Engineering Tripos Part IIA, 3G3: Introduction to Neuroscience, 2022-23
Module Leader
Lecturers
Dr G Hennequin, Dr Y Ahmadian
Lab Leader
Dr G Hennequin
Timing and Structure
Lent term. 16 lectures.
Aims
The aims of the course are to:
- Introduce students to how the brain processes sensory information, controls our actions, learns through experience and lays down memories.
- Elucidate the computational and engineering principles of brain function.
Objectives
As specific objectives, by the end of the course students should be able to:
- Have a basic grasp of neuroscience that can act as foundation for further study.
- Understand the basic principles of sensory processing, decision making, learning and memory and how engineering concepts can be applied to them.
Content
Perception and action (6L) (Dr G Hennequin)
- Neurons and synapses
- Perception as Bayesian inference
- Decision making
Dynamics of single neurons (2L) (Dr G Hennequin)
- Introduction to basic cell physiology and ion channels
- How do neurons communicate? The action potential and the Hodgkin-Huxley model
Learning and memory (8L) (Dr Y Ahmadian)
- The cellular basis of learning and memory
- Animal learning
- Memory
Coursework
Simulation of different types of neural coding of natural images. Laboratory report and/or Full Technical Report.
Efficient coding in visual cortex
Learning objectives:
- To apply basic techniques from linear algebra, optimization and statistics to understand how the primary visual cortex might efficiently encode natural scenes
- To learn (or consolidate) how to implement simple algorithms in Python
- To consolidate critical analysis and report-writing skills
Practical information:
- Sessions normally take place in the DPO, but could be done online if required by Covid19-related restrictions.
- This activity involves preliminary homework (estimated 30 min duration), consisting of mathematical derivations (including some basic vector calculus) to be performed before coming to the lab.
Full Technical Report:
Students will have the option to submit a Full Technical Report. This will take the form of a unifying review of 2 papers addressing efficient coding of sensory information in the brain.
Booklists
Please refer to the Booklist for Part IIA 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.
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.
E3
Ability to apply mathematical and computer based models for solving problems in engineering, and the ability to assess the limitations of particular cases.
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.
Last modified: 24/05/2022 12:59
Engineering Tripos Part IIA, 3G3: Introduction to Neuroscience, 2019-20
Module Leader
Lecturers
Dr G Hennequin, Dr M Lengyel, Dr T O'Leary
Lab Leader
Timing and Structure
Lent term. 16 lectures.
Aims
The aims of the course are to:
- Introduce students to how the brain processes sensory information, controls our actions, learns through experience and lays down memories.
- Elucidate the computational and engineering principles of brain function.
Objectives
As specific objectives, by the end of the course students should be able to:
- Have a basic grasp of neuroscience that can act as foundation for further study.
- Understand the basic principles of sensory processing, decision making, learning and memory and how engineering concepts can be applied to them.
Content
Perception and action (6L) (Dr G Hennequin)
- Neurons and synapses
- Perception as Bayesian inference
- Decision making
Dynamics of single neurons (2L) (Dr T O'Leary)
- Introduction to basic cell physiology and ion channels
- How do neurons communicate? The action potential and the Hodgkin-Huxley model
Learning and memory (8L) (Dr M Lengyel)
- The cellular basis of learning and memory
- Animal learning
- Memory
Coursework
Simulation of different types of neural coding of natural images. Laboratory report and/or Full Technical Report.
Efficient coding in visual cortex
Learning objectives:
- To apply basic techniques from linear algebra, optimization and statistics to understand how the primary visual cortex might efficiently encode natural scenes
- To learn (or consolidate) how to implement simple algorithms in Matlab
- To consolidate critical analysis and report-writing skills
Practical information:
- Sessions will take place in the DPO.
- This activity involves preliminary homework (estimated 30 min duration), consisting of mathematical derivations (including some basic vector calculus) to be performed before coming to the lab.
Full Technical Report:
Students will have the option to submit a Full Technical Report. This will take the form of a unifying review of 3 papers addressing efficient coding of sensory information in the brain.
