| CODE | GSC3409 | ||||||||||||
| TITLE | Data Analysis and Signal Processing | ||||||||||||
| UM LEVEL | 03 - Years 2, 3, 4 in Modular Undergraduate Course | ||||||||||||
| MQF LEVEL | 6 | ||||||||||||
| ECTS CREDITS | 6 | ||||||||||||
| DEPARTMENT | Geosciences | ||||||||||||
| DESCRIPTION | We live in a digital age in which the collection and storage of data happens continuously and in real-time. Large bases of data that store the recorded parameters are continuously being created and populated with measurements. However, only through the understanding of the underlying concepts and intelligent processing can new and valuable knowledge be extracted. In this study unit, processing techniques that are frequently applied to map the datasets onto different domains, extract hidden signals and enhance the signal-to-noise ratio, are put forward. The mathematical background and application of specialised algorithms that are used to extract signals from noisy measurements or to identify new hidden signals from complex dataset, will be presented. Students following this study-unit will become equipped with an important tool-set that is applicable to all fields of Geoscience. Study-Unit Aims: The aim of this study-unit is to introduce the students to the theories behind the processes that are routinely applied to field measurements before new and useful information can be extracted. Following the mathematical definitions, each method is demonstrated through a real application on collected data. Moving averages, smoothing and exponential smoothing techniques as well as error metrics will be initially covered. The correlation and convolution operators will be then put forward. This will be followed by different domain representations (Fourier and Wavelets) as well as principal and independence component analysis (PCA and ICA). Another aim is to show the complexities involved in recovering the original undistorted signals after been modified by processes beyond control such as turbulence in the atmosphere. Approximations to Inverse Problem solutions such as Least Squares, Monte Carlo and Norm minimization, will be presented. Learning Outcomes: 1. Knowledge & Understanding: By the end of the study-unit the student will be able to: - Explain the principals behind the convolution and the correlation operators on multi-dimensional datasets; - Explain the principals behind fourier and laspace transform; - Identify and analyze the errors in datasets; - Review how models can be used to obtain an understanding of the processes involved in producing the observed data; - Explain the benefits obtained by representing data in different domains; - Classify signals into different components to extract and identify any underlying periodicity; - Identify the complexity and solve simple inverse problems. 2. Skills: By the end of the study-unit the student will be able to: - Analyse a measured time series in Matlab; - Apply spatial and frequency filters to the data; - Compute error metrics between modeled and observed data; - Apply univariate and multivariate models; - Compute correlation, autocorrelation and cross-correlation; - Apply Fourier, Laplace and Wavelet transforms; - Perform component analysis; - Apply basic models to solve simple inverse problems. Main Text/s and any supplementary readings: Main Texts: - Ifeachor, Emmanuel C. and Jervis, Berrie W. (2001). Digital Signal Processing: A Practical Approach. Upper Saddle River, NJ: Prentice Hall. - Press, William H. and Teukolsky, Saul A. and Vetterling, William T. and Flannery, P. (1992). Numerical Recipes in C: the art of scientific computing. Cambridge University Press. |
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| STUDY-UNIT TYPE | Lecture and Tutorial | ||||||||||||
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The University makes every effort to ensure that the published Courses Plans, Programmes of Study and Study-Unit information are complete and up-to-date at the time of publication. The University reserves the right to make changes in case errors are detected after publication.
The availability of optional units may be subject to timetabling constraints. Units not attracting a sufficient number of registrations may be withdrawn without notice. It should be noted that all the information in the description above applies to study-units available during the academic year 2025/6. It may be subject to change in subsequent years. |
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