Tutor in the Department of Engineering Science (University of Oxford)

Third-Year Undergraduate Course

Paper B16: Software Engineering Laboratory (2024-present)

The purpose of this laboratory is to familiarise the student with the practical aspects of computer programming in C++, as well as to consolidate through practice the material taught in the other parts of B16: object-oriented programming, data structures, and algorithms. Throughout this practical, the student will: learn to use an integrated development environment to compile C++ programs; design a program structure; then implement and debug the program. Learning Outcomes:

1. Learn to use modern software development tools and integrated development environments.
2. Understand through practice fundamental structured programming notions: types, variables, control flow, functions.
3. Consolidate object-oriented programming skills: classes, constructors and destructors, and class hierarchies.
4. Learn to debug code using an integrated debugger.
5. Learn to use third-party libraries in your development.

Paper B18: Biomedical Modelling and Monitoring (2023-present)

Cellular Physiology, Systems Physiology, Wearable Technology, Medical Imaging

b1801: Cellular Physiology; Matric 2023, Y3; Paper B18, 4 Lectures, 1 Tutorial Sheet

The course consists of 4 lectures and will provide an introduction for using mathematical models for modelling certain physiological parameters of cellular systems. The structure of mammalian cells and a set of basic biochemical reactions will be introduced. The concept on cellular homeostasis and the membrane potential will be discussed. Key concepts of cellular signalling mechanisms will be presented. The aim of the course is to demonstrate what type phenomena and processes can be modelled with the help of mathematical models. Student will learn how their engineering or mathematical modelling skills can be applied to study cellular physiology. Learning Outcomes:

1. Be familiar the elementary structures of cells
2. Ability to model basic biochemical reactions using mass action kinetics and enzyme kinetics
3. Understand the concept of cellular homeostasis
4. Develop a simple cell model that incorporates the free principles: concentration of balance, electric neutrality and the Gibbs-Donnan equilibrium
5. Apply the Hodgkin-Huxley model for modelling basic action potentials
6. Apply reaction-diffusion equations for modelling carrier-mediated transport (example: glucose transport)

b1802: Systems Physiology; Matric 2023, Y3; Paper B18, 4 Lectures, 1 Tutorial Sheet This course introduces the basic concepts and applications of pharmacokinetic modelling, the structure of the cardiovascular system, electrical activity of the heart as well as the structure and function of the respiratory and nervous systems. This course also considers how these process give rise to changes that can be measured externally from the body. Learning Outcomes:

1. Have a good basic understanding about the anatomy and physiology of the human body.
2. Have knowledge of compartmental models describing compound or drug concentration in the human body.
3. Understand the physiological absorption, distribution, metabolism and elimination of foreign substances in the body and be able to calculate the concentration-time curves of these compounds using pharmacokinetic modelling techniques.
4. Understand the heart and cardiovascular system, the basics of electrocardiography as well as how to read a simple electrocardiogram and to measure blood pressure.
5. Be able to model the vasculature using an electrical equivalent circuit model and to calculate the relevant model parameters.
6. Be able to describe the function of the lung and to calculate lung volumes, respiratory capacity, gas exchange, blood pH and other relevant parameters.
7. Be able to describe the function of the nervous system, including afferent and efferent nerves, and the sympathetic and parasympathetic nerves.

b1803: Wearable Technology Matric 2023, Y3; Paper B18: 4 Lectures, 1 Tutorial Sheet The course consists of 4 lectures and will provide an introduction to the acquisition methods for important biosignals. The physiological processes that manifest themselves in these biosignals are discussed, and estimation of physiological parameters (such as the vital signs) is studied. This course extends the electrical engineering covered in previous years to the application of biosignal acquisition and conditioning, via biomedical instrumentation. Applications of the methods to the monitoring of patients within hospital and home settings are described, including the understanding of basic pathologies for which the vital-sign monitoring methods are used in practice. Learning Outcomes:

