Abstract
Long Period Fibre Gratings (LPFGs) were fabricated by femtosecond (fs) laser direct writing in a standard single-mode fibre (SMF-28e) to measure variations in the surrounding refractive index (SRI). The sensing sensitivity of these structures was optimized with the deposition of homogeneous thin layers of titanium dioxide (TiO2) by physical vapour deposition (PVD) process. A set of LPFGs were coated with different thickness layers of TiO2, and the spectral features were monitored for different SRI solutions. The wavelength shift and the optical power variation of the LPFG minimum attenuation band were measured achieving sensitivities of similar to 570 nm/RIU at using SRI near to 1.3600 in the case of the LPFG coated with 60 nm of TiO2, a 10-fold increase over the corresponding for a bare LPFG. For SRI values higher than the cladding refractive index, a sensitivity over similar to 3000 nm/RIU was determined for 30 nm of TiO2 thick film, a region where the bare LPFGs are useless. For 30 nm of TiO2, the optical power variation follows a quasi-linear function of the SRI, with a range of similar to 10 dB. Moreover, values as high as 50 and 120 dB/RIU at 1.3200 and 1.4200, respectively, can be obtained by choosing the proper film thickness. Preliminary studies revealed that coating fs-laser direct writing LPFGs with titanium dioxide improves their performance.

Abstract
An OFDM transmission system is reported based on a directly modulated blue LASER diode, for high bit rate under-water optical communication applications. The 256 subcarriers 16-QAM signal is transmitted over a total distance of 2.4 m underwater with an EVM lower than -28.5 dB for a 250 MHz bandwidth and -16.5 dB for a 2 GHz bandwidth, the BER being lower than the forward error corrector limit. At the maximum bandwidth of 2 GHz a transmission rate of 5.36 Gbit/s is achieved. © 2020 IEEE.

Abstract

Abstract

Abstract
The initial phase of the innovation process is widely accepted as an important driver of positive results for new products and for the success of businesses. The Front End of Innovation (FEI) is a multidisciplinary area that includes a variety of activities, such as ideation, opportunity identification and analysis, feasibility analysis, global trends analysis, concept definition, customer and competitor analysis, and even business model development. Due to the number and variety of FEI responsibilities, this phase entails a considerable level of complexity and decision making. This fact is reflected in the literature, where one finds a variety of FEI approaches and proposals, seldom overlapping and offering no clear consensual guidance. This work aimed at overcoming this gap by proposing an Ontology for the Front End of Innovation as a comprehensive knowledge representation of the FEI, the so-called Front End of Innovation Integrative Ontology (FEI2O). The ontology balanced the differences and addressed the shortcomings of the main FEI Reference Models and included contributions from the field. This research builds on a combination of qualitative and quantitative methodologies. It combines the qualitative methods of interviewing and focus group discussion to collect the views of domain experts, used to refine the artefact and later to evaluate the final ontology. Quantitative analysis of data was carried out using the Attribute Agreement approach. The FEI2O explicitly provides a description of a domain regarding concepts, properties and relations of concepts. The main benefit of the FEI2O is to provide a comprehensive formal reference model and a common vocabulary.

Abstract
Modern online web-based systems continuously generate data at very fast rates. This continuous flow of data encompasses web content - e.g. posts, news, products, comments -, but also user feedback - e.g. ratings, views, reads, clicks, thumbs up -, as well as context information - device used, geographic info, social network, current user activity, weather. This is potentially overwhelming for systems and algorithms design to train in offline batches, given the continuous and potentially fast change of content, context and user preferences. Therefore it is important to investigate online methods to be able to transparently adapt to the inherent dynamics of online systems. Incremental models that learn from data streams are gaining attention in the recommender systems community, given their natural ability to deal with data generated in dynamic, complex environments. User modeling and personalization can particularly benefit from algorithms capable of maintaining models incrementally and online, as data is generated. The objective of this workshop is to foster contributions and bring together a growing community of researchers and practitioners interested in online, adaptive approaches to user modeling, recommendation and personalization, as well as other related tasks, such as evaluation, reproducibility, privacy and explainability. © 2020 Owner/Author.