Booklists
Please see the Booklist for Part IIA Courses for references to 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.
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.
E3
Ability to apply mathematical and computer based models for solving problems in engineering, and the ability to assess the limitations of particular cases.
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.
Last modified: 03/09/2019 12:11
Engineering Tripos Part IIA, 3G3: Introduction to Neuroscience, 2018-19
Module Leader
Lecturers
Dr G Hennequin, Dr M Lengyel, Dr T O'Leary
Lab Leader
Timing and Structure
Lent term. 16 lectures.
Aims
The aims of the course are to:
- Introduce students to how the brain processes sensory information, controls our actions, learns through experience and lays down memories.
- Elucidate the computational and engineering principles of brain function.
Objectives
As specific objectives, by the end of the course students should be able to:
- Have a basic grasp of neuroscience that can act as foundation for further study.
- Understand the basic principles of sensory processing, decision making, learning and memory and how engineering concepts can be applied to them.
Content
Perception and action (6L) (Dr G Hennequin)
- Neurons and synapses
- Perception as Bayesian inference
- Decision making
Dynamics of single neurons (2L) (Dr T O'Leary)
- Introduction to basic cell physiology and ion channels
- How do neurons communicate? The action potential and the Hodgkin-Huxley model
Learning and memory (8L) (Dr M Lengyel)
- The cellular basis of learning and memory
- Animal learning
- Memory
Coursework
Simulation of different types of neural coding of natural images. Laboratory report and/or Full Technical Report.
Efficient coding in visual cortex
Learning objectives:
- To apply basic techniques from linear algebra, optimization and statistics to understand how the primary visual cortex might efficiently encode natural scenes
- To learn (or consolidate) how to implement simple algorithms in Matlab
- To consolidate critical analysis and report-writing skills
Practical information:
- Sessions will take place in the DPO during week 2 (3 sessions: Tuesday 30/01 from 11am-1pm and from 2-4pm; Wednesday 31/01 from 2-4pm).
- This activity involves primary work (estimated 30 min duration), consisting of mathematical derivations (including some basic vector calculus) to be performed before coming to the lab.
Full Technical Report:
Students will have the option to submit a Full Technical Report. This will take the form of a unifying review of 3 papers addressing efficient coding of sensory information in the brain.
Booklists
Please see the Booklist for Part IIA Courses for references to 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.
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.
E3
Ability to apply mathematical and computer based models for solving problems in engineering, and the ability to assess the limitations of particular cases.
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.
Last modified: 16/05/2018 13:46
Engineering Tripos Part IIA, 3G3: Introduction to Neuroscience, 2020-21
Module Leader
Lecturers
Prof M Lengyel, Dr T O'Leary, Dr Y Ahmadian
Lab Leader
Dr Y Ahmadian
Timing and Structure
Lent term. 16 lectures.
Aims
The aims of the course are to:
- Introduce students to how the brain processes sensory information, controls our actions, learns through experience and lays down memories.
- Elucidate the computational and engineering principles of brain function.
Objectives
As specific objectives, by the end of the course students should be able to:
- Have a basic grasp of neuroscience that can act as foundation for further study.
- Understand the basic principles of sensory processing, decision making, learning and memory and how engineering concepts can be applied to them.
Content
Perception and action (6L) (Dr G Hennequin)
- Neurons and synapses
- Perception as Bayesian inference
- Decision making
Dynamics of single neurons (2L) (Dr T O'Leary)
- Introduction to basic cell physiology and ion channels
- How do neurons communicate? The action potential and the Hodgkin-Huxley model
Learning and memory (8L) (Dr M Lengyel)
- The cellular basis of learning and memory
- Animal learning
- Memory
Coursework
Simulation of different types of neural coding of natural images. Laboratory report and/or Full Technical Report.
Efficient coding in visual cortex
Learning objectives:
- To apply basic techniques from linear algebra, optimization and statistics to understand how the primary visual cortex might efficiently encode natural scenes
- To learn (or consolidate) how to implement simple algorithms in Matlab
- To consolidate critical analysis and report-writing skills
Practical information:
- Sessions will take place in the DPO.
