Spatial–Temporal Temperature Forecasting Using Deep-Neural-Network-Based Domain Adaptation

Autor: Vu Tran, François Septier, Daisuke Murakami, Tomoko Matsui

Publikováno v: Atmosphere, Vol 15, Iss 1, p 90 (2024)

Accurate temperature forecasting is critical for various sectors, yet traditional methods struggle with complex atmospheric dynamics. Deep neural networks (DNNs), especially transformer-based DNNs, offer potential advantages, but face challenges with

Externí odkaz: https://doaj.org/article/6d74a1b8378e4cc4b45624cfbdfbd1ef

Impulsive noise modeling and robust receiver design

Autor: Laurent Clavier, Gareth W. Peters, François Septier, Ido Nevat

Publikováno v: EURASIP Journal on Wireless Communications and Networking, Vol 2021, Iss 1, Pp 1-30 (2021)

Abstract Interference is an important limitation in many communication systems. It has been shown in many situations that the popular Gaussian approximation is not adequate and interference exhibits an impulsive behavior. This paper surveys the diffe

Externí odkaz: https://doaj.org/article/1f06b615b50949f09218fe83de0bb08d

Pilot-Aided Sequential Monte Carlo Estimation of Phase Distortions and Transmitted Symbols in Multicarrier Systems

Autor: François Septier, Yves Delignon, Atika Menhaj-Rivenq, Christelle Garnier

Publikováno v: Journal of Electrical and Computer Engineering, Vol 2010 (2010)

We address the challenging problem of the joint estimation of transmitted symbols and phase distortions in standardized multicarrier systems, including pilot or virtual subcarriers. These subcarriers create time correlation on the useful transmitted

Externí odkaz: https://doaj.org/article/98e3bc7de80e494c8dda187bcfc010f9

State space partitioning based on constrained spectral clustering for block particle filtering

Autor: Rui Min, Christelle Garnier, François Septier, John Klein

Publikováno v: Signal Processing
Signal Processing, 2022, 201, pp.12. ⟨10.1016/j.sigpro.2022.108727⟩

The particle filter (PF) is a powerful inference tool widely used to estimate the filtering distribution in non-linear and/or non-Gaussian problems. To overcome the curse of dimensionality of PF, the block PF (BPF) inserts a blocking step to partitio

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::81ca038f71bbf140256c3530eaa32e17
https://doi.org/10.1016/j.sigpro.2022.108727

Block Kalman Filter: an Asymptotic Block Particle Filter in the Linear Gaussian Case

Autor: Christelle Garnier, John Klein, Rui Min, François Septier

Publikováno v: IEEE International Conference on Acoustics, Speech, and Signal Processing
IEEE International Conference on Acoustics, Speech, and Signal Processing, Jun 2021, Toronto, Canada
ICASSP

The curse of dimensionality in particle filtering can be mitigated by approximating the posterior distribution by a product of marginals on disjoint low dimensional subspaces of the state space. One such approach is known as the block particle filter

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c3dd4ce3880e8354e1f14ba95dc97548
https://hal.science/hal-03138584