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Sound Waves

Sound waves are mechanical vibrations that propagate through a medium, such as air or water, and can be detected by the human ear or specialized equipment.
These waves are characterized by their frequency, wavelength, and amplitude, and play a crucial role in various fields, including acoustics, communication, and medical imaging.
PubCompare.ai can help streamline your sound wave research by providing easy access to the best protocols from the literature, preprints, and patents, enabling you to identify the most effective methods and products for reproducibility and accuracy.
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Most cited protocols related to «Sound Waves»

1
Skull Bone Acoustic Characterization
The goal of this study was to establish functions for the coefficients of attenuation and speed of sound, corresponding to the longitudinal sound propagation in human skull bone. The study was performed for the frequencies of 0.27, 0.836, 1.402, 1.965 and 2.525 MHz. Both the coefficient of attenuation and speed of sound were expressed as the functions of the apparent skull density obtained from CT scans. Experimental measurements were made to obtain the relative absorbed energy by the skull bone, under conditions of normal incidence of ultrasound transmission through a cross-sectional area of small dimensions. The delay caused by the skull bone in the propagation of an acoustic wave was also measured.
A multi-layered scheme of sound transmission for plane waves was used to model the ultrasound propagation through the skull bone. An optimization process was executed to establish a population of best-fitted functions of the attenuation and speed of sound that match the experimental measurements with the propagation model. A robust, but computationally more time-consuming model of sound propagation was used to corroborate the precision of the multi-layer model, where the information of density was specified at the voxel level rather than at the layer level. This robust model, which is based on the Westervelt equation (Hamilton and Blackstock 1998 ), helped to pre-select the best functions of the attenuation and speed of sound.
With this subset of pre-selected functions, the optimization process was executed again, but using the Westervelt equation to calculate the cost function. It was expected that the multi-layered model would be precise enough to provide a ‘first good guess’ and the more precise solution of the Westervelt equation would help to establish a more definitive relationship between the density and the speed of sound and attenuation.
Pichardo S., Sin V.W, & Hynynen K. (2010). Multi-frequency characterization of the speed of sound and attenuation coefficient for longitudinal transmission of freshly excised human skulls. Physics in medicine and biology, 56(1), 219-250.
Publication 2010
Bones Cranium Echoencephalography Homo sapiens Sound Sound Waves Transmission, Communicable Disease Ultrasonics X-Ray Computed Tomography
2
Acoustic Attenuation Measurement of Ultrasound Contrast Agents

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Publication 2013
Acoustics Bath Cell Culture Techniques Chambers, Anterior Face Gravity Marshes Muscle Rigidity polycarbonate polyvinylidene fluoride Pulse Rate Pulses Reading Frames Sound Waves Tissue, Membrane Transducers Ultrasonics
3
Liver Stiffness Measurement by Transient Elastography
Liver stiffness was determined by transient elastography with use of a Fibroscan machine (EchoSens) [9 (link), 11 (link)]. In brief, an ultrasound transducer probe is mounted on the axis of a vibrator; vibrations of mild amplitude and low frequency induce an elastic shear wave that propagates through underlying liver tissue. Pulse-echo ultrasound acquisitions are used to follow propagation of the shear wave and measure its velocity. Results are instantaneously received as a single, quantitative parameter of liver stiffness measurement, reported in kilopascals. All elastography examinations were performed by certified operators (who were trained by the manufacturer) with use of a single device in the research clinic; the methods are described elsewhere [11 (link)]. Examinations with 8 validated measurements and a ≥60% success rate (the number of validated measurements divided by the total number of measurements) were considered to be reliable. During training, examinations were performed sequentially by 2 operators for 47 patients; the median interobserver difference was 0.0 kPa (interquartile range [IQR], −1.45 to 1.25 kPa).
Kirk G.D., Astemborski J., Mehta S.H., Spoler C., Fisher C., Allen D., Higgins Y., Moore R.D., Afdhal N., Torbenson M., Sulkowski M, & Thomas D.L. (2009). Assessment of Liver Fibrosis by Transient Elastography in Persons with Hepatitis C Virus Infection or HIV–Hepatitis C Virus Coinfection. Clinical infectious diseases : an official publication of the Infectious Diseases Society of America, 48(7), 963-972.
Publication 2009
ECHO protocol Elasticity Imaging Techniques Epistropheus Liver Medical Devices Patients Physical Examination Pulse Rate Sound Waves Tissues Transducers Transients Ultrasonics Vibration
4
Acoustic Microtapping OCT Elastography of the Cornea

