Behind Today’s Trends: The Technologies Driving Change

Big data. Cloud and mobile computing. The Internet of things. Low-cost, programmable microprocessors. Online education. These are some of today’s trends affecting the way engineers and scientists work. But there is a deeper, more enduring set of technology forces behind these trends. In this presentation, Chris Hayhurst discusses these larger forces, how MATLAB® and Simulink® are responding, and how you can seize the opportunities and meet the challenges ahead.

Chris Hayhurst, MathWorks

A Technology Platform with a MATLAB Backbone: A Financial Engineering True Story

At Munich Re Trading LLC (MRTL), a leading expert in risk solutions worldwide, the technology platform is a cutting-edge combination of algorithms and computations integrated into a .NET service architecture supporting a myriad of applications. This design relies heavily on our Risk and Market Analytics (RMA) library, which is developed in MATLAB®. While it is not always evident in the final product, MATLAB is the computational environment supporting our entire cloud-based solution.

The platform is in production and is a strategic asset for our business. Accessible from multiple countries in North America and Europe, the platform enables complex weather and commodity trades and supports market and quantitative research. Due in large part to the flexibility of MATLAB, the entire platform was developed and is maintained by only a handful of personnel. In a typical day, over 2400 MATLAB events occur, consuming nearly 3000 CPU minutes. In excess of 1.5 GB of data is captured or created daily, with the underlying database warehouse approaching 3 billion rows of readily accessible data.

This session discusses MRTL’s platform design and how MATLAB is integrated throughout the solution. Edward Byrns illustrates how the MATLAB language provides a comprehensive development environment suitable for a scalable production solution. Specifically, he follows a path from the command line, through user interfaces, into a service layer and ultimately demonstrates live application events. He further illustrates how MRTL’s application events are seamlessly integrated with the interactive MATLAB environment, returning from an application event back to the command line.

Edward V. Byrns. Jr., Munich Re Trading LLC

Introduction to MATLAB

Get an introduction to MATLAB®, a high-level language and interactive environment for numerical computation, visualization, and programming.

MATLAB includes built-in mathematical functions fundamental to solving engineering and scientific problems, and an interactive environment ideal for iterative exploration, design, and problem solving. Through product demonstrations, you will see how this combination allows you to quickly explore ideas, gain insight into your data, and document and share your results.

Sean de Wolski, MathWorks

Introduction to Simulink: Quadcopter Simulation and Control

This session shows you the benefits of utilizing Simulink® in your workflow. Using a quadcopter vehicle as a demonstration, Ryan gives a high-level overview of how you can utilize Simulink to perform modeling, simulation, and control.

Ryan Gordon, MathWorks

Using Arduino with MATLAB and Simulink

You may have heard about, or even played with, the ubiquitous Arduino® boards that are infiltrating classrooms and maker spaces around the world, but did you know that MATLAB® speaks Arduino?

In this session, you will learn how to control and program an Arduino from MATLAB and Simulink®. You will see how MATLAB makes it easy to explore the analog and digital inputs and outputs on your Arduino board. With this background, we build an example circuit and gauge to measure and display the light intensity in a room, using MATLAB to compute the gauge position. We then use Simulink to deploy this positioning algorithm to the device for standalone execution, allowing us to use the gauge without being tethered to a computer.

Dan Seal, MathWorks

Tackling Big Data with MATLAB

Are the data sets you need to analyze becoming uncomfortably large to work with in memory? Are they taking too long to compute? Are you finding it challenging to scale your algorithms to big data sets? In this session, you will learn strategies and techniques for handling large amounts of data in MATLAB®. New big data capabilities in MATLAB R2014b are highlighted.

Topics include:

  • Applying best practices for memory use in MATLAB
  • Accessing data in large text files, databases, or the Hadoop Distributed File System (HDFS)™
  • Leveraging distributed memory to work with large data sets
  • Processing data using the MapReduce programming technique
  • Developing algorithms on your desktop and scaling to a cluster, cloud, or Hadoop

Adam Filion, MathWorks

What’s New in MATLAB and Simulink R2015a

Learn about the latest capabilities offered in MATLAB® and Simulink® in releases 2014b and 2015a.

