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Chad, Jamal, Ryan. Chad Ostrowski Coursework/Interests Artificial Neural Networks & Learning Machines Analytical Modeling Usability Electrical Engineering.

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Presentation on theme: "Chad, Jamal, Ryan. Chad Ostrowski Coursework/Interests Artificial Neural Networks & Learning Machines Analytical Modeling Usability Electrical Engineering."— Presentation transcript:

1 Chad, Jamal, Ryan

2 Chad Ostrowski Coursework/Interests Artificial Neural Networks & Learning Machines Analytical Modeling Usability Electrical Engineering & Engineering Science

3 Jamal Spearmon Coursework/Interests Satellite Comm. Systems RF Engineering Comm. Networks Electrical Engineering

4 Ryan Clothier Coursework/Interests Industry Quality Engineering Product Realization MOSFET Semiconductor Fabrication Lab Optics Electrical Engineering

5 Ultrasonic Directional Speakers

6 Problems Finding Transducers that operate in desired range Finding components that sample at the rate needed Generating the desired output Parts are cost prohibitive

7 Technical Update Conceptual Verification from Product Realization Use of Tweeters as opposed to Transducers Eliminated V/F ~ F/V block in design Stereo Signal Output produced by Matlab

8 Tech Specs -Tweeters- 70 [W] RMS per channel 8 Ω ± 0.1% Signal to Noise Ratio – 99 dB Overall Frequency Response – 25 Hz to 40 KHz Input signal from soundcard mV

9 We Can Hear Ultrasound Beat Frequencies!

10 Matlab function this=noise(SecsOfPlay,FreqOfPlayLeft,FreqOfPlayRight) if nargin==2 FreqOfPlayRight=FreqOfPlayLeft; end Fs=FreqOfPlayLeft/.05; bits=SecsOfPlay*Fs; NumWavesLeft=.05*bits; NumWavesRight=FreqOfPlayRight*SecsOfPlay; t=linspace(0,bits,bits); y=zeros(bits,2); y(:,1)=sin(2*pi*NumWavesLeft*t); y(:,2)=sin(2*pi*NumWavesRight*t); sound(y,Fs)

11 Right Hz

12 Left Hz

13 Summary High directivity of sound Frequency shifted signal to hear beat freq Offering privacy in a congested world


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