultrasound, PET/SPECT, histopathology
An overview of medical imaging modalities and their clinical use
Introduction to medical image registration
-Clinical applications of image registration
-Linear transforms: rigid, affine
-Non-linear transforms: thin-plate spline, B-spline, diffeomorphic
-Challenges in image registrationMedical image segmentation in traditional techniques and deep learning techniques

Challenges in medical image segmentation
Semi-automated image segmentation
Medical image classification in traditional techniques
Medical image classification in deep learning techniques
Medical image reconstruction in traditional techniques and deep learning techniques
Medical image super-resolution in traditional techniques
Medical image super-resolution in deep learning techniques
Frontline of AI in medical imaging and case studies

Learning Outcome

Upon successful completion of this course, students will be able to:
1. have some basic ideas of artificial intelligence and machine learning;
2. know the medical applications of artificial intelligence and machine learning;
3. understand the limitations and possibilities of different approaches to artificial intelligence in medical image analysis;

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Objective
A series of lectures on current research in signal processing.

Syllabus

Learning Outcome

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