Skip to Main Content Skip to Search
Accelerating the pace of engineering and science


 

Abstracts

Expand all

Keynote Presentation

Getting to Smart

Engineers and scientists strive for “smart” in the systems they develop, the discoveries they make, the way they work and learn. While the value of “smart” is clear, it isn’t always apparent how to get there. In this presentation, MathWorks Fellow Jim Tung shows you how companies and universities are reaching their smart outcomes—using new computing hardware, global teamwork, and modeling and simulation—with MATLAB and Simulink.

Jim Tung Jim Tung, MathWorks
Jim is a MathWorks Fellow, focusing on business and technology strategy and analysis. Jim has more than 25 years of experience in the technical computing software markets, including 20 years at MathWorks, where he previously held the positions of vice president of marketing and vice president of business development. Earlier in his career, Jim held marketing and sales management positions at Lotus Development and Keithley DAS, a pioneering manufacturer of PC-based data acquisition systems. Jim holds a bachelor's degree from Harvard University.

Track 1 – Discover MATLAB and Simulink

Control Design Made Easy

Learn how to get started designing control systems with Simulink using a DC motor as a physical modeling example. We create models of dynamic systems and then show how you can design feedback controllers, by tuning a PID controller for the motor. You will see how to test the controller through simulation and generate C code for deployment to an embedded microprocessor.

Arkadiy Turevskiy Arkadiy Turevskiy, MathWorks
Arkadiy works in the technical marketing group at MathWorks supporting Simulink products for control design. Prior to joining MathWorks he worked at Pratt & Whitney, where he developed control systems for large aircraft engines. Arkadiy has a M.S. in aeronautics and astronautics from MIT.

Mathematical Modeling with MATLAB

MATLAB enables you to build mathematical models for forecasting and optimizing the behavior of complex systems. In this session, we demonstrate how you can:

  • Develop models using data fitting and first-principles modeling techniques
  • Simulate models and develop custom postprocessing routines
  • Generate reports that document models and simulation results

The session covers use of the MATLAB language, symbolic expressions, and prebuilt graphical tools for specific modeling tasks and other approaches you can use to develop models.

Jim Tung Tucker McClure, MathWorks
Tucker is an aerospace application engineer at MathWorks. Before joining MathWorks in 2011, he developed aerospace vehicle simulations and control algorithms for Bigelow Aerospace and Lockheed Martin. He has a bachelor's degree from Columbia University and a master's degree from Cornell University, where his research focused on state estimation for a morphing, autonomous aircraft.

Modeling a 4G LTE System in MATLAB

In this session, we discuss the iterative process of analysis, design, simulation, optimization, and implementation of major components of 4G LTE wireless systems in MATLAB. Orthogonal frequency-division multiplexing (OFDM) and multiple input, multiple output (MIMO) technologies are at the heart of modern communication systems. Because of the complexities of the underlying technologies, many companies are adopting MATLAB and Simulink to accelerate implementation and optimization of the next generation of wireless systems. Through demonstrations in MATLAB, we start with a simple communications system and progressively add components to approach a barebones prototype of a 4G LTE system.

Highlights of the presentation include:

  • Modeling, simulating, and visualizing the performance of the communications system in MATLAB
  • Using Communications System Toolbox to incorporate components such as modulators, channel models, convolutional and turbo coders, and MIMO and OFDM modules into your model
  • Performing system-level throughput analysis with adaptive modulation based on channel characteristics
  • Accelerating the speed of your MATLAB simulation at each step through parallel processing, code generation, efficient algorithms, and GPU processing
  • Generating C code from your MATLAB model with MATLAB Coder to prototype and test your model as a standalone desktop C/C++ application
  • Generating VHDL® or Verilog® code to implement the design using FPGAs
Arkadiy Turevskiy Houman Zarrinkoub, MathWorks
Houman is a senior product manager at MathWorks responsible for DSP System Toolbox, Communications System Toolbox, and the MATLAB to C workflow. He joined the company in 2001 as the development manager of the Signal Processing team. Prior to joining MathWorks, he spent six years at Nortel Networks as a member of the scientific staff specialized in wireless speech processing applications. He has a B.S.E.E. from McGill University and M.S.E.E. and Ph.D. from the Institut Nationale de la Recherche Scientifique, Universite du Quebec in Canada.

Track 2 – Find Out What's New

Can Teams Using Simulink Collaborate on Their Designs?

Modeling and simulation have been the cornerstone for Model-Based Design. With advances in computing technologies, recent trends in modeling have focused on building higher-fidelity models that result in better and more robust designs. The result has been the proliferation of large-scale projects that involve multidisciplinary groups of engineers that must collaborate to realize these models. Associated with these workflows are inherent complexities arising out of engineers having to organize, manage, and revision control files that contain algorithm implementations, data, utilities, and associated report artifacts. Consequently, the complexity faced by the engineer is twofold: design and file management. With attention divided between these two tasks, the productivity and effectiveness of the engineer is diminished. At the team level, interdependencies arising out of engineers contributing to a single design in a project-based setting complicate the matter further. The result has been a trend toward ad hoc project management where engineers have to learn to work with source control tools or depend heavily on a configuration management specialist for basic tasks. This can lead to process bottlenecks being created, or the abandonment of the process altogether. Lessons and best practices learned from project workflows are lost and not transferable to other projects. This session explores features introduced in Simulink that can help you answer the collaboration question with a resounding "yes."

Saurabh Mahapatra Saurabh Mahapatra, MathWorks
Saurabh is a product marketing manager at MathWorks. His interest is in the area of understanding distributed systems modeling and simulation.



MATLAB to C Made Easy

In this session, we demonstrate the workflow for generating readable and portable C code from your MATLAB algorithms using MATLAB Coder. Using the command line or the graphical project management tool, you can introduce implementation requirements to your MATLAB algorithms and generate readable source code, a standalone compiled executable, or a library that can be shared across your organization.

We also explore how you can automatically generate MEX functions to verify the behavior of the generated code back in MATLAB or to accelerate computationally intensive portions of your MATLAB code by running it at compiled speed.

