Undergraduate Teaching 2025-26

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[node:field-syllabus-course-year:parent:name], Engineering Tripos Part IIB, 2021-22

Module Leader

Dr F Iida

Lecturers

Dr F Iida, Dr F Forni, Dr A Prorok, Dr H Gunes

Timing and Structure

Lent term. 16 Lectures. Assessment: 100% coursework (1 project report and presentation)

Prerequisites

4M20 is recommended

Aims

The aims of the course are to:

  • Advanced robotics topics including underactuated robotics, robot learning, soft robotics, human-robot interactions, multi-robot systems
  • Fundamental theories and concepts of advanced robotics topics
  • Practical methods and tools to simulate and build robots

Objectives

As specific objectives, by the end of the course students should be able to:

  • Learning different design strategies and architectures of robots
  • Group projects to collaborate with others to develop research proposal and perform research projects
  • Learning of effective presentations and report writing

Content

 

1. Introduction (2L; F Iida, F Forni)
a. Course overview; 
b. History and landscape of robotics; 
c. basic knowledge and theories (kinematics, dynamics, planning/search); 
2. Underactuated Robotics (4L; F Forni)
a. Problem formulation and modelling
b. Control approaches of underactuated systems
c. Case studies
3. Robot Learning and Adaptation(2L; F Iida)
a. Model-based learning approaches
b. Model-free learning approaches 
c. Optimization methods and case studies
4. Soft Robotics (2L; F Iida)
a. Soft material/body robot modelling; 
b. Soft actuators and sensors; 
c. Control and learning of soft robots; 
5. Human-Robot Interaction 1 (2L; H Gunes)
a. Introduction to human-robot interaction
b. Theoretical frameworks (spatial, nonverbal, verbal interactions)
c. Research methods, applications, robots in society
6. Distributed Robotics, Multi-Agent Systems (2L; A Prorok)
a. Planning and control in multi-robot systems
b. Methods for learning coordination and cooperation in multi-agent systems
7. Coursework Presentations (F Iida, F Forni)
 

Coursework

The assessment will be 100% coursework and consist of three elements (1) first group report (30%), (2) intermediate group project presentation (20%), and (3) final individual written report (50%). The first report is  a research project proposal that your group will work in the second half of this module, which should be submitted by Week 5. The project will be conducted in groups of 2-3 students, and the topics should be either or both simulation/hardware. The intermediate presentation will be delivered by groups in Week 8). The final report is expected to be a professional presentation about the project, individually extended from the intermediate presentation, and should be handed in by Week 14 as a 6-page double-column report (conference-formatted). In addition, the summary of lecture topics (up to 4 pages) should be submitted together with the final report. 

Booklists

Please refer to the Booklist for Part IIB Courses for references to this module, this can be found on the associated Moodle course·      

  • Ronald C. Arkin 1949- author. Behavior-Based Robotics / Ronald C. Arkin. Cambridge, Mass. : MIT Press, c1998.; 1998.


  • Bruno Siciliano 1959- editor., Oussama Khatib editor., eds. Springer Handbook of Robotics / Edited by Bruno Siciliano, Oussama Khatib. 2nd Edition. Cham : Springer International Publishing, 2016.; 2016. 
  • Rolf Pfeifer. Understanding Intelligence / Rolf Pfeifer and Christian Scheier ; with Figures by Alex Riegler and Cartoons by Isabelle Follath. (Christian Scheier, ed.). MIT Press; 1999.
  • Fantoni, Isabelle, Lozano, Rogelio, Non-linear Control for Underactuated Mechanical Systems, Springer, 2002

Examination Guidelines

Please refer to Form & conduct of the examinations.

 
Last modified: 18/06/2021 14:57

Engineering Tripos Part IIB, 4M26: Algorithms and Data Structures, 2025-26

Module Leader

Prof Per Ola Kristensson

Lecturers

Prof Per Ola Kristensson, Dr A Tewari, Dr E Wu

Timing and Structure

Lent term. 16 lectures (including two integrated examples classes). Assessment: 100% exam.

