MATLAB EXPO 2023

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    15
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All Sessions

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  • ANA's Predictive Maintenance Challenge: Replace Aircraft Parts Before They Break
    Sadanari Shigetomi, All Nippon Airways Co., Ltd. Naoya Kaido, All Nippon Airways Co., Ltd.
    Thu, May 11 | 12:30 am - 1:00 am (CDT)
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    AI
    ANA's Predictive Maintenance Challenge: Replace Aircraft Parts Before They Break
    Sadanari Shigetomi, All Nippon Airways Co., Ltd. Naoya Kaido, All Nippon Airways Co., Ltd.
    Thu, May 11 | 12:30 am - 1:00 am (CDT)
    12:30 am - 1:00 am (CDT)
    AI
    All Nippon Airways Group started its air transport business in 1952 and celebrated its 70th anniversary in 2022. ANA Maintenance Center is working on predictive maintenance for aircraft maintenance. We detect failure signs of various aircraft components based on sensor data collected during flights, aiming to improve safety and on-time performance. To perform predictive maintenance, it is necessary to identify features to predict failures from sensor data. The ANA Maintenance Center leverages the deep domain knowledge of the maintenance crew and sensor data from more than 20 thousand flights around the world. The sensor data includes external factors, such as weather conditions, flight path, and number of passengers. In this talk, see how we used machine learning with MATLAB® to test hypotheses and create machine learning models. We successfully identified robust features that enabled early failure detection of the cabin air compressor (CAC), one of the main air conditioning components. The preprocessing pipeline and the trained model were integrated into an existing system using MATLAB Compiler™ for daily inspection.

    Highlights:

    • Predictive maintenance for aircraft maintenance

    • Hypothesis verification of failure-identifying features using domain knowledge and field data

    • A case study on detecting the deterioration of aircraft components using a neural network

  • Managing the Complexity of FPGA-Based Rapid Control Prototyping
    Henning Braess, Siemens Healthineers
    Thu, May 11 | 12:30 am - 1:00 am (CDT)
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    Modeling, Simulation, and Implementation
    Managing the Complexity of FPGA-Based Rapid Control Prototyping
    Henning Braess, Siemens Healthineers
    Thu, May 11 | 12:30 am - 1:00 am (CDT)
    12:30 am - 1:00 am (CDT)
    Modeling, Simulation, and Implementation
    Siemens Healthineers is exploring rapid control prototyping (RCP) for the development of power electronic components. RCP with FPGAs represents one of the most powerful workflows within the Simulink® framework. The workflow enables engineers to implement sophisticated and high-performing control systems with very fast design iterations. The tight integration between HDL Coder™ and Simulink Real-Time™ eliminates a lot of intricacies of FPGA design. Nevertheless, FPGA-based RCP remains one of the most complex Simulink workflows. Thus, managing the workflow complexity is one of the key factors for success. Three main topics can be employed to this end: workflow automation, a suitable model architecture, and a well-customized utility library for handling direct memory access (DMA). Developing an FPGA-based RCP application requires many iterations of successive builds with FPGA synthesis, which require approximately one hour each. It is highly recommended to automate this workflow with a continuous integration server to separate this procedure from the core modeling tasks. There are a number of considerations for an effective and sustainable RCP architecture. There needs to be a clear separation between the core responsibilities of experiment design, hardware interfacing, physical modeling, control design, and real-time signal tracing. To be effective, this architecture requires barrier-free communication between CPU and FPGA. Basic support for this comes with the DMA-blocks from Simulink Real-Time. To be really barrier-free and flexible, a DMA utility library has been designed. This presentation encourages the user to think about efficient modeling procedures, the robustness of their designs, and the validity of their model architecture.
  • Transforming Wireless System Design with MATLAB and NI
    Robin Getz, MathWorks Jeremy Twaits, NI
    Thu, May 11 | 12:30 am - 1:00 am (CDT)
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    Wireless Connectivity and Radar
    Transforming Wireless System Design with MATLAB and NI
    Robin Getz, MathWorks Jeremy Twaits, NI
    Thu, May 11 | 12:30 am - 1:00 am (CDT)
    12:30 am - 1:00 am (CDT)
    Wireless Connectivity and Radar
    Wireless communication, radar systems, software-defined radio (SDR), and instrumentation are all highly intricate areas of technology that require advanced mathematical and computational techniques for their design, simulation, and implementation. Engineers and researchers can utilize software and hardware tools from MathWorks and NI to facilitate this process, which enables the characterization, design, simulation, testing, and prototyping of real-world systems for over-the-air testing. These tools, based on flexible COTS systems, allow for the development and real-time testing of signals spanning a wide range of wireless standards such as 5G, LTE, WiFi, FMCW, pulse radar, and the characterization of power amplifiers used for digital predistortion (DPD), as well as the development of narrow-band bursty waveforms like Automatic Dependent Surveillance-Broadcast (ADS-B). The integration of mathematical modeling, simulation, code generation, and hardware connectivity capabilities in MATLAB with NI's expertise in data acquisition, instrument control, and real-time testing ensures that any system can be effectively tested in challenging real-world scenarios. This results in a comprehensive and efficient workflow for engineers and researchers across multiple domains, allowing for a streamlined design and implementation process without the need for extensive knowledge of the underlying hardware.
  • Accelerate Aerial Autonomy with Simulink and Microsoft Project AirSim
    Effendi Dufford, Microsoft Balinder Malhi, Microsoft Fred Noto, MathWorks
    Thu, May 11 | 12:30 am - 1:00 am (CDT)
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    Autonomous Systems and Robotics
    Accelerate Aerial Autonomy with Simulink and Microsoft Project AirSim
    Effendi Dufford, Microsoft Balinder Malhi, Microsoft Fred Noto, MathWorks
    Thu, May 11 | 12:30 am - 1:00 am (CDT)
    12:30 am - 1:00 am (CDT)
    Autonomous Systems and Robotics
    Autonomous aerial systems are critical systems that incorporate AI and require rigorous verification to ensure safety and performance. Aerial autonomous technologies are driving advanced air mobility use cases such as power line inspections, logistics, and flying taxis. Integration of airframe physics and sensors in scalable high-fidelity simulations with photorealistic 3D environments is needed to test and evaluate autonomous aerial systems while generating synthetic data to train AI models.

