MATLAB EXPO, Online May 10

All Sessions

Automated Driving Systems

30
Computational Biology

17
Control Systems

65
Data Science

33
Deep Learning

57
Embedded Systems

48
Enterprise and IT Systems

24
FPGA, ASIC, and SoC Design

25
Image Processing and Computer Vision

47
Internet of Things

21
Machine Learning

51
Mechatronics

24
Mixed-Signal Systems

15
Power Electronics Control Design

29
Power Systems Analysis and Design

32
Predictive Maintenance

29
Robotics

39
Signal Processing

38
Test and Measurement

42
Wireless Communications

33

All Sessions

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Process of Building AI Models for Predicting Engine Performance and Emissions

Shakti Saurabh, Cummins Britant Sureka, Cummins

Wed, May 10 | 5:00 am - 5:30 am (CDT)

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Algorithm Development and Data Analysis
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- Presenter Profile
Process of Building AI Models for Predicting Engine Performance and Emissions

Shakti Saurabh, Cummins Britant Sureka, Cummins

Wed, May 10 | 5:00 am - 5:30 am (CDT)

5:00 am - 5:30 am (CDT)

Algorithm Development and Data Analysis

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.

Shakti Saurabh

Dr. Shakti Saurabh is based in Columbus, Indiana. He joined Cummins in 2017 and has been a part of the thermal sciences and fluid systems group primarily focused on internal combustion engine/emissions modeling, where he is responsible for developing processes for accurate modeling of engine subsystems and engine out emission predictions. Over a career span of 12 years in academia and industry, Dr. Saurabh has acquired extensive experience in 1D-3D CFD, machine learning, fluid-structure interaction, FEA, advanced numerical methods in fluid dynamics, combustion, and emissions model development. He worked for GE India in the combustion CFD group for two years (2011–2013) before receiving his doctoral degree in aerospace engineering from the University of Illinois Urbana-Champaign.

Britant Sureka

Britant Sureka joined Cummins in 2017. In his current role, he supports advanced product development program teams by modeling and optimizing engine performance using GT SUITE. During his career, he performed studies such as developing digital twins, evaluating different thermal management strategies, modeling and optimizing dynamic skip firing, developing 1D predictive and semi-predictive combustion models for diesel and hydrogen fuel, and evaluating various air handling technologies for diesel engines. He received his bachelor’s in mechanical engineering from the Maulana Azad National Institute of Technology, Bhopal in India and his master’s in mechanical engineering from Michigan Technological University.
Accelerating Safe Railway Application Development Using Model-Based Design

Daran Smalley, Alstom

Wed, May 10 | 5:30 am - 6:00 am (CDT)

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Modeling, Simulation, and Implementation
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Accelerating Safe Railway Application Development Using Model-Based Design

Daran Smalley, Alstom

Wed, May 10 | 5:30 am - 6:00 am (CDT)

5:30 am - 6:00 am (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.

Daran Smalley

Daran Smalley has recently transitioned to brake subsystem manager at Alstom in Västerås, Sweden. He previously worked as a traction control engineer focusing on leading a team to develop and roll out Model-Based Design processes and tools within the MathWorks ecosystem and supporting train traction software product development and verification. Before joining Alstom, Daran worked as an electrical engineer automating cranes at Rio Tinto, an aluminum smelter in Tasmania, Australia. Daran studied mechatronics engineering at Monash University in Melbourne, Australia. He is now enjoying the freezing cold in Sweden.
Build Scalable AI Solutions with MATLAB Production Server in Kubernetes on Azure

Björn Müller, Aerzen Digital Systems GmbH

Wed, May 10 | 5:30 am - 6:00 am (CDT)

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Cloud, Enterprise, and DevOps
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Build Scalable AI Solutions with MATLAB Production Server in Kubernetes on Azure

Björn Müller, Aerzen Digital Systems GmbH

Wed, May 10 | 5:30 am - 6:00 am (CDT)

5:30 am - 6:00 am (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.

Björn Müller

Björn Müller is an expert data scientist, combining the latest machine learning methods with a clear process and automation understanding. He is part of an interdisciplinary team of mathematicians, cloud developers, and engineers at Aerzen Digital Systems. Before joining Aerzen Digital Systems, Björn worked in process automation at the Institute of Production Technology and Logistics at the University of Kassel. His research interests include data-based advanced process control systems with a focus on energy efficiency and optimization utilizing cutting-edge techniques. Björn holds a Master of Science degree from the University of Kassel.
How to Optimize the User Experience of Your MATLAB Apps

Gianluca Carnielli, MathWorks

Wed, May 10 | 5:30 am - 6:00 am (CDT)

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Algorithm Development and Data Analysis
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How to Optimize the User Experience of Your MATLAB Apps

Gianluca Carnielli, MathWorks

Wed, May 10 | 5:30 am - 6:00 am (CDT)

