Corning Optical Communications LLC

Corning Incorporated

Yi Zhang

Elias Panides

James Michael Brown

Yuehao Li

Jingru Zhang

Adama Tandia

Adam James Ruggles

Jingru Zhang Benner

A cleaning nozzle includes an outer housing having a central axis and an inner surface that defines an outer housing interior. An inner housing resides within the outer housing interior along the central axis and has an inner surface that defines an inner flow channel. The inner flow channel supports flow of the cleaning fluid and has a converging taper and a flow disrupter element. The nozzle assembly may include an adapter that receives a front end of the nozzle and that also holds a ferrule that supports an optical fiber having an end face. The nozzle assembly allows the nozzle to direct a jet stream of cleaning fluid to the ferrule end face and the fiber end face. The flow disrupter causes the jet stream to have a time-varying direction that enhances the cleaning of the ferrule end face and the optical fiber end face.

Cleaning nozzle and nozzle assembly and cleaning methods for optical fiber connectors

The nozzle includes a nozzle body having a front end with a recess defining a recessed wall. The recess is elongate in a first direction. The recess receives a front-end section of a ferrule of a multi-fiber connector to define gap, wherein the front-end section has an elongate end face. The nozzle has first and second channels that are elongate in the first direction and have respective first and second front-end openings at the recessed wall. The first and second front-end openings are spaced apart in a second direction perpendicular to the first direction. A cleaning fluid flows from the first channel into the gap and then out the second channel, including over the ferrule end faces and end faces of optical fibers supported by the ferrule. A flow-disrupting feature on the recessed wall generates turbulent flow to enhance cleaning.

Cleaning nozzle and nozzle assembly for multi-fiber connectors

Systems and techniques for image segmentation into overlapping tiles are described herein. In an example, an anomaly detection system for super-high-resolution images is adapted to divide the image into tiles with a size and overlap between each tile, wherein the size and overlap is determined using a machine-learning model. The anomaly detection system may be further adapted to use a classifier model for each tile to identify anomaly presence in the tile, wherein the classifier model is trained using a data set of tile size images that are labeled according to anomaly presence in each tile size image. The anomaly detection system may be further adapted to determine a classification for the image based on results from the classifier model for the tiles. The anomaly detection system may be further adapted to output the classification.

Image segmentation into overlapping tiles

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Painted Post, NY, United States of America

Yi Zhang