MATLAB Virtual Conference 2013


Keynote Presentation

Embracing Complexity

Jim Tung, MathWorks

Complexity is neither good nor bad. It is a reality of life. It is a condition that must be addressed whether you want to build a smart system or understand the world around you. So, how can we, as engineers and scientists, improve our ability to deal with complex challenges and perform increasingly complex tasks? In this presentation, MathWorks Fellow Jim Tung discusses how companies and universities are creating and adopting new ways to master the development of complex systems and the analysis of complex phenomena, using MATLAB and Simulink.

About the Speaker

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.

Discover MATLAB and Simulink

Signal Analysis and Measurement Techniques in MATLAB

Graham Reith, MathWorks

In this presentation, we illustrate techniques for generating, visualizing, and analyzing digital signals across various applications. Using MATLAB® functions and apps, we show you how to perform signal processing tasks such as spectral analysis, windowing, filtering, signal measurements, and statistical signal processing. Using Signal Processing Toolbox™ and DSP System Toolbox™, we demonstrate how you can:

  • Import and visualize signals in the time domain
  • Perform time-domain measurements of digital data and characterize the performance of levels, pulses, and transitions
  • Display time-domain measurements for streaming data using the Time Scope block in MATLAB
  • Apply spectral estimation techniques such as periodogram, Welch, and Yule-Walker to understand signal characteristics in the frequency domain
  • Use apps to accelerate signal analysis, filter design and analysis, and window design and analysis

This presentation is applicable to scientists, engineers, and students who may or may not be signal processing experts.

About the Speaker

Graham is an industry manager for the Communications, Electronics and Semiconductor sector. He works with leading organizations in Europe and Asia to adopt MathWorks technology in their development workflows. Graham joined MathWorks as an engineer in 2002, specializing in Signal Processing and Communications applications and design flows to embedded processors and FPGAs. Prior to MathWorks, Graham worked in applied research for the U.K. Government, working on high-performance signal processing applications. He has a master’s degree in electronic engineering from the University of York, U.K.

Machine Learning with MATLAB

Stefan Duprey, MathWorks

Learn how to get started using machine learning tools to detect patterns and build predictive models from your datasets. In this session you’ll learn when, why, and how to select from a variety of machine learning methods for:

  • Clustering: segmenting data into natural subgroups
  • Classification: building a model to predict groups for new observations
  • Regression: building a predictive model from continuous observations

Techniques include K-means clustering, discriminant analysis, decision trees including boosting and bagging, neural networks, and linear, logistic, and nonlinear regression.

About the Speaker

Stefan joined MathWorks in 2010 as an application engineer specializing in computational finance. Prior to that he worked as an IT quant in charge of the exotic products at the French bank Société Générale and developed algorithmic trading models at Crédit Agricole. In both roles he used machine learning to develop and deploy quantitative models. Stefan holds a master’s degree in engineering from the Ecole des Mines, a Ph.D. in applied mathematics and computer science from Institut National de Recherché en Informatique Appliquée, and a master’s degree from ESSEC Business School in mathematical computational finance.

Model-Based Design for Control Systems

Terry Denery and Sam Mirsky, MathWorks

In this presentation, you’ll learn how to use MATLAB and Simulink to develop an embedded control system including implementation and testing on hardware. The demonstration emphasizes how to design, simulate, and test a complex system that incorporates multiple domains—such as mechanical, electrical, and hydraulic—that are typically isolated across different software platforms and not simulated in a common framework.

Starting from underlying physical principles, we build models that can expand and accelerate the development process through simulation. You see how to simulate the controller and the plant together, optimize the control system, and generate code for HIL testing—all before building a prototype. We demonstrate the integration of simulated and real components to enable hardware-in-the-loop (HIL) testing in support of early verification and validation (V&V) against top-level requirements.

You will learn how to:

  • Build models of mechanical, electrical, and control software components
  • Employ models to optimize component designs against system requirements
  • Use models to support the production and testing of integrated systems

About the Speakers

Terry is an expert in modeling. He runs hundreds, if not thousands, of simulations to pursue the best design of electrical, mechanical, and control systems. Sam’s forte is working with the hardware. Their two skill sets complement each other perfectly. Using MATLAB and Simulink products, Sam can rapidly prototype Terry’s best design, and prove whether it will really work. This could not be done without good modeling and rapid conversion of these models into real hardware systems.

