Open University

Tampere

Advanced Signal Processing, Lectures

<p>Contact information<br>Email: <span class="spamspan"><span class="u">open.studies.tau</span> [at] <span class="d">tuni.fi</span></span><br>Phone: 0294 520 200</p><p>More information concerning Open University studies<br><a href="https://www.tuni.fi/open-university">tuni.fi/open-university</a></p>

Advanced studies

Faculty of Information Technology and Communication Sciences

Technology and Natural Sciences

<p>Apply for Open University studies by using the following links:</p><p><ul><li><a href="https://tau-avoin.ilmoittaudu.fi/lobby/?realization=J-otm-8d4a4dde-df52-4db6-87a3-a3e45c6b5de3_2023-tampere-COMPSGN200" target="_blank">COMP.SGN.200 Advanced Signal Processing, lectures, Tampere</a></li></ul></p><p>You can find a link to the Open University Admissions service and application instructions on the <a href="https://www.tuni.fi/en/study-with-us/open-higher-education-studies/open-university/applying-open-university">Applying to Open University studies</a> page.</p><p>Some of the studies require prior studies. For example, studying intermediate studies may require some completed basic studies. These are described on the Information about studies tab of the study unit under prior requirements.</p><p>When you have been admitted to the studies, you must enrol separately via the Sisu information system for the implementation of the unit of study and, for example if set, for a practise group of your choice. You will receive instructions on how to do this by e-mail after you have confirmed your acceptance of the study place by paying the required fee.</p>

English

Hervanta Campus

Contact teaching

<p>Look information at <a href="https://www.tuni.fi/en/study-with-us/open-higher-education-studies/open-university/applying-open-university">Applying to Open University studies</a>.</p>

<h5>Core content</h5><ul><li>1.&nbsp; &nbsp; &nbsp;Deterministic and random signals: review of Fourier transform, Z transform, random variables, random signals, correlation, &nbsp;AR,MA, ARMA </li><li>2.&nbsp; &nbsp; &nbsp;Optimal filter design (Wiener filter, Least squares, essentials of estimation, MLE, CramerRao)</li><li>3.&nbsp; &nbsp; &nbsp;Adaptive filter design (LMS, NLMS, RLS ) </li><li>4.&nbsp; &nbsp; &nbsp;Application areas of &nbsp;Optimal filter design and Adaptive filter design</li><li>5.&nbsp; &nbsp; &nbsp;Spectrum estimation:Frequency spectrum (needed in machine function regime diagnosis, finding periodicities in time series), &nbsp;Direction of Arrival spectrum </li><li>6.&nbsp; &nbsp; &nbsp;Nonlinear filters (median and order statistics filter family)</li></ul>

<h2>Common</h2><h3>Lecture: </h3><p>23.10.2023 10:00 - 12:00<br>30.10.2023 10:00 - 12:00<br>06.11.2023 10:00 - 12:00<br>13.11.2023 10:00 - 12:00<br>20.11.2023 10:00 - 12:00<br>27.11.2023 10:00 - 12:00<br>04.12.2023 10:00 - 12:00<br></p><h2>Groups</h2><h3>Group 1: Group 1</h3><p>26.10.2023 12:00 - 14:00<br>02.11.2023 12:00 - 14:00<br>09.11.2023 12:00 - 14:00<br>16.11.2023 12:00 - 14:00<br>23.11.2023 12:00 - 14:00<br>30.11.2023 12:00 - 14:00<br>07.12.2023 12:00 - 14:00<br></p><h3>Group 2: Group 2</h3><p>26.10.2023 10:00 - 12:00<br>02.11.2023 10:00 - 12:00<br>09.11.2023 10:00 - 12:00<br>16.11.2023 10:00 - 12:00<br>23.11.2023 10:00 - 12:00<br>30.11.2023 10:00 - 12:00<br>07.12.2023 10:00 - 12:00<br></p>

Student will learn advanced signal processing methods, especially linear optimal filter design, adaptive filters, spectrum estimation, nonlinear filters and how to select proper methods for signal processing tasks at hand.<br>After completing the course, the student<br>-&nbsp; &nbsp; &nbsp;Is familiar with the most important advanced signal processingr generic problems: optimal design, convergence, recursiveness in time, spectrum estimation;<br>-&nbsp; &nbsp; &nbsp;Is able to start from the formulation of a problem formulation and utilize a number of typical algorithmic tools to derive the solution;<br>-&nbsp; &nbsp; &nbsp;Knows what are the most important algorithms for optimal and adaptive filters: LMS, NLMS,RLS etc.<br>-&nbsp; &nbsp; &nbsp;Acquires practice on simulating optimal and adaptive algorithms with given input data and extracting useful performance indices helpful in comparing various algorithms. &nbsp; <br>-&nbsp; &nbsp; &nbsp;Knows how to integrate an optimal or adaptive filter in a number of important applications: echo cancelation, noise cancellation, channel equalization etc.<br><br>

<h2>Prerequisites</h2><h5>Prerequisite</h5><ul><li>Code:&#160;SGN-11000</li><li>Name:&#160;Basic Course in Signal Processing</li><li>ECTS credits:&#160;5</li><li>Mandatory:&#160;Advisable</li></ul><h5>Prerequisite</h5><ul><li>Code:&#160;SGN-11007</li><li>Name:&#160;Introduction to Signal Processing</li><li>ECTS credits:&#160;5</li><li>Mandatory:&#160;Advisable</li></ul><h2>Recommended Prerequisites</h2><ul><li>Introduction to Signal Processing, COMP.SGN.100, 5 cr</li></ul>

<h5>Material</h5><ul><li>Type:&#160;Book</li><li>Name:&#160;Adaptive Filter Theory</li><li>Author:&#160;Simon O. Haykin</li><li>Exam material:&#160;No</li><li>Language:&#160;English</li></ul><h5>Material</h5><ul><li>Type:&#160;Book</li><li>Name:&#160;Spectral analysis of signals</li><li>Author:&#160;Petre Stoica and Randolph Moses </li><li>Exam material:&#160;No</li><li>Language:&#160;English</li></ul><h5>Material</h5><ul><li>Type:&#160;Lecture slides</li><li>Author:&#160;Ioan Tabus</li><li>Exam material:&#160;Yes</li><li>URL:&#160;http://www.cs.tut.fi/~tabus/course/AdvSP.html</li><li>Language:&#160;English</li></ul><h5>Material</h5><ul><li>Type:&#160;Book</li><li>Name:&#160;Optimum Signal Processing</li><li>Author:&#160;S. J. Orfanidis</li><li>Exam material:&#160;No</li><li>URL:&#160;http://eceweb1.rutgers.edu/~orfanidi/osp2e/index.html</li><li>Language:&#160;English</li></ul>

Advanced Signal Processing, Lectures

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Advanced Signal Processing, 5 cr

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