Loading [MathJax]/extensions/MathZoom.js
Mauro Dalla Mura - IEEE Xplore Author Profile
Subscribe
ADVANCED SEARCH

Author details

< Back

Mauro Dalla Mura

Also published under: Mauro Dalla-Mura, M. Dalla Mura

Publications
93
Citations
5,761
Publications by Year
20082025
Co-Authors:
Show All Co-Authors (126)
Image of author Mauro Dalla Mura

Mauro Dalla Mura

Also published under: Mauro Dalla-Mura, M. Dalla Mura

Affiliation

CNRS
Grenoble INP
GIPSA-Lab, Université Grenoble Alpes, Grenoble, France
Institut Universitaire de France (IUF), Paris, France

Publication Topics

Biography

Mauro Dalla Mura (S#x0027;08–M#x0027;11) received the B.E. (laurea) and M.E. (laurea specialistica) degrees in telecommunication engineering from the University of Trento, Trento, Italy, in 2005 and 2007, respectively, and the joint Ph.D. degree in information and communication technologies (telecommunications area) and in electrical and computer engineering from the University of Trento and the University of Iceland, Reykjavik, Iceland, respectively, in 2011.,In 2011, he was a Research Fellow with the Fondazione Bruno Kessler, Trento, Italy, conducting research on computer vision. He is currently an Assistant Professor with the Grenoble Institute of Technology (Grenoble INP), Grenoble, France. He is conducting his research at the Grenoble ...
Publications
93
Citations
5,761
Publications by Year
20082025
Co-Authors:
Show All Co-Authors (126)
Author's Published Works

Showing 1-25 of 93 results

  • Sort

Filter Results

Show

Sort By

Results

Multiple-beam Interference Spectroscopy: Instrument Analysis and Spectrum Reconstruction

Mohamad Jouni;Daniele Picone;Mauro Dalla Mura

IEEE Transactions on Instrumentation and Measurement
Year: 2025 | Early Access Article |
Hyperspectral imaging systems based on multiple-beam interference (MBI), such as Fabry-Perot interferometry, are attracting interest due to their compact design, high throughput, and fine resolution. Unlike dispersive devices, which measure spectra directly, the desired spectra in interferometric systems are reconstructed from measured interferograms. Although the response of MBI devices is modele...Show More

Multiple-beam Interference Spectroscopy: Instrument Analysis and Spectrum Reconstruction

Mohamad Jouni;Daniele Picone;Mauro Dalla Mura

Year: 2025 | Early Access Article |

VOGTNet: Variational Optimization-Guided Two-Stage Network for Multispectral and Panchromatic Image Fusion

Peng Wang;Zhongchen He;Bo Huang;Mauro Dalla Mura;Henry Leung;Jocelyn Chanussot

IEEE Transactions on Neural Networks and Learning Systems
Year: 2025 | Volume: 36, Issue: 5 | Journal Article |
Cited by: Papers (18)
Multispectral image (MS) and panchromatic image (PAN) fusion, which is also named as multispectral pansharpening, aims to obtain MS with high spatial resolution and high spectral resolution. However, due to the usual neglect of noise and blur generated in the imaging and transmission phases of data during training, many deep learning (DL) pansharpening methods fail to perform on the dataset contai...Show More

VOGTNet: Variational Optimization-Guided Two-Stage Network for Multispectral and Panchromatic Image Fusion

Peng Wang;Zhongchen He;Bo Huang;Mauro Dalla Mura;Henry Leung;Jocelyn Chanussot

Year: 2025 | Volume: 36, Issue: 5 | Journal Article |

Pattern-Invariant Unrolling for Robust Demosaicking

Matthieu Muller;Daniele Picone;Mauro Dalla Mura;Magnus O. Ulfarsson

2024 32nd European Signal Processing Conference (EUSIPCO)
Year: 2024 | Conference Paper |
To acquire color images, most commercial cameras rely on color filter arrays (CFAs), which are a pattern of color filters overlaid over the sensor's focal plane. Demosaicking describes the processing techniques to reconstruct a full color image for all pixels on the focal plane array. Most demosaicking methods are tailored for a specific CFA, and tend to work poorly for others. In this work we pre...Show More

