J-MOD2^{2}: Joint Monocular Obstacle Detection and Depth Estimation

In this work, we propose an end-to-end deep architecture that jointly learns to detect obstacles and estimate their depth for MAV flight applications. Most of the existing approaches either rely on Visual SLAM systems or on depth estimation models to build 3D maps and detect obstacles. However, for the task of avoiding obstacles this level of complexity is not required. Recent works have proposed multi task architectures to both perform scene understanding and depth estimation. We follow their track and propose a specific architecture to jointly estimate depth and obstacles, without the need to compute a global map, but maintaining compatibility with a global SLAM system if needed. The network architecture is devised to exploit the joint information of the obstacle detection task, that produces more reliable bounding boxes, with the depth estimation one, increasing the robustness of both to scenario changes. We call this architecture J-MOD$^{2}$. We test the effectiveness of our approach with experiments on sequences with different appearance and focal lengths and compare it to SotA multi task methods that jointly perform semantic segmentation and depth estimation. In addition, we show the integration in a full system using a set of simulated navigation experiments where a MAV explores an unknown scenario and plans safe trajectories by using our detection model

Zero Emission Geothermal Flash Power Plant

The successful exploitation of geothermal energy for power production relies on the availability of nearly zero emission and efficient technologies. Two zero emission flash plant layouts, with full reinjection of the geothermal fluid (non-condensable gas included), are considered. This paper focusses on the CO2issue, and therefore only the carbon dioxide is considered as non-condensable gas present in the geothermal fluid; the CO2 flow is separated, compressed, and reinjected with the geothermal fluid. Both the reservoir and the power plant are simulated. A first scheme of plant presents a conventional layout in which the CO2is separated and compressed after the condenser. The second scheme presents a plant layout that allows the separation of the CO2at higher pressure with respect to the conventional layout, thus reducing the requested power consumption. The conventional plant scheme performs always better at higher temperature and at lower concentration of CO2. The new layout results better for low temperature and higher gas content

Forecasting consumer confidence through semantic network analysis of online news

This research studies the impact of online news on social and economic consumer perceptions through semantic network analysis. Using over 1.8 million online articles on Italian media covering four years, we calculate the semantic importance of specific economic-related keywords to see if words appearing in the articles could anticipate consumers' judgments about the economic situation and the Consumer Confidence Index. We use an innovative approach to analyze big textual data, combining methods and tools of text mining and social network analysis. Results show a strong predictive power for the judgments about the current households and national situation. Our indicator offers a complementary approach to estimating consumer confidence, lessening the limitations of traditional survey-based methods

AI-driven ground robots: mobile edge computing and mmWave communications at work

The seamless integration of multiple radio access technologies (multi-RAT) and cloud/edge resources is pivotal for advancing future networks, which seek to unify distributed and heterogeneous computing and communication resources into a cohesive continuum system, tailored for mobile applications. Many research projects and focused studies are proposing solutions in this area, the impact of which is undoubtedly increased by moving from theoretical and simulation studies to experimental validations. To this aim, this paper proposes a testbed architecture that combines contemporary communication and cloud technologies to provide microservice-based mobile applications with the ability to offload part of their tasks to cloud/edge data centers connected by multi-RAT cellular networks. The testbed leverages Kubernetes, Istio service mesh, OpenFlow, public 5G networks, and IEEE 802.11ad mmWave (60 GHz) Wi-Fi access points. The architecture is validated through a use case in which a ground robot autonomously follows a moving object by using an artificial intelligence-driven computer vision application. Computationally intensive navigation tasks are offloaded by the robot to microservice instances, which are executed on demand within cloud and edge data centers that the robot can exploit during its journey. The proposed testbed is flexible and can be reused to assess communication and cloud innovations focusing on multi-RAT cloud continuum scenarios

A decrease of calcitonin serum concentrations less than 50 percent 30 minutes after thyroid surgery suggests incomplete C-cell tumor tissue removal

The prognosis of medullary thyroid carcinoma (MTC) depends on the completeness of the first surgical treatment. To date, it is not possible to predict whether the tumor has been completely removed after surgery. The aim of this study was to evaluate the reliability of an intraoperative calcitonin monitoring as a predictor of the final outcome after surgery in patients with MTC

Transferring knowledge across robots: A risk sensitive approach

One of the most impressive characteristics of human perception is its domain adaptation capability. Humans can recognize objects and places simply by transferring knowledge from their past experience. Inspired by that, current research in robotics is addressing a great challenge: building robots able to sense and interpret the surrounding world by reusing information previously collected, gathered by other robots or obtained from the web. But, how can a robot automatically understand what is useful among a large amount of information and perform knowledge transfer? In this paper we address the domain adaptation problem in the context of visual place recognition. We consider the scenario where a robot equipped with a monocular camera explores a new environment. In this situation traditional approaches based on supervised learning perform poorly, as no annotated data are provided in the new environment and the models learned from data collected in other places are inappropriate due to the large variability of visual information. To overcome these problems we introduce a novel transfer learning approach. With our algorithm the robot is given only some training data (annotated images collected in different environments by other robots) and is able to decide whether, and how much, this knowledge is useful in the current scenario. At the base of our approach there is a transfer risk measure which quantifies the similarity between the given and the new visual data. To improve the performance, we also extend our framework to take into account multiple visual cues. Our experiments on three publicly available datasets demonstrate the effectiveness of the proposed approach

Humic-like substances from urban waste as auxiliaries for photo-Fenton treatment: a fluorescence EEM-PARAFAC study

In this work, analysis of excitation-emission-matrices (EEM) has been employed to gain further insight into the characterization of humic like substances (HLS) obtained from urban wastes (soluble bio-organic substances, SBOs). In particular, complexation of these substances with iron and changes along a photo-Fenton process have been studied. Recorded EEMs were decomposed by using parallel factor analysis (PARAFAC). Three fluorescent components were identified by PARAFAC modeling of the entire set of SBO solutions studied. The EEM peak locations (λex/λem) of these components were 310?330 nm/400?420 nm (C1), 340?360 nm/450?500 nm (C2), and 285 nm/335?380 nm (C3). Slight variations of the maximum position of each component with the solution pH were observed. The interaction of SBO with Fe(III) was characterized by determining the stability constants of the components with Fe(III) at different pH values, which were in the order of magnitude of the ones reported for humic substances and reached their highest values at pH = 5. Photochemical experiments employing SBO and Fe(III), with and without H2O2, showed pH-dependent trends for the evolution of the modeled components, which exhibited a strong correlation with the efficiency reported for the photo-Fenton processes in the presence of SBO at different pH values.Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicada