Results 221 to 230 of about 2,877,467 (378)

Optimization of ISAC Trade-Off via Covariance Matrix Allocation in Multi-User Systems. [PDF]

Entropy (Basel)
Prado-Alvarez D, Calabuig D, Inca S, Monserrat JF.   +3 more
europepmc   +1 more source

Monogenic Scale Space Based Region Covariance Matrix Descriptor for Face Recognition

, 2012
M. Sharmila Kumari
openalex   +1 more source

Riemannian Geometry for the Classification of Brain States with Intracortical Brain Recordings

Advanced Intelligent Systems, EarlyView.
Geometric machine learning is applied to decode brain states from invasive intracortical neural recordings, extending Riemannian methods to the invasive regime where data is scarcer and less stationary. A Minimum Distance to Mean classifier on covariance manifolds uses geodesic distances to outperform convolutional neural networks while reducing ...
Arnau Marin‐Llobet, Sergio Sánchez‐Manso, Arnau Manasanch, Lluc Tresserras, Xinhe Zhang, Yining Hua, Hao Zhao, Melody Torao‐Angosto, Maria V Sanchez‐Vives, Leonardo Dalla Porta   +9 more
wiley   +1 more source

A Permutation Test for High-Dimensional Covariance Matrix

, 2021
Shanshan Li
openalex   +1 more source

Polymerase Chain Reaction. Perturbation Theory and Machine Learning Artificial Intelligence‐Experimental Microbiome Analysis: Applications to Ancient DNA and Tree Soil Metagenomics Cases of Study

Advanced Intelligent Systems, EarlyView.
The polymerase chain reaction (PCR).Perturbation Theory and Machine Learning framework integrates perturbation theory and machine learning to classify genetic sequences, distinguishing ancient DNA from modern controls and predicting tree health from soil metagenomic data.
Jose L. Rodriguez, Estefania Ascencio, Jose M. Ageitos, Lucia Feijoo, Shan He, Amirreza Daghighi, Gerardo M. Casanola‐Martin, Cristian R. Munteanu, Santiago Rodriguez Yañez, Alejandro Pazos, Harbil Bediaga, Brenda Durand Ordiales, Raquel Esteban, Ana‐Maria Heres, Jorge Curiel Yuste, Lur Epelde, Bakhtiyor Rasulev, Tomas Gonzalez, Sonia Arrasate, Humberto González‐Diaz   +19 more
wiley   +1 more source

Robust adaptive beamforming based on covariance matrix reconstruction with annular uncertainty set constraints. [PDF]

PLoS One
Xing G, Yao Z, Wei H, Hu Y.
europepmc   +1 more source

When do improved covariance matrix estimators enhance portfolio optimization? An empirical comparative study of nine estimators [PDF]

, 2011
Ester Pantaleo, Michele Tumminello, Fabrizio Lillo, Rosario N. Mantegna   +3 more
openalex   +1 more source

Nonlinear Shrinkage of the Covariance Matrix for Portfolio Selection: Markowitz Meets Goldilocks

, 2017
Olivier Ledoit, Michael Wolf
semanticscholar   +1 more source

Synthetic Electrocardiogram Spectrogram Generation Using Generative Adversarial Network‐Based Models: A Comparative Study

Advanced Intelligent Systems, EarlyView.
Cardiovascular diseases are leading death causes; electrocardiogram (ECG) analysis is slow, motivating machine learning and deep learning. This study compares deep convolutional generative adversarial network, conditional GAN, and Wasserstein GAN with gradient penalty (WGAN‐GP) for synthetic ECG spectrograms; Fréchet Inception Distance (FID) and ...
Giovanny Barbosa‐Casanova, Norelli Schettini, Winston Percybrooks, Begoña García‐Zapirain   +3 more
wiley   +1 more source

Degeneracy Sensing Light Detection and Ranging‐Inertial Simultaneous Localization and Mapping with Dual‐Layer Resistant Odometry and Scan‐Context Loop‐Closure Detection Backend in Diverse Environments

Advanced Intelligent Systems, EarlyView.
This paper presents a degeneracy‐aware light detection and ranging (LiDAR)‐inertial framework that enhances LiDAR simultaneous localization and mapping performance in challenging environments. The proposed system integrates a dual‐layer robust odometry frontend with a Scan‐Context‐based loop‐closure detection backend.
Haoming Yang, Liye Zhao, Xukai Ding, Wenjun Li, Kehao Qi   +4 more
wiley   +1 more source