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    System and method for residual long short term memories (LSTM) network
    91.
    发明授权
    System and method for residual long short term memories (LSTM) network 有权

    公开(公告)号：US10810482B2

    公开(公告)日：2020-10-20

    申请号：US15343987

    申请日：2016-11-04

    Applicant: Samsung Electronics Co., Ltd.

    Inventor： JaeYoung Kim ,  Jungwon Lee ,  Mostafa El-Khamy

    IPC: G06N3/04 ,  G06N3/08

    Abstract: An apparatus and a method. The apparatus includes a plurality of long short term memory (LSTM) networks, wherein each of the plurality of LSTM networks is at a different network layer, wherein each of the plurality of LSTM networks is configured to determine a residual function, wherein each of the plurality of LSTM networks includes an output gate to control what is provided to a subsequent LSTM network, and wherein each of the plurality of LSTM networks includes at least one highway connection to compensate for the residual function of a previous LSTM network.

    System and method for acoustic echo cancelation using deep multitask recurrent neural networks
    92.
    发明授权
    System and method for acoustic echo cancelation using deep multitask recurrent neural networks 有权

    公开(公告)号：US10803881B1

    公开(公告)日：2020-10-13

    申请号：US16573573

    申请日：2019-09-17

    Applicant: Samsung Electronics Co., Ltd.

    Inventor： Amin Fazeli ,  Mostafa El-Khamy ,  Jungwon Lee

    IPC: G10L21/0232 ,  H04R3/04 ,  G06N20/10 ,  G06N3/08 ,  G10L21/0208

    Abstract: A method for performing echo cancellation includes: receiving a far-end signal from a far-end device at a near-end device; recording a microphone signal at the near-end device including: a near-end signal; and an echo signal corresponding to the far-end signal; extracting far-end features from the far-end signal; extracting microphone features from the microphone signal; computing estimated near-end features by supplying the microphone features and the far-end features to an acoustic echo cancellation module including: an echo estimator including a first stack of a recurrent neural network configured to compute estimated echo features based on the far-end features; and a near-end estimator including a second stack of the recurrent neural network configured to compute the estimated near-end features based on an output of the first stack and the microphone signal; computing an estimated near-end signal from the estimated near-end features; and transmitting the estimated near-end signal to the far-end device.

    SYSTEM AND METHOD FOR ACOUSTIC ECHO CANCELATION USING DEEP MULTITASK RECURRENT NEURAL NETWORKS
    93.
    发明申请
    SYSTEM AND METHOD FOR ACOUSTIC ECHO CANCELATION USING DEEP MULTITASK RECURRENT NEURAL NETWORKS 审中-公开

    公开(公告)号：US20200312345A1

    公开(公告)日：2020-10-01

    申请号：US16573573

    申请日：2019-09-17

    Applicant: Samsung Electronics Co., Ltd.

    Inventor： Amin Fazeli ,  Mostafa El-Khamy ,  Jungwon Lee

    IPC: G10L21/0232 ,  H04R3/04 ,  G06N3/08 ,  G06N20/10

    Abstract: A method for performing echo cancellation includes: receiving a far-end signal from a far-end device at a near-end device; recording a microphone signal at the near-end device including: a near-end signal; and an echo signal corresponding to the far-end signal; extracting far-end features from the far-end signal; extracting microphone features from the microphone signal; computing estimated near-end features by supplying the microphone features and the far-end features to an acoustic echo cancellation module including: an echo estimator including a first stack of a recurrent neural network configured to compute estimated echo features based on the far-end features; and a near-end estimator including a second stack of the recurrent neural network configured to compute the estimated near-end features based on an output of the first stack and the microphone signal; computing an estimated near-end signal from the estimated near-end features; and transmitting the estimated near-end signal to the far-end device.

    System and method for information highways in a hybrid feedforward-recurrent deep network
    95.
    发明授权
    System and method for information highways in a hybrid feedforward-recurrent deep network 有权

    公开(公告)号：US10599974B2

    公开(公告)日：2020-03-24

    申请号：US15343882

    申请日：2016-11-04

    Applicant: Samsung Electronics Co., Ltd.

