MATLAB EXPO 2023

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    6
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    24
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    18

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All Sessions

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  • Using Simulink with Python
    Yann Debray, MathWorks Weiwu Li, MathWorks
    Wed, May 10 | 12:00 pm - 12:30 pm (CDT)
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    Modeling, Simulation, and Implementation
    Using Simulink with Python
    Yann Debray, MathWorks Weiwu Li, MathWorks
    Wed, May 10 | 12:00 pm - 12:30 pm (CDT)
    12:00 pm - 12:30 pm (CDT)
    Modeling, Simulation, and Implementation
    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.
  • Techno-Economic​ Analysis of the Impact of EV Charging on the Power Grid
    Dr. Graham Dudgeon, MathWorks Chris Lee, MathWorks Ruth-Anne Marchant, MathWorks
    Wed, May 10 | 12:00 pm - 12:30 pm (CDT)
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    Electrification
    Techno-Economic​ Analysis of the Impact of EV Charging on the Power Grid
    Dr. Graham Dudgeon, MathWorks Chris Lee, MathWorks Ruth-Anne Marchant, MathWorks
    Wed, May 10 | 12:00 pm - 12:30 pm (CDT)
    12:00 pm - 12:30 pm (CDT)
    Electrification
    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.
  • MATLAB Based DevOps Workflow in AWS for Hospital Patient Monitoring Applications
    Mohammad Khair, GE HealthCare
    Wed, May 10 | 12:00 pm - 12:30 pm (CDT)
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    Cloud, Enterprise, and DevOps
    MATLAB Based DevOps Workflow in AWS for Hospital Patient Monitoring Applications
    Mohammad Khair, GE HealthCare
    Wed, May 10 | 12:00 pm - 12:30 pm (CDT)
    12:00 pm - 12:30 pm (CDT)
    Cloud, Enterprise, and DevOps
    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.
  • How to Optimize the User Experience of Your MATLAB Apps
    Gianluca Carnielli, MathWorks
    Wed, May 10 | 12:30 pm - 1:00 pm (CDT)
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    Algorithm Development and Data Analysis
    How to Optimize the User Experience of Your MATLAB Apps
    Gianluca Carnielli, MathWorks
    Wed, May 10 | 12:30 pm - 1:00 pm (CDT)
    12:30 pm - 1:00 pm (CDT)
    Algorithm Development and Data Analysis
    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

