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    System and method of modeling visual perception V1 area
    3.
    发明授权
    System and method of modeling visual perception V1 area 有权

    公开(公告)号：US11023808B1

    公开(公告)日：2021-06-01

    申请号：US16663195

    申请日：2019-10-24

    Applicant: HRL LABORATORIES, LLC

    Inventor： Qin Jiang ,  Narayan Srinivasa

    IPC: G06N3/08 ,  G06K9/62 ,  G06N3/063 ,  G06N3/04

    Abstract: A system to detect a feature in an input image comprising a processor to evaluate a model including: four layers including: a supragranular layer, a granular layer, a first infragranular layer, and a second infragranular layer, each of the layers including a base connection structure including: an excitatory layer including a excitatory neurons arranged in a two dimensional grid; and an inhibitory layer including a inhibitory neurons arranged in a two dimensional grid; within-layer connections between the neurons of each layer in accordance with a Gaussian distribution; between-layer connections between the neurons of different layers, the probability of a neuron of a first layer of the different layers to a neuron of a second layer of the different layers in accordance with a uniform distribution; and input connections from lateral geniculate nucleus (LGN) neurons of an input LGN layer to the granular layer in accordance with a uniform distribution.

    Methods for on-line, high-accuracy estimation of battery state of power
    5.
    发明授权
    Methods for on-line, high-accuracy estimation of battery state of power 有权

    公开(公告)号：US09989595B1

    公开(公告)日：2018-06-05

    申请号：US14586828

    申请日：2014-12-30

    Applicant: HRL Laboratories, LLC

    Inventor： Shuoqin Wang ,  Luan D. Vu ,  Qin Jiang

    IPC: G01R31/36

    CPC classification number: G01R31/3648 ,  G01R31/3606

    Abstract: Some variations provide a method for real-time estimation of state of charge and state of power of a battery, comprising: (a) cycling a battery with a driving profile; (b) utilizing a recursive algorithm that relates battery terminal voltage to battery current, wherein the algorithm includes open-circuit voltage and a finite-impulse-response filter to dynamically model kinetic voltage; measuring the battery terminal voltage and the battery current at least at a first time and a second time during cycling; calculating battery open-circuit voltage and finite-impulse-response filter parameters; calculating battery state of charge based on the open-circuit voltage; and calculating battery state of power based on the open-circuit voltage and the finite-impulse-response filter parameters. An extended Kalman filtering technique is incorporated for real-time updating of FIR model parameters. Only a single FIR filter is necessary, making these methods applicable for battery-powered systems with limited computing and storage capabilities.

    Method of modeling functions of orientation and adaptation on visual cortex
    6.
    发明授权
    Method of modeling functions of orientation and adaptation on visual cortex 有权

    公开(公告)号：US11289175B1

    公开(公告)日：2022-03-29

    申请号：US13691130

    申请日：2012-11-30

    Applicant: HRL LABORATORIES LLC

    Inventor： Narayan Srinivasa ,  Qin Jiang

    IPC: G16B5/00

    Abstract: A method is disclosed. The method models a plurality of visual cortex neurons, models one or more connections between at least two visual cortex neurons in the plurality of visual cortex neurons, assigns synaptic weight value to at least one of the one or more connections, simulates application of one or more electrical signals to at least one visual cortex neuron in the plurality of visual cortex neurons, adjusts the synaptic weight value assigned to at least one of the one or more connection based on the one or more electrical signals, and generates an orientation map of the plurality of visual cortex neurons based on the adjusted synaptic weight values.

    Method of malicious social activity prediction using spatial-temporal social network data
    7.
    发明授权
    Method of malicious social activity prediction using spatial-temporal social network data 有权

    公开(公告)号：US11195107B1

    公开(公告)日：2021-12-07

    申请号：US16705219

    申请日：2019-12-05

    Applicant: HRL Laboratories, LLC

    Inventor： Qin Jiang ,  Kang-Yu Ni ,  Tsai-Ching Lu

    IPC: G06N5/04 ,  G06N5/02 ,  H04L29/08 ,  G06Q50/26 ,  G06Q50/00

    Abstract: Described is a system for predicting future social activity. The system extracts social activities from spatial-temporal social network data collected in a first time period ranging from hours to days to capture spatial structures of social activities in a graph network representation. A graph matching technique is applied over a set of spatial-temporal social network data collected in a second time period ranging from weeks to months to capture temporal structures of the social activities. A spatial-temporal structure of each social activity is represented as an activity core, where each activity core is defined as active nodes that participate in the social activity with a frequency over a predetermined threshold over the second time period. For each activity core, the system computes statistics of the social activity and uses the statistics to generate a prediction of future behaviors of the social activity.

    METHOD OF REAL TIME VEHICLE RECOGNITION WITH NEUROMORPHIC COMPUTING NETWORK FOR AUTONOMOUS DRIVING
    10.
    发明申请
    METHOD OF REAL TIME VEHICLE RECOGNITION WITH NEUROMORPHIC COMPUTING NETWORK FOR AUTONOMOUS DRIVING 审中-公开

    公开(公告)号：US20200026287A1

    公开(公告)日：2020-01-23

    申请号：US16519814

    申请日：2019-07-23

    Applicant: HRL Laboratories, LLC

    Inventor： Qin Jiang ,  Youngkwan Cho ,  Nigel D. Stepp ,  Steven W. Skorheim ,  Vincent De Sapio ,  Praveen K. Pilly ,  Ruggero Scorcioni

    IPC: G05D1/00 ,  G05B13/02 ,  G05D1/02

    Abstract: Described is a system for online vehicle recognition in an autonomous driving environment. Using a learning network comprising an unsupervised learning component and a supervised learning component, images of moving vehicles extracted from videos captured in the autonomous driving environment are learned and classified. Vehicle feature data is extracted from input moving vehicle images. The extracted vehicle feature data is clustered into different vehicle classes using the unsupervised learning component. Vehicle class labels for the different vehicle classes are generated using the supervised learning component. Based on a vehicle class label for a moving vehicle in the autonomous driving environment, the system selects an action to be performed by the autonomous vehicle, and causes the selected action to be performed by the autonomous vehicle in the autonomous driving environment.

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