MATLAB EXPO 2023

Headline

Incidunt expedita est debitis amet qui est. Dolor aut qui velit. Iste molestiae tempora veniam fugit laboriosam sit.

All Sessions

FILTER BY

Day

Clear
  • Wednesday, May 10
  • Thursday, May 11

Region

Clear
  • APAC
  • EMEA
  • AMER

Session Type

Clear
  • Hands-On Workshop
  • Plenary
  • Track

Tracks

Clear
  • AI
  • Modeling, Simulation, and Implementation
  • Autonomous Systems and Robotics
  • Algorithm Development and Data Analysis
  • Electrification
  • Cloud, Enterprise, and DevOps
  • Preparing Future Engineers and Scientists
  • Wireless Connectivity and Radar
  • Plenary

Applications

Clear
  • Automated Driving Systems
  • Computational Biology
  • Control Systems
  • Data Science
  • Deep Learning
  • Embedded Systems
  • Enterprise and IT Systems
  • FPGA, ASIC, and SoC Design
  • Image Processing and Computer Vision
  • Internet of Things
  • Machine Learning
  • Mechatronics
  • Mixed-Signal Systems
  • Power Electronics Control Design
  • Power Systems Analysis and Design
  • Predictive Maintenance
  • Robotics
  • Signal Processing
  • Test and Measurement
  • Wireless Communications

Capabilities

Clear
  • Cloud Computing
  • Container, Web, and Desktop Deployment
  • Discrete-Event Simulation
  • Embedded Code Generation
  • GPU Computing
  • Hardware Support
  • Model-Based Design
  • Model Deployment
  • Parallel Computing
  • Physical Modeling
  • Real-Time Simulation and Testing
  • Report Generation
  • Systems Engineering
  • Verification, Validation, and Test