    Explore how to leverage Microsoft Project AirSim and Simulink® with Model-Based Design to accurately simulate vehicle physics with sensors and dynamic environments in high-fidelity simulations. Learn how to use co-simulation in software-in-the-loop workflows to safely build, train, test, and validate autonomous aerial systems.
  • NETH and University Collaboration for Talent Workforce Development
    Nattha Jindapetch, Prince of Songkla University (PSU) Kosin Pattanon, Toyota Tsusho NEXTY Electronics (Thailand) Co., Ltd.
    Thu, May 11 | 12:30 am - 1:00 am (CDT)
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    Preparing Future Engineers and Scientists
    NETH and University Collaboration for Talent Workforce Development
    Nattha Jindapetch, Prince of Songkla University (PSU) Kosin Pattanon, Toyota Tsusho NEXTY Electronics (Thailand) Co., Ltd.
    Thu, May 11 | 12:30 am - 1:00 am (CDT)
    12:30 am - 1:00 am (CDT)
    Preparing Future Engineers and Scientists
    The NEXTY Career Identity Program is NETH’s project for automotive software (SW) development industrial knowledge transfer to academia for future talent workforce development. The project’s objectives are to:

    • Acquire talented workforce with the SW development skills needed to become a SW engineer.

    • Build automotive SW development technology network between university and industry.

    • Transfer automotive software development knowledge to universities.

    • Develop automotive SW development curriculum for workforce upskilling.

    • Define skill evaluation criteria for target technology in the embedded systems SW development field.


