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    Resistive random-access memory for embedded computation
    112.
    发明授权
    Resistive random-access memory for embedded computation 有权

    公开(公告)号：US11501829B2

    公开(公告)日：2022-11-15

    申请号：US16921198

    申请日：2020-07-06

    Applicant: Arizona Board of Regents on behalf of Arizona State University

    Inventor： Jae-sun Seo ,  Shimeng Yu

    IPC: G11C7/22 ,  G11C13/00 ,  G06N3/02 ,  G11C7/18 ,  G11C11/54 ,  G11C7/10

    Abstract: A resistive random-access memory (RRAM) system includes an RRAM cell. The RRAM cell includes a first select line and a second select line, a word line, a bit line, a first resistive memory device, a first switching device, a second resistive memory device, a second switching device, and a comparator. The first resistive memory device is coupled between a first access node and the bit line. The first switching device is coupled between the first select line and the first access node. The second resistive memory device is coupled between a second access node and the bit line. The second switching device is coupled between the second select line and the second access node. The comparator includes a first input coupled to the bit line, a second input, and an output.

    High temperature heater lamp
    117.
    发明授权
    High temperature heater lamp 有权

    公开(公告)号：US11477855B2

    公开(公告)日：2022-10-18

    申请号：US17467090

    申请日：2021-09-03

    Applicant: ARIZONA BOARD OF REGENTS ON BEHALF OF ARIZONA STATE UNIVERSITY

    Inventor： Ivan Ermanoski ,  Xiang Gao ,  Ryan Milcarek

    IPC: H01K1/32 ,  H05B3/00 ,  H05B3/44 ,  H01K1/06 ,  H01K1/40 ,  H01K1/36 ,  H01K1/08 ,  H05B3/12

    Abstract: A high temperature heater lamp including a ceramic envelope is disclosed. The ceramic envelope is substantially infrared transparent and is composed of a refractory ceramic. The heater lamp also includes two lead wires communicatively coupled via a filament. The filament is enclosed within the ceramic envelope, which is evacuated. The heater lamp may include at least two metallic IR shields within the ceramic envelope, at least one located on either side of the filament. The filament may be tungsten, a carbon filament, or molybdenum. At least one end of the ceramic envelope may be sealed with a metal cap affixed to the ceramic envelope by a high vacuum sealant. The heater lamp may be configured to operate at above 1500° C. The ceramic envelope may have a wall thickness less than 1 mm thick.

    Systems, methods, and apparatuses for implementing a self-supervised chest x-ray image analysis machine-learning model utilizing transferable visual words
    120.
    发明授权
    Systems, methods, and apparatuses for implementing a self-supervised chest x-ray image analysis machine-learning model utilizing transferable visual words 有权

    公开(公告)号：US11436725B2

    公开(公告)日：2022-09-06

    申请号：US17098422

    申请日：2020-11-15

    Applicant: Arizona Board of Regents on Behalf of Arizona State University

    Inventor： Mohammad Reza Hosseinzadeh Taher ,  Fatemeh Haghighi ,  Jianming Liang

    IPC: G06T7/00 ,  G06T7/73 ,  A61B6/00

    Abstract: Not only is annotating medical images tedious and time consuming, but it also demands costly, specialty-oriented expertise, which is not easily accessible. To address this challenge, a new self-supervised framework is introduced: TransVW (transferable visual words), exploiting the prowess of transfer learning with convolutional neural networks and the unsupervised nature of visual word extraction with bags of visual words, resulting in an annotation-efficient solution to medical image analysis. TransVW was evaluated using NIH ChestX-ray14 to demonstrate its annotation efficiency. When compared with training from scratch and ImageNet-based transfer learning, TransVW reduces the annotation efforts by 75% and 12%, respectively, in addition to significantly accelerating the convergence speed. More importantly, TransVW sets new records: achieving the best average AUC on all 14 diseases, the best individual AUC scores on 10 diseases, and the second best individual AUC scores on 3 diseases. This performance is unprecedented, because heretofore no self-supervised learning method has outperformed ImageNet-based transfer learning and no annotation reduction has been reported for self-supervised learning. These achievements are contributable to a simple yet powerful observation: The complex and recurring anatomical structures in medical images are natural visual words, which can be automatically extracted, serving as strong yet free supervision signals for CNNs to learn generalizable and transferable image representation via self-supervision.

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