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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: 6/25/2025
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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
A Data-Driven Approach to Quality Assurance for Imagers (Radiologists) Individually and Imaging Departments as a Whole (Case No. 2014-501)
Summary: UCLA researchers have developed innovative software that streamlines radiological data analysis to enhance patient outcomes, evaluate radiologist performance, and assess AI algorithms in a way that mirrors radiologist decision-making. Background: Radiological imaging is critical to disease diagnosis and treatment planning for a wide array...
Published: 3/7/2025
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Inventor(s):
Dieter Enzmann
,
Corey Arnold
,
Alex Bui
,
William Hsu
Keywords(s):
3D tissue imaging
,
3D ultrasound imaging
,
bioimaging
,
cell imaging
,
computational imaging
,
computational imaging task
,
Imaging
,
Magnetic Resonance Imaging
,
Magnetic Resonance Imaging Medical Physics
,
Magnetic Resonance Imaging Pathology
,
Magnetic Resonance Imaging Spin Polarization
,
Medical Imaging
,
Molecular Imaging
,
multi-contrast imaging
,
non-invasive imaging
,
Radiology
,
Radiology / Radiomitigation
,
three dimensional imaging
Category(s):
Software & Algorithms
,
Software & Algorithms > AI Algorithms
,
Software & Algorithms > Image Processing
,
Software & Algorithms > Digital Health
,
Medical Devices
,
Medical Devices > Medical Imaging
,
Medical Devices > Monitoring And Recording Systems
The Growing Role of Digital Pathology and Machine Learning in Cancer Diagnostics (UCLA Case No. 2021-134)
Summary: Researchers at UCLA's Department of Radiological Sciences have developed an active learning methodology for digital pathology image analysis, addressing challenges posed by inconsistent annotations and revolutionizing the training of diagnostic algorithms. Background: The global digital pathology market is anticipated to experience significant...
Published: 2/14/2025
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Inventor(s):
Corey Arnold
,
Wenyuan Li
,
Jiayun Li
,
William Speier
Keywords(s):
active learning
,
Artifical Intelligence (Machine Learning, Data Mining)
,
Artificial Intelligence
,
Artificial Neural Network
,
artificial-intelligent materials
,
computational imaging task
,
convolutional networks
,
curriculum learning
,
Deep Learning
,
deep physical neural network
,
Digital Pathology
,
dynamic identification
,
Histopathological image analysis
,
histopathology images
,
Image Processing
,
Magnetic Resonance Imaging Pathology
,
Medical Devices and Materials
,
Medical Imaging
,
noisy annotations
,
noisy label detection
,
pathology image analysis
Category(s):
Software & Algorithms
,
Software & Algorithms > Image Processing
,
Electrical > Imaging
,
Medical Devices > Medical Imaging