  • Accelerating Safe Railway Application Development Using Model-Based Design
    Daran Smalley, Alstom
    Wed, May 10 | 12:30 pm - 1:00 pm (CDT)
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    Modeling, Simulation, and Implementation
    Accelerating Safe Railway Application Development Using Model-Based Design
    Daran Smalley, Alstom
    Wed, May 10 | 12:30 pm - 1:00 pm (CDT)
    12:30 pm - 1:00 pm (CDT)
    Modeling, Simulation, and Implementation
    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.
  • Build Scalable AI Solutions with MATLAB Production Server in Kubernetes on Azure
    Björn Müller, Aerzen Digital Systems GmbH
    Wed, May 10 | 12:30 pm - 1:00 pm (CDT)
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    Cloud, Enterprise, and DevOps
    Build Scalable AI Solutions with MATLAB Production Server in Kubernetes on Azure
    Björn Müller, Aerzen Digital Systems GmbH
    Wed, May 10 | 12:30 pm - 1:00 pm (CDT)
    12:30 pm - 1:00 pm (CDT)
    Cloud, Enterprise, and DevOps
    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.
  • What's New in MATLAB and Simulink R2023a
    Dr. Jason Ghidella, MathWorks Dr. Michelle Hirsch, MathWorks
    Wed, May 10 | 10:30 pm - 11:00 pm (CDT)
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    Plenary
    What's New in MATLAB and Simulink R2023a
    Dr. Jason Ghidella, MathWorks Dr. Michelle Hirsch, MathWorks
    Wed, May 10 | 10:30 pm - 11:00 pm (CDT)
    10:30 pm - 11:00 pm (CDT)
    Plenary
    Learn about new capabilities in MATLAB® and Simulink®. This talk highlights new tools such as interactive apps, Live Editor tasks, and high-level functions for completing tasks in MATLAB with little to no coding; and new features in Simulink for defining input signals, managing design variants, and debugging simulations. See new capabilities for building and testing your code and models, using Python® with both MATLAB and Simulink, and integrating with other tools and environments, including Jupyter®, VS Code, and Unreal®.
  • Project-Based Learning and Design with Simulation
    Professor Claire Lucas, King’s College London
    Wed, May 10 | 11:00 pm - 11:30 pm (CDT)
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    Plenary
    Project-Based Learning and Design with Simulation
    Professor Claire Lucas, King’s College London
    Wed, May 10 | 11:00 pm - 11:30 pm (CDT)
    11:00 pm - 11:30 pm (CDT)
    Plenary
    King's Engineering at King’s College London was relaunched in 2020 as a general engineering department. The aim is to deliver innovative engineering education that addresses new technological and societal challenges through an interdisciplinary, transdisciplinary, and intradisciplinary education. We are reimagining how project-based learning and design can be applied, at scale, in a research-intensive environment and how we can put people at the center of engineering. To support this vision, we are embedding simulation workflows and methodologies within our teaching modules. It allows students to work in agile sprints and fully explore scenarios and iterate design ideas within a multidomain systems environment. MATLAB® and Simulink® become both the learning environment and the assessment tool. In this talk, we discuss our pedagogic approach to designing assignments with Simulink and developing the students' modeling and simulation skills. We argue that project-based and practical learning can include hands-on experience of digital artifacts and that the parallels between Model-Based Design and engineering system design help students in their learning.
  • Understanding and Verifying Your AI Models
    Lucas Garcia, MathWorks
    Wed, May 10 | 11:30 pm - 12:00 am (CDT)
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    AI
    Understanding and Verifying Your AI Models
    Lucas Garcia, MathWorks
    Wed, May 10 | 11:30 pm - 12:00 am (CDT)
    11:30 pm - 12:00 am (CDT)
    AI
    Neural networks can obtain state-of-the-art performance in a wide variety of tasks, including image classification, object detection, speech recognition, and machine translation. Due to this impressive performance, there has been a desire to utilize neural networks for applications in industries with safety-critical components such as aerospace, automotive, and medical. While these industries have established processes for verifying and validating traditional software, it is often unclear how to verify the reliability of neural networks. In this talk, we explore a comprehensive workflow for verifying and validating AI models. Using an image classification example, we will discuss explainability methods for understanding the inner workings of neural networks. Learn how Deep Learning Toolbox™ Verification Library enables you to formally verify the robustness properties of networks and determine whether the data your model is seeing during inference time is out of distribution. By thoroughly testing the requirements of the AI component, you can ensure that the AI model is fit for purpose in applications where reliability is of the utmost importance.
