Nec Corporation

Nec Laboratories America, Inc.

Nec USA, Inc.

Other

At+t Corp.

Nec Electronics Corporation

Rutgers, the State University of New Jersey

Nec Research Institute, Inc.

Srimat T Chakradhar

Giuseppe Coviello

Kunal Rao

Yi Yang

Srihari Cadambi

Murugan Sankaradas

Biplob Kumar Debnath

Seongmoon Wang

Min Feng

Anand Raghunathan

Oliver Po

Cheng-Hong Li

Nishkam Ravi

Kedarnath J Balakrishnan

Rajat Phull

Utsav Drolia

Wang-Pin Hsiung

Venkata S Jakkula

Michela Becchi

Murugan Sankaradass

Arun Balakrishnan

Surendra K Bommu

Kiran B Doreswamy

Sibendu Paul

Haris Lekatsas

Srivaths Ravi

Wenlong Wei

Souvik Bhattacherjee

Hans Peter Graf

Kwang-Ting Cheng

Vishwani D Agrawal

Rabindra K Roy

Debayan Deb

Angela Krstic

Joerg Henkel

Arash Rezaei

Steven G Rothweiler

Chia-Tso Chao

Kittisak Sajjapongse

Abhinandan Majumdar

Michael S Hsiao

Arunkumar Balakrishnan

Savita Banerjee

Surendra Byna

Kiran Nagaraja

Florin Sultan

Eric Cosatto

Renqiang Min

Asim Kadav

Pranav N Ashar

Niraj K Jha

Sujit Dey

Erik Kruus

Farley Lai

Ganesh Lakshminarayana

Igor Durdanovic

Vijay Raghunathan

Marcello Lajolo

Jörg Henkel

Nupur Kothari

Yang Gao

Linhai Song

Naresh Rapolu

Ram Kumar Rengaswamy

Karthik Nandakumar

Da Li

Hirohiko Shibata

Xiao Liu

Loganathan Lingappan

Abhishek Verma

Wei Li

Joseph Kilian

Jacques Pienaar

M Ashraf Siddiquee

Haicheng Wu

M Mustafa Rafique

Nachiketh Potlapally

Linnan Wang

Tao Bao

Alper Yildirim

Xijiang Lin

Ozcan Ozturk

Viswani Agrawal

Jun Liu

Suman Kanjilal

Chao Li

Hyungmin Cho

Igor Rivin

Dong Li

Vijay Gangaram

Vivek Aswal

Neisarg Dave

Jiayuan Meng

Nitin Agrawal

Narayanan Sundaram

Tarang Chugh

Kanishka Lahiri

Valentina Pelliccia

Abhishek Mitra

A method for specifying and executing an application including multiple microservices on 5G slices within a multi-tiered 5G infrastructure is presented. The method includes managing compute requirements and network requirements of the application simultaneously by determining end-to-end application characteristics by employing an application slice specification including an application ID component, an application name component, an application metadata component, a function dependencies component, a function instances component, and an instance connections component, specifying a function slice specification including a function network slice specification and a function compute slice specification, and employing a runtime component including a resource manager, an application slice controller, and an application slice monitor, wherein the resource manager maintains a database and manages starting, stopping, updating, and deleting application instances.

Application-centric design for 5G and edge computing applications

Methods and systems for camera configuration include configuring an image capture configuration parameter of a camera according to a multi-objective reinforcement learning aggregated reward function. Respective quality estimates for analytics are determined after configuring the image capture parameters. The aggregated reward function is updated based on the quality estimates.

Camera parameter enhancement for multiple analytics

Methods and systems for context reduction include identifying a context document relating to a query. A number of sentences of the context document to preserve is determined. The sentences of the context document are ranked according to respective similarities between the sentences and the query. A reduced context is generated that preserves the determined number of highest ranked sentences of the context document and eliminates other sentences from the context document. The query is executed with a language model, including the reduced context in a prompt, to generate a response.

Domain-specific question answering with context reduction for decision making

Systems and methods are provided for dynamically tuning camera parameters in a video analytics system to optimize analytics accuracy. A camera captures a current scene, and optimal camera parameter settings are learned and identified for the current scene using a Reinforcement Learning (RL) engine. The learning includes defining a state within the RL engine as a tuple of two vectors: a first representing current camera parameter values and a second representing measured values of frames of the current scene. Quality of frames is estimated using a quality estimator, and camera parameters are adjusted based on the quality estimator and the RL engine for optimization. Effectiveness of tuning is determined using perceptual Image Quality Assessment (IQA) to quantify a quality measure. Camera parameters are adaptively tuned in real-time based on learned optimal camera parameter settings, state, quality measure, and set of actions, to optimize the analytics accuracy for video analytics tasks.

Adaptive perceptual quality based camera tuning using reinforcement learning

A method for automatically adjusting camera parameters to improve video analytics accuracy during continuously changing environmental conditions is presented. The method includes capturing a video stream from a plurality of cameras, performing video analytics tasks on the video stream, the video analytics tasks defined as analytics units (AUs), applying image processing to the video stream to obtain processed frames, filtering the processed frames through a filter to discard low-quality frames and dynamically fine-tuning parameters of the plurality of cameras. The fine-tuning includes passing the filtered frames to an AU-specific proxy quality evaluator, employing State-Action-Reward-State-Action (SARSA) reinforcement learning (RL) computations to automatically fine-tune the parameters of the plurality of cameras, and based on the reinforcement computations, applying a new policy for an agent to take actions and learn to maximize a reward.