5:30 am - 6:00 am (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
Gianluca Carnielli

Gianluca Carnielli is an application engineer at MathWorks focusing on MATLAB language and software development workflows. Prior to joining MathWorks, he held a research position in the field of space physics at Imperial College London. Gianluca holds a master’s degree in theoretical physics and a doctoral degree in space physics from Imperial College London.
Moonshots: How Engineers and Scientists Are Achieving the Impossible

Dr. Arun Mulpur, MathWorks

Wed, May 10 | 10:00 am - 10:30 am (CDT)

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Plenary
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Moonshots: How Engineers and Scientists Are Achieving the Impossible

Dr. Arun Mulpur, MathWorks

Wed, May 10 | 10:00 am - 10:30 am (CDT)

10:00 am - 10:30 am (CDT)

Plenary

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."

Dr. Arun Mulpur

Arun Mulpur is a senior industry group manager at MathWorks. He has worked in the areas of design automation and algorithm and system design for over 28 years, including 21 years at MathWorks. His team works with companies in the communications, electronics, semiconductors, software and internet, energy production, medical devices, and robotics industries. They focus on enabling customer engineering teams in the development of new technologies and products and improving engineering workflows for algorithm development, system design, simulation, prototyping, and implementation. Arun holds a B.E. degree in electrical engineering from Osmania University, where he was awarded the Khateeb Mahmood Hussain Gold Medal. He also holds M.S. and Ph.D. degrees in electrical engineering and an M.B.A. from the University of Massachusetts.
How Siemens Energy Enables the Global Energy Transition

Jens Dietrich, Siemens Energy AG

Wed, May 10 | 10:30 am - 11:00 am (CDT)

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How Siemens Energy Enables the Global Energy Transition

Jens Dietrich, Siemens Energy AG

Wed, May 10 | 10:30 am - 11:00 am (CDT)

10:30 am - 11:00 am (CDT)

Plenary

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.

Jens Dietrich

Jens Dietrich leads the development of control platforms for HVDC and FACTS applications at Siemens Energy, Germany. Previously, he worked as development manager, product owner, and developer for various control and simulation applications in power transmission. His group is responsible for the development, continuous improvement, and targeted use of a MATLAB and Simulink based software ecosystem for the delivery of customized control systems for HVDC and FACTS applications at Siemens Energy. Jens leads a team of passionate experts who love to bring state-of-the-art software development techniques to the model-driven world. He holds an M.Sc. and B.Sc. from RWTH Aachen University in electrical and computer engineering with a focus on power systems and control simulation.
Reduced Order Modeling with AI: Accelerating Simulink Analysis and Design

Kishen Mahadevan, MathWorks Terri Xiao, MathWorks

Wed, May 10 | 11:00 am - 11:30 am (CDT)

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AI
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Reduced Order Modeling with AI: Accelerating Simulink Analysis and Design

Kishen Mahadevan, MathWorks Terri Xiao, MathWorks

Wed, May 10 | 11:00 am - 11:30 am (CDT)

11:00 am - 11:30 am (CDT)

AI

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.

Kishen Mahadevan

Kishen Mahadevan is a product manager in the controls marketing group at MathWorks, where he drives awareness and adoption of the controls products and works closely with development teams to guide the roadmap for these tools. Prior to moving into product marketing, Kishen worked for two years as an application support engineer supporting customers with their workflows and questions related to Simulink products, with a focus on physical modeling, controls, and deep learning applications. Kishen holds an M.S. in electrical engineering with a specialization in control systems from Arizona State University and a B.E. in electrical and electronics engineering from Visvesvaraya Technological University in India.

Terri Xiao

Terri Xiao is the application engineering manager for AI and robotics at MathWorks. She leads a team of engineers to demonstrate the value of data analytics and Model-Based Design for customers‘ AI and robotics applications. Before becoming an application engineering manager, Terri worked for three years as an industry manager for the industrial automation and machinery market, serving as the technical liaison between MathWorks and the industry with a focus on digital twin applications. Before joining MathWorks, Terri worked at a consulting company specializing in guidance, navigation, and control for the aerospace industry. Terri received her B.S. and M.S. in mechanical engineering from the University of California, Los Angeles. While pursuing her graduate studies, her concentration was in systems and controls.
Low Code Data Analysis in MATLAB

Lola Davidson, MathWorks Onomitra Ghosh, MathWorks

Wed, May 10 | 11:00 am - 11:30 am (CDT)

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Algorithm Development and Data Analysis
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Low Code Data Analysis in MATLAB

Lola Davidson, MathWorks Onomitra Ghosh, MathWorks

Wed, May 10 | 11:00 am - 11:30 am (CDT)

11:00 am - 11:30 am (CDT)

Algorithm Development and Data Analysis

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.

