End-to-end Deep Learning for VCSEL’s Nonlinear Digital Pre-Distortion

We propose a novel optimization method for a Neural Network based Digital Pre-Distorter (DPD), applied in Intensity Modulation-Direct Detection transmission systems leveraging Multi-Modal Fiber and Vertical-Cavity Surface-Emitting Laser. We train the DPD using End-to-end Deep Learning of the optical link, together with a Direct Learning Approach leveraging experimental measurements for modeling the transmission channel. The optimization considers VCSEL amplitude constraints, the use of an FFE at the receiver side, and the presence of a receiver non-flat Colored Gaussian Noise (CGN). We verify our optimized DPD on an experimental setup transmitting a 92 Gbps PAM-4 modulated signal. We achieve, for BER=0.01, a performance gain of more than 1 dB in terms of Optical Path Loss with respect to the best performing non-pre-distorted scenario

Interaction-aware development environments: recording, mining, and leveraging IDE interactions to analyze and support the development flow

Nowadays, software development is largely carried out using Integrated Development Environments, or IDEs. An IDE is a collection of tools and facilities to support the most diverse software engineering activities, such as writing code, debugging, and program understanding. The fact that they are integrated enables developers to find all the tools needed for the development in the same place. Each activity is composed of many basic events, such as clicking on a menu item in the IDE, opening a new user interface to browse the source code of a method, or adding a new statement in the body of a method. While working, developers generate thousands of these interactions, that we call fine-grained IDE interaction data. We believe this data is a valuable source of information that can be leveraged to enable better analyses and to offer novel support to developers. However, this data is largely neglected by modern IDEs. In this dissertation we propose the concept of "Interaction-Aware Development Environments": IDEs that collect, mine, and leverage the interactions of developers to support and simplify their workflow. We formulate our thesis as follows: Interaction-Aware Development Environments enable novel and in- depth analyses of the behavior of software developers and set the ground to provide developers with effective and actionable support for their activities inside the IDE. For example, by monitoring how developers navigate source code, the IDE could suggest the program entities that are potentially relevant for a particular task. Our research focuses on three main directions: 1. Modeling and Persisting Interaction Data. The first step to make IDEs aware of interaction data is to overcome its ephemeral nature. To do so we have to model this new source of data and to persist it, making it available for further use. 2. Interpreting Interaction Data. One of the biggest challenges of our research is making sense of the millions of interactions generated by developers. We propose several models to interpret this data, for example, by reconstructing high-level development activities from interaction histories or measure the navigation efficiency of developers. 3. Supporting Developers with Interaction Data. Novel IDEs can use the potential of interaction data to support software development. For example, they can identify the UI components that are potentially unnecessary for the future and suggest developers to close them, reducing the visual cluttering of the IDE

Experimental VCSEL Digital Twin modeling for net 100 Gb/s/λ nonlinear Digital Pre-Distortion

We experimentally model a VCSEL-based optical transmitter for high speed intra data center interconnects using a convolutional neural network digital twin. The device is able to effectively reproduce the VCSEL linear and nonlinear distortions on PAM4 signals transmitted at 107.2 Gbps, thus enabling the optimization of nonlinear VCSEL-MMF digital pre-distorters

Nonlinear Pre-distortion through a Multi-rate End-to-end Learning Approach over VCSEL-MMF IM-DD Optical Links

We experimentally demonstrate a nonlinear digital pre-distorter for PAM-M shaping in VCSEL+MMF IM-DD links able to operate at a generic baud rate using a fractional sample-per-symbol Neural Network. We focus on efficient and practical multi-rate operation, signal amplitude constraints, and linear equalizer at the receiver

