Article 3 of Bringing Intelligence to the Edge Series: Balancing the critical metrics of accuracy, power consumption, latency, and memory requirements is key to unlocking the potential of Tiny Machine Learning (TinyML) in low-power microcontrollers and edge computing.
Article 3 of Bringing Intelligence to the Edge Series: Balancing the critical metrics of accuracy, power consumption, latency, and memory requirements is key to unlocking the potential of Tiny Machine Learning (TinyML) in low-power microcontrollers and edge computing.
Article #4 of Spotlight on Innovations in Edge Computing and Machine Learning: Discover the integration of TinyML and wearable tech as we delve into a project that detects falls in real-time, potentially saving lives in our aging population.
Article #3 of Spotlight on Innovations in Edge Computing and Machine Learning: A computer vision system that detects and localizes the surface cracks in concrete structures for predictive maintenance.
Artificial intelligence (AI) is a wide-ranging tool that enables people to rethink how we integrate information, analyze data, and use the resulting insights to improve decision making
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From leading experts at the CDFAM Computational Design Symposium
eCozy 2.0 system addresses the limitations of traditional thermostats for water heating radiators, delivering efficiency, convenience and sustainability benefits to households
Protect the cloud by securing the edge with hardware-based security, TPMs, HSMs, and resilient edge computing architectures.
Explore the rise of intelligent EV hardware and how real-time processing, ML acceleration, and hardware virtualization are enabling safer, smarter, software-defined electric vehicles.
EPFL researchers are developing AI models that could one day enable vision prosthetics able to restore meaningful, object-level sight for the blind.
A deep-dive listicle guiding controls, automation, and quality engineers toward the best vision systems for manufacturing applications in 2026.
Understanding industrial vision systems by examining their components, imaging fundamentals, AI integration since 2020, and how to choose the right solution for every application.
The AI-powered tool could inform the design of better sensors and cameras for robots or autonomous vehicles.
Article 3 of Bringing Intelligence to the Edge Series: Balancing the critical metrics of accuracy, power consumption, latency, and memory requirements is key to unlocking the potential of Tiny Machine Learning (TinyML) in low-power microcontrollers and edge computing.
Article #4 of Spotlight on Innovations in Edge Computing and Machine Learning: Discover the integration of TinyML and wearable tech as we delve into a project that detects falls in real-time, potentially saving lives in our aging population.
Article #3 of Spotlight on Innovations in Edge Computing and Machine Learning: A computer vision system that detects and localizes the surface cracks in concrete structures for predictive maintenance.
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Electronic Technology Supporting the Evolution of “Next Tech”
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The tinyML Foundation, Sony Semiconductor, and the City of San José are teaming up to use Machine Learning in improving pedestrian safety
Article 2 of Bringing Intelligence to the Edge Series: Advancements in AI and embedded vision technologies are revolutionizing various industries, enabling real-time decision-making, enhancing security, and facilitating automation in various applications.
There is a talent shortage in the Netherlands that is expected to persist until 2050. So concludes Ton Wilthagen, professor of Labor Market at Tilburg University, in the new High Tech Campus documentary The Talent Game: "Even if we attract more international knowledge workers, work more hours and involve people from the sidelines in the labor market, the talent shortage will remain."
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In this episode, we discuss a joint effort between the Laboratory for Information and Decisions Systems and the Institute for Data, Systems, and Society at MIT to tackle the trust issue with autonomous vehicles.
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From sleep monitoring wearables to face detection models, tinyML is making a big impact on the way we can gather and apply data.
In both analytics and machine learning (ML), the value of data cannot be overstated. Understanding its importance is essential for unlocking its full potential and driving informed decision-making, enhancing business processes, and exploring new opportunities across various industry sectors.
Highlights from the 3rd Annual tinyML EMEA Innovation Forum include exploring hardware developments, algorithm optimization, and deploying MLOps tools.
Article 1 of Bringing Intelligence to the Edge Series: With the introduction of AI, IoT devices can become more intelligent and less reliant on external systems— but not without trade-offs in performance and cost. Understanding how to make that decision is key.
Article 5 of Bringing Intelligence to the Edge Series: Integrating voice user interface technology into microcontroller units for offline, edge-based voice recognition is set to redefine the landscape of home automation and smart industrial applications.
Article #2 of Spotlight on Innovations in Edge Computing and Machine Learning: Edge AI techniques such as Keyword Spotting can turn an ordinary device into a smart appliance.
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Unsupervised learning is a machine learning approach that finds patterns, structure, or useful representations in unlabeled data. It groups similar observations, compresses high-dimensional features, detects outliers, discovers item relationships, and supports modern representation learning.
