48 research outputs found
ΠΠ΅ΡΠΎΠ΄Ρ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠΈ ΡΠΈΠ³Π½Π°Π»ΠΎΠ² Π² Π³ΠΈΠ΄ΡΠΎΠ»ΠΎΠΊΠ°ΡΠΈΠΎΠ½Π½ΡΡ ΡΠΈΡΡΠ΅ΠΌΠ°Ρ ΠΏΡΠΈ ΡΠ΅ΡΠ΅Π½ΠΈΠΈ Π·Π°Π΄Π°ΡΠΈ Π·ΠΎΠ½Π΄ΠΈΡΠΎΠ²Π°Π½ΠΈΡ Π΄ΠΎΠ½Π½ΠΎΠΉ ΠΏΠΎΠ²Π΅ΡΡ Π½ΠΎΡΡΠΈ
The paper deals with the processing of hydroacoustic data recorded with help of hydroacoustic research complexes. Particular attention to classic and interferometric sonars is paid. In accordance to the regulatory documentation, the minimum permissible measurement errors for the formation of bottom surface maps for various economic sectors are determined.
As one of the important problems affecting the effectiveness of survey work with sonar complexes, the authors determine the problem of primary data compression, which, as a rule, leads to information loss without the possibility of its recovery. These drawbacks of the methods of primary information compression-recovery and processing of hydroacoustic data used in complexes reduce the overall effectiveness of the complexes usage both with the use of sidescan sonar and with the use of an interferometric side-scan sonar.
In the framework of a numerical experiment, it has been shown that the use of chirp signals as probing pulses makes it possible to effectively apply the complex in the survey sonar mode.
The results of the numerical experiment for estimating the spatial position of the object at the bottom of the sonar images using the phase difference information of the received signals using an interferometric sonar are presented. Based on the results of the experiment, the requirements for recording quality of reflected signals of various types in interferometric side-scan sonar are determined.