    To transfer industrial knowledge to students, Model-Based Design and embedded SW development training related to Electrification was provided to them from November 2021 to November 2022. The challenge of project was to provide concrete SW development practices and examples with quality standard mindset while keeping in mind the various student majors and programming backgrounds. Imparting Model-Based Design philosophy is the key to successful training because NETH (also known as Toyota Tsusho NEXTY Electronics (Thailand) Co., Ltd.) uses Simulink® for industrial SW development. With NETH’s university collaboration network and business partnership support, the NEXTY Career Identity Program provided training courses to 52 students, including basic Model-Based Design training, embedded systems workshop, DC motor modeling with FPGA hardware-in-the-loop, and battery management system using Stateflow®. Eighteen professors from six universities joined the collaboration program, and nine of them plan to develop further curricula with NETH. These achievements show that our workforce development network is growing.
  • Breaking the Boundaries: Integrating GIS, AI, and Lidar for Digital Innovation
    Hong Tran, Spacesium
    Thu, May 11 | 1:00 am - 1:30 am (CDT)
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    AI
    Breaking the Boundaries: Integrating GIS, AI, and Lidar for Digital Innovation
    Hong Tran, Spacesium
    Thu, May 11 | 1:00 am - 1:30 am (CDT)
    1:00 am - 1:30 am (CDT)
    AI
    A key component of the digital transformation of GIS data is the rapid integration and interrogation of large data sets, lidar point clouds, and visual information to support rapid decision making. For example, smart city automation relies on accurate knowledge of street infrastructure, such as the location and condition of utility poles and power lines, while forestry managers require canopy and trunk estimation and digital terrain mapping to enhance their operations. Significant challenges remain, including the need for rapid but accurate processing of spatial point clouds and the integration of visual and multispectral data with lidar data to provide information beyond a simple (x,y,z) location.

    Traditional feature-based methods for processing point cloud data can be slow and imprecise. In some cases, such as on mine sites, feature-based algorithms may misinterpret ground profiles as buildings due to the planar shapes of spoil heaps, leading to inaccuracies. In contrast, at Spacesium we are using cutting-edge deep learning algorithms, such as R-CNN, DeepLab v3 and PointNet++, in conjunction with semi-supervised retraining, to rapidly segment and classify point clouds utilizing cloud scale resources.

    The integration of visual and multispectral data presents a unique set of challenges and opportunities. While it allows for more than just the specification of the position of an xyz point cloud, using both location and visual data can enable the determination of viable routes for autonomous vehicles. On the challenge side, it is computationally expensive to fuse these data sets that are captured in separate imaging systems. At Spacesium, we have implemented custom algorithms based on tools from Image Processing Toolbox™ and Computer Vision Toolbox™ to efficiently correct, compute, and fuse these data sets.
  • Implementing a PLCnext-Based Turbine Control System in Simulink
    Jakoba Reimann, Sokratel GmbH
    Thu, May 11 | 1:00 am - 1:30 am (CDT)
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    Modeling, Simulation, and Implementation
    Implementing a PLCnext-Based Turbine Control System in Simulink
    Jakoba Reimann, Sokratel GmbH
    Thu, May 11 | 1:00 am - 1:30 am (CDT)
    1:00 am - 1:30 am (CDT)
    Modeling, Simulation, and Implementation
    Model-Based Design provides an efficient approach to modeling and designing control systems. Both the controller and the plant model are implemented in MATLAB® and Simulink®. By linking these models, you can optimally analyze the controller and adapt it to the environment and the plant. Different stages of testing like unit tests or model-in-the-loop tests are optimally implemented with Simulink Test™. Further advantages like a visual representation of data flow, intuitive language learning, and smooth implementation of new features make Simulink the ideal choice for Sokratel’s task of implementing a PLCnext-based wind turbine control system.

    This talk focuses on the following topics:

    - What is Model-Based Design and why does Sokratel use it?