Aims

The aims of the course are to:

  • Introduce the principles behind algorithm and data structure design and evaluation.
  • Cover key topics including elementary and advanced data structures, including sorting algorithms, graph algorithms, and so on.
  • Provide an understanding of how to translate algorithms into code for selected engineering problems through coding-focused computerised examples papers.

Objectives

As specific objectives, by the end of the course students should be able to:

  • Analyse the computational efficiency of algorithms.
  • Re-implement and debug algorithms.
  • Correctly choose a suitable algorithmic solution and set of data structures for a computational problem.
  • Understand the theoretical and practical advantages and disadvantages of various algorithmic approaches and established solutions.
  • Devise and implement new algorithms and data structures, or modify existing algorithms and data structures, to solve previously unencountered tasks.

Content

  • Part 1: Fundamentals of Algorithms and Data Structures (7L + 1 Example Class)
    • Interpreting and writing pseudocode, demonstrating correctness, arriving at tight/lower/upper bounds of running time/storage, and solving computational problems using a repertoire of data structures and algorithmic approaches.
  • Part 2: Algorithms and Data Structures in Engineering (7L + 1 Example Class)
    • Translating pseudocode into code, debug implementations of algorithms and data structures, apply algorithms and data structures to solve a range of frequent engineering problems, such as finding shortest paths, resource allocation, and scheduling.

Booklists

Introduction to Algorithms (3rd ed) by Cormen, T., Leiserson, C., Rivest, R., Stein, C. The MIT Press. ISBN:978-0-262-03384-8.

Also, please refer to the Booklist for Part IIB Courses for references to this module, this can be found on the associated Moodle course.

Examination Guidelines

Please refer to Form & conduct of the examinations.

 
Last modified: 04/06/2025 13:33

Engineering Tripos Part IIB, 4M26: Algorithms and Data Structures, 2024-25

Module Leader

Prof Per Ola Kristensson

Lecturers

Prof Per Ola Kristensson

Timing and Structure

Lent term. 16 lectures (including two integrated examples classes). Assessment: 100% exam.

Aims

The aims of the course are to:

  • Introduce the principles behind algorithm and data structure design and evaluation.
  • Cover key topics including elementary and advanced data structures, including sorting algorithms, graph algorithms, and so on.
  • Provide an understanding of how to translate algorithms into code for selected engineering problems through coding-focused computerised examples papers.

Objectives

As specific objectives, by the end of the course students should be able to:

  • Analyse the computational efficiency of algorithms.
  • Re-implement and debug algorithms.
  • Correctly choose a suitable algorithmic solution and set of data structures for a computational problem.
  • Understand the theoretical and practical advantages and disadvantages of various algorithmic approaches and established solutions.
  • Devise and implement new algorithms and data structures, or modify existing algorithms and data structures, to solve previously unencountered tasks.

Content

  • Part 1: Fundamentals of Algorithms and Data Structures (7L + 1 Example Class)
    • Interpreting and writing pseudocode, demonstrating correctness, arriving at tight/lower/upper bounds of running time/storage, and solving computational problems using a repertoire of data structures and algorithmic approaches.
  • Part 2: Algorithms and Data Structures in Engineering (7L + 1 Example Class)
    • Translating pseudocode into code, debug implementations of algorithms and data structures, apply algorithms and data structures to solve a range of frequent engineering problems, such as finding shortest paths, resource allocation, and scheduling.

Booklists

Introduction to Algorithms (3rd ed) by Cormen, T., Leiserson, C., Rivest, R., Stein, C. The MIT Press. ISBN:978-0-262-03384-8.

Also, please refer to the Booklist for Part IIB Courses for references to this module, this can be found on the associated Moodle course.

Examination Guidelines

Please refer to Form & conduct of the examinations.

