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Sustainable marine environment intelligent monitoring
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Programs:
Sustainable marine environment intelligent monitoring
Units:
32 hours
Format:
Live Online
Duration:
Jun 23 2025 ~ Jun 30 2025
Cost:
Free
Credit(s):
2
Course Description

This course focuses on the theme of "protection and sustainable utilization of oceans and marine resources to promote sustainable development". The course adopts a combination of theory and practice to introduce related technologies and typical applications of ocean intelligent autonomous monitoring. Typically, the course includes unmanned surface vehicle(USV),unmanned aerial vehicle(UAV), autonomous underwater vehicle(AUV), and related algorithms for data processing. After successfully completing this course, students are able to:

  • have a comprehensive and preliminary understanding of the field of sustainable ocean intelligence autonomous monitoring. 
  • understand and master the overall architecture and key technologies of the three important autonomous systems of USV, UAV, and AUV. 
  • implement basic ocean intelligent autonomous monitoring system with programming software.

Relevant SDGs: Goal 14: Conserve and sustainably use the oceans, seas and marine resources

Academic Team

PI:

  • GAO Rui, Assistant Professor, rgao@sjtu.edu.cn
  • WANG Jian, Assistant Research Fellow, nsms_sjtu@sjtu.edu.cn

Collaborators:

  • Howard Li, University of New Brunswick, howard@unb.ca
  • Rubén Clavería Vega, Lecturer 
What skills will students get?
  1. Understand the meaning of autonomous monitoring of ocean intelligence, explain the key technologies of autonomous systems such as unmanned surface vehicle(USV),unmanned aerial vehicle(UAV), autonomous underwater vehicle(AUV).
  2. Exploit unmanned system technology to analyze and solve practical problems of sustainable ocean intelligent autonomous monitoring.
  3. Understand the basic algorithms of intelligent autonomous system.
Mode of Teaching

Lectures & Discussion & Exercises & Project demos

Grading
  1. Attendance: 30%;
  2. Group Discussion: 30%;
  3. Final Group Presentation: 40%
Course-specific Restrictions

Basic knowledge of electrical engineering

Class Schedule

Week

Date 

Week Day

Time 

Topic

Credit hours

Teaching mode

(Lecture/Tutorial/Discussion)

Instructor in charge

 

23/06

Monday

 

12:00-12:45
12:50-13:35

 

13:40-14:25

14:30-15:15

Ice Break&Background of ocean intelligent autonomous monitoring

4

Lecture&Discussion

Rui Gao

Jian Wang

 

24/06

Tuesday

 

12:00-12:45
12:50-13:35

 

13:40-14:25

14:30-15:15

Machine learning algorithms for intelligent autonomous monitoring

4

Lecture&Discussion

 

Zhaobo Zheng
Rui Gao

 

 

25/06

Wednesday

 

12:00-12:45
12:50-13:35

 

13:40-14:25

14:30-15:15

Machine learning algorithms for intelligent autonomous monitoring

4

Lecture&Discussion

 

Zhaobo Zheng
Rui Gao

 

 

26/06

Thursday

 

12:00-12:45
12:50-13:35

 

13:40-14:25

14:30-15:15

Machine learning algorithms for intelligent autonomous monitoring

4

Lecture&Discussion

 

Zhaobo Zheng
Rui Gao

 

 

27/06

Friday

 

12:00-12:45
12:50-13:35

 

13:40-14:25

14:30-15:15

Unmanned aerial vehicle (UAV)

4

Lecture &Discussion

Howard Li

 

28/06

Saturday

 

12:00-12:45
12:50-13:35

 

13:40-14:25

14:30-15:15

Autonomous underwater vehicle (AUV)

4

Lecture &Discussion

Howard Li

 

29/06

Sunday

 

12:00-12:45
12:50-13:35

 

13:40-14:25

14:30-15:15

Autonomous underwater vehicle (AUV)

4

Lecture &Discussion

Howard Li

 

30/06

Monday

 

12:00-12:45
12:50-13:35

 

13:40-14:25

14:30-15:15

Project demos of sustainable ocean intelligent autonomous monitoring

4

Project Demonstration

Rui Gao

Jian Wang

Total

32

 

 

 
Course Contact

GAO Rui, rgao@sjtu.edu.cn

Instructors
Rui Gao
Assistant Professor at the School of Naval Architecture Ocean and Civil Engineering, Shanghai Jiao Tong University
Jian Wang
Assistant Research Fellow at the School of Naval Architecture Ocean and Civil Engineering, Shanghai Jiao Tong University
Howard Li
professor in the Department of Electrical and Computer Engineering, University of New Brunswick
Rubén Clavería Vega
Received the Ph.D. degree in information engineering from the University of Cambridge, U.K., in 2025
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