Lola Davidson

Lola Davidson is a senior engineer at MathWorks focusing on developing software for data preprocessing workflows. Lola joined MathWorks in 2017 through the Engineering Development Group. Since then, she has worked on developing functionality in support of low-code data analysis workflows such as Live Editor Tasks and the Data Cleaner app. Lola has a B.A. in mathematics and physics from the University of the Cumberlands and an M.S. in mathematics from the University of Kentucky.

Onomitra Ghosh

Onomitra Ghosh is a principal product manager at MathWorks focused on improving the data science and big data workflows in MATLAB. He joined MathWorks in 2007 and has worked on software development and testing for a variety of product areas that include core mathematics, data analytics, image acquisition, data acquisition, vehicle network, audio/video IO, and parallel computing. Prior to joining MathWorks, Onomitra was a software consultant focused on database engineering. He has a B.S. in mechanical engineering from Jadavpur University (India), an M.S. in mechanical engineering from Boston University, and an M.B.A. from Cornell University.
Inclusive Innovation: Technology by and for All

Juliana Garaizar, Greentown Labs Milka Santana , Perfecto Labs Armani Hrobowski, Argonne National Laboratory Ji Yang Luo, Moderator, MathWorks

Wed, May 10 | 11:00 am - 11:30 am (CDT)

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Inclusive Innovation
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Inclusive Innovation: Technology by and for All

Juliana Garaizar, Greentown Labs Milka Santana , Perfecto Labs Armani Hrobowski, Argonne National Laboratory Ji Yang Luo, Moderator, MathWorks

Wed, May 10 | 11:00 am - 11:30 am (CDT)

11:00 am - 11:30 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.

Juliana Garaizar

Juliana Garaizar is the chief development and investment officer for Greentown Labs and head of their Houston incubator. She is a Kauffman Fellow; a venture partner at Portfolia, an investment fund designed for women; and a board member of many distinguished organizations, such as the Wells Fargo Innovation Incubator, the Latinx Startup Alliance, the Diversity Fund Houston, and the Investors of Color Network. Juliana holds an M.B.A. from the London Business School and Haas School of Business in Berkeley.

Milka Santana

Milka Santana is the founder and CEO of Perfecto Labs. After the death of her father, Perfecto Santana, she founded Perfecto Labs in 2020 with the mission of teaching technology to underprivileged children in Latin America. Before Perfecto Labs, Milka worked for J.P. Morgan Private Bank and Goldman Sachs, then moved to Xi’an, China, where she completed two graduate degrees from John's Hopkins University and Xi'An Jiaotong University.

Armani Hrobowski

Armani Hrobowski is a technology integration engineer at Argonne National Laboratory. In this role, he supports the Sustainable Transportation Education and Partnerships (STEP) Department’s diversity, equity, and inclusion (DEI), STEM education, and energy and environmental justice activities. He also leads the new DEI subteam for the EcoCAR EV Challenge. He focuses on integrating DEI initiatives into STEP’s workforce development portfolio and supporting the deployment of new programs. He previously worked for Ford Motor Company. Armani holds a Bachelor of Science in electrical and computer engineering from Ohio State.

Ji Yang Luo, Moderator

Ji Yang Luo is an associate application support engineer at MathWorks, helping users succeed with Model-Based Design, code generation, and verification tools. Ji Yang holds a Bachelor of Science in electrical engineering from Tufts University.
Infineon, Supporting Dependability for Automotive MCUs with Model-Based Design

Thomas Schneid, Infineon Technologies

Wed, May 10 | 11:00 am - 11:30 am (CDT)

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Modeling, Simulation, and Implementation
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Infineon, Supporting Dependability for Automotive MCUs with Model-Based Design

Thomas Schneid, Infineon Technologies

Wed, May 10 | 11:00 am - 11:30 am (CDT)

11:00 am - 11:30 am (CDT)

Modeling, Simulation, and Implementation

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.

Thomas Schneid

Thomas Schneid is the senior director of software, partner and ecosystem management at Infineon Automotive Microcontroller, where he has been responsible the software business and ecosystem solution portfolio for the last five years. Thomas has more than 20 years of experience in the automotive segment and 7 years in the security and chipcard market with different sales & management responsibilities. He has spent most of his career at Infineon, but has also worked for companies like Texas Instruments and Motorola.
Preparing Students for Impactful Careers in Industry

Moiz Khan, MathWorks

Wed, May 10 | 11:00 am - 11:30 am (CDT)

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Preparing Future Engineers and Scientists
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Preparing Students for Impactful Careers in Industry

Moiz Khan, MathWorks

Wed, May 10 | 11:00 am - 11:30 am (CDT)

11:00 am - 11:30 am (CDT)

Preparing Future Engineers and Scientists

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.