- This activity involves preliminary homework (estimated 30 min duration), consisting of mathematical derivations (including some basic vector calculus) to be performed before coming to the lab.
Full Technical Report:
Students will have the option to submit a Full Technical Report. This will take the form of a unifying review of 3 papers addressing efficient coding of sensory information in the brain.
Booklists
Please refer to the Booklist for Part IIA 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.
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.
E3
Ability to apply mathematical and computer based models for solving problems in engineering, and the ability to assess the limitations of particular cases.
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.
Last modified: 28/08/2020 11:08
Engineering Tripos Part IIA, 3G3: Introduction to Neuroscience, 2017-18
Module Leader
Lecturers
Dr G Hennequin, Dr M Lengyel, Dr T O'Leary
Lab Leader
Timing and Structure
Lent term. 16 lectures.
Aims
The aims of the course are to:
- Introduce students to how the brain processes sensory information, controls our actions, learns through experience and lays down memories.
- Elucidate the computational and engineering principles of brain function.
Objectives
As specific objectives, by the end of the course students should be able to:
- Have a basic grasp of neuroscience that can act as foundation for further study.
- Understand the basic principles of sensory processing, decision making, learning and memory and how engineering concepts can be applied to them.
Content
Perception and action (6L) (Dr G Hennequin)
- Neurons and synapses
- Perception as Bayesian inference
- Decision making
Dynamics of single neurons (2L) (Dr T O'Leary)
- Introduction to basic cell physiology and ion channels
- How do neurons communicate? The action potential and the Hodgkin-Huxley model
Learning and memory (8L) (Dr M Lengyel)
- The cellular basis of learning and memory
- Animal learning
- Memory
Coursework
Simulation of different types of neural coding of natural images. Laboratory report and/or Full Technical Report.
Efficient coding in visual cortex
Learning objectives:
- To apply basic techniques from linear algebra, optimization and statistics to understand how the primary visual cortex might efficiently encode natural scenes
- To learn (or consolidate) how to implement simple algorithms in Matlab
- To consolidate critical analysis and report-writing skills
Practical information:
- Sessions will take place in the DPO during week 2 (3 sessions: Tuesday 30/01 from 11am-1pm and from 2-4pm; Wednesday 31/01 from 2-4pm).
- This activity involves primary work (estimated 30 min duration), consisting of mathematical derivations (including some basic vector calculus) to be performed before coming to the lab.
Full Technical Report:
Students will have the option to submit a Full Technical Report. This will take the form of a unifying review of 3 papers addressing efficient coding of sensory information in the brain.
Booklists
Please see the Booklist for Part IIA Courses for references to 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.
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.
E3
Ability to apply mathematical and computer based models for solving problems in engineering, and the ability to assess the limitations of particular cases.
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.
Last modified: 16/01/2018 13:18
Engineering Tripos Part IIA, 3F4: Data Transmission, 2018-19
Module Leader
Lecturers
Dr R Venkataramanan, Prof. Ioannis Kontoyiannis
Lab Leader
Timing and Structure
Lent term. 16 lectures
Prerequisites
Knowledge of 3F1 assumed.
Aims
The aims of the course are to:
- Cover a range of topics which are important in modern communication systems.
- Extend the basic material covered in the Engineering Part IB Communications course to deal with data transmission over baseband (low frequency) channels as well bandpass (higher frequency) channels.
- Analyse the effects of noise in some detail.
- Understand the technique of convolutional coding to protect information transmitted over noisy channels.
- To understand basic congestion control protocols (TCP/IP), and routing algorithms used in the Internet.
Objectives
As specific objectives, by the end of the course students should be able to:
- Understand the different components of a communication network, in particular the role of the physical layer versus the network layer.
- Be able to represent waveforms as vectors in a signal space.
- Appreciate that pulses may be shaped to control the bandwidth of a signal and reduce inter-symbol interference, and be aware of the limits on transmission rate if ISI is to be avoided.