New MATLAB features include:

  • New MATLAB graphics system
  • Functions for processing big data on your desktop that can scale for use with Hadoop
  • Git and Subversion source control integration and access to projects on GitHub from File Exchange

New Simulink features include:

  • Capabilities to tune, test, and analyze simulations
  • Smart editing cues to accelerate model building
  • Fast restart to run consecutive simulations more quickly

Kevin Cohan and Michael Carone, MathWorks

Using MATLAB and Simulink for Robotics

The new Robot Operating System (ROS) interface from MathWorks allows you to leverage the power of MATLAB® and Simulink® to quickly prototype, test, and verify your robotics algorithms by providing direct access to all ROS-enabled robots and simulators such as Gazebo and V-REP. The interface enables you to develop your robotics algorithms in MATLAB and Simulink, while giving you the ability to exchange messages with other nodes on ROS networks. The interface has been used by experts from automotive, defense, medical devices, and other industries, and it has also been widely adopted in classroom teaching and academic research.

In this session, you will learn how to:

  • Create a ROS node inside MATLAB
  • Design and test robotics algorithms on a robot simulator such as Gazebo
  • Test robotics algorithms on a physical robot
  • Import rosbag log files into MATLAB
  • Generate a ROS node from Simulink models

Yanliang Zhang, MathWorks

What’s New in Wireless System Design

In this session, you will learn about new antenna-to-bits wireless design capabilities in MATLAB® and Simulink®. These capabilities enable wireless and radar engineers to design systems that incorporate multiple antennas, smart RF devices, and advanced receiver algorithms. New hardware support features enable designers to connect MATLAB algorithms to SDR devices and RF instruments, and to verify them with over-the-air testing with a range of custom waveforms.

The presentation includes case studies and examples:

  • MIMO-OFDM technologies, such as 802.11 WLAN systems, with analysis and measurements to assess end-to-end simulation performance
  • Fast RF behavioral modeling and simulation including ADI agile RF transceivers
  • Design, simulation, and testing of LTE and LTE-Advanced systems
  • Wireless connectivity to software-defined radios (SDR) and RF instruments for over-the-air testing of wireless signals
  • Simulation and testing multidomain radar applications including FMCW radar for automotive applications

Houman Zarrinkoub, MathWorks

Working with Large Sets of Images in MATLAB Just Got Easier

Modern image processing and computer vision problems often require large amounts of video and image data. For MATLAB® users, this means moving beyond traditional workflows on a desktop or laptop computer.

In this session, we explore new capabilities that will change the way you handle and process large sets of images in MATLAB. Topics include:

  • Reading and managing large collections of images in MATLAB
  • Using MATLAB apps to interactively test and visualize your image processing algorithms
  • Applying machine learning techniques to perform image recognition or object categorization
  • Scaling your algorithm for use on multiple cores on your desktop or a cluster

Avinash Nehemiah, MathWorks

Data Analytics with MATLAB

Using data analytics to turn large volumes of complex data into actionable information can help you improve engineering design and decision-making processes. However, developing effective analytics and integrating them into business systems can be challenging. In this session you will learn approaches and techniques available in MATLAB® to tackle these challenges.

Highlights include:

  • Accessing, exploring, and analyzing data stored in files, online, and in data warehouses
  • Techniques for cleaning, exploring, visualizing, and combining complex multivariate data sets
  • Prototyping, testing, and refining predictive models using machine learning methods
  • Integrating and running analytics within enterprise business systems and interactive web applications

Adam Filion, MathWorks

Presentation of the Platform for the Development of Aircraft Engine Monitoring Algorithms: SAMANTA

To monitor engines, Safran has created an environment for the design and development of health monitoring algorithms: SAMANTA (Safran AlgorithM ANd Test Application). This platform, used with MATLAB® and Simulink®, enables users to integrate algorithmic applications such as word processing, input/output, or display, as well as to continuously and automatically optimize algorithms.

Machine Learning Made Easy

Machine learning is ubiquitous. From medical diagnosis, speech, and handwriting recognition to automated trading and movie recommendations, machine learning techniques are being used to make critical business and life decisions every moment of the day. Each machine learning problem is unique, so it can be challenging to manage raw data, identify key features that impact your model, train multiple models, and perform model assessments.

In this session we explore the fundamentals of machine learning using MATLAB®. Through several examples we review typical workflows for both supervised learning (classification and regression) and unsupervised learning (clustering).