Programming with MATLAB

Loren Shure, MathWorks

MATLAB is a high-level language that includes mathematical functions for solving engineering and scientific problems. You can produce immediate results by interactively executing commands one at a time. However, MATLAB also provides features of traditional programming languages, including flow control, error handling, and object-oriented programming (OOP). Attend this session to learn more about programming capabilities in MATLAB and how to be more productive working with MATLAB.

Topics include:

  • Basics of the MATLAB programming language
  • Automating tasks using scripts and functions
  • Tools for efficient development

About the Speaker

Loren is a principal MATLAB developer and has worked at MathWorks for over 25 years. She has co-authored several MathWorks products in addition to adding core functionality to MATLAB. Loren currently works on the design of the MATLAB language.

She graduated from the Massachusetts Institute of Technology with a B.Sc. in physics and has a Ph.D in marine geophysics from the University of California, San Diego, Scripps Institution of Oceanography. Loren writes about MATLAB on her blog, The Art of MATLAB.

Find Out What's New

From Apps to Web Services: Sharing the Work You’ve Done in MATLAB

Bonita Vormawor, MathWorks

Using MATLAB and add-on toolboxes, you can create complete applications or algorithms for sharing with others or for incorporating into other applications. You can create a packaged MATLAB app for others to use in their own MATLAB sessions. With deployment tools including MATLAB Compiler™ and MATLAB builder products, you can share your work outside of MATLAB by packaging your MATLAB applications as an encrypted standalone executable, C/C++ shared library, or a component for use with Java®, .NET, or Excel®. MATLAB Production Server™ lets you run MATLAB programs as a service within your production systems, enabling you to run MATLAB programs as a part of web, database, and enterprise applications.

Attend this session to learn more about sharing the work you do in MATLAB with others. Session examples highlight new functionality from recent releases, including packaging MATLAB apps and MATLAB Production Server.

About the Speaker

Bonita joined MathWorks in 2011. She came with years of experience in numerous professional roles across various industries. Her past titles include senior software engineer, associate scientist, laboratory administrator, database developer, and research assistant. She previously worked at Raytheon Company, Polaroid, Tufts University School of Dental Medicine, and the University of Pennsylvania School of Medicine. Bonita earned an M.S. in computer science from the National Technological University and a B.A. in both computer mathematics and natural sciences from the University of Pennsylvania.

Using Microsoft Kinect with MATLAB and Simulink

Bruce Tannenbaum, MathWorks

Microsoft Kinect for Windows® has received attention in research and engineering communities as a low-cost 3D imaging system and natural interaction device with uses in robotics, kinesiology, civil engineering and other areas.

In this presentation, discover how to use the Kinect with MATLAB and Simulink. Product demonstrations show how to:

  • Configure Kinect using Image Acquisition Toolbox™ to acquire video, 3D depth, and skeleton tracking data in MATLAB
  • Configure Kinect with DSP System Toolbox to acquire 4-channel audio
  • Design algorithms in MATLAB that leverage data acquired from Kinect
  • Design systems in Simulink that leverage data acquired from Kinect

About the Speaker

Bruce works on image processing and computer vision applications in technical marketing at MathWorks. Earlier in his career, he developed computer vision and wavelet-based image compression algorithms at Sarnoff Research Center (now SRI). He holds an M.S.E.E. degree from University of Michigan and a B.S.E.E. degree from Penn State.

Design and Prototype Real-Time DSP Systems with MATLAB

Houman Zarrinkoub and Youssef Abdelilah, MathWorks

In this presentation, we demonstrate how you can use MATLAB to develop real-time DSP algorithms and test benches with the latest features in DSP System Toolbox.

We showcase an acoustic tracking system that uses the acoustic sensors in Microsoft® Kinect®. You’ll learn how to:

  • Use a library of highly efficient algorithm components (System objects™) to develop your real-time DSP IP
  • Automatically generate C code to accelerate simulation or integrate designs with other software
  • Discover and interface to your signal processing hardware more easily
  • Test your design with real-time streaming data

About the Speakers

Houman is a senior product manager responsible for Communications System Toolbox. He joined MathWorks in 2001 as a development manager for signal processing products. Prior to MathWorks, he spent six years at Nortel Networks as a research engineer specializing in speech processing for wireless systems. He holds a B.S.E.E. from McGill University and an M.S.E.E. and a Ph.D. from the Institut Nationale de la Recherche Scientifique, Universite du Quebec.

