Ricerc@Sapienza

Large scale deployments of Internet of Things (IoT) networks are becoming reality. From a technology perspective, a lot of information related to device parameters, channel states, network and application data are stored in databases and can be used for an extensive analysis to improve the functionality of IoT systems in terms of network performance and user services. LoRaWAN (Long Range Wide Area Network) is one of the emerging IoT technologies, with a simple protocol based on LoRa modulation.

The application of machine learning and soft computing techniques for function approximation is a widely explored topic in literature. Neural networks, evolutionary algorithms and support vector machines proved to be very effective, although these models suffer from very low level of interpretability by human operators. Conversely, Adaptive Neuro Fuzzy Inference Systems (ANFISs) demonstrated to be very accurate models featured by a considerable degree of interpretability. In this paper, a general framework for ANFIS training by clustering is proposed and investigated.

Quality of Service (QoS) management in IP networks today relies on static configuration of classes of service definitions and related forwarding priorities. Packets are actually classified according to the DiffServ architecture based on the RFC 4594, typically thanks to static configuration or filters matching packet features, at network access equipment. In this paper, we propose a dynamic classification procedure, referred to as Learning-powered DiffServ (L-DiffServ), able to detect the distinctive characteristics of traffic and to dynamically assign service classes to IP packets.

Palmprint verification is one of the most significant and popular approaches for personal authentication due to its high accuracy and efficiency. Using deep region of interest (ROI) and feature extraction models for palmprint verification, a novel approach is proposed where convolutional neural networks (CNNs) along with transfer learning are exploited. The extracted palmprint ROIs are fed to the final verification system, which is composed of two modules. These modules are (i) a pre-trained CNN architecture as a feature extractor and (ii) a machine learning classifier.

The goal of this special issue is to understand how and to what extent novel computational intelligence techniques based on the emerging end-to-end learning paradigm can be efficiently employed in Digital Audio, in the light of all aforementioned aspects.

The sounds of work activities and equipment operations at a construction site provide critical information regarding construction progress, task performance, and safety issues. The construction industry, however, has not investigated the value of sound data and their applications, which would offer an advanced approach to unmanned management and remote monitoring of construction processes and activities.

In this paper, a Machine Learning (ML), and more specifically, a Deep Learning (DL) approach, is applied to the resolution of a typical electromagnetic problem such as the analysis and classification of Ground Penetrating Radar (GPR) radargrams. In particular, the study employs a DL architecture, known as DenseNet, to classify a set of radargrams, generated through the gprMax simulation software, and representing the scattering from perfect electric conductor (PEC) cylinders of infinite length, buried in various media, at different depths, and with different radius amplitudes.

The aim of this work is to exploit Machine Learning (ML) for the analysis of Ground Penetrating Radar images.
In particular, the objective is to apply a scaled-down version of DenseNet [1] architecture with a multiporse
approach to extract from b-scan images of buried cylinders: the cylinder radius, the cylinder length, the depth
with respect to the ground, and the relative permittivity of the cylinder and of the medium in which the cylinder
is immersed. The cylinders have an infinite length or have a length much greater than the diameter. The main

Pseudomonas aeruginosa

Biofilm resistance to antimicrobials is a complex phenomenon, driven not only by genetic mutation induced resistance, but also by means of increased microbial cell density that supports horizontal gene transfer across cells. The prevention of biofilm formation and the treatment of existing biofilms is currently a difficult challenge; therefore, the discovery of new multi-targeted or combinatorial therapies is growing.