Moiz Khan

Moiz Khan is the academic manager of electrical and computer engineering at MathWorks, leading growth in areas including robotics, power systems, and AI. He has also served as a consultant for several industry leaders on robotics and design. Moiz received his B.S. and M.S. from Rutgers University and New York University in applied engineering and biomechanics, respectively. He earned his Ph.D. at Columbia University in mechanical engineering, working on rehabilitation robotic platforms. Immediately following, he was a postdoctoral fellow at Harvard Medical School and Brigham and Women’s Hospital, where his research focused on developing surgical planning and optimization algorithms and minimally invasive robotic devices.
Running MATLAB Machine Learning Jobs in Amazon SageMaker

Gandharv Kashinath, MathWorks Shun Mao, Amazon Web Services

Wed, May 10 | 11:00 am - 11:30 am (CDT)

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Cloud, Enterprise, and DevOps
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Running MATLAB Machine Learning Jobs in Amazon SageMaker

Gandharv Kashinath, MathWorks Shun Mao, Amazon Web Services

Wed, May 10 | 11:00 am - 11:30 am (CDT)

11:00 am - 11:30 am (CDT)

Cloud, Enterprise, and DevOps

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^®.

Gandharv Kashinath

Gandharv Kashinath is a senior product marketing manager for cloud platform integrations at MathWorks. In his role, he manages cloud solutions that support MATLAB and Simulink on public cloud platforms like AWS and Microsoft Azure. Prior to joining MathWorks, Gandharv worked as a senior developer in HPC and cloud for Flow Science, a scientific software firm specializing in computation fluid dynamics. Gandharv has more than 10 years of experience in building, supporting, and managing scientific and engineering software solutions in the cloud. Gandharv has two master’s degrees—one in computer science from the Georgia Institute of Technology in Atlanta, and another in aerospace engineering from the University of Michigan–Ann Arbor.

Shun Mao

Shun Mao is the senior partner solutions architect in AI/ML at Amazon Web Services (AWS), where he mostly supports the cloud product development for AWS’s AI ISV partners. Prior to joining AWS, he worked as a senior data scientist in multiple corporations, including Danaher, ConocoPhillips, and CGG, covering energy and life science industries. He has years of experience in building AI products, data processing, machine learning, and cloud development. He holds a Ph.D. in physics from Texas A&M University and a B.S. in applied physics from the University of Science and Technology of China.
6G Wireless Technology: Accelerate Your R&D with MATLAB

Ahmad Saad, MathWorks Houman Zarrinkoub, MathWorks

Wed, May 10 | 11:00 am - 11:30 am (CDT)

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Wireless Connectivity and Radar
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6G Wireless Technology: Accelerate Your R&D with MATLAB

Ahmad Saad, MathWorks Houman Zarrinkoub, MathWorks

Wed, May 10 | 11:00 am - 11:30 am (CDT)

11:00 am - 11:30 am (CDT)

Wireless Connectivity and Radar

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.

Ahmad Saad

Ahmad Saad is a senior application engineer at MathWorks specializing in wireless communications and signal processing. Prior to joining MathWorks, he was a researcher and team lead at the Fraunhofer Society in Munich, Germany, where he was involved in the design and modeling of cognitive communication systems with applications in ultra-reliable industrial and vehicular connectivity. He obtained his Ph.D. in communication systems, which was jointly supervised by the Fraunhofer Society and the University of Augsburg in Germany.

Houman Zarrinkoub

Dr. Houman Zarrinkoub is a senior product manager at MathWorks responsible for wireless communications products. During his 22-year tenure at MathWorks, he has also served as a development manager and has been responsible for multiple signal processing and communications software tools. Prior to MathWorks, he was a research scientist working on mobile and voice coding technologies in the Wireless Group at Nortel Networks. He has been awarded multiple patents on topics related to computer simulations of signal processing applications. Houman is the author of the book Understanding LTE with MATLAB: From Mathematical Modeling to Simulation and Prototyping. He holds a B.Sc. degree in electrical engineering from McGill University and M.Sc. and Ph.D. degrees in telecommunications from the University of Quebec, in Canada.
Optimized Motor Control Applications: From Idea to Deployment with NXP MBDT

Razvan Chivu, NXP Stefan Cinipeanu, NXP

Wed, May 10 | 11:00 am - 11:30 am (CDT)

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Electrification
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Optimized Motor Control Applications: From Idea to Deployment with NXP MBDT

Razvan Chivu, NXP Stefan Cinipeanu, NXP

Wed, May 10 | 11:00 am - 11:30 am (CDT)

11:00 am - 11:30 am (CDT)

Electrification

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.

Razvan Chivu

Razvan Chivu is the engineering manager for the Model-Based Design Toolbox (MBDT) team at NXP in Bucharest, Romania. He has been working in the semiconductor industry since 2017, focusing on automotive tools. Razvan holds a bachelor's degree in computer science and a Master of Engineering degree, both from the Polytechnical University of Bucharest.