Bill Chou Bill Chou, MathWorks
Bill is a product marketing manager for MATLAB Coder. Bill holds an M.S. in electrical engineering from the University of Southern California and a B.A.Sc. in electrical engineering from the University of British Columbia.

New Capabilities for Regression and Curve Fitting

Statistics Toolbox (R2012a) includes an enhanced interface for regression analysis including fitting, prediction, and plotting while providing native support for dataset arrays and categorical data. These new capabilities speed up data analysis, eliminate the requirement to manually manipulate matrices, and produce more compact and readable MATLAB code. Product demonstrations feature applied examples of linear, nonlinear, and logistic regression including:

Product demonstrations feature applied examples of linear, nonlinear, and logistic regression including:

  • Generating predictions
  • Evaluating goodness-of-fit
  • Plotting fits and residuals
  • Detecting outliers and performing robust regression
Richard Willey Richard Willey, MathWorks
Richard is a product marketing manager at MathWorks, where he focuses on statistics and computational biology. Prior to joining MathWorks in 2007, Richard worked at Wind River Systems and Symantec. Richard has dual master’s degrees in engineering and management from MIT and a master’s in economics from Indiana University.

Track 3 – See What Industry Experts Are Doing

Automatic Code Generation of AUTOSAR Software Components for Mass Production Application of Engine Management Systems: Process and Benefits

This session presents the VALEO Engine and Electrical Systems (E.E.S.) development cycle, which is optimized around MATLAB, Simulink, and Embedded Coder.

In the context of Model-Based Design for engine management control laws, VALEO E.E.S. deployed the latest engineering technologies to optimize the design, simulation, validation, and generation of AUTOSAR software components. The close collaboration of the various actors of the development cycle made it possible to bring the requirements for each skill into a consistent and robust workflow of deliverables at each stage of the cycle.

The combination of this global approach for the development cycle and the suppliers' integration enabled the successful deployment of these processes for a new project with mass production development constraints.

Franck Narcisse

Franck Narcisse, VALEO Engine and Electrical Systems, France
Franck Narcisse has been involved in the automotive industry since the 1990s after getting a master’s degree in signal processing. He joined Valeo in 2005 and is currently in charge of their Engine Management Systems (EMS) midterm plan. Concerned with software design and architecture issues from the start of his professional career, he began managing the EMS software department in 2006, with a focus on software engineering process improvement.

Narcisse always wanted to generate embedded code automatically from control system models. As market trends emerged that included more powerful CPU cores with embedded floating-point units and more efficient code generation tools, he became involved in the Valeo EMS process definition and helped drive adoption of Model-Based Design with automatic code generation from prototyping to mass production. This included implementing rules for creating models and mandatory requirements for simulating models to produce the correct functional behavior.

Since 2008, continuous process improvements and feature enhancements in Embedded Coder have enabled Valeo to better meet project needs. This includes support for new AUTOSAR releases and quality-level targets, resulting in continued development cost reduction.

Real-Time Research Platform Applied to Sound Processing Research in Cochlear Implants and Hearing Aids

The multichannel cochlear implant is a unique technological achievement, representing the application of a novel combination of science, technology, and medicine. It brings functional hearing to severely or profoundly deaf individuals, transforming not only their lives but those of their families.

In 1985 Cochlear released the first commercial multichannel Nucleus® implant system. Four further generations have been released in the 25 years since. Innovations in mechanical design, electronics, and signal processing have brought successive improvements in device reliability and clinical outcomes. As of April 2011, over 144,000 registered Cochlear Nucleus® Implant Systems were in use globally.

Recent advances in acoustic signal processing for cochlear implants have produced incremental, but significant, improvements in cochlear implant recipients’ speech understanding. In the past, these improvements were constrained by laborious, time-consuming algorithmic implementation using proprietary digital signal processor (DSP) devices. This investment in process can limit the time available for creative work and hence restrict technological innovation.

Cochlear circumvented this innovation bottleneck with a rapid prototyping platform built with MATLAB and Simulink. The accelerated development process greatly reduced the time from conception to realization and increased the potential for future innovation.

This session describes how Simulink and xPC Target were integrated into a PC-based system with real-time capability. It will also provide examples of day-to-day contributions to people with Cochlear implants or hearing aids.

John Heasman John Heasman, Cochlear Ltd.
John Heasman has worked within the Research and Applications (R&A) department of Cochlear Ltd since 2002. He is involved with the management, clinical trial planning, and development of research internal and external to Research and Applications. John's experience includes implant failure analysis, neurophysiology, and audio signal processing. He has a B.E. in electrical and information systems engineering from the University of Sydney, NSW, Australia. From 1997 to 2001, he worked toward a Ph.D. in biomedical engineering within the Quadriplegic Hand Research Unit located at Royal North Shore Hospital, Sydney.

Red Cloud: On-Demand Research Computing

The Cornell University Center for Advanced Computing recently launched Red Cloud, an on-demand research computing service, for Cornell University researchers, their collaborators, and researchers at other academic institutions available by subscription. Red Cloud services are also available to industry through the center's corporate program. Red Cloud offers two services. The basic offering, Red Cloud, is an infrastructure as a service (IaaS) that runs Eucalyptus, the open source cloud computing platform. Subscribers have root access to virtual servers and virtual disks. The second offering, Red Cloud with MATLAB, is a software as a service (SaaS) that runs MATLAB Distributed Computing Server and features NVIDIA GPUs. Subscribers program applications on their desktops using their licensed copy of Parallel Computing Toolbox and then scale up to Red Cloud with MATLAB using MATLAB Distributed Computing Server. In this session, we will describe this flexible computing and data analysis resource and how it provides economies of scale that can benefit your research.

