Engineering Tripos Part IIB, 4M23: Electricity and Environment, 2025-26
Module Leader
Lecturer
Lecturer
Timing and Structure
Lent term. 2 hour sessions delivered in person. Assessment: 100% coursework.
Prerequisites
A basic engineering knowledge of electricity (first year undergraduate) and a familiarity with the units and notation associated with energy science and engineering is an advantage, but not essential. Assessment will be structured so as to be accessible to students from a range of backgrounds.
Aims
The aims of the course are to:
- provide students with a firm foundation in modern electricity policy with an emphasis on the UK.
- introduce students to a wide a variety of mature and emergent electricity generation and demand side technologies.
- expose students to the local, regional and global environmental effects of energy use.
- introduce the key considerations of energy policy and develop frameworks by which progress against policy goals may be achieved.
- discuss issues with electrification of heating and transport.
Objectives
As specific objectives, by the end of the course students should be able to:
- critique scenarios for the future UK electricity system out to 2050
- evaluate and compare the efficacy of different electricity generation technologies
- understand current and future electricity policy options
- appreciate how economics and engineering interact in a sustainable electricity system
Content
This module is a postgraduate module of Cambridge Judge Business School. It has its origins as an elective course of the MPhil in Technology Policy and the MPhil in Engineering for Sustainable Development. The module is of the standard size adopted in the Engineering Department and the Judge Business School, i.e. a nominal 16 hours. The course is delivered via one two-hour lecture each week for eight weeks.
We take the Great Britain electricity system as a working example which we will refer to throughout the course.
Overview - Class Introduction (Michael Pollitt)
Lecture 1
- History of Electrical Power and Energy Policy
- Fundamentals of the UK and USA Electricity System
- The nature of the current UK electricity bill and electricity market
- UK Energy Policy and Politics
- Principles of good energy policy
Environmental Effects of Fossil Fuel Use and what to do about them (Michael Pollitt)
Lecture 2
- Local Emissions and Impacts
- Putting a Price on Damages?
- Economic approaches to externalities
- Pricing carbon
- Experiences of the EU Emissions Trading System and carbon pricing in Australia
Electricity Demand (Michael Pollitt)
Lecture 3
- Economics of Electricity Demand
- The economics of smart energy services
- Technological aspects of electricity demand
- Social aspects of electricity demand
- Demand side policy
Fossil fuel generation, storage and future electricity markets (Michael Pollitt)
Lecture 4
- Current status of fossil-fuel power generation
- Economics of Carbon Capture and Storage
- The economics of electricity storage
- Business models for the internet of energy
- Future electricity market design
Renewables and the Electricity System (Michael Pollitt)
Lecture 5
- Renewables context
- Potential for renewables in the UK
- Place of renewables in electricity system
- How to subsidise renewables
- Lessons from around the world
Electrification of heating and transport? Electricity in Net Zero (Michael Pollitt)
Lecture 6
- Electrification of everything?
- Decarbonising heating with electricity
- Decarbonising transport with electricity
- Sector coupling and modelling Net Zero
- Policy recommendations for Net Zero
Electricity Networks for Net Zero (Teng Long)
Lecture 7
- Conventional and modern electric power systems
- Power electronics – enabling technology in power conversion
- Transport electrification
- Datacentre power supply
Nuclear Power, Electricity Security and EU Policy (Michael Pollitt)
Lecture 8
- Nuclear Power Technology
- History and Economics of Nuclear Power
- EU and UK energy security
- National security of electricity supply
- Meeting UK targets by Electricity Market Reform
- Good electricity policy?
Coursework
| Coursework | Format |
Due date & marks |
|---|---|---|
|
Essay on the decarbonisation challenge facing the UK electricity system. Learning objectives:
|
Individual Report 2000 words anonymously marked |
27 March 2026 [100/100] |
Booklists
Expected reading:
Glachant, J-M., Joskow, P. and Pollitt, M. (eds.) (2021) Handbook on Electricity Markets. Cheltenham: Edward Elgar. Online on iDiscover.
Ozawa, M., Chaplin, J., Pollitt, M., Reiner, D. and Warde, P. (eds.) (2019) In Search of Good Energy Policy. Cambridge: Cambridge University Press. Online on iDiscover.
Recommended reading:
Taylor, S. (2016) The Fall and Rise of Nuclear Power in Britain Cambridge: UIT Printed book at: JBS: HD9698.G72 T39 F3 2016 UL: C212.c.2239
Jamasb, T. and Pollitt, M. (eds.) (2011) The Future of Electricity Demand Cambridge: Cambridge University Press Printed book at: JBS: HD9685.G72 J35 2011 Engineering: DE.190 UL: 235.c.201.356 (South Front 6)
MacKay, D.J.C. (2009) Sustainable energy without the hot air. Cambridge: UIT E-book via withouthotair http://www.withouthotair.com/download.html Printed book at: Engineering: DE.164
Examination Guidelines
Please refer to Form & conduct of the examinations.
UK-SPEC
This syllabus contributes to the following areas of the UK-SPEC standard:
Toggle display of UK-SPEC areas.
GT1
Develop transferable skills that will be of value in a wide range of situations. These are exemplified by the Qualifications and Curriculum Authority Higher Level Key Skills and include problem solving, communication, and working with others, as well as the effective use of general IT facilities and information retrieval skills. They also include planning self-learning and improving performance, as the foundation for lifelong learning/CPD.
IA1
Apply appropriate quantitative science and engineering tools to the analysis of problems.
IA2
Demonstrate creative and innovative ability in the synthesis of solutions and in formulating designs.
KU1
Demonstrate knowledge and understanding of essential facts, concepts, theories and principles of their engineering discipline, and its underpinning science and mathematics.
KU2
Have an appreciation of the wider multidisciplinary engineering context and its underlying principles.
