Intelligent Fusion Technology, Inc.

Yi Li

Genshe Chen

Khanh D Pham

Xin Tian

Dan Shen

Nichole Sullivan

Qi Zhao

Erik Philip Blasch

Biao Chen

Sixiao Wei

Wenhao Xiong

Lun Li

Jiaoyue Liu

Hui Huang

Huong Ngoc Dang

Mingjie Feng

Li Li

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.

Method and system for low-probability-of-intercept radar signal waveform recognition

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.

Edge network computing system with deep reinforcement learning based task scheduling

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.

Methods and systems of resilient mobile distributed computing

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.

Method, device, and system for TCP packet transmission over satellite communication network

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.

Method, device, and storage medium for communication waveform performance predication

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.

Method and system for free space optical communication performance prediction

A three-layer protocol stack in a wireless communication device and a wireless communication network are provided. The three-layer protocol stack includes a physical layer; a medium access control (MAC) layer; and a network layer. The physical layer includes one or more circuits to conduct a power consumption minimization and a waveform selection. The MAC layer is configured to perform a medium access control and a resource block reconfiguration. The network layer is configured to perform an energy efficient routing and connection maintenance. The physical layer, the MAC layer and the network layer cooperate with each other to at least reduce an energy consumption of the wireless communication device.

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Germantown, MD, United States of America

Yi Li