AI, or artificial intelligence, is powering a massive shift in the roles that computers play in our personal and professional lives. Most technical organizations expect to gain or strengthen their competitive advantage through the use of AI. But are you in a position to fulfill that expectation; to transform your research, your products, or your business using AI?
Richard Rovner looks at the techniques that compose AI (deep learning, computer vision, robotics, and more), enabling you to identify opportunities to leverage it in your work. You will also learn how MATLAB® and Simulink® are giving engineers and scientists AI capabilities that were previously available only to highly specialized software developers and data scientists.
Learn about new capabilities in the latest releases of MATLAB® and Simulink® that will help your research, design, and development workflows become more efficient. MATLAB highlights include updates for writing and sharing code (Live Editor), developing and sharing MATLAB apps (App Designer), and managing and analyzing data. Simulink highlights include the Simulation Manager to run multiple simulations in parallel and new smart editing capabilities to build up models even faster. There are also new tools to make it easier to automatically upgrade your projects to the newest release.
Interest in predictive maintenance is increasing as more and more companies see it as a key application for data analytics that run on the Internet of Things. This talk covers the development of these predictive maintenance algorithms, as well as their deployment on the two main nodes of the IoT—the edge and the cloud.
Mechatronic systems include a wide range of components, including motors, op-amps, and shaft encoders. Simulating these components together with mechanical and control systems is critical to optimizing system performance. To ensure that testing is efficient, MathWorks offers a number of ways to easily balance the trade-off of model fidelity and simulation speed. The ability to generate C code from the model enables engineers to use Model-Based Design for the entire system (plant and controller).
Years of engineering expertise and best practices form the basis for the industry standards used in developing high-integrity and mission-critical systems. The standards include proven guidelines which can improve the quality of any design. Learn how you can take advantage of best practices from standards such as ISO 26262, DO-178/DO-331, IEC 61508, MISRA®, and others to find errors earlier in your process and improve the quality of your Simulink® models.
Learn how MATLAB® and Simulink® help you develop 5G wireless systems, including new digital, RF, and antenna array technologies that enable the ambitious performance goals of the new mobile communications standard.
This talk presents and demonstrates available tools and techniques for designing and testing 5G new radio physical layer algorithms, massive MIMO architectures and hybrid beamforming techniques for mmWave frequencies, and details on modeling and mitigating channel and RF impairments.
ADAS and autonomous driving technologies are redefining the automotive industry, changing all aspects of transportation, from daily commutes to long-haul trucking. Engineers across the industry use Model-Based Design with MATLAB® and Simulink® to develop their automated driving systems. This talk demonstrates how MATLAB and Simulink serve as an integrated development environment for the different domains required for automated driving, including perception, sensor fusion, and control design.
This talk covers:
Deep learning can achieve state-of-the-art accuracy for many tasks considered algorithmically unsolvable using traditional machine learning, including classifying objects in a scene or recognizing optimal paths in an environment. Gain practical knowledge of the domain of deep learning and discover new MATLAB® features that simplify these tasks and eliminate the low-level programming. From prototype to production, you’ll see demonstrations on building and training neural networks and hear a discussion on automatically converting a model to CUDA® to run natively on GPUs.