Direct digital wavelet synthesis for embedded biomedical microsystems
File(s)2018_BioCAS_DDWS_Submitted.pdf (795.3 KB)
Accepted version
Author(s)
Leene, Lieuwe
Constandinou, TG
Type
Conference Paper
Abstract
This paper presents a compact direct digital wavelet
synthesizer for extracting phase and amplitude data from cortical
recordings using a feed-forward recurrent digital oscillator.
These measurements are essential for accurately decoding local-
field-potentials in selected frequency bands. Current systems
extensively to rely large digital cores to efficiently perform
Fourier or wavelet transforms which is not viable for many
implants. The proposed system dynamically controls oscillation to
generate frequency selective quadrature wavelets instead of using
memory intensive sinusoid/cordic look-up-tables while retaining
robust digital operation. A MachXO3LF Lattice FPGA is used to
present the results for a 16 bit implementation. This configuration
requires 401 registers combined with 283 logic elements and
also accommodates real-time reconfigurability to allow ultra-low-
power sensors to perform spectroscopy with high-fidelity.
synthesizer for extracting phase and amplitude data from cortical
recordings using a feed-forward recurrent digital oscillator.
These measurements are essential for accurately decoding local-
field-potentials in selected frequency bands. Current systems
extensively to rely large digital cores to efficiently perform
Fourier or wavelet transforms which is not viable for many
implants. The proposed system dynamically controls oscillation to
generate frequency selective quadrature wavelets instead of using
memory intensive sinusoid/cordic look-up-tables while retaining
robust digital operation. A MachXO3LF Lattice FPGA is used to
present the results for a 16 bit implementation. This configuration
requires 401 registers combined with 283 logic elements and
also accommodates real-time reconfigurability to allow ultra-low-
power sensors to perform spectroscopy with high-fidelity.
Date Issued
2018-12-24
Date Acceptance
2018-08-13
Citation
2018, pp.77-80
Publisher
IEEE
Start Page
77
End Page
80
Copyright Statement
© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Sponsor
Engineering & Physical Science Research Council (EPSRC)
Identifier
https://ieeexplore.ieee.org/abstract/document/8584787
Grant Number
EP/M020975/1
Source
IEEE Biomedical Circuits and Systems (BioCAS) Conference 2018
Subjects
Science & Technology
Technology
Computer Science, Information Systems
Engineering, Biomedical
Engineering, Electrical & Electronic
Computer Science
Engineering
TRANSFORM
Publication Status
Published
Start Date
2018-10-17
Finish Date
2018-10-19
Coverage Spatial
Cleveland, Ohio, USA
Date Publish Online
2018-12-24