Youssef is a senior product manager responsible for DSP System Toolbox. Prior to MathWorks, he spent over 20 years in positions ranging from engineering to management. He has experience in digital signal processing and communications, and solar renewable energy. He holds 19 U.S. patents. He studied electrical and computer engineering at the University of Texas at Austin, New Jersey Institute of Technology, and North Carolina State University.

The New Look and Feel of Simulink: Why Modeling and Simulation Just Got Easier

Saurabh Mahapatra, MathWorks

In this session, we explore new capabilities that change the way you model and simulate with Simulink. Topics include:

  • A brand-new graphical design environment
  • New analysis and simulation testing tools
  • Built-in support to run models directly on low-cost target hardware
  • Modeling control logic and discrete event-based algorithms

Attend this presentation and envision the possibilities for you and your colleagues with these exciting new capabilities.

About the Speaker

Saurabh is a senior product manager for the Simulink platform. Over the last several years, he has worked closely with customers in upcoming areas such as systems engineering, discrete event simulation, 3D animation, reporting, and team collaboration. Saurabh has a bachelor’s degree from the Indian Institute of Technology (IIT) and master’s degrees from University of Illinois and Cornell.

See What Industry Experts Are Doing

Utilization of Simulink Verification and Validation and Simulink Design Verifier for HVAC Controls Software

Dr. Arun Chakrapani Rao, Rolls-Royce UTC in Control and Systems Engineering, Sheffield, U.K.
Mohan Murugesan, General Motors Technical Centre India Pvt. Ltd.

This presentation introduces Simulink Verification and Validation™ and Simulink Design Verifier™. It highlights advanced verification and validation techniques (involving structural coverage analysis and formal methods) for testing various components within the HVAC Controls Software Readiness and Core Engineering groups. This session also summarizes some of General Motors Company’s results, the benefits already obtained, those we hope to achieve in the long run, and some of the challenges that confront us.

About the Speakers

Arun is currently a core member of research staff at the Rolls-Royce University Technology Centre in Control and Systems Engineering based at The University of Sheffield, U.K. His work involves multidomain modeling and analysis techniques for Rolls-Royce products including Future Aircraft Engines. He worked as a tech lead – ECS in the HVAC Controls Algo and Readiness Engineering Group and the Rigorous Control Software V&V R&D Group at GM Technical Centre India in a technology transfer role, involving verification and validation techniques and tools relating to Model-Based Design. Prior to GM, he worked at the International Automotive Research Centre, The University of Warwick, U.K., and OFFIS R&D Institute, Germany, in various R&D projects with many automotive companies. He has a Ph.D. in computing sciences and engineering from De Montfort University, U.K., and B.Tech. and M.Tech. degrees in engineering from IIT-Kanpur and IIT-Madras, respectively.

Mohan Murugesan is currently working as lead engineer in the HVAC Controls Algo and Readiness Group at GM Technical Centre India. He has over a decade of experience in MATLAB and Simulink based control software development. He has a master's degree from IIT-Bombay.

From Concepts to Chips and Beyond: Implementing Model-Driven Engineering Workflows Within Selex ES

Calum Brown, Selex ES

As Model-Driven Engineering matures as a design approach, it becomes increasingly important for developers to think in holistic terms to fully leverage the power of vertically integrated toolsets such as MATLAB and Simulink. This session presents a view of this paradigm shift from a Selex ES perspective and explores how the use of Model-Driven workflows based on MATLAB and Simulink products have become enshrined within the product development lifecycle. With examples of high-performance DSP applications drawn from across the business, this session provides a balanced review of Selex’s progress to date, along with an outlook for the future, and an assessment of some of the key challenges remaining.