- Be able to describe and analyse demodulation of digital bandpass modulated signals in noise.
- Calculate the probability of error of various modulation schemes as a function of the signal-to-noise-ratio.
- Appreciate how equalisation can correct for undesirable channel characteristics and be able to design simple equalisers.
- Understand the principles of Orthogonal Frequency Division Multiplexing for communication over multi-path wideband channels
- Understand the need for coding, i.e., adding redundancy to control the effects of transmission errors.
- Understand the principles of convolutional coding, and be able to design a Viterbi decoder for convolutional codes.
- Understand the operation of congestion control protocols (TCP/IP) and routing algorithms used in the internet
Content
Fundamentals of Modulation and Demodulation (7L)
- Introduction: The overall commuication network and the roles of the physical layer and the network layer
- Signal Space: representing waveforms as elements a vector space
- Baseband modulation: Desirable properties of the pulse for PAM; Nyquist criterion for no inter-symbol interference; Eye-diagrams
- Modelling the noise as a Gaussian random process. Additive White Gaussian Noise (AWGN)
- Optimal demodulation and detection at the receiver in the presence of AWGN: Matched filter demodulator, optimal detection using the maximum-a-posteriori probability (MAP) rule
- Passband modulation: QAM, M-ary FSK (Orthogonal signalling)
- Performance analysis of modulation schemes (PAM, QAM, Orthogonal signaling etc.): probability of detection error and bandwidth efficiency
Advanced Topics in PHY-layer (3L)
- Brief discussion of coded modulation
- Equalisation techniques to deal with inter-symbol interference: ZF and MMSE equalizers
- Orthogonal Frequency Division Multiplexing (OFDM)
Channel Coding (3L)
- Introduction to error correction and linear codes
- Convolutional codes: State Diagram and Trellis representations, Viterbi decoding algorithm
- Distance properties of convolutional codes using the transfer function derived from state diagram; free-distance of convolutional codes.
Network Algorithms (3L)
- Congestion control in the Internet: window-based congestion control: TCP-Reno; slow-start, congestion avoidance
- Routing algorithms in the Internet: Djikstra's algorithm, Bellman-Ford and the similarities to the Viterbi algorithm
Further notes
The syllabus for this module was updated (with significant changes) in 2017-18. A list of relevant past Tripos questions is available on Moodle.
Coursework
Digital transmission systems
Learning objectives:
- To investigate, using a hardware simulation of baseband transmission channels, the phenomenon of inter-symbol interference, and to measure bit error rate (BER) due to noise
- To use the eye diagram as a diagnostic tool, and to understand its limitations.
- To compare the measured dependence of BER on signal-to-noise Ratio (SNR) with theoretical predictions, and explain the differences by considering how the assumptions made in the theoretical analysis compare with the real situation.
Practical information:
- Sessions will take place in EIETL, during week(s) [xxx].
- This activity involves preliminary work-- reading the lab handout ([estimated duration: 1 hour]).
Full Technical Report:
Students will have the option to submit a Full Technical Report.
Booklists
For Physical-layer communications (first 13L):
- B. Rimoldi, Principles of Digital Communication: A Top-Down Approach, Cambridge University Press, 2016]
- R. Gallager, Principles of Digital Communication, Cambridge University Press, 2008
- U. Madhow, Fundamentals of Digital Communication, Cambridge University Press, 2008
For network algorithms (last 3L):
- R. Srikant and L. Ying, Communication Networks, Cambridge University Press, 2014.
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.
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.
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.
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).
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.
Last modified: 12/12/2018 17:46
Engineering Tripos Part IIA, 3F4: Data Transmission, 2017-18
Module Leader
Lecturers
Dr R Venkataramanan, Prof. Ioannis Kontoyiannis
Lab Leader
Timing and Structure
Lent term. 16 lectures
Prerequisites
Knowledge of 3F1 assumed.
Aims
The aims of the course are to:
- Cover a range of topics which are important in modern communication systems.