A method of resolving the reflected (with partial overlap and overlay) hydroacoustic tones, based on the method of dividing the spectra is proposed by the authors. To improve the efficiency of the chirp signal processing, the authors suggest to improve the accuracy of the detection of the signal detection time due to the phase correction calculated through the slope of the frequency change rate of the chirp signal.Π ΡΡΠ°ΡΡΠ΅ ΡΠ°ΡΡΠΌΠ°ΡΡΠΈΠ²Π°ΡΡΡΡ Π²ΠΎΠΏΡΠΎΡΡ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠΈ Π³ΠΈΠ΄ΡΠΎΠ°ΠΊΡΡΡΠΈΡΠ΅ΡΠΊΠΈΡ
Π΄Π°Π½Π½ΡΡ
, ΡΠ΅Π³ΠΈΡΡΡΠΈΡΡΠ΅ΠΌΡΡ
Ρ ΠΏΠΎΠΌΠΎΡΡΡ Π³ΠΈΠ΄ΡΠΎΠ°ΠΊΡΡΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°ΡΠ΅Π»ΡΡΠΊΠΈΡ
ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΠΎΠ². ΠΡΠΎΠ±ΠΎΠ΅ Π²Π½ΠΈΠΌΠ°Π½ΠΈΠ΅ ΡΠ΄Π΅Π»Π΅Π½ΠΎ ΠΎΠ±Π·ΠΎΡΠ½ΡΠΌ ΠΈ ΠΈΠ½ΡΠ΅ΡΡΠ΅ΡΠΎΠΌΠ΅ΡΡΠΈΡΠ΅ΡΠΊΠΈΠΌ Π³ΠΈΠ΄ΡΠΎΠ»ΠΎΠΊΠ°ΡΠΎΡΠ°ΠΌ. ΠΡΠΎΠ²Π΅Π΄Π΅Π½ Π°Π½Π°Π»ΠΈΠ· ΡΡΡΠ΅ΡΡΠ²ΡΡΡΠΈΡ
ΡΠ΅ΡΠ΅Π½ΠΈΠΉ ΠΏΠΎ ΡΠ΅ΠΌΠ΅ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ, ΠΎΡΠΌΠ΅ΡΠ΅Π½Ρ ΠΎΡΠ½ΠΎΠ²Π½ΡΠ΅ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ, Π΄ΠΎΡΡΠΈΠ³Π½ΡΡΡΠ΅ ΡΠΎΡΡΠΈΠΉΡΠΊΠΈΠΌΠΈ ΠΊΠΎΠ»Π»Π΅ΠΊΡΠΈΠ²Π°ΠΌΠΈ ΡΠ°Π·ΡΠ°Π±ΠΎΡΡΠΈΠΊΠΎΠ²-ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°ΡΠ΅Π»Π΅ΠΉ. Π ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²ΠΈΠΈ Ρ Π½ΠΎΡΠΌΠ°ΡΠΈΠ²Π½ΠΎΠΉ Π΄ΠΎΠΊΡΠΌΠ΅Π½ΡΠ°ΡΠΈΠ΅ΠΉ ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½Ρ ΠΌΠΈΠ½ΠΈΠΌΠ°Π»ΡΠ½ΠΎ Π΄ΠΎΠΏΡΡΡΠΈΠΌΡΠ΅ ΠΏΠΎΠ³ΡΠ΅ΡΠ½ΠΎΡΡΠΈ ΠΈΠ·ΠΌΠ΅ΡΠ΅Π½ΠΈΠΉ ΠΏΡΠΈ ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΠΈ ΠΊΠ°ΡΡ Π΄ΠΎΠ½Π½ΠΎΠΉ ΠΏΠΎΠ²Π΅ΡΡ
Π½ΠΎΡΡΠΈ Π΄Π»Ρ ΡΠ°Π·Π»ΠΈΡΠ½ΡΡ
Ρ
ΠΎΠ·ΡΠΉΡΡΠ²Π΅Π½Π½ΡΡ
ΠΎΡΡΠ°ΡΠ»Π΅ΠΉ.
Π ΠΊΠ°ΡΠ΅ΡΡΠ²Π΅ ΠΎΠ΄Π½ΠΎΠΉ ΠΈΠ· Π²Π°ΠΆΠ½ΡΡ
ΠΏΡΠΎΠ±Π»Π΅ΠΌ, Π²Π»ΠΈΡΡΡΠΈΡ
Π½Π° ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΡΡΡ ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½ΠΈΡ ΠΎΠ±Π·ΠΎΡΠ½ΡΡ
ΡΠ°Π±ΠΎΡ Ρ ΠΏΠΎΠΌΠΎΡΡΡ Π³ΠΈΠ΄ΡΠΎΠ»ΠΎΠΊΠ°ΡΠΈΠΎΠ½Π½ΡΡ
ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΠΎΠ² Π°Π²ΡΠΎΡΠ°ΠΌΠΈ ΠΎΠΏΡΠ΅Π΄Π΅Π»ΡΠ΅ΡΡΡ ΠΏΡΠΎΠ±Π»Π΅ΠΌΠ° ΡΠΆΠ°ΡΠΈΡ ΠΏΠ΅ΡΠ²ΠΈΡΠ½ΡΡ
Π΄Π°Π½Π½ΡΡ