Stefan Cinipeanu

Stefan Cinipeanu is a software engineer on the Model-Based Design Toolbox (MBDT) team at NXP in Bucharest, Romania. He joined NXP in 2020 and has been focusing on automotive tools ever since. Stefan graduated from the Polytechnical University of Bucharest with a bachelor’s degree in control systems and systems engineering. He is pursuing a master’s degree in advanced control systems and real-time systems engineering.
What’s New in MATLAB, Simulink, and RoadRunner for Automated Driving Development

Div Tiwari, MathWorks Linghui Zhang, MathWorks

Wed, May 10 | 11:00 am - 11:30 am (CDT)

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Autonomous Systems and Robotics
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What’s New in MATLAB, Simulink, and RoadRunner for Automated Driving Development

Div Tiwari, MathWorks Linghui Zhang, MathWorks

Wed, May 10 | 11:00 am - 11:30 am (CDT)

11:00 am - 11:30 am (CDT)

Autonomous Systems and Robotics
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
Div Tiwari

Div Tiwari is a senior product manager for automated driving at MathWorks. He has helped MathWorks customers to establish and evolve their workflows in domains such as autonomous systems, artificial intelligence, and high-performance computing. Div holds B.S. and M.S. degrees in electrical and computer engineering and computer science from Cornell University.

Linghui Zhang

Linghui Zhang leads the application engineering in automated driving segment at MathWorks and is responsible for helping customers leverage MATLAB and Simulink products and workflows to develop automated driving applications. Before MathWorks, she worked at the China Academy of Space Technology as a technical manager and associate professor in charge of the development of control systems for satellites and spacecraft. Linghui has a master's degree in mechanical engineering from the University of Akron and a master's degree in automatic control from the China Academy of Space Technology.
Can You See Me Now? Analyzing Satellite-to-Ground Station Visibility

Rameez Ahmed, MathWorks Mike McLernon, MathWorks Ahmad Saad, MathWorks

Wed, May 10 | 11:30 am - 1:00 pm (CDT)

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Wireless Connectivity and Radar
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Can You See Me Now? Analyzing Satellite-to-Ground Station Visibility

Rameez Ahmed, MathWorks Mike McLernon, MathWorks Ahmad Saad, MathWorks

Wed, May 10 | 11:30 am - 1:00 pm (CDT)

11:30 am - 1:00 pm (CDT)

Wireless Connectivity and Radar
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.

To participate in this workshop, you will need a MathWorks Account (create MathWorks Account).
Rameez Ahmed

Rameez Ahmed is an application engineer for wireless communications, wireless standards, and satellite communications at MathWorks. He has been with MathWorks for seven years and spent his first three years in development for Communications Toolbox and DSP System Toolbox, and then as a product specialist for WLAN Toolbox, LTE Toolbox, 5G Toolbox, and Satellite Communications Toolbox. He holds a Ph.D. in wireless communications from Northeastern University in Boston.

Mike McLernon

Mike McLernon is an advocate for communications and software-defined radio products at MathWorks. Since joining MathWorks in 2001, he has overseen the development of numerous PHY layer capabilities in Communications Toolbox and connectivity to multiple SDR hardware platforms. He has worked in the communications field for over 30 years in both the satellite and wireless industries. Mike received his B.S.E.E. from the University of Virginia and his M.E.E.E. from Rensselaer Polytechnic Institute.

Ahmad Saad

Ahmad Saad is a senior application engineer at MathWorks specializing in wireless communications and signal processing. Prior to joining MathWorks, he was a researcher and team lead at the Fraunhofer Society in Munich, Germany, where he was involved in the design and modeling of cognitive communication systems with applications in ultra-reliable industrial and vehicular connectivity. He obtained his Ph.D. in communication systems, which was jointly supervised by the Fraunhofer Society and the University of Augsburg in Germany.
Interactive Learning with MATLAB Apps, Live Scripts, and MATLAB Grader

Naini Dawar, MathWorks Stefan Kerber, MathWorks

Wed, May 10 | 11:30 am - 1:00 pm (CDT)

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Preparing Future Engineers and Scientists
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Interactive Learning with MATLAB Apps, Live Scripts, and MATLAB Grader

Naini Dawar, MathWorks Stefan Kerber, MathWorks

Wed, May 10 | 11:30 am - 1:00 pm (CDT)

11:30 am - 1:00 pm (CDT)

Preparing Future Engineers and Scientists
This hands-on 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.