Highlights include:

  • Accessing, exploring, analyzing, and visualizing data
  • Training a range of machine learning models, including linear regression models, support vector machines (SVMs), boosted and bagged decision trees, k-nearest neighbor, Naïve Bayes, discriminant analysis, and neural networks
  • Performing model assessment and model comparisons using statistical tests to help choose the best model for your data
  • Improving models using feature selection and feature transformation techniques
  • Sharing results in the form of reports or integrating models within production environments

Shashank Prasanna, MathWorks

Big Data Applied to Big Buildings to Give Big Savings on Big Energy Bills

Heating, ventilation, and air conditioning (HVAC) systems that regulate internal temperature and humidity in large-scale buildings (office buildings, hospitals, shopping centers, casinos, and so forth) account for approximately 30% of total global energy consumption. HVAC systems are highly inefficient, thereby resulting in unnecessary energy waste. This inefficiency stems from the fact that most HVAC control systems are passive and do not actively and predictively take into account changing weather patterns, weather forecasts, variable energy costs and tariffs, and the underlying building thermal properties in order to optimally control and regulate the building’s internal temperature and humidity so as to minimize total energy consumption.

In collaboration with the Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia’s national science agency, BuildingIQ has developed the first and only cloud-based software, employing sophisticated big data machine learning methods that continuously optimize HVAC performance in real time for minimum energy consumption, while ensuring maximum comfort for building occupants. The key advantage of this industry-leading software is that it seamlessly interfaces with current building control systems, requiring little to no capital investment for deployment within most existing building control systems. In addition to seamless integration, the software also delivers results for clients, generally achieving energy savings of 10–25% on HVAC operations, depending on the underlying building and HVAC dynamics.

This presentation gives a basic outline of the problem, implementation, energy savings achieved, and challenges in translating R&D into practice, as well as how MATLAB® was employed for basic algorithm development and for interfacing with the rest of the BuildingIQ cloud-based system.

Boris Savkovic, BuildingIQ

Journey to a Flipped Computation Course using MATLAB

The engineering computation course at Boston University has been taught using MATLAB® for nine years. The main thrust of the course is basic procedural programming concepts, but the efficient use of MATLAB, including vectorizing code, is also emphasized. The format of the course has evolved over the years from a traditional lecture to a combination of some lecture and some in-class problem sets, and now finally to a flipped format with all active learning in class.

This talk covers the guiding principles of the course itself, the recent transformation of the course format to a blended course utilizing preclass content on edX, the results from this new model, and planned future improvements.

The two main goals for enabling active learning and peer-to-peer instruction in class were to improve the learning outcomes and to improve the engagement of the students in the course. These were achieved systematically over a period of several years, using evidence-based teaching practices.

The results from this blended course included better achievement of the learning outcomes, as evidenced by higher average grades and fewer D/F/W grades, and increased engagement in the course as evidenced by increased attendance.

Future improvements in the course include more assessment questions in the online materials, and increased use of test scripts to allow students to submit solutions to problems online using the embedded MATLAB in edX and Cody Coursework™.

Stormy Attaway, Boston University

Sharing MATLAB Applications

In this session, you will see how you can share your MATLAB® work with others who may or may not have MATLAB. Deliverables could be full applications or components that are integrated with applications written in other languages.

Highlights include:

  • Publishing and reporting results
  • Packaging MATLAB apps for other MATLAB users
  • Creating standalone applications for those without MATLAB
  • Deploying components for integration with C, C++, Java®, Microsoft® .NET, Microsoft Excel®, and/or Python
  • Sharing research data and analytic tools with a larger community online

Bonita Vormawor, MathWorks

Controlling a Robotic System with MATLAB and Simulink Using a Desktop Computer

Learn about MathWorks support for student competitions and why competitions are a great way to learn Model-Based Design.

In this session you will learn how to run MATLAB® and Simulink® code on a computer with the goal of controlling a robotic system. The following will be demonstrated:

  • An autonomous ground vehicle controlled by MATLAB and Simulink on a desktop computer
  • The advantages of using MATLAB and Simulink to build algorithms and control a robotic system
  • The workflow to run your robotic system on a computer using MATLAB and Simulink

Sergio Biagioni, MathWorks

Project-Based Learning for Signal Processing and Communications with MATLAB and Simulink

In this presentation, we discuss new capabilities in MATLAB® and Simulink® in the area of signal processing and communications that make it easier to develop successful project-based curricula.

