Engineering data has become essential in business-critical systems and applications. Audio, image, real-time video, motion, machine performance metrics, and other sensor-generated data are being combined with business, transactional, and other IT data to create opportunities for sophisticated analytics on more complex phenomena. The flexibility to run those analytics, either on massive datasets in IT or cloud infrastructures or as the data are acquired on smart sensors and embedded devices, is enabling organizations in many industries to develop intelligent products, devices, and services that expand the business impact of their data and analytics. In this talk, you will see numerous examples of this in action and learn about new capabilities in MATLAB® and Simulink® that empower you to design and develop these systems and be a leading force in this new analytics-driven age.
The Singular Value Decomposition Saves the Universe
Cleve Moler, MathWorks
The Singular Value Decomposition, or SVD, is the Swiss Army knife of technical computation. Its wide variety of applications includes principal component analysis, the foundation of modern data analytics. A pioneering computer graphics film made in 1976 at Los Alamos displays what was then a newly discovered algorithm for computing the SVD. Hollywood borrowed the graphics. However, MATLAB® and the SVD were not enough to save one of the biggest failures in the history of Silicon Valley.
Developing Machine Learning and Deep Learning Algorithms Using MATLAB
Abhijit Bhattacharjee, MathWorks
Machine learning is ubiquitous. From medical diagnosis, speech, and object recognition to engine health monitoring and predictive maintenance, machine learning techniques are being used to make critical engineering and business decisions every moment of the day. In this session, we look at different machine learning techniques in MATLAB®, and in particular, we address the computer vision problem of object recognition using deep learning.
From Insight to Action: Analytics from Both Sides of the Brain
Michael O'Connell, TIBCO Software
Analytics have become central to setting business strategy, enabling tactics, and driving operations. These are different challenges and time scales, and require visual and predictive analytics to support decisions and automate actions. Spotfire’s visual analytics and the numerical algorithms of MATLAB® are the best in class, and the two software systems combine very naturally to form a productive platform for analytics app development.
This presentation showcases the Spotfire-MATLAB analytics platform, and describes case studies across a broad set of industries. Examples from both sides of the brain—from engineering and marketing—are discussed. The focus is on enterprise apps deployed at scale, and driving extreme business value. Highlight examples include context from the finance, energy, transportation, and manufacturing industries.
The Transformative Force of Robotics in Industry
Peter Corke, Queensland University of Technology
Robotics and artificial intelligence (AI) are the next transformative technologies that will impact virtually every industry, from automotive to medical devices, consumer electronics to industrial manufacturing. This talk explores the current state of robotics, and discusses the various segments of industry and society where robotics technologies are expected to have the largest impact, both in the short term and not-too-distant future.
Documenting and Debugging Complex Signal Processing Systems with MATLAB
Malcolm Slaney, Google
My most important tasks with MATLAB® are debugging and documenting complex signal processing systems. Over the course of more than 25 years I have used MATLAB to debug running speech-recognition systems, debug hardware errors in CDROM drivers, and solve numerous other difficult problems. I have used MATLAB to build a number of toolboxes that are widely used. These toolboxes have spanned the areas of auditory modeling, image processing, and now decoding brain waves. In this presentation, I share my experiences and describe what makes a successful publication and toolbox.
Hierarchical, Compositional Corelet Programming for Neuromorphic Chips
Arnon Amir, IBM
Recent results in deep convolutional neural networks on a new, spiking, core-based neuromorphic architecture approach state-of-the-art classification accuracy on a number of datasets while processing more than 1200 frames per second, (yet consuming only 25 to 275 mW). The corelet programming language (CPL) was developed to program this highly efficient, non von-Neumann architecture. Using MATLAB® object-oriented programming, CPL provides network abstraction, encapsulation, and efficient hierarchical composition of reusable network components (corelets). The talk introduces CPL, its development environment (CPE), the corelet library, and a short live demo as time permits. CPE has been released and taught to a community of more than 100 developers from dozens of institutes around the world, who use it to develop new neuromorphic algorithms, networks, and systems.
Navigating Big Data with MATLAB
Isaac Noh, MathWorks
Data is everywhere and each year users store more and more of it. Huge data sets present an amazing opportunity to discover new things about the world, the products made, and how people interact with them. However, big data sets also present some real challenges. How do you understand them? How do you interrogate them? How do you even read them? In this talk, new tools in 2016 MATLAB and Simulink product releases are discussed that help you work with even bigger data.
