Nano Science


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	7TH SEMESTER COURSES

Regarding all books, check if available at the central bookstore. Books available at Amazon UK. Contact the course responsible if needed. All 7th semester courses notes ahead Courses taught by the Nanobio group Molecular Interaction and Recognition Course Lector: Associate Professor Teresa Neves Petersen The students will be presented with a vast collection of spectroscopy tools allowing them to detect the interaction between nanosized molecules, from the ensemble to the single molecule level. The vast potential of such techniques to study diverse molecular phenomena will be presented. Book: Protein-ligand Interactions: A Practical Approach: Structure and Spectroscopy (Practical Approach S.) (Paperback) by S.E. Harding See course notes Lesson 1 Lecture Notes here Location: Skjernvej 4A, Room 1.212 Time: 8.30 am •Chapter 4 will be covered •Additional material will be presented •Prior to showing you spectroscopy tools allowing the detection of molecular interactions, we need to: – to give you an introduction to immunology •Fluorescence based techniques will be shown as ideal to monitor molecular interactions and recognition •Key fluorescent equipment and biosensors will be discussed Problems session here Answers to Problem session 1 here Lesson 2 Lecture Notes 2 Revision of fluorescence concepts. Notes Lesson2 Problems 1 Problems 2 Solutions to Problems 2 (soon) Lesson 3 Lecture Notes 3 Building the background to talk about SERS spectroscopy and Raman-AFM spectroscopy. NotesLesson3 Literature: a, b, c, d, e Lecture detailed plan: Building the background knowledge to SERS Spectroscopy 1- Raman Spectroscopy –Principles of RS –Applications –Scientific papers –Monitoring molecular interactions with RS –SERS – Surface Enhanced Raman Spectroscopy Problem session Lecture 3 here Lesson 4 Lecture Notes 4 NotesRamanSPM NotesPlasmons NotesSERS SERS – Surface Enhanced Raman Spectroscopy Building the background knowledge to Raman/AFM microscopy - Scanning Probe Microscopy (SPM) –Basic principles –Monitoring molecular interactions with RS: -Antibody-Antigene studies - Combining scanning probe microcopy and Raman microscopy Lesson 5 Lecture Notes 5 NotesNMR NMR Spectroscopy and NMR Imaging - basic concepts and applications Film "Tiny Machines" by Richard Feynman Advanced Data Analyses Course Lector: Professor Steffen B. Petersen Book: Digital Image Processing Using Matlab (Hardcover) by Rafael C. Gonzalez (Author), Richard E. Woods (Author), Steven L. Eddins (Author) Available at Amazon UK Advanced Data Analysis All students should download a copy of MATLAB 2007a – the Pass Code can be obtained from Steffen B. Petersen Textbook used: Gonzalez, Woods and Eddins: Digital Image Processing using MATLAB, Prentice Hall, 2004, ISBN 0-13-008519-7 Teaching Segments: 1: Introduction to Advanced Data Analysis. Notes lecture 1 Chapter 1 2. Basic Operations, MATLAB commands, Matrices and Graphical User Interface (GUI) a) First Assignment: Basic MATLAB here 2: Image Basics Notes lecture 2 Chapter 2 intro to Chapter 3 The concept of Pixels and Colors. True Color Images and Indexed images. Gray Scale Images, Binary Images b) Second Assignment : Reading and Writing Images, Basic Operations on Intensity Images here 3: Intensity Transformations and Spatial Domain Filtering Notes FFT lecture 3 Notes lecture 3 Chapter 3 Filtering the image in the spatial domain – linear filters, non-linear filters. Histogram operations. c) Third Assignment : Smoothing and feature extraction 4: Fourier Transformation and Frequency Domain Processing Notes lecture 4 Chapter 4 1D Fourier Transformation in Practice using MATLAB. 2D FFT and visualization. Filtering in the frequency domain. Low Pass and High Pass filtering. Removing glow. d) Fourth Assignment : Extracting frequency information in 1D and 2D . Removing frequency information from an image 5: Image Restoration Notes lecture 5 Chapter 5 What is Noise. Noise Models. Periodic Noise Filters to remove noise from image. Frequency domain filtering. Inverse Filtering. Point Spread Function PSF. Least Square Filtering. Blind Deconvolution. Geometric Transformations e) Fifth Assignment: Restoring Images – Images will be provided . Geometric Transformation of objects. 6: Color in Image Processing Notes lecture 6 Chapter 6 True Color and False Color. Converting from RGB to Gray scale or intensity images. Half Term Self Evaluation :/Assets/edge_detection.pdf HALF TERM EVALUATION TEST, available here on the 19th October at 8.30. Also, get pictures: Peppers Comet Mandril Xfile 7: Wavelets and Image Compression Notes lecture 7 Chapter 7 8 (Main weight on Chapter 7) Fast Wavelet Transform –using wavelets to decompose structures. Inverse Wavelet Transform. Comparison to Fast Fourier Transform. f) Sixth Assignment: Using wavelet analysis on images 8: Multidimensional datasets Notes lecture 8 Chapter 9 Dilation and Erosion, Opening and Closing, Labeling. Binary Morphology. Gray Scale Morphology. Object enumeration. g) Seventh assignment: Practical uses of morphology 9: Image Segmentation Chapter 10 Point, Line and Edge detection. Hough Transform. Thresholding. Region based segmentation. Watershed transform. h) Finding distinct object of a particular shape in an image. Analysis of the objects.