S1
The ability to make general evaluations of commercial risks through some understanding of the basis of such risks.
S3
Understanding of the requirement for engineering activities to promote sustainable development.
S4
Awareness of the framework of relevant legal requirements governing engineering activities, including personnel, health, safety, and risk (including environmental risk) issues.
E1
Ability to use fundamental knowledge to investigate new and emerging technologies.
E4
Understanding of and ability to apply a systems approach to engineering problems.
P1
A thorough understanding of current practice and its limitations and some appreciation of likely new developments.
P3
Understanding of contexts in which engineering knowledge can be applied (e.g. operations and management, technology, development, etc).
US1
A comprehensive understanding of the scientific principles of own specialisation and related disciplines.
US3
An understanding of concepts from a range of areas including some outside engineering, and the ability to apply them effectively in engineering projects.
US4
An awareness of developing technologies related to own specialisation.
Last modified: 01/10/2025 16:47
Engineering Tripos Part IIB, 4M9: Surveying Field Course, 2017-18
Module Leader
Timing and Structure
Long Vacation between Part IIA and Part IIB. 2 - 15 July 2017 for 2017/18. and 1 - 14 July for 2018/19 -Assessment: 100% coursework
Prerequisites
Surveying experience, e.g. from IIA Engineering Area Activity or Fieldwork project.
Aims
The aims of the course are to:
- give students experience in surveying to a high accuracy, on a larger scale (and at greater altitude) than is possible near Cambridge.
Objectives
As specific objectives, by the end of the course students should be able to:
- plan the work for a complex setting-out exercise.
- know how to use high-accuracy and long-range surveying equipment.
- understand the role of GNSS in modern survey.
- know the calculation methods needed for the reduction of three-dimensional survey data.
- have experience in leading a survey team, and the planning of logistics.
- understand the effects of small errors in measurement, and how to minimise their effects.
- understand the need for long-term record keeping, and the information to be recorded.
Content
This module gives students experience in surveying to a high accuracy, on a larger scale than is possible near Cambridge. The exercise includes three-dimensional position-fixing and setting-out in a hilly location, and involves the use of first-order surveying instruments and precise computation.
Throughout the course, short lectures will be given as necessary to explain the theory needed for the practical work in hand. Topics covered include: geoids, ellipsoids, projections and grids; the theory and practice of GNSS, including the verification of Geoid models; reduction of angles and distances; least-squares adjustment.
The course has a capacity of 16. If over-subscribed, a ballot will be held in May, but with preference given to Civil Engineering students.
Coursework
The Course runs continuously over a two week period, and includes the following:
- Exercise planning and siting of control stations;
- Fixing of control stations using GNSS;
- High-accuracy traversing and resectioning;
- Fixing of heights by precise digital levelling and trigonometric heighting;
- Long-range distance measurement;
- Three-dimensional setting out;
- Adjustment, computation and record keeping.
The output of this course will be a set of numerical calculations leading to the setting-out of one or more points in the field. Since incorrect answers will be systematically eliminated from this result, assessment will be based on the course demonstrators' estimation of each student's ability to:
- Take accurate readings efficiently with the equipment provided;
- Make a neat and decipherable record of other students' readings;
- Produce accurate and well laid-out calculations;
- Check the calculations of others;
- Plan and manage the activities of the team;
- Generally contribute to the efficiency and productivity of the team.
Booklists
Examination Guidelines
Please refer to Form & conduct of the examinations.
UK-SPEC
This syllabus contributes to the following areas of the UK-SPEC standard:
Toggle display of UK-SPEC areas.
GT1
Develop transferable skills that will be of value in a wide range of situations. These are exemplified by the Qualifications and Curriculum Authority Higher Level Key Skills and include problem solving, communication, and working with others, as well as the effective use of general IT facilities and information retrieval skills. They also include planning self-learning and improving performance, as the foundation for lifelong learning/CPD.
IA1
Apply appropriate quantitative science and engineering tools to the analysis of problems.
IA2
Demonstrate creative and innovative ability in the synthesis of solutions and in formulating designs.
KU1
Demonstrate knowledge and understanding of essential facts, concepts, theories and principles of their engineering discipline, and its underpinning science and mathematics.
KU2
Have an appreciation of the wider multidisciplinary engineering context and its underlying principles.
E1
Ability to use fundamental knowledge to investigate new and emerging technologies.
E2
Ability to extract data pertinent to an unfamiliar problem, and apply its solution using computer based engineering tools when appropriate.
E3
Ability to apply mathematical and computer based models for solving problems in engineering, and the ability to assess the limitations of particular cases.
P1
A thorough understanding of current practice and its limitations and some appreciation of likely new developments.
P3
Understanding of contexts in which engineering knowledge can be applied (e.g. operations and management, technology, development, etc).
P7
Awareness of quality issues.
P8
Ability to apply engineering techniques taking account of a range of commercial and industrial constraints.
US1
A comprehensive understanding of the scientific principles of own specialisation and related disciplines.
US2
A comprehensive knowledge and understanding of mathematical and computer models relevant to the engineering discipline, and an appreciation of their limitations.
US3
An understanding of concepts from a range of areas including some outside engineering, and the ability to apply them effectively in engineering projects.
US4
An awareness of developing technologies related to own specialisation.
Last modified: 24/08/2017 15:52
Engineering Tripos Part IIB, 4G4: Biomimetics, 2017-18
Module Leader
Lecturers
Dr M Oyen, Dr F Iida, and Dr W Federle
Timing and Structure
Lent term. 12 lectures + Group project work. Assessment: 100% coursework
Aims
The aims of the course are to:
- Develop an understanding the ways engineers adopt and adapt ideas from nature and make new engineering entities.