, ΠΊΠΎΡΠΎΡΠ°Ρ, ΠΊΠ°ΠΊ ΠΏΡΠ°Π²ΠΈΠ»ΠΎ, ΠΏΡΠΈΠ²ΠΎΠ΄ΠΈΡ ΠΊ ΠΏΠΎΡΠ΅ΡΠ΅ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ Π±Π΅Π· Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΡΡΠΈ Π΅Π΅ Π²ΠΎΡΡΡΠ°Π½ΠΎΠ²Π»Π΅Π½ΠΈΡ. Π£ΠΊΠ°Π·Π°Π½Π½ΡΠ΅ Π½Π΅Π΄ΠΎΡΡΠ°ΡΠΊΠΈ ΠΏΡΠΈΠΌΠ΅Π½ΡΠ΅ΠΌΡΡ
Π² ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΠ°Ρ
ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² ΡΠΆΠ°ΡΠΈΡ-Π²ΠΎΡΡΡΠ°Π½ΠΎΠ²Π»Π΅Π½ΠΈΡ ΠΏΠ΅ΡΠ²ΠΈΡΠ½ΠΎΠΉ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ ΠΈ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠΈ Π³ΠΈΠ΄ΡΠΎΠ°ΠΊΡΡΡΠΈΡΠ΅ΡΠΊΠΈΡ
Π΄Π°Π½Π½ΡΡ
ΡΠ½ΠΈΠΆΠ°ΡΡ ΠΎΠ±ΡΡΡ ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΡΡΡ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΡ ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΠΎΠ² ΠΊΠ°ΠΊ ΠΏΡΠΈ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠΈ ΠΎΠ±Π·ΠΎΡΠ½ΠΎΠ³ΠΎ Π³ΠΈΠ΄ΡΠΎΠ»ΠΎΠΊΠ°ΡΠΎΡΠ°, ΡΠ°ΠΊ ΠΈ ΠΏΡΠΈ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠΈ ΠΈΠ½ΡΠ΅ΡΡΠ΅ΡΠΎΠΌΠ΅ΡΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ Π³ΠΈΠ΄ΡΠΎΠ»ΠΎΠΊΠ°ΡΠΎΡΠ° Π±ΠΎΠΊΠΎΠ²ΠΎΠ³ΠΎ ΠΎΠ±Π·ΠΎΡΠ°. Π‘Π»Π΅Π΄ΡΠ΅Ρ ΠΎΡΠΌΠ΅ΡΠΈΡΡ, ΡΡΠΎ ΡΠΊΠ°Π·Π°Π½Π½Π°Ρ Π°Π²ΡΠΎΡΠ°ΠΌΠΈ ΠΏΡΠΎΠ±Π»Π΅ΠΌΠ° Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠ½Π° ΠΈΡΠΊΠ»ΡΡΠΈΡΠ΅Π»ΡΠ½ΠΎ ΠΏΡΠΈ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠΈ Π² ΠΊΠ°ΡΠ΅ΡΡΠ²Π΅ Π·ΠΎΠ½Π΄ΠΈΡΡΡΡΠΈΡ
ΠΈΠΌΠΏΡΠ»ΡΡΠΎΠ² ΠΏΡΠΎΡΡΡΡ
ΡΠΎΠ½Π°Π»ΡΠ½ΡΡ
ΡΠΈΠ³Π½Π°Π»ΠΎΠ². Π ΡΠ°ΠΌΠΊΠ°Ρ
ΡΠΈΡΠ»Π΅Π½Π½ΠΎΠ³ΠΎ ΡΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΠ° ΠΏΠΎΠΊΠ°Π·Π°Π½ΠΎ, ΡΡΠΎ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ Π² ΠΊΠ°ΡΠ΅ΡΡΠ²Π΅ Π·ΠΎΠ½Π΄ΠΈΡΡΡΡΠΈΡ
ΠΈΠΌΠΏΡΠ»ΡΡΠΎΠ² ΡΠΈΠ³Π½Π°Π»ΠΎΠ² Ρ Π»ΠΈΠ½Π΅ΠΉΠ½ΠΎ-ΡΠ°ΡΡΠΎΡΠ½ΠΎΠΉ ΠΌΠΎΠ΄ΡΠ»ΡΡΠΈΠ΅ΠΉ ΠΏΠΎΠ·Π²ΠΎΠ»ΡΠ΅Ρ Π΄ΠΎΡΡΠ°ΡΠΎΡΠ½ΠΎ ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎ ΠΏΡΠΈΠΌΠ΅Π½ΡΡΡ ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡ Π² ΡΠ΅ΠΆΠΈΠΌΠ΅ ΠΎΠ±Π·ΠΎΡΠ½ΠΎΠ³ΠΎ Π³ΠΈΠ΄ΡΠΎΠ»ΠΎΠΊΠ°ΡΠΎΡΠ°.