All Sessions

Display session times in
  • Catching Fire: Using Autonomous Drones to Detect and Track Wildfires
    Brenda Zhuang Sarah Mohamed Shweta Singh
    12:00 am - 1:30 am (CDT)
    Register/Log in to save session
    1
    APAC
    Control Systems
    Deep Learning
    Embedded Systems
    Image Processing and Computer Vision
    Robotics
    Model-Based Design
    Physical Modeling
    Systems Engineering
    AI
    Catching Fire: Using Autonomous Drones to Detect and Track Wildfires
    Brenda Zhuang Sarah Mohamed Shweta Singh
    12:00 am - 1:30 am (CDT)
    12:00 am - 1:30 am (CDT)
    1
    APAC
    Control Systems
    Deep Learning
    Embedded Systems
    Image Processing and Computer Vision
    Robotics
    Model-Based Design
    Physical Modeling
    Systems Engineering
    AI
    Can drones help prevent natural disasters? Wildfires have become highly destructive in recent years, ravaging the environment and human lives. In this hands-on workshop, build a wildfire detection system with autonomous drones. Explore cutting-edge methods to detect fire outbreaks and analyze the spread. Gain skills in simulation and AI that you can apply to life-saving problems in MATLAB® and Simulink Online™.
  • Low Code Data Analysis in MATLAB
    Onomitra Ghosh Lola Davidson
    11:30 pm - 1:00 am (CDT)
    Register/Log in to save session
    1
    APAC
    Algorithm Development and Data Analysis
    Data Science
    Machine Learning
    Predictive Maintenance
    Low Code Data Analysis in MATLAB
    Onomitra Ghosh Lola Davidson
    11:30 pm - 1:00 am (CDT)
    11:30 pm - 1:00 am (CDT)
    1
    APAC
    Algorithm Development and Data Analysis
    Data Science
    Machine Learning
    Predictive Maintenance
    Data analysis is an integral part of many engineering and scientific workflows, but applying the right data analysis techniques typically takes lots of manual coding before getting useful results. It can be particularly challenging when you are not familiar with your data. MATLAB® Live Editor Tasks and apps such as Data Cleaner make it easy to explore, clean, and prepare data. These apps and tasks can also automatically generate the equivalent MATLAB for you to build on. This demo shows how you can analyze data and generate reusable data analysis programs in MATLAB with minimal coding.
  • Cell Array, Table, Timetable, Struct, or Dictionary? Choosing a Container Type
    David Garrison Francesca Marini
    12:00 am - 12:30 am (CDT)
    Register/Log in to save session
    APAC
    Algorithm Development and Data Analysis
    2
    Cell Array, Table, Timetable, Struct, or Dictionary? Choosing a Container Type
    David Garrison Francesca Marini
    12:00 am - 12:30 am (CDT)
    12:00 am - 12:30 am (CDT)
    APAC
    Algorithm Development and Data Analysis
    2
    What do you do when you need to work with data that combines numbers, strings, datetimes, categoricals, and other types of data? Container types such as a cell array, table, timetable, struct, and dictionary can store heterogeneous data, but how do you choose the right one for your application? The choice of container type can have a big impact on your productivity and the performance of your code. Learn about the five major container types in MATLAB®, how they represent data, and the advantages and disadvantages of each.
  • Process of Building AI Models for Predicting Engine Performance and Emissions
    Britant Sureka Shakti Saurabh
    12:30 am - 1:30 am (CDT)
    Register/Log in to save session
    APAC
    Algorithm Development and Data Analysis
    Deep Learning
    Machine Learning
    Test and Measurement
    Model-Based Design
    Model Deployment
    Parallel Computing
    Real-Time Simulation and Testing
    Systems Engineering
    Process of Building AI Models for Predicting Engine Performance and Emissions
    Britant Sureka Shakti Saurabh
    12:30 am - 1:30 am (CDT)
    12:30 am - 1:30 am (CDT)
    APAC
    Algorithm Development and Data Analysis
    Deep Learning
    Machine Learning
    Test and Measurement
    Model-Based Design
    Model Deployment
    Parallel Computing
    Real-Time Simulation and Testing
    Systems Engineering
    Simulation-based product development requires efficient engine models that can not only predict engine performance and emissions accurately but also produce results at real-time computing speeds. These models are used to predict in a closed-loop system-level environment integrated with controls and other components. 0D/1D physics-based software can model engine performance with good accuracy, but these models cannot be deployed in a closed-loop environment with controls and other components because they have a significantly higher runtime—about 20 to 40 times slower than real time. Investigation was done to find a low-fidelity model or algorithm with similar performance that could replace high-fidelity physics-based models in the simulation domain and can be deployed in a system level environment. The robustness of AI models’ ability to learn from the data encouraged Cummins to try several machine learning models. A detailed approach was taken to build LSTM-based deep neural network models that achieved target model accuracy with a runtime of about one-eighth that of real time. Cummins developed a total of 26 models using MATLAB® to predict 26 different responses of the engine which consist of temperature, pressure, and flows across multiple locations of the engine along with emissions, efficiencies, and engine brake torque. In the process of building these deep learning models, multiple techniques were evaluated such as defining an optimal deep neural network structure consisting of the type and number of layers, using different activation functions, and optimizing associated hyperparameters. In future, the process of building these models will be automated, and the use of MATLAB to train the models in parallel will be a significant advantage. As these models meet all the requirements, future work would be done to integrate these models in the pure simulation domain, followed by integration with actual hardware and control.
  • How to Optimize the User Experience of Your MATLAB Apps
    Gianluca Carnielli
    1:00 am - 2:00 am (CDT)
    Register/Log in to save session
    APAC
    Algorithm Development and Data Analysis
    Data Science
    Container, Web, and Desktop Deployment
    How to Optimize the User Experience of Your MATLAB Apps
    Gianluca Carnielli
    1:00 am - 2:00 am (CDT)
    1:00 am - 2:00 am (CDT)
    APAC
    Algorithm Development and Data Analysis
    Data Science
    Container, Web, and Desktop Deployment
    With MATLAB®, non-professional software developers can develop professional applications. However, creating visually appealing and user-friendly applications requires design considerations which might elude the engineer who is—rightfully—focused on the app’s functionality rather than aesthetics or usability aspects. Nonetheless, an easy-to-use app is always desirable and sometimes a requirement when the app needs to be published. In this presentation, learn tips and tricks to design appealing and user-friendly apps with MATLAB, such as:
    • customizing graphics objects
    • resizing the app components to fit every screen
    • exploiting every corner of the canvas space
    • using custom HTML components inside the application
  • Infineon, Supporting Dependability for Automotive MCUs with Model-Based Design
    Thomas Schneid
    11:30 pm - 12:30 am (CDT)
    Register/Log in to save session
    APAC
    Modeling, Simulation, and Implementation
    Automated Driving Systems
    Control Systems
    Embedded Systems
    Machine Learning
    Power Electronics Control Design
    Embedded Code Generation
    Hardware Support
    Model-Based Design
    Model Deployment
    Infineon, Supporting Dependability for Automotive MCUs with Model-Based Design
    Thomas Schneid
    11:30 pm - 12:30 am (CDT)
    11:30 pm - 12:30 am (CDT)
    APAC
    Modeling, Simulation, and Implementation
    Automated Driving Systems
    Control Systems
    Embedded Systems
    Machine Learning
    Power Electronics Control Design
    Embedded Code Generation
    Hardware Support
    Model-Based Design
    Model Deployment
    Dependability is fundamental to Infineon and encompasses automotive safety, cybersecurity, availability, and commitment to quality and long-term product lifecycle support. As automotive systems become more complex, designing a hardware and software application architecture that is fit for purpose is crucial for our customers. Mastering this complexity requires the support of Model-Based Design methods. In this presentation, learn how Infineon and MathWorks strategically teamed up to offer model-based software design capabilities for Infineon’s latest AURIXTM TC4x microcontroller. The AURIX TC4x hardware support package has already seen success with customers, some examples of which will also be shared in the presentation.
  • Formalizing Requirements and Generating Requirements-Based Test Cases
    Dalton L‘Heureux Dallas Perkins
    12:00 am - 1:00 am (CDT)
    Register/Log in to save session
    APAC
    Modeling, Simulation, and Implementation
    Embedded Systems
    Test and Measurement
    Embedded Code Generation
    Model-Based Design
    Systems Engineering
    Verification, Validation, and Test
    Formalizing Requirements and Generating Requirements-Based Test Cases
    Dalton L‘Heureux Dallas Perkins
    12:00 am - 1:00 am (CDT)
    12:00 am - 1:00 am (CDT)
    APAC
    Modeling, Simulation, and Implementation
    Embedded Systems
    Test and Measurement
    Embedded Code Generation
    Model-Based Design
    Systems Engineering
    Verification, Validation, and Test
    Systems engineers typically capture requirements using text that can be incomplete and inconsistent, resulting in errors that become exponentially more expensive to fix over time. By formalizing requirements using the Requirements Table block, you can define the expected behavior and analyze the requirements for completeness and consistency before you even begin your design. Furthermore, since the model is independent of the design model, you can generate requirements-based tests from your modeled requirements and verify if your design is meeting those requirements without needing to manually write thousands of test cases.
  • Using Simulink with Python
    Weiwu Li Yann Debray
    12:30 am - 1:30 am (CDT)
    Register/Log in to save session
    APAC
    Modeling, Simulation, and Implementation
    Automated Driving Systems
    Control Systems
    Deep Learning
    Image Processing and Computer Vision
    Signal Processing
    Container, Web, and Desktop Deployment
    Model-Based Design
    Model Deployment
    Physical Modeling
    Using Simulink with Python
    Weiwu Li Yann Debray
    12:30 am - 1:30 am (CDT)
    12:30 am - 1:30 am (CDT)
    APAC
    Modeling, Simulation, and Implementation
    Automated Driving Systems
    Control Systems
    Deep Learning
    Image Processing and Computer Vision
    Signal Processing
    Container, Web, and Desktop Deployment
    Model-Based Design
    Model Deployment
    Physical Modeling
    Engineers use MATLAB® and Simulink® with other programming languages, for example C/C++ or Python®, to develop algorithm components for their increasingly complex projects. Integrating these components, which are developed in different environments, together for system-level simulation is becoming a key step of the project’s success. In this presentation, see a demonstration of major workflows using Python with Simulink. Learn how to use Python Importer and other methods to bring Python code (including AI models and data processing pipelines) easily into Simulink. In addition, discover how you can simulate your Simulink model from a Python-based environment through the MATLAB engine or compile your Simulink model as a Python package for deployment.
  • MBD for FPGA/SoC design and prototyping in Aerospace application
    1:00 am - 2:00 am (CDT)
    Register/Log in to save session
    APAC
    Modeling, Simulation, and Implementation
    MBD for FPGA/SoC design and prototyping in Aerospace application
    1:00 am - 2:00 am (CDT)
    1:00 am - 2:00 am (CDT)
    APAC
    Modeling, Simulation, and Implementation
  • 6G Wireless Technology: Accelerate Your R&D with MATLAB
    Ahmad Saad Houman Zarrinkoub
    11:30 pm - 12:30 am (CDT)
    Register/Log in to save session
    APAC
    Wireless Connectivity and Radar
    FPGA, ASIC, and SoC Design
    Machine Learning
    Signal Processing
    Wireless Communications
    Model-Based Design
    Verification, Validation, and Test
    6G Wireless Technology: Accelerate Your R&D with MATLAB