Objectives
As specific objectives, by the end of the course students should be able to:
- Understand how scientists are borrowing from nature across many different fields of engineering, with in-depth understanding on one topic (project)
- Identify new possibilities for biomimesis in design.
- Learn how to read the current biomimetics literature.
Content
Introduction and Project assignment ( M. Oyen, CUED) (2L)
Bioinspired Robotics (F. Iida, CUED) (2L)
- Legged robot locomotion and underactuated motion control
- Soft robotics and bio-inspired actuation
Biomimetic adhesion and adhesives (W. Federle, Zoology) (4L)
- Attachment devices and mechanisms in nature
- Approaches to develop biomimetic adhesives
Biomimetic materials (M. Oyen, CUED) (4L)
- Protein-based structural materials
- Protein folding, weak bonding, hydration
- Biomineralisation
- Biosilification, calcium carbonates, calcium phosphates
- Composite mechanics applied to natural materials
- Polymer amphiphiles
- Self-healing materials
Project Presentations (2L)
Coursework
Students will work in groups of 2-3 on a biomimetics design portfolio for one specific case from any of the following: biomimetic materials (e.g. bone, shell); natural structures (e.g. photonic crystals, lotus paint, adhesives); robots that swim, fly, or crawl like creatures; or any other biomimetics topic identified as acceptable via discussion with the module leader.
| Coursework | Format |
Due date & marks |
|---|---|---|
|
[Group Presentation] Comparison of natural vs engineering solutions to a specific problem Learning objective:
|
Group Presentation non-anonymously marked |
Week 8 Lent [12/60] |
|
[Preliminary Report] Comparison of natural vs engineering solutions to a specific problem Learning objective:
|
Individual Report non-anonymously marked |
Friday week 10 Lent [18/60] |
|
[Final Report] Biomimetic design dossier, written report plus additional drawings, calculations, computer simulations, and prototypes Learning objective:
|
Individual Report non-anonymously marked |
Tuesday week 1 of Easter Term [30/60] |
Booklists
Please see the Booklist for Group G Courses for references for this module.
Examination Guidelines
Please refer to Form & conduct of the examinations.
UK-SPEC
This syllabus contributes to the following areas of the UK-SPEC standard:
Toggle display of UK-SPEC areas.
GT1
Develop transferable skills that will be of value in a wide range of situations. These are exemplified by the Qualifications and Curriculum Authority Higher Level Key Skills and include problem solving, communication, and working with others, as well as the effective use of general IT facilities and information retrieval skills. They also include planning self-learning and improving performance, as the foundation for lifelong learning/CPD.
IA1
Apply appropriate quantitative science and engineering tools to the analysis of problems.
IA2
Demonstrate creative and innovative ability in the synthesis of solutions and in formulating designs.
KU1
Demonstrate knowledge and understanding of essential facts, concepts, theories and principles of their engineering discipline, and its underpinning science and mathematics.
KU2
Have an appreciation of the wider multidisciplinary engineering context and its underlying principles.
E1
Ability to use fundamental knowledge to investigate new and emerging technologies.
E2
Ability to extract data pertinent to an unfamiliar problem, and apply its solution using computer based engineering tools when appropriate.
P1
A thorough understanding of current practice and its limitations and some appreciation of likely new developments.
P3
Understanding of contexts in which engineering knowledge can be applied (e.g. operations and management, technology, development, etc).
US1
A comprehensive understanding of the scientific principles of own specialisation and related disciplines.
US3
An understanding of concepts from a range of areas including some outside engineering, and the ability to apply them effectively in engineering projects.
US4
An awareness of developing technologies related to own specialisation.
Last modified: 04/10/2017 13:59
Engineering Tripos Part IIB, 4G2: Bioelectronics, 2025-26
Module Leader
Lecturers
Timing and Structure
Michaelmas term. Lectures and coursework. Assessment: 100% coursework.
Aims
The aims of the course are to:
- To provide an introduction to the field of bioelectronics.
- To highlight the application of bioelectronic devices in the medical and consumer sectors.
Objectives
As specific objectives, by the end of the course students should be able to:
- Extend principles of engineering to the development of bioelectronic devices.
- Understand the principles of signal transduction between biology and electronics.
- Appreciate the basic configuration and distinction among bioelectronic devices.
- Demonstrate appreciation for the technical limits of performance.
- Make design and selection decisions in response to measurement and actuation problems amenable to the use of bioelectronic devices.
- Be able to evaluate novel trends in the field.
Content
One of the most important scientific and technological frontiers of our time is the interfacing of electronics with living systems. This endeavour promises to help gain a better understanding of biological phenomena and devliver new tools for diagnosis and treatment of pathologies including epilepsy and Partinson's disease. The aim of this course is to provide an introduction to the field of bioelectronics. The course will link science and engineering concepts to the principles, technologies, and applications of bioelectronics. The fundamentals of electrophysiology and electrochemistry will be applied to implantable and cutaneous bioelectronic devices and to in vitro systems to explain the principles of operation. Examples from current scientific literature will be analysed.
COURSE CONTENT
1. Introduction
Drivers for bioelectronics
What is bioelectronics?
Organisation of the module
Part I: Fundamentals
2. Elements of anatomy and function
The nervous system
The neuron
Neural circuits
Other systems of interest
3. Signal transduction across the biotic/abiotic interface
Types of electrodes
Electrochemical impedance
Electrochemical reactions
Neural recording and stimulation
Transistors as transducers
Complete systems
Part II: Technology
4. Implantable devices
Cardiac pacemaker
Auditory and visual prostheses
CNS and PNS implants
Implantable sensors and drug delivery systems
The foreign body response
5. Cutaneous devices
Recording devices for brain, heart, muscle
Stimulation devices for brain, heart, muscle
Wearable electronics and electronic skins
6. In vitro devices
Electrochemical biosensors
In vitro electrophysiology
Impedance biosensors
Body-on-a-chip
Part III: Translation and ethics
7. Translation
From the drawing board to patients at scale
Device discovery
Preclinical research and prototyping
Pathway to approval
Regulatory review
Post-market monitoring
8. Ethics
Medical ethics
When a device becomes part of you
What happens to the data?