David A. Lifka David A. Lifka, Cornell University
David is the Director of the Cornell University Center for Advanced Computing and Director of Research Computing at Weill Cornell Medical College. He is an HPC industry veteran with over 20 years’ experience in management and technology leadership positions at Cornell and Argonne National Laboratory. His areas of expertise include parallel job scheduling and resource management systems, data management, high throughput systems, Web services, and cloud computing. Lifka has a Ph.D. in computer science from the Illinois Institute of Technology and serves on a number of academic and corporate advisory boards. His scheduling technologies have been commercially licensed by industry and he has received a ComputerWorld/Smithsonian award for innovations in IT. Most recently, Lifka was named Architecture and Design Coordinator for the NSF XSEDE program, which will be replacing and enhancing the former TeraGrid program.

System-Level Design of Mixed-Signal ASICs using Simulink: Efficient Transitions to EDA Environments

In common design flows for mixed-signal ASICs, Simulink models are used as executable specifications. Based on these specifications, analog and digital components are directly implemented in mixed-signal design environments. This step constitutes a large leap of abstraction. In this presentation, we address this aspect by showing and discussing an approach for automated transitions from Simulink models representing analog and digital components to HDL descriptions using Simulink HDL Coder. The analog Simulink models are translated into continuous-value discrete-time HDL descriptions and can serve as reference behavioral models in the mixed-signal design environment. For digital Simulink components, we integrated custom modeling guidelines into Simulink Model Advisor for achieving HDL descriptions that are optimized with respect to area and power consumption. An evaluation of the presented design flow is shown by applying the flow to an automotive hardware design.

Andreas Mauderer Andreas Mauderer, Robert Bosch GmbH
Andreas Mauderer received his diploma in computer science from the University of Karlsruhe in 2009. He is currently working at the Robert Bosch GmbH in the field of design methodology for automotive integrated circuits. His main interests are in system-level modeling of embedded systems.

Verification of High-Efficiency Power Amplifier Performance

This presentation describes the approaches and tools we have used to verify the operation of our modulator designs. We use MATLAB and Simulink across the entire development life cycle of our products from research, through design, to verification and automated testing.

Radio frequency (RF) power amplifiers (PAs) ensure that the source RF signal, such as a DVB, 3G, LTE, or 4G signals, is powerful enough for transmission. Because PAs use a fixed supply voltage, they draw maximum power whatever the amplitude of the signal, making them notoriously inefficient for amplitude modulated RF signals. Conventional PAs waste as much as 80% of the energy they consume as dissipated heat. The PAs in a cellular base station, for example, account for half the total power consumed.

Although envelope tracking was first described more than 60 years ago, it has not been applied commercially until recently, largely due to the difficulty of implementing a power supply modulator that meets the efficiency, bandwidth, and noise requirements of wideband signals such as multicarrier WCDMA, WiMAX, or DVB.

Nujira’s envelope tracking technology can double the efficiency of PAs and dramatically reduce power dissipation, which lowers energy bills and substantially reduces the amount of cooling required. It also enables higher device output power, allowing broadcasters to extend the range of existing broadcasting towers. Lastly, the wideband operation of Nujira’s technology enables broadcasters to use fewer PA designs to cover their target broadcast spectrum. For handset applications, the use of envelope tracking also improves PA linearity. This is achieved by dynamically modulating the amplifier’s supply voltage according to the RF signal passing through the device.

Sean Lynch Sean Lynch, Nujira
Sean Lynch manages the development of Nujira’s RF development platform, which is used both internally and externally by customers to develop, evaluate, and integrate envelope tracking products. He has more than 20 years of telecommunications experience, having worked with most of the leading telecommunications companies. In particular he managed the integration of TTPCom’s application suite with Intel’s first 3G chipset for a phone being designed by ASUSTek. Sean also successfully built the embedded software development group at Radiant Networks, which proved mesh wireless technology to British Telecommunications. While at Cambridge Consultants, he led the “fastest” Synchronous Digital Hierarchy node manager development for Nokia, and later managed their DECT Development Program. Sean holds a first class honours degree in electrical and electronic engineering.

Track 4 – Explore MATLAB and Simulink in Academia

Enabling Project-Based Learning with MATLAB, Simulink, and Target Hardware

Project-based learning is effective because students can see, hear, and touch what would otherwise be very abstract. In this presentation, we show how you can use MATLAB and Simulink to prototype, test, and run models on a broad range of low-cost target hardware in courses focused on:

  • Mechatronics
  • Circuit design
  • Programming
  • Controls
  • Robotics
  • Renewable energy

New with Release 2012a, Simulink can automatically generate standalone applications to run in real time on the BeagleBoard and LEGO® MINDSTORMS® NXT hardware platforms without the need for MATLAB Coder or Simulink Coder. Using this new capability, we explore integrating simulation and hardware to show the following concepts:

  • Reading sensors and writing to actuators
  • Interactive prototyping of algorithms for control and signal processing
  • Testing algorithms with physical hardware components
  • Deploying real-time algorithms to standalone hardware
  • Integrating algorithms with robots and real-world systems
Todd Atkins Todd Atkins, MathWorks
Todd Atkins is a member of the Educational Technical Marketing team at MathWorks who is exploring how best to work with universities to help prepare the next generation of engineers and scientists. He has been on the technical staff for five years in a number of roles including support, development, and marketing. Todd holds a B.S. and M. Eng. in electrical engineering and computer science from Massachusetts Institute of Technology. His research was in the fields of artificial intelligence and computer vision. Additionally Todd was a teaching assistant for MIT’s 6.001: Structure and Interpretation of Computer Programs course for three semesters.

Enhancing Project-Based Learning with Modeling and Simulation

Modeling and simulation with MATLAB and Simulink help students gain deeper insight into physical problems, complementing the advantages of project-based learning. Many employers find recruiting graduates from science, technology, engineering, and math (STEM) subjects with appropriate skills very difficult. Much of their concern revolves around big picture thinking, ability to approach problems from a multidisciplinary systems perspective, building mathematical models with varying complexity, and using software simulation tools such as Simulink in the overall verification and validation processes. Universities and colleges on the other hand have to educate students in the underlying principles and science as well as a rapidly expanding set of technologies to prepare them to be researchers, developers, engineers, and scientists. Integration of MATLAB and Simulink throughout the courses in a typical engineering curriculum, coupled with use of modeling and simulations to complement experiments and real-life projects, provides educators with a powerful set of tools to tackle the widening gap in the skills employers require from graduates and the need to cover the fundamentals of engineering science.

