Prof. Anna C. Gilbert Profile

Prof. Anna C. Gilbert

Assistant Professo at Univ of Michigan

SPIE Involvement:

Author

Publications (3)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 27 March 2015 Paper

Sampling considerations for modal analysis with damping

Jae Young Park, Michael Wakin, Anna Gilbert

Proceedings Volume 9435, 94350U (2015) https://doi.org/10.1117/12.2084289

KEYWORDS: Structural health monitoring, Sensors, Statistical analysis, Shape analysis, Modal analysis, Matrices, Sensor networks, Buildings, Bridges, Mining

Read Abstract +

Structural health monitoring (SHM) systems are critical for monitoring aging infrastructure (such as buildings or bridges) in a cost-effective manner. Wireless sensor networks that sample vibration data over time are particularly appealing for SHM applications due to their flexibility and low cost. However, in order to extend the battery life of wireless sensor nodes, it is essential to minimize the amount of vibration data these sensors must collect and transmit. In recent work, we have studied the performance of the Singular Value Decomposition (SVD) applied to the collection of data and provided new finite sample analysis characterizing conditions under which this simple technique{also known as the Proper Orthogonal Decomposition (POD){can correctly estimate the mode shapes of the structure. Specifically, we provided theoretical guarantees on the number and duration of samples required in order to estimate a structure's mode shapes to a desired level of accuracy. In that previous work, however, we considered simplified Multiple-Degree-Of-Freedom (MDOF) systems with no damping. In this paper we consider MDOF systems with proportional damping and show that, with sufficiently light damping, the POD can continue to provide accurate estimates of a structure's mode shapes. We support our discussion with new analytical insight and experimental demonstrations. In particular, we study the tradeoffs between the level of damping, the sampling rate and duration, and the accuracy to which the structure's mode shapes can be estimated.

Proceedings Article | 11 April 2013 Paper

Implementation of a compressive sampling scheme for wireless sensors to achieve energy efficiency in a structural health monitoring system

Sean O'Connor, Jerome Lynch, Anna Gilbert

Proceedings Volume 8694, 86941L (2013) https://doi.org/10.1117/12.2010128

KEYWORDS: Sensors, Compressed sensing, Bridges, Structural health monitoring, Energy efficiency, Data acquisition, Reconstruction algorithms, Wireless communications, Magnetic sensors, Modal analysis

Read Abstract +

Wireless sensors have emerged to offer low-cost sensors with impressive functionality (e.g., data acquisition, computing, and communication) and modular installations. Such advantages enable higher nodal densities than tethered systems resulting in increased spatial resolution of the monitoring system. However, high nodal density comes at a cost as huge amounts of data are generated, weighing heavy on power sources, transmission bandwidth, and data management requirements, often making data compression necessary. The traditional compression paradigm consists of high rate (>Nyquist) uniform sampling and storage of the entire target signal followed by some desired compression scheme prior to transmission. The recently proposed compressed sensing (CS) framework combines the acquisition and compression stage together, thus removing the need for storage and operation of the full target signal prior to transmission. The effectiveness of the CS approach hinges on the presence of a sparse representation of the target signal in a known basis, similarly exploited by several traditional compressive sensing applications today (e.g., imaging, MRI). Field implementations of CS schemes in wireless SHM systems have been challenging due to the lack of commercially available sensing units capable of sampling methods (e.g., random) consistent with the compressed sensing framework, often moving evaluation of CS techniques to simulation and post-processing. The research presented here describes implementation of a CS sampling scheme to the Narada wireless sensing node and the energy efficiencies observed in the deployed sensors. Of interest in this study is the compressibility of acceleration response signals collected from a multi-girder steel-concrete composite bridge. The study shows the benefit of CS in reducing data requirements while ensuring data analysis on compressed data remain accurate.

Proceedings Article | 21 September 2005 Paper

Improved time bounds for near-optimal sparse Fourier representations

A. Gilbert, S. Muthukrishnan, M. Strauss

Proceedings Volume 5914, 59141A (2005) https://doi.org/10.1117/12.615931

KEYWORDS: Fourier transforms, Algorithms, Statistical analysis, Convolution, Boxcar filters, Analog electronics, Superposition, Interference (communication), Optical filters, Error analysis

Read Abstract +

•We study the problem of finding a Fourier representation R of m terms for a given discrete signal A of length N. The Fast Fourier Transform (FFT) can find the optimal N-term representation in time O(N log N) time, but our goal is to get sublinear time algorithms when m << N. Suppose ||A||₂ ≤M||A-R_opt||₂, where R_opt is the optimal output. The previously best known algorithms output R such that ||A-R||²₂≤(1+ε))||A-R_opt||²₂ with probability at least 1-δ in time* poly(m,log(1/δ),log N,log M,1/ε). Although this is sublinear in the input size, the dominating expression is the polynomial factor in m which, for published algorithms, is greater than or equal to the bottleneck at m² that we identify below. Our experience with these algorithms shows that this is serious limitation in theory and in practice. Our algorithm beats this m² bottleneck. Our main result is a significantly improved algorithm for this problem and the d-dimensional analog. Our algorithm outputs an R with the same approximation guarantees but it runs in time m•poly(log(1/δ),log N,log M,1/ε). A version of the algorithm holds for all N, though the details differ slightly according to the factorization of N. For the d-dimensional problem of size N₁ × N₂ × •• × N_d, the linear-in-m algorithm extends efficiently to higher dimensions for certain factorizations of the N_i's; we give a quadratic-in-m algorithm that works for any values of N_i's. This article replaces several earlier, unpublished drafts.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years