Nepean, Canada

Carter Demars

This inventor holds 1 USPTO granted patent and 1 published patent application. Top assignee: Ciena Corporation. Active years: 2026.

USPTO Granted Patents = 1 

Average Co-Inventor Count = 1.0

ph-index = 1


Company Filing History:


Years Active: 2026

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1 patent (USPTO):Explore Patents

Title: Carter Demars: Innovator in Optical Amplifier Technology

Introduction

Carter Demars is a notable inventor based in Nepean, Canada. He has made significant contributions to the field of optical technology, particularly in the area of optical amplifiers. His innovative work focuses on utilizing machine learning to predict failures in optical amplifiers, which is crucial for maintaining the efficiency of optical networks.

Latest Patents

Carter Demars holds a patent for "Optical amplifier failure prediction using machine learning." This patent describes systems and methods for predicting failures in optical amplifiers, such as the Erbium-Doped Fiber Amplifier (EDFA). The method involves obtaining multiple inputs from the optical amplifier, analyzing these inputs with a trained machine learning model, and estimating the total pump current of the amplifier. By comparing this estimate to the measured total pump current, the health of the optical amplifier can be determined.

Career Highlights

Carter is currently employed at Ciena Corporation, a leading company in the field of networking technology. His work at Ciena focuses on advancing optical technologies and improving network performance through innovative solutions. With a patent portfolio that includes 1 patent, he continues to push the boundaries of what is possible in optical amplifier technology.

Collaborations

Carter has collaborated with talented individuals such as Yinqing Pei and David W Boertjes. These collaborations have contributed to the development of cutting-edge technologies in the optical domain.

Conclusion

Carter Demars is a pioneering inventor whose work in optical amplifier technology is making a significant impact in the field. His innovative approach to failure prediction using machine learning showcases the potential of combining advanced technology with practical applications.

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