Results 101 to 110 of about 1,531 (279)

Updating Wireless Signal Map with Bayesian Compressive Sensing [PDF]

, 2016
In a wireless system, a signal map shows the signal strength at different locations termed reference points (RPs). As access points (APs) and their transmission power may change over time, keeping an updated signal map is important for applications such ...
Chan, S. H.Gary, Bo Yang, He, Suining, Yang, Bo, S.-H. Gary Chan, Suining He   +5 more
core   +1 more source

3D Imaging Algorithm for Down-Looking MIMO Array SAR Based on Bayesian Compressive Sensing

International Journal of Antennas and Propagation, 2014
Down-looking MIMO array SAR can reconstruct 3D images of the observed area in the inferior of the platform of the SAR and has wide application prospects.
Xiaozhen Ren, Lina Chen, Jing Yang
doaj   +1 more source

Integrating Automated Electrochemistry and High‐Throughput Characterization with Machine Learning to Explore Si─Ge─Sn Thin‐Film Lithium Battery Anodes

Advanced Energy Materials, Volume 15, Issue 11, March 18, 2025.
A closed‐loop, data‐driven approach facilitates the exploration of high‐performance Si─Ge─Sn alloys as promising fast‐charging battery anodes. Autonomous electrochemical experimentation using a scanning droplet cell is combined with real‐time optimization to efficiently navigate composition space.
Alexey Sanin, Jackson K. Flowers, Tobias H. Piotrowiak, Frederic Felsen, Leon Merker, Alfred Ludwig, Dominic Bresser, Helge Sören Stein   +7 more
wiley   +1 more source

Assessing Mesoscale Heterogeneities in Hard Carbon Electrodes Through Deep Learning‐Assisted FIB‐SEM Characterization, Manufacturing and Electrochemical Modeling

Advanced Energy Materials, EarlyView.
A combination of discrete and finite element method models for the current collector deformation and electrochemical performance analysis, respectively. The models are calibrated and validated with electrochemical and imaging data of hard carbon electrodes. These electrodes were manufactured with different parameters (slurry solid contents of 35 and 40
Soorya Saravanan, Imelda Cardenas‐Sierra, Pei Sun, Hari Raj, Daniele Duranti, Mahnoor Nafees, Edoardo Rossi, Marco Sebastiani, Paul Vigneaux, Martin Petit, Maxime Moreaud, Francisco Fernandez, Alejandro A. Franco   +12 more
wiley   +1 more source

Sparse Bayesian Generative Modeling for Compressive Sensing

Advances in Neural Information Processing Systems 37
This work addresses the fundamental linear inverse problem in compressive sensing (CS) by introducing a new type of regularizing generative prior. Our proposed method utilizes ideas from classical dictionary-based CS and, in particular, sparse Bayesian learning (SBL), to integrate a strong regularization towards sparse solutions.
Benedikt Böck, Sadaf Syed, Wolfgang Utschick   +2 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

Regularized Bayesian Compressed Sensing In Ultrasound Imaging

, 2012
Publication in the conference proceedings of EUSIPCO, Bucharest, Romania ...
Nicolas Dobigeon, Adrian Basarab, Denis Kouame, Jean-Yves Tourneret   +3 more
openaire   +4 more sources

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   +2 more sources

A Kinetic–Energetic Bottleneck of Charge‐Transfer Injection Governs Energy Loss in Organic Solar Cells

Advanced Energy Materials, EarlyView.
Kinetic–energetic projection of time‐resolved photoluminescence reveals that charge‐transfer injection acts as a universal bottleneck in organic solar cells. A physics‐constrained Bayesian framework identifies an emergent effective CT injection rate governing the trade‐off between charge generation and nonradiative energy loss.
Rong Wang, Tobias Osterrieder, Atul Shukla, Christos L. Chochos, Vasilis G. Gregoriou, Yakun He, Hua Tang, Julian Matthias Haffner‐Schirmer, Karen Forberich, Ning Li, Thomas Heumueller, Frédéric Laquai, Jens Hauch, Safa Shoaee, Dieter Neher, Larry Lüer, Christoph J. Brabec   +16 more
wiley   +1 more source

Robust diagnostics for Bayesian compressive sensing with applications to structural health monitoring [PDF]

, 2011
In structural health monitoring (SHM) systems for civil structures, signal compression is often important to reduce the cost of data transfer and storage because of the large volumes of data generated from the monitoring system.
Yong Huang, Beck, James L., Stephen Wu, Huang, Yong, Li, Hui, James L. Beck, Hui Li, Wu, Stephen   +7 more
core   +1 more source