Fast computation of spectral centroids

Massar, Melody L.; Fickus, Matthew; Bryan, Erik; Petkie, Douglas T.; Terzuoli, A.J.

doi:10.1007/s10444-010-9167-y

Cited by 15 publications

(6 citation statements)

References 13 publications

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“…Only “object recovering” (N6) deserves being considered in detail and therefore in Figure 12 the induced pulse for MRIR1 and MRIR2 is shown in the case of Machine 3.0, along with the proper ELV adjusted by the motion-related frequency [1.43 and 1.14 Hz, respectively, as estimated by the spectral centroid of the corresponding periodogram (Massar et al, 2011 )].…”

Section: Resultsmentioning

confidence: 99%

Experimental and Modeling Analyses of Human Motion Across the Static Magnetic Field of an MRI Scanner

Gurrera

Leardini

Ortolani

et al. 2021

Front. Bioeng. Biotechnol.

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It is established that human movements in the vicinity of a permanent static magnetic field, such as those in magnetic resonance imaging (MRI) scanners induce electric fields in the human body; this raises potential severe risks of health to radiographers and cleaners exposed routinely to these fields in MRI rooms. The relevant directives and parameters, however, are based on theoretical models, and accurate studies on the simulation of the effects based on human movement data obtained in real conditions are still lacking. Two radiographers and one cleaner, familiar with MRI room activities and these directives, were gait analyzed during the execution of routine job motor tasks at different velocities. Full body motion was recorded in a gait laboratory arranged to reproduce the workspace of a room with an MRI full-body scanner. Body segments were tracked with clusters of at least three markers, from which position and velocity of the centroids were calculated. These were used as input in an established computer physical model able to map the stray field in an MRI room. The spatial peak values of the calculated electric field induced by motion of the head and of the entire body during these tasks, for both the health and sensory effects, were found smaller than the thresholds recommended by the European directives, for both 1.5 T and 3.0 T MRI. These tasks therefore seem to guarantee the safety of MRI room operators according to current professional good practice for exposure risks. Physical modeling and experimental measures of human motion can also support occupational medicine.

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Section: Resultsmentioning

confidence: 99%

Experimental and Modeling Analyses of Human Motion Across the Static Magnetic Field of an MRI Scanner

Gurrera

Leardini

Ortolani

et al. 2021

Front. Bioeng. Biotechnol.

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“…The STFT of the baseband signal for trials (1) and (2) are shown in Fig 4. For these types of spectrograms, calculating the spectral centroid of the signal can provide an estimate of the vital signs measurements [12], however, it is clear that the analysis of the displacement calculation is much more beneficial. It should also be noted that for each trial, the only significant Fourier components are at ~20 Hz and lower.…”

Section: Vital Signaturesmentioning

confidence: 99%

Micro-Doppler radar signatures of human activity

et al. 2010

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We will present the continued development of a millimeter-wave/sub-THz radar system used to capture and assess micro-Doppler signatures of humans. This system is being developed to remotely monitor respiration and heartbeat rates at standoff distances of up to 100 meters. We will discuss the latest hardware and software developments and recent studies of the performance of the system under a variety of conditions.

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“…The post-processing techniques that we adopted include the identification of leak frequency ranges. This involves the use of spectral centroids [28] to isolate the frequency ranges of interest. A direct result of identifying the spectral centroids from Sensor 2 is shown in Figure 11.…”

Section: Deployment Of Water Leakage Detection Systemmentioning

confidence: 99%

AIN-Based MEMS (Micro-Electro-Mechanical System) Hydrophone Sensors for IoT Water Leakage Detection System

Phua¹,

Rabeek²,

Han³

et al. 2020

Water

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There is an urgent need for industrial Internet of things (IoT) solutions to deploy a smart hydrophone sensor grid to monitor pipeline health and to provide an accurate prediction in the event of any leakage. One solution is to develop an IoT water leakage detection system consisting of an interface to capture acoustic signals from aluminum nitride (AlN)-based micro-machined infrasonic hydrophone sensors that are fed as inputs and predict an approximate leak location as a form of output. Micro-electro-mechanical systems (MEMS) are particularly useful for IoT applications with low power consumption and small device footprint. Data analytics including characterization, pre/post processing are applied to determine the leaks. In this work, we have developed the process flow and algorithm to detect pipe leakage occurrence and pinpoint the location accurately. Our approach can be implemented to detect leaks for different pipe lengths, diameters and materials.

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Fast computation of spectral centroids

Cited by 15 publications

References 13 publications

Experimental and Modeling Analyses of Human Motion Across the Static Magnetic Field of an MRI Scanner

Experimental and Modeling Analyses of Human Motion Across the Static Magnetic Field of an MRI Scanner

Micro-Doppler radar signatures of human activity

AIN-Based MEMS (Micro-Electro-Mechanical System) Hydrophone Sensors for IoT Water Leakage Detection System

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