About the Speaker

Calum is a principal systems engineer at Selex ES Ltd., a Finmeccanica company, in Edinburgh, U.K. A MATLAB user since 1995, Calum currently develops end-to-end solutions within primarily Simulink based workflows. In addition to 18 years of physical modeling experience, he has over 10 years of expertise in the development and deployment of complex DSP and control capability in airborne radar, electro-optic, and communications systems. Calum holds a master of physics degree in computational physics and a Ph.D. in computational mechanics from Heriot-Watt University in Edinburgh, U.K.

Data Processing Framework Supporting Large-Scale Driving Data Analysis

Clément Val, CEESAR

The increasing number and complexity of advanced driving assistance systems require an ever-increasing amount of field data. Data corresponding to the actual use of vehicles by ordinary drivers in real driving conditions is needed to calibrate new systems, identify their shortcomings, and evaluate their impact. Although necessary, experiments in controlled conditions, such as test-bench, test-track, or driving simulator, only allow observations in a finite number of predefined and generally simplified scenarios.

In this session, we show how we have implemented, using MATLAB, a very powerful framework that automates data management and processing and completely separates technical tasks from more scientific ones. Using this software, analysts have all the necessary tools to browse and search for specific things in data, transform the logged records into usable results with their own algorithms, and visualize and annotate everything (original signals, derived data, geographical position, video, etc.) in a single interface.

About the Speaker

Clément took the lead of CEESAR’s experiments and human behavior science department in 2006. There he has led a number of projects, focusing on driver behavior analysis. In the recent large-scale project, EUROFOT, he organized an experiment where the use of 40 vehicles, driven by ordinary drivers, was extensively monitored during one year. Clément led the integration of a modular data acquisition system that was installed in each vehicle. He also designed and implemented data management and analysis tools and the corresponding IT infrastructure. Previously, he contributed to several R&D projects in the transportation industry, including working as a consultant for the French carmaker LAB and for PSA Peugeot Citroën. Clément has a master’s degree in engineering from Ecole Centrale de Lyon.

Keeping New Zealand's Lights On

Heidi Heath, Transpower

Transpower owns and operates the electricity grid in New Zealand, keeping energy flowing to 4 million residents. The New Zealand power system is long and skinny, with the major generation centers in the bottom of the South Island and the major load center at the top of the North Island. As such, the system is very susceptible to frequency deviations and oscillations when a generator or large load unexpectedly trips off.

Transpower uses MATLAB to calculate how much spinning reserve and interruptible load must be scheduled during each half hour trading period. This ensures that, in the event of a large disturbance, the system frequency does not deviate to the point where it could cause cascading outages, voltage stability issues, or blackout. Detailed models of each generator and governor are built in Simulink, along with models of the HVDC link between the two islands and some basic load models. All of the detailed models are combined into a large Simulink model of the New Zealand power system. Run in conjunction with other MATLAB code files, this tool, called the Reserve Management Tool or RMT, calculates how much spinning reserve is required under a variety of conditions.

During every half hour period, RMT uses MATLAB to calculate what the largest risk to the system will be (a large generator or the HVDC link), how fast the frequency will fall if the calculated unit trips, how much load will be shed under certain contingencies due to the last-resort Automatic Under-Frequency Load Shedding scheme, what the expected governor response will be from dispatched generators, and the system inertia. It uses these parameters to calculate a final figure of how much spinning reserve and interruptible load must be scheduled for the given conditions.

About the Speaker

Heidi Heath has been with Transpower for two years. She has a bachelor’s degree in electrical engineering from Utah State University.

Explore MATLAB and Simulink in Academia

MathExplorer: Exploring UG Engineering Maths Using MuPAD

Dr. Martin Brown and Dr. Colin Steele, University of Manchester, U.K.

There are several challenges in teaching mathematics to students, including engineers. The MathExplorer tool addresses these challenges and allows students to study various mathematical concepts in the context of real engineering problems. The tool is not a replacement for lectures, but it enables students to quickly carry out techniques learned in lectures. MathExplorer makes use of the MuPAD® language, which is part of Symbolic Math Toolbox™. MathExplorer consists of a series of notebooks, each pertaining to a particular mathematical or engineering topic, starting with a brief introduction with references to more detailed accounts of the theory. There are opportunities for students to change the various parameters and view demonstrations where equations are solved and plotted. There are also some suggested exercises that students can explore. Currently there are notebooks for mathematical topics ranging from introductions to vectors and differentiation in year 1 to Laplace transforms and eigenvalues in year 2. Lecturers can demonstrate a notebook in classes, adding powerfully to the message of the lecture.