- Extend the basic material covered in the Engineering Part IB Communications course to deal with data transmission over baseband (low frequency) channels as well bandpass (higher frequency) channels.
- Analyse the effects of noise in some detail.
- Understand the technique of convolutional coding to protect information transmitted over noisy channels.
- To understand basic congestion control protocols (TCP/IP), and routing algorithms used in the Internet.
Objectives
As specific objectives, by the end of the course students should be able to:
- Understand the different components of a communication network, in particular the role of the physical layer versus the network layer.
- Be able to represent waveforms as vectors in a signal space.
- Appreciate that pulses may be shaped to control the bandwidth of a signal and reduce inter-symbol interference, and be aware of the limits on transmission rate if ISI is to be avoided.
- Be able to describe and analyse demodulation of digital bandpass modulated signals in noise.
- Calculate the probability of error of various modulation schemes as a function of the signal-to-noise-ratio.
- Appreciate how equalisation can correct for undesirable channel characteristics and be able to design simple equalisers.
- Understand the principles of Orthogonal Frequency Division Multiplexing for communication over multi-path wideband channels
- Understand the need for coding, i.e., adding redundancy to control the effects of transmission errors.
- Understand the principles of convolutional coding, and be able to design a Viterbi decoder for convolutional codes.
- Understand the operation of congestion control protocols (TCP/IP) and routing algorithms used in the internet
Content
Fundamentals of Modulation and Demodulation (7L)
- Introduction: The overall commuication network and the roles of the physical layer and the network layer
- Signal Space: representing waveforms as elements a vector space
- Baseband modulation: Desirable properties of the pulse for PAM; Nyquist criterion for no inter-symbol interference; Eye-diagrams
- Modelling the noise as a Gaussian random process. Additive White Gaussian Noise (AWGN)
- Optimal demodulation and detection at the receiver in the presence of AWGN: Matched filter demodulator, optimal detection using the maximum-a-posteriori probability (MAP) rule
- Passband modulation: QAM, M-ary FSK (Orthogonal signalling)
- Performance analysis of modulation schemes (PAM, QAM, Orthogonal signaling etc.): probability of detection error and bandwidth efficiency
Advanced Topics in PHY-layer (3L)
- Brief discussion of coded modulation
- Equalisation techniques to deal with inter-symbol interference: ZF and MMSE equalizers
- Orthogonal Frequency Division Multiplexing (OFDM)
Channel Coding (3L)
- Introduction to error correction and linear codes
- Convolutional codes: State Diagram and Trellis representations, Viterbi decoding algorithm
- Distance properties of convolutional codes using the transfer function derived from state diagram; free-distance of convolutional codes.
Network Algorithms (3L)
- Congestion control in the Internet: window-based congestion control: TCP-Reno; slow-start, congestion avoidance
- Routing algorithms in the Internet: Djikstra's algorithm, Bellman-Ford and the similarities to the Viterbi algorithm
Further notes
The syllabus for this module has been revised for 2017-18, and therefore the lecture notes, examples papers etc. will be different from previous years. A list of relevant past Tripos questions will be provided towards the end of the module.
Coursework
Digital transmission systems
Learning objectives:
- To investigate, using a hardware simulation of baseband transmission channels, the phenomenon of inter-symbol interference, and to measure bit error rate (BER) due to noise
- To use the eye diagram as a diagnostic tool, and to understand its limitations.
- To compare the measured dependence of BER on signal-to-noise Ratio (SNR) with theoretical predictions, and explain the differences by considering how the assumptions made in the theoretical analysis compare with the real situation.
Practical information:
- Sessions will take place in EIETL, during week(s) [xxx].
- This activity involves preliminary work-- reading the lab handout ([estimated duration: 1 hour]).
Full Technical Report:
Students will have the option to submit a Full Technical Report.
