
Explore radio wave fundamentals, electromagnetic principles, and wireless network types in this comprehensive introduction to WLAN technology and standards.

Explore microwave proximity sensing with RCWL-0516. Learn standalone and Arduino-integrated setups, including latching and remote sensing applications. Gain practical skills in Doppler effect-based detection.

Learn to build an Arduino-based UV Index meter, explore UV light properties, and create two versions: a basic model and an advanced design with temperature and humidity sensors.

Explore the physics behind LEDs, from semiconductor basics to color generation. Gain insights into voltage-current relationships and diode connections for a deeper understanding of LED operation.

Learn to measure the speed of sound using an Arduino and HC-SR04 sensor. Practice dimensional analysis, unit conversion, and apply math skills in a practical, hands-on project.

Learn to use photoresistors with Arduino, exploring their physics and creating a light-sensitive LED project. Gain practical skills in circuit building and programming for real-world applications.

Explore how tinyML and neuroscience combine to create engaging STEM education experiences for K-12 students, featuring insights from a successful pilot collaboration with Backyard Brains.

Explore practical considerations for implementing computer vision in low-power IoT devices, covering hardware accelerators, algorithms, optics, and real-world challenges.

Explore data transmission using sound waves with Wave-share, an out-of-band technique utilizing built-in microphones and speakers in smartphones and computers.

Explore embedded ML applications in African agriculture and conservation, from cow health monitoring to elephant tracking, addressing unique challenges and local solutions.

Explore TinyML's potential in developing countries for medicine, agriculture, and conservation. Learn about low-cost, battery-powered machine learning applications adaptable to local needs.

Explore machine learning applications in Earth system modeling, from parameter calibration to feature detection, enhancing climate predictions and understanding of complex environmental processes.

Exploring machine learning for atmospheric physics parameterization, focusing on cloud superparameterization emulation. Discusses challenges, strategies, and lessons learned in applying ML to climate modeling.

Explore the benefits of acknowledging uncertainty in climate system analysis using machine learning. Learn about state-dependent predictability and controlled abstention networks for improved modeling accuracy.

Explore interpretable and generalizable machine learning techniques for fluid dynamics, focusing on sparse identification of nonlinear dynamics and incorporating physical constraints for improved accuracy.

Explores physics-guided deep learning for fluid dynamics, focusing on accelerating turbulence simulations and incorporating symmetry for generalization in ocean current forecasting.

Explore non-unitary dynamics in quantum systems, from dissipative to monitored, covering analytical approaches, entanglement scaling, and spacetime duality in quantum circuits and many-body physics.

Explores measurement-induced entanglement transitions in quantum circuits, focusing on characterization methods, dynamical symmetry, and connections to classical models. Discusses implications for non-equilibrium many-body physics.

Explore fundamental axioms of quantum mechanics for quantum computing, covering unitary matrices, qubit gates, and quantum circuits in this graduate-level lecture from Carnegie Mellon University.

Explore quantum mechanics fundamentals, focusing on understanding and measuring one qubit. Learn about measuring devices, inner products, and quantum notation.

Explore quantum error correction through surface code logical qubits, focusing on scaling strategies and performance improvements for practical applications in quantum computing.