    Ahmad Saad Houman Zarrinkoub
    11:30 pm - 12:30 am (CDT)
    11:30 pm - 12:30 am (CDT)
    APAC
    Wireless Connectivity and Radar
    FPGA, ASIC, and SoC Design
    Machine Learning
    Signal Processing
    Wireless Communications
    Model-Based Design
    Verification, Validation, and Test
    The world of wireless communications has begun the research and development to build sixth-generation (6G) wireless systems. 6G research and development aims to improve on the performance of the current 5G systems and develop networks that are faster, more intelligent, operate with lower latencies, and enable new applications. Enabling technologies for 6G may include new frequencies like sub-THz communication, as well as artificial intelligence and machine learning, reconfigurable intelligent surfaces, joint communication and sensing, and new digital waveforms. Hear an overview of the goals and vision for 6G systems, the enabling technologies, and how MATLAB® wireless communications tools can enable you to accelerate your 6G R&D process with reliable modeling and simulation.
  • Can You See Me Now? Analyzing Satellite-to-Ground Station Visibility
    Ahmad Saad Mike McLernon Rameez Ahmed
    12:00 am - 2:00 am (CDT)
    Register/Log in to save session
    APAC
    Wireless Connectivity and Radar
    Wireless Communications
    Systems Engineering
    Can You See Me Now? Analyzing Satellite-to-Ground Station Visibility
    Ahmad Saad Mike McLernon Rameez Ahmed
    12:00 am - 2:00 am (CDT)
    12:00 am - 2:00 am (CDT)
    APAC
    Wireless Connectivity and Radar
    Wireless Communications
    Systems Engineering
    This hands-on workshop walks you through exercises that will enable you to use Satellite Communications Toolbox to streamline your satellite-related workflows with little coding. Agenda:
    • Brief overview of Satellite Communications Toolbox
    • Hands-on exercises using MATLAB Online™ where you will:
      • - Set up and launch a satellite scenario viewer - Compute and visualize the visibility access between a satellite and a ground station If you complete these exercises with time to spare, you can complete a follow-on exercise to compute and visualize communications link closure between a satellite and a ground station.
  • Optimized Motor Control Applications: From Idea to Deployment with NXP MBDT
    Razvan Chivu Stefan Cinipeanu
    11:30 pm - 12:30 am (CDT)
    Register/Log in to save session
    APAC
    Electrification
    Control Systems
    Embedded Systems
    Mechatronics
    Power Electronics Control Design
    Embedded Code Generation
    Hardware Support
    Model-Based Design
    Model Deployment
    Optimized Motor Control Applications: From Idea to Deployment with NXP MBDT
    Razvan Chivu Stefan Cinipeanu
    11:30 pm - 12:30 am (CDT)
    11:30 pm - 12:30 am (CDT)
    APAC
    Electrification
    Control Systems
    Embedded Systems
    Mechatronics
    Power Electronics Control Design
    Embedded Code Generation
    Hardware Support
    Model-Based Design
    Model Deployment
    Developing complex applications for motor control requires a streamlined, efficient process that delivers high-performance results. In this presentation, explore NXP™ Model-Based Design Toolbox (MBDT), which seamlessly integrates with MATLAB® and Simulink® to provide an automated path to applications development on NXP S32 microcontrollers. With extensive automotive math and motor control function libraries, peripheral driver blocks, and processor-optimized motor control functions (running on a specialized co-processor, the Enhanced Time Processor Unit), the toolbox offers multiple simulation modes and code generation capabilities for NXP processors. By using MBDT with MATLAB and Simulink, NXP customers can easily prototype motor control applications. Discover how NXP MBDT enables the development of optimized performance applications for motor control.
  • Optimize EV Battery Performance Using Simulation
    Dr. Lorenzo Nicoletti Dr. Danielle Chu Dr. Mike Sasena
    12:00 am - 1:00 am (CDT)
    Register/Log in to save session
    APAC
    Electrification
    Power Systems Analysis and Design
    Physical Modeling
    Optimize EV Battery Performance Using Simulation
    Dr. Lorenzo Nicoletti Dr. Danielle Chu Dr. Mike Sasena
    12:00 am - 1:00 am (CDT)
    12:00 am - 1:00 am (CDT)
    APAC
    Electrification
    Power Systems Analysis and Design
    Physical Modeling
    In early vehicle design stages, engineers need to make decisions about battery pack sizing. This often involves trade-offs between competing objectives such as EV range and cost, making it difficult to select an optimal design. It requires significant time and modeling expertise to build the closed-loop system-level models needed to evaluate potential designs. In this talk, we present an accessible workflow to address these pains. We demonstrate how to create a system-level electric vehicle model using the Virtual Vehicle Composer app from Powertrain Blockset™. The model will be used to iteratively evaluate the vehicle’s performance using optimization techniques. The results of this study can then be used in Simscape Battery™ to generate an appropriate battery pack design and evaluate the pack behavior in more detail. This battery model is then integrated back into the system level model for verification against the requirements. This approach provides a rigorous numerical method to quantify trade-offs in the design problem. It also simplifies the process of model building, both at the system level and battery subsystem level. Domain experts who may not be tool experts can now take advantage of these powerful design workflows.
  • Techno-Economic​ Analysis of the Impact of EV Charging on the Power Grid
    Dr. Graham Dudgeon Chris Lee Ruth-Anne Marchant
    12:30 am - 1:30 am (CDT)
    Register/Log in to save session
    APAC
    Electrification
    Power Systems Analysis and Design
    Physical Modeling
    Techno-Economic​ Analysis of the Impact of EV Charging on the Power Grid
    Dr. Graham Dudgeon Chris Lee Ruth-Anne Marchant
    12:30 am - 1:30 am (CDT)
    12:30 am - 1:30 am (CDT)
    APAC
    Electrification
    Power Systems Analysis and Design
    Physical Modeling
    With more and more electric vehicles connecting to the power grid every day, there are concerns that existing grid infrastructure will be strained beyond acceptable operational limits. We can address these concerns by bringing operations, pricing, and forecasting into techno-economic models of power systems. Using these models, we can assess feasibility, risk, optimal operations, and profitability of charging infrastructure. These models provide key insights such as expected system performance over time, identification of factors that lead to bad outcomes, and right-sizing of components through optimization studies. In this talk, we consider a scenario where a system operator can command individual electric vehicle battery units to both store and supply electricity while connected to the grid. The operator applies techno-economic optimization to the charging profiles to minimize electricity cost while accounting for system requirements and constraints, such as limits on state of charge, grid supply, and charge/discharge rate. The optimization provides a fast and automated approach for leveraging all of the units connected to the grid for overall system benefit. Charging profiles are then assessed for the impact on voltage and power flow levels using a grid-level simulation.
  • NASA’s Spacecraft Power Capability (SPoC) Model and Applications Using MATLAB
    1:00 am - 2:00 am (CDT)
    Register/Log in to save session
    APAC
    Electrification
    NASA’s Spacecraft Power Capability (SPoC) Model and Applications Using MATLAB
    1:00 am - 2:00 am (CDT)
    1:00 am - 2:00 am (CDT)
    APAC
    Electrification
  • What’s New in MATLAB, Simulink, and RoadRunner for Automated Driving Development
    Div Tiwari Linghui Zhang
    11:30 pm - 12:30 am (CDT)
    Register/Log in to save session
    APAC
    Autonomous Systems and Robotics
    Automated Driving Systems
    Systems Engineering
    Verification, Validation, and Test
    What’s New in MATLAB, Simulink, and RoadRunner for Automated Driving Development
    Div Tiwari Linghui Zhang
    11:30 pm - 12:30 am (CDT)
    11:30 pm - 12:30 am (CDT)
    APAC
    Autonomous Systems and Robotics
    Automated Driving Systems
    Systems Engineering
    Verification, Validation, and Test
    MATLAB®, Simulink®, and RoadRunner help engineers to build automated driving systems with increasing levels of automation. Discover new features and examples in R2022b and R2023a that will allow you to:
    • Interactively design scenes and scenarios for driving simulation
    • Generate 3D scenes from HD map data
    • Generate driving scenarios from recorded sensor data
    • Simulate driving applications like emergency braking, lane following, lane change, platooning, and valet parking
  • Model Autonomous Navigation of a Mars Rover
    Ameya Rajendra Godbole Chinmayi Lanka Harshal Upadhyay Kritika Ramani
    12:00 am - 2:00 am (CDT)
    Register/Log in to save session
    APAC
    Autonomous Systems and Robotics
    Deep Learning
    Image Processing and Computer Vision
    Robotics
    Model-Based Design
    Physical Modeling
    Model Autonomous Navigation of a Mars Rover
    Ameya Rajendra Godbole Chinmayi Lanka Harshal Upadhyay Kritika Ramani
    12:00 am - 2:00 am (CDT)
    12:00 am - 2:00 am (CDT)
    APAC
    Autonomous Systems and Robotics
    Deep Learning
    Image Processing and Computer Vision
    Robotics
    Model-Based Design
    Physical Modeling
    Curious about how a Mars rover autonomously plans and navigates its path, millions of miles away? In this hands-on workshop, work with a virtual model of a six-wheeled autonomous rover and help it navigate around uncharted terrains. Use sensor data to estimate the rover position, deep learning to detect obstacles, and path planning algorithms to design an optimal path for the rover to pick up a soil sample using a 6-DOF arm. Be inspired and empowered to work on autonomous navigation and robotics challenges in your own applications and learn how deep learning and computer vision tools can inform and improve traditional path planning algorithms. This workshop demonstrates how multiple MathWorks products can be used together to model a challenging application. The entire application is modeled within a single simulation environment using the following:
    • MATLAB App Designer for offline path planning, camera sensor and front end for the workshop
    • Deep Learning Toolbox™ for perception module and rock detections
    • Simscape Multibody™ to model the rover plant, contact mechanics and the virtual terrain
    • Computer Vision Toolbox™ and Image Processing Toolbox™ for depth estimation of detected rocks
    • Robotics System Toolbox™ and Navigation Toolbox™ for path planning and controls.
  • Running MATLAB Machine Learning Jobs in Amazon SageMaker
    Gandharv Kashinath Shun Mao
    11:30 pm - 12:30 am (CDT)
    Register/Log in to save session
    APAC
    Cloud, Enterprise, and DevOps
    Machine Learning
    Cloud Computing
    Container, Web, and Desktop Deployment
    GPU Computing
    Hardware Support
    Running MATLAB Machine Learning Jobs in Amazon SageMaker
    Gandharv Kashinath Shun Mao
    11:30 pm - 12:30 am (CDT)
    11:30 pm - 12:30 am (CDT)
    APAC
    Cloud, Enterprise, and DevOps
    Machine Learning
    Cloud Computing
    Container, Web, and Desktop Deployment
    GPU Computing
    Hardware Support
    Amazon SageMaker is a popular AI platform in the cloud providing a broad range of capabilities and services for AI developers. One of the great advantages of SageMaker is that it lets developers run jobs on a wide selection of compute instances, including GPU support with the pay-as-you-go pricing model, without managing underlying infrastructure. Learn how to leverage SageMaker to train a machine learning model using MATLAB®.
  • A Cloud-Based MATLAB Visual Inspection System
    Arvind Hosagrahara Brett Shoelson
    12:00 am - 1:00 am (CDT)
    Register/Log in to save session
    APAC
    Cloud, Enterprise, and DevOps
    Image Processing and Computer Vision
    Machine Learning
    Predictive Maintenance
    Cloud Computing
    Container, Web, and Desktop Deployment
    Model Deployment
    Report Generation
    A Cloud-Based MATLAB Visual Inspection System
    Arvind Hosagrahara Brett Shoelson
    12:00 am - 1:00 am (CDT)
    12:00 am - 1:00 am (CDT)
    APAC
    Cloud, Enterprise, and DevOps
    Image Processing and Computer Vision
    Machine Learning
