Search Results for Signal processing.

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Biomedical Signal Processing and Inference in Wearable Sensing Applications

Gutta, Sandeep, author.

http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqm&rft_dat=xri:pqdiss:10247799

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Biomedical Signal Processing and Inference in Wearable Sensing Applications / Gutta, Sandeep

Electroencephalogram Signal Processing for Hybrid Brain Computer Interface Systems

Haider, Md. Ali, author.

http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqm&rft_dat=xri:pqdiss:10814455

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Electroencephalogram Signal Processing for Hybrid Brain Computer Interface Systems / Haider, Md

Embedded System Design of Low-Power Wearable Bioelectronic Devices

Hopper, Matthew S., author.

http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqm&rft_dat=xri:pqdiss:10751998

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With continued development needed in the signal processing algorithms, the eye-gaze tracking

Use of Machine Learning Algorithms in Electroencephalography based Brain-Computer Interfaces

Salyers, Jacob, author.

http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqm&rft_dat=xri:pqdiss:10823120

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challenge due to a low signal to noise ratio and a tremendous degree of signal complexity. The main method

Deep Learning Approach for Brain Machine Interface

Xie, Ziqian, author.

http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqm&rft_dat=xri:pqdiss:10822098

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differentiable signal processing modules and plug them together to build an adaptive online system. The system

Displacement Estimation in Pulsed Wave Ultrasound Based on Analysis of the Phase Spectra

Hecker, Joseph, author. (orcid)0000-0002-5334-6959

http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqm&rft_dat=xri:pqdiss:10813920

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fundamental assumptions of the underlying signal models on the processing algorithms used to calculate

Kernel and Manifold Framework for Magnetic Resonance Imaging

Nakarmi, Ukash, author. (orcid)0000-0002-5351-3956

http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqm&rft_dat=xri:pqdiss:10817180

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representation outside MR community and much theoretical advancement in signal reconstructions have been made

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