Michigan State University

University of Nebraska

Ford Global Technolgoies, LLC

Siemens Corporate Research, Inc.

Arun Ross

Muhammad Rabnawaz

Ali Tamayol

Bahar Aliakbarian

Susan E Selke

Carina Russell

Austin Cozzo

Anurag Chowdhury

Prem Chahal

Krystal Cheng

Kwaku O Prakah-Asante

Krishnaswamy Venkatesh Prasad

Anil K Jain

James G Reisman

Xiaoming Liu

Muhammad Jamal Afridi

Debayan Deb

Erik M Shapiro

A medication bottle includes an outer container wall, an inner container wall and a fluid, such as an aversive liquid, located between the walls. In another aspect, the fluid is located within separate compartments or channels located between the walls where the walls contact or are secured to each other. A further aspect provides a removeable cap including multiple sensors, at least one of which is a biometric sensor.

Medication bottle with anti-tampering features

A passive authentication method includes, in response to receiving a requested action from a first user, obtaining a set of sensor data and categorizing first sensor data of the set of sensor data into a first modality of a set of modalities. The method includes, for the first modality of the set of modalities, determining a distance value by applying a first modality model to the first sensor data and comparing the distance value to a first verified value of the first user for the first modality. The method includes, based on the comparison, determining a first authentication decision of the distance value. The method includes, in response to the first authentication decision indicating the first sensor data corresponds to the first user, performing the requested action.

Integrated systems and methods for passive authentication

A system and method are provided for tracking magnetically-labeled substances, such as transplanted cells, in subjects using magnetic resonance imaging (MRI). The method includes obtaining a quantity of a substance that comprises an MRI contrast compound or is otherwise magnetically-labeled for purposes of an MRI scan, administering the substance into a region of interest of a subject, performing an imaging scan of a portion of the subject comprising the region of interest, obtaining an imaging data set from the scan, reducing the dataset into pixel groupings based on intensity profiles, where the pixel groupings have a pixel size larger than the expected pixel size of a unit of the MRI contrast compound or magnetically-labeled substance, extracting candidate pixel matrices from the imaging data, training a machine learning (ML) module by using the candidate pixel matrices, quantifying the presence, number and/or location of units of the substance within the subject by using the ML module, and displaying a visual representation of an identification of the substances within the subject as a result of using the ML module.

System and method for quantifying cell numbers in magnetic resonance imaging (MRI)

A method for matching fingerprint images is provided. The method includes the steps of acquiring a query image of a fingerprint; extracting a minutiae set from the query image; comparing the minutiae set of the query image with a minutiae set of at least one template image to determine transformation parameters to align the query image to the at least one template image and to determine a minutiae matching score; constructing a ridge feature map of the query image; comparing the ridge feature map of the query image to a ridge feature map of the at least one template image to determine a ridge feature matching score; and combining the minutiae matching score with the ridge feature matching score resulting in an overall score, the overall score being compared to a threshold to determine if the query image and the at least one template image match.

Fingerprint matching using ridge feature maps

Growing community of inventors

East Lansing, MI, United States of America

Arun Ross