Coorous Mohtadi Coorous Mohtadi, MathWorks
Coorous is a member of MathWorks technical marketing team supporting universities focusing on the application of MATLAB and Simulink in laboratories and curriculum development. Prior to joining MathWorks in 2007, he was the European technical manager for Temperature, Process Control, and Component products at Omron Electronics Europe, where he worked in various capacities for 10 years. Before that, he was the chief control engineer for Eurotherm Controls for six years and a postdoctoral research fellow at Oxford and University of Alberta, Canada for four years. Coorous holds a D.Phil. in model-based predictive control and an M.A. in engineering science, both from University of Oxford.

A MATLAB Robot Control Interface for Education and Research

In this session, we present a hardware-in-the-loop robot control interface built on top of the MATLAB programming environment. Specifically, we leverage MATLAB integration with external languages to develop toolboxes to interface with off-the-shelf robotics simulators, sensors, and platforms. These tools are used to introduce programming and robotics concepts to mechanical engineering undergraduate students. In this presentation, we give an overview of our MATLAB based robot control interface and show how these tools are integrated into the classroom and used in our research.

M. Ani Hsieh M. Ani Hsieh, Drexel University
Dr. M. Ani Hsieh, assistant professor in the Mechanical Engineering and Mechanics Department, serves as the principal investigator of the Scalable Autonomous Systems (SAS) Lab, with Ph.D. candidates T. William Mather, James Worcester, and Kenneth Mallory. The SAS Lab is an interdisciplinary lab focusing on fundamental research in multi-agent robotic systems with a focus in decentralized control and coordination. The facility is housed in Drexel University's Mechanical Engineering and Mechanics Department and the lab’s members consist of undergraduate and graduate students from the Mechanical Engineering, Electrical and Computer Engineering, and Computer Science departments.

Real-Time Control and Analysis in Biomedical Applications Using MATLAB

The MATLAB product family enables real-time data acquisition and processing, but also gives the user flexibility in implementing their own extensions and interfaces to custom hardware. In this presentation, we show how we use the MathWorks tool chain to support our research in biomedical engineering, from data acquisition to real-time closed-loop control solutions, with examples on how different real-time requirements can be addressed and how the MATLAB APIs can be used to interface with external custom devices.

Henrik Gollee Henrik Gollee, University of Glasgow
Henrik Gollee has been a user of MATLAB for over 15 years. He is a lecturer in Control at the School of Engineering, University of Glasgow. His research interests are in the application of system analysis and control methods in rehabilitation engineering with a primary focus on applications in spinal cord injury. He is working closely with the Queen Elizabeth National Spinal Injuries Unit and is a member of the Scottish Centre for Innovation in Spinal Cord Injury. Previously, he worked with the Advanced Development Department for Vehicle Dynamic Systems at Daimler AG, Stuttgart. He graduated from the Department of Electrical Engineering, Technical University Berlin, Germany, and has a Ph.D. in systems and control from the University of Glasgow, UK.

Teaching Modern Physics with MATLAB: Simulations and Experiments

There is strong agreement within the physics community as to the important role of computation in physics, but at the undergraduate level, finding room for it in an otherwise crowded curriculum is difficult. There are many ways in which to expose students to computational physics. In a majority of physics departments, there is a separate one-semester course in computational techniques. In some cases, when such a course is not offered, individual faculty have embedded computation into their existing physics courses on an ad hoc basis. A few schools have developed approaches that integrate computation throughout the curriculum.

In this session, I present one such approach, concentrating on the sophomore-level Modern Physics course whose laboratory serves as an introduction to computational physics at the University of St. Thomas. In this course, we use simulations written by the students in MATLAB to increase their understanding of the physical systems studied, to explore the limitations of theory, and to relate theory to experiment. I talk about the advantages and challenges of this approach, and show a few examples of course materials.

Marie Lopez del Puerto Marie Lopez del Puerto, University of St. Thomas
Dr. Marie Lopez del Puerto is an assistant professor in the Physics Department at the University of St. Thomas in St. Paul, Minnesota. Her research interests include the structural, optical, and electronic properties of nanoscale systems, computational physics, and physics and engineering education. She has a B.S. in physics from Universidad de las Americas - Puebla in Puebla, Mexico, and a Ph.D. in physics from the University of Minnesota - Twin Cities in Minneapolis, Minnesota.

The Vanderbilt Haptic Paddle: Educational Haptics Using Simulink

In this presentation, we introduce the Vanderbilt University Haptic Paddle, a one-degree-of-freedom force-feedback robot used in teaching system dynamics. The haptic paddle was originally developed at Stanford University and has since been adapted by several other institutions. At Vanderbilt University, we have contributed to the evolution of the paddle, particularly in its software platform.

We have transitioned the software of the haptic paddle from its original C architecture to the MATLAB and Simulink environment, enabling students to take on a much greater role in modeling and controlling their paddle. Throughout the course of the laboratory, students learn how to build simple Simulink models; interface them with the paddle, which runs on an Arduino board; and compare experimental data collected in real-time with model predictions. Further, students interact with virtual systems in Simulink and actually “feel” the system’s response. Thanks to the Simulink environment, all of these exercises are done without the challenges often associated with low-level programming and encourage students to engage in inquiry and reflection.

This session introduces the haptic paddle and its laboratories, with an in-depth look at each lab.

Jenna L. Gorlewicz Jenna L. Gorlewicz, Vanderbilt University
Jenna L. Gorlewicz is currently in the fourth year of her Ph.D. work in mechanical engineering at Vanderbilt University, and she is a National Science Foundation Graduate Research Fellow. She was the recipient of a MathWorks Education Curriculum Grant for her work in designing and assessing engineering curriculum using haptic paddles in MATLAB and Simulink. Her current research interests are in developing novel devices and methods for engineering education, including haptic touchscreen interfaces to help teach graphical mathematics concepts to blind children. She received her B.S. in mechanical engineering from Southern Illinois University Edwardsville.

