When a cyclist is cycling on a suburban road, it’s a problem to notice fast rear approaching vehicles in some cases. Looking back frequently is not a good idea. Finding some technical way to help cyclist perceiving rear approaching vehicles is quite necessary. This project aims to find some proper sensor to detect rear approaching vehicles. It’s separated into three steps. First, choose the suitable sensor and capture data. Then, find proper analyzing tool to analyze the capture data. Last but not least, draw a conclusion after analyzing contrast. Microphone is chosen as the sensor to recording the sounds form rear approaching vehicles. ”iRig Recorder FREE” is the program to transfer audio format. And the analyzing tool is to be Matlab. Matlab audio analysis makes good frequency spectrum for each piece of audio data. According to the frequency spectrum, the unique amplitude change around 1000 Hz is found when there is a rear approaching vehicle. This change is always distinct with or without noise. After getting the spectrum of different audio sources, the cross-correlation coefficient between 800 Hz and 1200 Hz is computed to see the correlation level. Then according to cross-correlation coefficient between new captured data and knowledge data, we can determine if there is a rear approaching vehicle in the new data or not. So, this project proves that the cross-correlation coefficient of frequency spectrum can determine if there is rear approaching vehicles or not. The future work would be automatic computer detect depending on this method.

Aeroacoustic emissions were identified as a primary concern in the public acceptance of wind turbines. A review of literature involving sound localization was undertaken and led to the design of two microphone arrays to identify acoustic sources. A small-scale array composed of 27 sensors was produced with the intention of improving the quality of sound measurements over those made by a single microphone in a small, closed-loop wind tunnel. A large-scale array containing 30 microphones was also implemented to allow for measurements of aeroacoustic emissions from airfoils and rotating wind turbines. To minimize cost and pursue alternative sensor technologies, microelectromechanical microphones were selected for the array sensors and assembled into the arrays on printed circuit boards. Characterization of the microphones was completed using a combination of calibration techniques, primarily in a plane wave tube. Array response to known sources was quantified by analyzing source maps with respect to source location accuracy, beamwidth, and root mean square error. Multiple sources and rotating sources were tested to assess array performance. Following validation with known sources, wind tunnel testing of a 600 watt wind turbine was performed at freestream speeds of 2.5 m/s, 3.5 m/s, 4.5 m/s, and to 5.5 m/s. Significant aeroacoustic emissions were noted from the turbine in the 4.5 m/s and 5.5 m/s cases, with an increase of up to 12 dB over background levels. Source maps from the 5.5 m/s tests revealed that the primary location of aeroacoustic emissions was near the outer radii of the rotor, but not at the tip, and generally moved radially outward with increasing frequency. The azimuthal location of the greatest sound pressure levels was typically found to be between 120º and 130º measured counterclockwise from the upward vertical, coinciding with the predicted location of greatest emissions provided by an analytical model based on dipole directivity and convective amplification. Analysis of the acoustic spectra, turbine operating characteristics, and previous literature suggested that the sound emissions emanated from the trailing edge of the blades.

Recent years have brought many new developments in the processing of speech and acoustic signals. Yet, despite this, the process of acquiring signals has gone largely unchanged. Adding spatial diversity to the repertoire of signal acquisition has long been known to offer advantages for processing signals further. The processing capabilities of mobile devices had not previously been able to handle the required computation to handle these previous streams of information. But current processing capabilities are such that the extra workload introduced by the addition of mutiple sensors on a mobile device are not over-burdensome. How these extra data streams can best be handled is still an open question. The present work deals with the examination of one type of spatial processing technique, known as beamforming. A microphone array test platform is constructed and verified through a number of beamforming agorithms. Issues related to speech acquisition through microphones arrays are discussed. The algorithms used for verification are presented in detail and compared to one another.

No description available.

Aeroacoustic emissions were identified as a primary concern in the public acceptance of wind turbines. A review of literature involving sound localization was undertaken and led to the design of two microphone arrays to identify acoustic sources. A small-scale array composed of 27 sensors was produced with the intention of improving the quality of sound measurements over those made by a single microphone in a small, closed-loop wind tunnel. A large-scale array containing 30 microphones was also implemented to allow for measurements of aeroacoustic emissions from airfoils and rotating wind turbines. To minimize cost and pursue alternative sensor technologies, microelectromechanical microphones were selected for the array sensors and assembled into the arrays on printed circuit boards. Characterization of the microphones was completed using a combination of calibration techniques, primarily in a plane wave tube. Array response to known sources was quantified by analyzing source maps with respect to source location accuracy, beamwidth, and root mean square error. Multiple sources and rotating sources were tested to assess array performance. Following validation with known sources, wind tunnel testing of a 600 watt wind turbine was performed at freestream speeds of 2.5 m/s, 3.5 m/s, 4.5 m/s, and to 5.5 m/s. Significant aeroacoustic emissions were noted from the turbine in the 4.5 m/s and 5.5 m/s cases, with an increase of up to 12 dB over background levels. Source maps from the 5.5 m/s tests revealed that the primary location of aeroacoustic emissions was near the outer radii of the rotor, but not at the tip, and generally moved radially outward with increasing frequency. The azimuthal location of the greatest sound pressure levels was typically found to be between 120º and 130º measured counterclockwise from the upward vertical, coinciding with the predicted location of greatest emissions provided by an analytical model based on dipole directivity and convective amplification. Analysis of the acoustic spectra, turbine operating characteristics, and previous literature suggested that the sound emissions emanated from the trailing edge of the blades.

Thesis (Ph. D.)--University of Toronto, 2006. / Source: Dissertation Abstracts International, Volume: 67-06, Section: B, page: 3354. Advisor: Parham Aarabi. Includes bibliographical references.

Thesis (Ph. D.)--State University of New York at Binghamton, Thomas J. Watson School of Engineering and Applied Science, Department of Mechanical Engineering, 2009. / Includes bibliographical references.

Thesis (M. S.)--Mechanical Engineering, Georgia Institute of Technology, 2010. / Committee Chair: Degertekin, F. Levent; Committee Member: Baldwin, Daniel; Committee Member: Hesketh, Peter. Part of the SMARTech Electronic Thesis and Dissertation Collection.

Thesis (M.S.)--University of Alabama at Birmingham, 2006. / Description based on contents viewed Jan. 29, 2007; title from title screen. Includes bibliographical references.

Thesis (Ph. D.)--Ohio State University, 2005. / Title from first page of PDF file. Includes bibliographical references (p. 114-117).