ΠΡΠΈΠ²Π΅Π΄Π΅Π½Ρ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ ΡΠΈΡΠ»Π΅Π½Π½ΠΎΠ³ΠΎ ΡΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΠ° Π΄Π»Ρ ΠΎΡΠ΅Π½ΠΊΠΈ ΠΏΡΠΎΡΡΡΠ°Π½ΡΡΠ²Π΅Π½Π½ΠΎΠ³ΠΎ ΠΏΠΎΠ»ΠΎΠΆΠ΅Π½ΠΈΡ ΠΎΠ±ΡΠ΅ΠΊΡΠ° Π½Π° Π΄Π½Π΅ ΠΏΠΎ Π³ΠΈΠ΄ΡΠΎΠ»ΠΎΠΊΠ°ΡΠΈΠΎΠ½Π½ΡΠΌ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡΠΌ Ρ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ΠΌ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ ΠΎ ΡΠ°Π·Π½ΠΈΡΠ΅ ΡΠ°Π· ΠΏΡΠΈΠ½ΠΈΠΌΠ°Π΅ΠΌΡΡ
ΡΠΈΠ³Π½Π°Π»ΠΎΠ² ΠΏΡΠΈ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠΈ ΠΈΠ½ΡΠ΅ΡΡΠ΅ΡΠΎΠΌΠ΅ΡΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ Π³ΠΈΠ΄ΡΠΎΠ»ΠΎΠΊΠ°ΡΠΎΡΠ°. ΠΠ° ΠΎΡΠ½ΠΎΠ²Π°Π½ΠΈΠΈ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠΎΠ² ΡΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΠ° ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½Ρ ΡΡΠ΅Π±ΠΎΠ²Π°Π½ΠΈΡ Π΄Π»Ρ ΠΊΠ°ΡΠ΅ΡΡΠ²Π° ΡΠ΅Π³ΠΈΡΡΡΠ°ΡΠΈΠΈ ΠΎΡΡΠ°ΠΆΠ΅Π½Π½ΡΡ
ΡΠΈΠ³Π½Π°Π»ΠΎΠ² ΡΠ°Π·Π»ΠΈΡΠ½ΠΎΠ³ΠΎ ΡΠΈΠΏΠ° Π² ΠΈΠ½ΡΠ΅ΡΡΠ΅ΡΠΎΠΌΠ΅ΡΡΠΈΡΠ΅ΡΠΊΠΈΡ
Π³ΠΈΠ΄ΡΠΎΠ»ΠΎΠΊΠ°ΡΠΎΡΠ°Ρ
Π±ΠΎΠΊΠΎΠ²ΠΎΠ³ΠΎ ΠΎΠ±Π·ΠΎΡΠ°.
ΠΠ²ΡΠΎΡΠ°ΠΌΠΈ ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½ ΡΠΏΠΎΡΠΎΠ± ΡΠ°Π·ΡΠ΅ΡΠ΅Π½ΠΈΡ ΠΎΡΡΠ°ΠΆΠ΅Π½Π½ΡΡ
(Ρ ΡΠ°ΡΡΠΈΡΠ½ΡΠΌ ΠΏΠ΅ΡΠ΅ΠΊΡΡΡΠΈΠ΅ΠΌ ΠΈ Π½Π°Π»ΠΎΠΆΠ΅Π½ΠΈΠ΅ΠΌ) Π³ΠΈΠ΄ΡΠΎΠ°ΠΊΡΡΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠΎΠ½Π°Π»ΡΠ½ΡΡ
ΡΠΈΠ³Π½Π°Π»ΠΎΠ², ΠΎΡΠ½ΠΎΠ²Π°Π½Π½ΡΠΉ Π½Π° ΠΌΠ΅ΡΠΎΠ΄Π΅ Π΄Π΅Π»Π΅Π½ΠΈΡ ΡΠΏΠ΅ΠΊΡΡΠΎΠ². ΠΠ»Ρ ΠΏΠΎΠ²ΡΡΠ΅Π½ΠΈΡ ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΡΡΠΈ ΠΏΡΠΈ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠ΅ ΡΠΈΠ³Π½Π°Π»ΠΎΠ² Ρ Π»ΠΈΠ½Π΅ΠΉΠ½ΠΎ-ΡΠ°ΡΡΠΎΡΠ½ΠΎΠΉ ΠΌΠΎΠ΄ΡΠ»ΡΡΠΈΠ΅ΠΉ Π°Π²ΡΠΎΡΠ°ΠΌΠΈ ΠΏΡΠ΅Π΄Π»Π°Π³Π°Π΅ΡΡΡ ΡΠ»ΡΡΡΠ°ΡΡ ΡΠΎΡΠ½ΠΎΡΡΡ ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΡ ΠΌΠΎΠΌΠ΅Π½ΡΠ° ΠΎΠ±Π½Π°ΡΡΠΆΠ΅Π½ΠΈΡ ΡΠΈΠ³Π½Π°Π»Π° Π·Π° ΡΡΠ΅Ρ ΠΊΠΎΡΡΠ΅ΠΊΡΠΈΡΠΎΠ²ΠΊΠΈ ΠΏΠΎ ΡΠ°Π·Π΅, ΡΠ°ΡΡΡΠΈΡΠ°Π½Π½ΠΎΠΉ ΡΠ΅ΡΠ΅Π· Π½Π°ΠΊΠ»ΠΎΠ½ ΡΠΊΠΎΡΠΎΡΡΠΈ ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΡ ΡΠ°ΡΡΠΎΡΡ ΠΌΠΎΠ΄ΡΠ»ΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠ³ΠΎ ΡΠΈΠ³Π½Π°Π»Π°
3D reconstruction and object recognition from 2D SONAR data
Accurate and meaningful representations of the environment are required for autonomy in underwater applications. Thanks to favourable propagation properties in water, acoustic sensors are commonly preferred to video cameras and lasers but do not provide direct 3D information. This thesis addresses the 3D reconstruction of underwater scenes from 2D imaging SONAR data as well as the recognition of objects of interest in the reconstructed scene. We present two 3D reconstruction methods and two model-based object recognition methods. We evaluate our algorithms on multiple scenarios including data gathered by an AUV. We show the ability to reconstruct underwater environments at centimetre-level accuracy using 2D SONARs of any aperture. We demonstrate the recognition of structures of interest on a medium-sized oil-ο¬eld type environment providing accurate yet low memory footprint semantic world models. We conclude that accurate 3D semantic representations of partially-structured marine environments can be obtained from commonly embedded 2D SONARs, enabling online world modelling, relocalisation and model-based applications
Embodied neuromorphic intelligence
The design of robots that interact autonomously with the environment and exhibit complex behaviours is an open challenge that can benefit from understanding what makes living beings fit to act in the world. Neuromorphic engineering studies neural computational principles to develop technologies that can provide a computing substrate for building compact and low-power processing systems. We discuss why endowing robots with neuromorphic technologies β from perception to motor control β represents a promising approach for the creation of robots which can seamlessly integrate in society. We present initial attempts in this direction, highlight open challenges, and propose actions required to overcome current limitations
