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Youssef Ibrahim Ph.D. StudentPhone Number: 519-243-3000 extension 3393 E-mail: ibrahi9@uwindsor.ca |
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Education
Thesis: Ultra Low-Noise Arithmetic using Cellular Neural Networks In this work we develop circuit architectures for mixed-signal applications where the presence of digital switching noise is a major problem; for example, digital circuitry adjacent to sensitive biosensors. We describe methods for building ultra low-noise binary arithmetic circuits using analog cellular neural networks (CNNs), essentially implementing asynchronous digital circuits but replacing uncontrolled digital switching transitions with smooth analog transitions. Each node in our asynchronous architectures uses controlled current sources driving into capacitors; providing both low current and voltage time derivatives (di/dt and dv/dt) and, as a result, reducing both instantaneous and average system and cross-talk noise. Supervisors: Dr. G.A.Jullien, Dr. W.C. Miller |
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| Publications: | ||||||||||
| [1] J.J. Yeboah Jr., Y. Ibrahim, G.A. Jullien, and J.W. Haslett, "Ultra low noise arithmetic using cellular neural networks," Micronet Workshop 2004, April 26 & 27, 2004, Aylmer, Quebec | ||||||||||
| [2] Y. Ibrahim, A. Darwish, and S. Shaheen, "Energy Matching Based on Deformable Templates," The IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP-99, USA | ||||||||||
Awards:
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© 2004 University of Windsor