Wolfram Technology Conference 2011—October 19–21, 2011 | Wolfram Headquarters, Champaign, Illinois, USA



Optica Creates CDF Content on the Web for Optics

with Donald Barnhart/Ann Williamson
Optica Software (A Division of Barnhart Optical Research)

Optica can be used to create interactive, dynamic Computable Document Format (CDF) content on the web of optical systems for teaching optics and sharing of optical models. The implications of this new paradigm could be revolutionary for the optics community. Since Optica can import the file formats of other popular optical design software packages, such as Zemax and Code V, the consequences of this new format could be quite far ranging. In particular, we hope to provide an online service for users of other optical modeling packages to upload their data files and immediately link to an interactive web-based model of their specific optical design. As such, new users not need purchase Optica or Mathematica to begin reaping the benefits of CDF. In this presentation, we will demonstrate how Optica can be used to generate CDF material and discuss our strategy to exploit this exciting new format.

Jan Brugård

Introduction to Systems Engineering with Wolfram MathModelica

with Jan Brugård

Whether you need to understand how to maximize the performance of a car, the reliability of a welding system, or the effect of a new drug, dynamic modeling and simulation is an essential tool. This seminar gives an example-driven introduction to the Wolfram MathModelica software and how it can be used to improve system performance and reliability.

Jan Brugård/Peter Aronsson

Wolfram MathModelica and Mathematica Analysis and Design Workshop

with Jan Brugård/Peter Aronsson

Learn how to use Mathematica when designing and analyzing MathModelica models. In this workshop you will learn how to analyze simulation results as well as learn how to get MathModelica model equations into Mathematica to analyze and design your dynamic system.

Jan Brugård/Peter Aronsson

Wolfram MathModelica Modeling and Simulations Workshop

with Jan Brugård/Peter Aronsson

Learn how to develop models of dynamic systems in a variety of fields, such as mechanical, electrical, thermal, and biological. This workshop gives direct experience with the basic MathModelica features needed to become a proficient MathModelica user. The course focuses on drag-and-drop modeling using existing libraries.

Jan Brugård/Bob Sandheinrich

Wolfram MathModelica—Control Design

with Jan Brugård/Bob Sandheinrich

With the release of Version 8, tools for control design have been included in Mathematica. Together with Wolfram MathModelica, it will give you all the tools you need to design controllers for advanced systems. This seminar will show you how you can develop a model of a dynamic system in MathModelica and design its control system in Mathematica.

Surface Envelopes for Engineering Applications

with Steve Bush
The Procter & Gamble Company

Applications of envelopes of manifolds (especially surfaces) are presented in the context of mechanical engineering. The mathematical analog of cutting away material to create a useful shape, envelopes find myriad applications where analytically determined, physically derived shapes are required. The generation and exchange of surfaces of envelopes take on greater importance as computer numerically controlled (CNC) machine tools are able to create surfaces directly from computer aided drawing (CAD) data, and as many mathematically precise surfaces cannot be generated directly by CAD tools. Three steps in the process of generating and using envelope surfaces are discussed: surface generation, interpolation to NURBS form, and parsing to a CAD interchange format.

Symbolic Computing Package and Its Application to Mathematical Physics

with Youngjoo Chung
Gwangju Institute of Science & Technology, Korea

The Symbolic Computing package is a Mathematica add-on package that facilitates symbolic computation in Mathematica. It enables display and interpretation of derivatives, differential vector operators, and brackets using the traditional notation based on the low-level box language and contains more than 500 user-defined functions for manipulation and evaluation of various mathematical expressions. These functions can be categorized into:

  • Basic Algebra
  • Complex Variables
  • Differential Calculus
  • Display and Graphics
  • Elementary Functions
  • Equation Solving
  • Equations
  • Export of Data
  • Flow Control
  • Formula Manipulation
  • Fourier Analysis
  • Function Analysis
  • Geometry
  • Integral Calculus
  • Numerical Functions
  • Operator Analysis
  • Patterns
  • Polynomials and Series
  • Products
  • Rules
  • Special Functions
  • String Operations
  • Summations
  • Symbols
  • Trigonometric Functions
  • Variables
  • Vectors and Matrices

The development of the package and application to mathematical physics, especially classical mechanics, electromagnetism, and quantum mechanics, will be presented.

A Financial Model of Medication Wastage Avoidance by Using Genetic Testing

with Susan B. Garavaglia
Medco Health Services, Inc.