    - The iterative process of refining a requirement from the idea to the prototype

             - Introduction to the process based on a small example

             - Toolchain integration: How Sokratel automates daily work

             - Testing concept: Hardware-in-the-loop testing with the PLCnext system

    After this talk, you will have an idea of how to successfully lead your team on the road from MATLAB and Simulink development to the final product on hardware.
  • Development of Signal Processor and Extractor Module for 3D Surveillance Radar
    Mohit Gaur, Bharat Electronics Limited
    Thu, May 11 | 1:00 am - 1:30 am (CDT)
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    Wireless Connectivity and Radar
    Development of Signal Processor and Extractor Module for 3D Surveillance Radar
    Mohit Gaur, Bharat Electronics Limited
    Thu, May 11 | 1:00 am - 1:30 am (CDT)
    1:00 am - 1:30 am (CDT)
    Wireless Connectivity and Radar
    In this session, explore radar signal processing and data extractor modules developed for 3D surveillance radar for a ground-based air surveillance system. Radar design starts from user specifications such as unambiguous range, range resolution, and azimuth and range resolution and accuracies. We evaluated the Radar Designer app to perform initial radar design calculations. Radar waveform selection is crucial to the radar design meeting its performance specifications. Using the Pulse Waveform Analyzer app, we selected and verified radar waveforms. Matched filtering–based digital pulse compression was verified using MATLAB® scripting. A radar signal processor (RSP) is responsible for target detection under environmental clutter. Many RSP algorithms are available whose performance verification requires high fidelity simulated input data. We successfully designed a complete RSP chain using Phased Array System Toolbox™ and Radar Toolbox. We built a simulated radar data extractor module consisting of elevation extraction and range and azimuth estimation methods in MATLAB using the toolboxes to provide better assessment of site recorded data. We overcame challenges encountered with range, azimuth, and elevation estimation of the data. MATLAB visualization enabled us to present our findings in a comprehensive manner. The RSP chain designed in MATLAB was validated on actual radar site data and found to be matching with actual performance with high fidelity. MATLAB proved to be extremely useful by reducing our design cycle and we were able to demonstrate our concept with a higher degree of confidence.
  • Bringing the iCub Humanoid Towards Real-World Applications
    Dr. Daniele Pucci, Italian Institute of Technology
    Thu, May 11 | 1:00 am - 1:30 am (CDT)
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    Autonomous Systems and Robotics
    Bringing the iCub Humanoid Towards Real-World Applications
    Dr. Daniele Pucci, Italian Institute of Technology
    Thu, May 11 | 1:00 am - 1:30 am (CDT)
    1:00 am - 1:30 am (CDT)
    Autonomous Systems and Robotics
    The iCub project was launched in 2004 as part of the RobotCub European Project, whose main aim was to study embodied cognition—the theory that an organism develops cognitive skills as it interacts with its environment. The main outcome of the iCub project is a one-meter-tall, 53-degrees-of-freedom humanoid currently being developed at the Italian Institute of Technology (IIT). Over the years, the iCub robot has been used as a research platform for diverse fields of science ranging from neuroscience to engineering. This presentation focuses on the work that the Artificial and Mechanical Intelligence research lab at IIT is carrying out with the iCub along the three axes: physical human-robot collaboration; avatar systems; and aerial humanoid robots (jet powered humanoid robots). The presentation shows how MATLAB® and Simulink® are fundamental for research and development for the iCub in control, planning, estimation, and artificial intelligence—and how these tools can be beneficial for humanoid robotics as a whole.
  • Engaging First-Year Engineering Students with Deep Learning and IoT
    Chao Wang, Arizona State University
    Thu, May 11 | 1:00 am - 1:30 am (CDT)
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    Preparing Future Engineers and Scientists
    Engaging First-Year Engineering Students with Deep Learning and IoT
    Chao Wang, Arizona State University
    Thu, May 11 | 1:00 am - 1:30 am (CDT)
    1:00 am - 1:30 am (CDT)
    Preparing Future Engineers and Scientists
