公开(公告)号：US20210377763A1

公开(公告)日：2021-12-02

申请号：US17332534

申请日：2021-05-27

Applicant: INTELLIGENT FUSION TECHNOLOGY, INC.

Inventor： Xin TIAN ,  Yi LI ,  Genshe CHEN ,  Khanh PHAM ,  Erik BLASCH

IPC: H04W24/08 ,  H04W52/24 ,  H04W72/08 ,  G06N3/08

Abstract: Various embodiments of the present disclosure provide a method, a device, and a storage medium for performance prediction of a communication waveform in a communication system. The method includes measuring, by a receiver, an actual SNR distribution of a communication link between a transmitter and the receiver; further includes evaluating, by a waveform performance prediction device, a normalized minimum SNR shift required for the communication waveform to operate, where the normalized minimum SNR shift is obtained based on a normalized SNR distribution using a neural network (NN), the normalized SNR distribution corresponding to the actual SNR distribution; and further includes, according to the normalized minimum SNR shift, obtaining, by a waveform performance prediction device, an actual minimum SNR shift for the actual SNR distribution, where according to the actual minimum SNR shift, the communication system is adjusted for operation.

公开(公告)号：US20220375134A1

公开(公告)日：2022-11-24

申请号：US17734858

申请日：2022-05-02

Applicant: Intelligent Fusion Technology, Inc.

Inventor： Qi ZHAO ,  Yi LI ,  Xin TIAN ,  Genshe CHEN ,  Erik BLASCH ,  Khanh PHAM

IPC: G06T9/00 ,  G06T3/00 ,  G06V10/25 ,  G01S17/89

Abstract: A method for point cloud compression of an intelligent cooperative perception (iCOOPER) for autonomous air vehicles (AAVs) includes: receiving a sequence of consecutive point clouds; identifying a key point cloud (K-frame) from the sequence of consecutive point clouds; transforming each of the other consecutive point clouds (P-frames) to have the same coordinate system as the K-frame; converting each of the K-frame and P-frames into a corresponding range image; spatially encoding the range image of the K-frame by fitting planes; and temporally encoding each of the range images of the P-frames using the fitting planes.

公开(公告)号：US20220085878A1

公开(公告)日：2022-03-17

申请号：US17021289

申请日：2020-09-15

Applicant: INTELLIGENT FUSION TECHNOLOGY, INC.

Inventor： Lun LI ,  Yi LI ,  Sixiao WEI ,  Dan SHEN ,  Genshe CHEN

IPC: H04B10/077 ,  G06N3/04 ,  H04B10/11 ,  G06K9/62 ,  G06N3/08

Abstract: Various embodiments provide a method for free space optical communication performance prediction method. The method includes: in a training stage, collecting a large number of data representing FSOC performance from external data sources and through simulation in five feature categories; dividing the collected data into training datasets and testing datasets to train a prediction model based on a deep neural network (DNN); evaluating a prediction error by a loss function and adjusting weights and biases of hidden layers of the DNN to minimize the prediction error; repeating training the prediction model until the prediction error is smaller than or equal to a pre-set threshold; in an application stage, receiving parameters entered by a user for an application scenario; retrieving and preparing real-time data from the external data sources for the application scenario; and generating near real-time FSOC performance prediction results based on the trained prediction model.

公开(公告)号：US20240402298A1

公开(公告)日：2024-12-05

申请号：US17382931

申请日：2021-07-22

Applicant: Intelligent Fusion Technology, Inc.

Inventor： Hui HUANG ,  Yi LI ,  Erik BLASCH ,  Khanh PHAM ,  Jiaoyue LIU ,  Nichole SULLIVAN ,  Dan SHEN ,  Genshe CHEN

IPC: G01S7/41 ,  G01S7/40

Abstract: A method for recognizing a low-probability-of-interception (LPI) radar signal waveform includes: obtaining, by a radar signal receiver, an LPI radar signal s(t), s(t) varying with time t; extracting, by a radar signal processor, an adaptive feature and a pre-defined analytical feature from the LPI radar signal s(t); combining, by the radar signal processor, the adaptive feature with the pre-defined analytical feature to generate a constructed adaptive feature; and applying, by the radar signal processor, a convolutional neural network (CNN) model to classify the constructed adaptive feature to recognize the LPI radar signal waveform.