To participate in this workshop, you will need a MathWorks Account (create MathWorks Account).
Naini Dawar

Naini Dawar is part of the academic technical team at MathWorks in Singapore. She works with universities and educational institutes in the ASEAN region to adopt MATLAB and Simulink in research, curriculum, and industry–academia collaboration projects. She has around five years of experience in incorporating computational thinking and Model-Based Design workflows into curriculum. Prior to joining MathWorks, Naini worked as a Model-Based Design engineer with a Tier 1 automotive supplier in India. She holds a master’s degree in electronics and communication engineering and her areas of interest include power electronics control design, automatic code generation, systems engineering, and vehicle electrification.

Stefan Kerber

Dr. Stefan Kerber is part of the academia group at MathWorks, partnering with universities in northeast Germany to accelerate their discovery and learning. Prior to joining MathWorks, he held several academic positions and an industry assignment bringing active noise cancellation systems to mass production for passenger cars. Stefan holds a doctoral degree in electrical engineering from the Technical University of Munich.
A Cloud-Based MATLAB Visual Inspection System

Arvind Hosagrahara, MathWorks Brett Shoelson, MathWorks

Wed, May 10 | 11:30 am - 12:00 pm (CDT)

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Cloud, Enterprise, and DevOps
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A Cloud-Based MATLAB Visual Inspection System

Arvind Hosagrahara, MathWorks Brett Shoelson, MathWorks

Wed, May 10 | 11:30 am - 12:00 pm (CDT)

11:30 am - 12:00 pm (CDT)

Cloud, Enterprise, and DevOps

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.

Arvind Hosagrahara

Arvind Hosagrahara is the technical lead of a team that helps organizations deploy MATLAB algorithms in critical engineering applications, with a focus on integrating MATLAB into the enterprise IT/OT systems. Arvind has extensive hands-on experience developing MATLAB and Simulink applications and integrating them with external technologies. He has helped design the software and workflow for a variety of production applications focusing on robustness, security, scalability, maintainability, usability, and forward compatibility across automotive, energy and production, finance, and other industries.

Brett Shoelson

Brett Shoelson is a principal engineer at MathWorks focusing on image and vision processing, machine learning, and deep learning. Brett owned and operated a publishing company before returning to school for engineering. He did postdoctoral research at Harvard Medical School and was a fellow at the National Institutes of Health for five years. The 13 years prior to his employment at MathWorks were spent focused on process automation with MATLAB in the biomedical arena. Brett holds a B.A. in anthropology from the University of Florida, a B.S. in biomedical engineering from Mercer University, and an M.S. and Ph.D. in biomedical engineering from Tulane University.
Model Autonomous Navigation of a Mars Rover

Ameya Rajendra Godbole, MathWorks Chinmayi Lanka, MathWorks Kritika Ramani, MathWorks Harshal Upadhyay, MathWorks

Wed, May 10 | 11:30 am - 1:00 pm (CDT)

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Autonomous Systems and Robotics
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Model Autonomous Navigation of a Mars Rover

Ameya Rajendra Godbole, MathWorks Chinmayi Lanka, MathWorks Kritika Ramani, MathWorks Harshal Upadhyay, MathWorks

Wed, May 10 | 11:30 am - 1:00 pm (CDT)

11:30 am - 1:00 pm (CDT)

Autonomous Systems and Robotics
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.

To participate in this workshop, you will need a MathWorks Account (create MathWorks Account).
Ameya Rajendra Godbole

Ameya Rajendra Godbole is a senior quality engineer at MathWorks. He is responsible for testing and qualifying Simscape Multibody, which provides a multibody simulation environment for mechanical systems such as robots, vehicle suspensions, and aircraft landing gear. His interests span robotics, modeling and simulation of physical systems, control systems design, state and parameter estimation, and unmanned vehicle systems. He supported the content creation for the workshops “Digital Twins in a Pandemic” for the Grace Hopper Celebration in 2020, and “AI takes Autonomous Navigation to Mars and Beyond’’ for GHC 22. Ameya was part of the MathWorks speaker team that delivered the workshop “Mission Planning of a Quadcopter Using a Digital Twin’’ at MATLAB EXPO in May 2021, which had more than 35,000 attendees globally.  Ameya holds a Ph.D. and an M.S. in mechanical engineering from the University of Texas at Arlington, and a B.E. in mechanical engineering from the University of Mumbai.

Chinmayi Lanka

Chinmayi Lanka is a senior team lead for the quality engineering team at MathWorks. Her team is responsible for designing and implementing cutting-edge tools to qualify the deployment of deep learning algorithms to various embedded devices such as Raspberry Pi, FPGA, and NVIDIA Jetson. During her time at MathWorks, she has contributed to development and qualification of several key deep learning functionalities in the product suite. Chinmayi has delivered talks at various Women in Technology events, such as the Grace Hopper Celebration and the Society of Women Engineers. She has been promoting Women in Technology by mentoring students at the Boston Museum of Science. She holds a master’s degree in electrical engineering focusing on signal processing and computer vision from the University of California, Los Angeles.

