Search Results - andreas+spanias

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Hybrid Quantum Neural Network and Imaging for Brain Tumor Classification
Invention Description Accurate classification of brain tumors from MRI scans is critical for diagnosis and treatment planning, but distinguishing between tumor types can be challenging due to similarities in imaging features. Traditional deep learning models often require substantial computational resources and may struggle to efficiently capture complex...
Published: 6/11/2026   |   Updated: 6/11/2026   |   Inventor(s): Andreas Spanias, Niraj Babar, Glen Uehara
Keywords(s):  
Category(s): Artificial Intelligence/Machine Learning, Cancer, Medical Imaging, Physical Science
Quantum Machine Learning for Enhanced Fault Detection in Photovoltaic Arrays
Invention Description Detecting faults in photovoltaic (PV) systems is essential for maintaining efficiency and reliability in large-scale solar energy installations. However, traditional methods often struggle to identify complex fault patterns due to the high interdependence between system variables. As PV systems grow in size and complexity, accurately...
Published: 5/26/2026   |   Updated: 5/26/2026   |   Inventor(s): Glen Uehara, Andreas Spanias
Keywords(s):  
Category(s): Artificial Intelligence/Machine Learning, Alternative Energy, Energy & Power, Physical Science
Quantum Image Fusion Methods for Remote Sensing
Background Image fusion is an image processing technique that has grown in prevalence in recent years. Image fusion can be used for many different applications including medical imaging, aerospace, remote sensing, military surveillance, and manufacturing flaw identification. Quantum image fusion is a more recent development that uses quantum computing...
Published: 2/26/2026   |   Updated: 5/22/2025   |   Inventor(s): Leslie Miller, Glen Uehara, Andreas Spanias
Keywords(s):  
Category(s): Physical Science, Applied Technologies, Imaging
Quantum Positive Unlabeled Learning Algorithms with Applications to Energy
Background Positive unlabeled (PU) learning is a semi-supervised machine learning approach to a binary classification problem in which most of the data is unlabeled. PU learning algorithms reduce the computational cost of training machine learning classifiers, because the labeling of data is time-intensive for supervised machine learning algorithms....
Published: 2/26/2026   |   Updated: 5/21/2025   |   Inventor(s): Andreas Spanias, Salil Naik, Leslie Miller, Kristen Jaskie, Glen Uehara
Keywords(s):  
Category(s): Physical Science, Artificial Intelligence/Machine Learning, Energy & Power, Wireless & Networking
Systems and Methods for Global Horizontal Irradiance Forecasting for Photovoltaic Systems
Background As more large-scale solar arrays (utility-scale) are connected to the energy grid, predicting solar irradiance (e.g., the amount of sunlight hitting the panels) becomes crucial for accurately forecasting the power of these photovoltaic (PV) arrays. This is because solar power generation is highly dependent on sunlight, which is variable...
Published: 2/26/2026   |   Updated: 5/13/2025   |   Inventor(s): Andreas Spanias, Sameeksha Katoch, David Ramirez, Pavan Turaga, Cihan Tepedelenlioglu
Keywords(s):  
Category(s): Physical Science, Microelectronics, Artificial Intelligence/Machine Learning, Advanced Materials/Nanotechnology, Energy & Power
Quantum Linear Prediction Using QFT
Quantum computing has the potential to significantly impact massive signal processing operations. Research is being done in exploring ways to harness its potential for signal processing applications. The use of quantum computing for signal processing holds great promise for improving the speed and accuracy in several signal processing applications...
Published: 2/26/2026   |   Updated: 8/7/2024   |   Inventor(s): Andreas Spanias, Aradhita Sharma, Glen Uehara
Keywords(s): PS-Computing and Information Technology, Quantum Computing, Signal Processing
Category(s): Computing & Information Technology, Physical Science
Quantum Autocorrelation Computation Using QFT
Quantum computing offers unprecedented computational power, enabling the solution of complex problems beyond the reach of classical computers. In signal processing, autocorrelation is used to analyze signals and extract valuable information, but traditional methods involve high computational complexity due to numerous multiplications and additions....
Published: 2/26/2026   |   Updated: 8/7/2024   |   Inventor(s): Aradhita Sharma, Glen Uehara, Andreas Spanias
Keywords(s): PS-Computing and Information Technology, Quantum Computing, Signal Processing
Category(s): Computing & Information Technology, Physical Science
Gaussian Process Modeling for Heterogeneous Functions
Many modern science and engineering applications, such as machine learning, hyperparameter optimization of neural networks, robotics, cyber-physical systems, etc., call for modeling techniques to model black-box functions. Gaussian Process (GP) modeling is a popular Bayesian non-parametric framework heavily employed to model expensive black-box functions...
Published: 2/26/2026   |   Updated: 5/13/2024   |   Inventor(s): Mohit Malu, Giulia Pedrielli, Gautam Dasarathy, Andreas Spanias
Keywords(s): Machine Learning, Modeling, PS-Computing and Information Technology
Category(s): Computing & Information Technology, Physical Science
Quantum Fourier Transform Tools for Signal Analysis-Synthesis and Compression
Quantum computing (QC) is a multidisciplinary field comprising aspects of computer science, physics, and mathematics that utilizes quantum mechanics to solve complex problems faster than on classical computers (e.g., QC promises to process data with estimated speeds exceeding 100 million times relative to classical computers). Signal processing is...
Published: 2/26/2026   |   Updated: 3/27/2024   |   Inventor(s): Andreas Spanias, Aradhita Sharma, Leslie Miller, Glen Uehara
Keywords(s): Algorithm Development, Circuits, PS-Computing and Information Technology, Quantum Computing, Signal Processing
Category(s): Computing & Information Technology, Physical Science
Adaptive Asymmetric Loss Function for Positive Unlabeled Learning
Unlike traditional semi-supervised learning, positive unlabeled learning requires only some labeled data from the positive class. All other data, both positive and negative, is unlabeled. The goal is the same, i.e., to construct a classification model that correctly labels unlabeled images and create a model to label future images. This problem is...
Published: 2/26/2026   |   Updated: 10/25/2023   |   Inventor(s): Kristen Jaskie, Nolan Vaughn, Vivek Sivaraman Narayanaswamy, Sahba Zaare, Joseph Marvin, Andreas Spanias
Keywords(s): Algorithm Development, PS-Computing and Information Technology
Category(s): Computing & Information Technology, Physical Science
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