1. Have a good understanding of biopotentials, including electrodes and the conversion of ionic currents to electrical currents.
2. Understand ECG instrumentation amplifiers and noise reduction using driven right-leg circuitry.
3. Have an understanding of the estimation of respiratory rate from biosignals, including electrical impedance changes of the chest due to breathing and blood flow.
4. Be able to describe 2 and 4-electrode measurement of electrical impedance, and the derivation of respiration rate from the amplitude and frequency-modulation of the ECG and photoplethysmogram.
5. Understand the basic principles of oximetry, including the measurement of arterial oxygen saturation using visible and infra-red light.
6. Be able to describe the separation of a.c. and d.c. components of light absorption in pulse oximetry, and understand standard circuitry used within pulse oximeters.
7. Have an understanding of the estimation of systolic and diastolic blood pressure, using non-invasive measurements from inflatable cuffs, via oscillometry, and Korotkoff sounds.

b1804: Medical Imaging; Matric 2023, Y3; Paper B18, 4 Lectures, 1 Tutorial Sheet This course introduces methods by which images from within the human body can be reconstructed from signals measured externally. It covers a number of widely used medical imaging modalities, briefly introducing the underlying principles and methods for image construction. The concept of imaging metabolic processes using specifically labelled molecules and imaging apparatus is also introduced.

1. Transmission, reflection and emission.
2. Tomographic reconstruction.
3. Resonance and frequency reconstruction.
4. Metabolic imaging with contrast agents. Learning Outcomes:
5. Understand how electromagnetic and sound waves can be used to identify structure within the body based on attenuation and reflection.
6. Describe the process of tomographic reconstruction, including simple reconstruction methods such as filtered back projection.
7. Understand the concept of a point spread function and sources of error the limit the resolution of tomographic techniques.
8. Describe how magnetic resonance can be used to selectively image body tissues.
9. Understand why magnetic resonance imaging gives rise to signals in the frequency domain and be able to describe the resulting relationships between frequency sampling and image resolution.
10. Appreciate how molecules can be labelled such that they can be detected by imaging apparatus so that physiological processes can be monitored.
11. Quantify uptake and binding of contrast agents through the use of kinetic models.

b1805: Wearable Technology Laboratory; Matric 2023, Y3; Paper B18 This lab session aims to provide practical experience of acquiring biosignals such as the EEG and the ECG. We relate the signals to the various physiological processes from which the observed data arise, and investigate the role of conditioning (such as filtering and amplification) as would typically be required to acquire and use these signals in practice. The main sources of error in the acquisition of these signals will be investigated, including gaining an understanding of appropriate sensors and their placement on the patient. Learning Outcomes:

1. Gain practical experience in the acquisition of ECG waveforms using two different sensor systems.
2. Investigate the typical difficulties involved in instrumenting a patient with non-invasive sensors.
3. Investigate the acquisition of EEG waveforms using scalp-mounted sensors.
4. Be able to relate the acquired signals to the physiological processes from which the waveforms arise.
5. Gain an understanding of signal conditioning methods, based on electrical engineering principles that have been learned in previous years.
6. Be able to select suitable parameters for data acquisition systems, making appropriate design decisions concerning sampling rate, filter characteristics, etc.
7. Be able to describe the main sources of error and potential for artefact in the resulting signals

Paper B14: Information Engineering Systems (2022-2023)

Image Processing, Signal Analysis, Estimation, Inference

Information Engineering Laboratory (2022-present)

In this laboratory the students will learn to implement some of the techniques presented during the lectures in MATLAB. The topics covered are: Basic image manipulation, image filtering in the spatial (convolution) and frequency domains (FFT), image denoising and deblurring, how to generate random samples from a distribution, manipulate Normal distributions and apply statistical inference to infer their parameters, how to combine information from different sensors to obtain more accurate estimations (sensor fusion), compute panoramic mosaics using planar homographies, train a classifier to learn facial expressions.

Learning Outcomes:

1. Understand how to implement Weiner filters and FFT’s and use them for image denoising
2. Understand how to combine iid multivariate normal measurements
3. Understand how to implement feature extraction and classification
4. Understand how to fuse multi-view imagery