MATLAB Virtual Conference 2015

Learn how to get the most out of MATLAB and Simulink

MATLAB Virtual Conference 2015

The MATLAB Virtual Conference showed attendees how to get the most out of MATLAB® and Simulink® for technical computing and Model-Based Design. This free event offered four tracks covering all experience levels:

  • Introduction to MATLAB and Simulink
    MathWorks engineers demonstrated the core capabilities of the MATLAB and Simulink product families. This introductory content helped attendees learn the basics so that they can get started quickly.
  • What's New
    MathWorks engineers highlighted new features and capabilities, including a new graphics system, increased support for big data, and source control integration in MATLAB. The engineers also presented new features for accelerating model building, running consecutive simulations, and testing with graphical controls and displays in Simulink. A spotlight on signal processing and communications showcased new capabilities for RF-to-antenna-to-bits design, over-the-air testing of wireless systems, streaming for real-time DSP applications, and simplified signal processing for data analysts.
  • Data Analytics
    Data is fueling the future, but extracting value from data still has many challenges, including accessing data in disparate sources, cleaning and merging data, applying machine learning algorithms, and integrating analytics with enterprise systems. Sessions in this track showed how MATLAB is used to meet these challenges, enabling organizations to gain deeper insights and make more informed decisions.
  • MATLAB and Simulink in Academia
    Hands-on learning inspires students and gives them practical experience inside and outside the classroom. These presentations covered techniques and best practices for integrating MATLAB and Simulink into curriculum and research.
  • Sesiones en Español
    En esta sesión en español tendrá acceso a 4 presentaciones en las que se abordan temas tales como: Novedades en MATLAB, Extracción de modelos dinámicos directamente de datos experimentales usando identificación de sistemas, Fundamentos de Big Data usando MATLAB, y Modelado y simulación de baterías recargables.