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1597 条记录 (耗时 0.034 秒)

    Tunable oblivious RAM
    105.
    发明授权
    Tunable oblivious RAM 有权

    公开(公告)号：US10229068B2

    公开(公告)日：2019-03-12

    申请号：US15393822

    申请日：2016-12-29

    申请人： Sameer Wagh ,  Paul Cuff ,  Prateek Mittal

    发明人： Sameer Wagh ,  Paul Cuff ,  Prateek Mittal

    IPC分类号： G06F12/14 ,  G06F21/62 ,  G06F21/00

    摘要： An approach to implementing or configuring an Oblivious RAM (ORAM), which in addition to behaving as a RAM, provides a way to meet a specified degree of privacy in a manner that avoids applying unnecessary computation resources (computation time and/or storages space and/or data transfer) to achieve the specified degree of privacy. In this way, a tradeoff between privacy and computation resources may be tuned to address requirements of a particular application. This ability to tune this tradeoff is not found in other ORAM implementations, which in general aim to achieve complete privacy. In some implementations, the ORAM provides a constant bandwidth overhead compared to conventional RAMs, while achieving a statistical privacy as desired by the user.

    Simultaneous ranging and remote chemical sensing utilizing optical dispersion or absorption spectroscopy
    106.
    发明授权
    Simultaneous ranging and remote chemical sensing utilizing optical dispersion or absorption spectroscopy 有权

    公开(公告)号：US10215845B2

    公开(公告)日：2019-02-26

    申请号：US15151812

    申请日：2016-05-11

    申请人： Andreas Hangauer ,  Gerard Wysocki

    发明人： Andreas Hangauer ,  Gerard Wysocki

    IPC分类号： G01S7/48 ,  G01S17/32 ,  G01S17/95 ,  G01N21/39 ,  G01J3/02 ,  G01J3/433 ,  G01N21/17 ,  G01J1/42

    摘要： A system for simultaneous optical pathlength determination and remote chemical sensing of a sample disposed along an optical path. A modulated laser source configured for modulated light emission so that at least one spectral sideband with a sideband frequency is created, the modulated light emission is directed along the optical path and sideband frequency is varied over time. A detector is configured to detect transmitted light along the optical path and generate a detected light intensity signal. A frequency down-converter is configured to receive the detected light emission signal and generate a frequency down-converted light intensity signal. A demodulator is configured to demodulate the frequency of the down-converted light intensity signal and output an instantaneous frequency. A pathlength calculator is configured to determine an optical pathlength to the sample based on the instantaneous frequency. A frequency down-converted light intensity signal is simultaneously output for spectroscopic chemical sensing.

    HETEROGENEOUS MICROPROCESSOR FOR ENERGY-SCALABLE SENSOR INFERENCE USING GENETIC PROGRAMMING
    109.
    发明申请
    HETEROGENEOUS MICROPROCESSOR FOR ENERGY-SCALABLE SENSOR INFERENCE USING GENETIC PROGRAMMING 审中-公开

    公开(公告)号：US20180349142A1

    公开(公告)日：2018-12-06

    申请号：US15614081

    申请日：2017-06-05

    申请人： The Trustees of Princeton University

    发明人： Hongyang Jia ,  Naveen Verma

    IPC分类号： G06F9/30 ,  G06F17/30 ,  G06N5/04

    摘要： A heterogeneous microprocessor configured to perform classification on an input signal. The heterogeneous microprocessor includes a die with a central processing unit (CPU) a programmable feature-extraction accelerator (FEA) and a classifier. The FEA is configured to perform feature extraction on the input signal to generate feature data. The classifier is configured to perform classification on the feature data and the CPU is configured to provide processing after classification. The FEA may be configured with a plurality of Gene-Computation (GC) Cores. The FEA may be configured for genetic programing with gene depth constraints, gene number constraints and base function constraints. The classifier may be a support-vector machine accelerator (SVMA). The SVMA may include training data based on error-affected feature data. The heterogeneous microprocessor may also include an automatic-programming & classifier training module. An automatic-programming & classifier training module may be configured to receive input-output feature data and training labels and generate gene code and a classifier model.