Group members Luke Makischuk Abderahmane Sebaa Ameneh Sadat Yazdaninik Asma Faizi Professor: R Habash TA: Wei Yang Introduction The purpose of our project was to implement an efficient voice recognition algorithm and improve upon the idea’s from the IEEE paper:

ByFundamentals of Audio Signals Two signals of different amplitudes A greater amplitude represents a louder sound. Fundamentals of Audio Signals Two signals of different frequencies A greater frequency represents a higher pitched sound. Fundamentals of Audio Signals

ByAudio Compression Usha Sree CMSC 691M 10/12/04 Motivation Efficient Storage Streaming Interactive Multimedia Applications Compression Goals Reduced bandwidth Make decoded signal sound as close as possible to original signal Lowest Implementation Complexity Robust Scalable

ByFrequency Shifting for Patients with High Frequency Hearing Loss Jack Ho, Joseph Yuen, Nate Werbekes, Kuya Takami Advisor: Thomas Yen, Ph. D. Design Criteria. Abstract. find maximum and minimum frequencies patient can hear gets entire range of what normal humans can hear

ByThe Taming of The Shrew: Mitigating Low-Rate TCP-targeted Attack. Chia-Wei Chang, Seungjoon Lee , Bill Lin, Jia Wang. Shrew Attack [Kuzmanovic03]. TCP-targeted low-rate denial-of-service attack Exploits TCP’s retransmission timeout

BySpectrogram & its reading. What is spectrogram?. Begin to be used since 1940s Another representation of frequency domain analysis The most popular way of representing spectral information 3 dimensional representation X-axis: Time Y-axis: Frequency Darkness (or color): Energy .

ByUniform Quantization. It was discussed in the previous lecture that the disadvantage of using uniform quantization is that low amplitude signals are drastically effected. This fact can be observed by considering the simulation results in the next four slides.

BySampling and Reconstruction. The sampling and reconstruction process Real world: continuous Digital world: discrete Basic signal processing Fourier transforms The convolution theorem The sampling theorem Aliasing and antialiasing Uniform supersampling Nonuniform supersampling.

ByLecture 28. Review: Frequency domain circuit analysis Superposition Frequency domain system characterization Frequency Response Related educational materials: Chapter 10.5, 10.6. Note during summary that we will be changing our mindset, rather than doing anything fundamentally new

ByChapter 3 Data and Signals. Copyright © The McGraw-Hill Companies, Inc. Permission required for reproduction or display. Note. To be transmitted, data must be transformed to electromagnetic signals. 3-1 ANALOG AND DIGITAL.

ByCo-registered Vibrometry & Imaging: A Combined Synthetic-Aperture Radar & Fractional-Fourier Transform Approach University of New Mexico FY2008 University Project. May 2009 NCMR Technology Review. PI & Presenter: Majeed Hayat. Project Information.

ByIntroduction to Converter Sampled-Data Modeling . ECEN 5807 Dragan Maksimovi ć. Objectives. Better understanding of converter small-signal dynamics, especially at high frequencies Applications DCM high-frequency modeling Current mode control Digital control. v* ( t ). D/A. A/D.

ByAlong-wind dynamic response . Wind loading and structural response Lecture 12 Dr. J.D. Holmes. Dynamic response. Significant resonant dynamic response can occur under wind actions for structures with n 1 < 1 Hertz (approximate).

ByIntroduction of Fractional Fourier Transform (FRFT). Speaker: Chia-Hao Tsai Research Advisor: Jian - Jiun Ding Digital Image and Signal Processing Lab Graduate Institute of Communication Engineering National Taiwan University. Outlines.

ByNovel Multiple-Antenna Systems. Mati Wax. Topics. Location Fingerprinting Beamforming and SDMA for outdoors WLAN. Location Fingerprinting. What is Location Fingerprinting?. A position location technology for rich multipath environments. The key idea:

ByMOTION ESTIMATION. An Overview BY: ABHISHEK GIROTRA Trainee Design Engineer. In Video Coding for Compression, the basic idea is to exploit redundant data. 2 types of Redundancy in Moving Picture: a) Spatial Redundancy b) Temporal Redundancy Cause for Temporal redundancy:

BySpatial Filtering. Background. Filter term in “ Digital image processing ” is referred to the subimage There are others term to call subimage such as mask, kernel, template, or window The value in a filter subimage are referred as coefficients, rather than pixels.

ByConvolution in Matlab. The convolution in matlab is accomplished by using “conv” command. If “u” is a vector with length ‘n’ and “v” is a vector with length ‘m’, then their convolution will be of length “n+m-1” Convolution is a commutative operation.

ByVibrational Spectroscopy for Pharmaceutical Analysis . Part IV. Fourier Transform Infrared (FT-IR) Spectroscopy Rodolfo J. Romañach, Ph.D. LAB INSTRUMENTS. Spectrum One – Perkin Elmer. Scimitar-Varian . Thermo Nicolet 6700. Tensor 27 – Bruker Optics. ABB - 100. PROCESS INSTRUMENTS.

ByAudio Compression. Usha Sree CMSC 691M 10/12/04. Motivation. Efficient Storage Streaming Interactive Multimedia Applications. Compression Goals. Reduced bandwidth Make decoded signal sound as close as possible to original signal Lowest Implementation Complexity Robust Scalable.

ByView Frequency domain PowerPoint (PPT) presentations online in SlideServe. SlideServe has a very huge collection of Frequency domain PowerPoint presentations. You can view or download Frequency domain presentations for your school assignment or business presentation. Browse for the presentations on every topic that you want.