Modern logistic operations in warehouses consist of human and robots working in the same large indoor or outdoor areas. To design safe and efficient area and avoid collisions or accidents, localization and tracking of humans need to be deployed. The development of location based service, such as human activity monitoring, has been one of the drivers for the last decade interest in indoor or outdoor positioning and localization. In this work, a novel tracking system integrating inertial measurements and an ultra-wide band infrastructure is proposed to follow and localize humans in a warehouse scenario. The system has been designed to be self-configurable, able to learn online most of the needed parameters. Being the computational load low, it can be implemented on wearable devices. We tested the tracking system in a real outdoor scenario where the adaptive online algorithms have shown their effectiveness and improvements with respect to existing approaches.

Navigation in pedestrian environments is critical to enabling independent mobility for the blind and visually impaired (BVI) in their daily lives. White canes have been commonly used to obtain contact feedback for following walls, curbs, or man-made trails, whereas guide dogs can assist in avoiding physical contact with obstacles or other pedestrians. However, the infrastructures of tactile trails or guide dogs are expensive to maintain. Inspired by the autonomous lane following of self-driving cars, we wished to combine the capabilities of existing navigation solutions for BVI users. We proposed an autonomous, trail-following robotic guide dog that would be robust to variances of background textures, illuminations, and interclass trail variations. A deep convolutional neural network (CNN) is trained from both the virtual and realworld environments. Our work included major contributions: 1) conducting experiments to verify that the performance of our models trained in virtual worlds was comparable to that of models trained in the real world; 2) conducting user studies with 10 blind users to verify that the proposed robotic guide dog could effectively assist them in reliably following man-made trails.

This work introduces a wearable system to provide situational awareness for blind and visually impaired people. The system includes a camera, an embedded computer and a haptic device to provide feedback when an obstacle is detected. The system uses techniques from computer vision and motion planning to (1) identify walkable space; (2) plan step-by-step a safe motion trajectory in the space, and (3) recognize and locate certain types of objects, for example the location of an empty chair. These descriptions are communicated to the person wearing the device through vibrations. We present results from user studies with low- and high-level tasks, including walking through a maze without collisions, locating a chair, and walking through a crowded environment while avoiding people.

We present an innovative smartphone-centric tracking system for indoor and outdoor environments, based on the joint utilization of dead-reckoning and computer vision (CV) techniques. The system is explicitly designed for visually impaired people (although it could be easily generalized to other users) and it is built under the assumption that special reference signals, such as painted lines, colored tapes or tactile pavings are deployed in the environment for guiding visually impaired users along pre-defined paths. Thanks to highly optimized software, we are able to execute the CV and sensor-fusion algorithms in run-time on low power hardware such as a normal smartphone, precisely tracking the users movements.

Interoperability is the key to enable clinical information systems for General Practitioners (GP) and Hospital Specialists (HS) in order to exchange and manage the Chronic Care Models (CCM) medical records, Patient Summary (PS), and Electronic Prescription (e-Prescription) documents while accessing the electronic health record. We present a localization experience for PS and e-prescription, based on the Health Level Seven Version 3 Clinical Document Architecture Release 2, developed for Italian healthcare. We describe also an experience on the implementation of CCM for sharing patient clinical data among healthcare providers in the management of diagnostic and therapeutic pathways for chronic diseases (diabetes). Finally, we propose, as a case study, a project for the integration of various services for GP/HS, in line with the context of Italian normative both at the national and regional levels (Tuscany region).

In WiFi networks, mobile nodes compete for accessing a shared channel by means of a random access protocol called Distributed Coordination Function (DCF). Although this protocol is in principle fair, since all the stations have the same probability to transmit on the channel, it has been shown that unfair behaviors may emerge in actual networking scenarios because of non-standard configurations of the nodes. Due to the proliferation of open source drivers and programmable cards, enabling an easy customization of the channel access policies, we propose a game-theoretic analysis of random access schemes. We show that even when stations are selfish, efficient equilibria conditions can be reached when they are interested in both uploading and downloading traffic. We explore the utilization of the Access Point as an arbitrator for improving the global network performance. Finally, we propose and evaluate some simple DCF extensions for practically implementing our theoretical findings.

The localization experience for the Patient Summary, based on the Health Level Seven Version 3 (HL7v3) Clinical Document Architecture, Release 2 (CDA Rel.2), is presented. An overview of the Chronic Care Model (CCM) is introduced with particular attention to the clinical information systems, in order to organize patient and population clinical data by sharing information among healthcare providers in management of chronic diseases. We propose, as case study, a project for the integration of various services for General Practitioners (GP) and Hospital Specialists (HS), accessing the Electronic Health Record (EHR), implementing the Patient Summary and managing the exchange of Chronic Care medical records. This project is in line with the state of the art in the normative referring context for the Italian healthcare, both at national and regional level (Tuscany Region).

In this paper we propose a game-theoretic approach for characterizing WiFi network performance in presence of intelligent nodes employing cognitive functionalities. We assume that a cognitive WiFi node is aware of its application requirements and is able to dynamically estimate the network status, in order to dynamically change its access strategy by tuning the contention window settings. We prove that, for infrastructure networks with bidirectional traffic and homogeneous application requirements, selfish access strategies are able to reach equilibrium conditions, which are also Pareto optimal. Indeed, we show that the station strategies converge toward values which maximize a per-node utility function, while maintaining performance fairness.

In this paper, we highlight that multiple bottlenecks can affect the performance of active queue management controllers, which are usually configured on a single bottleneck basis, as if each controller were the only element regulating the TCP traffic along its path. To see this, we consider a network scenario where RED is configured at each router, according to previously developed control theoretic techniques. These configuration rules assure stability in a single bottleneck scenario. Yet, we show that instability may arise when two link become congested. We justify this result through a multiple bottleneck model and give guidelines for new cooperative AQM controllers.