Angle-of-Arrival Estimation in Formation Flying Satellites: Concept and Demonstration

Autor: Alexandru Mihai Crisan, Alexandru Martian, Remus Cacoveanu, Daniela Coltuc

Publikováno v: IEEE Access, Vol 7, Pp 114116-114130 (2019)

One of the challenges of future space missions is the deployment of instruments distributed across multiple satellites that fly in an autonomous formation. This type of mission requires that the spacecraft communicate using inter-satellite links (ISL

Externí odkaz: https://doaj.org/article/9ff16055370c4f368167af2dae56ce53

On the Use of Normalized Compression Distances for Image Similarity Detection

Autor: Dinu Coltuc, Mihai Datcu, Daniela Coltuc

Publikováno v: Entropy, Vol 20, Iss 2, p 99 (2018)

This paper investigates the usefulness of the normalized compression distance (NCD) for image similarity detection. Instead of the direct NCD between images, the paper considers the correlation between NCD based feature vectors extracted for each ima

Externí odkaz: https://doaj.org/article/6d9ea4ec944a4edf9c8e102ed381eae2

Computing negentropy based signatures for texture recognition

Autor: Daniela COLTUC, Laurentiu FRANGU, Ana-Elena LUNGU

Publikováno v: Analele Universităţii "Dunărea de Jos" Galaţi: Fascicula III, Electrotehnică, Electronică, Automatică, Informatică, Vol 2007, Iss 1, Pp 47-52 (2007)

The proposed method aims to provide a new tool for texture recognition. For this purpose, a set of texture samples are decomposed by using the FastICA algorithm and characterized by a negentropy based signature. In order to do recognition, the textur

Externí odkaz: https://doaj.org/article/87951973bb764c78a0731330b7be1dbf

iDFD: A Dataset Annotated for Depth and Defocus

Autor: Saqib Nazir, Zhouyan Qiu, Daniela Coltuc, Joaquın Martınez-Sanchez, Pedro Arias

Depth estimation and image deblurring from a single defocused image are fundamental tasks in Computer Vision (CV). Many methods have previously been proposed to solve these two tasks separately, using Deep Learning (DL) powerful learning capability.

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::fd733c403352a3de62b5d832cc20c6f2

Optical Compressive Imaging Technologies for Space Big Data

Autor: Cinzia Lastri, Raffaele Vitulli, Giulio Coluccia, Daniela Coltuc, Ivan Pippi, Chiara Ravazzi, Alessandro Zuccaro Marchi, Enrico Magli, Valentina Raimondi, Donatella Guzzi, Vanni Nardino, Lorenzo Palombi, Florin Garoi

Publikováno v: IEEE Transactions on Big Data (2019). doi:10.1109/TBDATA.2019.2907135
info:cnr-pdr/source/autori:Giulio Coluccia, Cinzia Lastri, Donatella Guzzi, Enrico Magli, Vanni Nardino, Lorenzo Palombi, Ivan Pippi, Valentina Raimondi, Chiara Ravazzi, Florin Garoi, Daniela Coltuc, Raffaele Vitulli, Alessandro Zuccaro Marchi/titolo:Optical Compressive Imaging Technologies for Space Big Data/doi:10.1109%2FTBDATA.2019.2907135/rivista:IEEE Transactions on Big Data/anno:2019/pagina_da:/pagina_a:/intervallo_pagine:/volume

The increasing amount of data generated by space applications poses several challenges due to limited resources available onboard: power, memory, computation, data rate. In this paper, we propose Compressed Sensing (CS) as the key tool to face those

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::52348cfa756030efb12448a2c6b0ee85
https://doi.org/10.1109/tbdata.2019.2907135