    Predictive Maintenance
    Cloud Computing
    Container, Web, and Desktop Deployment
    Model Deployment
    Report Generation
    Hear about a use case study describing a repurposable cloud-based visual inspection system powered by image processing and computer vision algorithms running on MATLAB Production Server™. See how to use mobile device–based workflows to visually catalog and inspect objects of interest via streaming video and image capture and implement a DevOps workflow.
  • MATLAB Based DevOps Workflow in AWS for Hospital Patient Monitoring Applications
    Mohammad Khair
    12:30 am - 1:30 am (CDT)
    Register/Log in to save session
    APAC
    Cloud, Enterprise, and DevOps
    Deep Learning
    Image Processing and Computer Vision
    Cloud Computing
    Container, Web, and Desktop Deployment
    Model Deployment
    Parallel Computing
    MATLAB Based DevOps Workflow in AWS for Hospital Patient Monitoring Applications
    Mohammad Khair
    12:30 am - 1:30 am (CDT)
    12:30 am - 1:30 am (CDT)
    APAC
    Cloud, Enterprise, and DevOps
    Deep Learning
    Image Processing and Computer Vision
    Cloud Computing
    Container, Web, and Desktop Deployment
    Model Deployment
    Parallel Computing
    The Maternal Infant Care division at GE HealthCare is interested in remote baby monitoring in neonatal intensive care units (NICUs). Some of the babies admitted in NICU are fragile and must be isolated for special care in an optimal incubator environment. Remote monitoring enables healthcare professionals to assess babies’ health information and enable parental bonding with the baby in an incubator. This is enabled with a video camera attached to the NICU bed which streams data to MATLAB® on the cloud. The doctors and parents can access baby information via apps deployed on web and mobile applications. MATLAB Production Server™ hosts the MATLAB runtime environment in a Kubernetes-based AWS cloud platform integrated with Edison™ cloud services developed internally by GE HealthCare. As new patient data is collected, the research teams tune processing algorithms developed using MATLAB following the standard DevOps workflow. Deployment is scaled using MATLAB Production Server for multiple data streams.
  • Using IOT/AI on Sensing Non-Visual Disease Symptoms on Maize
    1:00 am - 2:00 am (CDT)
    Register/Log in to save session
    APAC
    Cloud, Enterprise, and DevOps
    Using IOT/AI on Sensing Non-Visual Disease Symptoms on Maize
    1:00 am - 2:00 am (CDT)
    1:00 am - 2:00 am (CDT)
    APAC
    Cloud, Enterprise, and DevOps
  • Preparing Students for Impactful Careers in Industry
    Moiz Khan
    11:30 pm - 12:30 am (CDT)
    Register/Log in to save session
    APAC
    Preparing Future Engineers and Scientists
    Control Systems
    Mechatronics
    Robotics
    Signal Processing
    Wireless Communications
    Embedded Code Generation
    Physical Modeling
    Real-Time Simulation and Testing
    Systems Engineering
    Verification, Validation, and Test
    Preparing Students for Impactful Careers in Industry
    Moiz Khan
    11:30 pm - 12:30 am (CDT)
    11:30 pm - 12:30 am (CDT)
    APAC
    Preparing Future Engineers and Scientists
    Control Systems
    Mechatronics
    Robotics
    Signal Processing
    Wireless Communications
    Embedded Code Generation
    Physical Modeling
    Real-Time Simulation and Testing
    Systems Engineering
    Verification, Validation, and Test
    In this session, hear about the challenges that deans and department heads face as they evolve their curricula to meet the ever-changing needs of industry and students. See how MathWorks aligns as a university partner by developing teaching tools and resources that help prepare students to fit industry and research workflows. This talk integrates MathWorks resources such as assignment autograding, online training courses, courseware, integrations with open source software (OSS), student programs, and capstone projects in a conversation about enabling educators to teach complex engineering concepts in an effective and engaging manner.
  • MATLAB Apps, Live Scripts, and MATLAB Grader for Teaching and Research
    Naini Dawar Stefan Kerber
    12:00 am - 2:00 am (CDT)
    Register/Log in to save session
    APAC
    Preparing Future Engineers and Scientists
    Data Science
    Container, Web, and Desktop Deployment
    MATLAB Apps, Live Scripts, and MATLAB Grader for Teaching and Research
    Naini Dawar Stefan Kerber
    12:00 am - 2:00 am (CDT)
    12:00 am - 2:00 am (CDT)
    APAC
    Preparing Future Engineers and Scientists
    Data Science
    Container, Web, and Desktop Deployment
    This workshop develops a lab experiment to learn about the prime MATLAB® applications for research and teaching. Participants will:
    • Use the MATLAB App Designer to create a simple application to generate data and execute a Stroop test that allows experimenters to easily collect data from participants who rate the color of printed words.
    • Design Live Scripts to interactively illustrate and evaluate the generated data. Visualizations produced by the app will analyze participant responses and reaction times and introduce participants to more advanced analysis techniques (such as machine learning).
    • Exchange data between the app and Live Scripts in CSV format to foster exchange and accessibility in commonly used tools like Microsoft Excel—a popular tool in many disciplines.
    • Demonstrate how to develop parts of the application code in MATLAB Grader™ and perform automated assessment in case the application is gradually built over the course of a lecture.
    The workshop is intended for an audience with basic MATLAB programming skills. Participants are strongly encouraged to complete the MATLAB Onramp course prior to this workshop.
  • Moonshots: How Engineers and Scientists Are Achieving the Impossible
    Arun Mulpur
    3:00 am - 3:30 am (CDT)
    Register/Log in to save session
    EMEA
    Plenary
    Control Systems
    Data Science
    Machine Learning
    Power Systems Analysis and Design
    Robotics
    Embedded Code Generation
    Model-Based Design
    Physical Modeling
    Moonshots: How Engineers and Scientists Are Achieving the Impossible
    Arun Mulpur
    3:00 am - 3:30 am (CDT)
    3:00 am - 3:30 am (CDT)
    EMEA
    Plenary
    Control Systems
    Data Science
    Machine Learning
    Power Systems Analysis and Design
    Robotics
    Embedded Code Generation
    Model-Based Design
    Physical Modeling
    Moonshots—projects with lofty and seemingly impossible goals—are the engines that drive innovation, increase human knowledge, and improve our standard of living. Over 50 years ago, NASA's Apollo program landed the first people on the moon, accomplishing a literal moonshot and fostering emerging technologies that are now ubiquitous, such as integrated circuits, photovoltaic cells, and digital image processing. Today, engineers and scientists are aiming to generate unlimited clean energy, create advanced medical devices to save and improve lives, travel to Mars, and explore the universe. In this talk, learn about some of these visionary projects, the role MATLAB® and Simulink® played in helping engineers and scientists achieve their goals, and how you can apply these same tools and techniques to your own "moonshots."
  • How Siemens Energy Enables the Global Energy Transition
    Jens Dietrich
    3:30 am - 4:00 am (CDT)
    Register/Log in to save session
    EMEA
    Plenary
    Automated Driving Systems
    Computational Biology
    Control Systems
    Data Science
    Deep Learning
    Embedded Systems
    Enterprise and IT Systems
    FPGA, ASIC, and SoC Design
    Image Processing and Computer Vision
    Internet of Things
    Machine Learning
    Mechatronics
    Mixed-Signal Systems
    Power Electronics Control Design
    Power Systems Analysis and Design
    Predictive Maintenance
    Robotics
    Signal Processing
    Test and Measurement
    Wireless Communications
    Cloud Computing
    Container, Web, and Desktop Deployment
    Discrete-Event Simulation
    Embedded Code Generation
    GPU Computing
    Hardware Support
    Model-Based Design
    Model Deployment
    Parallel Computing
    Physical Modeling
    Real-Time Simulation and Testing
    Report Generation
    Systems Engineering
    Verification, Validation, and Test
    How Siemens Energy Enables the Global Energy Transition
    Jens Dietrich
    3:30 am - 4:00 am (CDT)
    3:30 am - 4:00 am (CDT)
    EMEA
    Plenary
    Automated Driving Systems
    Computational Biology
    Control Systems
    Data Science
    Deep Learning
    Embedded Systems
    Enterprise and IT Systems
    FPGA, ASIC, and SoC Design
    Image Processing and Computer Vision
    Internet of Things
    Machine Learning
    Mechatronics
    Mixed-Signal Systems
    Power Electronics Control Design
    Power Systems Analysis and Design
    Predictive Maintenance
    Robotics
    Signal Processing
    Test and Measurement
    Wireless Communications
    Cloud Computing
    Container, Web, and Desktop Deployment
    Discrete-Event Simulation
    Embedded Code Generation
    GPU Computing
    Hardware Support
    Model-Based Design
    Model Deployment
    Parallel Computing
    Physical Modeling
    Real-Time Simulation and Testing
    Report Generation
    Systems Engineering
    Verification, Validation, and Test
    The transition to carbon-neutral energy production is one of the cornerstones for limiting global warming. Recent discussions about energy transition have focused on the "energy trilemma"—the need to find the right balance between affordability, reliability, and sustainability of energy. Part of the solution is to strengthen the power grid for highly fluctuating distributed generation and feed-in from renewable energy sources. Siemens Energy is accomplishing this with solutions such as HVDC (high-voltage direct current transmission) and FACTS (flexible AC transmission). The power grid of the future relies on power electronics and their control software. Therefore, the energy transition is also driven by software development. For this purpose, it is elementary to understand the grid as well as possible and to test the software thoroughly. Working closely with MathWorks as a technology partner, Siemens Energy's Grid Solutions Control and Protection Department has extended Model-Based Design to study, develop, and verify complex power transmission systems. Simulating the plant on a desktop PC helps to "push left" in the v-model development cycle to test system behavior as early as possible with the same control software that is later deployed on hardware. This enables the development of a digital twin to analyze and understand fault scenarios and incorporate final system validation for the end customer with MathWorks products. Leveraging this approach as an end-to-end development ecosystem helps in all phases of project execution—be it bidding, engineering, or system studies. Modern cloud-based CI/CD workflows extend this ecosystem to automate test and code generation and make it even more accessible to all engineers. Using a model-based engineering ecosystem helps Siemens Energy provide answers and solutions to our customers and support them on their journey to clean and available energy for all.
  • Reduced Order Modeling with AI: Accelerating Simulink Analysis and Design
    Kishen Mahadevan Terri Xiao
    4:00 am - 4:30 am (CDT)
    Register/Log in to save session
    EMEA
    AI
    Control Systems
    Deep Learning
    Machine Learning
    Model-Based Design
    Reduced Order Modeling with AI: Accelerating Simulink Analysis and Design
    Kishen Mahadevan Terri Xiao
    4:00 am - 4:30 am (CDT)
    4:00 am - 4:30 am (CDT)
    EMEA
    AI
    Control Systems
    Deep Learning
    Machine Learning
    Model-Based Design