Reinforcement-learning based system for camera parameter tuning to improve analytics

Methods and systems of training a neural network include training a feature extractor and a classifier using a first set of training data that includes one or more base cases. The classifier is trained with few-shot adaptation using a second set of training data, smaller than the first set of training data, while keeping parameters of the feature extractor constant.

Few-shot action recognition

Methods and systems for image filtering include detecting a distortion level of input images, using a distortion detection model that is trained using confidence values generated by a pre-trained image classifier with a set of distorted training images. An analysis is performed on input images having a detected distortion level that is lower than a threshold, with input images having an above-threshold detected distortion level being filtered out.

Distortion-based filtering for image classification

A method for free flow fever screening is presented. The method includes capturing a plurality of frames from thermal data streams and visual data streams related to a same scene to define thermal data frames and visual data frames, detecting and tracking a plurality of individuals moving in a free-flow setting within the visual data frames, and generating a tracking identification for each individual of the plurality of individuals present in a field-of-view of the one or more cameras across several frames of the plurality of frames. The method further includes fusing the thermal data frames and the visual data frames, measuring, by a fever-screener, a temperature of each individual of the plurality of individuals within and across the plurality of frames derived from the thermal data streams and the visual data streams, and generating a notification when a temperature of an individual exceeds a predetermined threshold temperature.

Free flow fever screening

A method for performing resource orchestration for microservices-based 5G applications in a dynamic, heterogenous, multi-tiered compute and network environment is presented. The method includes managing compute requirements and network requirements of a microservices-based application jointly by positioning computing nodes distributed across multiple layers, across edges and at a central cloud, identifying and modeling coupling relationships between compute and network resources for a plurality of microservices, when only application-level requirements are provided, to build coupling functions, solving a multi-objective optimization problem to identify how each of the plurality of microservices are deployed in the dynamic, heterogenous, multi-tiered compute and network environment by employing the coupling functions to jointly optimize resource usage of the compute and network resources across different compute and network slices, and deriving optimal joint network and compute resource allocation and function placement decisions.

Resource orchestration for microservices-based 5G applications

A method for employing a semi-supervised learning approach to improve accuracy of a small model on an edge device is presented. The method includes collecting a plurality of frames from a plurality of video streams generated from a plurality of cameras, each camera associated with a respective small model, each small model deployed in the edge device, sampling the plurality of frames to define sampled frames, performing inference to the sampled frames by using a big model, the big model shared by all of the plurality of cameras and deployed in a cloud or cloud edge, using the big model to generate labels for each of the sampled frames to generate training data, and training each of the small models with the training data to generate updated small models on the edge device.

Dynamic, contextualized AI models

Methods and systems for executing an application include extending a container orchestration system application programming interface (API) to handle objects that specify components of an application. An application representation is executed using the extended container orchestration system API, including the instantiation of one or more services that define a data stream path from a sensor to a device.

Data exchange and transformation in stream computing systems

A method for real-time cross-spectral object association and depth estimation is presented. The method includes synthesizing, by a cross-spectral generative adversarial network (CS-GAN), visual images from different data streams obtained from a plurality of different types of sensors, applying a feature-preserving loss function resulting in real-time pairing of corresponding cross-spectral objects, and applying dual bottleneck residual layers with skip connections to accelerate real-time inference and to accelerate convergence during model training.

Real-time cross-spectral object association and depth estimation

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.

Flexible and efficient communication in microservices-based stream analytics pipeline

A method for implementing a self-optimized video analytics pipeline is presented. The method includes decoding video files into a sequence of frames, extracting features of objects from one or more frames of the sequence of frames of the video files, employing an adaptive resource allocation component based on reinforcement learning (RL) to dynamically balance resource usage of different microservices included in the video analytics pipeline, employing an adaptive microservice parameter tuning component to balance accuracy and performance of a microservice of the different microservices, applying a graph-based filter to minimize redundant computations across the one or more frames of the sequence of frames, and applying a deep-learning-based filter to remove unnecessary computations resulting from mismatches between the different microservices in the video analytics pipeline.

Self-optimizing video analytics pipelines

A method for implementing application self-optimization in serverless edge computing environments is presented. The method includes requesting deployment of an application pipeline on data received from a plurality of sensors, the application pipeline including a plurality of microservices, enabling communication between a plurality of pods and a plurality of analytics units (AUs), each pod of the plurality of pods including a sidecar, determining whether each of the plurality of AUs maintains any state to differentiate between stateful AUs and stateless AUs, scaling the stateful AUs and the stateless AUs, enabling communication directly between the sidecars of the plurality of pods, and reusing and resharing common AUs of the plurality of AUs across different applications.

System for application self-optimization in serverless edge computing environments

Growing community of inventors

North Brunswick, NJ, United States of America

Srimat T Chakradhar