 
Last modified: 26/07/2024 14:46

Engineering Tripos Part IIB, 4M26: Algorithms and Data Structures, 2023-24

Module Leader

Dr S Albanie

Lecturers

Dr S Albanie & Prof Per Ola Kristensson

Timing and Structure

Lent term. 16 lectures (including 3 examples classes).

Aims

The aims of the course are to:

  • Introduce the principles behind algorithm and data structure design and evaluation.
  • Cover key topics including elementary and advanced data structures, sorting algorithms, graph algorithms, etc.
  • Provide an extensive hands-on understanding of the aforementioned topics via coding-focused computerised examples papers and exam.

Objectives

As specific objectives, by the end of the course students should be able to:

  • Analyse computational efficiency of most algorithms.
  • Re-implement and debug algorithms taught under time constraints.
  • Correctly choose the right algorithmic solution and data structures for the problem encountered.
  • Understand relative theoretical and practical advantages and disadvantages of various methods.
  • Devise and implement new algorithms or modify existing algorithms to solve previously unencountered tasks.

Content

  • Introduction (1L)
    • Algorithms and Data Structures: what are algorithms, why study algorithms and how? Introduction of the coding platform and other resources. Applications.
  • Fundamentals of Algorithms (2L)
    • Elementary data structures - stacks and ques, linked-lists, arrays, dictionaries. Algorithmic complexity. Strategies for algorithmic design: divide and conquer, dynamic algorithms, greedy algorithms.
  • Advanced Data Structures (2L)
    • Hash tables, binary search trees, red-black trees, B-trees.
  • Sorting Algorithms (2L)
    • Sorting algorithms - Heapsort, Quicksort, sorting in linear time.
  • Graph Algorithms (3L)
    • Graph algorithms - shortest path (BFS, DFS, Dijkstra, Bellman-Ford), topological sorting, strongly connected components, maximum flow (Ford-Fulkerson), minimum spanning trees (Kruskal’s, Primm’s).
  • Further Topics (2L)
    • Parallel algorithms, NP-completeness
  • Recent Developments (1L)
    • Large language models for code generation.
  • Example classes (3L)
    • Discussion of examples papers and past examination papers.

Booklists

Introduction to Algorithms (3rd ed) by Cormen, T., Leiserson, C., Rivest, R., Stein, C. The MIT Press. ISBN:978-0-262-03384-8.

Also, please refer to the Booklist for Part IIB Courses for references to this module, this can be found on the associated Moodle course.

Examination Guidelines

Please refer to Form & conduct of the examinations.

 
Last modified: 01/10/2023 20:27

Engineering Tripos Part IIB, 4M26: Algorithms and Data Structures, 2022-23

Module Leader

Dr I Budvytis

Lecturers

Dr I Budvytis and Dr S Albanie

Timing and Structure

Lent term. 16 lectures (including 3 examples classes). Intake: Part IIB students only.

Prerequisites

Due to a novel form of assessment (coding based exam) the intake of this course will be limited to approximately 30 Part IIB students for the first year (2022-2023). Only students who have strong skills in coding with Python are expected to attend this module. Students who are interested in taking this module will be required to pass a coding test in Michaelmas 2022. It is important to have an alternative course in mind for Michaelmas/Lent term in case one does not pass the test.

Aims

The aims of the course are to:

  • Introduce the principles behind algorithm and data structure design and evaluation.
  • Cover key topics including elementary and advanced data structures, sorting algorithms, graph algorithms, etc.
  • Provide an extensive hands-on understanding of the aforementioned topics via coding-focused computerised examples papers and exam.

Objectives

As specific objectives, by the end of the course students should be able to:

  • Analyse computational efficiency of most algorithms.
  • Re-implement and debug algorithms taught under time constraints.
  • Correctly choose the right algorithmic solution and data structures for the problem encountered.
  • Understand relative theoretical and practical advantages and disadvantages of various methods.
  • Devise and implement new algorithms or modify existing algorithms to solve previously unencountered tasks.

