To fulfil the requirements of 5G vision of “everything everywhere and always connected”, a new waveform must contain the features to support a greater number of users on high data rate. Although Orthogonal Frequency Division Multiplexing (OFDM) has been widely used in the 4th generation, but it can hardly meet the needs of 5G vision. However, many waveforms have been proposed to cope with new challenges. In this paper, we have presented a comparative analysis of several waveform candidates (FBMC, GFDM, UFMC, F-OFDM) on the basis of complexity, hardware design and other valuable characteristics. Filter based waveforms have much better Out of Band Emission (OoBE) as compared to OFDM. However, F-OFDM has smaller filter length compared to filter-based waveforms and provides better transmission with multiple antenna system without any extra processing, while providing flexible frequency multiplexing, shorter latency and relaxed synchronization as compared to other waveforms.

Laguerre-Hahn families on the real line are characterized in terms of second order differential equations with matrix coefficients for vectors involving the orthogonal polynomials and their associated polynomials, as well as in terms of second order differential equation for the functions of the second kind. Some characterizations of the classical families are derived.

In this paper we present a system for the detection of immunogold particles and a Transfer Learning (TL) framework for the recognition of these immunogold particles. Immunogold particles are part of a high-magnification method for the selective localization of biological molecules at the subcellular level only visible through Electron Microscopy. The number of immunogold particles in the cell walls allows the assessment of the differences in their compositions providing a tool to analise the quality of different plants. For its quantization one requires a laborious manual labeling (or annotation) of images containing hundreds of particles. The system that is proposed in this paper can leverage significantly the burden of this manual task. For particle detection we use a LoG filter coupled with a SDA. In order to improve the recognition, we also study the applicability of TL settings for immunogold recognition. TL reuses the learning model of a source problem on other datasets (target problems) containing particles of different sizes. The proposed system was developed to solve a particular problem on maize cells, namely to determine the composition of cell wall ingrowths in endosperm transfer cells. This novel dataset as well as the code for reproducing our experiments is made publicly available. We determined that the LoG detector alone attained more than 84\% of accuracy with the F-measure. Developing immunogold recognition with TL also provided superior performance when compared with the baseline models augmenting the accuracy rates by 10\%.

In this paper we characterize sequences of orthogonal polynomials on the unit circle whose corresponding Carathéodory function satisfies a Riccati differential equation with polynomial coefficients, in terms of second order matrix differential equations.

It is proved a characterization theorem for semi-classical orthogonal polynomials on non- uniform lattices that states the equivalence between the Pearson equation for the weight and some systems involving the orthogonal polynomials as well as the functions of the second kind. As a consequence, it is deduced the analogue of the so-called compatibility conditions in the ladder operator scheme. The classical orthogonal polynomials on non- uniform lattices are then recovered under such compatibility conditions, through a closed formula for the recurrence relation coefficients.

Transfer Learning (TL) aims to transfer knowledge acquired in one problem, the source problem, onto another problem, the target problem, dispensing with the bottom-up construction of the target model. Due to its relevance, TL has gained significant interest in the Machine Learning community since it paves the way to devise intelligent learning models that can easily be tailored to many different applications. As it is natural in a fast evolving area, a wide variety of TL methods, settings and nomenclature have been proposed so far. However, a wide range of works have been reporting different names for the same concepts. This concept and terminology mixture contribute however to obscure the TL field, hindering its proper consideration. In this paper we present a review of the literature on the majority of classification TL methods, and also a distribution-based categorization of TL with a common nomenclature suitable to classification problems. Under this perspective three main TL categories are presented, discussed and illustrated with examples.

The so-called 5G will revolutionize the way we live, and work. In order to demonstrate the profound changes, we can expect to experience within the next 5 to 10 years, we present use cases for the planned research within the TeamUp5G project. Some use cases are strongly linked to the network layer and aim at developing solutions capable of optimizing the main promising benefits of 5G: extremely low latency and extremely high bandwidth (e.g., handle video streams, traffic congestion, user profiles), in the most efficient way possible. Other use cases focus on commercial applications that make use of middleware applications to enhance their performance. The latter fall into two main areas: real-time virtual reality and live video streaming, which are extremely demanding in terms of latency and bandwidth to provide an acceptable QoE/QoS to multiple users. The use cases presented are built assuming that 5G is essential for their support with appropriate QoE/QoS. Key performance indicators and their range of variation are also identified.

With the growing demand for new blend of applications, the user’s dependency on the Internet is increasing day by day. Mobile Internet users are giving more attention to their own experience, especially in terms of communication reliability, high data rate and service stability on the move. This increase in the demand is causing saturation of existing radio frequency bands. To address these challenges, many researchers are finding the best approach, Carrier Aggregation (CA) is one of the newest innovations which seems to fulfil the demands of future spectrum, CA is one the most important feature for Long Term Evolution - Advanced. In direction to get the upcoming International Mobile Telecommunication Advanced (IMT-Advanced) mobile requirements 1 Gb/s peak data rate, the CA scheme is presented by 3GPP to sustain high data rate using widespread frequency bandwidth up to 100 MHz. Technical issues containing the aggregation structure, its implementation, deployment scenarios, control signal technique and challenges for CA technique in LTE-Advanced, with consideration backward compatibility are highlighted. Performance evaluation in macrocellular scenarios through a simulation approach shows the benefits of applying CA and low-complexity multi-band schedulers in service quality and system capacity enhancement. The Enhanced multi-band scheduler is less complex than the General multi-band scheduler and performs better for cell radius longer than 1800 m (and a PLR threshold of 2%).