996 research outputs found
Polarization dependent Brillouin gain in randomly birefringent fibers
An extensive study of the alignment between the pump, the signal and the
polarization dependent gain (PDG) vectors in stimulated Brillouin amplification
in randomly birefringent fibers is realized by numerically integrating the
equations governing the propagation. At the fiber output, the signal tends to
align to the PDG vector for large pump power because of the nonlinear
polarization pulling effect. The PDG vector, for large random birefringence,
aligns to a state that has the same linear component of the pump but opposite
circular component.Comment: 3 pages submitted to IEEE Photonics Technology Letter
Efficient and Accurate Disparity Estimation from MLA-Based Plenoptic Cameras
This manuscript focuses on the processing images from microlens-array based plenoptic cameras. These cameras enable the capturing of the light field in a single shot, recording a greater amount of information with respect to conventional cameras, allowing to develop a whole new set of applications. However, the enhanced information introduces additional challenges and results in higher computational effort. For one, the image is composed of thousand of micro-lens images, making it an unusual case for standard image processing algorithms. Secondly, the disparity information has to be estimated from those micro-images to create a conventional image and a three-dimensional representation. Therefore, the work in thesis is devoted to analyse and propose methodologies to deal with plenoptic images. A full framework for plenoptic cameras has been built, including the contributions described in this thesis. A blur-aware calibration method to model a plenoptic camera, an optimization method to accurately select the best microlenses combination, an overview of the different types of plenoptic cameras and their representation. Datasets consisting of both real and synthetic images have been used to create a benchmark for different disparity estimation algorithm and to inspect the behaviour of disparity under different compression rates. A robust depth estimation approach has been developed for light field microscopy and image of biological samples
Phase-Sensitive Mode Conversion and Equalization in a Few Mode Fiber Through Parametric Interactions
The parametric interaction in few mode fibers is theoretically and numerically studied in the particular case in which the signal and the idler waves are frequency degenerate but mode nondegenerate. Under simplifying hypotheses, we derive analytical formulas for the phase-insensitive and phase-sensitive amplification gain and conversion efficiency. The analytical formulas are in very good agreement with the numerical solutions of a full vectorial model that takes into account losses, mode coupling, and all possible four-wave mixing interactions. In the phase-sensitive regime, we predict that for small input pump powers, a large and tunable phase-sensitive extinction ratio can be achieved on one mode, whereas the other mode power remains essentially unaffected. Finally, in the high-gain regime, the self-equalization of the output power on different modes can be also achieved
Bloom filter variants for multiple sets: a comparative assessment
In this paper we compare two probabilistic data structures for association
queries derived from the well-known Bloom filter: the shifting Bloom filter
(ShBF), and the spatial Bloom filter (SBF). With respect to the original data
structure, both variants add the ability to store multiple subsets in the same
filter, using different strategies. We analyse the performance of the two data
structures with respect to false positive probability, and the inter-set error
probability (the probability for an element in the set of being recognised as
belonging to the wrong subset). As part of our analysis, we extended the
functionality of the shifting Bloom filter, optimising the filter for any
non-trivial number of subsets. We propose a new generalised ShBF definition
with applications outside of our specific domain, and present new probability
formulas. Results of the comparison show that the ShBF provides better space
efficiency, but at a significantly higher computational cost than the SBF
An anonymous inter-network routing protocol for the Internet of Things
With the diffusion of the Internet of Things (IoT), computing is becoming increasingly pervasive, and different heterogeneous networks are integrated into larger systems. However, as different networks managed by different parties and with different security requirements are interconnected, security becomes a primary concern. IoT nodes, in particular, are often deployed “in the open”, where an attacker can gain physical access to the device. As nodes can be deployed in unsurveilled or even hostile settings, it is crucial to avoid escalation from successful attacks on a single node to the whole network, and from there to other connected networks. It is therefore necessary to secure the communication within IoT networks, and in particular, maintain context information private, including the network topology and the location and identity of the nodes. In this paper, we propose a protocol achieving anonymous routing between different interconnected networks, designed for the Internet of Things and based on the spatial Bloom filter (SBF) data structure. The protocol enables private communication between the nodes through the use of anonymous identifiers, which hide their location and identity within the network. As routing information is encrypted using a homomorphic encryption scheme, and computed only in the encrypted domain, the proposed routing strategy preserves context privacy, preventing adversaries from learning the network structure and topology. This, in turn, significantly reduces their ability to gain valuable network information from a successful attacks on a single node of the network, and reduces the potential for attack escalation
Peliosis hepatis. Personal experience and literature review
Peliosis hepatis (PH) is a disease characterized by
multiple and small, blood-filled cysts within the
parenchymatous organs. PH is a very rare disease,
more common in adults, and when it affects the liver, it
comes to the surgeon’s attention only in an extremely
urgent situation after the lesion’s rupture with the
resulting hemoperitoneum. This report describes the
case of a 29-year-old woman affected by recurring
abdominal pain. CT scans showed a hepatic lesion
formed by multiple hypodense areas, which showed
an early acquisition of the contrast during the arterial
phase. Furthermore, it remained isodense with the
remaining parenchyma during the late venous phase.
