The concept of deep diffractive neural networks (D 2 NN) was introduced by Professor Ozcan’s research team in 2018. D 2 NN combines the principles of light diffraction with the functionalities of ...

Layered metasurfaces trained as optical neural networks enable multifunctional holograms and security features, integrating neural computation principles with nanostructured optics to create a ...

With the exponential growth in demand for high-speed data transmission, the 5G system infrastructure, despite its impressive peak data rate of 10 gigabits per second, is increasingly inadequate for ...

Scientists in India have proposed using a multilayer neural network to find line-to-ground, line-to-line, and bypass diode faults in PV module strings. They tested the new approach on a 22.5 kW solar ...

Forbes contributors publish independent expert analyses and insights. Philip Maymin, a professor of analytics and AI, covers finance and AI. Is this a deep learning neural network, with blue inputs, ...

A new technical paper titled “Hardware implementation of backpropagation using progressive gradient descent for in situ training of multilayer neural networks” was published by researchers at ...

Forbes contributors publish independent expert analyses and insights. Dr. Lance B. Eliot is a world-renowned AI scientist and consultant. In today’s column, I examine some exciting research that could ...

Researchers at the University of California, Los Angeles (UCLA) have developed an optical computing framework that performs large-scale nonlinear computations using linear materials. Subscribe to our ...

Learn what pooling layers are and why they’re essential in deep neural networks! This beginner-friendly explanation covers max pooling, average pooling, and how they help reduce complexity while ...