Booklists
For Physical-layer communications (first 13L):
- B. Rimoldi, Principles of Digital Communication: A Top-Down Approach, Cambridge University Press, 2016]
- R. Gallager, Principles of Digital Communication, Cambridge University Press, 2008
- U. Madhow, Fundamentals of Digital Communication, Cambridge University Press, 2008
For network algorithms (last 3L):
- R. Srikant and L. Ying, Communication Networks, Cambridge University Press, 2014.
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.
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.
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.
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).
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.
Last modified: 18/02/2018 16:43
Engineering Tripos Part IIA, 3F3: Statistical Signal Processing, 2018-19
Module Leader
Lecturers
Dr S.S. Singh
Lab Leader
Timing and Structure
Michaelmas term. 16 lectures.
Prerequisites
3F1
Aims
The aims of the course are to:
- Study more advanced probability theory, leading into random process theory.
- Study random process theory and focus on discrete time methods.
- Introduce inferential methodology, including maximum likelihood and Bayesian procedures, and examples drawn from signal processing. Objectives
Objectives
As specific objectives, by the end of the course students should be able to:
- By the end of the course students should be familiar with the fundamental concepts of statistical signal processing, including random processes, probability, estimation and inference.
Content
Lectures 1-8: Advanced Probability and Random Processes
-
Probability and random variables
-
Sample space, events, probability measure, axioms.
-
Conditional probability, probability chain rule, independence, Bayes rule.
-
Random variables (discrete and continuous), probability mass function (pmf), probability density function (pdf), cumulative distribution function, transformation of random variables.
-
Bivariate: conditional pmf, conditional pdf, expectation, conditional expectation.
-
Multivariates: marginals, Gaussian (properties), characteristic function, change of variables (Jacobian.)
-
-
Random processes
-
Definition of a random process, finite order densities.
-
Markov chains.
-
Auto-correlation functions.
-
Stationarity–strict sense, wide sense. Examples: iid process, random-phase sinusoid.
-
Ergodicity, Central limit theorem.
-
Spectral density.
-
Response of linear systems to stochastic inputs – time and frequency domain.
-
Time series models: AR, MA, ARMA
-
Lectures 9-16: Detection, Estimation and Inference
-
Basic linear estimation theory: BLUE, MMSE, bias, variance
-
Wiener filters
-
Matched filters
-
Least squares, maximum likelihood, Bayesian inference.
-
The ML/Bayesian linear Gaussian model
-
Maximum likelihood and Bayesian estimation
-
Example inference models: frequency estimation, AR model, Estimation of parameters for discrete Markov chain.
Coursework
Random variables and random number generation
Learning objectives:
- Understand random variables and functions of random variables and their simulation
- To study the Jacobian as used with random variables
- To experiment with methods for non-uniform random number generation
Practical information:
- Sessions will take place in [Location], during week(s) [xxx].
- This activity involves preliminary work.
Full Technical Report:
Students will have the option to submit a Full Technical Report.
Booklists
Please see the Booklist for Part IIA 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.
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.
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).
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.
Last modified: 13/09/2018 15:53
Engineering Tripos Part IIA, 3F3: Statistical Signal Processing, 2022-23
Module Leader
Lecturers
Prof S.S. Singh, Prof S. J. Godsill
Lab Leader
Prof S Godsill
Timing and Structure
Michaelmas term. 16 lectures.
Aims
The aims of the course are to:
- Study more advanced probability theory, leading into random process theory.
- Study random process theory and focus on discrete time methods.
- Introduce inferential methodology, including maximum likelihood and Bayesian procedures, and examples drawn from signal processing. Objectives
Objectives
As specific objectives, by the end of the course students should be able to:
- By the end of the course students should be familiar with the fundamental concepts of statistical signal processing, including random processes, probability, estimation and inference.
Content
Lectures 1-8: Advanced Probability and Random Processes
-
Probability and random variables
-
Sample space, events, probability measure, axioms.
-
Conditional probability, probability chain rule, independence, Bayes rule.
-
Random variables (discrete and continuous), probability mass function (pmf), probability density function (pdf), cumulative distribution function, transformation of random variables.
-
Bivariate: conditional pmf, conditional pdf, expectation, conditional expectation.