Project-based learning is an effective method for creating excitement in the student body, deepening their skill level, and connecting their theoretical knowledge to hands-on practice. In technical fields of study, particularly signal processing and communications, the challenge for introducing these programs is bringing together a diverse set of expertise in hardware, software, and curriculum development.

The widespread use of MATLAB and Simulink in science and engineering has enabled many professors to develop MATLAB based curricula. With the introduction of hardware support packages, all the pieces are now in place to create effective project-based learning courses.

Through case studies and examples, we highlight multiple applications, including:

  • Signal analytics and classification applied to data gathered from wearable and mobile devices
  • Audio and heart sensor applications with low-latency desktop processing and prototyping on ARM® Cortex®-M and Cortex®-A
  • Wireless connectivity to software-defined radios (SDR) and over-the-air testing of wireless signals

Through demonstrations, we show how you can set up labs and develop courseware to facilitate your research and teaching projects.

Houman Zarrinkoub, MathWorks

Novedades en MATLAB

En esta sesión, se destacan las nuevas características y capacidades en MATLAB® R2014b y toolboxes asociados. A través de demostraciones de productos, usted aprenderá acerca de las nuevas capacidades de la familia de productos MATLAB.

En esta sesión, Gerardo Hernandez describirá las nuevas capacidades introducidas recientemente en MATLAB, tales como:

  • Un nuevo sistema de gráficos
  • Un mayor apoyo para “Big Data”
  • Nueva funcionalidades para empaquetar y compartir código
  • Integración de control de fuente

Extracción de Modelos Dinámicos Directamente de Datos Experimentales usando Identificación de Sistemas

En esta presentación mostraremos los últimos avances en las herramientas de MathWorks para el diseño y sintonización de controladores y la identificación de sistemas.

Usando datos experimentales, los ejemplos seleccionados demostraran el uso de los nuevos interfaces gráficos del Control System Toolbox™ y el System Identification Toolbox™ para extraer de manera rápida e interactiva modelos matemáticos y dinámicos de las componentes o los sistemas que están siendo analizados. Estos modelos matemáticos pueden ser utilizados para la predicción del comportamiento de estas componentes o sistemas, o para el diseño y la sintonización de los controladores asociados a ellos.

Carlos Osorio, MathWorks

Fundamentos de Big Data utilizando MATLAB

El volumen de datos en la mayoría de las organizaciones actualmente es bastante alto. Hoy en día el principal reto no es la captura ni la recolección de datos, sino más bien el procesamiento ágil y rápido de los mismos, para generar soluciones o respuestas de gran valor para las empresas. En esta sesión veremos los fundamentos del procesamiento de grandes volúmenes de datos (big data) utilizando MATLAB®.

Yersinio Jiménez Campos, Banco Nacional de Costa Rica

Modelado y Simulación de Baterías Recargables con MATLAB y Simulink

Las baterías recargables han sido un importante objeto de estudio y desarrollo científico e ingenieril durante la última década por su promesa de propulsar vehículos con menor impacto ambiental que los combustibles derivados del petróleo, y de energizar instrumentos electrónicos estáticos y portátiles tales como teléfonos celulares y computadoras.

Esta fuente de energía requiere sofisticadas estrategias de control y monitoreo para su carga y descarga, control de temperatura, lectura de estado de carga, y corrección por envejecimiento. El diseño de tales métodos de control demanda indefectiblemente el trabajo con modelos dinámicos capaces de reproducir el comportamiento electro-térmico de la batería, y que sean a su vez pasibles de formar parte del algoritmo de control. Esto a su vez implica la necesidad de una herramienta flexible para el modelado, la simulación, y el despliegue de algoritmos en microprocesadores.

Este seminario describirá una serie de métodos desarrollados por ingenieros de MathWorks para el modelado, la identificación de parámetros en base a mediciones, la simulación, y la integración de baterías de ion de litio. Se hará también una reseña de aplicaciones como balance de carga, simulación de tiempo real, estimación de estado de carga, y monitoreo de degradación.

Javier Gazzarri, MathWorks