Optimization of 50G-PON APD-based receivers

In this paper, we approach the optimization of IM-DD systems leveraging APD-based receivers and Feed-Forward Equalization (FFE) at the receiver side (RX). We review the role of APD-based receivers in recent evolutions of IEEE and ITU-T standards for high-speed Passive Optical Networks (PON) and in particular in 50G-PON as discussed in the recently approved Recommendation ITU-T G.9804.3. We analyze different optimization methods for PAM-2 (NRZ) and PAM-4 modulation formats, based on grid search methods and analytical closed-form solutions for decision thresholds and PAM-4 internal levels. We develop our study on a simulated optical IM-DD system, transmitting at a Baud Rate R s =50 GBaud (i.e., 50 Gbps for NRZ, 100 Gbps for PAM-4). We assess power budget gain in terms of Optical Path Loss (OPL) with respect to nominal thresholds and levels, determining that optimizations are effective (i.e., approximately 1 dB OPL gain) when the system has moderate bandwidth limitations (B3dB>0.5⋅Rs). Our study can be of interest in the future for a better definition of the TDEC parameter for APD-based receivers

Silicosis mortality in Italy: temporal trends 1990-2012 and spatial patterns 2000-2012

The present study investigates the occurrence of silicosis in Italy and its temporal trends and spatial patterns using mortality data. The aim is to give a contribution, albeit with a conservative estimate inferred from mortality data, to epidemiological knowledge of silicosis in Italy. Trends in mortality due to silicosis from 1990 to 2012 were evaluated and a municipal cluster analysis was performed. It shows that mortality due to silicosis is declining but still not eradicated and that one of its main features is regional variability: in this respect, the cluster analysis performed allowed to identify 34 different geographic areas. The results obtained may help display a more detailed picture of silicosis epidemiology and contribute to the fight against exposure to silica, an undisputable public health commitment

A Multi-Rate Approach for Nonlinear Pre-Distortion Using End-to-End Deep Learning in IM-DD Systems

Modern intra-data center (IDC) interconnects leverage robust and low-cost intensity modulation (IM) and direct detection (DD) optical links, based on multimode fibers (MMFs) and vertical-cavity surface-emitting lasers (VCSELs). Current solutions, based on on-off keying (OOK) modulations, reach up to 25-50 Gbps per lane over nearly 100 meters. The actual target for IDCs is to increase VCSEL-MMF links capacity up to 100 Gbps, using PAM-4 on the same devices. To counteract the consequent linear and nonlinear distortions affecting the transmitted signals, an effective solution is to exploit digital signal processing (DSP). In this manuscript, we propose a novel method to optimize a nonlinear artificial neural network (ANN) digital pre-distorter (DPD), based on End-to-end (E2E) learning, that, trained jointly with a Feed-Forward Equalizer (FFE), fulfills physical amplitude constraints and handles different ratio between the sampling rates incurring along with an optical IM-DD system. We indeed propose an E2E ANN system operating simultaneously at different sampling frequencies. We moreover propose in our training method a substitution to the time-domain injection of the receiver noise in the system with an additive regularization term in the FFE gradient loss. We experimentally show the advantages of our proposed DPD comparing the bit error rate (BER) performance against the same scenario without DPD. We assess the gain in terms of Gross Bit Rate and Optical Path Loss (OPL), at given BER targets, for different fiber lengths

Estatística do Câncer Cutâneo no Instituto de Câncer de Londrina no Ano de 1975

Os autores apresentam a incidência de CA cutâneo, com especial atenção para o CA basocelular e CA espinocelular, no Serviço de Dermatologia do Instituto de Câncer de Londrina, no ano de 175. Dos 432 pacientes atendidos, 114 apresentavam Carcionoma cutâneo, sendo que 86 destes tinham Carcinoma Basocelular, enquanto que 17 apresentavam Carcinoma Espinocelular. Esses pacientes foram distribuídos estatisticamente em diversos tópicos, tais como: sexo, estado civil, idade, cor, profissão e localização das lesões. Através da alta incidência de Carcinoma cutâneo em pacientes de pele clara, que desenvolviam atividades que exigiam grandes períodos de exposição solar e pelas condições climáticas em nossa região, conclui-se que deva existir correlação entre estes- fatores, correlação esta já relatada por outros autores