Developing an Artificial Pancreas Using Model-Based Design
Lane Desborough, Bigfoot Biomedical
Just 18 months after we founded Bigfoot, our first system is now in human clinical trials. Model-Based Design is used to design, develop, verify, and validate an investigational automated insulin delivery system―a so-called artificial pancreas―which uses a mobile phone app, a continuous glucose monitor, a control algorithm, and an insulin pump to reduce the burden―and increase the safety―of living with insulin-dependent diabetes. This presentation details how modeling and simulation enable unprecedented speed as we progress on our journey to receive FDA approval to commercialize our Class III medical device system.
Evaluating the Production Consequences of Design Decisions Using MATLAB and Simulink
George Thiers, ModGeno
A global aerospace company needs to answer production-related questions about design ideas much earlier in a program's life cycle than is possible today. A fundamental difficulty is that the time and expertise required to formulate appropriate analysis models prevents their routine use, especially in new program development. A multi-year research project was undertaken to remedy this, and this presentation discusses the results. MATLAB®, Simulink®, and SimEvents® can enable a dramatic reduction in the time, cost, and expertise required to answer routine questions about the design and operation of production systems.
A Mixed-Signal Model-Based Design Flow for Automotive Sensors
Jamie Haas, Allegro Microsystems
It should come as no surprise that sensors are an integral part of today’s complex automotive systems. Traditional ASIC design flows are significantly challenged when the demand for innovation is high and the development schedule is aggressive. This session discusses Allegro’s evolution of a model-based mixed-signal ASIC design flow for the development of high-integrity automotive sensor ICs. See how the advanced sensor technology team is leveraging MATLAB® and Simulink® for rapid prototyping, streamlined UVM-based verification, and automatic RTL code generation for mixed signal sensor ICs.
The Road to 5G: Simulating and Prototyping Wireless Systems
John Wang, MathWorks
Wireless engineers are pursuing 5G and other advanced technologies such as LTE/WLAN to achieve gigabit data rates, ubiquitous coverage, and massive connectivity for many applications such as IoT and V2X. These applications present numerous technical challenges that require coordinated design of DSP, RF, and antenna components, as well as their implementations. This talk discusses how MATLAB® and Simulink® provide an integrated environment for simulating, testing, and prototyping today's wireless technologies.
Design Challenges for Sensor Data Analytics in the Internet of Things (IoT)
Corey Mathis, MathWorks
IoT solutions are built for many vertical applications such as environmental monitoring and control, health monitoring, vehicle fleet monitoring, industrial monitoring and control, and home automation. In an increasing number of these applications, signal/image processing and machine learning techniques are needed to process the sensor data. In this talk, we explore examples that illustrate how the development of data analytics and sensor processing systems can be accelerated using MATLAB® and Simulink®.
Designing Perception Systems for Autonomous Driving
Avinash Nehemiah, MathWorks
The use of LiDAR, cameras, and radar as sensors for perception in Level 3 and Level 4 automated driving functionality is gaining popularity. MATLAB® and Simulink® can acquire and process a wide array of sensor data for algorithm development for autonomous driving functions such as free space detection. This talk shows how you can use LiDAR, cameras, and radar for autonomous driving.
Development for the Intel Curie Module Using MATLAB
Sharon Xue Yang, Intel
The Intel® Curie module is a complete low-power solution designed for wearable devices and consumer and industrial edge products. Intel's vision is to integrate intelligent connectivity into virtually anything people can wear, from the everyday to the extraordinary. Along the way, Intel has been working with partners to make the creation of wearable devices faster, simpler, and more accessible. In this talk, Embedded Coder® support for Intel Curie module is discussed. Using Embedded Coder, MATLAB® code can be converted to readable, compact C/C++ code that can be directly run on the Curie processor, therefore significantly reducing the development cycle for Curie. Additionally, the Curie module provides a rich set of features including a built-in pattern-matching engine. This session demonstrates several examples of how MATLAB developers can take advantage of Embedded Coder to fully utilize Curie’s unique hardware capabilities.
Robotics Development Workflow with MATLAB and Simulink
Carlos Santacruz-Rosero, MathWorks
Robotics System Toolbox™ provides algorithms and hardware connectivity for developing autonomous mobile robotics applications. Toolbox algorithms include map representation, path planning, and path following for differential drive robots. You can design and prototype motor control, computer vision, and state machine applications in MATLAB® or Simulink® and integrate them with core algorithms in Robotics System Toolbox. The system toolbox provides an interface between MATLAB and Simulink and the Robot Operating System (ROS) that enables you to test and verify applications on ROS-enabled robots and robot simulators such as Gazebo. It supports C++ code generation, enabling you to generate a ROS node from a Simulink model and deploy it to a ROS network.
In this session you will learn about new capabilities in MATLAB and Simulink for the design and development of robotics and autonomous systems. MathWorks engineers will demonstrate how to develop robotics applications with MATLAB, Simulink, and Robotics System Toolbox.
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