Animal research
Further notes
The course will be interdispersed with discussions highlighting the state-of-the art in the field.
Coursework
The coursework will be assessed on two marked assignments. The first assignment will involve a laboratory session illustrating the functional demonstration of glucose sensor technology. The second assignment will involve a laboratory session illustrating the principle of a quartz crystal microbalance and related acoustic sensor technologies.
| Coursework | Format |
Due date & marks |
|---|---|---|
|
Coursework activity #1 : Cutaneous electrophysiology Learning objectives:
|
Individual Report anonymously marked |
Typically week 5 [30/60] |
|
Coursework activity #2 : Mock design of a bioelectronic system Learning objectives:
|
Individual Report anonymously marked |
Typically week 9 [30/60] |
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.
UK-SPEC
This syllabus contributes to the following areas of the UK-SPEC standard:
Toggle display of UK-SPEC areas.
GT1
Develop transferable skills that will be of value in a wide range of situations. These are exemplified by the Qualifications and Curriculum Authority Higher Level Key Skills and include problem solving, communication, and working with others, as well as the effective use of general IT facilities and information retrieval skills. They also include planning self-learning and improving performance, as the foundation for lifelong learning/CPD.
IA1
Apply appropriate quantitative science and engineering tools to the analysis of problems.
IA2
Demonstrate creative and innovative ability in the synthesis of solutions and in formulating designs.
KU1
Demonstrate knowledge and understanding of essential facts, concepts, theories and principles of their engineering discipline, and its underpinning science and mathematics.
KU2
Have an appreciation of the wider multidisciplinary engineering context and its underlying principles.
D1
Wide knowledge and comprehensive understanding of design processes and methodologies and the ability to apply and adapt them in unfamiliar situations.
D4
Ability to generate an innovative design for products, systems, components or processes to fulfil new needs.
E1
Ability to use fundamental knowledge to investigate new and emerging technologies.
E2
Ability to extract data pertinent to an unfamiliar problem, and apply its solution using computer based engineering tools when appropriate.
P1
A thorough understanding of current practice and its limitations and some appreciation of likely new developments.
P3
Understanding of contexts in which engineering knowledge can be applied (e.g. operations and management, technology, development, etc).
US1
A comprehensive understanding of the scientific principles of own specialisation and related disciplines.
US3
An understanding of concepts from a range of areas including some outside engineering, and the ability to apply them effectively in engineering projects.
US4
An awareness of developing technologies related to own specialisation.
Last modified: 22/07/2025 21:51
Engineering Tripos Part IIB, 4G2: Biosensors, 2017-18
Leader
Lecturers
Prof A Seshia and Professor E A Hall
Timing and Structure
Lent term. Lectures and coursework. Assessment: 100% coursework.
Aims
The aims of the course are to:
- link engineering principles to understanding of biosystems in sensors and bioelectronics
Objectives
As specific objectives, by the end of the course students should be able to:
- extend principles of engineering to the development of bioanalytical devices and the design of biosensors.
- understand the principles of linking cell components and biological pathways with energy transduction, sensing and detection
- appreciate the basic configuration and distinction among biosensor systems.
- demonstrate appreciation for the technical limits of performance.
- make design and selection decisions in response to measurement problems amenable to the use of biosensors.
Content
This course covers the principles, technologies, methods and applications of biosensors and bioinstrumentation. The objective of this course is to link engineering principles to understanding of biosystems in sensors and bioelectronics. It will provide the student with detail of methods and procedures used in the design, fabrication and application of biosensors and bioelectronic devices. The fundamentals of measurement science are applied to optical, electrochemical, mass, and pressure signal transduction. Upon successful completion of this course, students are expected to be able to explain biosensing and transduction techniques, as well as design and construct biosensor instrumentation.
Introduction
- Overview of Biosensors
- Fundamental elements of biosensor devices
- Engineering sensor proteins
Electrochemical Biosensors
- Electrochemical principles
- Amperometric biosensors and charge transfer pathways in enzymes
- Glucose biosensors
- Engineering electrochemical biosensors
Optical Biosensors
- Optics for biosensors
- Attenuated total reflection systems
Acoustic Biosensors
- Analytical models
- Acoustic sensor formats
- Quartz crystal microbalance
Micro- and Nano-technologies for biosensors
- Microfluidic interfaces for biosensors
- DNA and protein microarrays
- Microfabricated PCR technology
Diagnostics for the real world
- Communication and tracking in health monitoring
- Detection in resource limited settings
Coursework
The coursework will be assessed on two marked assignments. The first assignment will involve a laboratory session illustrating the functional demonstration of glucose sensor technology. The second assignment will involve a laboratory session illustrating the principle of a quartz crystal microbalance and related acoustic sensor technologies.
| Coursework | Format |
Due date & marks |
|---|---|---|
|
Coursework activity #1 Glucose biosensors Learning objectives:
|
Individual Report anonymously marked |
Mon week 5 [30/60] |
|
[Coursework activity #2 Quartz crystal microbalance] Learning objectives:
|
Individual Report anonymously marked |
Wed week 9 [30/60] |
Booklists
Please see the Booklist for Group G Courses for references for this module.
Examination Guidelines
Please refer to Form & conduct of the examinations.
UK-SPEC
This syllabus contributes to the following areas of the UK-SPEC standard:
Toggle display of UK-SPEC areas.
