公开(公告)号：US20240037188A1

公开(公告)日：2024-02-01

申请号：US18484839

申请日：2023-10-11

Applicant: NEC Laboratories America, Inc.

Inventor： Yi-Hsuan Tsai ,  Xiang Yu ,  Bingbing Zhuang ,  Manmohan Chandraker ,  Donghyun Kim

IPC: G06F18/213 ,  G06N3/08 ,  G06V10/75 ,  G06F18/22 ,  G06F18/214

CPC classification number: G06F18/213 ,  G06N3/08 ,  G06V10/751 ,  G06F18/22 ,  G06F18/2155

Abstract: Video methods and systems include extracting features of a first modality and a second modality from a labeled first training dataset in a first domain and an unlabeled second training dataset in a second domain. A video analysis model is trained using contrastive learning on the extracted features, including optimization of a loss function that includes a cross-domain regularization part and a cross-modality regularization part.

公开(公告)号：US11881688B2

公开(公告)日：2024-01-23

申请号：US17717088

申请日：2022-04-10

Applicant: NEC Laboratories America, Inc.

Inventor： Yangmin Ding ,  Sarper Ozharar ,  Yuandu Xu ,  Yue Tian ,  Ting Wang

IPC: H02G1/02 ,  G01S19/01 ,  G01D5/353

CPC classification number: H02G1/02 ,  G01D5/35354 ,  G01S19/01

Abstract: Systems and methods for performing the dynamic anomaly localization of utility pole aerial/suspended/supported wires/cables by distributed fiber optic sensing. In sharp contrast to the prior art, our inventive systems and methods according to aspects of the present disclosure advantageously identify a “location region” on a utility pole supporting an affected wire/cable, thereby permitting the identification and reporting of service personnel that are uniquely responsible for responding to such anomalous condition(s).

公开(公告)号：US20240005163A1

公开(公告)日：2024-01-04

申请号：US18362193

申请日：2023-07-31

Applicant: NEC Laboratories America, Inc.

Inventor： Wenchao Yu ,  Haifeng Chen

IPC: G06N3/084 ,  G16H50/30 ,  G06N20/20 ,  G06N5/046 ,  G06N3/045

CPC classification number: G06N3/084 ,  G16H50/30 ,  G06N3/045 ,  G06N5/046 ,  G06N20/20

Abstract: Methods and systems for responding to changing conditions include training a model, using a processor, using trajectories that resulted in a positive outcome and trajectories that resulted in a negative outcome. Training is performed using an adversarial discriminator to train the model to generate trajectories that are similar to historical trajectories that resulted in a positive outcome, and using a cooperative discriminator to train the model to generate trajectories that are dissimilar to historical trajectories that resulted in a negative outcome. A dynamic response regime is generated using the trained model and environment information. A response to changing environment conditions is performed in accordance with the dynamic response regime.

公开(公告)号：US20240005154A1

公开(公告)日：2024-01-04

申请号：US18370092

申请日：2023-09-19

Applicant: NEC Laboratories America, Inc.

Inventor： Jingchao Ni ,  Wei Cheng ,  Haifeng Chen

IPC: G06N3/08 ,  G06N7/01

CPC classification number: G06N3/08 ,  G06N7/01

Abstract: A computer-implemented method for model building is provided. The method includes receiving a training set of medical records and model hyperparameters. The method further includes initializing an encoder as a Dual-Channel Combiner Network (DCNN) and initialize distribution related parameters. The method also includes performing, by a hardware processor, a forward computation to (1) the DCNN to obtain the embeddings of the medical records, and (2) the distribution related parameters to obtain class probabilities. The method additionally includes checking by a convergence evaluator if the iterative optimization has converged. The method further includes performing model personalization responsive to model convergence by encoding the support data of a new patient and using the embeddings and event subtype labels to train a personalized classifier.

公开(公告)号：US20240003717A1

公开(公告)日：2024-01-04

申请号：US18320993

申请日：2023-05-21

Applicant: NEC Laboratories America, Inc.

Inventor： Milad SALEMI ,  Ming-Fang HUANG ,  Yuheng CHEN ,  Shaobo HAN

IPC: G01D5/353 ,  G01H9/00 ,  G06N3/09

CPC classification number: G01D5/35361 ,  G06N3/09 ,  G01H9/004

Abstract: A DFOS system and method providing detection and localization of vehicle emergency stops which employs DFOS and machine learning techniques as an integrated solution for automatic, real-time, detection and localization of vehicle emergency stop events. Real time-location data from a buried optical sensing fiber located along a roadway is used to derive continuous vehicle trajectories while providing a wide coverage area for more accurate assessments. AI techniques are employed which track vehicles' speed and acceleration, locate vehicle deceleration events, and localize emergency stop events. Danger assessment is determined by analyzing a vehicle's anticipated and reproducible trajectory after an emergency breaking event—while ignoring stop-and-go events as low-risk events—and discovering stop-no-go events exhibiting large deceleration as high-risk events.

公开(公告)号：US11848851B2

公开(公告)日：2023-12-19

申请号：US17331112

申请日：2021-05-26

Applicant: NEC Laboratories America, Inc.

