Honeywell International Inc.

Ratna Dinakar Tumuluri

Andy Walker Brown

Adam Dewey Mcbrady

Ethan Wang

Andrey Shtylenko

Vikram Bhat

Brown Andy Walker

A fluid sampling device, the device having a fluid composition sensor configured to receive a fluid sample and capture a plurality of particles from the fluid sample at a collection media, wherein the fluid composition sensor is further configured to generate particle data associated with the plurality of particles using a particle imaging operation, and a controller, the controller being configured to: determine an optimal sample volume associated with a sample collection operation based at least in part on a particle load condition defined by the plurality of particles captured at the collection media during the sample collection operation, and update one or more operational characteristics of the fluid composition sensor such that the sample collection operation is defined at least in part by the optimal sample volume.

Device for optimizing fluid sample volume and method of using the same

Embodiments of the present disclosure provide for improved generation and outputting of object identification data indicating object classifications for object representations. Such objects representations may correspond to depictions of objects in images captured using digital holographic microscopy. Some embodiments generate object identification data by comparing object representations in focused image(s) with specially configured annotated focused images, for example using a specially trained neural network or other machine learning model trained based on such annotated focused images. The annotated focused images are generated including a plurality of channels, each associated with a different grayscale focused image at a different target focal length of a range of target focal lengths. In this regard, model(s), algorithm(s), and/or other specially configured implementations may learn the spatial features of particular object representations and associated object identification data. The trained models may be used to perform accurate comparisons with the annotated focused images.

Apparatuses, computer-implemented methods, and computer program products for improved generation of object identification data

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Alpharetta, GA, United States of America

Ratna Dinakar Tumuluri