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    VIDEO ANALYTICS ACCURACY USING TRANSFER LEARNING
    32.
    发明公开
    VIDEO ANALYTICS ACCURACY USING TRANSFER LEARNING 审中-公开

    公开(公告)号：US20240037778A1

    公开(公告)日：2024-02-01

    申请号：US18361340

    申请日：2023-07-28

    Applicant: NEC Laboratories America, Inc.

    Inventor： Kunal Rao ,  Giuseppe Coviello ,  Murugan Sankaradas ,  Oliver Po ,  Srimat Chakradhar ,  Sibendu Paul

    IPC: G06T7/70 ,  G06T7/00 ,  G06V10/25 ,  G06V40/10

    CPC classification number: G06T7/70 ,  G06T7/0002 ,  G06V10/25 ,  G06V40/103 ,  G06V2201/07 ,  G06T2207/30242

    Abstract: Systems and methods are provided for increasing accuracy of video analytics tasks in real-time by acquiring a video using video cameras, and identifying fluctuations in the accuracy of video analytics applications across consecutive frames of the video. The identified fluctuations are quantified based on an average relative difference of true-positive detection counts across consecutive frames. Fluctuations in accuracy are reduced by applying transfer learning to a deep learning model initially trained using images, and retraining the deep learning model using video frames. A quality of object detections is determined based on an amount of track-ids assigned by a tracker across different video frames. Optimization of the reduction of fluctuations includes iteratively repeating the identifying, the quantifying, the reducing, and the determining the quality of object detections until a threshold is reached. Model predictions for each frame in the video are generated using the retrained deep learning model.

    Dynamic self-optimization of application quality-of-service requests to mobile networks
    33.
    发明授权
    Dynamic self-optimization of application quality-of-service requests to mobile networks 有权

    公开(公告)号：US11784945B2

    公开(公告)日：2023-10-10

    申请号：US17958820

    申请日：2022-10-03

    Applicant: NEC Laboratories America, Inc.

    Inventor： Kunal Rao ,  Murugan Sankaradas ,  Srimat Chakradhar ,  Vivek Aswal

    IPC: H04L47/83 ,  H04W28/02 ,  H04L43/0894 ,  H04L41/5019

    CPC classification number: H04L47/83 ,  H04L41/5019 ,  H04L43/0894 ,  H04W28/0268

    Abstract: Systems and methods for network bandwidth optimization, including transmitting sensor data from one or more sensors over a wireless network into a generated network slice, submitting a Quality-of-Service (QoS) request for one or more applications by specifying desired network slice characteristics, and predicting network bandwidth needed for granting the QoS request for the one or more applications using a cost function based on magnitude, direction, and frequency of error. Time-varying network bandwidth usage is continuously monitored, and new QoS requests for the one or more applications are periodically requested based on the monitoring. An updated prediction for updated bandwidth needed for the new QoS request is generated using the cost function, and network bandwidth reservations are iteratively adjusted based on the updated prediction for the new QoS request to provide an amount of network resources to the one or more applications to support the new QoS request.

    RESOURCE ORCHESTRATION FOR MICROSERVICES-BASED 5G APPLICATIONS
    35.
    发明申请
    RESOURCE ORCHESTRATION FOR MICROSERVICES-BASED 5G APPLICATIONS 有权

    公开(公告)号：US20230035024A1

    公开(公告)日：2023-02-02

    申请号：US17863685

    申请日：2022-07-13

    Applicant: NEC Laboratories America, Inc.

    Inventor： Kunal Rao ,  Wang-Pin Hsiung ,  Oliver Po ,  Murugan Sankaradas ,  Srimat Chakradhar ,  Anousheh Gholami

    IPC: G06F9/50 ,  H04W4/60 ,  H04W24/02

    Abstract: 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.

    DYNAMIC, CONTEXTUALIZED AI MODELS
    36.
    发明申请
    DYNAMIC, CONTEXTUALIZED AI MODELS 有权

    公开(公告)号：US20220230421A1

    公开(公告)日：2022-07-21

    申请号：US17577664

    申请日：2022-01-18

    Applicant: NEC Laboratories America, Inc.

    Inventor： Yi Yang ,  Murugan Sankaradas ,  Srimat Chakradhar

    IPC: G06V10/774 ,  H04L67/01 ,  H04L67/00

    Abstract: 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.

    FACE CLUSTERING IN VIDEO STREAMS
    37.
    发明申请
    FACE CLUSTERING IN VIDEO STREAMS 有权

    公开(公告)号：US20210319226A1

    公开(公告)日：2021-10-14

    申请号：US17194911

    申请日：2021-03-08

    Applicant: NEC Laboratories America, Inc.

