MEMS ACCELEROMETER CALIBRATION AT LOW FREQUENCIES

2008 ◽  
Author(s):  
F. LO CASTRO ◽  
G. BRAMBILLA ◽  
P. VERARDI ◽  
A. D'AMICO
Keyword(s):  
Low Frequencies ◽  
Mems Accelerometer

scholarly journals Shake, Rattle, and Roll

2001 ◽  
Vol 123 (11) ◽  
pp. 56-58
Author(s):  
John DeGaspari
Keyword(s):  
High Frequency ◽  
Low Frequency ◽  
High Frequency Data ◽  
Frequency Data ◽  
Low Frequencies ◽  
Mems Accelerometer ◽  
Running Shoes ◽  
Low Costs ◽  
New Applications ◽  
Thin Reservoir

This article highlights that airbag has been a boon to MEMS business. Sales of the tiny accelerometers that sense when the bags should deploy have helped to drive down prices significantly since the devices were first implemented. Now, the high volumes, low costs, and dependable performance of micro devices are opening the way to new applications. Sneaker companies are looking at MEMS accelerometers in running shoes to act as speedometers of sorts. Advantage of the MEMS accelerometer is that it has a wide bandwidth, capable of reading high as well as low frequencies. High-frequency data provides information about thin reservoir zones, faults, and changes that are taking place as fluids are being drained from pores in the rock, said Denver. Higher frequency signals are critical to accurate interpretation. Low-frequency signals are useful in identifying the type of rock, be it sandstone, shale, or carbonate, for example. The VectorSeis is as rugged as a conventional geophone and can be successfully deployed in down-hole environments to get a closer reading of a reservoir.

The S-SH Confusion Test and the Effects of Frequency Lowering

2019 ◽  
Vol 62 (5) ◽  
pp. 1486-1505
Author(s):  
Joshua M. Alexander
Keyword(s):  
Cognitive Processing ◽  
Hearing Aids ◽  
Response Times ◽  
Nonlinear Frequency ◽  
Low Frequencies ◽  
Frequency Compression ◽  
Negative Side ◽  
Minimal Word ◽  
Low Pass ◽  
Negative Side Effects

PurposeFrequency lowering in hearing aids can cause listeners to perceive [s] as [ʃ]. The S-SH Confusion Test, which consists of 66 minimal word pairs spoken by 6 female talkers, was designed to help clinicians and researchers document these negative side effects. This study's purpose was to use this new test to evaluate the hypothesis that these confusions will increase to the extent that low frequencies are altered.MethodTwenty-one listeners with normal hearing were each tested on 7 conditions. Three were control conditions that were low-pass filtered at 3.3, 5.0, and 9.1 kHz. Four conditions were processed with nonlinear frequency compression (NFC): 2 had a 3.3-kHz maximum audible output frequency (MAOF), with a start frequency (SF) of 1.6 or 2.2 kHz; 2 had a 5.0-kHz MAOF, with an SF of 1.6 or 4.0 kHz. Listeners' responses were analyzed using concepts from signal detection theory. Response times were also collected as a measure of cognitive processing.ResultsOverall, [s] for [ʃ] confusions were minimal. As predicted, [ʃ] for [s] confusions increased for NFC conditions with a lower versus higher MAOF and with a lower versus higher SF. Response times for trials with correct [s] responses were shortest for the 9.1-kHz control and increased for the 5.0- and 3.3-kHz controls. NFC response times were also significantly longer as MAOF and SF decreased. The NFC condition with the highest MAOF and SF had statistically shorter response times than its control condition, indicating that, under some circumstances, NFC may ease cognitive processing.ConclusionsLarge differences in the S-SH Confusion Test across frequency-lowering conditions show that it can be used to document a major negative side effect associated with frequency lowering. Smaller but significant differences in response times for correct [s] trials indicate that NFC can help or hinder cognitive processing, depending on its settings.

Prediction of Atmospheric Turbulence Characteristics for Surface Boundary Layer using Empirical Spectral Methods

2020 ◽  
Author(s):  
Yagya Dutta Dwivedi ◽  
Vasishta Bhargava Nukala ◽  
Satya Prasad Maddula ◽  
Kiran Nair
Keyword(s):  
Boundary Layer ◽  
Time Series ◽  
Surface Roughness ◽  
Wind Speed ◽  
Atmospheric Turbulence ◽  
Surface Boundary ◽  
Low Frequencies ◽  
Surface Boundary Layer ◽  
Power Spectral ◽  
Mean Wind Speed

Abstract Atmospheric turbulence is an unsteady phenomenon found in nature and plays significance role in predicting natural events and life prediction of structures. In this work, turbulence in surface boundary layer has been studied through empirical methods. Computer simulation of Von Karman, Kaimal methods were evaluated for different surface roughness and for low (1%), medium (10%) and high (50%) turbulence intensities. Instantaneous values of one minute time series for longitudinal turbulent wind at mean wind speed of 12 m/s using both spectra showed strong correlation in validation trends. Influence of integral length scales on turbulence kinetic energy production at different heights is illustrated. Time series for mean wind speed of 12 m/s with surface roughness value of 0.05 m have shown that variance for longitudinal, lateral and vertical velocity components were different and found to be anisotropic. Wind speed power spectral density from Davenport and Simiu profiles have also been calculated at surface roughness of 0.05 m and compared with k−1 and k−3 slopes for Kolmogorov k−5/3 law in inertial sub-range and k−7 in viscous dissipation range. At high frequencies, logarithmic slope of Kolmogorov −5/3rd law agreed well with Davenport, Harris, Simiu and Solari spectra than at low frequencies.

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