DSC3004 : Vision and Cyberphysical AI
- Inactive for Year: 2026/27
- Module Leader(s): Dr Tomasz Szydlo
- Lecturer: Dr Varun Ojha, Dr Zhuang Shao, Dr Bo Wei
- Owning School: Computing
- Teaching Location: Newcastle City Campus
Semesters
Your programme is made up of credits, the total differs on programme to programme.
| Semester 1 Credit Value: | 20 |
| ECTS Credits: | 10.0 |
| European Credit Transfer System | |
Aims
This module develops your understanding of how intelligent systems perceive, interpret, and interact with the physical world through visual and multimodal sensing. You will study approaches to computer vision, perception pipelines, sensor integration, and embodied or cyberphysical AI systems operating within real environments. The emphasis is on understanding how perception supports decision making and action, evaluating the reliability, efficiency, and robustness of deployed systems, and critically considering the ethical, safety, and societal implications associated with embodied and cyberphysical AI technologies.
Outline Of Syllabus
Topics covered by this module include:
Foundations of computer vision and image processing
Deep learning approaches for vision, including convolutional neural networks and vision transformers
Image classification, object detection, object tracking, and image segmentation
Video understanding, pose estimation, and activity recognition
Multimodal and foundation models for vision and perception tasks
Sensor fusion and multimodal perception systems
Embodied AI and robotic perception pipelines
Autonomous and cyberphysical AI systems
Perception–decision–action loops within intelligent agents
Edge AI, TinyML, and embedded inference systems
IoT-enabled intelligent systems and real-time AI deployment
Robustness, safety, and adversarial challenges within cyberphysical and embodied AI systems
Teaching Methods
Teaching Activities
| Category | Activity | Number | Length | Student Hours | Comment |
|---|---|---|---|---|---|
| Guided Independent Study | Assessment preparation and completion | 1 | 49:00 | 49:00 | Independent study and revision for final written exam |
| Scheduled Learning And Teaching Activities | Lecture | 21 | 1:00 | 21:00 | Lectures |
| Guided Independent Study | Assessment preparation and completion | 1 | 35:00 | 35:00 | Completion of in course assessments / preparation for Report 2 |
| Guided Independent Study | Assessment preparation and completion | 1 | 35:00 | 35:00 | Completion of in course assessments / preparation for Report 1 |
| Structured Guided Learning | Lecture materials | 1 | 20:00 | 20:00 | Reading through lecture materials – preparing / follow up |
| Guided Independent Study | Directed research and reading | 1 | 20:00 | 20:00 | Wider reading as per provided reading list |
| Scheduled Learning And Teaching Activities | Practical | 20 | 1:00 | 20:00 | Supervised practical sessions focused on hands on implementation |
| Total | 200:00 |
Teaching Rationale And Relationship
This module includes an encounter with the leading edge of research and practice.
Assessment Methods
The format of resits will be determined by the Board of Examiners
Exams
| Description | Length | Semester | When Set | Percentage | Comment |
|---|---|---|---|---|---|
| Written Examination | 90 | 1 | A | 40 | N/A |
Other Assessment
| Description | Semester | When Set | Percentage | Comment |
|---|---|---|---|---|
| Report | 1 | M | 30 | Up to six page report, including all figures |
| Report | 1 | M | 30 | Up to six page report, including all figures |
Assessment Rationale And Relationship
The written examination assesses students’ understanding of the principles, methods, and operational challenges associated with computer vision, perception systems, and embodied AI within controlled conditions. The examination evaluates students’ ability to reason about perception pipelines, sensor integration, intelligent agents, robustness, deployment constraints, and the role of perception within cyberphysical systems. The examination assesses all module learning outcomes through conceptual and applied questions relating to perception and intelligent system design. (MLO1, MLO2, MLO3, MLO4)
The reports provide students with opportunities to apply perception and computer vision approaches within practical and analytical contexts. Students will evaluate system behaviour, analyse deployment challenges, interpret performance under realistic conditions, and communicate findings through structured technical reports supported by appropriate figures and discussion. The assessments support the development of practical, analytical, and evaluative skills associated with embodied and cyberphysical AI systems. (MLO2, MLO4)
The assessment structure supports the development of both conceptual understanding and applied technical skills, enabling students to analyse, evaluate, and communicate the behaviour and limitations of perception systems operating within realistic and dynamic environments.
Alternative assessment:
For students with a Student Support Plan (SSP) who have alternative assessment as a reasonable adjustment and/or students with supported Personal Extenuating Circumstances (PEC), it may be necessary to set an alternative assessment as presented below. Due to the professional requirements of some programmes, alternative assessments may not be available even where students are eligible.
An oral examination comprising a 15-minute prepared topic followed by 15 minutes of questions from the overall module syllabus may be offered as an alternative to the written examination where appropriate.
Reading Lists
Timetable
- Timetable Website: www.ncl.ac.uk/timetable/
- DSC3004's Timetable