  • MATLAB for Control of Cryogenic DT Fuel for Nuclear Fusion Ignition Experiments
    Dr. Suhas Bhandarkar, Lawrence Livermore National Laboratory
    Wed, May 10 | 11:30 pm - 12:00 am (CDT)
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    Algorithm Development and Data Analysis
    MATLAB for Control of Cryogenic DT Fuel for Nuclear Fusion Ignition Experiments
    Dr. Suhas Bhandarkar, Lawrence Livermore National Laboratory
    Wed, May 10 | 11:30 pm - 12:00 am (CDT)
    11:30 pm - 12:00 am (CDT)
    Algorithm Development and Data Analysis
    The National Ignition Facility (NIF) houses the world’s most energetic laser that can deliver over 2 MJ of energy at the 500 TW level of power of 351 nm UV light. This immense optical energy is focused on a small target that can create such high energy densities that the unique physics underlying this extreme regime can be explored. One of the primary goals of the NIF is to explore controlled nuclear fusion where the energy from the laser is used to compress hydrogen (more specifically, its isotopes deuterium and tritium or DT) to 100 billion atmospheres where temperatures reach 100 million Kelvin, and at which point the atoms can overcome Coulombic repulsion and fuse. This grand challenge of ignition where the nuclear fusion energy out was greater than the optical energy in was successfully achieved in December 2022 in a landmark experiment. This is expected to usher in a new age of nuclear fusion research with diverse and far-reaching goals. One of the many challenges of this experiment was the formation of a spherical DT ice layer at ~19 K with very high dimensional precision and smoothness. In this presentation, see a brief overview of the key aspects of the complex and multifaceted system needed to carry out these experiments and a detailed explanation of the use of MATLAB® for the formation of the DT ice layer for the ignition experiment—as well as previous experiments that explored the physics needed to achieve that. In particular, the image analysis tools in MATLAB were used to control the ice layering process. This use case serves as a good example of the wide-ranging applications of this software tool.
  • Verification of Avionics Systems Using Simulink Test and Simulink Real-Time
    Maciej Stefaniak, GE Aerospace
    Wed, May 10 | 11:30 pm - 12:00 am (CDT)
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    Modeling, Simulation, and Implementation
    Verification of Avionics Systems Using Simulink Test and Simulink Real-Time
    Maciej Stefaniak, GE Aerospace
    Wed, May 10 | 11:30 pm - 12:00 am (CDT)
    11:30 pm - 12:00 am (CDT)
    Modeling, Simulation, and Implementation
    The current market situation forces companies to search for methods to accelerate the development of new avionics systems in accordance with guidelines and industry standards (including ARP4754A and DO-178C) while maintaining high quality and cost competitiveness. In this talk, hear about challenges related to the automation of the system verification process of avionics devices, solutions using Simulink Test™ and Simulink Real-Time™, and future development directions of the test environment.
  • Optimizing the Design and Operation of Radar and Antenna Systems in MATLAB
    Sumit Garg, MathWorks Chris Lee, MathWorks
    Wed, May 10 | 11:30 pm - 12:00 am (CDT)
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    Wireless Connectivity and Radar
    Optimizing the Design and Operation of Radar and Antenna Systems in MATLAB
    Sumit Garg, MathWorks Chris Lee, MathWorks
    Wed, May 10 | 11:30 pm - 12:00 am (CDT)
    11:30 pm - 12:00 am (CDT)
    Wireless Connectivity and Radar
    Multifunction radar systems perform many tasks including search and track, classification of targets, communications, environmental assessments, and interference mitigation. Engineers designing these systems must deal with multiple design challenges:

