Crowdsourced Reconstruction of Cellular Networks to Serve Outdoor Positioning: Modeling, Validation and Analysis.

Positioning via outdoor fingerprinting, which exploits the radio signals emitted by cellular towers, is fundamental in many applications. In most cases, the localization performance is affected by the availability of information about the emitters, such as their coverage. While several projects aim at collecting cellular network data via crowdsourcing observations, none focuses on information about the structure of the networks, which is paramount to correctly model their topology.

OSASUD: A dataset of stroke unit recordings for the detection of Obstructive Sleep Apnea Syndrome.

Polysomnography (PSG) is a fundamental diagnostical method for the detection of Obstructive Sleep Apnea Syndrome (OSAS). Historically, trained physicians have been manually identifying OSAS episodes in individuals based on PSG recordings. Such a task is highly important for stroke patients, since in such cases OSAS is linked to higher mortality and worse neurological deficits.

AIOSA: An approach to the automatic identification of obstructive sleep apnea events based on deep learning.

Obstructive Sleep Apnea Syndrome (OSAS) is the most common sleep-related breathing disorder. It is caused by an increased upper airway resistance during sleep, which determines episodes of partial or complete interruption of airflow. The detection and treatment of OSAS is particularly important in patients who suffered a stroke, because the presence of severe OSAS is associated with higher mortality, worse neurological deficits, worse functional outcome after rehabilitation, and a higher likelihood of uncontrolled hypertension.

Virtual Sensing and Sensors Selection for Efficient Temperature Monitoring in Indoor Environments.

Real-time estimation of temperatures in indoor environments is critical for several reasons, including the upkeep of comfort levels, the fulfillment of legal requirements, and energy efficiency. Unfortunately, setting an adequate number of sensors at the desired locations to ensure a uniform monitoring of the temperature in a given premise may be troublesome. Virtual sensing is a set of techniques to replace a subset of physical sensors by virtual ones, allowing the monitoring of unreachable locations, reducing the sensors deployment costs, and providing a fallback solution for sensor failures.