Louis B. Kratchman Louis B. Kratchman, Vanderbilt University
Louis B. Kratchman joined the Medical and Electromechanical Design Laboratory at Vanderbilt University in 2009, where he is currently working toward a Ph.D. in mechanical engineering. He was the recipient of a MathWorks Education Curriculum Grant for work in designing and assessing engineering curriculum using haptic paddles in MATLAB and Simulink. His research interests include medical robotics, parallel robots, image-guided surgery, and haptics. Currently he is developing tools and techniques for minimally invasive cochlear implant surgery. He has a B.S. in mechanical engineering and a B.A. in psychology, both from the University of Michigan, Ann Arbor.

Sesiones en Español

Generación de código C y C++ desde MATLAB con MATLAB Coder

En este presentación, mostraremos el flujo de trabajo para generar código legible y portable C y C++ desde sus algoritmos de MATLAB® utlizando MATLAB Coder. Se pueden introducir los requisitos de implementación de los algortimos escritos en MATLAB, bien usando la herramienta gráfica de gestión de proyectos o en la línea de comandos, y después generar el código fuente legible, ejecutable compilado, o una libería que puede ser compartida con terceros.

El Ingeniero de MathWorks le enseñará además cómo generar automáticamente funciones MEX para verificar el comportamiento del código generado en MATLAB y para acelerar el cálculo de partes intensivas de código MATLAB mediante su ejecución a velocidad de compilación.

Carlos Osorio

Carlos Osorio, MathWorks
Carlos recibio su Bachillerato en Ciencias de la Pontificia Universidad Catolica del Peru y su grado de Maestria en Ciencias de la Universidad de California en Berkeley, ambos en Ingenieria Mecanica. El se especializa en Sistemas de Control Automatico y Dinamica de Vehiculos. Antes de unirse a MathWorks en Octubre del año 2007, el trabajo en la industria automotriz en el Departamento de Tecnologia Avanzada de Chasis de Vehiculos en la corporacion Visteon, donde se dedico al desarrollo e implementacion de prototipos de sistemas de suspension electronica activa y semi-activa, direccion-por-cable y freno-por-cable para vehiculos de pasajeros.

Modelado Matemático con MATLAB

La familia de productos de MATLAB te permite construir modelos matemáticos para hacer predicciones y optimizar el comportamiento de sistemas complejos. En esta sesión demostramos como puedes:

  • Desarrollar modelos usando regresión de datos y a partir de principios fundamentales.
  • Simular modelos y desarrollar rutinas para post-procesamiento.
  • Generar reportes que documentan los modelos y los resultados de las simulaciones.

La sesión cubre el uso del lenguaje MATLAB, expresiones simbólicas, herramientas gráficas para tareas específicas de modelado, así como otras estrategias que se pueden usar para desarrollar modelos.

Gerardo Hernandez Correa

Gerardo Hernandez Correa, MathWorks
Gerardo ostenta un título en Física de la Universidad de Puerto Rico en Mayagüez y una Maestría an Matemáticas Aplicadas de la misma institución. Su área de investigación durante sus estudios de Maestría se enfocaron en el estudio de la teoría de distribuciones y en problemas inversos, en particular el problema de la identificación de sistemas lineales. En su tesis de Maestría, Gerardo diseñó e implementó en MATLAB un método iterativo y no destructivo para reconstruir el kernel de convolucion de sistemas lineales a partir de datos experimentales.

Gerardo también ostenta una Maestría en Ingeniería Mecánica de WPI y actualmente se encuentra completando los requisitos para un Doctorado en Matemáticas en la misma institución. En sus tesis de doctorado "An adaptive, multiresolution agent-based model of glioblastoma multiforme", Gerardo diseñó e implementó en MATLAB un modelo adaptativo de resolución variable de la evolución de Tumores cerebrales, en particular de Glioblastoma Multiforme.

Entre las áreas de interés de Gerardo se encuentra el Análisis Numérico, en particular, methodos numéricos para ecuaciones diferenciales parciales y ordinarias, sistemas dinámicos, computación en paralelo, etc.

Predicción de Demanda de Electricidad Utilizando Redes Neuronales Preentrenadas

En esta presentación, Gonzalo Mora compartirá su experiencia creando un modelo para predecir la demanda de energía eléctrica con la ayuda del Neural Network Toolbox.

La presentación muestra cómo entrenar redes neuronales utilizando MATLAB y Neural Network Toolbox. Los comandos utilizados para entrenar las redes son nftool y nctool. Con la red entrenada grabada dentro de un archivo *.mat, el siguiente paso es crear un GUI (Graphic User Interfase – Interface Gráfica de Usuario) para obtener la proyección de demanda eléctrica. La entrada del GUI es la muestra de la demanda, y cada cinco minutos la información se pasa a un archivo de Excel. El GUI toma la entrada, utilizando dos tipos de redes y menús de GUI que el usuario de la interface tiene que usar. El GUI produce dos proyecciones: una cada 30 minutos con datos sobre la demanda del día, lo cual es el objetivo, y otra con la demanda de cada hora. El último paso es crear la aplicación independiente sobre el sistema operativo Linux (Ubuntu), la cual puede correr sin MATLAB.

Gonzalo Mora Jiménez

Gonzalo Mora Jiménez, Instituto Costarricense de Electricidad
El Ing. Gonzalo Mora obtuvo el grado de Ingeniero Eléctrico de la Universidad de Costa Rica, y obtuvo su grado de Maestría con énfasis en Sistemas de Alta y Media Potencia en la misma institución. En el ámbito académico, ha sido profesor en la Universidad de Costa Rica desde el 2008. Del 2002 al 2009 trabajó como Auditor de Energía para la Compañía Nacional de Fuerza y Luz, y a partir del 2009 ha trabajado en el Instituto Costarricense de Electricidad como Investigador en Sistemas de Potencia, y ha estado involucrado en discusiones de varios tópicos sobre el Sistema Nacional Interconectado de Costa Rica.

