2009 AMSC/CMSC 660 Term Project Information

The assignment: Suppose that you are the instructor for 660. Write a case study for the class that solves an interesting application problem, using one or more of the algorithms studied this semester. Also write a solution for the case study, including well-documented Matlab code.

Deadlines and points:

  • By November 9, but not before October 19, you should send me e-mail with the title and a short description of your project.
  • The project is due at 4 pm Thursday, December 17.
  • It is worth 100 points.
  • There will be a 15% penalty for projects turned in up to 24 hours late, 30% penalty for projects turned in 24--48 hours late, etc.

    • Model your project on the Case Study chapters in the textbook, the corresponding chapters in the solution manual, and the software provided with it. A case study should typically have 4-5 challenges.

    The project should include:

  • one or more written challenges to explore the basis of the algorithm or the nature of the problem.
  • one or more Matlab challenges that solve the problem using one or more algorithms and then evaluate the goodness of the answers.
  • a discussion component.
  • a list of references.

    • How to get started: Each person is required to have a unique project, so send me your idea by email, and I will add it to the list of claimed topics on this page. If you don't have any ideas, let's talk.

    What to submit for the project:

  • The assignment, as you would hand it out to students.
  • The answers to the written problems, Matlab programs (documented to the standards in Chapter 4), any necessary data files, and a discussion.
  • Also include a list of relevant references.

    • How to submit: Submit your project by e-mail. The legitimate time stamp on the e-mail will determine whether the project is on-time or late. I will acknowledge receipt of your project as soon as I have saved the attachment to your email.

  • The Matlab programs should be in plain text, stored in files that can actually be run by Matlab.
  • Data files should be in .mat format.
  • The assignment, answers, and discussion should be in plain text, html, pdf, or ps format.
    Microsoft-formatted documents (Word, Excel, Powerpoint, etc.) will not be accepted; submit a pdf or ps file for these.
  • The entire set of files should be bundled into a single file (in tar, zip, or gzip format) and attached to the e-mail.
  • Remember to send all data files, since I will run your programs. Include a "readme" file if there might be any doubt about how your files are organized and named. To make the organization clear, consider naming the program that solves Challenge 1(a) "chal_1a.m", etc.
  • My workstation runs Linux, so Microsoft-specific features are unlikely to work properly. For example, don't use dll.

    • Some questions that will be asked while evaluating a project:

  • The assignment:
  • Is it clear and correct? Was it spell-checked?
  • Does it reinforce ideas taught in the class and make it clear why they are useful?
  • Does it contain written questions and programming?
  • Is the application explained well to a novice?
  • Do the students have all of the information they need to complete the assignment?
  • Is it appropriately difficult for 660 students?
  • Is it interesting and does it show some creativity?
  • If appropriate, are references given where a student could go for more information?
  • Do the students learn important lessons by completing the assignment?
  • The answer:
  • Is it clear and correct? Was it spell-checked?
  • Are the Matlab codes well designed and well documented?

    • Warning: The only failing grades I have given on this assignment have been for lateness or plagiarism. If you use someone's ideas, cite the source. If you use a direct quote, use quotation marks and cite the source. And don't expect a good grade on a project that is mostly someone else's work.

    A note on formatting: The format for your project does not need to be the same as the homeworks that I have given you, but here is a Latex template (which uses the class file found here ) and the resulting pdf output, in case you find it useful.

    A note on accessing journals over the internet at UMD:

  • Some journals (e.g., SIAM journals) can be accessed just by being on the UMD domain and going to the journal's website.
  • Others (e.g., for-profit journals published by Springer, Elsevier, etc.) require that you go to the UMD library research port and connect to the journal from there. This also works if you are off-campus.
  • And for really "old" things you might have to actually walk over to the Engineering and Physical Sciences Library, just like generations of scholars before you.

    • If you can't think of a topic, here are some ideas to consider:

  • Solve an optimal control problem using variants on methods discussed in the book.
  • Form and solve an ecological model.
  • Model the spread of wildfire.
  • Give an application of semidefinite programming and solve the problem.
  • Present parallel algorithms for factoring matrices. (One person could consider vector processors while another considered a network of connected processors.)
  • Present an application of surface fitting and (stable) algorithms to do it.
  • Present an application of multidimensional integration and solve it.
  • Present an application that produces a system of nonlinear equations and solve it.
  • Base your project on an interesting talk you have attended (That is where the capacitance project came from) or an interesting paper you have read.
    • Projects chosen by other students this semester and in previous semesters: Your project must be different from all of these, so either pick a different topic or check with me to make sure that your ideas are sufficiently different from what these students did.