Content

  • Introduction (1L)
    • Algorithms and Data Structures: what are algorithms, why study algorithms and how? Introduction of the coding platform and other resources. Applications.
  • Fundamentals of Algorithms (2L)
    • Elementary data structures - stacks and ques, linked-lists, arrays, dictionaries. Algorithmic complexity. Strategies for algorithmic design: divide and conquer, dynamic algorithms, greedy algorithms.
  • Advanced Data Structures (2L)
    • Hash tables, binary search trees, red-black trees, b-trees.
  • Sorting Algorithms (2L)
    • Sorting algorithms - Heapsort, Quicksort, sorting in linear time.
  • Graph Algorithms (3L)
    • Graph algorithms - shortest path (BFS, DFS, Dijkstra, Bellman-Ford), topological sorting, strongly connected components, maximum flow (Ford-Fulkerson), minimum spanning trees (Kruskal’s, Primm’s).
  • Further Topics (2L)
    • Parallel algorithms, matrix operations, NP-completeness, approximation algorithms.
  • Recent Developments (1L)
    • Large language models for code generation.
  • Example classes (3L)
    • Discussion of examples papers and past examination papers.

Further notes

The information regarding coding test in Michaelmas 2022 will be made available via Moodle page of 4M26 - Algorithms and Data Structures.

Examples papers

A computerised exam is held at the Design Project Office (DPO). A mixture of (i) coding, (ii) simple pen-and-paper algorithm run-through and (iii) short theoretical questions are provided in the exam paper. See example question here: http://mi.eng.cam.ac.uk/~ib255/files/4M26-Algorithms_and_Data_Structures...

Booklists

Introduction to Algorithms (3rd ed) by Cormen, T., Leiserson, C., Rivest, R., Stein, C. The MIT Press. ISBN:978-0-262-03384-8.

Also, please refer to the Booklist for Part IIB Courses for references to this module, this can be found on the associated Moodle course.

Examination Guidelines

Please refer to Form & conduct of the examinations.

 
Last modified: 17/01/2023 13:59

Engineering Tripos Part IIB, 4M26: Algorithms and Data Structures, 2021-22

Module Leader

Dr I Budvytis

Lecturer

Dr I Budvytis

Timing and Structure

Lent term. 16 lectures (including 3 examples classes). Intake: Part IIB students, Part IIA (optional).

Prerequisites

Due to a novel form of assessment (coding based exam) the intake of this course will be limited to 30-60 students for the first year (2021-2022). Only students who have strong skills in coding with Python are expected to attend this module. Students who are interested in taking this module will be required to pass a coding test in Michaelmas 2021. It is important to have an alternative course in mind for Lent term in case one does not pass the test.

Aims

The aims of the course are to:

  • Introduce the principles behind algorithm and data structure design and evaluation.
  • Cover key topics including elementary and advanced data structures, sorting algorithms, graph algorithms, etc.
  • Provide an extensive hands-on understanding of the aforementioned topics via coding-focused computerised examples papers and exam.

Objectives

As specific objectives, by the end of the course students should be able to:

  • Analyse computational efficiency of most algorithms.
  • Re-implement and debug algorithms taught under time constraints.
  • Correctly choose the right algorithmic solution and data structures for the problem encountered.
  • Understand relative theoretical and practical advantages and disadvantages of various methods.
  • Devise and implement new algorithms or modify existing algorithms to solve previously unencountered tasks.

