公开(公告)号：US11251881B2

公开(公告)日：2022-02-15

申请号：US16888270

申请日：2020-05-29

Applicant: UNIVERSITY OF UTAH RESEARCH FOUNDATION

Inventor： Thomas Becnel ,  Pierre-Emmanuel Gaillardon ,  Kerry Elizabeth Kelly

IPC: G06F15/16 ,  H04B17/11 ,  H04L67/12 ,  H04L67/00 ,  H04L41/12

Abstract: A computer system for recursive calibration of a sensor network receives a first data communication from a first sensor node that is a neighbor to a calibrated sensor node. The computer system then updates a set of linear regressions between the first sensor node and a set of neighboring sensor nodes, which include the neighboring, calibrated sensor node. The computer system calibrates the first sensor node using an average of the set of linear regressions weighted by a correlation. When the first sensor node is calibrated, the computer system uses the calibrated first sensor node in calibration of a neighboring, uncalibrated sensor node. The computer system then gathers, at the first sensor node, a calibrated sensor reading.

公开(公告)号：US20230244906A1

公开(公告)日：2023-08-03

申请号：US17592230

申请日：2022-02-03

Applicant: University of Utah Research Foundation

Inventor： Thomas Becnel ,  Pierre-Emmanuel Gaillardon

IPC: G06N3/04

CPC classification number: G06N3/0445

Abstract: Techniques for implementing a multi-branch neural network in an edge network are disclosed, where the multi-branch neural network is configured to infer latent features from fused sensor time series exogenous inputs. A multi-branch neural network is configured to include a LSTM branch and two FC branches. The multi-branch neural network is deployed on an edge node, which receives raw input from sensors. The raw input is fed into the LSTM branch and into the second FC branch. The raw input is fed into a normalization block that performs feature-wise normalization to generate normalized input. The normalized input is fed into the first FC block. The multi-branch neural network is used to generate a latent inference based on outputs provided by the LSTM branch and the two FC branches.

公开(公告)号：US20210376937A1

公开(公告)日：2021-12-02

申请号：US16888270

申请日：2020-05-29

Applicant: UNIVERSITY OF UTAH RESEARCH FOUNDATION

Inventor： Thomas Becnel ,  Pierre-Emmanuel Gaillardon ,  Kerry Elizabeth Kelly

IPC: H04B17/11 ,  H04L29/08

Abstract: A computer system for recursive calibration of a sensor network receives a first data communication from a first sensor node that is a neighbor to a calibrated sensor node. The computer system then updates a set of linear regressions between the first sensor node and a set of neighboring sensor nodes, which include the neighboring, calibrated sensor node. The computer system calibrates the first sensor node using an average of the set of linear regressions weighted by a correlation. When the first sensor node is calibrated, the computer system uses the calibrated first sensor node in calibration of a neighboring, uncalibrated sensor node. The computer system then gathers, at the first sensor node, a calibrated sensor reading.