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Publication 2021
Acoustics Cornea Eye Neoplasm Metastasis Precipitating Factors Sonoelastography Sound Waves Strains Toxic Epidermal Necrolysis Transducers
5
Estimating Probe Source Characteristics
The first step in estimating the Thévenin-equivalent source characteristics of the probe is to measure wideband pressure responses in known acoustic loads. For tubes of known lengths, an ideal expression for cavity impedance is Zc=−iZ0cot(kL), where Z0 is the acoustic impedance of a plane wave propagating in the tube, L is cavity length and k is the wavenumber (Keefe et al., 1992 (link)). The cavity set used for this study consisted of five brass tubes (11/32 in. o.d., 8 mm i.d.) that were 83, 54.3, 40, 25.6 and 18.5 mm in length. Tube lengths were selected so that, with the coupler and probe in place, resonant peaks would occur at approximately 2, 3, 4, 6 and 8 kHz. A wideband chirp stimulus with a sampling rate of 32 kHz was presented to each tube of the cavity set, and the pressure response was measured with the probe microphone. Total averaging time was approximately four seconds per source channel per cavity, resulting in a total of 160 averaged chirps. Ideal cavity impedance (Zc) and measured cavity pressure (Pc) are related to source pressure (Ps) and source impedance (Zs) by the following equation: PsZs+Zc=PcZc. An improved set of cavity lengths and corresponding Thévenin-equivalent source characteristics (Ps and Zs) was obtained by iterative solution of a set of five linear equations that minimized deviation between measured and expected pressure responses based on Eq. 2 (Allen, 1986 ). Cavity calibration to determine Ps and Zs was performed daily. Figure 1 shows the agreement typically observed between empirical and ideal cavity impedances. The separation between empirical and ideal data sets (error) increases with frequency and is the greatest at the pressure nulls, where measured pressure is higher than what would have been ideal. These trends are consistent with the effects of cross-talk described by Siegel (1995) (link) for the ER-10C probe; however, the implications on the calculation of Ps and Zs are small. The error calculation weights the peaks, and, consequently, estimated Thévenin-equivalent characteristics should have little dependence on whether the nulls are inaccurate.
Prior to initiating DPOAE measurements, a preliminary investigation demonstrated that air temperature within the calibration cavities significantly affects the estimated source characteristics (see Appendix A). In light of this result and the absence of a procedure to adjust for temperature, the cavities were warmed to approximate body temperature (95−105° F) during each daily probe calibration.
In-situ calibration was performed on each subject with the same DPOAE probe and chirp stimulus that was used for the source calibration. Load pressure ( Pℓ ) was measured by the probe microphone, and ear-canal load impedance ( Zℓ ) was calculated: Zℓ=ZsPℓPs−Pℓ. Load impedance provides the information necessary to convert load pressure, which is measured as SPL, into the corresponding SIL and FPL.
Scheperle R.A., Neely S.T., Kopun J.G, & Gorga M.P. (2008). Influence of in situ, sound-level calibration on distortion-product otoacoustic emission variability. The Journal of the Acoustical Society of America, 124(1), 288-300.
Publication 2008
Acoustics Body Temperature brass Cross Reactions Dental Caries External Auditory Canals Neoplasm Metastasis Pressure Sound Waves

Most recents protocols related to «Sound Waves»

1
Infrasound Detection using Fiber Flow Sensors
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Example 2

In some applications, an infrasonic sensor is desired, with a frequency response fl that extends to an arbitrarily low frequency, such as a tenth of hundredth of a Hertz. Such a sensor might be useful for detecting fluid flows associated with movement of objects, acoustic impulses, and the like. Such an application works according to the same principles as the sonic sensor applications, though the length of individual runs of fibers might have to be greater.

In addition, the voltage response of the electrode output to movements is proportional to the velocity of the fiber, and therefore one would typically expect that the velocity of movement of fluid particles at infrasonic frequencies would low, leading to low output voltages. However, in some applications, the fluid movement is macroscopic, and therefore velocities may be appreciable. For example, in wake detection applications, the amplitude may be quite robust.