    With Model-Based Design, you use virtual models to design, implement, and deliver complex systems. Creating high-fidelity virtual models that accurately capture hardware behavior is valuable and can be time consuming. However, these high-fidelity models are not suitable for all stages of the development process. For example, a computational fluid dynamics model that is useful for detailed component design will be too slow to include in system-level simulations to verify your control system or to perform system analysis that requires many simulation runs. A high-fidelity model for analyzing NOx emissions will be too slow to run in real time in your embedded system. Does this mean you have to start from scratch to create faster approximations of your high-fidelity models? This is where reduced order modeling (ROM) comes to help. ROM is a set of automated computational techniques that helps you reuse your high-fidelity models for creating faster-running, lower-fidelity approximations. In this talk, learn about different ROM techniques and methods, covering AI-based approaches, linear-parameter varying (LPV) modeling, and strategies for bringing large-scale sparse state-space matrices from FEA tools into MATLAB® and Simulink® for applications such as flexible body modeling and control. The focus of the talk, however, will be on AI-based ROM. See how you can perform a thorough design of experiments and use the resulting data to train AI models using LSTM, neural ODE, and nonlinear ARX algorithms. Learn how to integrate these AI models into your Simulink simulations, whether for hardware-in-the-loop testing or deployment to embedded systems for virtual sensor applications. Learn about the pros and cons of different ROM approaches to help you choose the best one for your next project.
  • Catching Fire: Using Autonomous Drones to Detect and Track Wildfires
    Brenda Zhuang Sarah Mohamed Shweta Singh
    4:30 am - 6:00 am (CDT)
    Register/Log in to save session
    EMEA
    AI
    Control Systems
    Deep Learning
    Embedded Systems
    Image Processing and Computer Vision
    Robotics
    Model-Based Design
    Physical Modeling
    Systems Engineering
    Catching Fire: Using Autonomous Drones to Detect and Track Wildfires
    Brenda Zhuang Sarah Mohamed Shweta Singh
    4:30 am - 6:00 am (CDT)
    4:30 am - 6:00 am (CDT)
    EMEA
    AI
    Control Systems
    Deep Learning
    Embedded Systems
    Image Processing and Computer Vision
    Robotics
    Model-Based Design
    Physical Modeling
    Systems Engineering
    Can drones help prevent natural disasters? Wildfires have become highly destructive in recent years, ravaging the environment and human lives. In this hands-on workshop, build a wildfire detection system with autonomous drones. Explore cutting-edge methods to detect fire outbreaks and analyze the spread. Gain skills in simulation and AI that you can apply to life-saving problems in MATLAB® and Simulink Online™.
  • Low Code Data Analysis in MATLAB
    Lola Davidson Onomitra Ghosh
    4:00 am - 4:30 am (CDT)
    Register/Log in to save session
    EMEA
    Algorithm Development and Data Analysis
    Data Science
    Machine Learning
    Predictive Maintenance
    Low Code Data Analysis in MATLAB
    Lola Davidson Onomitra Ghosh
    4:00 am - 4:30 am (CDT)
    4:00 am - 4:30 am (CDT)
    EMEA
    Algorithm Development and Data Analysis
    Data Science
    Machine Learning
    Predictive Maintenance
    Data analysis is an integral part of many engineering and scientific workflows, but applying the right data analysis techniques typically takes lots of manual coding before getting useful results. It can be particularly challenging when you are not familiar with your data. MATLAB® Live Editor Tasks and apps such as Data Cleaner make it easy to explore, clean, and prepare data. These apps and tasks can also automatically generate the equivalent MATLAB for you to build on. This demo shows how you can analyze data and generate reusable data analysis programs in MATLAB with minimal coding.
  • Cell Array, Table, Timetable, Struct, or Dictionary? Choosing a Container Type
    David Garrison Francesca Marini
    4:30 am - 5:00 am (CDT)
    Register/Log in to save session
    EMEA
    Algorithm Development and Data Analysis
    Cell Array, Table, Timetable, Struct, or Dictionary? Choosing a Container Type
    David Garrison Francesca Marini
    4:30 am - 5:00 am (CDT)
    4:30 am - 5:00 am (CDT)
    EMEA
    Algorithm Development and Data Analysis
    What do you do when you need to work with data that combines numbers, strings, datetimes, categoricals, and other types of data? Container types such as a cell array, table, timetable, struct, and dictionary can store heterogeneous data, but how do you choose the right one for your application? The choice of container type can have a big impact on your productivity and the performance of your code. Learn about the five major container types in MATLAB®, how they represent data, and the advantages and disadvantages of each.
  • Process of Building AI Models for Predicting Engine Performance and Emissions
    Britant Sureka Shakti Saurabh
    5:00 am - 5:30 am (CDT)
    Register/Log in to save session
    1
    EMEA
    Algorithm Development and Data Analysis
    Deep Learning
    Machine Learning
    Test and Measurement
    Model-Based Design
    Model Deployment
    Parallel Computing
    Real-Time Simulation and Testing
    Systems Engineering
    Process of Building AI Models for Predicting Engine Performance and Emissions
    Britant Sureka Shakti Saurabh
    5:00 am - 5:30 am (CDT)
    5:00 am - 5:30 am (CDT)
    1
    EMEA
    Algorithm Development and Data Analysis
    Deep Learning
    Machine Learning
    Test and Measurement
    Model-Based Design
    Model Deployment
    Parallel Computing
    Real-Time Simulation and Testing
    Systems Engineering
    Simulation-based product development requires efficient engine models that can not only predict engine performance and emissions accurately but also produce results at real-time computing speeds. These models are used to predict in a closed-loop system-level environment integrated with controls and other components. 0D/1D physics-based software can model engine performance with good accuracy, but these models cannot be deployed in a closed-loop environment with controls and other components because they have a significantly higher runtime—about 20 to 40 times slower than real time. Investigation was done to find a low-fidelity model or algorithm with similar performance that could replace high-fidelity physics-based models in the simulation domain and can be deployed in a system level environment. The robustness of AI models’ ability to learn from the data encouraged Cummins to try several machine learning models. A detailed approach was taken to build LSTM-based deep neural network models that achieved target model accuracy with a runtime of about one-eighth that of real time. Cummins developed a total of 26 models using MATLAB® to predict 26 different responses of the engine which consist of temperature, pressure, and flows across multiple locations of the engine along with emissions, efficiencies, and engine brake torque. In the process of building these deep learning models, multiple techniques were evaluated such as defining an optimal deep neural network structure consisting of the type and number of layers, using different activation functions, and optimizing associated hyperparameters. In future, the process of building these models will be automated, and the use of MATLAB to train the models in parallel will be a significant advantage. As these models meet all the requirements, future work would be done to integrate these models in the pure simulation domain, followed by integration with actual hardware and control.
  • How to Optimize the User Experience of Your MATLAB Apps
    Gianluca Carnielli
    5:30 am - 6:00 am (CDT)
    Register/Log in to save session
    EMEA
    Algorithm Development and Data Analysis
    Data Science
    Container, Web, and Desktop Deployment
    How to Optimize the User Experience of Your MATLAB Apps
    Gianluca Carnielli
    5:30 am - 6:00 am (CDT)
    5:30 am - 6:00 am (CDT)
    EMEA
    Algorithm Development and Data Analysis
    Data Science
    Container, Web, and Desktop Deployment
    With MATLAB®, non-professional software developers can develop professional applications. However, creating visually appealing and user-friendly applications requires design considerations which might elude the engineer who is—rightfully—focused on the app’s functionality rather than aesthetics or usability aspects. Nonetheless, an easy-to-use app is always desirable and sometimes a requirement when the app needs to be published. In this presentation, learn tips and tricks to design appealing and user-friendly apps with MATLAB, such as:
    • customizing graphics objects
    • resizing the app components to fit every screen
    • exploiting every corner of the canvas space
    • using custom HTML components inside the application
  • Infineon, Supporting Dependability for Automotive MCUs with Model-Based Design
    Thomas Schneid
    4:00 am - 4:30 am (CDT)
    Register/Log in to save session
    EMEA
    Modeling, Simulation, and Implementation
    Automated Driving Systems
    Control Systems
    Embedded Systems
    Machine Learning
    Power Electronics Control Design
    Embedded Code Generation
    Hardware Support
    Model-Based Design
    Model Deployment
    Infineon, Supporting Dependability for Automotive MCUs with Model-Based Design
    Thomas Schneid
    4:00 am - 4:30 am (CDT)
    4:00 am - 4:30 am (CDT)
    EMEA
    Modeling, Simulation, and Implementation
    Automated Driving Systems
    Control Systems
    Embedded Systems
    Machine Learning
    Power Electronics Control Design
    Embedded Code Generation
    Hardware Support
    Model-Based Design
    Model Deployment
    Dependability is fundamental to Infineon and encompasses automotive safety, cybersecurity, availability, and commitment to quality and long-term product lifecycle support. As automotive systems become more complex, designing a hardware and software application architecture that is fit for purpose is crucial for our customers. Mastering this complexity requires the support of Model-Based Design methods. In this presentation, learn how Infineon and MathWorks strategically teamed up to offer model-based software design capabilities for Infineon’s latest AURIXTM TC4x microcontroller. The AURIX TC4x hardware support package has already seen success with customers, some examples of which will also be shared in the presentation.
  • Formalizing Requirements and Generating Requirements-Based Test Cases
    Dalton L‘Heureux Dallas Perkins
    4:30 am - 5:00 am (CDT)
    Register/Log in to save session
    EMEA
    Modeling, Simulation, and Implementation
    Embedded Systems
    Test and Measurement
    Embedded Code Generation
    Model-Based Design
    Systems Engineering
    Verification, Validation, and Test
    Formalizing Requirements and Generating Requirements-Based Test Cases
    Dalton L‘Heureux Dallas Perkins
    4:30 am - 5:00 am (CDT)
    4:30 am - 5:00 am (CDT)
    EMEA
    Modeling, Simulation, and Implementation
    Embedded Systems
    Test and Measurement
    Embedded Code Generation
    Model-Based Design
    Systems Engineering
    Verification, Validation, and Test
    Systems engineers typically capture requirements using text that can be incomplete and inconsistent, resulting in errors that become exponentially more expensive to fix over time. By formalizing requirements using the Requirements Table block, you can define the expected behavior and analyze the requirements for completeness and consistency before you even begin your design. Furthermore, since the model is independent of the design model, you can generate requirements-based tests from your modeled requirements and verify if your design is meeting those requirements without needing to manually write thousands of test cases.
  • Using Simulink with Python
    Weiwu Li Yann Debray
    5:00 am - 5:30 am (CDT)
    Register/Log in to save session
    EMEA
    Modeling, Simulation, and Implementation
    Automated Driving Systems
    Control Systems
    Deep Learning
    Image Processing and Computer Vision
    Signal Processing
    Container, Web, and Desktop Deployment
    Model-Based Design
    Model Deployment
    Physical Modeling
    Using Simulink with Python
    Weiwu Li Yann Debray
    5:00 am - 5:30 am (CDT)
    5:00 am - 5:30 am (CDT)
    EMEA
    Modeling, Simulation, and Implementation
    Automated Driving Systems
    Control Systems
    Deep Learning
    Image Processing and Computer Vision
    Signal Processing
    Container, Web, and Desktop Deployment
    Model-Based Design
    Model Deployment
    Physical Modeling
    Engineers use MATLAB® and Simulink® with other programming languages, for example C/C++ or Python®, to develop algorithm components for their increasingly complex projects. Integrating these components, which are developed in different environments, together for system-level simulation is becoming a key step of the project’s success. In this presentation, see a demonstration of major workflows using Python with Simulink. Learn how to use Python Importer and other methods to bring Python code (including AI models and data processing pipelines) easily into Simulink. In addition, discover how you can simulate your Simulink model from a Python-based environment through the MATLAB engine or compile your Simulink model as a Python package for deployment.
