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Highly-accurate, AI-enabled fault monitoring for real-time control systems
In real-time control systems, sensor data plays a crucial role in monitoring the system’s operational status and detecting any anomalies that might indicate a fault. Artificial intelligence (AI) can significantly enhance a system’s ability to accurately detect faults by leveraging advanced algorithms, data processing, and predictive analytics. In this session, which includes two real-life examples, discover how our new C2000 real-time microcontroller product can use AI to improve the accuracy of your fault-monitoring application, while also simultaneously handling your system's control functions.
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Technical trends and solutions in industrial real-time control systems
As real-time control systems become increasingly complex, keeping up with the latest trends is critical for maintaining a competitive edge. In this session, we’ll highlight five key trends to be aware of: cybersecurity, multi-protocol industrial communication, enhanced control loop performance, optimized power efficiency, and AI-based fault detection. You’ll learn how to navigate these trends and their related challenges using innovative approaches, supported by our latest application-specific microcontroller products and reference designs.
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Robust wireless solar solutions using TI wireless solar management system
Wireless connectivity is essential for scalable, reliable, and traceable solar networks. In this session, you’ll learn about our Wireless Solar Management System (WSMS) software stack, which offers fast re-join times, streamlined maintenance, and rapid shutdown for solar micro inverters and tracker applications. WSMS-Mesh supports market unification with interoperable, royalty-free software and standards-based security, while WSMS-Star is a cost-effective solution ensuring robust performance without compromising essential features.
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Navigating smart home connectivity: Unlocking TI’s affordable Zigbee-enabled low-power MCUs
In smart homes today, interoperability, scalability, range, energy efficiency, affordability, and security are all in high demand. Join us for this session to learn about the trends shaping these demands and the technical hurdles in integrating diverse communication protocols in the smart home. We’ll focus on Zigbee, a key smart home technology, and demonstrate how our CC2340 and CC2755 wireless MCUs not only meet today’s wireless standards but also offer the flexibility to adapt to emerging technologies such as Zigbee.
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Solve common MCU design challenges with TI MSPM0 subsystems
Microcontrollers offer increased control, reduced board space, and cost savings compared to discrete analog components. However, software development can be a barrier. Our MSPM0 MCU subsystems accelerate development by integrating building blocks that simplify your software efforts. Each subsystem can be adapted to your needs using our instructions, insights, and software. In this session, you’ll learn about TI’s Arm Cortex-M0+ MCU portfolio and explore subsystems, from analog and sensing to communication bridges and control, designed to help you develop faster.
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Seamlessly transition to the most comprehensive M0+ MCU portfolio available
With the right tools and resources, transitioning to new technology can be seamless. In this presentation, we’ll show you how to easily migrate your existing Arm® Cortex®-M0+ projects from platforms such as STM8, Microchip and Renesas to our MSPM0 platform, which includes the most comprehensive portfolio of M0+ microcontrollers available today. You’ll learn how to use our STM8 migration tool, discover pin-compatible options available at TI, and review guides and software tools that simplify hardware and software migration.
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Prepare for security regulations: 3 steps to take now
Upcoming security regulations like the Cyber Resilience Act (CRA), which impacts device manufacturers shipping products to the European Union, have many designers scrambling to understand compliance requirements. The scope of these regulations can be daunting, especially for those adding security to their products for the first time. Even experienced designers may benefit from a clear action plan. This talk introduces three actionable steps: using field-securable devices, creating a software bill-of-materials, and establishing a root-of-trust to help you start your journey toward compliance with our Arm-based processors.
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A scalable vision for the future through edge AI
Artificial Intelligence (AI) is becoming increasingly relevant across industries, but understanding how AI adds value to embedded systems can be challenging. The resource constraints of embedded systems make integrating AI daunting, raising concerns about investment and scalability. Our Arm-based processors offer a scalable solution, letting you invest in AI with confidence that your work is reusable across product revisions. In this session, you’ll learn about our products, tools and ecosystem that enable edge AI, see example applications, and learn how to get started today with Edge AI Studio.
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mmWave radar test-methodologies and tools for validating application performance
TI mmWave radar devices deliver powerful detection performance for various applications, from video doorbells to HVAC systems. However, optimizing performance across different applications can be challenging. This session will guide you through using ready-made software and analysis frameworks to quickly test any TI mmWave radar application, ensuring robust performance. You’ll learn how to set up the IWRL6432AOP, adjust detection parameters to increase range or reduce false detections, use tools for data collection and analysis, and validate performance metrics for new algorithms, hardware, or applications.
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Machine learning with mmWave radar data: Simple steps to get started
Machine learning enables powerful pattern recognition and performance across various data sets, including mmWave radar. While there are numerous examples for video and audio data, fewer resources exist for starting with radar sensors. This session presents a simple procedure using open-source tools and readily available information to train and deploy a machine learning model at the edge with the IWRL6432. You’ll explore the IWRL6432’s capabilities, understand the advantages and considerations of ML in embedded systems, and follow a full development flow from data collection to deployment for surface classification applications, helping you develop and test new ML-based radar applications.