Hand Held Products, Inc.

Honeywell International Inc.

Adam Dewey Mcbrady

Moin Shafai

Gennady Germaine

Thomas Merritt Haggerty

Philip C Foster

Aoife Celoria

Giorgio Carlo Isella

Dustin Michael Brandt

Jose Guadalupe Sanchez De La Rosa

Oscar Ponte Rivas

Emir Rahislic

Amanda Esther Sall Childers

Chen Feng

Stephan M Bork

Andy Walker Brown

Suresh Venkatarayalu

Ratna Dinakar Tumuluri

Andrey Shtylenko

Stephan Michael Bork

Ryadh Abdullah Zakaria

Methods, apparatuses, and systems associated with a sample testing device are provided. For example, an example sample testing device may include a substrate layer defining a bottom surface of the sample testing device, as well as a waveguide disposed on the substrate layer and includes at least one reference channel and at least one sample channel.

Apparatuses, systems, and methods for sample testing

Methods, apparatuses, and systems associated with aerosol collection devices (such as, but not limited to, breath-aerosol collector devices, breathalyzers) are provided. An example aerosol collection device includes a sample transfer adapter configured to receive a sample and a device body connected to the sample transfer adapter. In some examples, the device body defines a flow channel that guides the sample to a filter component, and the filter component contains a buffer solution.

Apparatuses, systems, and methods for sample capture and extraction

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

Various embodiments described herein relate to apparatuses and methods for detecting fluid particles and their characteristics. In various embodiments, a device for detecting fluid particles and their characteristics may comprise a fluid composition sensor configured to receive a volume of fluid. The fluid composition sensor has a collection media housing configured to receive a portion of a collection media, a pump for moving a volume of fluid over the collection media housing, an imaging device configured to capture an image of particles on the collection media, and a particle matter mass concentration calculation circuitry configured to calculate a total particle matter mass. The particle matter mass concentration calculation circuitry is connected with the imaging device and the pump. The particle matter mass concentration calculation circuitry is configured to adjust the volume of fluid over the collection media housing.

Fluid composition sensor device and method of using the same

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Dallas, TX, United States of America

Adam Dewey Mcbrady