Ricerc@Sapienza

In this paper, we propose a Deep Recurrent Neural Network (DRNN) approach based on Long-Short Term Memory (LSTM) units for the classification of audio signals recorded in construction sites. Five classes of multiple vehicles and tools, normally used in construction sites, have been considered. The input provided to the DRNN consists in the concatenation of several spectral features, like MFCCs, mel-scaled spectrogram, chroma and spectral contrast. The proposed architecture and the feature extraction have been described.

In parallel with the vast medical research on clinical treatment of COVID-19, an important action to have the disease completely under control is to carefully monitor the patients. What the detection of COVID-19 relies on most is the viral tests, however, the study of X-rays is helpful due to the ease of availability. There are various studies that employ Deep Learning (DL) paradigms, aiming at reinforcing the radiography-based recognition of lung infection by COVID-19.

The large-scale penetration of renewable energy sources is forcing the transition towards
the future electricity networks modeled on the smart grid paradigm, where energy clusters call for new
methodologies for the dynamic energy management of distributed energy resources and foster to form
partnerships and overcome integration barriers. The prediction of energy production of renewable energy
sources, in particular photovoltaic plants that suffer from being highly intermittent, is a fundamental tool

Civil infrastructures, such as tunnels and bridges, are directly related to the overall economic and demographic growth of countries.
The aging of these infrastructures increases the probability of catastrophic failures that results in loss of lives and high repair costs;
all over the world, these factors drive the need for advanced infrastructure monitoring systems. For these reasons, in the last years,
different types of devices and innovative infrastructure monitoring techniques have been investigated to automate the process and

Automated pavement crack detection and measurement are important road issues. Agencies have to guarantee the improvement of road safety. Conventional crack detection and measurement algorithms can be extremely time-consuming and low efficiency. Therefore, recently, innovative algorithms have received increased attention from researchers. In this paper, we propose an ensemble of convolutional neural networks (without a pooling layer) based on probability fusion for automated pavement crack detection and measurement.

Automatic crack detection from images is an important task that is adopted to ensure road safety and durability for Portland cement concrete (PCC) and asphalt concrete (AC) pavement. Pavement failure depends on a number of causes including water intrusion, stress from heavy loads, and all the climate effects. Generally, cracks are the first distress that arises on road surfaces and proper monitoring and maintenance to prevent cracks from spreading or forming is important. Conventional algorithms to identify cracks on road pavements are extremely time-consuming and high cost.

In developing countries, crop field productivity is particularly vulnerable to spreading diseases, including viruses and fungi. This is mostly due to the lack of skilled plant pathologists as well as to the scarce fund and poor infrastructure (e.g., roads, power and water lines) availability.
The PlantVillage project through its mobile application named Nuru provides an AI digital assistant to recognize plants and their diseases through image analysis.

The management of plastic debris is a serious issue due to its durability. Unfortunately, million
tons of plastic end up in the sea becoming one of the biggest current environmental problems. One way to
monitor the amount of plastic in beaches is to collect samples and visually count and sort the plastic particles
present in them. This is a very time-consuming task. In this work, we present a Computer Vision-based
system which is able to automatically count and classify microplastic particles (1-5 mm) into five different

Let us consider questions like: What is the current state of output? What will be the evolution of prices in the short future? Are they increasing or decreasing? Is it thundering right now and what is the situation off the sea? These questions which at a first glance seem unrelated have one thing in common; they all challenge researchers to make very quick decisions about the conditions around them, to nowcast, as it is commonly called. Every airport needs a nowcasting mechanism for the weather; likewise, every Central Bank should have a nowcasting system for economic aggregates.

A novel deep learning approach is proposed for the predictive analysis of trends in energy related time series, in particular those relevant to photovoltaic systems. Aim of the proposed approach is to grasp the trend of the time series, namely, if the series goes up, down or keep stable, instead of predicting the future numerical value. The modeling system is based on Long Short-Term Memory networks, which are a type of recurrent neural network able to extract information in samples located very far from the current one.