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Utah State - BIOL - 3300
20 Common Amino Acidsand their properties
Utah State - BIOL - 3300
GENERAL MICROBIOLOGY (BIOL 3300)Fall Semester 2008Reading Assignments for: Microbiology 6th Ed., by Prescott, Harley & Klein (ISBN 0-07-255678-1)Tentative ScheduleDate Aug. 25 27 Sep. 1 3 8 10 15 17 22 24 29 Oct. 1 6 8 13 15 20 22 27 29 Nov. 3 5
Utah State - BIOL - 3300
BIOL 3300 Objectives (cont.)8Topic 6. Microbial Genetics Be able to: 1. Describe the processes of conjugation, transduction, and transformation. 2. Distinguish between generalized and specialized transduction. 3. Describe the factors that determi
Utah State - BIOL - 3300
BIOL 3300 Objectives (cont.)5Topic 4. Microbial Growth Be able to: 1. Describe three major steps in the process of binary fission. 2. Describe the functions of topoisomerase, helicase, primase, DNA polymerase III, DNA polymerase I, DNA binding pr
Utah State - BIOL - 3300
BIOL 3300 Objectives (cont.)7Topic 5. Viruses Be able to: 1. Describe the basic structures of naked and enveloped viruses. 2. Describe the different types of genetic material that may be used by viruses. 3. Name a virus that has a segmented genom
Utah State - BIOL - 3300
BIOL 3300 Objectives (cont.)10Topic 7. Medical Microbiology Be able to: 1. Distinguish between epidemiology and pathology. 2. Explain why morbidity data may be more useful than mortality data. 3. Describe three general approaches to controlling t
Utah State - BIOL - 3300
BIOL 3300 Objectives (cont.)6Topic 4. Microbial Growth (continued) 11. Provide an equation that describes the effect of substrate concentration on the growth rate constant. Draw a graph showing how the Monod relationship would differ between copi
Utah State - CS - 6890
Gene Finding With A H idden M arkov model Of Genomic Structure and Evolution.Jakob Skou Pedersen and Jotun Hein Deepak Verghese CS 6890GPHMM CONSERVED Exon method 2 step GLASS n ROSETTA TWINSCAN which extends GENESCAN etc Do not exploit all info
Utah State - CS - 5890
Meta-Analysis Combines Affymetrix Microarray Results Across LaboratoriesJohn R. Stevens1 & R.W. Doerge1,2 1 Department of Statistics, 2 Department of Agronomy Purdue University, West Lafayette, IN 47907Email: jrsteven@stat.purdue.edu, doerge@purdue
Utah State - REU - 06
LEARNING FROM RELEVANCE FEEDBACK SESSIONS USING A SEMANTIC SPACE Matthew Royal1 and Dr. Xiaojun Qi2 matthew.royal@gmail.com Pensacola Christian College, 250 Brent Lane, Pensacola, FL 32503 2 xqi@cc.usu.edu Computer Science Department, Utah State Univ
Utah State - REU - 06
IMAGE COMPRESSION USING GENE EXPRESSION PROGRAMMING Robert Gempeler rrgemp@cc.usu.eduABSTRACT Gene expression programming (GEP) has a variety of applications for solving problems. The purpose of our study was to test whether it was possible and effi
Utah State - REU - 06
Gene Expression Programming and Image CompressionSamuel Ashworth and Robert Gempeler REU CVIPPR Program Summer 2006 Utah State UniversityOutlineIntroduction Methods Implementation Results Future Work & DiscussionGene Expression Programming
Utah State - REU - 06
SUPERVISED HEAT KERNEL LPP METHOD FOR FACE RECOGNITION Crystal Whittier and Xiaojun Qi Computer Science Department, Utah State University, Logan UT, 84321-4205 cryshan@cc.usu.edu and xqi@cc.usu.eduABSTRACT This paper details the advantages of using
Utah State - REU - 06
ADAPTIVE COMPOSITE APPROACH FOR BLIND DIGITAL IMAGE WATERMARKING Elliot First Ripon College Mathematics and Computer Science Department Ripon, WI 54971ABSTRACT This paper presents an adaptive, blind digital watermarking technique, called CompMark, w
Utah State - REU - 06
Content-Based Watermarking Robust to Both Affine and JPEG DistortionsChad Coats and Qiaojun Qi Utah State UniversityWatermarking TermsDistortion/Attack- -Operation performed on image that may make the presence of a watermark undetectable Ma
Utah State - REU - 06