About the Speakers

Martin is a senior lecturer in the school of Electrical and Electronic Engineering and is a member of the control systems center at the University of Manchester.

Colin is a reader in the school of Mathematics at the University of Manchester and specializes in the teaching engineers and other “users” of mathematics. He promotes the use of technology in mathematics teaching through interactive demonstrations and computerized assessment.
Note: the presentation will be delivered by Colin Steele.

The Role of MATLAB in the CDIO-Based Design-Build Projects at Telecom-BCN

Ramon Bragós and Eduard Alarcon, Universitat Politecnica de Catalunya

The ICT degrees at Telecom-BCN, the Electrical and Telecom Engineering School of the Technical University of Catalonia (UPC), in Barcelona, were redesigned four years ago according to the CDIO Initiative Standards. This framework defines a set of tools to design and implement engineering programs that promote the learning of personal, interpersonal, and engineering-specific skills while ensuring a deep knowledge of basic and disciplinary knowledge. One of the more relevant tools is the inclusion of several design-build project subjects in the curricula. In our ICT engineering programs, four specific project-centered courses were scattered along the curricula, in the second semester of each academic year. MATLAB is pervasively used in these courses, with gradually increasing complexity. This session discusses the implementation of these projects into the curriculum using MATLAB.

About the Speakers

Ramon is associate professor in the Electronics Engineering Department and associate dean of Academic Innovation at Telecom-BCN, Universitat Politecnica de Catalunya UPC-Barcelona Tech. His current research focuses on electrical impedance spectroscopy applications in biomedical engineering. For the work discussed in his conference presentation, he collaborates with a team of more than 25 faculty members, including Albert Oliveras, Josep Pegueroles, Adriano Camps, Eduard Alarcon, and Ferran Marques.
Note: the presentation will be delivered by Eduard Alarcon.

Model-Based Design for a Self-Balancing Transporter

Kevin Craig, Marquette University

The self-balancing, two-wheeled transporter is a marvel of mechatronics engineering. Over the past decade it has been commercialized for both human and robotic motion. It has also been used in academia mainly for senior- and graduate-level projects, but it has the potential to be used throughout the undergraduate engineering curriculum to illustrate how practicing engineers conceive, model, simulate, control, and virtually prototype every system before construction. The key is to uncover the fundamental engineering principles and the integration required through Model-Based Design to create such a multidisciplinary system. This presentation describes a self-balancing transporter designed and built with the most basic, low-cost engineering components to facilitate understanding of fundamental engineering principles on which all such systems are based. More importantly, it describes the mechatronic system design process used to accomplish this goal. MATLAB, Simulink, and the inexpensive, open-source, single-board Arduino microcontroller are used for encoder decoding, for balancing and steering control, and for radio-frequency communication. Human-centered Model-Based Design is the future of modern engineering practice, and engineering education must reflect that in the context of real-world problem solving. This engineering platform, together with Simulink programming and automatic code generation, enable this to happen now.

About the Speaker

Kevin joined the faculty of the Marquette University College of Engineering as professor of mechanical engineering and the Robert C. Greenheck Chair in Engineering Design. Through his 20-year career, he has conducted hands-on, integrated, customized, mechatronics workshops for practicing engineers nationally and internationally. He writes a monthly column on mechatronics for Design News magazine. He is a Fellow of the ASME and a member of the IEEE and ASEE. He has previously held tenured positions at the U.S. Merchant Marine Academy, Hofstra University, and Rensselaer Polytechnic Institute. Kevin has a B.S. from the United States Military Academy, West Point. He received M.S., M.Phil., and Ph.D. degrees from Columbia University, New York.

Using Web-Based Tutorials to Teach Controls Fundamentals with MATLAB and Simulink

Richard Hill, University of Detroit Mercy

This session demonstrates the recently redesigned Control Tutorials for MATLAB and Simulink. These popular web-based tutorials now feature a modern design, enhanced content, and the latest control and analysis capabilities available in MATLAB and Simulink. Learn about the various ways in which these tutorials have been used in control system courses to enhance how students learn essential control design techniques.