Pattern-Invariant Unrolling for Robust Demosaicking

Matthieu Muller;Daniele Picone;Mauro Dalla Mura;Magnus O. Ulfarsson

Year: 2024 | Conference Paper |

Koopman Ensembles for Probabilistic Time Series Forecasting

Anthony Frion;Lucas Drumetz;Guillaume Tochon;Mauro Dalla Mura;Abdeldjalil Aissa El Bey

2024 32nd European Signal Processing Conference (EUSIPCO)
Year: 2024 | Conference Paper |
In the context of an increasing popularity of data-driven models to represent dynamical systems, many machine learning-based implementations of the Koopman operator have recently been proposed. However, the vast majority of those works are limited to deterministic predictions, while the knowledge of uncertainty is critical in fields like meteorology and climatology. In this work, we investigate th...Show More

Koopman Ensembles for Probabilistic Time Series Forecasting

Anthony Frion;Lucas Drumetz;Guillaume Tochon;Mauro Dalla Mura;Abdeldjalil Aissa El Bey

Year: 2024 | Conference Paper |

Denoising of Geodetic Time Series Using Spatiotemporal Graph Neural Networks: Application to Slow Slip Event Extraction

Giuseppe Costantino;Sophie Giffard-Roisin;Mauro Dalla Mura;Anne Socquet

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Year: 2024 | Volume: 17 | Journal Article |
Cited by: Papers (1)
Geospatial data have been transformative for the monitoring of the Earth, yet, as in the case of (geo) physical monitoring, the measurements can have variable spatial and temporal sampling and may be associated with a significant level of perturbations degrading the signal quality. Denoising geospatial data is, therefore, essential, yet often challenging because the observations may comprise noise...Show More

Denoising of Geodetic Time Series Using Spatiotemporal Graph Neural Networks: Application to Slow Slip Event Extraction

Giuseppe Costantino;Sophie Giffard-Roisin;Mauro Dalla Mura;Anne Socquet

Year: 2024 | Volume: 17 | Journal Article |

MSF: A Multi-Scale Fusion Generative Adversarial Network for SAR-to-Optical Image Translation

YongKang Chen;Zuguo Zhu;Yan Huang;Peng Wang;Bo Huang;Mauro Dalla Mura

IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium
Year: 2024 | Conference Paper |
Cited by: Papers (1)
This paper proposes an image translation method based on multi-scale fusion GAN (MFS) network. In MFS network, there are two modules: optical image generation sub-network (OGS), optical image generation sub-network (OGS) and SAR image regressive sub-network (SRS). Firstly, we design a multi-scale fusion generator (MFG) to perform SAR-to-optical image translation and SAR image regression in OGS and...Show More

MSF: A Multi-Scale Fusion Generative Adversarial Network for SAR-to-Optical Image Translation

YongKang Chen;Zuguo Zhu;Yan Huang;Peng Wang;Bo Huang;Mauro Dalla Mura

Year: 2024 | Conference Paper |

Advanced graph deep learning for High-dimensional image analysis: challenges and opportunities

Refka Hanachi;Akrem Sellami;Imed Riadh Farah;Mauro Dalla Mura

2024 IEEE 7th International Conference on Advanced Technologies, Signal and Image Processing (ATSIP)
Year: 2024 | Conference Paper |
High-dimensional image analysis, such as Hyperspectral Imaging (HSI) data, poses unique challenges due to their high dimensionality and non-Euclidean structures, making their analysis and classification complex. In this study, we explore the use of both graph deep learning (GDL) and multi-view graph representation learning for HSI classification. Furthermore, we present our proposed approach of mu...Show More

Advanced graph deep learning for High-dimensional image analysis: challenges and opportunities

Refka Hanachi;Akrem Sellami;Imed Riadh Farah;Mauro Dalla Mura

Year: 2024 | Volume: 1 | Conference Paper |

Geometric Deep Learning Techniques for Analyzing Brain 3D Meshes

Mariem Ayad;Akrem Sellami;Imed Riadh Farah;Mauro Dalla Mura

2024 IEEE 7th International Conference on Advanced Technologies, Signal and Image Processing (ATSIP)
Year: 2024 | Conference Paper |
Geometric deep learning (GDL) has emerged as a powerful paradigm for analyzing complex data represented in non-Euclidean domains. In the field of neuroimaging, 3D meshes have become a prevalent representation for capturing the intricate structures of the brain. This survey paper provides a comprehensive overview of the recent advancements and techniques in using GDL to analyze brain 3D meshes. We ...Show More