Learning From Relevance Feedback Sessions Using a Semantic SpaceMatthew Royal Pensacola Christian CollegeEHD Edge Histogram Descriptor8x8 superpixels4x4 BlocksEHD Edge Histogram Descriptor Filtersverticalhorizontalnon-directional45
Utah State - REU - 06
Gene Expression Programming and Image CompressionSamuel Ashworth and Robert Gempeler REU CVIPPR Program Summer 2006 Utah State UniversityOutlineIntroduction Methods Implementation Results Future Work & DiscussionGene Expression Programming
Utah State - REU - 06
Content-Based Image RetrievalKonstantin Shkurko Cornell U.IntroductionPrevious Systems started it all matched colors and textures worked only ok failfor large databasesRelevance Feedback saves the day! new approach able to learn wh
Utah State - REU - 06
GENE EXPRESSION PROGRAMMING APPLIED TO IMAGE COMPRESSION Samuel Ashworth 426 S 1000 E Apt 508 Salt Lake City, UT 84102ABSTRACT We here describe an image compression algorithm that generates a set of mathematical functions capable of approximately r
Utah State - REU - 06
Introduction Basic (Low-Level) DIP - Part IImage Enhancement in the Spatial Domain Xiaojun Qi Objective: To process an image so that the result is more suitable than the original image for a specific application. Problem Oriented To suppress undes
Utah State - REU - 06
Step 1: Face Detection Different Color Spaces RGB YCbCr HSI UCS (Perceptually uniform color system)Face RecognitionXiaojun Qi Histogram-based Approaches Skin color distribution model Hair color distribution model1 Morphological Operat
Utah State - REU - 06
REU Site Program in CVIP Summer 2006 Lab 5 Exercises on Transformation, EA, and NN Lab Exercises For Monday 6/5 1.) Pick any of the Matlab demo .tif images and display it on the screen 2.) Compute the 2D fft of the image, center it, and display its
Utah State - REU - 06
Information Hiding Techniques Information Hiding: Digital Watermarking and SteganographyXiaojun Qi12CryptographySteganography The idea of communicating secretly is as old as communication itself. Steganography, derived from the Greek langu
Utah State - REU - 06
Content-Based Image Retrieval (CBIR) and Annotation System The driving forcesContent-Based Image Retrieval (CBIR)Xiaojun Qi Internet Storage devices Computing power Two approaches Text-based approach Content-based approach1 2Text-Based
Utah State - REU - 06
REU Site Program in CVIP Summer 2006 Lab 2 Exercise on Image Enhancement in the Spatial DomainWarm-up Demo: Play with the "imadjdemo" program in Matlab, to see the effect of different choice of the transformation functions on image contrast and br
Utah State - REU - 06
ROBUST AND FRAGILE IMAGE ADAPTIVE WATERMARKING USING DWT AND SVDGary Brimley Computer Science Department Utah State University Logan, UT 84322-4205 garybrimley@cc.usu.edu ABSTRACT A novel algorithm combining two watermarking techniques for digital i
Utah State - REU - 06
CONTENT-BASED WATERMARKING ROBUST TO BOTH AFFINE AND JPEG DISTORTIONS Chad Coats and Xiaojun Qi Computer Science Department, Utah State University, Logan, UTAbstract - The problem of reliably detecting watermarks in digital images has been given muc
Utah State - REU - 06
A LEARNING APPROACH TO CONTENT-BASED IMAGE RETRIEVAL COMBINING RADIAL BASIS FUNCTIONS AND SEMANTIC SPACE Konstantin Shkurko1 and Xiaojun Qi2 kis9@cornell.edu Mathematics and Physics Departments, Cornell University, Ithaca, NY 14853 2 xqi@cc.usu.edu C
Utah State - REU - 06
CompMarkA blind watermarking schemeElliot First firste@ripon.edu Ripon CollegeCompMark is.a blind watermarking scheme. a grayscale logo based method. key based so only an authorized user can extract the logo. robust against common attacks such
Utah State - REU - 06
Robust and Fragile Image Adaptive Watermarking Using DWT and SVDGary Brimley Utah State University August 8, 2006Overview Algorithm for embedding two binary watermarks Both are embedded in the Discrete Wavelet Transform coefficients Extraction
Utah State - REU - 06
Face RecognitionREU Summer 2006 Crystal WhittierFace RecognitionOverview PCA and LDA LPP Weights Experiments Conclusion Future WorkOverviewAirport security Computer security Criminal detection ATM recognitionOverviewControlled background Co