GT1
Develop transferable skills that will be of value in a wide range of situations. These are exemplified by the Qualifications and Curriculum Authority Higher Level Key Skills and include problem solving, communication, and working with others, as well as the effective use of general IT facilities and information retrieval skills. They also include planning self-learning and improving performance, as the foundation for lifelong learning/CPD.
IA1
Apply appropriate quantitative science and engineering tools to the analysis of problems.
IA2
Demonstrate creative and innovative ability in the synthesis of solutions and in formulating designs.
KU1
Demonstrate knowledge and understanding of essential facts, concepts, theories and principles of their engineering discipline, and its underpinning science and mathematics.
KU2
Have an appreciation of the wider multidisciplinary engineering context and its underlying principles.
D1
Wide knowledge and comprehensive understanding of design processes and methodologies and the ability to apply and adapt them in unfamiliar situations.
D4
Ability to generate an innovative design for products, systems, components or processes to fulfil new needs.
E1
Ability to use fundamental knowledge to investigate new and emerging technologies.
E2
Ability to extract data pertinent to an unfamiliar problem, and apply its solution using computer based engineering tools when appropriate.
P1
A thorough understanding of current practice and its limitations and some appreciation of likely new developments.
P3
Understanding of contexts in which engineering knowledge can be applied (e.g. operations and management, technology, development, etc).
US1
A comprehensive understanding of the scientific principles of own specialisation and related disciplines.
US3
An understanding of concepts from a range of areas including some outside engineering, and the ability to apply them effectively in engineering projects.
US4
An awareness of developing technologies related to own specialisation.
Last modified: 05/10/2017 11:12
Engineering Tripos Part IIB, 4G2: Biosensors, 2018-19
Leader
Lecturers
Prof A Seshia and Professor E A Hall
Timing and Structure
Lent term. Lectures and coursework. Assessment: 100% coursework.
Aims
The aims of the course are to:
- link engineering principles to understanding of biosystems in sensors and bioelectronics
Objectives
As specific objectives, by the end of the course students should be able to:
- extend principles of engineering to the development of bioanalytical devices and the design of biosensors.
- understand the principles of linking cell components and biological pathways with energy transduction, sensing and detection
- appreciate the basic configuration and distinction among biosensor systems.
- demonstrate appreciation for the technical limits of performance.
- make design and selection decisions in response to measurement problems amenable to the use of biosensors.
Content
This course covers the principles, technologies, methods and applications of biosensors and bioinstrumentation. The objective of this course is to link engineering principles to understanding of biosystems in sensors and bioelectronics. It will provide the student with detail of methods and procedures used in the design, fabrication and application of biosensors and bioelectronic devices. The fundamentals of measurement science are applied to optical, electrochemical, mass, and pressure signal transduction. Upon successful completion of this course, students are expected to be able to explain biosensing and transduction techniques, as well as design and construct biosensor instrumentation.
Introduction
- Overview of Biosensors
- Fundamental elements of biosensor devices
- Engineering sensor proteins
Electrochemical Biosensors
- Electrochemical principles
- Amperometric biosensors and charge transfer pathways in enzymes
- Glucose biosensors
- Engineering electrochemical biosensors
Optical Biosensors
- Optics for biosensors
- Attenuated total reflection systems
Acoustic Biosensors
- Analytical models
- Acoustic sensor formats
- Quartz crystal microbalance
Micro- and Nano-technologies for biosensors
- Microfluidic interfaces for biosensors
- DNA and protein microarrays
- Microfabricated PCR technology
Diagnostics for the real world
- Communication and tracking in health monitoring
- Detection in resource limited settings
Coursework
The coursework will be assessed on two marked assignments. The first assignment will involve a laboratory session illustrating the functional demonstration of glucose sensor technology. The second assignment will involve a laboratory session illustrating the principle of a quartz crystal microbalance and related acoustic sensor technologies.
| Coursework | Format |
Due date & marks |
|---|---|---|
|
Coursework activity #1 Glucose biosensors Learning objectives:
|
Individual Report anonymously marked |
Mon week 5 [30/60] |
|
[Coursework activity #2 Quartz crystal microbalance] Learning objectives:
|
Individual Report anonymously marked |
Wed week 9 [30/60] |
Booklists
Please see the Booklist for Group G Courses for references for this module.
Examination Guidelines
Please refer to Form & conduct of the examinations.
UK-SPEC
This syllabus contributes to the following areas of the UK-SPEC standard:
Toggle display of UK-SPEC areas.
GT1
Develop transferable skills that will be of value in a wide range of situations. These are exemplified by the Qualifications and Curriculum Authority Higher Level Key Skills and include problem solving, communication, and working with others, as well as the effective use of general IT facilities and information retrieval skills. They also include planning self-learning and improving performance, as the foundation for lifelong learning/CPD.
IA1
Apply appropriate quantitative science and engineering tools to the analysis of problems.
IA2
Demonstrate creative and innovative ability in the synthesis of solutions and in formulating designs.
KU1
Demonstrate knowledge and understanding of essential facts, concepts, theories and principles of their engineering discipline, and its underpinning science and mathematics.
KU2
Have an appreciation of the wider multidisciplinary engineering context and its underlying principles.
D1
Wide knowledge and comprehensive understanding of design processes and methodologies and the ability to apply and adapt them in unfamiliar situations.
D4
Ability to generate an innovative design for products, systems, components or processes to fulfil new needs.
E1
Ability to use fundamental knowledge to investigate new and emerging technologies.
E2
Ability to extract data pertinent to an unfamiliar problem, and apply its solution using computer based engineering tools when appropriate.
P1
A thorough understanding of current practice and its limitations and some appreciation of likely new developments.
P3
Understanding of contexts in which engineering knowledge can be applied (e.g. operations and management, technology, development, etc).
US1
A comprehensive understanding of the scientific principles of own specialisation and related disciplines.
US3
An understanding of concepts from a range of areas including some outside engineering, and the ability to apply them effectively in engineering projects.