No medication is 100% effective for 100% of patients. With certain chronic diseases, such as depression and rheumatoid arthritis, the patient may not be aware that the medication is not helping him or her and use non-efficacious medications for several years. Thus, the medication is a wasted cost. Spear, et al. (1) produced a meta-analysis of efficacy of drugs for 14 diseases and, more recently, Kitzmiller, et al. (5) provides a useful overview of current knowledge of pharmacogenomics. In several therapeutic areas, the value of genetic testing or genotyping has been demonstrated in enabling better clinical outcomes and lower medical costs (2,3,4). In some cases, genotyping establishes a phenotype of metabolization rates and, in other cases, genotyping leads the prescriber to the right drug for the patient versus a non-efficacious or even toxic drug. A genetic test needs to be performed only once on a patient; however, to identify the subgroup of patients that require adjustments to therapy or dosage, all patients must be tested, creating a cost barrier to testing. An interactive model is introduced to estimate how much wasted medication could be avoided with an effective genetic test. Selectable parameters include disease of interest, patient population size, inflation rates, nominal improvement percent, and cost per test. The model also has hard-coded parameters that could be modified, such as Average Wholesale Price (AWP) Per Member Per Year (PMPY) costs and total enrollment. Medication spent and wastage are calculated as annuities. Mathematica functions used include Manipulate, PieChart, TimeValue, and Annuity.

Quantum Mechanics on the GPU

with Richard Gass
Department of Physics, University of Cincinnati

Mathematica 8 provides tools for running code on your computer's GPU using either CUDA or OpenCL. These tools lower the barriers to GPU computing dramatically. I will show how by writing a small amount of GPU kernel code that you can use to solve interesting quantum mechanics problems on your GPU. Speed-ups of several orders of magnitude can be obtained even on modest graphics cards. Once your GPU function is written, it can used just like any other Mathematica function, making dynamic visualization of your results easy.

Analysis and Design of Feedback Control Systems Using Mathematica

with Pradipto Ghosh

Feedback control systems find widespread application in physical, biological, information, and economic systems. This talk will focus on the modeling, analysis, and design of such systems using Mathematica. Topics covered will include the use of Mathematica in studying representations of both continuous and discrete-time systems; their stability, controllability, and observability properties; and classical control aspects such as root locus, Bode design methods, and Nyquist stability. Techniques available in Mathematica for the design of control systems with specific desired characteristics and the synthesis of state estimators and feedback controllers for both deterministic and stochastic linear systems will also be discussed.

J. Brendan Hagan

Direct Exoplanet Imaging Using Advanced Post Processing in Mathematica

with J. Brendan Hagan
Space Telescope Science Institute

HR 8799 is one of only three exoplanetary systems observed with direct imaging. Discovered in 2008, it is currently the only multiple-exoplanetary system discovered with this method. The ability to measure the planet's orbital motion is critical to understanding this system. Indeed, orbital information brings insight into the dynamics, stability, and formation mechanisms for the planets. Yet measuring the orbital motion is a very difficult task because of the long-period orbits (50–500 year), which requires long time baselines and high-precision astrometry. We present the precovery of the three planets HR 8799b, c, and d using the archival dataset of the star HR 8799 obtained with the Hubble Space Telescope (HST) NICMOS coronagraph in 1998. This data provides a 10-year baseline with the discovery images, and therefore offers a very unique opportunity to constrain their orbital motion now. Recent dynamical studies of this system showed that it is mostly unstable with only a few possible stable solutions involving mean motion resonances. We study the compatibility of a few of these stable scenarios with the new astrometric data, and offer new dynamical constraints on the HR 8799 system. These results were achieved by developing sophisticated image-processing and orbit-fitting techniques using Mathematica, where we apply a statistical approach to get high-precision astrometry and then test for compatibility with previously published stable solutions.

Stanislav Hledík

Interactive Model of Spin-Stabilized Magnetic Levitation

with Stanislav Hledík
Institute of Physics, Silesian University in Opava, Czech Republic

Stable equilibrium of a fixed spatial configuration of mutually interacting macroscopic permanent magnets and/or paramagnetic or ferromagnetic bodies in free space—alone or under gravity—is precluded by Earnshaw's theorem (1842), thereby making static magnetic levitation intrinsically impossible. However, there are a few ways of getting around the theorem assumptions that allow magnetic levitation. The method of passive spin-stabilized levitation of a small magnetized top above a massive ring-like base magnet employed by a popular levitation toy known as LEVITRON© or U-CAS involves quite subtle underlying physics—a unique coupling of the magnetic forces and torques with the gyroscopic action. The present work develops an interactive mathematical model of such a levitating device, taking advantage of capabilities of Mathematica in symbolic manipulations, numerical computations, and interactivity. The model will be used as a case study and an illustrative example in lectures on mechanics, electrodynamics, and mathematical modeling.