    Inventor： Mostafa El-Khamy ,  Jungwon Lee ,  Jaeyoung Kim

    IPC: G06N3/04 ,  G06N3/063 ,  G06N3/08

    Abstract: An apparatus and a method. The apparatus includes a first recurrent network in a first layer; a second recurrent network in a second layer connected to the first recurrent network; a distant input gate connected to the second recurrent network; a first highway gate connected to the distant input gate and the second recurrent network; a first elementwise product projection gate connected to the distant input gate, the highway gate, and the second recurrent network; a second highway gate connected to the first recurrent network and the second recurrent network; and a second elementwise product projection gate connected to the first recurrent network, the second highway gate, and the second recurrent network.

    SYSTEM AND METHOD FOR DEEP LEARNING IMAGE SUPER RESOLUTION
    96.
    发明申请
    SYSTEM AND METHOD FOR DEEP LEARNING IMAGE SUPER RESOLUTION 审中-公开

    公开(公告)号：US20200090305A1

    公开(公告)日：2020-03-19

    申请号：US16693146

    申请日：2019-11-22

    Applicant: Samsung Electronics Co., Ltd.

    Inventor： Mostafa El-Khamy ,  Jungwon Lee ,  Haoyu Ren

    IPC: G06T3/40 ,  G06N3/08 ,  G06N3/04

    Abstract: In a method for super resolution imaging, the method includes: receiving, by a processor, a low resolution image; generating, by the processor, an intermediate high resolution image having an improved resolution compared to the low resolution image; generating, by the processor, a final high resolution image based on the intermediate high resolution image and the low resolution image; and transmitting, by the processor, the final high resolution image to a display device for display thereby.

    System and method for higher order long short-term memory (LSTM) network
    98.
    发明授权
    System and method for higher order long short-term memory (LSTM) network 有权

    公开(公告)号：US10241684B2

    公开(公告)日：2019-03-26

    申请号：US15480086

    申请日：2017-04-05

    Applicant: Samsung Electronics Co., Ltd.

    Inventor： Jaeyoung Kim ,  Inyup Kang ,  Mostafa El-Khamy ,  Jungwon Lee

    IPC: G06F3/06 ,  G06N3/04 ,  G06N3/08

    Abstract: A method and apparatus are provided. The method includes configuring a plurality of long short term memory (LSTM) networks, wherein each of the plurality of LSTM networks is at a different network layer, configuring a plurality of memory cells in a spatial domain of the plurality of LSTM networks, configuring the plurality of memory cells in a temporal domain of the plurality of LSTM networks, controlling an output of each of the plurality of LSTM networks based on highway connections to outputs from at least one previous layer and at least one previous time of the plurality of LSTM networks, and controlling the plurality of memory cells based on highway connections to memory cells from the at least one previous time.

    System and methods for low complexity list decoding of turbo codes and convolutional codes
    99.
    发明授权
    System and methods for low complexity list decoding of turbo codes and convolutional codes 有权

    公开(公告)号：US10205470B2

    公开(公告)日：2019-02-12

    申请号：US14565082

    申请日：2014-12-09

    Applicant: Samsung Electronics Co., Ltd.

    Inventor： Mostafa El-Khamy ,  Jinhong Wu ,  Jungwon Lee ,  Inyup Kang

    IPC: H03M13/00 ,  H03M13/39 ,  G06F11/10 ,  G11B20/18 ,  H03M13/37 ,  H03M13/45

    Abstract: A method and system for decoding a signal are provided. The method includes receiving a signal, where the signal includes at least one symbol; decoding the signal in stages, where each at least one symbol is decoded into at least one bit per stage, wherein a Log-Likelihood Ratio (LLR) and a path metric are determined for each possible path for each at least one bit at each stage; determining the magnitudes of the LLRs; identifying K bits of the signal with the smallest corresponding LLR magnitudes; identifying, for each of the K bits, L possible paths with the largest path metrics at each decoder stage for a user-definable number of decoder stages; performing forward and backward traces, for each of the L possible paths, to determine candidate codewords; performing a Cyclic Redundancy Check (CRC) on the candidate codewords, and stopping after a first candidate codeword passes the CRC.

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