Utah State - REU - 06
Introduction Eye tracking (Gaze Finding/Tracking) is a technique mainly used in machine vision, cognitive science, and human-computer interaction (HCI). Eye tracking is to determine where the user is looking (within the boundary of a computer displ
Utah State - CS - 7680
4/10/2008Outline Activity Discovery and Segmentation A smart system for monitoring the activities outside a buildingVision-Based TrackingXiaojun Qi Vision-Based Vehicle Detection and Tracking T ki A real-time vehicle tracking on a highway
Utah State - CS - 5650
Basic Pattern Recognition ConceptXiaojun Qi1Concepts of Pattern Recognition Pattern: A pattern is the description of an object. According to the nature of the patterns to be recognized, we may divide our acts of recognition into two major type
Utah State - CS - 7680
OverviewPedestrian and VisionBased Vehicle Detection and TrackingXiaojun Qi Goal: Perform real-time tracking of moving vehicles on highways. In addition to tracking regular cars, the proposed method can also track a vehicle performing a lane cha
Utah State - CS - 7680
What is Computer Vision? Deals with the development of the theoretical and algorithmic basis by which useful information about the 3D world can be automatically extracted and analyzed from a single or multiple 2D images of the world. That is: 2D Ima
Utah State - CS - 2450
The Current Version of UML Like all modern computer languages, UML is constantly changing When this book was written, the latest version of UML was Version 2.0 By now, some aspects of UML may have changedMore on UMLXiaojun Qi UML is now under
Utah State - REU - 06
REU Site Program in CVIP Summer 2006 Lab 3 Exercise on Filter Techniques for Image EnhancementWarm-up Demo:Play with the "firdemo" program in Matlab to see the effect of different highpass and lowpass filters on an image.(If boundary extension
Utah State - REU - 06
TOWARDS A ROBUST FEATURE-BASED WATERMARKING SCHEME Jonathan Weinheimera, Xiaojun Qib, and Ji Qib jw010m@mail.rochester.edu Computer Science Department, University of Rochester, Rochester, NY 14627 b xqi@cc.usu.edu and jiqi@cc.usu.edu Computer Science
Utah State - REU - 06
Introduction Active Shape Model and Active Appearance ModelXiaojun Qi Model-based approach towards image interpretation named deformable models has proven very successful. This is especially true in the case of images containing objects with large
Utah State - REU - 06
0255Digital Image FundamentalsXiaojun Qi12Image areas occupying less than half of the pixel area will not be considered for quantizationQuestions: x: Row Suppose that the y: Column image is sampled as shown on the left side: 1. How many r
Utah State - REU - 06
REU Site Program in CVIP Summer 2006 Lab 6 Exercise on Filter Techniques in Frequency DomainThe extra notes related to frequency domain can be downloaded at the following website: http:/www.cs.usu.edu/~xqi/Teaching/REU06/Notes/Supplements.pdfPro
Utah State - REU - 06
REU Site Program in CVIP Summer 2006 Lab 1 Matlab Warm-up Exercises Problems: 1. Load the image peppers.bmp into a variable A. Display the loaded image A on figure 1 with the message "RGB Original Image" as the figure title. {Think: What is the data
Utah State - REU - 06
Outline Classification MethodsXiaojun Qi Cluster Seeking: K-Means algorithm Feature Selection: Karhunen-Love Expansion Principal Components Analysis (PCA) Classification: Linear Discriminant Analysis (LDA) Statistical Classification: Quadratic
Utah State - CS - 3100
1 2Chapter 13: I/O Systems Chapter 13: I/O Systems I/O Hardware Application I/O Interface Kernel I/O Subsystem Transforming I/O Requests to Hardware Operations Streams Performance3Objectives Explore the structure of an OSs I/O subsystem
Utah State - CS - 3100
1 2Chapter 3: Processes Chapter 3: Processes Process Concept Process Scheduling Operations on Processes Cooperating Processes Interprocess Communication Communication in Client-Server Systems3Process Concept An operating system executes
