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 Ⅶ 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