Sintonización de PID de forma sencilla

Aprenda como diseñar sistemas de control en Simulink usando un motor DC como ejemplo. Crearemos modelos de sistemas dinámicos y luego les mostraremos como diseñar controles de retroalimentación, ajustando un controlador PID para el motor. También verán como probar el controlador mediante simulaciones y como generar código C para desplegar en microprocesadores embebidos.

Carlos Osorio

Carlos Osorio, MathWorks
Carlos recibio su Bachillerato en Ciencias de la Pontificia Universidad Catolica del Peru y su grado de Maestria en Ciencias de la Universidad de California en Berkeley, ambos en Ingenieria Mecanica. El se especializa en Sistemas de Control Automatico y Dinamica de Vehiculos. Antes de unirse a MathWorks en Octubre del año 2007, el trabajo en la industria automotriz en el Departamento de Tecnologia Avanzada de Chasis de Vehiculos en la corporacion Visteon, donde se dedico al desarrollo e implementacion de prototipos de sistemas de suspension electronica activa y semi-activa, direccion-por-cable y freno-por-cable para vehiculos de pasajeros.

中文会场

航空航天行业主题:运用MATLAB/Simulink 开发航空航天防御控制系统

我们将重点介绍:

  • 航空航天防御控制系统开发在全球的主要发展趋势
  • 控制系统设计的通用开发过程
  • 如何使用MATLAB/Simulink进行具有挑战性的开发,测试和验证与确认
  • 针对控制系统设计的MATLAB/Simulink中的关键技术和功能
张灵惠

张灵惠, MathWorks
MathWorks公司亚太地区高级项目支持总负责人,主要负责领导技术团队去指导帮助亚太地区的客户采用和实施基于模型的设计和自动代码生成。

2001年加入MathWorks总部至今,曾有7年从事MathWorks自动代码生成产品的开发以及针对大客户的高级项目支持,包括美国三大汽车公司,Caterpillar, 波音,NASA, Toyota等。

她曾就职于中国空间技术研究院,技术项目主管和教授,从事卫星和飞船控制系统的设计开发。获得美国Akron大学机械工程硕士学位及中国空间技术研究院自控硕士学位。

通信行业主题:使用 MATLAB 加速4G通信系统的设计

我们将对4G LTE无线系统的核心模块展开建模,仿真、加速和验证。在演讲过程中,我们将通过一系列的具体通信系统案例介绍最新的通信和信号处理工具的使用,如何使用MATLAB Coder进行C代码优化和并行加速,以及定点化和流式信号处理。最后,我们还将邀请业界领先企业和您共享他们的成功案例。

成功案例: 上海贝尔LTE加速LTE系统仿真和算法开发

我们会讨论上海贝尔LTE算法开发团队使用MathWorks产品实现LTE系统仿真的经验,包括:

  • 运用MATLAB和可视化模块高效开发LTE的物理层算法
  • 运用MATLAB并行计算加速LTE系统仿真
  • 运用MATLAB Coder进一步加速LTE系统仿真
  • MATLAB仿真链路和DSP代码输出结果的比较验证
陈建平

陈建平, MathWorks China
MathWorks 中国区高级应用工程师,专注于信号处理和通信方向,于北京大学获得电子学学士学位和通信系统硕士学位。在加入MathWorks之前,他在NTT DOCOMO北京研究中心从事无线通信技术的研究工作,研究范围包括MIMO检测和均衡技术,信息论和信道编码。他有多年从事FPGA设计和无线通信系统 的设计经验。

江浩博士, 上海贝尔.阿尔卡特朗讯股份有限公司
MathWorks 中国区高级应用工程师,专注于信号处理和通信方向,于北京大学获得电子学学士学位和通信系统硕士学位。在加入MathWorks之前,他在NTT DOCOMO北京研究中心从事无线通信技术的研究工作,研究范围包括MIMO检测和均衡技术,信息论和信道编码。他有多年从事FPGA设计和无线通信系统 的设计经验。

嘉宾演讲者:江浩博士,上海贝尔.阿尔卡特朗讯股份有限公司,从事LTE系统中基站信号处理和检测算法的设计和开发工作,其专注于LTE eNodeB的上行接收算法,特别是FDD和TDD

LTE系统中随机接入信道(RACH)检测算法的设计和实现工作。毕业于北京邮电大学,获信号与信息处理专业博士学位,曾先后供职于华为科技有限公司和松下电器研究开发中国有限公司,从事3GPP LTE标准化工作中的关键技术研究。

汽车行业主题: 基于模型的设计在汽车控制系统开发中的运用

基于模型的设计理念在汽车控制系统设计方面得到了广泛的应用。本次研讨会将与大家共同探讨这一先进的设计理念在汽车控制系统的软件开发中的具体运用。主要内容包括控制模型的构建与模型构架,模型的测试与验证,代码生成技术,系统的集成等。详细介绍快速原型控制软件与产品控制软件的比较,产品控制器软件的开发思路与流程等。

成功案例:上海捷能汽车技术有限公司-

基于模型设计在新能源汽车控制系统开发中的应用

新能源汽车,无论是混合动力汽车还是纯电动汽车,捷能在开发整车控制策略过程中,开发工具链的核心都是围绕着MathWorks 公司的MATLAB/Simulink.

