Search Results - neural+networks

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Method of Proficient Typing Using a Limited Number of Classes (Case No. 2024-063)
Summary: UCLA researchers in the Department of Electrical and Computer Engineering have developed a novel software algorithm to rapidly predict text using small keyboards for various applications, including mobile computing, gaming, and human-computer interactions. Background: Advancements in mobile computing have drastically changed everyday life...
Published: 6/10/2024   |   Inventor(s): Jonathan Kao, Shreyas Kaasyap, John Zhou, Johannes Lee, Nima Hadidi
Keywords(s): Advanced Computing / AI, advanced computing methods, all-optical diffractive computing, Artificial Neural Network, Artificial Neural Network Artificial Neuron, assistive communication, background radiation, Bandwidth (Computing), Brain computer interface, brain machine interface, Classroom management software, Cloud Computing, composite scintillators, Database management/data entry, deep physical neural network, design software, edge computing, fast scintillators, gamma spectroscopy, graph neural network, HCI (Human Computer Interaction), high-Z organometallics, Human/Brain computer interfaces (BCI/HCI), human-centered computing, material characterization, Medical science computing, mobile computing, modular robotic system, nanocomposite scintillators, neural network, neural networks, neutrino detection, Optical computing, positron emission tomography (PET), predictive text, primary school software, radiation detection, recurrent neural networks, Robotics, robotics control, scintillators, second harmonic generation, secondary school software, self-sustaining computing, soft robotics, Software, Software & Algorithms, Software Development Tools, Software-enabled learning, Spatial computing, Stochastic Computing (SC), T6 keyboard, T9 keyboard, visual computing, wafer-scale computing
Category(s): Software & Algorithms, Software & Algorithms > Artificial Intelligence & Machine Learning, Software & Algorithms > Communication & Networking
Universal Linear Intensity Transformations Using Spatially-Incoherent Diffractive Processors (Case No. 2023-192)
Summary: UCLA researchers in the Department of Electrical and Computer Engineering have developed a novel platform technology to facilitate the design of all-optical visual processors, which can be used to perform advanced computational tasks at the speed of light. Background: Information processing via light is a cutting-edge field among optics...
Published: 4/5/2024   |   Inventor(s): Aydogan Ozcan, MD Sadman Rahman, Xilin Yang
Keywords(s): Adaptive Optics, Algorithm Optical Coherence Tomography, all-optical diffractive computing, all-optical transformation, Artifical Intelligence (Machine Learning, Data Mining), Artificial Intelligence, Atomic Force Microscopy Optical Tweezers, computational imaging, deep diffractive network, Deep Learning, Deep learning-based sensing, diffractive processor, Dispersion (Optics), Electron Microscope, Electro-Optics, fluorescence microscopy, Focus (Optics), Infrared Electromagnetic Spectrum Dispersion (Optics), interference processor, large language model (LLNMs), linear optics, linear transformations, Machine Learning, Microscope, Microscopy, Microscopy And Imaging, Near-Field Scanning Optical Microscope, neural networks, Nonlinear Optics, non-linear optics, Optical Coherence , Optical Communication , Optical computing, Optical Fiber Copper Wire And Cable, optical implementation, Optical Microscope, Optical networks, optical processor, optical transmission, Optics Parabolic Reflector Curved Mirror, phase-only diffractive network, reverse engineered optical system, Software, Software & Algorithms, Software Development Tools, spatially-incoherent light, start to end optics design, Surgical Instrument Optical Coherence Tomography, three dimensional imaging, visual computing, Waferscale Processors
Category(s): Optics & Photonics, Optics & Photonics > Microscopy, Platforms, Software & Algorithms > Image Processing, Electrical, Electrical > Signal Processing, Electrical > Computing Hardware
Machine Learning and/or Neural Networks to Validate Stem Cells and Their Derivatives for Use in Cell Therapy, Drug Delivery, and Diagnostics
Abstract: Many biological and clinical procedures require functional validation of a desired cell type. Current techniques to validate rely on various assays and methods, such as staining with dyes, antibodies, and nucleic acid probes, to assess stem cell health, death, proliferation, and functionality. These techniques potentially destroy stem cells...
Published: 4/8/2024   |   Inventor(s): Kapil Bharti, Nicholas Schaub, Carl Simon, Nathan Hotaling
Keywords(s): Bharti, Cell therapy, Cell Validation, Less Invasive Diagnostics, Machine Learning, Neural Networks, QUALITY CONTROL, Stem Cell Diagnostics, Stem Cell Therapy
Category(s): TherapeuticArea > Cardiology, Collaboration Sought > Collaboration, Collaboration Sought > Licensing, TherapeuticArea > Oncology, Application > Software / Apps, TherapeuticArea > Dermatology
Semi-Stochastic Boolean-Neural Hybrids for Solving Hard Problems
Reference #: 01457 The University of South Carolina is offering licensing opportunities for Semi-Stochastic Boolean-Neural Hybrids for Solving Hard Problems Background: Currently, there exist several emerging approaches for solving hard computational problems. Quantum computation offers algorithms that in principle allow finding solutions of some...
Published: 9/4/2022   |   Inventor(s): Yuriy Pershyn
Keywords(s): computing networks, factorization, neural networks, NP-complete problems, traveling salesman problem
Category(s): Research Tools, Software and Computing
A New Risk Management Tool for Medical Imaging Software
Deep neural networks (DNNs) help radiologists locate, characterize, and segment tumors. These software tools augment imagery in regions that are otherwise obscured by signal noise or streaks. DNNs reduce imaging session times and thereby reduce radiation exposure. On the other hand, “black box” DNNs defy clinical scrutiny, make patient consultations...
Published: 2/15/2024   |   Inventor(s): Nidhal Bouaynaya, Ghulam Rasool, Dimah Dera
Keywords(s): Artificial Intelligence, MRI, Neural Networks, Safety
Category(s): Software
Correntropy Loss Function that Promotes Robust Training in Artificial Neural Networks
Algorithm Resists Outliers to Provide Accurate Data Classification for Random Sets of DataThe Correntropy Loss, or C-loss, function stabilizes signals in artificial neural networks to optimally classify random data points and resist the presence of outliers in a given sample. Artificial neural networks are statistical models that process information...
Published: 6/27/2021   |   Inventor(s): Jose Principe, Abhishek Singh
Keywords(s): classification, Data Mining, Neural networks, Robust Training
Category(s): Technology Classifications > Engineering > Computer Science