Stevens Institute of Technology

Nikolay Sedunov

Dimitri M Donskoy

Alexander M Sutin

Hady Salloum

Alexander Sedunov

Malcolm Kahn

Michael R Bruno

Edward A Whittaker

Laurent Fillinger

Barry Bunin

Jeremy Turner

Howard Goheen

Mikhail Tsionskiy

An acoustic sensing system and method includes at least one cluster of acoustic sensors in communication with a computing device. The computing device is configured to process received acoustic signals, and provide at least one of detection of the acoustic source presence; determination of direction of arrival of an acoustic wave emitted by an acoustic source; and classification of the acoustic source as to its nature. The cluster may include at least two sensors and the computing device may be configured to process the received acoustic signals and provide localization of the acoustic source in three dimensions. The cluster of acoustic sensors may comprise at least one seismic wave sensor.

Passive acoustic detection, tracking and classification system and method

A portable threat detection apparatus and method is disclosed which may comprise a plurality of acoustic emission sensors arranged in a cluster, forming a polygon defining at least two axes of alignment between respective pairs of acoustic emission sensors; a computing device in cooperation with a non-transitory computer readable storage medium comprising computer readable instructions for performing: receiving a first signal produced by a first acoustic emission sensor within a respective pair of acoustic emission sensors, in response to the first acoustic emission sensor detecting an acoustic emission from a source, and receiving a second signal produced by a second acoustic emission sensor within the respective pair of acoustic emission sensors, in response to the second acoustic emission sensor detecting the acoustic emission; determining a cross-correlation factor between the first signal and the second signal; and, determining a bearing to the source based on the cross correlation factor.

Passive acoustic underwater intruder detection system

An acoustic vector sensor mounted in a housing is provided for measuring ultra low frequency acoustic wave particle velocities in a liquid, comprising a horn for amplifying the acoustic wave particle velocities, and a neutrally buoyant object supported in a liquid contained in the horn. The neutrally buoyant object, in reacting to the amplified acoustic wave particle velocities, produces displacements that are sensitively measured.

Ultra low frequency acoustic vector sensor

A method and apparatus () is provided which employs phase or amplitude modulated electromagnetic probing waves () (in optical or microwave frequency ranges or both) emitted toward a vibrating object (). The optical and/or microwave probing signals () remotely irradiate an object () of interest. The object () reflects and/or scatters the probing wave () toward a receiver (). The reflected/scattered modulated signal () is received with a remote sensor (receiver) (). Vibration causes additional phase modulation to the probing wave (). At the receiving end, the signal is demodulated to extract and analyze the vibration waveform (). The present invention can be utilized for nondestructive testing, monitoring of technological processes, structural integrity, noise and vibration control, mine detection, etc.

Method and apparatus for remote measurement of vibration and properties of objects

A device and method for detecting the presence of insects in a structure utilize a plurality of transceivers, each of which generates separate and distinct microwave signals and receives separate and distinct signals reflected from a structure being tested. The reflected signals received by each of the transceivers are processed, for instance by a microprocessor, so as to provide output signals that indicate the presence or absence of insects in the structure. If most or all of the transceivers receive positive signals indicative of the possible presence of insects in the structure, which is a false indication of the presence of insects, the microprocessor distinguishes this false indication from an actual indication of the presence of insects, thereby enabling the detection of insects despite the existence of motion signals caused by non-insect motion.

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Hoboken, NJ, United States of America

Nikolay Sedunov