Geometric Deep Learning Techniques for Analyzing Brain 3D Meshes

Mariem Ayad;Akrem Sellami;Imed Riadh Farah;Mauro Dalla Mura

Year: 2024 | Volume: 1 | Conference Paper |

Neural Koopman Prior for Data Assimilation

Anthony Frion;Lucas Drumetz;Mauro Dalla Mura;Guillaume Tochon;Abdeldjalil Aïssa El Bey

IEEE Transactions on Signal Processing
Year: 2024 | Volume: 72 | Journal Article |
Cited by: Papers (2)
With the increasing availability of large scale datasets, computational power and tools like automatic differentiation and expressive neural network architectures, sequential data are now often treated in a data-driven way, with a dynamical model trained from the observation data. While neural networks are often seen as uninterpretable black-box architectures, they can still benefit from physical ...Show More

Neural Koopman Prior for Data Assimilation

Anthony Frion;Lucas Drumetz;Mauro Dalla Mura;Guillaume Tochon;Abdeldjalil Aïssa El Bey

Year: 2024 | Volume: 72 | Journal Article |

Potential of an Embedded Hyperspectral Compressive Imaging System for Remote Sensing Applications

Olivier Lim;Stéphane Mancini;Mauro Dalla Mura

IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium
Year: 2023 | Conference Paper |
Cited by: Papers (1)
The utilization of hyperspectral imaging in remote sensing has seen an increasing trend, as it enables to capture a greater amount of information. In this context, emerging snapshot sensors based on compressed sensing have been employed for various remote sensing applications. This work presents a prospective study by proposing a method to evaluate the performances we can expect when reconstructin...Show More

Potential of an Embedded Hyperspectral Compressive Imaging System for Remote Sensing Applications

Olivier Lim;Stéphane Mancini;Mauro Dalla Mura

Year: 2023 | Conference Paper |

Joint Demosaicing and Fusion of Multiresolution Coded Acquisitions: A Unified Image Formation and Reconstruction Method

Daniele Picone;Mauro Dalla Mura;Laurent Condat

IEEE Transactions on Computational Imaging
Year: 2023 | Volume: 9 | Journal Article |
Cited by: Papers (4)
Novel optical imaging devices allow for hybrid acquisition modalities such as compressed acquisitions with locally different spatial and spectral resolutions captured by a single focal plane array. In this work, we propose to model the imaging camera system for a multiresolution coded acquisition (MRCA) in a unified framework, which includes conventional devices such as those based on spectral/col...Show More

Joint Demosaicing and Fusion of Multiresolution Coded Acquisitions: A Unified Image Formation and Reconstruction Method

Daniele Picone;Mauro Dalla Mura;Laurent Condat

Year: 2023 | Volume: 9 | Journal Article |

Deep SURE for Unsupervised Remote Sensing Image Fusion

Han V. Nguyen;Magnus O. Ulfarsson;Johannes R. Sveinsson;Mauro Dalla Mura

IEEE Transactions on Geoscience and Remote Sensing
Year: 2022 | Volume: 60 | Journal Article |
Cited by: Papers (17)
Image fusion is utilized in remote sensing (RS) due to the limitation of the imaging sensor and the high cost of simultaneously acquiring high spatial and spectral resolution images. Optical RS imaging systems usually provide images of high spatial resolution but low spectral resolution and vice versa. Therefore, fusing those images to obtain a fused image having both high spectral and spatial res...Show More

Deep SURE for Unsupervised Remote Sensing Image Fusion

Han V. Nguyen;Magnus O. Ulfarsson;Johannes R. Sveinsson;Mauro Dalla Mura

Year: 2022 | Volume: 60 | Journal Article |

A Benchmarking Protocol for Pansharpening: Dataset, Preprocessing, and Quality Assessment

