Dallas, TX, United States of America

Michelle L Crawford


Average Co-Inventor Count = 1.0

ph-index = 1

Forward Citations = 3(Granted Patents)


Company Filing History:


Years Active: 2021

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1 patent (USPTO):Explore Patents

Title: The Innovative Contributions of Michelle L Crawford

Introduction

Michelle L Crawford is a prominent inventor based in Dallas, TX (US). She has made significant strides in the field of technology, particularly in the area of radio frequency (RF) identification systems. Her work exemplifies the intersection of engineering and machine learning, showcasing her innovative spirit and dedication to advancing technology.

Latest Patents

Michelle L Crawford holds a patent for a "Smart receiver with compressive sensing and machine learning." This invention outlines a system and method for identifying an RF emitter. The system includes channelizers for channelizing RF signals into several channels, a compressive sensing (CS) encoder for each channel to CS encode the channelized signal, and a summer to sum the encoded channelized signals. Additionally, it features a channelized pulse detection circuit to detect pulses in each channel, producing encoded pulse snippets from the I/Q data. The invention also incorporates a CS decoder for each channel, a first machine learning device to characterize the decoded pulse snippets, and a second machine learning device to associate the pulse description words (PDWs) with one or more RF emitters for identification.

Career Highlights

Michelle L Crawford is currently employed at Raytheon Applied Signal Technology, Inc. Her role at the company allows her to apply her expertise in developing advanced technologies that enhance RF identification systems. Her contributions have been instrumental in pushing the boundaries of what is possible in this field.

Collaborations

Due to space constraints, the collaborations section has been omitted.

Conclusion

Michelle L Crawford's innovative work in RF identification systems highlights her significant contributions to technology and engineering. Her patent demonstrates her ability to integrate machine learning with compressive sensing, paving the way for future advancements in the field.

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