    Inventor： Biplob Debnath ,  Srimat Chakradhar ,  Giuseppe Coviello ,  Murugan Sankaradas

    IPC: G06K9/00 ,  G06K9/40 ,  G06K9/62

    Abstract: Methods and systems for video analysis and response include detecting face images within video streams. Noisy images are filtered from the detected face images. Batches of the remaining detected face images are clustered to generate mini-clusters, constrained by temporal locality. The mini-clusters are globally clustered to generate merged clusters formed of face images for respective people, using camera-chain information to constrain a set of the video streams being considered. Analytics are performed on the merged clusters to identify a tracked individual's movements through an environment. A response is performed to the tracked individual's movements.

    LambdaLib: In-Memory View Management and Query Processing Library for Realizing Portable, Real-Time Big Data Applications
    39.
    发明申请
    LambdaLib: In-Memory View Management and Query Processing Library for Realizing Portable, Real-Time Big Data Applications 审中-公开
    LambdaLib：内存视图管理和查询处理库，用于实现便携式实时大数据应用程序

    公开(公告)号：US20160300157A1

    公开(公告)日：2016-10-13

    申请号：US15089667

    申请日：2016-04-04

    Applicant: NEC Laboratories America, Inc.

    Inventor： Murugan Sankaradas ,  Giuseppe Coviello ,  Srimat Chakradhar ,  Marco Gianfico ,  Emanuel Di Nardo

    IPC: G06N99/00 ,  G06F17/30

    CPC classification number: G06F16/252 ,  G06F16/24568

    Abstract: A big data processing system includes a memory management engine having stream buffers, realtime views and models, and batch views and models, the stream buffers coupleable to one or more stream processing frameworks to process stream data, the batch models coupleable to one or more batch processing frameworks; one or more processing engines including Join, Group, Filter, Aggregate, Project functional units and classifiers; and a client layer engine communicating with one or more big data applications, the client layer engine handling an output layer, an API layer, and an unified query layer.

    Abstract translation: 大数据处理系统包括具有流缓冲器，实时视图和模型以及批视图和模型的存储器管理引擎，流缓冲器可耦合到一个或多个流处理框架以处理流数据，批处理模型可耦合到一个或多个批处理 处理框架; 一个或多个处理引擎，包括Join，Group，Filter，Aggregate，Project功能单元和分类器; 以及与一个或多个大数据应用通信的客户层引擎，处理输出层的客户层引擎，API层和统一查询层。

    SYSTEMS AND METHODS FOR SWAPPING PINNED MEMORY BUFFERS
    40.
    发明申请
    SYSTEMS AND METHODS FOR SWAPPING PINNED MEMORY BUFFERS 有权
    用于切换PINNED内存缓冲区的系统和方法

    公开(公告)号：US20150212733A1

    公开(公告)日：2015-07-30

    申请号：US14603813

    申请日：2015-01-23

    Applicant: NEC Laboratories America, Inc.

    Inventor： Cheng-Hong LI ,  Giuseppe Coviello ,  Kunal Rao ,  Murugan Sankaradas ,  Srihari Cadambi ,  Srimat Chakradhar ,  Rajat Phull

    IPC: G06F3/06

    CPC classification number: G06F3/061 ,  G06F3/0656 ,  G06F3/0673 ,  G06F9/50 ,  G06F12/023

    Abstract: Systems and methods for swapping out and in pinned memory regions between main memory and a separate storage location in a system, including establishing an offload buffer in an interposing library; swapping out pinned memory regions by transferring offload buffer data from a coprocessor memory to a host processor memory, unregistering and unmapping a memory region employed by the offload buffer from the interposing library, wherein the interposing library is pre-loaded on the coprocessor, and collects and stores information employed during the swapping out. The pinned memory regions are swapped in by mapping and re-registering the files to the memory region employed by the offload buffer, and transferring data of the offload buffer data from the host memory back to the re-registered memory region.

    Abstract translation: 用于在主存储器和系统中的单独存储位置之间交换出和被固定的存储器区域的系统和方法，包括在插入库中建立卸载缓冲器; 通过将协处理器存储器中的卸载缓冲器数据传送到主机处理器存储器来交换出固定的存储器区域，从插入库取消注册和解映射卸载缓冲器所使用的存储器区域，其中插入库被预加载到协处理器上，并且收集 并存储在交换期间使用的信息。 通过将文件映射并重新注册到由卸载缓冲器采用的存储器区域并将卸载缓冲器数据的数据从主机存储器传送回重新注册的存储器区域来交换被固定的存储器区域。

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