Generally, low frequency sound is detected by sensors which are sensitive to pressure such as infrasound microphones and microbarometers. As pressure is a scaler, multiple sensors should be used to identify the source location. Meanwhile, due to the long wave length of low frequency sound, multiple sensors have to be aligned far away to distinguish the pressure difference so as to identify the source location. As velocity is a vector, sensing sound flow can be beneficial to source localization. There is no available flow sensor that can detect infrasound flow in a broad frequency range with a flat frequency response currently. However, as discussed above, thin fibers can follow the medium (air, water) movement with high velocity transfer ratio (approximate to 1 when the fiber diameter is in the range of nanoscale), from zero Hertz to tens of thousands Hertz. If a fiber is thin enough, it can follow the medium (air, water) movement nearly exactly. This provides an approach to detect low frequency sound flow directly and effectively, with flat frequency response in a broad frequency range. This provides an approach to detect low frequency sound flow directly. The fiber motion due to the medium flow can be transduced by various principles such as electrodynamic sensing of the movement of a conductive fiber within a magnetic field, capacitive sensing, optical sensing and so on. Application example based on electromagnetic transduction is given. It can detect sound flow with flat frequency response in a broad frequency range.

For the infrasound detection, this can be used to detect manmade and natural events such as nuclear explosion, volcanic explosion, severe storm, chemical explosion. For the source localization and identification, the fiber flow sensor can be applied to form a ranging system and noise control to find and identify the low frequency source. For the low frequency flow sensing, this can also be used to detect air flow distribution in buildings and transportations such as airplanes, land vehicles, and seafaring vessels.

The infrasound pressure sensors are sensitive to various environmental parameters such as pressure, temperature, moisture. Limited by the diaphragm of the pressure sensor, there is resonance. The fiber flow sensor avoids the key mentioned disadvantages above. The advantages include, for example: Sensing sound flow has inherent benefit to applications which require direction information, such as source localization. The fiber flow sensor is much cheaper to manufacture than the sound pressure sensor. Mechanically, the fiber can follow the medium movement exactly in a broad frequency range, from infrasound to ultrasound. If the fiber movement is transduced to the electric signal proportionally, for example using electromagnetic transduction, the flow sensor will have a flat frequency response in a broad frequency range. As the flow sensor is not sensitive to the pressure, it has a large dynamic range. As the fiber motion is not sensitive to temperature, the sensor is robust to temperature variation. The fiber flow sensor is not sensitive to moisture. The size of the flow sensor is small (though parallel arrays of fibers may consume volume). The fiber flow sensor can respond to the infrasound instantly.

Note that a flow sensor is, or would be, sensitive to wind. The sensor may also respond to inertial perturbances. For example, the pressure in the space will be responsive to acceleration of the frame. This will cause bulk fluid flows of a compressible fluid (e.g., a gas), resulting in signal output due to motion of the sensor, even without external waves. This can be advantages and disadvantages depends on the detailed applications. For example, it can be used to detect flow distribution in the buildings. If used to detect infrasound, the wind influence be overcome by using an effective wind noise reduction approach.

