Zobrazeno 1 - 10
of 303
pro vyhledávání: '"Richard, Cédric"'
Personal sound zone (PSZ) systems, which aim to create listening (bright) and silent (dark) zones in neighboring regions of space, are often based on time-varying acoustics. Conventional adaptive-based methods for handling PSZ tasks suffer from the c
Externí odkaz:
http://arxiv.org/abs/2311.07729
Autor:
Yuan, Siyuan, Ende, Martijn van den, Liu, Jingxiao, Noh, Hae Young, Clapp, Robert, Richard, Cédric, Biondi, Biondo
Distributed Acoustic Sensing (DAS) that transforms city-wide fiber-optic cables into a large-scale strain sensing array has shown the potential to revolutionize urban traffic monitoring by providing a fine-grained, scalable, and low-maintenance monit
Externí odkaz:
http://arxiv.org/abs/2212.03936
Deconvolution is a widely used strategy to mitigate the blurring and noisy degradation of hyperspectral images~(HSI) generated by the acquisition devices. This issue is usually addressed by solving an ill-posed inverse problem. While investigating pr
Externí odkaz:
http://arxiv.org/abs/2211.15307
Hyperspectral and multispectral image fusion allows us to overcome the hardware limitations of hyperspectral imaging systems inherent to their lower spatial resolution. Nevertheless, existing algorithms usually fail to consider realistic image acquis
Externí odkaz:
http://arxiv.org/abs/2208.11376
We propose the adaptive random Fourier features Gaussian kernel LMS (ARFF-GKLMS). Like most kernel adaptive filters based on stochastic gradient descent, this algorithm uses a preset number of random Fourier features to save computation cost. However
Externí odkaz:
http://arxiv.org/abs/2207.07236
Spectral unmixing is one of the most important quantitative analysis tasks in hyperspectral data processing. Conventional physics-based models are characterized by clear interpretation. However they may not be suitable for analyzing scenes with unkno
Externí odkaz:
http://arxiv.org/abs/2206.05508
To overcome inherent hardware limitations of hyperspectral imaging systems with respect to their spatial resolution, fusion-based hyperspectral image (HSI) super-resolution is attracting increasing attention. This technique aims to fuse a low-resolut
Externí odkaz:
http://arxiv.org/abs/2201.09851
The recursive least-squares algorithm with $\ell_1$-norm regularization ($\ell_1$-RLS) exhibits excellent performance in terms of convergence rate and steady-state error in identification of sparse systems. Nevertheless few works have studied its sto
Externí odkaz:
http://arxiv.org/abs/2109.06749
In many areas such as computational biology, finance or social sciences, knowledge of an underlying graph explaining the interactions between agents is of paramount importance but still challenging. Considering that these interactions may be based on
Externí odkaz:
http://arxiv.org/abs/2104.13687
Multitemporal spectral unmixing (SU) is a powerful tool to process hyperspectral image (HI) sequences due to its ability to reveal the evolution of materials over time and space in a scene. However, significant spectral variability is often observed
Externí odkaz:
http://arxiv.org/abs/2104.02837