Kritika Ramani

Kritika is a senior manager of quality engineering at MathWorks with more than 18 years of experience in technical computing and design automation. She manages several diverse teams of talented engineers who are responsible for the testing and qualification of various products in the physical modeling, automotive systems, and code generation space. Kritika has shepherded several key quality initiatives and has helped improve the software testing processes and testing infrastructures at the MathWorks.  Kritika holds a master’s degree in mechanical engineering from the University of Michigan–Ann Arbor. She is passionate about her role as a mentor to help others grow and succeed and has actively contributed to several diversity, inclusion, and community outreach opportunities as part of the MathWorks workshop teams for the Grace Hopper Celebration, the Society of Women Engineers, Black Girls CODE, and the Open Data Science conferences. 

Harshal Upadhyay

Harshal Upadhyay is a senior software engineer in test for Simscape Multibody at MathWorks, focusing on product quality and the development of automated test suites for physical modeling and multibody dynamics software. He has also supported customers and developed features and demos in the areas of robotics, control systems, and mechanics simulation since 2016. Prior to joining MathWorks, he was focused on assistive robotics research as a graduate student researcher. Harshal holds a master of science degree in mechanical engineering from Purdue University.
Optimize EV Battery Performance Using Simulation

Dr. Danielle Chu, MathWorks Dr. Lorenzo Nicoletti, MathWorks

Wed, May 10 | 11:30 am - 12:00 pm (CDT)

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Electrification
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Optimize EV Battery Performance Using Simulation

Dr. Danielle Chu, MathWorks Dr. Lorenzo Nicoletti, MathWorks

Wed, May 10 | 11:30 am - 12:00 pm (CDT)

11:30 am - 12:00 pm (CDT)

Electrification

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.

Dr. Danielle Chu

Danielle Chu is a product manager at MathWorks supporting the Simscape product family and specializing in power electronics and battery systems. Prior to joining MathWorks, Danielle was at John Deere for seven years developing power electronics control systems. Prior to employment at John Deere, she performed postdoctoral work at the Center for Advanced Power Systems (CAPS) at Florida State University and received her Ph.D. in electrical engineering from the University of South Carolina and her M.S. in electrical engineering from Mississippi State University.

Dr. Lorenzo Nicoletti

Dr. Lorenzo Nicoletti is an application engineer in the MathWorks Munich office. His main application focuses are electrification, virtual vehicles, and physical modeling. Prior to joining MathWorks, Lorenzo collaborated in a research project with AUDI AG focusing on the parametric modeling of battery electric vehicles. In the scope of the project, he obtained a Ph.D. in mechanical engineering from the Technical University of Munich. Prior to the Ph.D., he obtained an M.Sc. in mechanical engineering and an M.Sc. in automotive engineering from the Technical University of Munich.
Cell Array, Table, Timetable, Struct, or Dictionary? Choosing a Container Type

David Garrison, MathWorks Francesca Marini, MathWorks

Wed, May 10 | 11:30 am - 12:00 pm (CDT)

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Algorithm Development and Data Analysis
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Cell Array, Table, Timetable, Struct, or Dictionary? Choosing a Container Type

David Garrison, MathWorks Francesca Marini, MathWorks

Wed, May 10 | 11:30 am - 12:00 pm (CDT)

11:30 am - 12:00 pm (CDT)

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.

David Garrison

David Garrison is a MATLAB Product Manager focusing on the MATLAB language, MATLAB core data types, and external interfaces. David has had previous product management responsibility for MATLAB graphics and app building and for the MATLAB desktop, Editor, and Live Editor. Prior to joining MathWorks, he held product management positions at Aspen Technology, Netegrity, and BEA Systems. David has a master‘s degree in chemical engineering and a master‘s degree in mathematics from Vanderbilt University.

Francesca Marini

Francesca Marini is a MATLAB product manager focusing on the MATLAB desktop and editor. Francesca previously worked as an application engineer supporting academic customers at universities and research institutions to achieve success. Prior to joining MathWorks, she worked for Microsoft, where she was dedicated to guiding customers to innovate, expand, and realize real business value using cloud technologies. Francesca has a Ph.D. in bioengineering and robotics from the University of Genoa and a passion for neuroscience.
Catching Fire: Using Autonomous Drones to Detect and Track Wildfires

Sarah Mohamed, MathWorks Shweta Singh, MathWorks Brenda Zhuang, MathWorks Arushi Kapurwan, MathWorks Archana Narayanan , MathWorks Sathya Chikotri, MathWorks

Wed, May 10 | 11:30 am - 1:00 pm (CDT)

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AI
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Catching Fire: Using Autonomous Drones to Detect and Track Wildfires

Sarah Mohamed, MathWorks Shweta Singh, MathWorks Brenda Zhuang, MathWorks Arushi Kapurwan, MathWorks Archana Narayanan , MathWorks Sathya Chikotri, MathWorks

Wed, May 10 | 11:30 am - 1:00 pm (CDT)

11:30 am - 1:00 pm (CDT)

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™.