Utah State - CS - 5400
xVertex Arrays Efficiency Number of function calls Redundant specification of vertices Enable Arrays glEnableClientState(GLenum array); GL_VERTEX_ARRAY GL_COLOR_ARRAY GL_INDEX_ARRAY GL_NORMAL_ARRAY GL_TEXTURE_COORD_ARRAY GL_EDGE_FLAG_ARRAY Enabling
Utah State - CS - 6890
Network Security Relies on host and application security Networks allow computers to communicate Vulnerabilities are exposed to the world Network accessible vs. inaccessible programs Eavesdropping and network vulnerabilitiesBasic Terminology
Utah State - CS - 6890
Master Project ReportStudent: Min Wu Director: Robert F. Erbacher1Text Categorization Techniques for Intrusion Detection - A N-Gram-Based MethodMin Wu minwu@cs.albany.eduAbstractText categorization techniques have been used in anomaly intru
Utah State - CS - 3100
1 2Chapter 1: Introduction Chapter 1: Introduction What Operating Systems Do Computer-System Organization Computer-System Architecture Operating-System Structure Operating-System Operations Process Management Memory Management Storage M
Utah State - CS - 3100
Chapter 3: Processes Process Concept Process Scheduling Operations on Processes Cooperating Processes Interprocess Communication Communication in Client-Server SystemsChapter 3: ProcessesProcess Concept An operating system executes program
Utah State - CS - 3100
Chapter 1: Introduction What Operating Systems Do Computer-System Organization Computer-System Architecture Operating-System Structure Operating-System Operations Process Management Memory Management Storage Management Protection and Secu
Utah State - CS - 3100
Chapter 8: Memory Management Background Swapping Contiguous Allocation Paging Segmentation Segmentation with PagingChapter 8: Memory ManagementBackground Program must be brought into memory and placed within a process for it to be run Inp
Utah State - CS - 5400
Explicit RepresentationDependant variable is given in terms of the Independant Variable y=f(x) x=g(y), inverted relationship or in 3D y=f(x), z=g(x) surface, z=f(x, y)Implicit Representationf(x, y)=0 line, ax+by+c=0 circle, x2+y2-r2=0 Membership
Utah State - CS - 3100
Objectives To describe the services an operating system provides to users, processes, and other systems To discuss the various ways of structuring an operating system To explain how operating systems are installed and customized and how they boot
Utah State - CS - 6890
OS Security Host-based security Multi-programmed environment Shared resources Programs can interfere with one another Separate from networked issuesObjects Require Protection Memory Sharable IO devices Serially reusable devices, printers
Utah State - CS - 5890
Gene Expression an Overview of Problems & Solutions: 3&4Utah State University Bioinformatics: Problems and Solutions Summer 2006ReviewConsidering several problems & solutions with gene expression data Previously:1: Data quality checks (GIGO; us
Utah State - CS - 6890
Similar Sequence Similar FunctionCharles Yan Spring 2006From Sequence to FunctionProtein sequence determine protein function. Thus similar protein sequences have similar functions One approach to predict function for a new protein is to sea
Utah State - CS - 6670
Matching Problems in BioinformaticsCharles Yan Fall 2008Matching ProblemGiven a string P (pattern) and a long string T (text), find all occurrences, if any, of P in T.Example T: Given a string P (pattern) and a long string T (text), find all o
Utah State - CS - 6890
Gene FindingCharles Yan 1Gene FindingContent sensors Extrinsic content sensorsIntrinsic content sensorsCompare with protein sequences Compare with cDNA and ESTs Genomic comparisons Prediction methodsSignal sensors 2In
Utah State - CS - 5050
ShortestPaths8 B 2 8 2 7 5 E 3 C A 0 4 1 8 F D 5 3 2 9ShortestPaths1OutlineandReadingWeightedgraphs(7.1) Shortestpathproblem Shortestpathproperties Algorithm EdgerelaxationDijkstrasalgorithm(7.1.1) TheBellmanFordalgorithm(7.1.2) Short