# US 11,909,434 B2 · Acoustic sensing nodes and related systems and methods

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- Number: US 11,909,434 B2
- Document ID: US11909434B2
- Status: Granted
- Kind: B2
- Application number: 18/076,965
- Filed: 2022-12-07
- Published: 2023-11-09
- Granted: 2024-02-20
- Earliest claimed: 2019-09-27
- Assignees: Case Western Reserve University; Virtual Em Inc.
- Applicants: Case Western Reserve University
- Inventors: Xinyao Tang; Soumyajit Mandal; Joel B. Harley; Tayfun Ozdemir
- Web page: https://patentgrep.com/patent/US11909434B2

## CPC

- G01M 5/0033: {by determining damage, crack or wear}
- H04B 1/40: Circuits
- G01M 5/0066: {by exciting or detecting vibration or acceleration (vibration testing of structures G01M7/00)}
- G01M: TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- H04B: TRANSMISSION

## Abstract

In an example, a system includes a plurality of acoustically coupled nodes. Each of the nodes includes a transducer, a communication circuit and a controller. The transducer is adapted to be mechanically coupled to a medium. The communication circuit is coupled to the transducer to send and receive acoustic signals via the medium according to at least one communication parameter. The controller is to adaptively configure the at least one communication parameter of the communication circuit based on an acoustic signal received from at least one other of the nodes.

## Claims

### Claim 1

A method comprising:
receiving, at a given wireless sensor node, at least one acoustic signal transmitted by at least one other wireless sensor node, each of the wireless sensor nodes being acoustically coupled via a medium to form a wireless sensor network;
converting, at the given wireless sensor node, the received at least one acoustic signal to a corresponding acoustic data representing the at least one acoustic signal;
using machine learning to analyze the corresponding acoustic data and determine classification results that classify at least one spatial region as containing and abnormality or other unexpected condition; and
localizing abnormality or other unexpected condition based on the classification results.