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1
Intro
2
Motivation
3
Data collection
4
Equations of motion
5
Free vibration
6
Modal analysis techniques
7
Sampling model
8
Joint sparse frequency estimation
9
Classical spectral estimation
10
Method #1: Proper Orthogonal Decomposition
11
SVD of data matrix
12
POD considerations
13
Minimum separation condition
14
Uniform time sampling
15
Experiment: Telegraph Bridge data
16
Experiment, ctd
17
Method #2: Atomic norm minimization
18
Synchronous random sampling
19
Random temporal compression
20
Random spatial compression
21
Benefits of compression
22
Sampling rate scaling
23
Orthogonality
24
Synchronous vs. asynchronous
25
More sensors
26
Open questions
Description:
Explore modal analysis techniques for structural health monitoring in this 52-minute lecture from the Alan Turing Institute. Delve into sampling and compression strategies for wireless sensor networks, focusing on SVD-based methods and atomic norm minimization for mode shape and frequency recovery. Examine theoretical bounds on sample complexity and recovery accuracy, and compare various sampling/compression approaches. Learn about applications in civil and structural engineering, signal processing, and applied mathematics. Gain insights from speaker Michael B. Wakin, an accomplished researcher in compressive sensing and signal processing, as he presents case studies, experimental results, and discusses open questions in the field.

Modal Analysis from Random and Compressed Samples

Alan Turing Institute
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