Results 111 to 120 of about 1,446,045 (274)

Bayesian compressive sensing for ultra-wideband channel models

2012 35th International Conference on Telecommunications and Signal Processing (TSP), 2012
Considering the sparse structure of ultra-wideband (UWB) channels, compressive sensing (CS) is suitable for UWB channel estimation. Among various implementations of CS, the inclusion of Bayesian framework has shown potential to improve signal recovery as statistical information related to signal parameters is considered.
Ozgor, Mehmet, Erküçük, Serhat, Erküçük, Serhat, Cirpan, Hakan Ali   +3 more
openaire   +3 more sources

Machine Learning Interatomic Potentials for Energy Materials: Architectures, Training Strategies, and Applications

Advanced Energy Materials, EarlyView.
Machine learning interatomic potentials bridge quantum accuracy and computational efficiency for materials discovery. Architectures from Gaussian process regression to equivariant graph neural networks, training strategies including active learning and foundation models, and applications in solid‐state electrolytes, batteries, electrocatalysts ...
In Kee Park, Saeed Pourasad, Jinhong Mun, Arunkumar Chitteth Rajan, Tae Hyeon Park, Anand Rohit, Mohammad Zafari, Tan‐Lien Pham, Eunjae Jung, Bonseung Koo, Miran Ha, Gi Beom Sim, Hyun Gyu Park, Won Woong Choi, Kwanghyeon Jo, Soohaeng Yoo Willow, David ChangMo Yang, Chang Woo Myung, Geunsik Lee, Kwang S. Kim   +19 more
wiley   +1 more source

Bayesian Exploration of Metal‐Organic Framework‐Derived Nanocomposites for High‐Performance Supercapacitors

Advanced Intelligent Discovery, EarlyView.
An AI‐assisted approach is introduced to decode synthesis–performance relationships in metal‐organic framework‐derived supercapacitor materials using Bayesian optimization and predictive modeling, streamlining the search for optimal energy storage properties.
David Gryc, Pritam Banerjee, Felipe Jacques Lima Duarte, Joerg R. Jinschek, Premkumar Murugesan, Babak Rezaei, Stephan Sylvest Keller, Roland A. Fischer, Mian Zahid Hussain   +8 more
wiley   +1 more source

Bayesian Learning-Assisted Compressive Sensing Improves Multi-Target Parameter Estimation in 5G-NR Inspired ISAC

IEEE Open Journal of Vehicular Technology
Integrated sensing and communication (ISAC) has emerged as a key technology for 6G communication systems, enabling both spectrum and hardware sharing between radar and communication systems.
Prabhanshu Yadav, Kunal Kumar Singh, Suraj Srivastava, Lajos Hanzo   +3 more
doaj   +1 more source

A Data-Adaptive Compressed Sensing Approach to Polarimetric SAR Tomography of Forested Areas [PDF]

, 2013
Super-resolution imaging via compressed sensing (CS)-based spectral estimators has been recently introduced to synthetic aperture radar (SAR) tomography.
Nannini, Matteo, Aguilera, Esteban, Reigber, Andreas   +2 more
core   +1 more source

Advances in Thermal Modeling and Simulation of Lithium‐Ion Batteries with Machine Learning Approaches

Advanced Intelligent Discovery, EarlyView.
Heat generation in lithium‐ion batteries affects performance, aging, and safety, requiring accurate thermal modeling. Traditional methods face efficiency and adaptability challenges. This article reviews machine learning‐based and hybrid modeling approaches, integrating data and physics to improve parameter estimation and temperature prediction ...
Qi Lin, Kang Fu, Junjie Ding, Kai Sun, Peng Tan   +4 more
wiley   +1 more source

Simultaneous Bayesian Compressive Sensing And Blind Deconvolution

, 2012
Publication in the conference proceedings of EUSIPCO, Bucharest, Romania ...
Leonidas Spinoulas, Bruno Amizic, Miguel Vega, Rafael Molina 0001, Aggelos K. Katsaggelos   +4 more
openaire   +3 more sources

Compressed sensing applied to modeshapes reconstruction [PDF]

, 2011
Modal analysis classicaly used signals that respect the Shannon/Nyquist theory. Compressive sampling (or Compressed Sampling, CS) is a recent development in digital signal processing that offers the potential of high resolution capture of physical ...
Dimitri Bettebghor, Joseph Morlier, Morlier, Joseph, Bettebghor, Dimitri   +3 more
core   +1 more source

Self‐Driving Laboratory Optimizes the Lower Critical Solution Temperature of Thermoresponsive Polymers

Advanced Intelligent Discovery, EarlyView.
A low‐cost, self‐driving laboratory is developed to democratize autonomous materials discovery. Using this "frugal twin" hardware architecture with Bayesian optimization, the platform rapidly converges to target lower critical solution temperature (LCST) values while self‐correcting from off‐target experiments, demonstrating an accessible route to data‐
Guoyue Xu, Renzheng Zhang, Tengfei Luo
wiley   +1 more source

Compressed Sensing with nonlinear observations and related non-linear optimisation problems

, 2013
Non-convex constraints have recently proven a valuable tool in many optimisation problems. In particular sparsity constraints have had a significant impact on sampling theory, where they are used in Compressed Sensing and allow structured signals to be ...
Blumensath, Thomas
core   +1 more source