Gemine Vivone;Mauro Dalla Mura;Andrea Garzelli;Fabio Pacifici

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Year: 2021 | Volume: 14 | Journal Article |
Cited by: Papers (91)
Comparative evaluation is a requirement for reproducible science and objective assessment of new algorithms. Reproducible research in the field of pansharpening of very high resolution images is a difficult task due to the lack of openly available reference datasets and protocols. The contribution of this article is threefold, and it defines a benchmarking framework to evaluate pansharpening algor...Show More

A Benchmarking Protocol for Pansharpening: Dataset, Preprocessing, and Quality Assessment

Gemine Vivone;Mauro Dalla Mura;Andrea Garzelli;Fabio Pacifici

Year: 2021 | Volume: 14 | Journal Article |

Tensor-Based Learning Framework for Automatic Multichannel Volcano-Seismic Classification

Antonio Augusto Teixeira Peixoto;Carlos Alexandre Rolim Fernandes;Pablo Eduardo Espinoza Lara;Adolfo Inza;Jêrome I. Mars;Jean-Philippe Métaxian;Mauro Dalla Mura;Marielle Malfante

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Year: 2021 | Volume: 14 | Journal Article |
Cited by: Papers (5)
This article proposes a supervised tensor-based learning framework for classifying volcano-seismic events from signals recorded at the Ubinas volcano, in Peru, during a period of great activity in 2009. The proposed method is fully tensorial, as it integrates the three main steps of the automatic classification system (feature extraction, dimensionality reduction, and classifier) in a general mult...Show More

Tensor-Based Learning Framework for Automatic Multichannel Volcano-Seismic Classification

Antonio Augusto Teixeira Peixoto;Carlos Alexandre Rolim Fernandes;Pablo Eduardo Espinoza Lara;Adolfo Inza;Jêrome I. Mars;Jean-Philippe Métaxian;Mauro Dalla Mura;Marielle Malfante

Year: 2021 | Volume: 14 | Journal Article |

A New Benchmark Based on Recent Advances in Multispectral Pansharpening: Revisiting Pansharpening With Classical and Emerging Pansharpening Methods

Gemine Vivone;Mauro Dalla Mura;Andrea Garzelli;Rocco Restaino;Giuseppe Scarpa;Magnus O. Ulfarsson;Luciano Alparone;Jocelyn Chanussot

IEEE Geoscience and Remote Sensing Magazine
Year: 2021 | Volume: 9, Issue: 1 | Magazine Article |
Cited by: Papers (233)
Pansharpening refers to the fusion of a multispectral (MS) image and panchromatic (PAN) data aimed at generating an outcome with the same spatial resolution of the PAN data and the spectral resolution of the MS image. In the last 30 years, several approaches to deal with this issue have been proposed. However, the reproducibility of these methods is often limited, making the comparison with the st...Show More

A New Benchmark Based on Recent Advances in Multispectral Pansharpening: Revisiting Pansharpening With Classical and Emerging Pansharpening Methods

Gemine Vivone;Mauro Dalla Mura;Andrea Garzelli;Rocco Restaino;Giuseppe Scarpa;Magnus O. Ulfarsson;Luciano Alparone;Jocelyn Chanussot

Year: 2021 | Volume: 9, Issue: 1 | Magazine Article |

Automatic Multichannel Volcano-Seismic Classification Using Machine Learning and EMD

Pablo Eduardo Espinoza Lara;Carlos Alexandre Rolim Fernandes;Adolfo Inza;Jérôme I. Mars;Jean-Philippe Métaxian;Mauro Dalla Mura;Marielle Malfante

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Year: 2020 | Volume: 13 | Journal Article |
Cited by: Papers (19)
This article proposes the design of an automatic classifier using the empirical mode decomposition (EMD) along with machine learning techniques for identifying the five most important types of events of the Ubinas volcano, the most active volcano in Peru. The proposed method uses attributes from temporal, spectral, and cepstral domains, extracted from the EMD of the signals, as well as a set of pr...Show More

Automatic Multichannel Volcano-Seismic Classification Using Machine Learning and EMD

Pablo Eduardo Espinoza Lara;Carlos Alexandre Rolim Fernandes;Adolfo Inza;Jérôme I. Mars;Jean-Philippe Métaxian;Mauro Dalla Mura;Marielle Malfante

Year: 2020 | Volume: 13 | Journal Article |

Superresolution Land Cover Mapping Based on Pixel-, Subpixel-, and Superpixel-Scale Spatial Dependence With Pansharpening Technique

