Intelligent Fusion Technology, Inc.

Nichole Sullivan

Genshe Chen

Dan Shen

Khanh D Pham

Erik Philip Blasch

Yi Li

Xin Tian

Jingyang Lu

Biao Chen

Bin Jia

Wenhao Xiong

Qi Zhao

Hua-mei Chen

Zhijiang Chen

Kuo-Chu Chang

Yiran Xu

Jiaoyue Liu

Huong Ngoc Dang

Roman Ilin

Hui Huang

Cailing Dong

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

Embodiments of the present disclosure provide a method, a device, and a storage medium for domain adaptation for efficient learning fusion (DAELF). The method includes acquiring data from a plurality of data sources of a plurality of sensors; for each of the plurality of sensors, training an auxiliary classifier generative adversarial network (AC-GAN) by a hardware processor with data from each data source of the plurality of data sources, thereby obtaining a trained feature extraction network and a trained label prediction network for each data source; forming a decision-level fusion network or a feature-level fusion network; and training the decision-level fusion network or the feature-level fusion network with a source-only mode or a generate to adapt (GTA) mode; and applying the trained decision-level fusion network or the trained feature-level fusion network to detect a target of interest.

Method, device, and storage medium for deep learning based domain adaptation with data fusion for aerial image data analysis

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

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.

Method and system for wave propagation 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.

Method and system for energy efficient wireless communications

A method for pattern discovery and real-time anomaly detection based on knowledge graph, comprising: based on a dataset including messages collected within a certain period, constructing a local knowledge graph (KG); applying a statistical relational learning (SRL) model to predict hidden relations between entities to obtain an updated local KG; from all SPO triples of the updated local KG, discovering a normalcy pattern that includes frequent entities, frequent relations, and frequent SPO triples; and in response to receiving streaming data from a message bus, extracting a plurality of entities, a plurality of relations, and a plurality of SPO triples, from the streaming data for comparison with the normalcy pattern using semantic distance, thereby determining whether there is an abnormal entity, relation, or SPO triple in the streaming data.

Method and system for pattern discovery and real-time anomaly detection based on knowledge graph

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Germantown, MD, United States of America

Nichole Sullivan