Algorithms for propagation-aware underwater ranging and localization
MenciΓ³n Internacional en el tΓtulo de doctorWhile oceans occupy most of our planet, their exploration and conservation are one of
the crucial research problems of modern time. Underwater localization stands among the
key issues on the way to the proper inspection and monitoring of this significant part of our
world. In this thesis, we investigate and tackle different challenges related to underwater
ranging and localization. In particular, we focus on algorithms that consider underwater
acoustic channel properties. This group of algorithms utilizes additional information
about the environment and its impact on acoustic signal propagation, in order to improve
the accuracy of location estimates, or to achieve a reduced complexity, or a reduced
amount of resources (e.g., anchor nodes) compared to traditional algorithms.
First, we tackle the problem of passive range estimation using the differences in the
times of arrival of multipath replicas of a transmitted acoustic signal. This is a costand
energy- effective algorithm that can be used for the localization of autonomous
underwater vehicles (AUVs), and utilizes information about signal propagation. We study
the accuracy of this method in the simplified case of constant sound speed profile (SSP)
and compare it to a more realistic case with various non-constant SSP. We also propose
an auxiliary quantity called effective sound speed. This quantity, when modeling acoustic
propagation via ray models, takes into account the difference between rectilinear and
non-rectilinear sound ray paths. According to our evaluation, this offers improved range
estimation results with respect to standard algorithms that consider the actual value of
the speed of sound.
We then propose an algorithm suitable for the non-invasive tracking of AUVs or
vocalizing marine animals, using only a single receiver. This algorithm evaluates the
underwater acoustic channel impulse response differences induced by a diverse sea
bottom profile, and proposes a computationally- and energy-efficient solution for passive
localization.
Finally, we propose another algorithm to solve the issue of 3D acoustic localization
and tracking of marine fauna. To reach the expected degree of accuracy, more sensors
are often required than are available in typical commercial off-the-shelf (COTS) phased
arrays found, e.g., in ultra short baseline (USBL) systems. Direct combination of multiple
COTS arrays may be constrained by array body elements, and lead to breaking the optimal array element spacing, or the desired array layout. Thus, the application of
state-of-the-art direction of arrival (DoA) estimation algorithms may not be possible. We
propose a solution for passive 3D localization and tracking using a wideband acoustic
array of arbitrary shape, and validate the algorithm in multiple experiments, involving
both active and passive targets.Part of the research in this thesis has been supported by the EU H2020 program under
project SYMBIOSIS (G.A. no. 773753).This work has been supported by IMDEA Networks InstitutePrograma de Doctorado en IngenierΓa TelemΓ‘tica por la Universidad Carlos III de MadridPresidente: Paul Daniel Mitchell.- Secretario: Antonio FernΓ‘ndez Anta.- Vocal: Santiago Zazo Bell