Bodidata, Inc.

University of Pittsburgh

Hoa V Pham

Hai Nguyen

Tuoc V Luong

Tung Viet Vu

Long Hai Nguyen

Albert J Charpentier

Yang Liu

Shikhar Uttam Fnu

Randall E Brand

Michael Boylan

Kelsey MacDonald

J Bruce Terry

Albert Charpentier

Quan Hong Hua

Systems and methods for automating clothing transactions. The methods comprise: obtaining user input data and/or sensor data specifying characteristics of a body for an individual; transforming the user input data and/or sensor data into a 3D graphical representation of the body; analyzing the 3D graphical representation to derive 3D body measurements for the individual; identifying clothing items based on results from comparing the 3D body measurements to reference measurements associated with clothing items having different styles, sizes and brand associations; analyzing how a fabric elasticity and a garment construction could impact a fit of each identified clothing item relative to each body part of the individual based on the 3D graphical representation of the body; and filtering the clothing items based on results of the analyzing and fit preferences of the individual to generate recommended clothing items for the individual.

Systems and methods for automating clothing transaction

System and methods for obtaining sizing of an irregularly shaped object. The methods comprise: displaying, by a display of a handheld scanner system, a 3D optical figure of a subject; displaying a grid superimposed over the 3D optical figure of the subject, the grid comprising a plurality of marks arranged in rows and columns; generating, by a radar module of the handheld scanner system, range data specifying a sensed spacing between the radar module and at least a surface of a first portion of the irregularly shaped object which is covered by at least one item; receiving, by a processor of the handheld scanner system, the range data from the radar module as the subject is being scanned; and causing at least one first visual characteristic of a first mark of said plurality of marks to change when corresponding range data is successfully received by the processor.

Systems and methods for improved radar scanning coverage and efficiency

Systems, methods and other embodiments associated with spatial-domain Low-coherence Quantitative Phase Microscopy (SL-QPM) are described herein. SL-QPM can detect structural alterations within cell nuclei with nanoscale sensitivity (0.9 nm) (or nuclear nano-morphology) for 'nano-pathological diagnosis' of cancer. SL-QPM uses original, unmodified cytology and histology specimens prepared with standard clinical protocols and stains. SL-QPM can easily integrate in existing clinical pathology laboratories. Results quantified the spatial distribution of optical path length or refractive index in individual nuclei with nanoscale sensitivity, which could be applied to studying nuclear nano-morphology as cancer progresses. The nuclear nano-morphology derived from SL-QPM offers significant diagnostic value in clinical care and subcellular mechanistic insights for basic and translational research. Techniques that provide for depth selective investigation of nuclear and other cellular features are disclosed.

Spatial-domain low-coherence quantitative phase microscopy

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Hanoi, Vietnam

Hoa V Pham