中科微点-全球专利检索

  1. Login
  2. Sign Up

Search Results

1764 records (took 0.038 seconds)

    ROBUST, LOW POWER, RECONFIGURABLE THRESHOLD LOGIC ARRAY
    322.
    发明申请
    ROBUST, LOW POWER, RECONFIGURABLE THRESHOLD LOGIC ARRAY 有权
    坚固，低功耗，可重新启动的阈值逻辑阵列

    公开(公告)号：US20160164526A1

    公开(公告)日：2016-06-09

    申请号：US14903428

    申请日：2014-07-08

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

    Inventor： Sarma Vrudhula ,  Niranjan Kulkarni

    IPC: H03K19/177 ,  H03K19/23 ,  H03K19/00

    CPC classification number: H03K19/1778 ,  H03K19/0013 ,  H03K19/17736 ,  H03K19/17768 ,  H03K19/23

    Abstract: A field programmable threshold-logic array (FPTLA) includes a number of threshold logic gates and a number of programmable interconnect elements. Each one of the programmable interconnect elements are connected between two or more of the threshold logic gates, such that the programmable interconnect elements route signals between the threshold logic gates. By using threshold logic gates for the FPTLA, the size of the FPTLA may be significantly smaller than conventional solutions. Further, using threshold logic gates results in significant improvements in the computation speed of the FPTLA when compared to conventional solutions.

    Abstract translation: 现场可编程阈值逻辑阵列（FPTLA）包括多个阈值逻辑门和多个可编程互连元件。 每个可编程互连元件连接在两个或更多个阈值逻辑门之间，使得可编程互连元件在阈值逻辑门之间路由信号。 通过使用FPTLA的阈值逻辑门，FPTLA的大小可能会明显小于传统的解决方案。 此外，与常规解决方案相比，使用阈值逻辑门导致FPTLA的计算速度显着提高。

    METHOD FOR IMPROVING THE ACCURACY OF CHEMICAL IDENTIFICATION IN A RECOGNITION-TUNNELING JUNCTION
    325.
    发明申请
    METHOD FOR IMPROVING THE ACCURACY OF CHEMICAL IDENTIFICATION IN A RECOGNITION-TUNNELING JUNCTION 审中-公开
    提高识别隧道结中化学鉴定精度的方法

    公开(公告)号：US20150142327A1

    公开(公告)日：2015-05-21

    申请号：US14493961

    申请日：2014-09-23

    Applicant: ARIZONA BOARD OF REGENTS on behalf of ARIZONA STATE UNIVERSITY

    Inventor： Brian Alan ASHCROFT ,  Stuart LINDSAY ,  John SHUMWAY

    IPC: G01N27/414 ,  G01N33/68

    CPC classification number: G01N33/68 ,  G01N33/48721

    Abstract: A method to identify a chemical target trapped in a tunnel junction with a high probability of a correct assignment based on, a single read of the tunnel current signal. The method recognizes and rejects background signals produced in the absence of target molecules, and do so accurately without rejecting useful signals from the target molecules. The identity of signals generated by electron tunneling through an analyte is provided and comprises determining a plurality of characteristics of each signal current spike, generating one or more training signals with a set of analytes, where the analytes may comprise a first analyte, and using the training signals to find one or more boundaries in a space of dimension equal to one or more parameters, wherein the space is partitioned such that a signal from the first analyte of interest is separated from a signal from the second analyte of interest.

    Abstract translation: 基于隧道电流信号的单次读取，识别被捕获在隧道结中的化学目标的方法，其具有正确分配的高概率。 该方法识别和排除在不存在目标分子的情况下产生的背景信号，并且准确地进行，而不会排除来自目标分子的有用信号。 提供通过通过分析物的电子穿隧产生的信号的身份，并且包括确定每个信号电流尖峰的多个特征，用一组分析物产生一个或多个训练信号，其中分析物可以包括第一分析物，并且使用 训练信号以在等于一个或多个参数的维度空间中找到一个或多个边界，其中所述空间被分割，使得来自所述第一分析物的信号与来自所述感兴趣的第二分析物的信号分离。

    ANTIMICROBIAL MATERIALS AND NANOPARTICLES AND METHODS OF USE THEREOF
    327.
    发明申请
    ANTIMICROBIAL MATERIALS AND NANOPARTICLES AND METHODS OF USE THEREOF 有权

    公开(公告)号：US20250114324A1

    公开(公告)日：2025-04-10

    申请号：US18833613

    申请日：2023-02-10

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

    Inventor： Shu Wang ,  Zhaoyang Fan

    IPC: A61K31/355 ,  A23B7/16 ,  A23L33/15 ,  A61K9/51 ,  A61K45/06

    Abstract: The present invention provides antimicrobial nanoparticles or compositions thereof comprising at least one phytochemical or bioactive compound, at least one lipid, at least one surfactant, and at least one vitamin E. In some embodiments, the antimicrobial nanoparticles or compositions thereof further comprise at least one coating agent. In some embodiments, the antimicrobial nanoparticles or compositions thereof are an edible coating. In various aspects, the present invention relates to methods of reducing or inhibiting the activity or level of or preventing the growth of at least one microorganism on a surface of an element. In some aspects, the present invention relates to methods of increasing a shelf-life of food and/or maintaining and improving food nutrients and/or nutrition values. In other aspects, the present invention relates to methods of delivering at least one nutritional or bioactive agent for preventing and treating various disorders and diseases.

    Systems, methods, and apparatuses for the generation of source models for transfer learning to application specific models used in the processing of medical imaging
    330.
    发明授权
    Systems, methods, and apparatuses for the generation of source models for transfer learning to application specific models used in the processing of medical imaging 有权

    公开(公告)号：US12260622B2

    公开(公告)日：2025-03-25

    申请号：US17625313

    申请日：2020-07-17

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

    Inventor： Zongwei Zhou ,  Vatsal Sodha ,  Md Mahfuzur Rahman Siddiquee ,  Ruibin Feng ,  Nima Tajbakhsh ,  Jianming Liang

    IPC: G06V10/82 ,  G06V10/774 ,  G06V10/776 ,  G06V10/98

    Abstract: Described herein are means for generating source models for transfer learning to application specific models used in the processing of medical imaging. In some embodiments, the method comprises: identifying a group of training samples, wherein each training sample in the group of training samples includes an image; for each training sample in the group of training samples: identifying an original patch of the image corresponding to the training sample; identifying one or more transformations to be applied to the original patch; generating a transformed patch by applying the one or more transformations to the identified patch; and training an encoder-decoder network using a group of transformed patches corresponding to the group of training samples, wherein the encoder-decoder network is trained to generate an approximation of the original patch from a corresponding transformed patch, and wherein the encoder-decoder network is trained to minimize a loss function that indicates a difference between the generated approximation of the original patch and the original patch. The source models significantly enhance the transfer learning performance for many medical imaging tasks including, but not limited to, disease/organ detection, classification, and segmentation. Other related embodiments are disclosed.

Patent Agency Ranking