从整车模型的开发到整车性能仿真,从控制策略开发到代码生成,从单元测试到MIL/HIL,MATLAB/Simulink为我们提供了直观而强大的功能。

董淑成

董淑成, MathWorks China
MathWorks公司中国区高级应用工程师,主要负责自动代码生成在汽车及其他工业领域中的应用。毕业于北京航空航天大学,并获得控制工程硕士学位。加入MathWorks之前曾供职于德尔福中国研发中心,长期从事汽车电子产品软件开发,在基于模型设计的工具链应用以及流程优化方面有丰富的经验。

蒋新华, 上海捷能汽车技术有限公司
嘉 宾演讲者:蒋新华,上海捷能汽车技术有限公司主任工程师,主要负责新能源汽车整车控制器的软件集成和软件测试工作。毕业于同济大学,并获得控制理论与控制 工程硕士学位。加入捷能之前曾供职于华为上海研究所,长期从事嵌入式产品软件开发,在基于模型设计的工具链、代码生成、集成及测试方面有丰富的经验。

能源行业主题: MATLAB工具链在能源领域的应用

MathWorks工程师将通过产品研发的V流程,为您介绍能源领域可能用到的MATLAB产品工具链,包括建模仿真,模型校准,控制算法开发,代码生成以及测试验证。同时,会以一个演示示例介绍风能领域中发电机励磁系统的数学建模和校准验证。 最后,会与您分享业内人士如何运用MATLAB软件进行项目开发。

成功案例:通用电气(中国)有限公司 - 使用MathWorks工具链进行电机控制设计

在电动大巴的电机驱动研发过程中,GE引入了基于模型设计(Model-Based Design)的开发流程。这个流程把仿真、实现和验证结合在一起,可以大大提高开发效率,也是高度依赖与工具链的一种方法。MathWorks提供了完整的工具链,支持从建模仿真,代码生成,SIL,PIL和HIL的所的过程,加速电动大巴的控制器的设计与实现。

吴菁

吴菁, MathWorks China
MathWorks 公司中国区高级应用工程师,机械电子工程硕士,专注于控制系统设计的行业应用。她从北京航空航天大学毕业,获得机械电子工程学士学位,最初在中国运载火箭 技术研究院(CASA),从事运载火箭的推力矢量控制系统的开发工作,并获得硕士学位。曾在恒润科技(MathWorks的分销商)担任控制应用工程。

吴恒博士, GE通用电气全球研发中心信号与信息处理实验室
嘉宾演讲者:吴恒博士, GE通用电气全球研发中心信号与信息处理实验室-研发工程师,主要负责电动汽车驱动的控制器设计。毕业于中科院上海技术物理研究所, 获得电路与系统博士学位。长期从事嵌入式产品的软硬件开发。

日本語トラック

数学的モデリングその1 数式処理と数値計算の連携

数式処理と最適化計算をMATLAB上で連携させるデモンストレーションをご紹介します。

数学モデルは、複雑なシステムの挙動を理解し、正確なシミュレーションを行う上で不可欠なものです。MathWorks 製品には、データと科学原理に基づいた数学モデルの開発に必要なツールが用意されています。

MATLABによる数学的モデリングをご紹介するセミナーの前編として、本セッションでは、具体的な数式が手元にある場合を取り上げます。

ガントリークレーンの運動方程式を数式処理ツールSymbolic Math Toolboxを用いて解きます。そして、その解を最適化するためにOptimization Toolboxを、更により広域での最適解を探索するためにGlobal Optimization Toolboxを利用します。

対象:

  • 数式モデルとその数値解法に興味のある方
  • 数式モデルとその数値解の最適化に興味のある方
  • Symbolic Math Toolboxに興味がある方
中川 慶子

中川 慶子, MathWorks Japan アプリケーションエンジニア。
大学院在籍時よりMATLABを使用し、電磁場に関する諸方程式の解析的及び数値的解法に取り組む。それを半導体デバイスシミュレーション等の実務に活かす。現在はMATLABの未経験者から中級ユーザーを対象に、より広いMATLAB活用方法の提案と情報提供する活動に注力している。

大学教育における MATLAB/Simulink の活用

MATLAB/Simulink プロダクト

ファミリは、大学教育における必須の科学技術計算用プログラミング言語として、全世界5,000 校以上の大学に導入され、工学・科学分野の効率的な学習、教育、研究に活用されています。

MATLAB/Simulink を利用されたことのない教員の方を対象に、大学教育においてどのように導入し活用すれば効果が期待できるか、統計学と信号処理を例にご説明します。

対象製品

  • MATLAB
  • Simulink
  • DSP System Toolbox
沖田 芳雄

沖田 芳雄, MathWorks Japan アカデミックテクニカルエバンジェリスト。
修士課程終了後、沖電気工業に入社。信号処理技術の研究開発、新規ビジネス開発、技術マーケティング等に従事。在職中、社内制度により南カリフォルニア大学大学院に留学、アレイ信号処理の研究によりEngineer’s Degreeを取得。帰国後、留学中出会ったMATLABの普及活動を社内外で展開。MATLABを用いた信号処理の技術セミナーの講師を担当、共著出版。2011年よりマスワークスジャパンにてMATLAB/Simulinkの学術・教育利用、カリキュラム開発・導入支援を担当。

モデルベースデザインによる制御系設計入門

モデルベースデザイン (MBD)

は、開発効率向上や検証作業の前倒し等、制御系設計の課題を解決するための方法として、実機の代替となるモデルを活用した開発アプローチです。MATLAB/Simulink は MBD の中心となるソフトであり、制御ソフト/制御装置/制御対象のモデリングからシステム応答解析、シミュレーションによる仮想実験、モデル生成コードを用いた実機実験までをシームレスに行うことができます。本セッションでは、MBD の概要および

MATLAB/Simulink 製品を用いた制御系設計の“いろは”についてデモを中心にして紹介します。

対象:

  • 制御系設計者、MATLAB/Simulink に興味のある方
  • キーワード:
  • モデルベースデザイン、モデルベース開発、MBD、制御系設計
山本 順久

山本 順久, MathWorks Japan シニアアプリケーションエンジニア。
MATLAB/Simulink関連業務に10年以上携わり、現在は組込み制御システム/ソフトウェアに関するモデルベースデザインの紹介や導入支援、顧客課題解決を担当している。ETロボコンで採用されているLEGO倒立振子ロボットの制御プログラム開発を担当(http://www.youtube.com/watch?v=4ulBRQKCwd4)。

具体例で学ぶ信号処理ワークフロー~すぐに使えるテクニック: 実践編~

MATLABには周波数解析を行う関数だけでも十個以上もあり、フィルタを設計する関数に至っては何十個もあるので、とっつきにくく難しいと感じる方も多いのではないでしょうか?このセッションでは簡単な音声信号処理の例を用いて、MATLABでどのように解析・設計を行い、さらにSimulinkでどのようにシミュレーションを行うことが出来るのかをデモベースでご紹介いたします。

