Bioinformatics

Information Theory • Introduction: What is information? • Statistical measures of information • Entropy • Conditional entropy • Discrete memoryless channel capacity • Binary noise-free channel • Binary noisy channel • Binary symmetric channel • Discrete channel capacity • Source coding: Shannon–Fano and Huffman coding • Channel coding: linear codes and block codes Signal Processing • Properties of signals and noise • Physically realizable signals • Time average operator • DC value • Power and decibels • Fourier transform and spectra • Properties of the Fourier transform • Parseval’s theorem and energy spectral density • Dirac delta function and unit step function • Rectangular and triangular pulses • Convolution • Band-limited signals and noise • Band-limited waveforms • Sampling theorem • Impulse sampling • Discrete Fourier Transform (DFT)

A basic understanding of mathematics and physics

Lecture notes prepared by the instructors as part of the monograph "Wireless Access in Communication Networks". Scientific articles for further study selected by the instructors.

Class attendance is optional, but highly recommended.

The assessment will be based on an oral examination of the knowledge of the course topics.

D. Anastassiou, "Genomic signal processing," in IEEE Signal Processing Magazine, vol. 18, no. 4, pp. 8-20, July 2001, doi: 10.1109/79.939833. Shmulevich I, Dougherty E. Genomic Signal Processing. Princeton, NJ: Princeton University Press; 2007. Genomic Signal Processing IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 54, NO. 6, JUNE 2006 Dougherty ER, Astola J, Chen J, Goutsias J, Shmulevich editors

The course includes traditional lectures that present the topics covered, combining an in-depth treatment of analytical aspects with examples of how the studied techniques and protocols are applied in existing and emerging systems.