D etector C haracterization R obot (Progress Since January 2001)

June 1, 2001 GEO DC Workshop

Detector Characterization Robot(Progress Since January 2001)

• Design (Already exists)

• Algorithmic and Statistical studies (In progress)

• Algorithm development and fast prototyping in MATLAB

Soumya Mohanty, Soma Mukherjee

• Computational Implementaion issues (C++)

• Software Environment (Standalone)

• Hardware requirements

Soumya Mohanty, B.Sathyaprakash,

R.Balasubramanian, D.Churches

Two Main Areas of Development Now


What are the Robot’s Functions?

• Detect Changes in the Multi-channel data produced by an interferometric detector.– Change in each channel by itself,

– In the inter-dependence of channels.

• Each change point is of potential interest. – For Data analysts: Need to adjust GW search algorithm

parameters/thresholds,

– For Experimentalists: Change in behaviour of detector components or the detector’s environment.

• Perform with known reliability and sensitivity.

Microsoft Word Document


History (January 2001 – Present)• MATLAB v6.0 acquired (May 2001):

– Enhanced external interfaces (MEX), enhanced Signal Processing/Statistics Toolboxes and more.

– Started developing better Matlab to Frame interface: frmake.c (MEX file) and frgui (GUI for browsing and executing FrDump).

• Changes made to line removal algorithm (MBLT) code.– Eliminated resizing of matrices inside for loops, Ways to make the code faster

identified.– Present code is completely unoptimised and very slow.

• Investigation of alternatives to MBLT (Continuing). – Coding almost complete (Kernel Density Estimates, LOWESS).

• Time series modeling functionality of Matlab investigated. – Severely lacking compared to advanced statistical software such as SAS but

sufficient to start with. – PBURG produces a fixed offset (30dB) in PSD irrespective of model order.

• Discussions started on Computational aspects. Several telecons held.• Identified data set for experimentation: 40m Nov 1994 run.

– Started generating data cleaned of lines using MBLT.


Thinking on Computational Front• Preliminary accounting shows DCR to be computationally expensive.

– Mainly due to the line removing algorithm being used in the first stage.– The first stage line removal will be applied to several channels so one needs a model independent,

transient resistant method.– However, much improvement is possible.

• Converging to a C++ Digital Signal Processing library (very focussed in the beginning).– C++ so that it can be merged, if required, with TRIANA, DMT, LDAS.– Want to make a portable, well-structured library (data structures based on the Standard Template

Library and not custom built). – Will be useful when iterating over several DCR designs and for rapid prototyping.– Extensive search over the web for free software turned up only one such project (with only one

author) without much development.– But bits and pieces exist: Especially DSP appropriate data structures based on STL.– Explored other options such as MATLAB C++ Math library or OCTAVE’s (free Matlab clone)

library. MATLAB is very restrictive as far as sharing concerned. OCTAVE being explored by Stas Babak in a different context (?).

• Looking at Class hierarchy designs. (Book on C++ DSP algorithms found – not freely distributable software/more for teaching)

• Scoping for manpower and hardware requirements started.


Plans on the design front• Immediate plan is to manually execute the DCR on 0.5 hours of 40m data

(locked section).– Writing a monolithic Matlab script not useful since algorithms are being updated

and are being investigated individually. At least not yet.– Manual execution strictly follows the DCR implementation. There should be no

hidden human help.

• Continue coding and development of alternative tools at every stage.– Exploring line removal alternatives.– noise floor PSD tracking alternatives:

• Median filtered PSD (New), • Time-Frequency distributions.

• Refine algorithms and remove performance bottlenecks.– Example: In MBLT, Upsampling proving to be the main bottleneck in Matlab

though it shouldn’t be. May have to write own MEX file. • Apply DCR to several auxiliary channels also.

– Note: simple problems take up time. Getting to know the channel names in old 40m data: FrDump of the old FrameLib version does not produce a list of channel names.


Line Removal (Median Based Line Tracker)

Bandpass & Demodulate

Bandpass & Demodulate

Adjust Filter Delay

Adjust Filter Delay

Block wise median

Block wise median

UpsampleUpsample

ModulateModulate

Bandpass & DMBandpass & DM

Adjust Filter Delay

Adjust Filter Delay

Block wise median

Block wise median

UpsampleUpsample

ModulateModulate


Adjust Filter Delay

Adjust Filter Delay

Block wise median

Block wise median

UpsampleUpsample

ModulateModulate


Adjust Filter Delay

Adjust Filter Delay

Block wise median

Block wise median

UpsampleUpsample

ModulateModulate

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DATA

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f_info=[30.718 29.9 31.5 29.0 32.0 800;

39.6 39.0 40.2 39.0 40.2 1000;

41.1 40.9 41.5 40.9 41.5 1000;

58.8 58.0 59.6 57.0 61.0 800;

70.0 64.0 76.0 64.0 76.0 800;

79.5 78.0 81.0 78.0 81.0 850;

109.4 108.0 111.0 106.0 113.0 800;

120.0 119.4 120.6 117 123.0 800;

131.9 131.4 132.4 131.5 132.5 1000;

One-time Database of frequencies, filterband limits, block sizes required

47Entries


Histograms: Original versus Residual data


Time Series


Autocorrelation Function


• Time series modeling: Estimate a filter function such that the observed time series is statistically (upto second moments) similar to white noise passed through this filter.

– In general a zero-pole, stable, filter can be found.– Anything can be fit with a sufficiently large order: Therefore some penalty on

model order needed (AIC,BIC,MLD,…).

• Parametric model of PSD obtained as a side benefit. Frequency resolution is not tied to data duration.

• Time evolution of Model coefficients should indicate non-stationarity (both short and long term).

• AR models (all pole) can model spectra with sharp spikes.– AR models only a special cases of more general models: ARMA (AR and Moving

Average).– Restricted to AR right now because Matlab has only AR modeling.– System Identification Toolbox arrived yesterday! (Also Neural Net, Wavelet and

Database).

Tracking PSD variation: Time Series modeling

New


Order of fitted model: Necessity of line removal


Comparision with Spectrogram

D etector C haracterization R obot (Progress Since January 2001)

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