Peng Wang;Lei Zhang;Gong Zhang;Hui Bi;Mauro Dalla Mura;Jocelyn Chanussot

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Year: 2019 | Volume: 12, Issue: 10 | Journal Article |
Cited by: Papers (16)
In this article, a novel superresolution mapping (SRM) method based on pixel-, subpixel-, and superpixel-scale spatial dependence (PSSSD) with pansharpening technique is proposed. First, an original coarse resolution remote sensing image and a high-resolution panchromatic image are fused by the pansharpening technique to produce a pansharpened result. A segmentation image with numerous superpixels...Show More

Superresolution Land Cover Mapping Based on Pixel-, Subpixel-, and Superpixel-Scale Spatial Dependence With Pansharpening Technique

Peng Wang;Lei Zhang;Gong Zhang;Hui Bi;Mauro Dalla Mura;Jocelyn Chanussot

Year: 2019 | Volume: 12, Issue: 10 | Journal Article |

Dynamic Multicontext Segmentation of Remote Sensing Images Based on Convolutional Networks

Keiller Nogueira;Mauro Dalla Mura;Jocelyn Chanussot;William Robson Schwartz;Jefersson Alex dos Santos

IEEE Transactions on Geoscience and Remote Sensing
Year: 2019 | Volume: 57, Issue: 10 | Journal Article |
Cited by: Papers (119)
Semantic segmentation requires methods capable of learning high-level features while dealing with large volume of data. Toward such goal, convolutional networks can learn specific and adaptable features based on the data. However, these networks are not capable of processing a whole remote sensing image, given its huge size. To overcome such limitation, the image is processed using fixed size patc...Show More

Dynamic Multicontext Segmentation of Remote Sensing Images Based on Convolutional Networks

Keiller Nogueira;Mauro Dalla Mura;Jocelyn Chanussot;William Robson Schwartz;Jefersson Alex dos Santos

Year: 2019 | Volume: 57, Issue: 10 | Journal Article |

Sentinel-2 Sharpening Using a Reduced-Rank Method

Magnus O. Ulfarsson;Frosti Palsson;Mauro Dalla Mura;Johannes R. Sveinsson

IEEE Transactions on Geoscience and Remote Sensing
Year: 2019 | Volume: 57, Issue: 9 | Journal Article |
Cited by: Papers (43)
Recently, the Sentinel-2 (S2) satellite constellation was deployed for mapping and monitoring the Earth environment. Images acquired by the sensors mounted on the S2 platforms have three levels of spatial resolution: 10, 20, and 60 m. In many remote sensing applications, the availability of images at the highest spatial resolution (i.e., 10 m for S2) is often desirable. This can be achieved by gen...Show More

Sentinel-2 Sharpening Using a Reduced-Rank Method

Magnus O. Ulfarsson;Frosti Palsson;Mauro Dalla Mura;Johannes R. Sveinsson

Year: 2019 | Volume: 57, Issue: 9 | Journal Article |

Soft-Then-Hard Super-Resolution Mapping Based on Pansharpening Technique for Remote Sensing Image

Peng Wang;Mauro Dalla Mura;Jocelyn Chanussot;Gong Zhang

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Year: 2019 | Volume: 12, Issue: 1 | Journal Article |
Cited by: Papers (18)
Super-resolution mapping (SRM) technique can explore the spatial distribution information of land cover classes in mixed pixels for multispectral image (MSI) or hyspectral image (HSI). Soft-then-hard super-resolution mapping (STHSRM) is an important type of SRM technique. STHSRM first utilizes the subpixel sharpening to produce the high-resolution fractional images with the soft attribute values f...Show More

Soft-Then-Hard Super-Resolution Mapping Based on Pansharpening Technique for Remote Sensing Image

Peng Wang;Mauro Dalla Mura;Jocelyn Chanussot;Gong Zhang

Year: 2019 | Volume: 12, Issue: 1 | Journal Article |

Pansharpening Based on Deconvolution for Multiband Filter Estimation

Gemine Vivone;Paolo Addesso;Rocco Restaino;Mauro Dalla Mura;Jocelyn Chanussot

IEEE Transactions on Geoscience and Remote Sensing
Year: 2019 | Volume: 57, Issue: 1 | Journal Article |
Cited by: Papers (50)
The combination of a multispectral (MS) image and a panchromatic (PAN) image, the so-called pansharpening, allows to produce very appealing images that are useful both for visual interpretation and for feature extraction. The state-of-the-art multiresolution analysis pansharpening algorithms are based on the extraction of spatial details from the PAN image through image filters matched with the MS...Show More

