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?
Rick Hyde 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.
On-road or off, Jaguar Land Rover’s vehicles are renowned for their outstanding steering, ride, and handling. In this talk, Chris will describe the challenges in systematically quantifying and optimising vehicle dynamics across the company’s product lines. He will then explain how his team of vehicle dynamics and software engineers develops and applies a suite of advanced MATLAB® based tools which vastly improve productivity and decision-making power.
In this session, Ned introduces new capabilities in the MATLAB® product family in Releases 2018a and 2018b. He shares his insights into how MATLAB is designed to be the language of choice for millions of engineers and scientists worldwide. Attend this session for a unique opportunity to learn from one of the key designers of MATLAB.
Machine learning and deep learning can be used to automate a range of tasks. Shell and the Advanced Analytics Center of Excellence (AACoE) are using these techniques to speed up processes while increasing their reliability. In geomatics, terrain classification can be improved using a rich training dataset of labelled satellite images. Automatic tag detection in large (panoramic) plant images also leads to more efficient maintenance.
James and Amjad will show how MATLAB® make using these techniques easy. With minimal setup, MATLAB Distributed Computing Server™ allows the team to train networks on multiple remote GPUs in the cloud. MATLAB Production Server lets the team create thin web clients that operators in the field can use, with minimal physical hardware such as a smartphone.
Shell leverages all those techniques and tools so that its engineers can easily and painlessly use the latest findings.
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.
Emilio will present the approach developed at Aberdeen Asset Management for the practical implementation of machine learning to analyse financial market trends, in order to generate tactical trades on multi-asset class portfolios. The intensive use of computing power to build solid tests about the validity of the design involved high-performance computing. Microsoft® Azure™ and MATLAB® as the tools of choice to produce and accelerate the process.
In this talk you will learn how the University of Bristol and BAE Systems Submarines are using MATLAB® to develop and test powerful ultrasonic imaging algorithms to detect manufacturing defects within safety-critical engineering components.
The instrumental control, GPU, and GUI building capabilities within MATLAB have been leveraged to develop BRAIN: a real-time acquisition, processing, and analysis platform for ultrasonic data, providing a step-change in imaging resolution and sensitivity.
Anthony Croxford will discuss the development and implementation of the platform and Tom Barber will provide an overview of the industrial application of this capability to BAE Systems Submarines and highlight how MATLAB has helped transfer new technology from academia into a production environment.
Learn how to use computer vision and image processing techniques in MATLAB® to solve practical image analysis, automation, and detection problems using real-world examples. Explore the latest features in image processing and computer vision such as interactive apps, new image enhancement algorithms, data pre-processing, and deep learning.
The response of a loudspeaker at low frequencies is affected both by its location and that of the listener within the room. The Bowers & Wilkins DB Series subwoofers use a proprietary algorithm allowing customers to automatically equalize them in their homes for a consistent and high-performance listening experience. In the original DB1, the process required external hardware along with MATLAB® code running directly on a home PC. In the new DB Series the algorithm is embedded in a mobile application and users can auto-optimize their loudspeaker setup simply using their phones. In this session, Sean will talk about his work in loudspeaker equalization, from algorithm exploration to software implementation. He will discuss how Bowers & Wilkins used MATLAB to simplify both the customer experience and product development workflow, including early testing through to automatic code generation.
In this session, you will learn how Qualcomm used MATLAB® to develop 5G RF front-end digital and analogue components and their associated calibration and control algorithms. Sean covers the full development cycle from 5G standards specification to device validation by customers.
Topics covered will include:
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.
In this talk, Graham demonstrates tools and techniques for designing and testing 5G radio physical layer algorithms, massive MIMO architectures, and hybrid beamforming techniques for mmWave frequencies. Modelling and mitigating channel and RF impairments will be discussed.
In this talk Andrew and Rory discuss how Leonardo is using model-driven engineering to promote a centralised and cross-functional workflow using MATLAB® and Simulink®. They will discuss how Leonardo is applying the MathWorks toolset to develop common reference designs that demonstrate best practices and promote cutting-edge technologies in the industry. They will also discuss how they are engaging young engineering talent through academic placements that investigate disruptive technologies, which are then pulled through to projects.