    2009:

  • Transportation modeling
  • Active vibration control of a beam
  • Randomized algorithms for testing primality
  • Image segmentation using Markov random fields
  • Monte Carlo method in computing PageRank
  • Multi-core processor performance optimization under temperature constraints
  • Interferometric technique for high-accuracy emitter location
  • Modelling the swine flu epidemic in a university
  • Potential parallelism in QR factorization
  • Potential parallelism in fast algorithms for Toeplitz matrices
  • Computation of equilibrium bids in auctions
  • Tracking vortices using Monte Carlo
  • Modeling queues with Markov chains
  • Monte Carlo methods for making business decisions
  • Diffusion Tensor Imaging - Tensor modeling of water diffusion in the brain
  • Estimating time required for computing graph metrics
  • Stochastic differential equation models of oscillators
  • Optimal decoding/encoding for digital transmission
  • Detecting Anomalies and DOS events in computer networks using wavelets
  • HITS for document retrieval
  • Differential Equation Models for Subcutaneous Insulin Kinetics
  • Using ODE Methods to evaluate Steady-State Approximation in Chemical Reactions
  • project topic
  • Studying Bacterial Antibiotic Resistance

    • Previous semesters:

  • Speech analysis and modeling based on rotational invariant techniques (ESPRIT)
  • Toeplitz matrices and least squares problems
  • A Comparison of Clustering Algorithms for Gene Expression Microarray Data
  • Image Segmentation using Spectral Clustering Methods
  • Failure prediction of MLCCs under temperature-humidity-bias testing conditions
  • Motion of 2 and 3 particle systems in a central force
  • A Monte Carlo Approximate SVD
  • Evolutionary Game Equilibrium Points/Opponent Modeling in a Negotiation Game
  • Non-linear PCA for feature selection and clustering
  • Inverse Kinematics for Redundant Robotic Manipulators
  • Optimization for guardian placement on campus
  • Expectation-Maximization algorithm and Image Segmentation
  • Numerical solution of boundary value problems in chemical vapor deposition reactor systems
  • Product Sales Estimations using Monte Carlo methods
  • Use of the improved fast Gaussian transform in linear system solving
  • Monte Carlo and finite differences in option pricing
  • Monte Carlo methods in natural language processing
  • Digital photography post-processing
  • Social learning strategies
  • Photon mapping with Monte Carlo
  • Contaminant Source Reconstruction Using Monte Carlo Techniques
  • Approximating images with wavelet dictionaries
  • Circuit Analysis of Winner Take All (WTA) networks
  • Monte Carlo simulation of a vapor deposition process: minimizing surface roughness
  • Direct Linear Transformation of 2D Images
  • Estimation of Characteristics of Ellipsoid Shaped Objects
  • ODEs in solving first-price auctions
  • Use variants of latent semantic indexing (SVD and other decompositions) to perform document retrieval.
  • Perform image compression using various matrix-based approaches.
  • Present the fast multipole algorithm in matrix terms and solve a problem using it.
  • Survivable Network Design
  • Formulate the data assimilation problem in meteorology in terms of our matrix factorizations.
  • Use wavelets to approximate a signal, and compare with Fourier analysis.
  • Designing a helicopter seat to damp vibration
  • Illustrate the role of unitary matrices in quantum computing.
  • Analysis of poker
  • Support vector machines
  • Mobile emergency communcation
  • Derivative-free methods for constrained optimization
  • Solution of convection-diffusion equation using ODEs
  • Protein folding using homotopy methods
  • Monte Carlo models of raindrops
  • Hydro-mechanical Analysis of a Magnetorheological Energy Absorber (MREA) with Bifold valves for Shock Load Mitigation
  • Plasma physics particle simulation
  • Independent component analysis
  • Monte Carlo for Markov chains and Bayesian Networks
  • Parallel Algorithms for Scalar Product and LU Decomposition
  • Health diagnostics and performance diagnostics of electronic systems
  • Linear rational equations
  • Location estimation using gps
  • Solving the human heart dipole problem using tabu search
  • FIR eigenfilters design
  • Kalman filtering, linear and nonlinear
  • Face recognition by PCA
  • Metropolis algorithm for finding independent sets in a graph
  • Simulated annealing for particles with Lennard-Jones potential
  • Neuronal layout optimization
  • Maximum entropy design of computer experiments
  • CMOS circuit optimization using geometric programming
  • Preconditioning conjugate gradients
  • SVD filtering for video images
  • ODE models of structured population dynamics
  • Metropolis for DSP address optimization
  • Spectral clustering methods for image segmentation
  • Solution of the secular equation
  • Monte Carlo description of a dynamic terrain
  • A Metropolis-based algorithm for solving the Prisoner's Dilemma
  • Singular value analysis of cryptograms
  • Handwritten Postcode recognition by PCA
  • Document clustering through matrix factorization.
  • epipolar alignment of stereo cameras
  • Finding Fundamental Matrix for Stereo Vision
  • minimizing helicopter vibration using flap control