  • Accelerating Safe Railway Application Development Using Model-Based Design
    Daran Smalley
    5:30 am - 6:00 am (CDT)
    Register/Log in to save session
    EMEA
    Modeling, Simulation, and Implementation
    Control Systems
    Embedded Systems
    Power Electronics Control Design
    Power Systems Analysis and Design
    Test and Measurement
    Embedded Code Generation
    Model-Based Design
    Real-Time Simulation and Testing
    Systems Engineering
    Verification, Validation, and Test
    Accelerating Safe Railway Application Development Using Model-Based Design
    Daran Smalley
    5:30 am - 6:00 am (CDT)
    5:30 am - 6:00 am (CDT)
    EMEA
    Modeling, Simulation, and Implementation
    Control Systems
    Embedded Systems
    Power Electronics Control Design
    Power Systems Analysis and Design
    Test and Measurement
    Embedded Code Generation
    Model-Based Design
    Real-Time Simulation and Testing
    Systems Engineering
    Verification, Validation, and Test
    Alstom’s train traction team uses MATLAB® and Simulink® to develop traction control software adhering to Software Safety Level 2 standard EN 50657 (including former EN 50128). Hear a brief summary of the earlier talk from MATLAB EXPO 2018 when the traction control team used MATLAB and Simulink for prototyping code generation on traction controllers for the very first time. Learn how Alstom transformed its software development processes and tools from traditional tools such as Visio, textual documents in combination with IEC 1131 design tools, and hardware-based testing using MATLAB and Simulink for requirement management, software development, and verification on personal laptops according to safety standards EN 50128/50657. Alstom has developed software products and projects to successfully have trains running in passenger service with independent safety assessment using a Model-Based Design certified workflow from MathWorks. Alstom upskilled its software developers and verifiers to transform into this new way of working and new tools. For certain validation activities, the team has managed to cut cost by 80% compared to traditional way of working. Hear about the opportunities Alstom sees in MATLAB and Simulink and what the team is prototyping to create an even more efficient system development process. Learn about some of the challenges faced and how to overcome them, as such transformations don’t always go smoothly.
  • 6G Wireless Technology: Accelerate Your R&D with MATLAB
    Ahmad Saad Houman Zarrinkoub
    4:00 am - 4:30 am (CDT)
    Register/Log in to save session
    EMEA
    Wireless Connectivity and Radar
    FPGA, ASIC, and SoC Design
    Machine Learning
    Signal Processing
    Wireless Communications
    Model-Based Design
    Verification, Validation, and Test
    6G Wireless Technology: Accelerate Your R&D with MATLAB
    Ahmad Saad Houman Zarrinkoub
    4:00 am - 4:30 am (CDT)
    4:00 am - 4:30 am (CDT)
    EMEA
    Wireless Connectivity and Radar
    FPGA, ASIC, and SoC Design
    Machine Learning
    Signal Processing
    Wireless Communications
    Model-Based Design
    Verification, Validation, and Test
    The world of wireless communications has begun the research and development to build sixth-generation (6G) wireless systems. 6G research and development aims to improve on the performance of the current 5G systems and develop networks that are faster, more intelligent, operate with lower latencies, and enable new applications. Enabling technologies for 6G may include new frequencies like sub-THz communication, as well as artificial intelligence and machine learning, reconfigurable intelligent surfaces, joint communication and sensing, and new digital waveforms. Hear an overview of the goals and vision for 6G systems, the enabling technologies, and how MATLAB® wireless communications tools can enable you to accelerate your 6G R&D process with reliable modeling and simulation.
  • Can You See Me Now? Analyzing Satellite-to-Ground Station Visibility
    Ahmad Saad Mike McLernon Rameez Ahmed
    4:30 am - 6:00 am (CDT)
    Register/Log in to save session
    EMEA
    Wireless Connectivity and Radar
    Wireless Communications
    Systems Engineering
    Can You See Me Now? Analyzing Satellite-to-Ground Station Visibility
    Ahmad Saad Mike McLernon Rameez Ahmed
    4:30 am - 6:00 am (CDT)
    4:30 am - 6:00 am (CDT)
    EMEA
    Wireless Connectivity and Radar
    Wireless Communications
    Systems Engineering
    This hands-on workshop walks you through exercises that will enable you to use Satellite Communications Toolbox to streamline your satellite-related workflows with little coding. Agenda:
    • Brief overview of Satellite Communications Toolbox
    • Hands-on exercises using MATLAB Online™ where you will:
      • - Set up and launch a satellite scenario viewer - Compute and visualize the visibility access between a satellite and a ground station If you complete these exercises with time to spare, you can complete a follow-on exercise to compute and visualize communications link closure between a satellite and a ground station.
  • Optimized Motor Control Applications: From Idea to Deployment with NXP MBDT
    Razvan Chivu Stefan Cinipeanu
    4:00 am - 4:30 am (CDT)
    Register/Log in to save session
    EMEA
    Electrification
    Control Systems
    Embedded Systems
    Mechatronics
    Power Electronics Control Design
    Embedded Code Generation
    Hardware Support
    Model-Based Design
    Model Deployment
    Optimized Motor Control Applications: From Idea to Deployment with NXP MBDT
    Razvan Chivu Stefan Cinipeanu
    4:00 am - 4:30 am (CDT)
    4:00 am - 4:30 am (CDT)
    EMEA
    Electrification
    Control Systems
    Embedded Systems
    Mechatronics
    Power Electronics Control Design
    Embedded Code Generation
    Hardware Support
    Model-Based Design
    Model Deployment
    Developing complex applications for motor control requires a streamlined, efficient process that delivers high-performance results. In this presentation, explore NXP™ Model-Based Design Toolbox (MBDT), which seamlessly integrates with MATLAB® and Simulink® to provide an automated path to applications development on NXP S32 microcontrollers. With extensive automotive math and motor control function libraries, peripheral driver blocks, and processor-optimized motor control functions (running on a specialized co-processor, the Enhanced Time Processor Unit), the toolbox offers multiple simulation modes and code generation capabilities for NXP processors. By using MBDT with MATLAB and Simulink, NXP customers can easily prototype motor control applications. Discover how NXP MBDT enables the development of optimized performance applications for motor control.
  • Optimize EV Battery Performance Using Simulation
    Dr. Lorenzo Nicoletti Dr. Danielle Chu Dr. Mike Sasena
    4:30 am - 5:00 am (CDT)
    Register/Log in to save session
    EMEA
    Electrification
    Power Systems Analysis and Design
    Model-Based Design
    Physical Modeling
    Optimize EV Battery Performance Using Simulation
    Dr. Lorenzo Nicoletti Dr. Danielle Chu Dr. Mike Sasena
    4:30 am - 5:00 am (CDT)
    4:30 am - 5:00 am (CDT)
    EMEA
    Electrification
    Power Systems Analysis and Design
    Model-Based Design
    Physical Modeling
    In early vehicle design stages, engineers need to make decisions about battery pack sizing. This often involves trade-offs between competing objectives such as EV range and cost, making it difficult to select an optimal design. It requires significant time and modeling expertise to build the closed-loop system-level models needed to evaluate potential designs. In this talk, we present an accessible workflow to address these pains. We demonstrate how to create a system-level electric vehicle model using the Virtual Vehicle Composer app from Powertrain Blockset™. The model will be used to iteratively evaluate the vehicle’s performance using optimization techniques. The results of this study can then be used in Simscape Battery™ to generate an appropriate battery pack design and evaluate the pack behavior in more detail. This battery model is then integrated back into the system level model for verification against the requirements. This approach provides a rigorous numerical method to quantify trade-offs in the design problem. It also simplifies the process of model building, both at the system level and battery subsystem level. Domain experts who may not be tool experts can now take advantage of these powerful design workflows.
  • Techno-Economic​ Analysis of the Impact of EV Charging on the Power Grid
    Chris Lee Dr. Graham Dudgeon Ruth-Anne Marchant
    5:00 am - 5:30 am (CDT)
    Register/Log in to save session
    EMEA
    Electrification
    Power Systems Analysis and Design
    Physical Modeling
    Techno-Economic​ Analysis of the Impact of EV Charging on the Power Grid
    Chris Lee Dr. Graham Dudgeon Ruth-Anne Marchant
    5:00 am - 5:30 am (CDT)
    5:00 am - 5:30 am (CDT)
    EMEA
    Electrification
    Power Systems Analysis and Design
    Physical Modeling
    With more and more electric vehicles connecting to the power grid every day, there are concerns that existing grid infrastructure will be strained beyond acceptable operational limits. We can address these concerns by bringing operations, pricing, and forecasting into techno-economic models of power systems. Using these models, we can assess feasibility, risk, optimal operations, and profitability of charging infrastructure. These models provide key insights such as expected system performance over time, identification of factors that lead to bad outcomes, and right-sizing of components through optimization studies. In this talk, we consider a scenario where a system operator can command individual electric vehicle battery units to both store and supply electricity while connected to the grid. The operator applies techno-economic optimization to the charging profiles to minimize electricity cost while accounting for system requirements and constraints, such as limits on state of charge, grid supply, and charge/discharge rate. The optimization provides a fast and automated approach for leveraging all of the units connected to the grid for overall system benefit. Charging profiles are then assessed for the impact on voltage and power flow levels using a grid-level simulation.
  • NASA’s Spacecraft Power Capability (SPoC) Model and Applications Using MATLAB
    5:30 am - 6:00 am (CDT)
    Register/Log in to save session
    EMEA
    Electrification
    NASA’s Spacecraft Power Capability (SPoC) Model and Applications Using MATLAB
    5:30 am - 6:00 am (CDT)
    5:30 am - 6:00 am (CDT)
    EMEA
    Electrification
  • What’s New in MATLAB, Simulink, and RoadRunner for Automated Driving Development
    Div Tiwari Linghui Zhang
    4:00 am - 4:30 am (CDT)
    Register/Log in to save session
    EMEA
    Autonomous Systems and Robotics
    Automated Driving Systems
    Systems Engineering
    Verification, Validation, and Test
    What’s New in MATLAB, Simulink, and RoadRunner for Automated Driving Development
    Div Tiwari Linghui Zhang
    4:00 am - 4:30 am (CDT)
    4:00 am - 4:30 am (CDT)
    EMEA
    Autonomous Systems and Robotics
    Automated Driving Systems
    Systems Engineering
    Verification, Validation, and Test
    MATLAB®, Simulink®, and RoadRunner help engineers to build automated driving systems with increasing levels of automation. Discover new features and examples in R2022b and R2023a that will allow you to:
    • Interactively design scenes and scenarios for driving simulation
    • Generate 3D scenes from HD map data
    • Generate driving scenarios from recorded sensor data
    • Simulate driving applications like emergency braking, lane following, lane change, platooning, and valet parking
  • Model Autonomous Navigation of a Mars Rover
    Ameya Rajendra Godbole Chinmayi Lanka Harshal Upadhyay Kritika Ramani
    4:30 am - 6:00 am (CDT)
    Register/Log in to save session
    EMEA
    Autonomous Systems and Robotics
    Deep Learning
    Image Processing and Computer Vision
    Robotics
    Model-Based Design
    Physical Modeling
    Model Autonomous Navigation of a Mars Rover
    Ameya Rajendra Godbole Chinmayi Lanka Harshal Upadhyay Kritika Ramani
    4:30 am - 6:00 am (CDT)
    4:30 am - 6:00 am (CDT)
    EMEA
    Autonomous Systems and Robotics
    Deep Learning
    Image Processing and Computer Vision
    Robotics
    Model-Based Design
    Physical Modeling
