K.E. Thomson
Michigan State University
6 Papers
33 Citations
K.E. Thomson is an academic researcher from Michigan State University. The author has contributed to research in topics: Discrete wavelet transform & Wavelet. The author has an hindex of 5, co-authored 6 publications.
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Papers
A Scalable Wavelet Transform VLSI Architecture for Real-Time Signal Processing in High-Density Intra-Cortical Implants
TL;DR: Results indicate that signal integrity is not compromised by quantization down to 5-bit filter coefficient and 10-bit data precision at intermediate stages, and results from analog simulation and modeling show that a hardware-minimized computational core executing filter steps sequentially is advantageous over the pipeline approach commonly used in DWT implementations.
Design optimization of integer lifting DWT circuitry for implantable neuroprosthetics
Andrew J. Mason,J. Li,K.E. Thomson,Yasir Suhail,Karim Oweiss +4 more
- 12 May 2005
TL;DR: This work identifies an optimal VLSI architecture for computing a 1-dimensional multilevel discrete wavelet transform for multiple electrode channels simultaneously based on the lifting-scheme for wavelet computation and integer fixed point precision for real-time processing under constraints imposed by implantability requirements.
Augmenting real-time DSP in implantable high-density neuroprosthetic devices
Karim Oweiss,Yasir Suhail,K.E. Thomson,J. Li,Andrew J. Mason +4 more
- 12 May 2005
TL;DR: A systems approach for reducing the complexity of on-chip discrete wavelet transform (DWT) computation for multiple data channels by exploiting regularity in the filtering steps of the lifting-based DWT algorithm associated with negligible degradation in signal fidelity with integer fixed point arithmetic representation is described.
Scalable architecture for streaming neural information from implantable multichannel neuroprosthetic devices
K.E. Thomson,Yasir Suhail,Karim Oweiss +2 more
- 23 May 2005
TL;DR: Two hardware architectures for implementing lifting-based discrete wavelet transform suitable for implantable, real-time operation of high-density sensor array neuroprosthetic devices are suitable for achieving scalability to an arbitrary number of channels.
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A Systems Approach for Real-Time Data Compression in Advanced Brain-Machine Interfaces
Karim Oweiss,K.E. Thomson,David J. Anderson +2 more
- 16 Mar 2005
TL;DR: It is shown that the tradeoff between transmission bit rate and processing complexity requires a smart coding mechanism to yield a fast and efficient neural interface capable of transmitting the information from the CNS in real-time without compromising issues of communication bandwidth and signal fidelity.
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