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    COMMUNICATION SYSTEM WITH REPEAT-RESPONSE COMBINING MECHANISM AND METHOD OF OPERATION THEREOF
    101.
    发明申请
    COMMUNICATION SYSTEM WITH REPEAT-RESPONSE COMBINING MECHANISM AND METHOD OF OPERATION THEREOF 有权
    具有重复响应组合机制的通信系统及其操作方法

    公开(公告)号：US20130343271A1

    公开(公告)日：2013-12-26

    申请号：US13908765

    申请日：2013-06-03

    Applicant: Samsung Electronics Co., Ltd.

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

    IPC: H04L1/00

    CPC classification number: H04L1/004 ,  H04L1/1845

    Abstract: A communication system includes: a validation module configured to transmit a repeat request corresponding to a preceding data including a communication content; an inter-block processing module, coupled to the validation module, configured to determine a previous communication value based on the preceding data; a detection module, coupled to the inter-block processing module, configured to identify a repeat data corresponding to the repeat request from a receiver signal; an accumulator module, coupled to the detection module, configured to generate an accumulation output based on the preceding data and the repeat data; and a decoding module, coupled to the accumulator module, configured to determine the communication content using the previous communication value and the accumulation output across instances of transmission blocks for communicating with a device.

    Abstract translation: 通信系统包括：验证模块，被配置为发送与包括通信内容的先前数据相对应的重复请求; 耦合到所述验证模块的块间处理模块，被配置为基于所述先前数据来确定先前的通信值; 耦合到所述块间处理模块的检测模块，被配置为从接收器信号识别与所述重复请求对应的重复数据; 耦合到所述检测模块的累加器模块，被配置为基于所述先前数据和所述重复数据生成累加输出; 以及耦合到所述累加器模块的解码模块，被配置为使用所述先前通信值来确定所述通信内容，并且跨越传输块的实例在与设备进行通信的情况下的累积输出。

    METHOD AND APPARATUS FOR DATA-FREE POST-TRAINING NETWORK QUANTIZATION AND GENERATING SYNTHETIC DATA BASED ON A PRE-TRAINED MACHINE LEARNING MODEL
    102.
    发明申请
    METHOD AND APPARATUS FOR DATA-FREE POST-TRAINING NETWORK QUANTIZATION AND GENERATING SYNTHETIC DATA BASED ON A PRE-TRAINED MACHINE LEARNING MODEL 有权

    公开(公告)号：US20250086457A1

    公开(公告)日：2025-03-13

    申请号：US18957380

    申请日：2024-11-22

    Applicant: Samsung Electronics Co., Ltd.

    Inventor： Yoo Jin Choi ,  Mostafa El-Khamy ,  Jungwon Lee

    IPC: G06N3/08 ,  G06F17/18 ,  G06F18/2113 ,  G06F18/214 ,  G06F18/22 ,  G06N3/045 ,  G06N7/01

    Abstract: A method for training a generator, by a generator training system including a processor and memory, includes: extracting training statistical characteristics from a batch normalization layer of a pre-trained model, the training statistical characteristics including a training mean μ and a training variance σ2; initializing a generator configured with generator parameters; generating a batch of synthetic data using the generator; supplying the batch of synthetic data to the pre-trained model; measuring statistical characteristics of activations at the batch normalization layer and at the output of the pre-trained model in response to the batch of synthetic data, the statistical characteristics including a measured mean, and {circumflex over (μ)}ψ measured variance {circumflex over (σ)}ψ2; computing a training loss in accordance with a loss function Lψ based on μ, σ2, {circumflex over (μ)}ψ, and {circumflex over (σ)}ψ2; and iteratively updating the generator parameters in accordance with the training loss until a training completion condition is met to compute the generator.

    Method and apparatus for reducing computational complexity of convolutional neural networks
    103.
    发明授权
    Method and apparatus for reducing computational complexity of convolutional neural networks 有权

    公开(公告)号：US12248866B2

    公开(公告)日：2025-03-11

    申请号：US17473813

    申请日：2021-09-13

    Applicant: Samsung Electronics Co., Ltd.

    Inventor： Mostafa El-Khamy ,  Yoo Jin Choi ,  Jungwon Lee

    IPC: G06N3/06 ,  G06N3/04 ,  G06N3/0464 ,  G06N3/08 ,  G06F5/01 ,  G06F17/16

    Abstract: A convolutional neural network (CNN) system for generating a classification for an input image is presented. The CNN system comprises circuitry running on clock cycles and configured to compute a product of two received values, and at least one non-transitory computer-readable medium that stores instructions for the circuitry to derive a feature map based on at least the input image; puncture at least one selection among the feature map and a kernel by setting the value of an element at an index of the at least one selection to zero and cyclic shifting a puncture pattern to achieve a 1/d reduction in number of clock cycles, where d is an integer and puncture interval value >1. The feature map is convolved with the kernel to generate an output, and a classification of the input image is generated based on the output.

    SYSTEM AND METHOD FOR ACOUSTIC ECHO CANCELATION USING DEEP MULTITASK RECURRENT NEURAL NETWORKS
    104.
    发明申请
    SYSTEM AND METHOD FOR ACOUSTIC ECHO CANCELATION USING DEEP MULTITASK RECURRENT NEURAL NETWORKS 有权

    公开(公告)号：US20240420720A1

    公开(公告)日：2024-12-19

    申请号：US18815570

    申请日：2024-08-26

    Applicant: Samsung Electronics Co., Ltd.

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

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

    Abstract: A system for performing echo cancellation includes: a processor configured to: receive a far-end signal; record a microphone signal including: a near-end signal; and an echo signal corresponding to the far-end signal; extract far-end features from the far-end signal; extract microphone features from the microphone signal; compute estimated near-end features by supplying the microphone features and the far-end features to an acoustic echo cancellation module including a recurrent neural network including: an encoder including a plurality of gated recurrent units; and a decoder including a plurality of gated recurrent units; compute an estimated near-end signal from the estimated near-end features; and transmit the estimated near-end signal to the far-end device. The recurrent neural network may include a contextual attention module; and the recurrent neural network may take, as input, a plurality of error features computed based on the far-end features, the microphone features, and acoustic path parameters.

    Video depth estimation based on temporal attention
    110.
    发明授权
    Video depth estimation based on temporal attention 有权

    公开(公告)号：US11527005B2

    公开(公告)日：2022-12-13

    申请号：US16841618

    申请日：2020-04-06

    Applicant: Samsung Electronics Co., Ltd.

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

    IPC: G06T7/579 ,  G06T7/194 ,  G06T3/00

    Abstract: A method of depth detection based on a plurality of video frames includes receiving a plurality of input frames including a first input frame, a second input frame, and a third input frame respectively corresponding to different capture times, convolving the first to third input frames to generate a first feature map, a second feature map, and a third feature map corresponding to the different capture times, calculating a temporal attention map based on the first to third feature maps, the temporal attention map including a plurality of weights corresponding to different pairs of feature maps from among the first to third feature maps, each weight of the plurality of weights indicating a similarity level of a corresponding pair of feature maps, and applying the temporal attention map to the first to third feature maps to generate a feature map with temporal attention.

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