    Curious about how a Mars rover autonomously plans and navigates its path, millions of miles away? In this hands-on workshop, work with a virtual model of a six-wheeled autonomous rover and help it navigate around uncharted terrains. Use sensor data to estimate the rover position, deep learning to detect obstacles, and path planning algorithms to design an optimal path for the rover to pick up a soil sample using a 6-DOF arm. Be inspired and empowered to work on autonomous navigation and robotics challenges in your own applications and learn how deep learning and computer vision tools can inform and improve traditional path planning algorithms. This workshop demonstrates how multiple MathWorks products can be used together to model a challenging application. The entire application is modeled within a single simulation environment using the following:
    • MATLAB App Designer for offline path planning, camera sensor and front end for the workshop
    • Deep Learning Toolbox™ for perception module and rock detections
    • Simscape Multibody™ to model the rover plant, contact mechanics and the virtual terrain
    • Computer Vision Toolbox™ and Image Processing Toolbox™ for depth estimation of detected rocks
    • Robotics System Toolbox™ and Navigation Toolbox™ for path planning and controls.
  • Running MATLAB Machine Learning Jobs in Amazon SageMaker
    Gandharv Kashinath Shun Mao
    4:00 am - 4:30 am (CDT)
    Register/Log in to save session
    EMEA
    Cloud, Enterprise, and DevOps
    Image Processing and Computer Vision
    Machine Learning
    Predictive Maintenance
    Cloud Computing
    Container, Web, and Desktop Deployment
    GPU Computing
    Hardware Support
    Running MATLAB Machine Learning Jobs in Amazon SageMaker
    Gandharv Kashinath Shun Mao
    4:00 am - 4:30 am (CDT)
    4:00 am - 4:30 am (CDT)
    EMEA
    Cloud, Enterprise, and DevOps
    Image Processing and Computer Vision
    Machine Learning
    Predictive Maintenance
    Cloud Computing
    Container, Web, and Desktop Deployment
    GPU Computing
    Hardware Support
    Amazon SageMaker is a popular AI platform in the cloud providing a broad range of capabilities and services for AI developers. One of the great advantages of SageMaker is that it lets developers run jobs on a wide selection of compute instances, including GPU support with the pay-as-you-go pricing model, without managing underlying infrastructure. Learn how to leverage SageMaker to train a machine learning model using MATLAB®.
  • A Cloud-Based MATLAB Visual Inspection System
    Arvind Hosagrahara Brett Shoelson
    4:30 am - 5:00 am (CDT)
    Register/Log in to save session
    EMEA
    Cloud, Enterprise, and DevOps
    Image Processing and Computer Vision
    Machine Learning
    Predictive Maintenance
    Cloud Computing
    Container, Web, and Desktop Deployment
    Model Deployment
    Report Generation
    A Cloud-Based MATLAB Visual Inspection System
    Arvind Hosagrahara Brett Shoelson
    4:30 am - 5:00 am (CDT)
    4:30 am - 5:00 am (CDT)
    EMEA
    Cloud, Enterprise, and DevOps
    Image Processing and Computer Vision
    Machine Learning
    Predictive Maintenance
    Cloud Computing
    Container, Web, and Desktop Deployment
    Model Deployment
    Report Generation
    Hear about a use case study describing a repurposable cloud-based visual inspection system powered by image processing and computer vision algorithms running on MATLAB Production Server™. See how to use mobile device–based workflows to visually catalog and inspect objects of interest via streaming video and image capture and implement a DevOps workflow.
  • MATLAB Based DevOps Workflow in AWS for Hospital Patient Monitoring Applications
    Mohammad Khair
    5:00 am - 5:30 am (CDT)
    Register/Log in to save session
    EMEA
    Cloud, Enterprise, and DevOps
    Deep Learning
    Image Processing and Computer Vision
    Cloud Computing
    Container, Web, and Desktop Deployment
    Model Deployment
    Parallel Computing
    MATLAB Based DevOps Workflow in AWS for Hospital Patient Monitoring Applications
    Mohammad Khair
    5:00 am - 5:30 am (CDT)
    5:00 am - 5:30 am (CDT)
    EMEA
    Cloud, Enterprise, and DevOps
    Deep Learning
    Image Processing and Computer Vision
    Cloud Computing
    Container, Web, and Desktop Deployment
    Model Deployment
    Parallel Computing
    The Maternal Infant Care division at GE HealthCare is interested in remote baby monitoring in neonatal intensive care units (NICUs). Some of the babies admitted in NICU are fragile and must be isolated for special care in an optimal incubator environment. Remote monitoring enables healthcare professionals to assess babies’ health information and enable parental bonding with the baby in an incubator. This is enabled with a video camera attached to the NICU bed which streams data to MATLAB® on the cloud. The doctors and parents can access baby information via apps deployed on web and mobile applications. MATLAB Production Server™ hosts the MATLAB runtime environment in a Kubernetes-based AWS cloud platform integrated with Edison™ cloud services developed internally by GE HealthCare. As new patient data is collected, the research teams tune processing algorithms developed using MATLAB following the standard DevOps workflow. Deployment is scaled using MATLAB Production Server for multiple data streams.
  • Build Scalable AI Solutions with MATLAB Production Server in Kubernetes on Azure
    Björn Müller
    5:30 am - 6:00 am (CDT)
    Register/Log in to save session
    EMEA
    Cloud, Enterprise, and DevOps
    Deep Learning
    Cloud Computing
    Model Deployment
    Real-Time Simulation and Testing
    Build Scalable AI Solutions with MATLAB Production Server in Kubernetes on Azure
    Björn Müller
    5:30 am - 6:00 am (CDT)
    5:30 am - 6:00 am (CDT)
    EMEA
    Cloud, Enterprise, and DevOps
    Deep Learning
    Cloud Computing
    Model Deployment
    Real-Time Simulation and Testing
    With more than 150 years of experience in the industry, the AERZEN Group is one of the top 3 application specialists for high-performance blowers. The compressors, blowers, and turbos are mainly used in wastewater treatment plants, in the process industry, and in oil-free pneumatic conveying of bulk materials. Sustainability, smart energy, resource usage, and reliability of machinery are important concerns for AERZEN’s customers when operating their plants all over the world. The Aerzen Digital Systems business unit is working on smart services so that users can operate the machines even more efficiently and reliably with the help of AI and machine learning. Aerzen Digital Systems designs AI models for forecasting, condition monitoring, and predictive maintenance for this purpose. Functions and models may be used interactively for data exploration as well as automatic processing of streaming IoT data. While planning to operationalize these models, several challenges surfaced and certain requirements for the platform were defined: - Flexibility and scalability for many unique plants and sets of machinery - MLOps to monitor and adapt AI models over decades of equipment lifetimes - Agile and quick deployment of new functionality on availability of improved AI techniques - Integration with applications from different frameworks developed both in-house and by customers In this talk, we detail a sample solution to these challenges centered around MATLAB Production Server™ running in Kubernetes on Microsoft Azure. The Aerzen Digital Systems libraries of MATLAB functions and AI models are deployed to MATLAB Production Server through an MLOps pipeline. In the present case study, data from a large wastewater treatment plant is analyzed and an anomaly detection algorithm for a single blower is developed. This model is then uploaded to the cloud and trained individually for all blowers. During runtime execution, every model is monitored and automatically retrained if necessary.
  • Preparing Students for Impactful Careers in Industry
    Moiz Khan
    4:00 am - 4:30 am (CDT)
    Register/Log in to save session
    EMEA
    Preparing Future Engineers and Scientists
    Control Systems
    Mechatronics
    Robotics
    Signal Processing
    Wireless Communications
    Embedded Code Generation
    Physical Modeling
    Real-Time Simulation and Testing
    Systems Engineering
    Verification, Validation, and Test
    Preparing Students for Impactful Careers in Industry
    Moiz Khan
    4:00 am - 4:30 am (CDT)
    4:00 am - 4:30 am (CDT)
    EMEA
    Preparing Future Engineers and Scientists
    Control Systems
    Mechatronics
    Robotics
    Signal Processing
    Wireless Communications
    Embedded Code Generation
    Physical Modeling
    Real-Time Simulation and Testing
    Systems Engineering
    Verification, Validation, and Test
    In this session, hear about the challenges that deans and department heads face as they evolve their curricula to meet the ever-changing needs of industry and students. See how MathWorks aligns as a university partner by developing teaching tools and resources that help prepare students to fit industry and research workflows. This talk integrates MathWorks resources such as assignment autograding, online training courses, courseware, integrations with open source software (OSS), student programs, and capstone projects in a conversation about enabling educators to teach complex engineering concepts in an effective and engaging manner.
  • MATLAB Apps, Live Scripts, and MATLAB Grader for Teaching and Research
    Naini Dawar Stefan Kerber
    4:30 am - 6:00 am (CDT)
    Register/Log in to save session
    2
    EMEA
    Preparing Future Engineers and Scientists
    Data Science
    Container, Web, and Desktop Deployment
    MATLAB Apps, Live Scripts, and MATLAB Grader for Teaching and Research
    Naini Dawar Stefan Kerber
    4:30 am - 6:00 am (CDT)
    4:30 am - 6:00 am (CDT)
    2
    EMEA
    Preparing Future Engineers and Scientists
    Data Science
    Container, Web, and Desktop Deployment
    This workshop develops a lab experiment to learn about the prime MATLAB® applications for research and teaching. Participants will:
    • Use the MATLAB App Designer to create a simple application to generate data and execute a Stroop test that allows experimenters to easily collect data from participants who rate the color of printed words.
    • Design Live Scripts to interactively illustrate and evaluate the generated data. Visualizations produced by the app will analyze participant responses and reaction times and introduce participants to more advanced analysis techniques (such as machine learning).
    • Exchange data between the app and Live Scripts in CSV format to foster exchange and accessibility in commonly used tools like Microsoft Excel—a popular tool in many disciplines.
    • Demonstrate how to develop parts of the application code in MATLAB Grader™ and perform automated assessment in case the application is gradually built over the course of a lecture.
    The workshop is intended for an audience with basic MATLAB programming skills. Participants are strongly encouraged to complete the MATLAB Onramp course prior to this workshop.
  • Moonshots: How Engineers and Scientists Are Achieving the Impossible
    Arun Mulpur
    10:00 am - 10:30 am (CDT)
    Register/Log in to save session
    AMER
    Plenary
    Control Systems
    Data Science
    Machine Learning
    Power Systems Analysis and Design
    Robotics
    Embedded Code Generation
    Model-Based Design
    Physical Modeling
    Moonshots: How Engineers and Scientists Are Achieving the Impossible
    Arun Mulpur
    10:00 am - 10:30 am (CDT)
    10:00 am - 10:30 am (CDT)
    AMER
    Plenary
    Control Systems
    Data Science
    Machine Learning
    Power Systems Analysis and Design
    Robotics
    Embedded Code Generation
    Model-Based Design
    Physical Modeling
    Moonshots—projects with lofty and seemingly impossible goals—are the engines that drive innovation, increase human knowledge, and improve our standard of living. Over 50 years ago, NASA's Apollo program landed the first people on the moon, accomplishing a literal moonshot and fostering emerging technologies that are now ubiquitous, such as integrated circuits, photovoltaic cells, and digital image processing. Today, engineers and scientists are aiming to generate unlimited clean energy, create advanced medical devices to save and improve lives, travel to Mars, and explore the universe. In this talk, learn about some of these visionary projects, the role MATLAB® and Simulink® played in helping engineers and scientists achieve their goals, and how you can apply these same tools and techniques to your own "moonshots."
← Previous 1 3 4
Session Title
Presenter, Company Name