Content

  • Introduction (1L)
    • Algorithms and Data Structures: what is it, why study it and how? Introduction of the coding platform and other resources. Applications.
  • Fundamentals of Algorithms (2L)
    • Elementary data structures - stacks and ques, linked-lists, arrays, dictionaries. Algorithmic complexity and NP completeness. Strategies for algorithmic design: divide and conquer, dynamic algorithms, greedy algorithms.
  • Advanced Data Structures (2L)
    • Hash tables, binary search trees, red-black trees, etc.
  • Sorting Algorithms (2L)
    • Sorting algorithms - Bubblesort, Heapsort, Quicksort, etc. Sorting in linear time.
  • Graph Algorithms (3L)
    • Graph algorithms - shortest path (BFS, DFS, Dijkstra, Bellman-Ford, etc), maximum flow (Ford-Fulkerson), minimum spanning trees (Kruskal’s, Primm’s).
  • Computational Geometry and Mathematical Algorithms (2L)
    • Line segment intersection, closest pair, convex hull. DFT and FFT, Matrix multiplication and inversion, number theoretic algorithms.
  • Advanced Topics (1L)
    • Multi-threaded algorithms, approximation algorithms, differentiable programming.
  • Example classes (3L)
    • Discussion of examples papers and past examination papers.

Examples papers

A computerised exam is held at the Design Project Office (DPO). A mixture of (i) coding, (ii) simple pen-and-paper algorithm run-through and (iii) short theoretical questions are provided in the exam paper. See example question here: http://mi.eng.cam.ac.uk/~ib255/files/4M26-Algorithms_and_Data_Structures...

Booklists

Introduction to Algorithms (3rd ed) by Cormen, T., Leiserson, C., Rivest, R., Stein, C. The MIT Press. ISBN:978-0-262-03384-8.

Also, please refer to the Booklist for Part IIB Courses for references to this module, this can be found on the associated Moodle course.

Examination Guidelines

Please refer to Form & conduct of the examinations.

 
Last modified: 04/06/2021 15:57

Engineering Tripos Part IIB, 4G9: Biomedical Engineering, 2025-26

Module Leader

Dr T Bashford

Lecturers

Prof M Sutcliffe (MPFS), Dr T Bashford (TB), Prof T Makin (TM), Prof A Flewitt (AJF)

Timing and Structure

11 lectures; four discussion meetings. Assessment: 100% coursework. Lectures will be recorded.

Aims

The aims of the course are to:

  • Provide a comprehensive overview of biomedical engineering
  • Outline the key principles of good engineering design in a biomedical context
  • Introduce the concept of system design approach for sustainable improvement
  • Describe the technology adoption pathway in healthcare

Objectives

As specific objectives, by the end of the course students should be able to:

  • Conduct research and define the issues with existing medical devices or clinical procedures
  • Understand how to apply engineering knowledge to solve biomedical challenges
  • Communicate and work with healthcare professionals to validate the engineering designs
  • Use a broader systems design toolkit to address larger and more complex issues in healthcare

Content

The course has four case studies. Students will 'major' on one case study, but will need to attend (either in person or via recorded lectures) the lectures pertaining to the other case studies to cover all the required elements of the course.

General introduction (3L total) [TB (2L); MPFS (0.33L); GMB (0.33L); AJF (0.33L)]

Introduction of biomedical engineering and systems approach to systems improvement; introduction of four case studies

Monitoring after brain injury case study (2L) [TB]

Monitoring after brain injury; novel technology; stakeholder acceptance regulatory pathway.

Biomechanics case study (2L) [MPFS]

Knee biomechanics/kinematics; design for the knee replacement; clinical/patient acceptance

Wearable motor augmentation case study (2L) [TM]

Neurological, neuroanatomical and user considerations in the design of augmentation technology,  Basics of anatomy, user needs, patient and public engagement, and rapid iterative design cycling.