Pansharpening Based on Deconvolution for Multiband Filter Estimation

Gemine Vivone;Paolo Addesso;Rocco Restaino;Mauro Dalla Mura;Jocelyn Chanussot

Year: 2019 | Volume: 57, Issue: 1 | Journal Article |

Use of Deep Features for the Automatic Classification of Fish Sounds

Marielle Malfante;Omar Mohammed;Cédric Gervaise;Mauro Dalla Mura;Jérôme I. Mars

2018 OCEANS - MTS/IEEE Kobe Techno-Oceans (OTO)
Year: 2018 | Conference Paper |
Cited by: Papers (7)
The work presented in this paper focuses on the environmental monitoring of underwater areas using acoustic signals. In particular, we propose to compare the effectiveness of various feature sets used to represent the underwater acoustic data for the automatic processing of fish sounds We focus on the detection and classification tasks. Specifically, we compare the use of features issued from sign...Show More

Use of Deep Features for the Automatic Classification of Fish Sounds

Marielle Malfante;Omar Mohammed;Cédric Gervaise;Mauro Dalla Mura;Jérôme I. Mars

Year: 2018 | Conference Paper |

A Low-Rank Method for Sentinel-2 Sharpening Using Cyclic Descent

M. O. Ulfarsson;M. Dalla Mura

IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium
Year: 2018 | Conference Paper |
Cited by: Papers (5)
Multiresolution optical remote sensing systems often have a spatial resolution that varies between bands. An example is the Sentinel-2 (S2) constellation which has three levels of spatial resolution 10m, 20m, and 60m. Recently, researchers have exploited the spectral/spatial correlation inherent in multispectral data to sharpen the lower resolution S2 bands. In this paper, we propose a low-rank me...Show More

A Low-Rank Method for Sentinel-2 Sharpening Using Cyclic Descent

M. O. Ulfarsson;M. Dalla Mura

Year: 2018 | Conference Paper |

Image Fusion and Reconstruction of Compressed Data: A Joint Approach

Daniele Picone;Laurent Condat;Florian Cotte;Mauro Dalla Mura

2018 25th IEEE International Conference on Image Processing (ICIP)
Year: 2018 | Conference Paper |
Cited by: Papers (2)
In the context of data fusion, pansharpening refers to the combination of a panchromatic (PAN) and a multispectral (MS) image, aimed at generating an image that features both the high spatial resolution of the former and high spectral diversity of the latter. In this work we present a model to jointly solve the problem of data fusion and reconstruction of a compressed image; the latter is envision...Show More

Image Fusion and Reconstruction of Compressed Data: A Joint Approach

Daniele Picone;Laurent Condat;Florian Cotte;Mauro Dalla Mura

Year: 2018 | Conference Paper |

A Variational Pansharpening Approach Based on Reproducible Kernel Hilbert Space and Heaviside Function

Liang-Jian Deng;Gemine Vivone;Weihong Guo;Mauro Dalla Mura;Jocelyn Chanussot

IEEE Transactions on Image Processing
Year: 2018 | Volume: 27, Issue: 9 | Journal Article |
Cited by: Papers (74)
Pansharpening is an important application in remote sensing image processing. It can increase the spatial-resolution of a multispectral image by fusing it with a high spatial-resolution panchromatic image in the same scene, which brings great favor for subsequent processing such as recognition, detection, etc. In this paper, we propose a continuous modeling and sparse optimization based method for...Show More

A Variational Pansharpening Approach Based on Reproducible Kernel Hilbert Space and Heaviside Function

Liang-Jian Deng;Gemine Vivone;Weihong Guo;Mauro Dalla Mura;Jocelyn Chanussot

Year: 2018 | Volume: 27, Issue: 9 | Journal Article |

IEEE Account

Purchase Details

Profile Information

Need Help?

A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.
© Copyright 2025 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.