Search Results - analog+computing

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Monitoring Structural Health Using Diffractive Optical Processors (Case No. 2025-201)

Summary: UCLA researchers in the Department of Electrical and Computer Engineering have developed a novel structural health monitoring system that is highly accurate and cost effective, addressing limitations in current infrastructure and civil health monitoring and a rise in public safety concerns. Background: The need for structural health monitoring...

Published: 7/25/2025 | Inventor(s): Aydogan Ozcan, Ertugrul Taciroglu, Yuntian Wang, Yuhang Li

Keywords(s): 3D structures, Adaptive Optics, AI-generated images and content, all-optical diffractive computing, all-optical transformation, analog computing, analog optical computing, Analogue Electronics, Artifical Intelligence (Machine Learning, Data Mining), Artificial Intelligence, artificial intelligence algorithms, artificial intelligence augmentation, artificial intelligence/machine learning models, artificial-intelligent materials, civil engineering, civil infrastructure, civil monitoring, computational imaging, computational imaging task, Construction, deep diffractive network, Diffraction, diffractive design, diffractive image reconstruction, diffractive network, diffractive processor, diffractive surface, digital image reconstruction, electromagnetic spectrum, Electro-Optics, Image Analysis, Image Processing, Image Resolution, image restoration, image signal processing, Imaging, Infrastructure, Lens (Optics), linear optics, Nanostructure, optical processor, optically-guided structural monitoring, Optics, passive light-matter interactions, security imaging, Signal Reconstruction, Structural health monitoring, structural health monitoring (SHM), structure monitoring, Structures

Category(s): Electrical, Electrical > Signal Processing, Electrical > Imaging, Materials, Materials > Construction Materials, Electrical > Visual Computing, Electrical > Computing Hardware, Electrical > Instrumentation, Energy & Environment, Energy & Environment > Energy Efficiency, Software & Algorithms, Software & Algorithms > Artificial Intelligence & Machine Learning, Software & Algorithms > Image Processing, Software & Algorithms > Programs

Event-Driven Integrate and Fire (EIF) Neuron Circuit for Neuromorphic Computing System (Case No. 2024-275)

Summary: Researchers in the UCLA Department of Electrical and Computer Engineering have developed an energy efficient neuromorphic computing architecture. Background: Widespread growth in demand for artificial intelligence systems has highlighted limitations in current central processing unit (CPU) designs, particularly in terms of energy efficiency...

Published: 3/4/2025 | Inventor(s): Mau-Chung Chang, Chao Jen Tien, Yong Hei

Keywords(s): Advanced Computing / AI, advanced computing methods, AI hardware, analog computing, Artifical Intelligence (Machine Learning, Data Mining), artificial electromagnetic materials, Artificial Intelligence, artificial intelligence algorithms, artificial intelligence augmentation, artificial intelligence/machine learning models, artificial intelligence-generated content, Artificial Neural Network, Artificial Neural Network Artificial Neuron, artificial presenting cells, artificial-intelligent materials, Cloud Computing, computational efficiency, computational imaging, compute-in-memory, Computer Aided Learning, Computer Architecture, Computer Monitor, Computer Vision, CPU design, deep neural networks (DNN), Energy Density, Energy Efficiency, event-driven processing, generative artificial intelligence, latency encoding, low latency computing, low-power architecture, matrix multiplication, Medical artificial intelligence (AI), Neuromorphic computing, offline learning, online learning, spike neural networks (SNN), Supercomputer

Category(s): Electrical, Electrical > Signal Processing, Electrical > Electronics & Semiconductors, Electrical > Computing Hardware, Software & Algorithms, Software & Algorithms > Artificial Intelligence & Machine Learning