Biosensor case study (2L) [AJF]

Concept of point-of-care; microfluidic platform-assisted biosensors; manufacturing

Discussion meetings (5L) [Guest mentors (2L); all lecturers (3L)]

Short presentation sessions from guest mentors (University, NHS, industry) and panel discussions; open discussion meetings with lecturers

Coursework

Coursework Format

Due date

& marks

Initial coursework mapping 'canvas'

One-page document focusing on the big picture of the chosen case study

Learning objective:

  • demonstrate the framework of systematic engineering design
  • encourage the student to plan the case study by raising questions
  • adapt a genetic system design framework to a specific project at a high level
  • make an initial list of foci under each key topic on the canvas template

Individual Report

anonymously marked

End of week 2

[10%]

Expanded courswork mapping ' canvas'

A much expanded version of the first coursework element

Learning objective:

  • provide further guidance on the canvas on the activities that need to be considered by providing example questions
  • reflect on an accurate problem identification, risk management, the interdependency between technical and social components in the project

Individual Report

anonymously marked

End of week 5

[30%]

Final report

Final report - 20 page upper limit, 3,000 word upper limit

Learning objective:

  • provide information on the problem formulation, requirement specification, design, risk assessment, stakeholder acceptance, marketing/policy strategy, design solution, etc.

Individual Report

anonymously marked

End of week 9

[60%]

 

Booklists

Please refer to the Booklist for Part IIB Courses for references to this module, this can be found on the associated Moodle course.

Examination Guidelines

Please refer to Form & conduct of the examinations.

 
Last modified: 16/03/2026 17:09

Engineering Tripos Part IIB, 4G9: Biomedical Engineering, 2024-25

Module Leader

Dr T Bashford

Lecturers

Prof M Sutcliffe (MPFS), Dr T Bashford (TB), Prof A Flewitt (AJF)

Timing and Structure

11 lectures; four discussion meetings. Assessment: 100% coursework. Lectures will be recorded.

Aims

The aims of the course are to:

  • Provide a comprehensive overview of biomedical engineering
  • Outline the key principles of good engineering design in a biomedical context
  • Introduce the concept of system design approach for sustainable improvement
  • Describe the technology adoption pathway in healthcare

Objectives

As specific objectives, by the end of the course students should be able to:

  • Conduct research and define the issues with existing medical devices or clinical procedures
  • Understand how to apply engineering knowledge to solve biomedical challenges
  • Communicate and work with healthcare professionals to validate the engineering designs
  • Use a broader systems design toolkit to address larger and more complex issues in healthcare

Content

The course has four case studies. Students will 'major' on one case study, but will need to attend (either in person or via recorded lectures) the lectures pertaining to the other case studies to cover all the required elements of the course.

General introduction (3L total) [TB (2L); MPFS (0.33L); GMB (0.33L); AJF (0.33L)]

Introduction of biomedical engineering and systems approach to systems improvement; introduction of four case studies

Brain injury case study (2L) [TB]

Monitoring after brain injury; novel technology; stakeholder acceptance regulatory pathway.

Biomechanics case study (2L) [MPFS]

Knee biomechanics/kinematics; design for the knee replacement; clinical/patient acceptance

Wearable motor augmentation case study (2L) [TM]

Neurological, neuroanatomical and user considerations in the design of augmentation technology,  Basics of anatomy, user needs, patient and public engagement, and rapid iterative design cycling.

Biosensor case study (2L) [AJF]

Concept of point-of-care; microfluidic platform-assisted biosensors; manufacturing

Discussion meetings (5L) [Guest mentors (2L); all lecturers (3L)]

Short presentation sessions from guest mentors (University, NHS, industry) and panel discussions; open discussion meetings with lecturers

Further notes

Please note that the number of places is limited and if the module looks likely to be oversubscribed preference will be given to those who initially selected this module in their preliminary selections on COMET.

Coursework

Coursework Format

Due date

& marks

Initial coursework mapping 'canvas'

One-page document focusing on the big picture of the chosen case study

Learning objective:

  • demonstrate the framework of systematic engineering design
  • encourage the student to plan the case study by raising questions
  • adapt a genetic system design framework to a specific project at a high level
  • make an initial list of foci under each key topic on the canvas template

Individual Report

anonymously marked

End of week 2

[10%]

Expanded courswork mapping ' canvas'

A much expanded version of the first coursework element

Learning objective:

  • provide further guidance on the canvas on the activities that need to be considered by providing example questions
  • reflect on an accurate problem identification, risk management, the interdependency between technical and social components in the project

Individual Report

anonymously marked

End of week 5

[30%]

Final report

Final report - 20 page upper limit, 5,000 word upper limit

Learning objective:

  • provide information on the problem formulation, requirement specification, design, risk assessment, stakeholder acceptance, marketing/policy strategy, design solution, etc.

