公开(公告)号：US20180278425A1

公开(公告)日：2018-09-27

申请号：US15469667

申请日：2017-03-27

Applicant: Intelligent Fusion Technology, Inc

Inventor： Yiran XU ,  Gang WANG ,  Sixiao WEI ,  Genshe CHEN ,  Khanh PHAM ,  Erik BLASCH

IPC: H04L9/32 ,  H04W28/04 ,  H04L29/06

Abstract: Methods and systems for cyber secure data communications are provided. In some embodiments, a method for transmitting data comprises: performing a marker-based data encoding process to embed a digital watermark into each of a plurality of original data flows to be transmitted to a plurality of receivers; performing a non-orthogonal multiple access process to allocate transmission powers to the plurality of original data flows, such that the plurality of original data flows are simultaneously superposed on a carrier frequency to generate a superposed signal; performing a noise modulation process to modulate the superposed signal to generate a noise-like signal and a reference noise signal; transmitting the noise-like signal and the reference noise signal through orthogonally polarized antennas; and performing a portal-based data integrity analysis process to check whether a receiver in the plurality of receivers is compromised or manipulated.

公开(公告)号：US20210134046A1

公开(公告)日：2021-05-06

申请号：US16674929

申请日：2019-11-05

Applicant: INTELLIGENT FUSION TECHNOLOGY, INC.

Inventor： Jingyang LU ,  Yiran XU ,  Dan SHEN ,  Nichole SULLIVAN ,  Genshe CHEN ,  Khanh PHAM ,  Erik BLASCH

IPC: G06T15/06 ,  G06N20/00 ,  G06N5/04

Abstract: The present disclosure provides a method for wave propagation prediction based on a 3D ray tracing engine and machine-learning based dominant ray selection. The method includes receiving, integrating, and processing input data. Integrating and processing the input data includes dividing a cone of the original millimeter wave (mmWave) into a plurality of sub cones; determining a contribution weight of rays coming from each sub cone to the received signal strength (RSS) at a receiving end of interest; and determining rays coming from one or more sub cones that have a total contribution weight to the RSS larger than a preset threshold value as dominant rays using a neural network obtained through a machine learning approach. The method further includes performing ray tracing based on the input data and the dominant rays to predict wave propagation.