About the Speaker

Richard joined the faculty of the Mechanical Engineering Department at the University of Detroit Mercy in 2008. Since joining the faculty, he has spent two visiting stints at Ford Motor Company first working on diagnostics for hybrid electric vehicles and then to develop a course on the modeling and control of advanced electric vehicles. His research interests lie in vehicle control, control and diagnosis of discrete-event systems, modular and hierarchical control, and engineering education. Previously, he was a high school math and science teacher. From 2000 to 2002, he worked at Lockheed Martin Corporation on satellite attitude determination and control. Richard has a Ph.D. in mechanical engineering and an M.S. in applied mathematics from the University of Michigan, Ann Arbor. He also has a B.S. in mechanical engineering from the University of Southern California and an M.S. in mechanical engineering from the University of California, Berkeley.

Computational Science and Engineering for New Undergraduates

K.-Y. Daisy Fan, Cornell University

To engineering and science students of the 21st century, computing is an area of fundamental importance. At Cornell, a traditional introductory programming course taken by first-year engineering students was transformed into a foundational course in computational science and engineering. This course teaches MATLAB programming and helps students develop an appreciation for fuzziness, error, approximation, randomness, and dimension. Computational concepts such as model, parameter, and sensitivity are examined, emphasizing that the goal of computing is insight to a problem, not just one answer. This session presents course materials developed to uplift the profile of computational science and engineering while faithfully teaching the traditional programming curriculum. Specific examples discussed include exercises in image processing, models for water quality simulation, and a MATLAB based robot simulator.

About the Speaker

Daisy teaches at Cornell University in the areas of programming, scientific computing, and optimization. Her recently co-authored book, "Insight Through Computing: A MATLAB Introduction to Computational Science and Engineering," is a culmination of years of teaching programming and scientific computing using MATLAB, Java, and LEGO® MINDSTORMS® robotics. Her research interests include collaborative learning methodologies and technologies in engineering education, application of systems-analysis techniques for water resources and environmental problems, and development of numerically efficient optimization methods for engineering applications.

En Español

Enfoque VAR-Bayesiano para Proyecciones en MATLAB

Alan Ledesma, Banco Central de Reserva del Perú

Las autoridades monetarias buscan anticiparse a eventos que afecten el correcto desempeño de la economía. Así, los ejercicios de proyección de las principales variables macroeconómicas son fundamentales. Consecuentemente, los bancos centrales vienen desarrollando modelos con el objetivo de anticipar la evolución de algunas variables de interés, los cuales suelen requerir cierta sofisticación analítica. Además, es también de interés de un banco central que los distintos agentes tengan una lectura similar respecto de su percepción del entorno económico. De esta forma se fomenta la estabilidad macroeconómica y se facilita la administración monetaria.

No obstante, con frecuencia los modelos desarrollados por los bancos centrales no son fáciles de replicar por el público, por lo tanto se requiere de modelos que busquen cubrir esta dificultad. Por este motivo, en esta presentación se discutirán algunos modelos estadísticos de vectores autoregresivos de fácil implementación, cuya capacidad de pronóstico se puede mejorar significativamente con técnicas bayesianas.

Será evidente en esta sesión que MATLAB constituye una herramienta bastante versátil en la implementación y evaluación de modelos con estas características. Más aún, además de facilitar este tipo de ejercicios, MATLAB es sumamente útil para reportar resultados. Debido a la facilidad con la que MATLAB interactúa con plataformas para edición de texto como Latex y archivos PostScripts encapsulados para gráficos.

About the Speaker

El Sr. Alan Ledesma Arista es miembro del personal permanente del Banco Central de Reserva del Perú (BCRP). Él se unió a esta institución en el año 2007, al culminar sus estudios de pregrado y luego de participar exitosamente en el 54° Curso de Extensión Universitaria del BCRP (curso de selección). El Sr. Ledesma inició sus labores en el departamento de estabilidad financiera donde se especializó en temas de banca. Posteriormente, en el año 2009, fue promovido al departamento de Modelos Macroeconómicos, donde se desempeña actualmente. En este departamento, el Sr. Ledesma se vio envuelto en el estudio y aplicación de técnicas econométricas para las proyecciones macroeconómicas, estimación de variables no observables y diversos proyectos de interés del BCRP.

