MATLAB EXPO 2017 SWITZERLAND

Demo Stations

New Regression Learner App in R2017a

New Regression Learner App in R2017a

MATLAB® enables domain experts (engineers and scientists) to do data science. The Regression Learning app makes it easy to get started fitting regression models—a common task in machine learning. The app makes it easy to:

  • Get started with regression machine learning problems
  • Quickly compare models and choose the best one
  • Generate MATLAB code

Creating a Cloud-Based People Counter Using MATLAB

Creating a Cloud-Based People Counter Using MATLAB

Learn how to use MATLAB to create a cloud-based people counter. This demo uses a USB webcam and Computer Vision System Toolbox™ to detect faces within a video feed. The ThingSpeak Internet of Things (IoT) platform is used to track the number of people by transferring data to the cloud.

Visit this showcase area to see and discuss how MATLAB is used to:

  • Perform real-time object recognition with live video acquisition
  • Track features in a video and count the number of occurrences
  • Create a cloud-based visualization updated with live data

Model-Based Design of a Motor Controller for Xilinx Zynq SoCs

Model-Based Design of a Motor Controller for Xilinx Zynq SoCs

From model to FPGA system-on-chip (SoC), see the implementation of a motor regulation-algorithm on a heterogeneous SoC made of an FPGA and an ARM®9 processor through IP core generation workflows and automatic HDL- and C-code generation. This demo is based on a Xilinx® ZedBoard™ using an Analog Devices® motor control FMC board, and it runs a field-oriented controller (FOC) for a permanent magnet synchronous machine (PMSM).

Visit this showcase area to see and discuss how MATLAB is used to:

  • Reduce dependency on hardware with simulation
  • Avoid implementation errors with C- and HDL-code generation
  • Perform faster hardware iterations with deployment automation

Virtual Commissioning and Verification of a Manufacturing Cell

Virtual Commissioning and Verification of a Manufacturing Cell

Virtual commissioning and early design verification are key processes in today’s automation industry. Explore a model of a manufacturing cell to learn how you can combine your existing CAD models with mechatronic actuators to confidently make component sizing decisions. The manufacturing cell consists of a robot arm with 5 degrees of freedom that transfers parts between two conveyor belts. The model is used to determine mechanical loads, size motors, and determine overall power requirements. Optimization algorithms are used to determine optimal trajectories that minimize power consumption. Model-Based Design does not stop at pure simulation: See how you can use the model to produce fully verified supervisory algorithms ready to be deployed through automatic code generation in embedded environments. Visit this demo to learn how to:

  • Increase productivity by importing and reusing CAD models in Simulink®
  • Quickly model and integrate complex multidomain physical systems
  • Reduce time-to-market by using simulation and automatic code generation

Real-Time Simulation of Large-Scale Electrical Networks

Real-Time Simulation of Large-Scale Electrical Networks

Today’s power grids are one of the most complex technological infrastructures ever built. The emerging smart grid and the increasing adoption of renewable energy systems raise new challenges for utilities and equipment suppliers. In this demo, large network layouts from third-party tools are automatically imported in MATLAB and re-implemented in Simulink, addressing the demand for test automation and simulation. Automatic code generation enables the rapid deployment of power-grid models on multicore, real-time machines for hardware-in-the-loop testing and verification.

Visit this demo to learn how MATLAB and Simulink work together to:

  • Facilitate the importing of large data sets and extraction of relevant information
  • Use this information to automatically build models of complex systems
  • Deploy these models on real-time machines to investigate hardware and prototype integration

Macro-economic Stress Testing with MATLAB

Macro-economic Stress Testing with MATLAB

The financial and sovereign debt crises highlighted how important it is for banks to have solid capital buffers to withstand shocks to their balance sheets and ensure they can continue to be effective financial intermediaries even in periods of turbulence. A macro stress-testing framework can help assess the resilience of the banking sector to macroeconomic and financial change.

Visit this showcase area to see and discuss how MATLAB® is used to:

  • Employ MATLAB high-level and object-oriented model and data structures for efficient development and deployment of a stress test application
  • Obtain seamless integration of the stress test application with data import and dashboard-based reporting export.
  • Utilize a wide range of versatile time-series models for the forecasting and scenario analysis

Code Verification

Code Verification

See the power of static analysis in action. Reduce time and effort spent on dynamic test of software and minimize the danger of costly recall campaigns. If you produce machines or systems with safety constraints, or simply want to avoid sending technicians all over the world to fix bugs, see how Polyspace® can help you:

  • Find bugs and possible run-time errors during the early phases of code design and implemen-tation
  • Measure the compliancy of the code to MISRA® rules and quality criteria
  • Obtain certification credits according to IEC 61508 and other standards