US4
An awareness of developing technologies related to own specialisation.
Last modified: 17/05/2018 14:26
Engineering Tripos Part IIB, 4F12: Computer Vision, 2019-20
Module Leader
Lecturers
Prof R Cipolla and Dr I Budvytis
Timing and Structure
Michaelmas term. 16 lectures (including 3 examples classes). Assessment: 100% exam
Aims
The aims of the course are to:
- introduce the principles, models and applications of computer vision.
- cover image structure, projection, stereo vision, structure from motion and object detection and recognition.
- give case studies of industrial (robotic) applications of computer vision, including visual navigation for autonomous robots, robot hand-eye coordination and novel man-machine interfaces.
Objectives
As specific objectives, by the end of the course students should be able to:
- design feature detectors to detect, localise and track image features.
- model perspective image formation and calibrate single and multiple camera systems.
- recover 3D position and shape information from arbitrary viewpoints;
- appreciate the problems in finding corresponding features in different viewpoints.
- analyse visual motion to recover scene structure and viewer motion, and understand how this information can be used in navigation;
- understand how simple object recognition systems can be designed so that they are independent of lighting and camera viewpoint.
- appreciate the commerical and industrial potential of computer vision but understand the limitations of current methods.
Content
- Introduction (1L)
Computer vision: what is it, why study it and how ? The eye and the camera, vision as an information processing task. A geometrical framework for vision. 3D interpretation of 2D images. Applications.
- Image structure (3L)
Image intensities and structure: edges, corners and blobs. Edge detection, the aperture problem. Corner and blob detection. Contour extraction using B-spline snakes. Texture. Feature descriptors and matching.
- Projection (3L)
Orthographic projection. Planar perspective projection. Vanishing points and lines. Projection matrix, homogeneous coordinates. Camera calibration, recovery of world position. Weak perspective and the affine camera. Projective invariants.
- Stereo vision and Structure from Motion (3L)
Epipolar geometry and the essential matrix. Recovery of depth. Uncalibrated cameras and the fundamental matrix. The correspondence problem. Structure from motion. 3D shape from multiple view stereo.
- Object detection and recognition (3L)
Basic architectures for deep learning in computer vision. Object detection, classification and semantic segmentation. Object recognition, feature embedding and metric learning. Reconstruction, localisation and structured deep learning.
- Example classes (3L)
Discussion of examples papers and past examination papers.
Booklists
Please see the Booklist for Group F Courses for references for this module.
Examination Guidelines
Please refer to Form & conduct of the examinations.
UK-SPEC
This syllabus contributes to the following areas of the UK-SPEC standard:
Toggle display of UK-SPEC areas.
GT1
Develop transferable skills that will be of value in a wide range of situations. These are exemplified by the Qualifications and Curriculum Authority Higher Level Key Skills and include problem solving, communication, and working with others, as well as the effective use of general IT facilities and information retrieval skills. They also include planning self-learning and improving performance, as the foundation for lifelong learning/CPD.
IA1
Apply appropriate quantitative science and engineering tools to the analysis of problems.
IA2
Demonstrate creative and innovative ability in the synthesis of solutions and in formulating designs.
KU1
Demonstrate knowledge and understanding of essential facts, concepts, theories and principles of their engineering discipline, and its underpinning science and mathematics.
KU2
Have an appreciation of the wider multidisciplinary engineering context and its underlying principles.
D1
Wide knowledge and comprehensive understanding of design processes and methodologies and the ability to apply and adapt them in unfamiliar situations.
D4
Ability to generate an innovative design for products, systems, components or processes to fulfil new needs.
E1
Ability to use fundamental knowledge to investigate new and emerging technologies.
E2
Ability to extract data pertinent to an unfamiliar problem, and apply its solution using computer based engineering tools when appropriate.
E3
Ability to apply mathematical and computer based models for solving problems in engineering, and the ability to assess the limitations of particular cases.
P1
A thorough understanding of current practice and its limitations and some appreciation of likely new developments.
P3
Understanding of contexts in which engineering knowledge can be applied (e.g. operations and management, technology, development, etc).
US1
A comprehensive understanding of the scientific principles of own specialisation and related disciplines.
US2
A comprehensive knowledge and understanding of mathematical and computer models relevant to the engineering discipline, and an appreciation of their limitations.
US3
An understanding of concepts from a range of areas including some outside engineering, and the ability to apply them effectively in engineering projects.
US4
An awareness of developing technologies related to own specialisation.
Last modified: 16/09/2019 16:49
Engineering Tripos Part IIB, 4F12: Computer Vision, 2023-24
Module Leader
Lecturers
Prof R Cipolla and Dr S Albanie
Timing and Structure
Michaelmas term. 16 lectures (including 3 examples classes). Assessment: 100% exam
Aims
The aims of the course are to:
- introduce the principles, models and applications of computer vision.
- cover image structure, projection, stereo vision, structure from motion and object detection and recognition.
- give case studies of industrial (robotic) applications of computer vision, including visual navigation for autonomous robots, robot hand-eye coordination and novel man-machine interfaces.
Objectives
As specific objectives, by the end of the course students should be able to:
- design feature detectors to detect, localise and track image features.
- model perspective image formation and calibrate single and multiple camera systems.
- recover 3D position and shape information from arbitrary viewpoints;
- appreciate the problems in finding corresponding features in different viewpoints.
- analyse visual motion to recover scene structure and viewer motion, and understand how this information can be used in navigation;
- understand how simple object recognition systems can be designed so that they are independent of lighting and camera viewpoint.
- appreciate the commerical and industrial potential of computer vision but understand its limitations.
Content
- Introduction (1L)
Computer vision: what is it, why study it and how ? The eye and the camera, vision as an information processing task. Geometrical and statistical frameworks for vision. 3D interpretation of 2D images. Applications.