Asimismo, en el año 2011 el Sr. Ledesma fue seleccionado por el programa de becas de postgrado del BCRP y culminó exitosamente sus estudios de maestría en la especialidad de econometría en la Universidad de Ámsterdam. Cabe destacar que además de sus actividades profesionales en el BCRP, el Sr. Ledesma siempre ha estado vinculado a la academia, ya que en repetidas ocasiones se desempeño como asistente de cátedra y posteriormente como docente universitario en varias de las universidades más prestigiosas del Perú.

Programando con MATLAB

Gerardo Hernandez Correa, MathWorks

MATLAB es un lenguaje de programación de alto nivel que incluye funciones matemáticas para solucionar problemas científicos y de ingeniería. Usted puede producir resultados inmediatos por medio a la ejecución interactiva de comandos uno a uno. Sin embargo, MATLAB también provee características de un lenguaje de programación tradicional, incluyendo control de flujo, manejo de error y programación orientada a objetos (POO). Participe en esta sesión para aprender más sobre las capacidades de programación en MATLAB y para aprender cómo ser más productivo trabajando con MATLAB.

Los temas abarcados incluirán:

  • Fundamentos Básicos del lenguaje de programación de MATLAB
  • Automatización mediante secuencias de comandos (scripts) y funciones
  • Herramientas para el desarrollo eficaz de programas

About the Speaker

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.

La Nueva Apariencia y Estilo de Simulink: Porque Ahora es Más Fácil el Modelamiento y la Simulación

Carlos Osorio, MathWorks

En esta charla, vamos a explorar las nuevas capacidades que cambian la forma de modelar y simular con Simulink. Vamos a cubrir los siguientes temas:

  • Un nuevo entorno de diseño grafico
  • Nuevas herramientas de pruebas de análisis y simulación
  • Soporte integrado para correr modelos en hardware de bajo costo
  • Modelamiento de algoritmos de control lógico y discreto basado en eventos

Vea esta presentación e imagine las posibilidades que tendrán usted y sus colegas con estas capacidades ¡nuevas y emocionantes!

About the Speaker

Carlos recibió su Bachillerato en Ciencias de la Pontificia Universidad Católica del Peru y su grado de Maestría en Ciencias de la Universidad de California en Berkeley, ambos en Ingeniería Mecánica. El se especializa en Sistemas de Control Automático y Dinámica de Vehículos. Antes de unirse a MathWorks en Octubre del año 2007, el trabajo en la industria automotriz en el Departamento de Tecnología Avanzada de Chasis de Vehículos en la corporación Visteon, donde se dedico al desarrollo e implementación de prototipos de sistemas de suspensión electrónica activa y semi-activa, dirección-por-cable y freno-por-cable para vehículos de pasajeros.

De Aplicaciones a Servicios de Web: Compartiendo el Trabajo que has Hecho en MATLAB

Gerardo Hernandez Correa, MathWorks

Los productos de MathWorks le permiten crear algoritmos y aplicaciones completas para compartir con los demás o para incorporar en otras aplicaciones, utilizando MATLAB y Toolboxes. Cree una aplicación MATLAB empaquetada para que otros puedan usar en sus propias sesiones de MATLAB o utilice las herramientas de MathWorks para despliegue de aplicaciones - incluyendo el MATLAB Compiler y los Builders - para compartir su trabajo fuera de MATLAB. El MATLAB Compiler y los Builders le permiten empaquetar las aplicaciones de MATLAB como un archivo ejecutable encriptado, una librería compartida C/C++, o como un componente para usar con Java, .NET o Excel. El MATLAB Production Server le permite ejecutar programas de MATLAB como un servicio dentro de sus sistemas de producción, lo que le permite ejecutar programas de MATLAB como parte de la web, base de datos y aplicaciones empresariales. Asista a esta sesión para aprender más acerca de compartir el trabajo que haces en MATLAB con otros. Los ejemplos exhibirán las nuevas funcionalidades de las versiones más recientes e incluyen el empaquetado de MATLAB Apps y el MATLAB Production Server.

About the Speaker

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.

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