US11869476B2. Acoustic metamaterial (2024-01-09). The Research Foundation for The State University of New York [US]. Inventors: Steven M. Hoffberg [US].
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Patent 2024
Acceleration Acoustics A Fibers Blast Injuries Blood Vessel Cloning Vectors Dietary Fiber Electric Conductivity Electricity Electromagnetics Fibrosis Magnetic Fields Movement Pressure Reading Frames Sound Sound Waves Toxic Epidermal Necrolysis Ultrasonics Vaginal Diaphragm Vibration Water Movements Wind
2
Acoustic Radiation Force Manipulation
A traveling acoustic wave scatters at the solid-liquid interface, producing a forward time-averaged acoustic radiation force (ARF, FARF ) following the direction of wave propagation. The acoustic pressure can be expressed as:
Where YT is the ARF factor dominated by density, r is the radius of the manipulated particle, and E is the average energy density of propagating wave. The calculation of YT has been illustrated in our previous works [31 (link)]. The results in Fig. 4A indicate that YT of droplets is basically unity after 10 ​μm in dimeters, and about 3 orders of magnitude larger than MCF-7. Thus, the ARF acting on droplets is independent of droplet content.
In experiments, after receiving the activation signal from the impedance analyser, the RF generator gives an RF signal at the resonance frequency of 132 ​MHz (acoustic wavelength of 30 ​μm) with acoustic power about 50 ​mW after RF amplification.
Zhong J., Liang M., Tang Q, & Ai Y. (2023). Selectable encapsulated cell quantity in droplets via label-free electrical screening and impedance-activated sorting. Materials Today Bio, 19, 100594.
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Publication 2023
Acoustics AN 132 Electromagnetic Radiation Pressure Radius Reproduction Sound Waves Vibration
3
Photoacoustic Flow Cytometry for Bacterial Detection
Our photoacoustic system is shown in Figure 2 and consisted of a laser coupled to an optical fiber directed at a flow chamber through which saline suspensions were directed. An acoustic transducer was coupled to detection electronics for data processing and analysis. The system was calibrated using 10 μm dyed polystyrene microspheres (Polybead, Warrington, Pennsylvania) and phosphate buffered saline (Fisher Scientific, Pittsburgh, Pennsylvania) as positive and negative controls. As a control for bacteriophage binding, we used American Type Culture Collection strain 35556 (S. aureus strain SA113, ATCC, Manassas, Virginia). Modified bacteriophage SP1 was used as tags at a ratio of 1,000 bacteriophage per S. aureus cell. Bacteriophage were added to each culture and incubated at room temperature for 10 minutes to ensure phage binding. The combined culture and bacteriophage mixture was passed through the PAFC system at a flow rate of 60 μL/min.
The photoacoustic flow cytometer used a frequency doubled Nd:YAGlaser operating at 532 nmwith a 5 nspulse duration and a 20 Hzpulse repetition rate. These laser parameters are appropriate for inducing acoustic waves in labeled bacteriophage attached to bacterial cells. Laser light was launched into a 1,000 μmoptical fiber with a numerical aperture of 0.22 (Thorlabs, Newton, New Jersey). The optical fiber was directed to a flow chamber made from 3D printed polylactic acid (PLA) filament. The chamber is shown in Figure 3. An immersion acoustic transducer (Olympus, Waltham, Massachusetts) fixed to the flow chamber with a center frequency of 2.25 MHzand a focal length of 0.5 inches was used to sense the generated acoustic waves.
Rather than sending a continuous flow of cell suspension through the flow chamber, we induced two phase flow by introducing an immiscible fluid to the saline suspension. We used mineral oil, thus creating alternating droplets of cell suspension and oil (22 (link), 23 (link)). These alternating droplets created a fluidic conveyor belt that allowed for localized detection of photoacoustic events. This arrangement allowed for microfluidic capture of droplets that generated photoacoustic waves which identified bacterial cells of interest.
The transducer was coupled to a high frequency digitizer and amplifier (National Instruments, Austin, Texas) connected to a desktop computer (Dell, Round Rock, Texas). Photoacoustic waves were identified by a LabVIEW (National Instruments, Austin, Texas) program made for this photoacoustic flow cytometer. Photoacoustic events were classified by a simple threshold of the voltage signal from the transducer. The threshold was set at three times the standard deviation of the noise. Each photoacoustic wave was assumed to be generated from a single bacterial cell, which was reasonable from the dilute concentration of bacterial cells. The bacterial count was recorded for each patient sample which as split into two subsamples, one of which was treated with oxacillin, and one was untreated. These numbers were used for determination of antibiotic resistance.
For quality control, we calibrated the photoacoustic system before each use. We ran a sample of phosphate buffered saline (PBS) as a negative control. In all PBS samples, we detected no photoacoustic events, as expected, as there were no optical absorbers present. For a positive control, we ran a suspension of 1 μmblack latex microspheres to ensure we were successfully detecting photoacoustic events. In all such cases, we showed constant detections, as the microspheres generated photoacoustic waves.
Edgar R.H., Samson A.P., Kowalski R.P., Kellum J.A., Hempel J., Viator J.A, & Jhanji V. (2023). Differentiating methicillin resistant and susceptible Staphylococcus aureus from ocular infections using photoacoustic labeling. Frontiers in Medicine, 10, 1017192.
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Publication 2023
Acoustics Antibiotic Resistance, Microbial austin Bacteria Bacteriophages Cells Counts, Bacterial Cytoskeletal Filaments Fibrosis Latex Light Microspheres Oil, Mineral Oxacillin Phosphates poly(lactic acid) Polystyrenes Preauricular Fistulae, Congenital Saline Solution Sound Waves Staphylococcus aureus Strains Submersion Technique, Dilution Transducers
4
SAW-based Spray-drying Particle Production
Particles are produced using a surface acoustic wave (SAW)-based spray-drying method. The device is composed of a LiNbO3 wafer with a size of 2 × 3 cm2 onto which an Interdigital transducer (IDT) finger cross pattern is sputtered as described previously.30 The wafer is connected to a PowerSAW Generator (Belektronig GmbH) via a Printed Circuit Board (PCB). The solution is delivered onto the chip through a poly(dimethylsiloxane) (PDMS) (Sylgard 184, Dow Corning) based microfluidic channel that is bound to the piezoelectric wafer using oxygen plasma. The solution is injected into the PDMS-based channel with a width and height of 100 μm using a volume-controlled syringe pump (Cronus Sigma 1000, Labhut) at a flow rate of 1.5 ml h−1. The powerSAW Generator is operated at 3.3 W. The dried particles are collected on one side polished silicon wafers for further characterization.
Okur A.C., Erni P., Ouali L., Benczedi D, & Amstad E. (2023). Controlling the crystal structure of succinic acid via microfluidic spray-drying. RSC Advances, 13(12), 7731-7737.
Publication 2023
DNA Chips Fingers lithium niobate Medical Devices Oxygen Plasma polydimethylsiloxane Silicon Sound Waves Syringes Transducers
5
Crowdsourced Noise Monitoring in Guayaquil
This study began as part of an Escuela Superior Politécnica del Litoral (ESPOL) community service project. As a result, the measurement period corresponds to part of the academic cycles, from June to August, in the years 2020 and 2021. A total of 95 students contributed actively to the data collection, supervised by 12 volunteer teachers from ESPOL. Both groups resided in the city of Guayaquil during the development of the project.
In 2020, 70 students participated, while in 2021, only 25 students did. The difference in the number of participants is due to the restrictions implemented during the COVID-19 pandemic in 2020. These restrictions resulted in students having fewer places to choose from when participating in internships. As a result, a greater number of students joined in 2020.
The project implemented a crowdsourcing methodology, carrying out measurements collaboratively to study noise levels in the city. The sound pressure was recorded using the Sound Meter (Smart Tools Co., 2020 ) and Decibel X: dB Sound Level Meter applications (SkyPaw, 2019 ), for smartphones with Android and iOS operating systems, respectively. For an adequate noise characterization, measurements were made daily in 4 different time frames: 9:00 a.m., 1:00 p.m., 4:00 p.m., and 8:00 p.m.
Prior to measuring the sound pressure waves, the students needed to stabilize the noise measurement application. To achieve this, the App had to be left operating for approximately 5 min without registering the measurement (OSHA, 2015 ). Once the stabilization was achieved, the participants completed the measurements in 2 min from their respective homes, ensuring that the noise came from sources outside the home. In addition, mobile applications made it possible to record the maximum, minimum and average levels through the sound pressure measurement period.
Subsequently, the ArcGIS Survey123 application (Esri, 2022 ) was used to record the noise data and automatically obtain the geolocation of measurements. Additionally, the participants specified the date and time of each measurement, the noise levels, the predominant emitting source, and any important observations during the measurement.
Velastegui-Montoya A., Guerrero-Cabrera G., Gonzalez-Camba S., Jaramillo-Lindao Y., Murillo-Portillo R., Hidalgo-Crespo J, & Encalada-Abarca L. (2023). Acoustic contamination assessment during the transition between the COVID-19 restrictions and reactivation: A exploratory analysis in Guayaquil. Geography and Sustainability, 4(2), 138-149.
Publication 2023
COVID 19 Medical Internship Pressure Reading Frames Sound Sound Waves Student Ultrasonography Voluntary Workers