    A recent survey by the American Society of Engineering Education Corporate Member Council highlighted two areas in which engineering graduates are inadequately prepared to meet industry demands: AI and Internet of Things (IoT). At the Ira A. Fulton Schools of Engineering at Arizona State University, we’re taking steps to address this skills gap by introducing engineering students to AI and IoT concepts early in their college careers. Specifically, a new learning module was added to the first-year Introduction to Engineering course in which students complete hands-on AI and IoT exercises using MATLAB®. In these exercises, students perform image classification with a deep learning network and then send the results of their classifications to the ThingSpeak IoT analytics platform for aggregation and analysis. The module requires no previous programming experience in MATLAB and no additional hardware—students use their own laptops, tablets, and webcams. Just as importantly, the module requires minimal instructor preparation because the exercises were designed, implemented, and validated by MathWorks engineers and are ready to use in the course.
  • What's New in MATLAB and Simulink R2023a
    Dr. Jason Ghidella, MathWorks Dr. Michelle Hirsch, MathWorks
    Thu, May 11 | 3:00 am - 3:30 am (CDT)
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    Plenary
    What's New in MATLAB and Simulink R2023a
    Dr. Jason Ghidella, MathWorks Dr. Michelle Hirsch, MathWorks
    Thu, May 11 | 3:00 am - 3:30 am (CDT)
    3:00 am - 3:30 am (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
    Thu, May 11 | 3:30 am - 4:00 am (CDT)
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    Plenary
    Project-Based Learning and Design with Simulation
    Professor Claire Lucas, King’s College London
    Thu, May 11 | 3:30 am - 4:00 am (CDT)
    3:30 am - 4:00 am (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
    Thu, May 11 | 4:00 am - 4:30 am (CDT)
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    AI
    Understanding and Verifying Your AI Models
    Lucas Garcia, MathWorks
    Thu, May 11 | 4:00 am - 4:30 am (CDT)
    4:00 am - 4:30 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
    Thu, May 11 | 4:00 am - 4:30 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
    Thu, May 11 | 4:00 am - 4:30 am (CDT)
    4:00 am - 4:30 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
    Thu, May 11 | 4:00 am - 4:30 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
    Thu, May 11 | 4:00 am - 4:30 am (CDT)
    4:00 am - 4:30 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
    Thu, May 11 | 4:00 am - 4:30 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
    Thu, May 11 | 4:00 am - 4:30 am (CDT)
    4:00 am - 4:30 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
    Thu, May 11 | 4:00 am - 4:30 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
    Thu, May 11 | 4:00 am - 4:30 am (CDT)
    4:00 am - 4:30 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.
    Thu, May 11 | 4:00 am - 4:30 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.
    Thu, May 11 | 4:00 am - 4:30 am (CDT)
    4:00 am - 4:30 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
    Thu, May 11 | 4:00 am - 4:30 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
    Thu, May 11 | 4:00 am - 4:30 am (CDT)
    4:00 am - 4:30 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
    Thu, May 11 | 4:00 am - 4:30 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
    Thu, May 11 | 4:00 am - 4:30 am (CDT)
    4:00 am - 4:30 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
    Thu, May 11 | 4:00 am - 4:30 am (CDT)
    Register/Log in to save session
    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
    Thu, May 11 | 4:00 am - 4:30 am (CDT)
    4:00 am - 4:30 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.
  • Using Deep Learning and Kalman Filters for Temperature Soft Sensing
    Bruno Dandine, Poclain Hydraulics
    Thu, May 11 | 4:30 am - 5:00 am (CDT)
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    AI
    Using Deep Learning and Kalman Filters for Temperature Soft Sensing
    Bruno Dandine, Poclain Hydraulics
    Thu, May 11 | 4:30 am - 5:00 am (CDT)
    4:30 am - 5:00 am (CDT)
    AI
    See how to leverage AI to assess internal temperatures of hydraulic motors. The aim was to create an embedded algorithm for temperature estimation based on the motor loads and environment. The challenges were to consider the load history, and generate data using a single internal tool, with fewer measurements and less prior data available. The size of the AI model was also important. MATLAB® enabled Poclain Hydraulics to use pretrained AI models and quickly ramp up their expertise in deep learning, deal with load history issues, and accelerate the project. The benefits of this project will be preventing field catastrophic failures due to prolonged time at unexpected high loads and improving the size of the overall hydraulic transmission.
  • Using MATLAB with Python
    Yann Debray, MathWorks Dr. Heather Gorr, MathWorks
    Thu, May 11 | 4:30 am - 6:00 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 | 4:30 am - 6:00 am (CDT)
    4:30 am - 6:00 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 | 4:30 am - 5:00 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 | 4:30 am - 5:00 am (CDT)
    4:30 am - 5:00 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.
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