公开(公告)号：US20230186620A1

公开(公告)日：2023-06-15

申请号：US17551436

申请日：2021-12-15

Applicant: Intelligent Fusion Technology, Inc.

Inventor： Qi ZHAO ,  Huong Ngoc DANG ,  Yi LI ,  Xin TIAN ,  Nichole SULLIVAN ,  Genshe CHEN ,  Khanh PHAM

IPC: G06V10/94 ,  G06V10/764 ,  G06V10/82 ,  G06V10/96 ,  G06V10/77

CPC classification number: G06V10/95 ,  G06V10/764 ,  G06V10/82 ,  G06V10/96 ,  G06V10/7715

Abstract: A system includes: a named data networking (NDN) based Spark distributed computing network including a Spark distributed computing network including a master computer node and a plurality of slave computer nodes, and a named data networking (NDN) protocol installed on the Spark distributed computing network, and a coded distributed computing (CDC) target recognition model deployed on the NDN-based Spark distributed computing network. The NDN-based Spark distributed computing network is configured to: receive one or more batches of input images; generate a parity image from each batch of the input images; predict a label for each image of the batch of the input images; process the generated parity image; upon a label prediction of one image of the batch of the input images being unavailable, reconstruct the unavailable label prediction; and classify labels for the input images.

公开(公告)号：US20230153124A1

公开(公告)日：2023-05-18

申请号：US17490861

申请日：2021-09-30

Applicant: Intelligent Fusion Technology, Inc.

Inventor： Qi ZHAO ,  Yi LI ,  Mingjie FENG ,  Li LI ,  Genshe CHEN

IPC: G06F9/445 ,  G06F9/48

CPC classification number: G06F9/44594 ,  G06F9/4806 ,  G06F2209/509

Abstract: An edge network computing system includes: a plurality of terminal devices; a plurality of edge servers connected to the terminal device through an access network; and a plurality of cloud servers connected to the plurality of edge servers through a core network. Each edge server is configured to: receive a plurality of computing tasks originated from one of the plurality of terminal devices; use a deep Q-learning neural network (DQN) with experience replay to select one of the plurality of could servers to offload a portion of the plurality of computing tasks; and send the portion of the plurality of computing tasks to the selected cloud server and forward results of the portion of the plurality of computing tasks received from the selected cloud server to the originating terminal device.

公开(公告)号：US20220086261A1

公开(公告)日：2022-03-17

申请号：US17021674

申请日：2020-09-15

Applicant: INTELLIGENT FUSION TECHNOLOGY, INC.

Inventor： Xin TIAN ,  Qi ZHAO ,  Yi LI ,  Genshe CHEN ,  Khanh PHAM

IPC: H04L29/06 ,  H04L12/26 ,  H04L1/18 ,  H04L12/801

Abstract: Various embodiments provide a method for transmission control protocol (TCP) packet transmission. The method includes receiving, by a receiver performance enhancing node (PEN), one or more TCP packets each with a timestamp and a sequence number from a sender PEN; evaluating a packet delivery time from the sender PEN to the receiver PEN; detecting whether any TCP packet is lost based on a packet sequence and determining a delay shaping time for each TCP packet based on a maximum number of retransmissions and an evaluated delivery time distribution; in response to a lost TCP packet being detected, determining whether the lost TCP packet needs to be retransmitted based on the maximum number of retransmissions; and in response to the determined delay shaping time, determining when a received TCP packet needs to be forwarded based on the determined delay shaping time and a timestamp associated with the received TCP packet.