We decided on performing a liver resection of segment
â…¦ while avoiding a biopsy for safety reasons. The
histopathologic examination confirmed the diagnosis
of focal PH. PH should always be considered in the
differential diagnosis of hepatic lesions. Clinicians
should discuss the possible causes and issues related to
the differential diagnosis in addition to the appropriate
therapeutic approach. The fortuitous finding of a
lesion, potentially compatible with PH, requires elective
surgery with diagnostic and therapeutic intents. The
main aim is to prevent the risk of a sudden bleeding
that, in absence of properly equipped structures, may
have a fatal outcome
Ricostruzione di scene 3D a colori
La tesi tratta il miglioramento di un programma di ricostruzione di modelli tridimensionali. Tale programma, attraverso l'acquisizione della scena utilizzando sensori di profondità (depth-cameras) come il Kinect [4] e in grado di elaborare i dati no a completare una riproduzione della geometria della scena. Il lavoro è stato progettato come un'implementazione di un'estensione successiva che sopperisce alla mancanza di un'accurata gestione del colore nel modelloopenEmbargo per motivi di segretezza e/o di proprietà dei risultati e/o informazioni sensibil
Model-aware Deep Learning Method for Raman Amplification in Few-Mode Fibers
One of the most promising solutions to overcome
the capacity limit of current optical fiber links is space-division
multiplexing, which allows the transmission on various cores of
multi-core fibers or modes of few-mode fibers. In order to realize
such systems, suitable optical fiber amplifiers must be designed.
In single mode fibers, Raman amplification has shown significant
advantages over doped fiber amplifiers due to its low-noise and
spectral flexibility. For these reasons, its use in next-generation
space-division multiplexing transmission systems is being studied
extensively. In this work, we propose a deep learning method that
uses automatic differentiation to embed a complete few-mode
Raman amplification model in the training process of a neural
network to identify the optimal pump wavelengths and power
allocation scheme to design both flat and tilted gain profiles.
Compared to other machine learning methods, the proposed
technique allows to train the neural network on ideal gain
profiles, removing the need to compute a dataset that accurately
covers the space of Raman gains we are interested in. The ability
to directly target a selected region of the space of possible gains
allows the method to be easily generalized to any type of Raman
gain profiles, while also being more robust when increasing the
number of pumps, modes, and the amplification bandwidth. This
approach is tested on a 70 km long 4-mode fiber transmitting
over the C+L band with various numbers of Raman pumps in
the counter-propagating scheme, targeting gain profiles with an
average gain in the interval from 5 dB to 15 dB and total tilt in
the interval from 1.425 dB to 1.425 dB. We achieve wavelengthand
mode-dependent gain fluctuations lower than 0.04 dB and
0.02 dB per dB of gain, respectively
Securing PIN-based Authentication in Smartwatches With just Two Gestures
Smartwatches are becoming increasingly ubiquitous as they offer new capabilities to
develop sophisticated applications that make daily life easier and more convenient
for consumers. The services provided include applications for mobile payment, ticketing,
identification, access control, etc. While this makes modern smartwatches very
powerful devices, it also makes them very attractive targets for attackers. Indeed,
PINs and Pattern Lock have been widely used in smartwatches for user authentication.
However, such authentication methods are not robust against various forms of
cybersecurity attacks, such as side channel, phishing, smudge, shoulder surfing, and
video recording attacks. Moreover, the recent adoption of hardware-based solutions,
like the Trusted Execution Environment (TEE), can mitigate only partially such problems.
Thus, the user’s security and privacy are at risk without a strong authentication
scheme in place. In this work, we propose 2GesturePIN, a new authentication framework
that allows users to authenticate securely to their smartwatches and related
sensitive services through solely two gestures. 2GesturePIN leverages the rotating
bezel or crown, which are the most intuitive ways to interact with a smartwatch, as a
dedicated hardware. 2GesturePIN improves the resilience of the regular PIN authentication
method against state-of-the-art cybersecurity attacks while maintaining a
high level of usability
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