This Small Business Innovation Research (SBIR) Phase I project proposes to perform fundamental research into a novel method for signal analysis, using standing waves. The intellectual merit of this proposal is the development of an innovative set of physical design rules which could lead to seamless spectral component analysis of arbitrarily complex analog signals. Fourier analysis is a ubiquitous technique for encoding and decoding information from physical systems as well as electronic signals. Yet there have been minimal innovations in the methods for producing, say Fourier spectra, aside from improvements in computational speeds with which Fast Fourier Transforms (FFTs) and Discrete Fourier Transforms (DDFTs) are performed by Digital Signal Processors (DSPs) or computers. The device proposed is an electronic component called an Electronic Neural Loop (ENL), designed to promote resonances in a particular frequency. The method can be used to perform very fast frequency component identification. Several ENLs in parallel would have the capacity to decompose a broadband signal streams into frequency bins similar to Fourier frequency components, only faster. The ENL approach uses physics in place of computational methods and has not been investigated or suggested by any other company or research institution to date.

The broader impact of this technology affects the essential methods currently used for signals processing. The ENL being an analog device does not require a digital sampling and framing stage, which introduces processing artifacts and limitations. As a circuit element it has countless application areas, many to yet be discovered. It replaces existing circuits comprised of more parts with simpler solutions having lower power consumption which is critical to today's handheld applications. It has tremendous market potential, allowing for continuous, real-time, on-chip Fourier analysis, with game-changing innovations to scientific spectrometric instrumentation, telecommunications, encryption, sensors for high radiation environments (nuclear reactors, space exploration), medical systems as well as numerous military uses.

Project Report

In this NSF SBIR Phase I project Variance Dynamical performed fundamental research into a novel method and device for very fast Direct Frequency Component Decomposition (DFCD), used in an innovative analog device called an Electronic Neural Loop (ENL). An ENL employs unique physical principles which take advantage of the physical dimensionality and scale of its electronic chip architecture, to decompose broadband signal streams into a frequency bin similar to a Fourier frequency component. The physical principles are those encountered in studies of resonances in waveguides, used here to promote the formation of standing waves corresponding to the fundamental frequency of the ENL. < Note: DFCD is an analog electronic process, whereas FFTs are digital numberical algorithms, which approximate Fourier Transformations. > Proof-of-concept ENL prototype devices have been constructed validating the basic functionality of the approach. This Phase I research established the basic design specifications and tuning capabilities of ENL devices. We have successfully constructed ENLs at frequencies from 1 MHz to 4 GHz, which were found to be in accordance to the basic principles. The technical result of this Phase I work focused on validating Electronic Neural Loop technology as of a new method for constructing bandpass filters, and for extracting Fourier frequency components. < See figures for technical results.> The ENL and DFCD process is a novel platform technology with tremendous commercial potential as a fundamental building block for any number of electronic devices which require fast signal processing. ENL technology can be used as as bandpass filters, resonators, oscillators and band reject/notch filters. An ENL filter bank, arranged as an Automatic Fourier Analyzer (AFA), can be used to perform continuous Fourier-like analysis on analog signals without the need for a digital sampling or computation. The broader impact of this technology relate to the essential methods currently used for signal processing. The ENL/DFCD differs from current signal analysis and processing approaches, as it does not use numerical Fourier Transformation methods as used in Digital Signal Processors (DSPs), or electromechanical / piezoelectric systems as applied to Surface Acoustic Wave (SAW) and Bulk Acoustic Wave (BAW) devices. Furthermore, the technology is scalable, miniaturaziable and theoretically scalable to any frequency, circumventing material property limitations imposed on current solutions. ENLs as analog devices do not require digital sampling and framing stages, which introduce processing artifacts and speed limitations. ENLs have tremendous market potential as they allow for continuous, on-the-fly Fourier analysis, applicable to speech/speaker recognition systems, scientific instrumentation, telecommunications, encryption, sensors for high radiation environments (nuclear reactors, space exploration), medical systems and applications of interest for national security. Our research continues into Phase II, by extending these capabilities towards the development of commercial prototype bandpass filters operating at a wide range of frequencies relevant to MHz and GHz-class wireless telecommunications, GPS, WiFi, Bluetooth and RFID systems. A technology readiness assessment of the ENL/DFCD operating at THz frequencies will establish their applicability to emergent medical, remote sensing and military/medical THz applications.

National Science Foundation (NSF)
Division of Industrial Innovation and Partnerships (IIP)
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Juan E. Figueroa
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Variance Dynamical Corporation
United States
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