-
Multivariates: marginals, Gaussian (properties), characteristic function, change of variables (Jacobian.)
-
-
Random processes
-
Definition of a random process, finite order densities.
-
Markov chains.
-
Auto-correlation functions.
-
Stationarity–strict sense, wide sense. Examples: iid process, random-phase sinusoid.
-
Ergodicity, Central limit theorem.
-
Spectral density.
-
Response of linear systems to stochastic inputs – time and frequency domain.
-
Time series models: AR, MA, ARMA
-
Lectures 9-16: Detection, Estimation and Inference
-
Basic linear estimation theory: BLUE, MMSE, bias, variance
-
Wiener filters
-
Matched filters
-
Least squares, maximum likelihood, Bayesian inference.
-
The ML/Bayesian linear Gaussian model
-
Maximum likelihood and Bayesian estimation
-
Example inference models: frequency estimation, AR model, Estimation of parameters for discrete Markov chain.
Coursework
Random variables and random number generation
Learning objectives:
- Understand random variables and functions of random variables and their simulation
- To study the Jacobian as used with random variables
- To experiment with methods for non-uniform random number generation
Practical information:
- Sessions will take place in [Location], during week(s) [xxx].
- This activity involves preliminary work.
Full Technical Report:
Students will have the option to submit a Full Technical Report.
Booklists
Please refer to the Booklist for Part IIA 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.
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.
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).
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.
Last modified: 23/11/2022 08:40
Engineering Tripos Part IIA, 3F3: Statistical Signal Processing, 2019-20
Module Leader
Lecturers
Dr S.S. Singh, Prof S. J. Godsill
Lab Leader
Timing and Structure
Michaelmas term. 16 lectures.
Prerequisites
3F1
Aims
The aims of the course are to:
- Study more advanced probability theory, leading into random process theory.
- Study random process theory and focus on discrete time methods.
- Introduce inferential methodology, including maximum likelihood and Bayesian procedures, and examples drawn from signal processing. Objectives
Objectives
As specific objectives, by the end of the course students should be able to:
- By the end of the course students should be familiar with the fundamental concepts of statistical signal processing, including random processes, probability, estimation and inference.
Content
Lectures 1-8: Advanced Probability and Random Processes
-
Probability and random variables
-
Sample space, events, probability measure, axioms.
-
Conditional probability, probability chain rule, independence, Bayes rule.
-
Random variables (discrete and continuous), probability mass function (pmf), probability density function (pdf), cumulative distribution function, transformation of random variables.
-
Bivariate: conditional pmf, conditional pdf, expectation, conditional expectation.
-
Multivariates: marginals, Gaussian (properties), characteristic function, change of variables (Jacobian.)
-
-
Random processes
-
Definition of a random process, finite order densities.
-
Markov chains.
-
Auto-correlation functions.
-
Stationarity–strict sense, wide sense. Examples: iid process, random-phase sinusoid.
-
Ergodicity, Central limit theorem.
-
Spectral density.
-
Response of linear systems to stochastic inputs – time and frequency domain.
-
Time series models: AR, MA, ARMA
-
Lectures 9-16: Detection, Estimation and Inference
-
Basic linear estimation theory: BLUE, MMSE, bias, variance
-
Wiener filters
-
Matched filters
-
Least squares, maximum likelihood, Bayesian inference.
-
The ML/Bayesian linear Gaussian model
-
Maximum likelihood and Bayesian estimation
-
Example inference models: frequency estimation, AR model, Estimation of parameters for discrete Markov chain.
Coursework
Random variables and random number generation
Learning objectives:
- Understand random variables and functions of random variables and their simulation
- To study the Jacobian as used with random variables
- To experiment with methods for non-uniform random number generation
Practical information:
- Sessions will take place in [Location], during week(s) [xxx].
- This activity involves preliminary work.
Full Technical Report:
Students will have the option to submit a Full Technical Report.
Booklists
Please see the Booklist for Part IIA 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.
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.
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).
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.
Last modified: 23/09/2019 14:25
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