'Linear space' diaporamas de présentation

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Digital Image Processing

Digital Image Processing. STMIK MDP. Image Distortion Model. Restoration depends on distortion Common model: convolve plus noise Special case: noise alone (no convolution). Thoughts on Restoration. Enhancement vs restoration Enhancement: cosmetic Restoration: substantive

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Heuristic alignment algorithms; Cost matrices

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Math 307 Spring, 2003 Hentzel Time: 1:10-2:00 MWF Room: 1324 Howe Hall

Math 307 Spring, 2003 Hentzel Time: 1:10-2:00 MWF Room: 1324 Howe Hall Instructor: Irvin Roy Hentzel Office 432 Carver Phone 515-294-8141 E-mail: hentzel@iastate.edu http://www.math.iastate.edu/hentzel/class.307.ICN Text: Linear Algebra With Applications,

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Sequence Alignment

Sequence Alignment. Lecture 2, Thursday April 3, 2003. Review of Last Lecture. Lecture 2, Thursday April 3, 2003. Sequence conservation implies function. Interleukin region in human and mouse. Sequence Alignment. AGGCTATCACCTGACCTCCAGGCCGATGCCC TAGCTATCACGACCGCGGTCGATTTGCCCGAC.

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L3: Blast: Keyword match basics

L3: Blast: Keyword match basics. Silly Quiz. TRUE or FALSE: In New York City at any moment, there are 2 people (not bald) with exactly the same number of hairs!. Assignment 1 is online. Due 10/6 (Thursday) in class. An O(nm) algorithm for score computation. For i = 1 to n

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Sequence Alignment

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Linear-Space Alignment

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Linear-Space Alignment

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Linear-Space Alignment

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The Complexity of Tree Transducer Output Languages

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Presented by Daniel Khashabi

Pattern-independent Demosaicing ;. Presented by Daniel Khashabi Joint work with Sebastian Nowozin, Jeremy Jancsary, Andrew W. Fitzgibbon and Bruce Lindbloom. Outline. Demosaicing problem Creating input-output pairs Our demosaicing model Experiments. Raw Spcae.

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Sequence Alignment

Sequence Alignment. Tanya Berger-Wolf CS502: Algorithms in Computational Biology January 25, 2011. Comparing Genes in Two Genomes. Small islands of similarity corresponding to similarities between exons Such comparisons are quite common in biology research. String Similarity.

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I/O-Efficient Structures for Orthogonal Range Max and Stabbing Max Queries

I/O-Efficient Structures for Orthogonal Range Max and Stabbing Max Queries. Second Year Project Presentation Ke Yi Advisor: Lars Arge Committee: Pankaj K. Agarwal and Jun Yang. Problem Definition: Range Max Queries. Range-aggregate queries : range-count, range-sum, range-max

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A Linear Space Algorithm for Computing Maximal Common Subsequences

A Linear Space Algorithm for Computing Maximal Common Subsequences. Author : D.S. Hirschberg Publisher: Communications of the ACM 1975 Presenter: Han-Chen Chen Date: 2010/04/07. Outline. Introduction Algorithm A Algorithm B Algorithm C. Introduction.

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Space Efficient Alignment Algorithms

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Process_Memory_Allocation

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