信号処理分野の業務に従事するエンジニアで、フィルタ設計、周波数解析、適応フィルタに興味があり、業務をもっと効率的にしたい方は必見です。

なお、プレゼンテーションの後に、参加者からのご質問にお答えするQ&Aセッションがあります。

参加対象者:

  • 信号処理分野の業務に携わるエンジニア
松本 充史

松本 充史, MathWorks Japan シニアアプリケーションエンジニア
大学院で現代制御理論を用いた電源制御の研究でMATLAB/Simulinkを初めて利用しその便利さに感動する。その後電子機器メーカーに入社し信号処理分野のアルゴリズム開発および実装業務に従事する。現在はC/HDLコード生成ツールや信号処理分野のツールを担当し、モデルベースデザインによる開発フローを広めるべく活動している。

한국어 세션

MATLAB을 이용한 수학적 모델링

MATLAB으로 수학적 모델링을 통해 사용자는 복잡한 시스템의 특성을 파악하실 수 있으며 최적화를 통해 성능 향상을 하실 수 있습니다. 이번 세션에서 소개되는 내용은 아래와 같습니다.

  • 데이터 피팅(fitting) 및 데이터 기반으로 모델을 도출
  • 모델의 시뮬레이션 및 후처리 과정
  • 모델의 특성 및 시뮬레이션 결과의 보고서 생성

이번 세션에서는 MATLAB, 심볼릭 컴퓨팅, 그래프 작성 기능을 이용하여 수학적 모델링에 있어서 일련의 과정에 대해 소개해 드립니다.

엄준상 대리

엄준상 대리, MathWorks Korea
엄준상 대리는 MathWorks 한국지사에서 Application Engineer로 근무하고 있습니다. MathWorks 제품중 수학, 데이터 분석, 최적화, 병렬처리관련 제품을 담당하고 있습니다.

시뮬링크를 이용한 손쉬운 PID 제어기 설계

본 세션에서는 시뮬링크가 제공하는 손쉬운 PID 제어기 설계방법을 소개할 것입니다. 데모를 통해 어떻게 PID 제어기를 설계 튜닝하고, 연속에서 불연속 시스템으로의 변환, output saturation, anti-windup protection, gain-scheduling, 외란제거 등과 같은 PID 제어기 설계 시 나타나는 까다로운 문제들을 어떻게 해결하는지 설명합니다.

김종헌 과장

김종헌 과장, MathWorks Korea
김종헌 과장은 ㈜ 만도와 LG 전자에서 9년간 자동차 샤시 전자 제어시스템 개발을 했으며, MathWorks 한국지사에서 시뮬링크와 Control Design 제품과 관련하여 Application Engineer로 일하고 있습니다.

MATLAB을 이용한 통신 기본 이론으로부터 4G LTE 시스템 모델링하기

본 세션에서는 4G LTE 무선 통신 시스템의 주요 부분을 MATLAB으로 분석, 설계, 시뮬레이션,최적화 및 구현하는 과정을 소개합니다. OFDM(Orthogonal frequency-division multiplexing)과 MIMO(multiple input, multiple output) 기술은 현대 통신 시스템의 가장 중요한 부분으로서, 굉장히 복잡한 기술을 필요로 합니다. 따라서 많은 회사들이 차세대 무선통신 시스템의 최적화 및 구현을 가속화 하기 위해서 MATLAB과 Simulink을 채택하고 있습니다. 본 세션에서는 MATLAB 데모를 통해 간단한 통신시스템에서부터 시작해 통신 모듈들을 점차적으로 추가해가면서 최종적으로 프로토타입의 4G LTE 시스템 모델링을 보여 드릴 예정입니다.

이번 프리젠테이션에서는 아래와 같은 내용을 다루게 됩니다.

  • MATLAB을 이용한 통신시스템 의 모델링, 시뮬레이션 및 성능 시각화
  • Communication System Toolbox을 이용하여 변조기, 채널 모델, 길쌈부호기, 터보코드, MIMO 및 OFDM과 같은 모듈들을 전체 하나의 시스템으로 모델링.
  • 채널 특성에 따라 변조 수준을 달리하는 적응변조방식을 통한 시스템수준의 성능 분석 수행
  • 각 단계에서 병렬 프로세싱, 코드 생성, 효과적인 알고리듬, GPU프로세싱등을 통한 MATLAB 시뮬레이션 속도 가속화 데모
  • 만들어진 모델로 부터 MATLAB Coder™를 이용하여 MATLAB 코드를 C코드로 생성한 뒤, C코드 단독으로 실행 및 테스트
  • VHDL 또는 Verilog 코드를 생성한 뒤 FPGA로 구현 데모
김용정 부장

김용정 부장, MathWorks Korea
김용정 부장은 C&S Technology, 삼성전자 등에서 8년간 신호처리 및 통신시스템 설계를 했으며, MathWorks 한국지사에서 신호처리 및 통신 시스템 관련 분야를 담담하고 있습니다.

MATLAB Coder를 이용한 C 코드 생성

본 세션에서는 MATLAB Coder를 소개하고, MATLAB 알고리즘으로부터 C 코드를 생성하기 위한 전반적인 워크플로우를 설명합니다. 구성은 다음과 같습니다.

  • MATLAB 알고리즘으로부터 PC에서 실행 가능한  Standalone Executable 생성
  • 자동으로 MEX를 생성하여 MATLAB 알고리즘을 가속화
  • 설계된 알고리즘을 컴파일 된 Library로 만들어 Custom Software와 통합
  • 핸드코딩 수준의 C 소스코드 생성
김용정 부장

이웅재 차장, MathWorks Korea
이웅재 차장은 LG Innotek과 LIG Nex1에서 10년간 전자전, 통신 분야에서 일했으며, MathWorks 한국지사에서 신호처리 및 통신분야의 Application Engineer로 일하고 있습니다.