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PHYS52115: Data Acquisition and Image Processing

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Type	Tied
Level	5
Credits	15
Availability	Available in 2024/2025
Module Cap	None.
Location	Durham
Department	Physics

Prerequisites

None

Corequisites

PHYS51915 Core Ia: Introduction to Machine Learning and Statistics and PHYS52015 Core Ib: Introduction to Scientific and High-performance Computing

Excluded Combinations of Modules

None

Aims

Provide basic knowledge and critical understanding of data acquisition and image analysis
Provide basic knowledge and critical understanding of paradigms, fundamental ideas and methods of data acquisition and image processing.

Content

Data acquisition
Image Processing

Learning Outcomes

Subject-specific Knowledge:

understanding and critical reflection of fundamental ideas and techniques in the application of data acquisition.
understanding and critical reflection of fundamental ideas and techniques in the application of image processing.

Subject-specific Skills:

Competent and educated selection and application of data acquisition techniques and image processing for specific problems.

Key Skills:

Familiarity with basic paradigms and modern concepts underlying data acquisition and image processing.

Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module

Teaching will be by lectures, workshops and practical classes.
The lectures provide the means to give a concise, focused presentation of the subject matter of the module.
When appropriate, the lectures will also be supported by the distribution of written material, or by information and relevant links on DUO
Regular problem exercises and workshops will give students the chance to develop their theoretical understanding and problem solving skills.
Students will be able to obtain further help in their studies by approaching their lecturers, either after lectures or at other mutually convenient times.
Student performance will be summatively assessed through coursework.
The formative coursework provides opportunities for feedback, for students to gauge their progress and for staff to monitor progress throughout the duration of the module.

Teaching Methods and Learning Hours

Activity	Number	Frequency	Duration	Total
Data Acquistion	8	8 per week	1 hour	8
Practical Classes in Data Acquisition	8	8 per week	1 hour	8
Image Processing	8	8 per week	1 hour	8
Practical Classes in Image Processing	8	8 per week	1 hour	8
Self-study				118
Total				150

Summative Assessment

Component: Coursework		Component Weighting: 100%
Element	Length / Duration	Element Weighting	Resit Opportunity
Data Acquisition Coursework		50
Image Processing Coursework		50

Formative Assessment

Feedback on coursework

More information

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