- Image structure (4L)
Image intensities and structure: edges, corners and blobs. Edge detection, the aperture problem and corner detection. Image pyramids, blob detection with band-pass filtering. The SIFT feature descriptor for matching. Characterising textures.
- Projection (4L)
Orthographic projection. Planar perspective projection. Vanishing points and lines. Projection matrix, homogeneous coordinates. Camera calibration, recovery of world position. Weak perspective and the affine camera. Projective invariants.
- Stereo vision and Structure from Motion (2L)
Epipolar geometry and the essential matrix. Recovery of depth by triangulation. Uncalibrated cameras and the fundamental matrix. The correspondence problem. Structure from motion. 3D shape examples from multiple view stereo.
- Deep Learning for Computer Vision (5L)
Basic architectures for deep learning in computer vision. Object detection, classification and semantic segmentation. Object recognition, feature embedding and metric learning. Transformer architectures and self-supervised learning.
- Example classes
Discussion of examples papers and past examination papers will be integrated with lectures.
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.
UK-SPEC
This syllabus contributes to the following areas of the UK-SPEC standard:
Toggle display of UK-SPEC areas.
GT1
Develop transferable skills that will be of value in a wide range of situations. These are exemplified by the Qualifications and Curriculum Authority Higher Level Key Skills and include problem solving, communication, and working with others, as well as the effective use of general IT facilities and information retrieval skills. They also include planning self-learning and improving performance, as the foundation for lifelong learning/CPD.
IA1
Apply appropriate quantitative science and engineering tools to the analysis of problems.
IA2
Demonstrate creative and innovative ability in the synthesis of solutions and in formulating designs.
KU1
Demonstrate knowledge and understanding of essential facts, concepts, theories and principles of their engineering discipline, and its underpinning science and mathematics.
KU2
Have an appreciation of the wider multidisciplinary engineering context and its underlying principles.
D1
Wide knowledge and comprehensive understanding of design processes and methodologies and the ability to apply and adapt them in unfamiliar situations.
D4
Ability to generate an innovative design for products, systems, components or processes to fulfil new needs.
E1
Ability to use fundamental knowledge to investigate new and emerging technologies.
E2
Ability to extract data pertinent to an unfamiliar problem, and apply its solution using computer based engineering tools when appropriate.
E3
Ability to apply mathematical and computer based models for solving problems in engineering, and the ability to assess the limitations of particular cases.
P1
A thorough understanding of current practice and its limitations and some appreciation of likely new developments.
P3
Understanding of contexts in which engineering knowledge can be applied (e.g. operations and management, technology, development, etc).
US1
A comprehensive understanding of the scientific principles of own specialisation and related disciplines.
US2
A comprehensive knowledge and understanding of mathematical and computer models relevant to the engineering discipline, and an appreciation of their limitations.
US3
An understanding of concepts from a range of areas including some outside engineering, and the ability to apply them effectively in engineering projects.
US4
An awareness of developing technologies related to own specialisation.
Last modified: 28/09/2023 15:45
Engineering Tripos Part IIB, 4F12: Computer Vision, 2020-21
Module Leader
Lecturers
Timing and Structure
Michaelmas term. 16 lectures (including 3 examples classes). Assessment: 100% exam
Aims
The aims of the course are to:
- introduce the principles, models and applications of computer vision.
- cover image structure, projection, stereo vision, structure from motion and object detection and recognition.
- give case studies of industrial (robotic) applications of computer vision, including visual navigation for autonomous robots, robot hand-eye coordination and novel man-machine interfaces.
Objectives
As specific objectives, by the end of the course students should be able to:
- design feature detectors to detect, localise and track image features.
- model perspective image formation and calibrate single and multiple camera systems.
- recover 3D position and shape information from arbitrary viewpoints;
- appreciate the problems in finding corresponding features in different viewpoints.
- analyse visual motion to recover scene structure and viewer motion, and understand how this information can be used in navigation;
- understand how simple object recognition systems can be designed so that they are independent of lighting and camera viewpoint.
- appreciate the commerical and industrial potential of computer vision but understand the limitations of current methods.
Content
- Introduction (1L)
Computer vision: what is it, why study it and how ? The eye and the camera, vision as an information processing task. A geometrical framework for vision. 3D interpretation of 2D images. Applications.
- Image structure (3L)
Image intensities and structure: edges, corners and blobs. Edge detection, the aperture problem. Corner and blob detection. Contour extraction using B-spline snakes. Texture. Feature descriptors and matching.
- Projection (3L)
Orthographic projection. Planar perspective projection. Vanishing points and lines. Projection matrix, homogeneous coordinates. Camera calibration, recovery of world position. Weak perspective and the affine camera. Projective invariants.
- Stereo vision and Structure from Motion (3L)
Epipolar geometry and the essential matrix. Recovery of depth. Uncalibrated cameras and the fundamental matrix. The correspondence problem. Structure from motion. 3D shape from multiple view stereo.
- Object detection and recognition (3L)
Basic architectures for deep learning in computer vision. Object detection, classification and semantic segmentation. Object recognition, feature embedding and metric learning. Reconstruction, localisation and structured deep learning.
- Example classes (3L)
Discussion of examples papers and past examination papers.
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.
UK-SPEC
This syllabus contributes to the following areas of the UK-SPEC standard:
Toggle display of UK-SPEC areas.
GT1
Develop transferable skills that will be of value in a wide range of situations. These are exemplified by the Qualifications and Curriculum Authority Higher Level Key Skills and include problem solving, communication, and working with others, as well as the effective use of general IT facilities and information retrieval skills. They also include planning self-learning and improving performance, as the foundation for lifelong learning/CPD.
IA1
Apply appropriate quantitative science and engineering tools to the analysis of problems.