    • RF spectrum congestion: 5G applications are pushing wireless systems to utilize higher frequency bands, which causes interference challenges for radar applications.

    • Resource allocation: Radar systems have limited resources (e.g., bandwidth, transmit energy/time budget, computational resources) that must be allocated to multiple tasks to achieve mission objectives.

    • Physical design and cost constraints: For example, designers must minimize the antenna size while maintaining the impedance matching and maximizing the antenna gain.


    In this talk, we explore how engineers can address these radar design challenges with automated design optimization workflows using the Optimization Toolbox™. We will cover several examples:

    • Optimal beam pattern synthesis to null the power in the direction of interfering signals using Phased Array System Toolbox™

    • Optimal resource allocation for a multisector surveillance radar using Radar Toolbox

    • Antenna design optimization using Antenna Toolbox™

  • Toward Zero-Emission Shipping with Fuel Cells and Model-Based Design
    Kjersti Wergeland Krakhella, Clara Venture Labs and Alma Clean Power
    Wed, May 10 | 11:30 pm - 12:30 am (CDT)
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    Electrification
    Toward Zero-Emission Shipping with Fuel Cells and Model-Based Design
    Kjersti Wergeland Krakhella, Clara Venture Labs and Alma Clean Power
    Wed, May 10 | 11:30 pm - 12:30 am (CDT)
    11:30 pm - 12:30 am (CDT)
    Electrification
    The shipping sector accounts for a significant amount of global CO2 emissions. Fuel cell technology enables electrified transportation and hence realizes a vision of carbon-free transportation. In converting diverse fuels like ammonia, methane, hydrogen with oxygen into electricity, fuel cells could replace combustion engines to ensure long-range autonomy in freight transportation and contribute to cleaner and more efficient propulsion. But what are the intrinsic challenges to fuel cells and their commercial upscaling in the maritime sector? Is there one fuel cell technology–such as solid oxide—with an edge? How do engineering methods allow for a thorough yet agile concept evaluation? Please attend this presentation by Clara Venture Labs and Alma Clean Power to discover how Model-Based Design is used to evaluate the robustness and efficiency of various fuel cell solutions in high demand by the maritime sector. Furthermore, the benefits of model customization and tunability in the process of developing industrial partnership proposals will be highlighted.
  • Development and Validation of ADAS/AD Features Using MATLAB Solutions
    Jihas Khan, Tata Elxsi Ltd.
    Wed, May 10 | 11:30 pm - 12:00 am (CDT)
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    Autonomous Systems and Robotics
    Development and Validation of ADAS/AD Features Using MATLAB Solutions
    Jihas Khan, Tata Elxsi Ltd.
    Wed, May 10 | 11:30 pm - 12:00 am (CDT)
    11:30 pm - 12:00 am (CDT)
    Autonomous Systems and Robotics
    Autonomous driving (AD) and advanced driver assistance systems (ADAS) are technologies which will see widespread adoption globally. The design, development, and validation of complex ADAS/AD algorithms are critical to assure the safety of the vehicle occupants. Simulation is an ideal choice to develop the ADAS/AD algorithms and validate them in a PC-based environment before the hardware availability. During the session, hear about the work done by Tata Elxsi to simulate ADAS/AD algorithms using the product portfolio from MATLAB® and Simulink®. Dive into topics like sensor modeling, scenario creation, environment modeling, control algorithm development, vehicle dynamics modeling, closed loop integration, and validation. Learn how ISO safety of the intended functionality can be validated in a virtual space using MathWorks solutions and about their intuitive nature for enhancing work productivity. See examples of the advantages of the MathWorks solutions that helped Tata Elxsi to accelerate the development process, and watch demonstrations of the interoperability of MATLAB solutions with 3D simulation tools for better visualization of the scenario. Finally, see how MathWorks solutions are used for ADAS/AD hardware-in-the-loop simulation validation for validation of ADAS/AD electronic control units.
  • DevOps with MATLAB: A Predictive Maintenance System for Streaming Data
    Christine Bolliger, MathWorks Nicole Bonfatti, MathWorks Seth DeLand, MathWorks
    Wed, May 10 | 11:30 pm - 12:00 am (CDT)
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    Cloud, Enterprise, and DevOps
    DevOps with MATLAB: A Predictive Maintenance System for Streaming Data
    Christine Bolliger, MathWorks Nicole Bonfatti, MathWorks Seth DeLand, MathWorks
    Wed, May 10 | 11:30 pm - 12:00 am (CDT)
    11:30 pm - 12:00 am (CDT)
    Cloud, Enterprise, and DevOps
    Many organizations use MATLAB® and Simulink® to develop algorithms—but how do they deploy, monitor, and manage them over their lifetime? DevOps refers to the set of capabilities, tools, and best practices needed to operationalize software applications, usually in an IT context. But Gartner reports that more than 50% of data science projects do not result in business value due to difficulties with operationalization.

    In this session, see a demonstration of a complete predictive maintenance DevOps system for monitoring the state of health (SOH) of a battery fleet. Learn how to develop an SOH prediction model and a drift detection model in MATLAB. Then, see how to automatically test and deploy these algorithms in operation using a CI/CD pipeline, MATLAB Production Server™ on Microsoft Azure®, and dashboards for performance monitoring.