Amet nihil et nostrum perspiciatis. Porro voluptatem ipsum sed nam omnis et nam commodi incidunt. Eum quae eos consectetur. Eos deserunt est omnis unde error ut aspernatur voluptatibus dolorum. Ea reprehenderit quae iure velit saepe.

Amet nihil et nostrum perspiciatis. Porro voluptatem ipsum sed nam omnis et nam commodi incidunt. Eum quae eos consectetur. Eos deserunt est omnis unde error ut aspernatur voluptatibus dolorum. Ea reprehenderit quae iure velit saepe.

Session Title
Presenter, Company Name

Amet nihil et nostrum perspiciatis. Porro voluptatem ipsum sed nam omnis et nam commodi incidunt. Eum quae eos consectetur. Eos deserunt est omnis unde error ut aspernatur voluptatibus dolorum. Ea reprehenderit quae iure velit saepe.

Amet nihil et nostrum perspiciatis. Porro voluptatem ipsum sed nam omnis et nam commodi incidunt. Eum quae eos consectetur. Eos deserunt est omnis unde error ut aspernatur voluptatibus dolorum. Ea reprehenderit quae iure velit saepe.

Session Title
Presenter, Company Name

Amet nihil et nostrum perspiciatis. Porro voluptatem ipsum sed nam omnis et nam commodi incidunt. Eum quae eos consectetur. Eos deserunt est omnis unde error ut aspernatur voluptatibus dolorum. Ea reprehenderit quae iure velit saepe.

Amet nihil et nostrum perspiciatis. Porro voluptatem ipsum sed nam omnis et nam commodi incidunt. Eum quae eos consectetur. Eos deserunt est omnis unde error ut aspernatur voluptatibus dolorum. Ea reprehenderit quae iure velit saepe.

Ead-messages-offsite-icon-1