Signal Processing

with Mariusz Jankowski

An Engineer's Box of Chocolates

with Yves Klett
Institute of Aircraft Design & University of Stuttgart, Germany

While mechanical engineers certainly are responsible for huge amounts of computation, their approach to doing math can be somewhat practical: they need some way to get things done (and please, no metaphysical debates about error representations of floating point numbers, because there in fact is a deadline for this car/airplane/rocket/toaster crash test). Especially in R&D, one is faced with all kinds of ever-changing tasks involving dirty real-world processes that obstinately refuse to behave according to theory (and sometimes depend more on the day of the week and the weather than on calculation), but still need to be dealt with. Thus the box of chocolates analogy: sometimes you really don't know what you're going to get—but you'd better get it done by next week, thank you very much, chop-chop. On the plus side, no one is going to fire you because you used a horribly inefficient root-finding method (as long as it works, that is). Because many of those tasks do involve really non-standard problems and processes, you need a flexible, rapidly deployable tool. As it turns out, Mathematica does exceptionally well here. We'll present a few select examples where we make particularly good use of Mathematica in everyday (and not so everyday) research, ranging from test data analysis to computational origami.

Dave Lawrence

Computational Integration of Biomedical Research Workflows at the Oregon National Primate Research Center

with Dave Lawrence
Oregon National Primate Research Center

The Oregon National Primate Research Center (ONPRC) is one of eight NPRCs applying animal models in developing therapies for AIDS, metabolic and neurodegenerative disease, and reproductive health. Research into human disease is complemented by veterinary medicine in development of methods and resources to support animal husbandry—capabilities crucial to engineering breeding colonies that are supportive of evolving research needs. An additional challenge concerns allocation of animals to competing research projects, in particular doing so in a way that is fair to scientific, economic, and ethical perspectives. In accordance with these needs, Mathematica technologies are revolutionizing ONPRC efforts to transform organizational workflows throughout, from powering market-based mechanisms for animal allocation to providing automated animal-record extracts for researchers to integrating surgical and animal-care activity to related work across the consortium of NPRCs. First-of-a-kind capabilities have been developed in minimal time and at minimal expense, substantiating the claim that Mathematica offers a months-to-days improvement in development efficiency. The diversity of deployment mechanisms make the Mathematica product accessible to users throughout the organization, from animal care technicians to veterinarians to researchers and staff.

Some accomplishments include reduction of surgical staff coupled with reduction in process variation and improvement in procedural discipline and efficiency, online access to animal information strengthening integration of research and husbandry, and generation of datasets in minutes that previously would have required hours and days (if possible at all.) Never before at ONPRC has "IT" evoked expressions like "... Amazing what you can do," "...Love it...," "This is cool," and simply "Awesome."

Malte Lenz/Johan Rhodin

Wolfram MathModelica—Analysis and Design

with Malte Lenz/Johan Rhodin

Discover the improved interface between MathModelica and Mathematica. Learn how the combined strengths of MathModelica and Mathematica can be used to design models and analyze simulation results.

GPS Timing-Code-Based Positioning in Mathematica

with Thomas Meyer
Dept. of Natural Resources and the Environment, University of Connecticut

Global Positioning System (GPS) receiver positions are computed from observed timing-code pseudoranges between GPS satellites and the receiver plus the coordinates computed for the satellites at transmission time. The Mathematica code reads observations and satellite ephemerides from RINEX files, computes satellite positions using orbital mechanics, computes and corrects the pseudorange error budget, to compute the final position using a nonlinear least-squares adjustment of the multi-lateration problem. Tests show computed positions agree with published control coordinates within the standard error expected for code-only GPS positioning.

Image Processing with Mathematica

with Matthias Odisio

Mathematica 8 includes an advanced set of image processing functions to perform segmentation, registration, feature detection restoration, morphological analysis, and much more, all seamlessly integrated into Mathematica. In this talk we introduce some of the new and enhanced, state-of-the-art functions along with many examples in a variety of fields ranging from fun image effects to real problems in industry and scientific research.