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More about "Sound Waves"

Sound waves, also known as acoustic waves or pressure waves, are mechanical vibrations that propagate through a medium, such as air, water, or solid materials.
These oscillations are characterized by their frequency, wavelength, and amplitude, and play a crucial role in numerous fields, including acoustics, communication, and medical imaging.
In the world of scientific research and instrumentation, sound waves are studied and utilized in various ways.
COMSOL Multiphysics, a powerful simulation software, enables the modeling and analysis of sound wave propagation, acoustic-structure interaction, and related phenomena.
The latest version, COMSOL Multiphysics 5.5, offers advanced capabilities for simulating complex sound wave dynamics.
Closely tied to sound wave research is the use of specialized equipment, such as the PCM-D50 and PCM-D100 digital sound recorders, which capture high-quality audio data for analysis.
Similarly, the FibroScan 502 Touch and Sahara Clinical Bone Sonometer utilize ultrasound technology to assess the health of tissues and bones, respectively.
In the field of signal processing, MATLAB, a widely used programming environment, provides robust tools for the analysis and manipulation of sound wave data, enabling researchers to extract valuable insights and develop innovative applications.
When it comes to optimizing sound wave research protocols, the AI-driven platform PubCompare.ai can be a valuable resource.
By providing easy access to the best protocols from the literature, preprints, and patents, PubCompare.ai helps researchers identify the most effective methods and products, ensuring reproducibility and accuracy in their studies.
Whether you're investigating the propagation of sound waves, developing new acoustic-based technologies, or exploring medical applications, understanding the fundamental principles and latest advancements in this field can lead to groundbreaking discoveries and breakthroughs.
Embrace the power of sound waves and let your research soar to new heights.