Secure Training of Multi-Party Deep Neural Network

    公开(公告)号:US20200349435A1

    公开(公告)日:2020-11-05

    申请号:US16934685

    申请日:2020-07-21

    Abstract: A deep neural network may be trained on the data of one or more entities, also know as Alices. An outside computing entity, also known as a Bob, may assist in these computations, without receiving access to Alices' data. Data privacy may be preserved by employing a “split” neural network. The network may comprise an Alice part and a Bob part. The Alice part may comprise at least three neural layers, and the Bob part may comprise at least two neural layers. When training on data of an Alice, that Alice may input her data into the Alice part, perform forward propagation though the Alice part, and then pass output activations for the final layer of the Alice part to Bob. Bob may then forward propagate through the Bob part. Similarly, backpropagation may proceed backwards through the Bob part, and then through the Alice part of the network.

    Methods and Apparatus for Imaging and 3D Shape Reconstruction

    公开(公告)号:US20200146543A1

    公开(公告)日:2020-05-14

    申请号:US16732220

    申请日:2019-12-31

    Abstract: An otoscope may project a temporal sequence of phase-shifted fringe patterns onto an eardrum, while a camera in the otoscope captures images. A computer may calculate a global component of these images. Based on this global component, the computer may output an image of the middle ear and eardrum. This image may show middle ear structures, such as the stapes and incus. Thus, the otoscope may “see through” the eardrum to visualize the middle ear. The otoscope may project another temporal sequence of phase-shifted fringe patterns onto the eardrum, while the camera captures additional images. The computer may subtract a fraction of the global component from each of these additional images. Based on the resulting direct-component images, the computer may calculate a 3D map of the eardrum.

    Methods and Apparatus for Modulo Sampling and Recovery

    公开(公告)号:US20190103876A1

    公开(公告)日:2019-04-04

    申请号:US16025991

    申请日:2018-07-02

    Abstract: A self-reset ADC may take a set of temporally equidistant, modulo samples of a bandlimited, analog signal, at a sampling rate that is greater than πe samples per second, where π is Archimedes' constant and is Euler's number. The bandlimited signal may have a bandwidth of 1 Hertz and a maximum frequency of 0.5 Hertz. These conditions of sampling rate, bandwidth and maximum frequency may ensure that an estimated signal may be recovered from the set of modulo samples. This estimated signal may be equal to the bandlimited signal plus a constant. The constant may be equal to an integer multiple of the modulus of the centered modulo operation employed to take the modulo samples.

    Methods and apparatus for time-of-flight imaging

    公开(公告)号:US10191154B2

    公开(公告)日:2019-01-29

    申请号:US15431713

    申请日:2017-02-13

    Abstract: In some implementations, scene depth is extracted from dual frequency of a cross-correlation signal. A camera may illuminate a scene with amplitude-modulated light, sweeping the modulation frequency. For each modulation frequency in the sweep, each camera pixel may measure a cross-correlation of incident light and of a reference electrical signal. Each pixel may output a vector of cross-correlation measurements acquired by the pixel during a sweep. A computer may perform an FFT on this vector, identify a dual frequency at the second largest peak in the resulting power spectrum, and calculate scene depth as equal to a fraction, where the numerator is the speed of light times this dual frequency and the denominator is four times pi. In some cases, the two signals being cross-correlated have the same phase as each other during each cross-correlation measurement.

    Methods and apparatus for optical fiber imaging

    公开(公告)号:US09894254B2

    公开(公告)日:2018-02-13

    申请号:US15151417

    申请日:2016-05-10

    CPC classification number: H04N5/2256 G02B6/06 H04N5/2257

    Abstract: An open-ended, incoherent bundle of optical fibers transmits light from a nearby scene. A camera captures images of the back end of the fiber bundle. Because the fiber bundle is incoherent, the captured image is shuffled, in the sense that the relative position of pixels in the image differs from the relative position of the scene regions that correspond to the pixels. Calibration is performed in order to map from the front end positions to the back-end positions of the fibers. In the calibration, pulses of light are delivered, in such a way that the time at which light reflecting from a given pulse enters a given fiber directly correlates to the position of the front end of the given fiber. A time-of-flight sensor takes measurements indicative of these time signatures. Based on the map obtained from calibration, a computer de-shuffles the image.

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