Inventor： Mohammad Khojastepour ,  Shahram Shahsavari

IPC: H04L43/12 ,  H04W16/28 ,  H04B7/0408 ,  H04B7/06 ,  H04W72/044

CPC classification number: H04L43/12 ,  H04B7/0408 ,  H04B7/0617 ,  H04B7/0619 ,  H04B7/0695 ,  H04W16/28 ,  H04W72/046

Abstract: Transmission systems include a configurable antenna, that transmits according to a configured beam, a hardware processor, and a memory that stores a computer program product. When the computer program product is executed by the hardware processor, it causes the hardware processor to send a first probing packet on the antenna using a first scanning beam, selected from a set of scanning beams, to determine feedback about the first probing packet, to send a second probing packet on the antenna using a second scanning beam, selected from the set of scanning beams based on the determined feedback about the first probing packet, to determine feedback about the second probing packet, to determine a data transmission beam based on the set of scanning beams and the received feedback about the first probing packet and the second probing packet, and to transmit data using the antenna, configured according to the determined transmission beam.

公开(公告)号：US20230403340A1

公开(公告)日：2023-12-14

申请号：US18321880

申请日：2023-05-23

Applicant: NEC Laboratories America, Inc.

Inventor： Giuseppe Coviello ,  Kunal Rao ,  Srimat Chakradhar

IPC: H04L67/55 ,  H04L67/563

CPC classification number: H04L67/55 ,  H04L67/563

Abstract: A pull-based communication method for microservices-based real-time streaming video analytics pipelines is provided. The method includes receiving a plurality of frames from a plurality of cameras, each camera including a camera sidecar, arranging a plurality of detectors in layers such that a first detector layer includes detectors with detector sidecars and detector business logic, and the second detector layer includes detectors with only sidecars, arranging a plurality of extractors in layers such that a first extractor layer includes extractors with extractor sidecars and extractor business logic, and the second extractor layer includes extractors with only sidecars, and enabling a mesh controller, during registration, to selectively assign inputs to one or more of the detector sidecars of the first detector layer and one or more of the extractor sidecars of the first extractor layer to pull data items for processing.

公开(公告)号：US20230400350A1

公开(公告)日：2023-12-14

申请号：US18333490

申请日：2023-06-12

Applicant: NEC Laboratories America, Inc.

Inventor： Shaobo HAN ,  Ming-Fang HUANG ,  Jian FANG

IPC: G01H9/00 ,  G01D5/353 ,  F41A19/01

CPC classification number: G01H9/004 ,  G01D5/35361 ,  F41A19/01

Abstract: A machine learning (ML)/artificial intelligence (AI) based distributed fiber optic sensing (DFOS) system and method providing detection and localization of gunshot events. In addition to the ML/AI DFOS, a signal processing pipeline that compresses an audible distributed acoustic sensing (DAS) waveform data into a small set of features that protects privacy of individuals while preserving the utility of acoustic events to detect the gunshot events and discriminate same from other events. A data-driven deep learning approach automatically predicts acoustic event types with higher accuracy that realized by prior art methods.

公开(公告)号：US20230377179A1

公开(公告)日：2023-11-23

申请号：US18319476

申请日：2023-05-17

Applicant: NEC Laboratories America, Inc.

Inventor： Zhuocheng JIANG ,  Yue TIAN ,  Yangmin DING ,  Sarper OZHARAR ,  Ting WANG

IPC: G06T7/50 ,  H04N13/207 ,  G06V10/74

CPC classification number: G06T7/50 ,  H04N13/207 ,  G06V10/761 ,  G06T2207/20084 ,  G06T2207/20228

Abstract: Computer vision based, wide-area snow/water level estimation methods using disparity maps. In one embodiment our method provides rich depth-information using a stereo camera and image processing. Scene images at normal and snow/rain weather conditions are obtained by a double-lens stereo camera and a disparity map is generated from the scene images at left and right lenses using a self-supervised deep convolutional network. In another embodiment, our method uses a single point snow/water level sensor, a stationary monocular camera to measure snow/water levels covering a wide area.

公开(公告)号：US20230376783A1

公开(公告)日：2023-11-23

申请号：US18319472

申请日：2023-05-17

Applicant: NEC Laboratories America, Inc.

Inventor： Philip JI ,  Ting WANG ,  Zilong YE

IPC: G06N3/092 ,  G06N3/045

CPC classification number: G06N3/092 ,  G06N3/045

Abstract: An advance in the art is made according to aspects of the present disclosure directed to a method that determines virtual topology design and resource allocation for dynamic intent-based computing jobs in a mobile edge computing infrastructure when client requests are dynamic. Our method according to aspects of the present disclosure is an unsupervised machine learning approach, so that there is no need for manual labeling or pre-processing in advance, while a training process and decision making is performed online. In sharp contrast to the prior art, our method according to aspects of the present disclosure utilizes reinforcement learning techniques to make an efficient assignment in which two neural networks—a policy neural network and a value neural network—are used interactively to achieve the assignment. A training process is performed through a batch (or group) processing style in an online manner.