To participate in this workshop, you will need a MathWorks Account (create MathWorks Account).

Sarah Mohamed

Sarah Mohamed is a software engineer at MathWorks, working in the Statistics and Machine Learning Group on deep learning. Her team builds tools that enable scientists and engineers to exchange deep neural networks between MATLAB and other tools, such as ONNX and TensorFlow. She has also worked as a customer-facing application support engineer, helping engineers to investigate and overcome technical challenges in their own machine and deep learning applications. Sarah loves teaching and building tech that empowers women to tackle tough problems in a fun and engaging way. She has delivered workshops at Grace Hopper Celebration, Women in Data Science, and Open Data Science Conference. Sarah holds dual bachelor’s degrees in computer science and biology from the University of Virginia and a master's degree in computational science and engineering from Harvard University.

Shweta Singh

Shweta Singh is a software development engineer at MathWorks working in the Product Security group on securing software development lifecycle and online operations. At MathWorks, she also worked as a customer-facing application support engineer supporting customers with resolving issues related to multiple MATLAB toolboxes. Prior joining MathWorks, she worked as an assistant professor at Delhi Technological University in India. She holds master’s and bachelor’s degrees in computer science and engineering from Guru Gobind Singh Indraprastha University and Delhi Technological University, respectively. Shweta is working on her Ph.D. thesis at Northeastern University in Boston where her research focus is detecting undesirable emergent behaviors in autonomous systems. Shweta has multiple publications in conferences and journals, including the International Conference on Complex Systems and the Society for Modeling & Simulation International.

Brenda Zhuang

Dr. Brenda Zhuang is a consulting engineer and development manager at MathWorks, where she leads a team responsible for software tools for automatic deployment of embedded applications such as motor controls and deep learning to microprocessors and FPGAs. Brenda joined MathWorks in 2007. She is active in the research community of numerical simulation and design automation. She also serves on the technical program committee for many international conferences in control theory, modeling, and simulation. She received her Ph.D. in systems engineering from Boston University, developing optimal scheduling approaches in smart sensors networks. She received a bachelor of engineering with honors from Huazhong University of Science and Technology in China and a master of philosophy from Hong Kong University of Science and Technology.

Arushi Kapurwan

Archana Narayanan

Sathya Chikotri
Formalizing Requirements and Generating Requirements-Based Test Cases

Dalton L‘Heureux, MathWorks Dallas Perkins, MathWorks

Wed, May 10 | 11:30 am - 12:00 pm (CDT)

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Modeling, Simulation, and Implementation
- Session Information
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Formalizing Requirements and Generating Requirements-Based Test Cases

Dalton L‘Heureux, MathWorks Dallas Perkins, MathWorks

Wed, May 10 | 11:30 am - 12:00 pm (CDT)

11:30 am - 12:00 pm (CDT)

Modeling, Simulation, and Implementation

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.

Dalton L‘Heureux

Dalton L‘Heureux joined MathWorks in 2020 as an application engineer helping aerospace customers best utilize MathWorks products for systems engineering, verification and validation, and code generation for DO-178C certification and other high integrity/safety-critical applications. Prior to MathWorks, Dalton worked as a systems engineer at Collins Aerospace, developing flight software for the Boeing 777X and Bombardier C-Series Aircraft. At Collins, his main responsibilities were in specification modeling and test case development and generation. Dalton obtained a B.S.E. in aerospace engineering and an M.S.E. in unmanned and autonomous systems engineering from Embry-Riddle Aeronautical University.

Dallas Perkins

Dallas Perkins joined MathWorks in 2020 as an application engineer. Prior to MathWorks, he worked as a software controls engineer at General Atomics Electromagnetic Systems developing hardware-in-the-loop experiments for electromechanical systems and their controllers. Dallas received his B.S., M.S., and Ph.D. in electrical engineering from Florida State University with a focus on energy conversion and control in electrical distribution systems.
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
- Session Information
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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.

Yann Debray

Yann Debray is a MATLAB product manager focusing on the use of MATLAB with Python. Prior to joining MathWorks in 2020, he worked in the field of open source scientific computing since 2014. He holds an engineering master’s degree from the Arts et Métiers ParisTech engineering school.

Weiwu Li

Weiwu Li is a product manager responsible for simulation capabilities of Simulink and Simulink Compiler. Prior to joining MathWorks in 2016, he had many years of experience in the automotive industry as a system controls engineer. Weiwu has a Ph.D. in controls engineering from Zhejiang University, an M.S.M.E. from The Ohio State University, and an M.B.A. from Purdue University.

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MATLAB EXPO 2023

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