    Discover how engineering teams can use MATLAB to operationalize their algorithms and how to bridge the gap with IT/OT teams.
  • Teaching Robotics and Controls Made Easier
    Jennifer J. Gago Muñoz, MathWorks Melda Ulusoy, MathWorks
    Wed, May 10 | 11:30 pm - 12:00 am (CDT)
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    Preparing Future Engineers and Scientists
    Teaching Robotics and Controls Made Easier
    Jennifer J. Gago Muñoz, MathWorks Melda Ulusoy, MathWorks
    Wed, May 10 | 11:30 pm - 12:00 am (CDT)
    11:30 pm - 12:00 am (CDT)
    Preparing Future Engineers and Scientists
    Robotics and controls, as rapidly advancing engineering disciplines, are transforming various industries and sectors across the board. Educators who teach these subjects face limited time to create and update their curriculum while trying to keep up with advancing technology and scientific trends. Discover how to apply tools used in industry and interactive courseware to teach robotics and controls. Analyze a typical academic curriculum from early courses to final projects to understand how you can approach the different branches of knowledge that form these disciplines, from the different phases of the teaching activity. Learn how to supplement your teaching using free self-paced online training, educational videos, and course assignments to automatically grade code. You will also learn how to incorporate interactive apps and scripts, cloud solutions, virtual labs, and low-cost hardware projects developed by MathWorks and leading universities to make your lessons more engaging.
  • Inclusive Innovation: Technology by and for All
    Dr. Shima Barakt, University of Cambridge Daniela Dobreva-Nielsen , AZO Anwendungszentrum GmbH Oberpfaffenhofen Eva Pelster, Moderator, MathWorks Mohan Sundaram, ARTILAB Foundation
    Wed, May 10 | 11:30 pm - 12:00 am (CDT)
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    Inclusive Innovation
    Inclusive Innovation: Technology by and for All
    Dr. Shima Barakt, University of Cambridge Daniela Dobreva-Nielsen , AZO Anwendungszentrum GmbH Oberpfaffenhofen Eva Pelster, Moderator, MathWorks Mohan Sundaram, ARTILAB Foundation
    Wed, May 10 | 11:30 pm - 12:00 am (CDT)
    11:30 pm - 12:00 am (CDT)
    Inclusive Innovation
    Inclusive innovation is of fundamental importance to develop engineering solutions that support the needs of diverse communities. These solutions must be accessible, affordable, and sustainable to transform the lives of individuals and communities. How do we build learning environments and workplaces that recognize community needs in order to engineer inclusive solutions? Hear thought leaders discuss the needs, challenges, and social factors that impact inclusive innovation.
  • How a Pseudo-Pressure Sensor Improves Diagnostics in a Solenoid Actuated Valve
    Arun Natarajan, Coca-Cola
    Thu, May 11 | 12:00 am - 12:30 am (CDT)
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    AI
    How a Pseudo-Pressure Sensor Improves Diagnostics in a Solenoid Actuated Valve
    Arun Natarajan, Coca-Cola
    Thu, May 11 | 12:00 am - 12:30 am (CDT)
    12:00 am - 12:30 am (CDT)
    AI
    The Coca-Cola Freestyle dispenser uses a solenoid actuated valve for dispensing water. There is no pressure sensor on the water line, and it is difficult to distinguish between an upstream pressure loss issue and inherent valve failure. This results in the unnecessary replacement of good valves in dispensers in the field. In this talk, hear about the development of a novel pseudo-pressure sensor that estimates the line pressure based on the solenoid valve current signature. MATLAB® was used to analyze the current signature test data, extract features, and develop a regression model to predict line pressure. This model was then deployed to a memory-constrained ARM Cortex-M microprocessor using Simulink® for testing and deployment to production. This pseudo-pressure sensor feature has improved dispenser diagnostics and reduced valve replacements, leading to a significant drop in field service cost. In addition, learn about future work plans to develop diagnostics around valve blockages using MATLAB and Simulink for machine learning and deployment.
  • Using MATLAB with Python
    Yann Debray, MathWorks Dr. Heather Gorr, MathWorks
    Thu, May 11 | 12:00 am - 1:30 am (CDT)
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    Algorithm Development and Data Analysis
    Using MATLAB with Python
    Yann Debray, MathWorks Dr. Heather Gorr, MathWorks
    Thu, May 11 | 12:00 am - 1:30 am (CDT)
    12:00 am - 1:30 am (CDT)
    Algorithm Development and Data Analysis
    In this hands-on workshop, learn how to use MATLAB® and Python® together with practical examples, such as predicting weather data. Specifically, discover how to:

    • Call Python libraries from MATLAB

    • Call user-defined Python commands, scripts, and modules

    • Manage and convert data between MATLAB and Python

    • Deploy MATLAB algorithms to call from Python


    To participate in this workshop, you will need a MathWorks Account (create MathWorks Account).
  • Using Model-Based Design to Develop SOA Applications for In-Vehicle OS
    Yiming Luo, ZEEKR Intelligent Technology
    Thu, May 11 | 12:00 am - 12:30 am (CDT)
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    Modeling, Simulation, and Implementation
    Using Model-Based Design to Develop SOA Applications for In-Vehicle OS
    Yiming Luo, ZEEKR Intelligent Technology
    Thu, May 11 | 12:00 am - 12:30 am (CDT)
    12:00 am - 12:30 am (CDT)
    Modeling, Simulation, and Implementation
    With the growth of the service-oriented architecture paradigm in the automotive industry, several middleware options have emerged, along with the widely known AUTOSAR Adaptive Platform. Many powerful companies like ZEEKR are also looking to develop their own middleware for in-vehicle operating systems (OS). ZEEKR implements multiple function clusters for such an in-vehicle operating system for smart EVs. When we talk about how to develop software for SOA or do the actual coding, the first thing that comes to mind is handwritten C++ language, which puts forward high requirements not only on the programmer's ability but also on the accompanying toolchain. In contrast, Model-Based Design is the dominant approach in traditional onboard embedded system development and there are a lot of experienced engineers. Starting with R2022a, Simulink® provides some new features like a client-server interface, which enables modeling instead of handwriting SOA software. Companies can use the power of MATLAB® and Simulink to quickly transition from the embedded software era to the emerging SOA software era. There are two main challenges in using Model-Based Design to develop SOA software running on in-vehicle OS: how to model the software behavior and how to maintain complex software clusters. This talk focuses on solving these two problems. ZEEKR made full use of the powerful capabilities of MATLAB and Simulink for SOA software development, formulated many strategies according to local conditions, and facilitated the implementation of SOA application software running on in-vehicle OS.
  • Intel Agilex FPGA-in-the-Loop Simulation: Enabling DSP Emulation for Space-BACN
    Ziyi Zhu, Intel
    Thu, May 11 | 12:00 am - 12:30 am (CDT)
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    Wireless Connectivity and Radar
    Intel Agilex FPGA-in-the-Loop Simulation: Enabling DSP Emulation for Space-BACN
    Ziyi Zhu, Intel
    Thu, May 11 | 12:00 am - 12:30 am (CDT)
    12:00 am - 12:30 am (CDT)
    Wireless Connectivity and Radar
    The U.S. Defense Advanced Research Projects Agency (DARPA) Space-Based Adaptive Communications Node (Space-BACN) program will provide communication in hard-to-reach areas by using a network of small low-Earth orbit satellites as relay stations to forward and amplify signals from ground-based radios, aircraft, and other communication systems, essentially acting as a space-based repeater system to extend the range of communication systems that would be limited by the curvature of the earth or interference from terrain. In the development phase, DARPA has selected Intel, with others, to design a reconfigurable optical modem that will support both current and new communication standards and protocols to enable interoperability among satellite constellations. Intel is developing its optical modem solution by bringing together experts from its field-programmable gate array (FPGA) product group, packaging technologists from its Assembly Test Technology Development (ATTD) division, and researchers from Intel Labs. Based on its leading-edge low-power Intel® Agilex™ FPGA, Intel is designing new chiplets that will be integrated using Intel’s embedded multi-die interconnect bridge (EMIB) and advanced interface bus (AIB) packaging technologies, into a single multi-chip package (MCP). To ensure first silicon success, Intel is using FPGA-in-the-loop technologies in MATLAB® and Simulink®. This enables the verification of HDL implementations directly against algorithms in MATLAB or Simulink, the application of data and test scenarios from MATLAB or Simulink to the HDL design on the FPGA, and the integration of existing HDL code with models under development in MATLAB or Simulink. Once completed, Space-BACN will be used in the future for humanitarian assistance, disaster relief, and operations in denied areas. Communication barriers that have long hindered operations in difficult environments will be overcome, allowing for greater efficiency and connectivity.
  • Simscape Battery Essentials
    Dr. Danielle Chu, MathWorks Dr. Graham Dudgeon, MathWorks
    Thu, May 11 | 12:00 am - 1:30 am (CDT)
    Register/Log in to save session
    Electrification
    Simscape Battery Essentials
    Dr. Danielle Chu, MathWorks Dr. Graham Dudgeon, MathWorks
    Thu, May 11 | 12:00 am - 1:30 am (CDT)
    12:00 am - 1:30 am (CDT)
    Electrification
    This hands-on workshop provides engineers with a strong foundation for understanding and using the capabilities of Simscape Battery™ for both battery pack design and battery management system design. This workshop covers workflows that align with shipping examples and gives added insights where appropriate to enhance the learning experience. Topics include:

    • Building, visualizing, and simulating a battery module.

    • Building and parameterizing a battery pack.

    • Configuring a battery module with a cooling plate.

    • Battery passive cell balancing.

    • Battery state-of-charge estimation.

    • Battery charging and discharging.

    • Thermal analysis of new and aged battery packs.


    To participate in this workshop, you will need a MathWorks Account (create MathWorks Account).
  • Applying AI to Enable Autonomy in Robotics Using MATLAB
    Tohru Kikawada, MathWorks YJ Lim, MathWorks
    Thu, May 11 | 12:00 am - 12:30 am (CDT)
    Register/Log in to save session
    Autonomous Systems and Robotics
    Preparing Future Engineers and Scientists
    Applying AI to Enable Autonomy in Robotics Using MATLAB
    Tohru Kikawada, MathWorks YJ Lim, MathWorks
    Thu, May 11 | 12:00 am - 12:30 am (CDT)
    12:00 am - 12:30 am (CDT)
    Autonomous Systems and Robotics
    Preparing Future Engineers and Scientists
    The AI applications in robotics greatly expanded in recent years to include voice command, object identification, pose estimation, and motion planning, to name a few. AI-enabled robots continue to grow for manufacturing facilities, power plants, warehouses, and other industrial sites. Warehouse bin-picking is a good example. Deep learning and reinforcement learning enable robots to handle various objects with minimal help from humans, reducing workplace injuries due to repetitive motions.

    Learn how to empower your robots using AI for perception and motion control in autonomous robotics applications. MATLAB® and Simulink® provide a powerful platform for successful AI adoption in robotics and autonomous systems. You can use the same development platform to design, test, and deploy your AI applications in intelligent bin-picking collaborative robots (cobots), autonomous mobile robots (AMRs), UAVs, and other robotics systems. This reduces development time as well as time-to-market.

    Gain insights into: 

    • Reducing manual effort with automatic data labeling

    • Detecting and classifying objects using deep learning for robotics applications

    • Motion planning using deep learning

    • Controlling robot motion using reinforcement learning

    • Deploying deep learning algorithms as CUDA-optimized ROS nodes

  • What's New for Managing, Testing, and Building Your MATLAB Code
    Adam Sifounakis, MathWorks Vamshi Kumbham, Mr
    Thu, May 11 | 12:00 am - 1:30 am (CDT)
    Register/Log in to save session
    Cloud, Enterprise, and DevOps
    What's New for Managing, Testing, and Building Your MATLAB Code
    Adam Sifounakis, MathWorks Vamshi Kumbham, Mr
    Thu, May 11 | 12:00 am - 1:30 am (CDT)
    12:00 am - 1:30 am (CDT)
    Cloud, Enterprise, and DevOps
    Is your code growing in size, becoming more critical to others, or involving more collaborators?

    Are your development and building processes requiring more intermediate steps and becoming increasingly complex?

    In this hands-on workshop, hear about the significant improvements to the software development workflows in MATLAB®. Learn how you can use these features to:

    • Track and manage collaborative development with the Git™ branch manager

    • Identify and fix code issues with the Code Analyzer app

    • Iteratively develop and test code with the test browser

    • Package your toolbox with a scriptable interface

    • Automate and streamline repeated tasks with the build tool


    To participate in this workshop, you will need a MathWorks Account (create MathWorks Account).
Session Title
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Session Title
Presenter, Company Name

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