Learn about new capabilities in the latest release of Simulink that will help your research, design, and development workflows become more efficient.
UK Autodrive is an ambitious three-year project that is trialing the use of connected and self-driving vehicles on the streets of Milton Keynes and Coventry. As part of this work, Tata Motors are developing autonomous (self-driving) cars. The autonomous trials began on a controlled test-track environment, before moving through progressively complex urban scenarios, culminating on the streets of UK cities.
In this talk, you will learn what it takes to develop the complex control systems required for an autonomous vehicle, and how Tata Motors have used Simulink®, Robotics System Toolbox™, and Simulink Real-Time™ to develop the algorithms for trajectory planning and motion control and deploy them into the autonomous vehicles for testing.
Complete system modelling is now an integral part of any design process, both for de-risking project and design decisions, and providing system data when experimental measurements are not possible. With the development of the SABRE (Synergetic Air Breathing Rocket Engine) ground test, the dynamic modelling of the complete system is tantamount to the design of the engine itself. The engine cycle of SABRE is one of complexity and high coupling, involving multiple pieces of turbomachinery, heat exchangers, valves and ducting. This engine cycle has been modelled in Simscape™ using a mixture of built-in and bespoke blocks to provide a test environment in which both the operability of the engine can be designed and the extremes of the system tested.
Optimizing the performance of a robotic system is a complex task that involves mechanical, electrical, and algorithm design. Only by integrating these systems in a single environment can you detect integration issues early and reduce cost. In this session, Steve will show you how to integrate 3D assemblies from CAD software with electrical actuation and supervisory logic.
Complex systems, which typically require rigorous safety justifications, are increasingly common in marine vehicles. Model-Based Design (MBD) fully describes the operation of a system in an executable model and helps manage complexity. When used at a system level, MBD facilitates development and integration.
This presentation gives an overview of the Model Descriptive Development Process (MDDP). MDDP combines MBD with the text-based requirement approach traditionally used to meet safety justifications. The presentation also describes the methods used to break down the requirements and confirm correct implementation within an FPGA. Finally, it reviews lessons learned from 10 years’ experience of employing MathWorks tools to generate HDL for safety-critical systems.
Simulink Requirements™ provides a requirements-centric view, enabling the requirements traceability, consistency checking, verification, and reporting required to support high-integrity workflows.
In this session, Fraser will demonstrate the functionality and workflows of Simulink Requirements, including the prime source of requirements in an external tool such as IBM DOORS.
In this session, Lianne introduces MATLAB®, the interactive environment and high-level language for numerical computation, visualisation, and programming. Topics discussed in this session include:
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.
In this session, Jonathan introduces the Simulink® product family. Topics include:
This presentation is ideal for Simulink beginners and MATLAB users interested in learning more about Simulink
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. Marc will demonstrate 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.
Processing signals from a variety of sensors is a key task for many engineers today—whether performing offline analysis of captured data to understand system performance, or performing real-time signal processing on sensor data in an embedded system. In this session Steven demonstrates the latest signal processing capabilities of MATLAB® and Simulink®, with examples using signals from a range of sources including accelerometers, microphones, and RF receivers.
You will learn how you can:
As models become increasingly complex, or the number of scenarios to be analysed increases, the desire for faster simulation is ever present. In this master class, Sonia demonstrates tools and techniques you can use to increase the simulation performance of your Simulink® models by:
Join this session to get the most out of the processing power available to you when running a Simulink model.
Fleet data can provide valuable insights and information regarding the performance of the systems you are monitoring. The size and speed at which data is collected makes the discoverability of those insights a challenge. See a practical case study of how to work with vehicle fleet data using MATLAB® and Apache™ Spark™. Highlights include data preparation, data exploration, and building functions to compute key performance indicators across the fleet. Along the way, you will learn about the new data types that make this process possible, as well as dive deep into specific scenarios like how to detect “events” in your data or perform aggregate calculations. The ability to consider your data at both the macro (fleet) and micro (sensor) perspectives enables you to ask questions of your data that were not previously feasible.
Whether writing a MATLAB® application from scratch, or restructuring existing scripts and prototypes into functions and classes, you want to ensure that users can run your code without encountering unexpected behaviour or errors. You also want to prevent bugs being introduced as your application grows in complexity and features.
This master class covers: