Abstract
Heart rate variability (HRV) analysis has been used as a quantitative marker of the autonomous nervous system activity to measure mental stress. Wearable sensors have been emerging as a solution to collect HRV data for stress assessment in a real context, however such studies raise additional requirements. The wearable system must be minimally obtrusive to allow the subjects to perform their tasks without interference, and inconspicuous to avoid the anxiety associated with wearing medical devices in public. The purpose of this study was to quantify the accuracy trade-off in the use of a chest band heart rate sensor that is less intrusive and less costly than a wearable electrocardiogram (ECG). The HRV metrics extracted from a chest band heart rate monitor, Zephyr HxM (TM) (Zph (TM)), were compared with those extracted from an ECG certified medical device, Vital Jacket (TM) (VJ (TM)). The two systems were worn simultaneously. under laboratory conditions by a population of 14 young and healthy subjects, aged 20 to 26 years, under the stress induced by the Trier Social Stress Test (TSST) procedure. The results showed a mean difference between RR intervals of 9 ms; a. root-mean square error. (RMSE) of less than 8% and. a Pearson's correlation higher than 0.946, considering all TSST phases. In the HRV analysis, the average of all normal intervals (AVNN) showed errors less than 2% between the two systems with a correlation higher than 0.99 for all TSST phases. We thus conclude that the used chest band sensor represents an alternative to the current wearable medical devices to monitor RR intervals, and could be used for mental stress monitoring similar to the TSST protocol.

Abstract
Background and Objectives: In spite of the existence of a multitude of techniques that allow the estimation of stress from physiological indexes, its fine-grained assessment is still a challenge for biomedical engineering. The short-term assessment of stress condition overcomes the limits to stress characterization with long blocks of time and allows to evaluate the behaviour change in real-world settings and also the stress level dynamics. The aim of the present study was to evaluate time and frequency domain and nonlinear heart rate variability (HRV) metrics for stress level assessment using a short-time window. Methods: The electrocardiogram (ECG) signal from 14 volunteers was monitored using the Vital Jacketml while they performed the Trier Social Stress Test (TSST) which is a standardized stress-inducing protocol. Window lengths from 220 s to 50 s for HRV analysis were tested in order to evaluate which metrics could be used to monitor stress levels in an almost continuous way. Results: A sub-set of HRV metrics (AVNN, rMSSD, SDNN and pNN20) showed consistent differences between stress and non-stress phases, and showed to be reliable parameters for the assessment of stress levels in short-term analysis. Conclusions: The AVNN metric, using 50 s of window length analysis, showed that it is the most reliable metric to recognize stress level across the four phases of TSST and allows a fine-grained analysis of stress effect as an index of psychological stress and provides an insight into the reaction of the autonomic nervous system to stress.

Abstract
The ability to detect pathological changes early and in a non-invasive way represents important advantages in the medical field. Diagnosis should become less intrusive, more accurate and less expensive in order to implement in the clinical routine. Infrared thermography has the advantages of being non-invasive, fast, reliable, capable of producing multiple recordings in short intervals, and absolutely safe for patients and clinicians. Thermographic image (TI) came to be an extensively studied technique to quantify sensitive changes in skin temperature in relation to certain diseases: early in the pathological process (lesions, inflammation and infection) the circulation fluxes are altered and, consequently, the tissues’ temperature is reflected in thermography pattern, before structural or functional changes can be observed. This technique proved to be able to give relevant clinical information, such as breast cancer, foot disease in diabetes, rheumatoid arthritis and sports injuries. Monitoring the temperature profile of a patient will allow understanding the physiological evolution of some diseases or monitoring the pharmacologic therapy effect. However, the high cost of this technology and the small number of commercial solutions do not allow a general implementation in the clinical environmental. The future direction is the combination of this technique with the other images techniques in order to add clinical information for a more reliable diagnostic.

Abstract
To gain competitive advantage in today's mobile market, cellular network testing, monitoring and improving customer experience is crucial. Today independent benchmarking companies are hired by mobile operators to run drive tests in a certain geographical areas. The high cost for running these tests results in a low frequency of execution, typically this benchmarking is executed no more than two or three times per year, which is not sufficient to follow the dynamics of an LTE network in a dense urban area. The majority of the drive testing costs come from the car, driver, and the in-car technician. Another approach is to take advantage of existing transportation companies to carry on network benchmarking services to Mobile Network Operators. Unattended measurement nodes can be deployed in existing transportation fleets without the need for dedicated field personnel, reducing the cost of testing up to 70%. This demo uses nodes placed in buses, available in several cities in Europe, to create and validate an automatic LTE network benchmark. The tool allows an easy comparative analyses of mobile network quality of Service and quality of experience parameters based on the operators raw data.

Abstract
This paper presents a methodology for forecasting the average downlink throughput for an LTE cell by using real measurement data collected by multiple LTE probes. The approach uses data analytics techniques, namely forecasting algorithms to anticipate cell congestion events which can then be used by Self-Organizing Network (SON) strategies for triggering network re-configurations, such as shifting coverage and capacity to areas where they are most needed, before subscribers have been impacted by dropped calls or reduced data speeds. The presented implementation results show the prediction of network behaviour is possible with a high level of accuracy, effectively allowing SON strategies to be enforced in time.

Abstract