Individual Report

anonymously marked

End of week 9

[60%]

 

Booklists

Please refer to the Booklist for Part IIB Courses for references to this module, this can be found on the associated Moodle course.

Examination Guidelines

Please refer to Form & conduct of the examinations.

 
Last modified: 31/05/2024 10:09

Engineering Tripos Part IIB, 4G9: Biomedical Engineering, 2024-25

Module Leader

Dr T Bashford

Lecturers

Prof M Sutcliffe (MPFS), Dr T Bashford (TB), Prof T Makin (TM), Prof A Flewitt (AJF)

Timing and Structure

11 lectures; four discussion meetings. Assessment: 100% coursework. Lectures will be recorded.

Aims

The aims of the course are to:

  • Provide a comprehensive overview of biomedical engineering
  • Outline the key principles of good engineering design in a biomedical context
  • Introduce the concept of system design approach for sustainable improvement
  • Describe the technology adoption pathway in healthcare

Objectives

As specific objectives, by the end of the course students should be able to:

  • Conduct research and define the issues with existing medical devices or clinical procedures
  • Understand how to apply engineering knowledge to solve biomedical challenges
  • Communicate and work with healthcare professionals to validate the engineering designs
  • Use a broader systems design toolkit to address larger and more complex issues in healthcare

Content

The course has four case studies. Students will 'major' on one case study, but will need to attend (either in person or via recorded lectures) the lectures pertaining to the other case studies to cover all the required elements of the course.

General introduction (3L total) [TB (2L); MPFS (0.33L); GMB (0.33L); AJF (0.33L)]

Introduction of biomedical engineering and systems approach to systems improvement; introduction of four case studies

Monitoring after brain injury case study (2L) [TB]

Monitoring after brain injury; novel technology; stakeholder acceptance regulatory pathway.

Biomechanics case study (2L) [MPFS]

Knee biomechanics/kinematics; design for the knee replacement; clinical/patient acceptance

Wearable motor augmentation case study (2L) [TM]

Neurological, neuroanatomical and user considerations in the design of augmentation technology,  Basics of anatomy, user needs, patient and public engagement, and rapid iterative design cycling.

Biosensor case study (2L) [AJF]

Concept of point-of-care; microfluidic platform-assisted biosensors; manufacturing

Discussion meetings (5L) [Guest mentors (2L); all lecturers (3L)]

Short presentation sessions from guest mentors (University, NHS, industry) and panel discussions; open discussion meetings with lecturers

Further notes

Please note that the number of places is limited and if the module looks likely to be oversubscribed preference will be given to those who initially selected this module in their preliminary selections on COMET.

Coursework

Coursework Format

Due date

& marks

Initial coursework mapping 'canvas'

One-page document focusing on the big picture of the chosen case study

Learning objective:

  • demonstrate the framework of systematic engineering design
  • encourage the student to plan the case study by raising questions
  • adapt a genetic system design framework to a specific project at a high level
  • make an initial list of foci under each key topic on the canvas template

Individual Report

anonymously marked

End of week 2

[10%]

Expanded courswork mapping ' canvas'

A much expanded version of the first coursework element

Learning objective:

  • provide further guidance on the canvas on the activities that need to be considered by providing example questions
  • reflect on an accurate problem identification, risk management, the interdependency between technical and social components in the project

Individual Report

anonymously marked

End of week 5

[30%]

Final report

Final report - 20 page upper limit, 5,000 word upper limit

Learning objective:

  • provide information on the problem formulation, requirement specification, design, risk assessment, stakeholder acceptance, marketing/policy strategy, design solution, etc.