IA2
Demonstrate creative and innovative ability in the synthesis of solutions and in formulating designs.
KU1
Demonstrate knowledge and understanding of essential facts, concepts, theories and principles of their engineering discipline, and its underpinning science and mathematics.
KU2
Have an appreciation of the wider multidisciplinary engineering context and its underlying principles.
D1
Wide knowledge and comprehensive understanding of design processes and methodologies and the ability to apply and adapt them in unfamiliar situations.
D4
Ability to generate an innovative design for products, systems, components or processes to fulfil new needs.
E1
Ability to use fundamental knowledge to investigate new and emerging technologies.
E2
Ability to extract data pertinent to an unfamiliar problem, and apply its solution using computer based engineering tools when appropriate.
E3
Ability to apply mathematical and computer based models for solving problems in engineering, and the ability to assess the limitations of particular cases.
P1
A thorough understanding of current practice and its limitations and some appreciation of likely new developments.
P3
Understanding of contexts in which engineering knowledge can be applied (e.g. operations and management, technology, development, etc).
US1
A comprehensive understanding of the scientific principles of own specialisation and related disciplines.
US2
A comprehensive knowledge and understanding of mathematical and computer models relevant to the engineering discipline, and an appreciation of their limitations.
US3
An understanding of concepts from a range of areas including some outside engineering, and the ability to apply them effectively in engineering projects.
US4
An awareness of developing technologies related to own specialisation.
Last modified: 17/09/2020 08:07
Engineering Tripos Part IIB, 4F12: Computer Vision, 2021-22
Module Leader
Lecturers
Timing and Structure
Michaelmas term. 16 lectures (including 3 examples classes). Assessment: 100% exam
Aims
The aims of the course are to:
- introduce the principles, models and applications of computer vision.
- cover image structure, projection, stereo vision, structure from motion and object detection and recognition.
- give case studies of industrial (robotic) applications of computer vision, including visual navigation for autonomous robots, robot hand-eye coordination and novel man-machine interfaces.
Objectives
As specific objectives, by the end of the course students should be able to:
- design feature detectors to detect, localise and track image features.
- model perspective image formation and calibrate single and multiple camera systems.
- recover 3D position and shape information from arbitrary viewpoints;
- appreciate the problems in finding corresponding features in different viewpoints.
- analyse visual motion to recover scene structure and viewer motion, and understand how this information can be used in navigation;
- understand how simple object recognition systems can be designed so that they are independent of lighting and camera viewpoint.
- appreciate the commerical and industrial potential of computer vision but understand the limitations of current methods.
Content
- Introduction (1L)
Computer vision: what is it, why study it and how ? The eye and the camera, vision as an information processing task. Geometrical and statistical frameworks for vision. 3D interpretation of 2D images. Applications.
- Image structure (3L)
Image intensities and structure: edges, corners and blobs. Edge detection, the aperture problem and corner detection. Image pyramids, blob detection with band-pass filtering. The SIFT feature descriptor for matching. Characterising textures.
- Projection (3L)
Orthographic projection. Planar perspective projection. Vanishing points and lines. Projection matrix, homogeneous coordinates. Camera calibration, recovery of world position. Weak perspective and the affine camera. Projective invariants.
- Stereo vision and Structure from Motion (2L)
Epipolar geometry and the essential matrix. Recovery of depth by triangulation. Uncalibrated cameras and the fundamental matrix. The correspondence problem. Structure from motion. 3D shape examples from multiple view stereo.
- Deep Learning for Computer Vision (4L)
Basic architectures for deep learning in computer vision. Object detection, classification and semantic segmentation. Object recognition, feature embedding and metric learning. Transformers, scaling laws for computer vision, neural architecture search. Self-supervised learning and pseudo-labelling.
- Example classes (3L)
Discussion of examples papers and past examination papers.
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.
UK-SPEC
This syllabus contributes to the following areas of the UK-SPEC standard:
Toggle display of UK-SPEC areas.
GT1
Develop transferable skills that will be of value in a wide range of situations. These are exemplified by the Qualifications and Curriculum Authority Higher Level Key Skills and include problem solving, communication, and working with others, as well as the effective use of general IT facilities and information retrieval skills. They also include planning self-learning and improving performance, as the foundation for lifelong learning/CPD.
IA1
Apply appropriate quantitative science and engineering tools to the analysis of problems.
IA2
Demonstrate creative and innovative ability in the synthesis of solutions and in formulating designs.
KU1
Demonstrate knowledge and understanding of essential facts, concepts, theories and principles of their engineering discipline, and its underpinning science and mathematics.
KU2
Have an appreciation of the wider multidisciplinary engineering context and its underlying principles.
D1
Wide knowledge and comprehensive understanding of design processes and methodologies and the ability to apply and adapt them in unfamiliar situations.
D4
Ability to generate an innovative design for products, systems, components or processes to fulfil new needs.
E1
Ability to use fundamental knowledge to investigate new and emerging technologies.
E2
Ability to extract data pertinent to an unfamiliar problem, and apply its solution using computer based engineering tools when appropriate.
E3
Ability to apply mathematical and computer based models for solving problems in engineering, and the ability to assess the limitations of particular cases.
P1
A thorough understanding of current practice and its limitations and some appreciation of likely new developments.
P3
Understanding of contexts in which engineering knowledge can be applied (e.g. operations and management, technology, development, etc).
US1
A comprehensive understanding of the scientific principles of own specialisation and related disciplines.
US2
A comprehensive knowledge and understanding of mathematical and computer models relevant to the engineering discipline, and an appreciation of their limitations.
US3
An understanding of concepts from a range of areas including some outside engineering, and the ability to apply them effectively in engineering projects.
US4
An awareness of developing technologies related to own specialisation.
Last modified: 24/09/2021 16:40
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