A Formal Approach for Modeling and Simulation

with Yves Papegay

Modeling some behaviors of a system is a complex multi-step and multi-layered description process closely related to the numerical simulation and visualization of these behaviors. At the bottom level layer, physical characteristics are defined in terms of quantities and associated to symbols, units, and values. At the top level layer, systems are expressed as a set of interconnected components involving such quantities. Intermediate layers are used to express how components are acting on each other, how quantities are dependent together, and how that can be effectively represented in terms of equations with the help of physical formalisms and theories. By adding a few constructions, we show how to turn the rich programming and mathematical language of Mathematica into a generic declarative modeling language that is rich enough to represent the most complex models of large-scale systems. Ad-hoc compilers are then able to produce from these source models any kind of simulation code as target. An industrial application of these theoretical concerns has led to the development of an integrated environment for modeling and simulation used by one of the leaders in the aircraft industry for the design of flight dynamics models and the automatic generation of components of the flight simulators.

Oliver Rübenkönig

Numerical Solutions of Partial Differential Equations

with Oliver Rübenkönig

Impact of Slowing Spin on the Trajectory of a Baseball

with Haiduke Sarafian
Pennsylvania State University

A flying baseball in the air not only is subject to gravity's pull, it is also subject to air resistance. A spinning ball in addition to these two forces experiences a spin-dependent force. We consider a practical scenario where the speed dependent drag force not only retards the motion, it slows the spin as well. The analysis of the kinematic and dynamic of the proposed scenario entails solving super nonlinear coupled ODEs. Utilizing Mathematica's NDSolve function, we solve these equations numerically. We compile numeric and graphic lists of quantities of interests versus their comparative counterparts of non-retarded case.

Tom Sherlock

The Wolfram Machinist Professional Assistant

with Tom Sherlock

An introduction to the Wolfram Machinist Professional Assistant will be given, including examples for a variety of common machine shop calculations, including decimal and fractional equivalents, feeds and speeds for a wide variety of materials, drilling and tapping and holes, single point threading calculations, dovetail calculations, chord calculations, rotary table and bolt circle calculations, ball end mills, and geometrical calculations.

Visual Depth Perception and Mathematica

with Keith Stroyan/Mark Nawrot
Department of Mathematics, The University of Iowa / Center for Visual and Cognitive Neuroscience, North Dakota State University

A translating observer viewing a rigid environment experiences "motion parallax," the relative movement upon the observer's retina of variously positioned objects in the scene. This retinal movement of images provides a cue to the relative depth of objects in the environment; however, retinal motion alone cannot mathematically determine the relative depth of objects. Also, Nadler, et al. (Nature 2008) showed that monkeys cannot perceive even "near" or "far" without an extra retinal signal. Nawrot and Joyce (Vis Res 2006) showed experimentally that the ratio of the rate of image motion on the retina and the rate of smooth eye pursuit was important in perception of depth from lateral motion. Nawrot and Stroyan (Vis Res 2009 and J of Math Bio 2011) developed a mathematical theory of the motion/pursuit ratio and verified that people use this cue in central vision. Nadler, Nawrot, et al. (Neuron 2009) showed that monkeys use the extra retinal pursuit signal neurologically to perceive depth sign. Work is ongoing to understand the role of the motion/pursuit ratio, psychophysically, neurologically, and mathematically. Mathematica is playing an important role in the discoveries and communication across disciplines because it can provide powerful, but easy-to-use interactive graphical programs in accessible ways.

Otto Tronarp/Peter Aronsson

Wolfram MathModelica—Modeling and Simulation

with Otto Tronarp/Peter Aronsson

Get familiar with MathModelica and how to use it for modeling and simulation of dynamic systems, including an overview of the model libraries, graphical user interface with drag-and-drop modeling, and new features using example-driven material.

Image Processing Workshop

with Markus van Almsick/Piotr Wendykier

This workshop will explore some of Mathematica's powerful image processing capabilities. We will focus on rapid prototyping of image processing algorithms and develop step-by-step solutions for a variety of problems. Moreover, we will show how to make use of Mathematica's built-in features to improve performance and to easily create powerful interactive tools.

*During the conference, not only will you hear about what's new, but you will also be privy to details about what's on the horizon in talks given by Wolfram executives, developers, and more. As such, you will be required to sign a non-disclosure agreement to attend our talks labeled "NDA".