Individual Report

anonymously marked

End of week 9

[60%]

 

Booklists

Please refer to the Booklist for Part IIB Courses for references to this module, this can be found on the associated Moodle course.

Examination Guidelines

Please refer to Form & conduct of the examinations.

 
Last modified: 27/10/2024 12:22

Engineering Tripos Part IIB, 4G9: Biomedical Engineering, 2022-23

Module Leader

Prof M P F Sutcliffe

Lecturers

Prof M Sutcliffe (MPFS), Dr T Bashford (TB), Dr G Bale (GMB), Prof A Flewitt (AJF)

Timing and Structure

11 lectures; four discussion meetings. Assessment: 100% coursework. Lectures will be recorded.

Aims

The aims of the course are to:

  • Provide a comprehensive overview of biomedical engineering
  • Outline the key principles of good engineering design in a biomedical context
  • Introduce the concept of system design approach for sustainable improvement
  • Describe the technology adoption pathway in healthcare

Objectives

As specific objectives, by the end of the course students should be able to:

  • Conduct research and define the issues with existing medical devices or clinical procedures
  • Understand how to apply engineering knowledge to solve biomedical challenges
  • Communicate and work with healthcare professionals to validate the engineering designs
  • Use a broader systems design toolkit to address larger and more complex issues in healthcare

Content

The course has four case studies. Students will 'major' on one case study, but will need to attend (either in person or via recorded lectures) the lectures pertaining to the other case studies to cover all the required elements of the course.

General introduction (3L total) [TB (2L); MPFS (0.33L); GMB (0.33L); AJF (0.33L)]

Introduction of biomedical engineering and systems approach to systems improvement; introduction of four case studies

Engineering design case study (2L) [TB]

System approach in healthcare design; e.g. design of face mask/technology in the home; safety

Biomechanics case study (2L) [MPFS]

Knee biomechanics/kinematics; design for the knee replacement; clinical/patient acceptance

Biosignal processing case study (2L) [GMB]

Basics of anatomy, pathophysiology; design for optical brain monitoring; clinical trials

Biosensor case study (2L) [AJF]

Concept of point-of-care; microfluidic platform-assisted biosensors; manufacturing

Discussion meetings (5L) [Guest mentors (2L); all lecturers (3L)]

Short presentation sessions from guest mentors (University, NHS, industry) and panel discussions; open discussion meetings with lecturers

Further notes

Please note that the number of places is limited and if the module looks likely to be oversubscribed preference will be given to those who initially selected this module in their preliminary selections on COMET.

Coursework

Coursework Format

Due date

& marks

Initial coursework mapping 'canvas'

One-page document focusing on the big picture of the chosen case study

Learning objective:

  • demonstrate the framework of systematic engineering design
  • encourage the student to plan the case study by raising questions
  • adapt a genetic system design framework to a specific project at a high level
  • make an initial list of foci under each key topic on the canvas template

Individual Report

anonymously marked

End of week 2

[10%]

Expanded courswork mapping ' canvas'

A much expanded version of the first coursework element

Learning objective:

  • provide further guidance on the canvas on the activities that need to be considered by providing example questions
  • reflect on an accurate problem identification, risk management, the interdependency between technical and social components in the project

Individual Report

anonymously marked

End of week 5

[30%]

Final report

Final report - 20 page upper limit, 5,000 word upper limit

Learning objective:

  • provide information on the problem formulation, requirement specification, design, risk assessment, stakeholder acceptance, marketing/policy strategy, design solution, etc.

Individual Report

anonymously marked

End of week 9

[60%]

 

Booklists

Please refer to the Booklist for Part IIB Courses for references to this module, this can be found on the associated Moodle course.

Examination Guidelines

Please refer to Form & conduct of the examinations.

 
Last modified: 23/07/2022 17:56

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