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Place recognition and user positioning methods are becoming trending topics among the research community thanks to the need of providing cutting-edge solutions to enable innovative and efficient Location-Based Services (LBS) and applications. However, the mobile framework suffers from typical constraints on energy and computational capabilities due to the inherent characteristics of wireless mobile devices. For this reason, in order to be suitable for deploying in such framework, a place recognition algorithm should be thrifty in terms of resource consumption. In this paper we consider six different WiFi-based outdoor positioning approaches and we analytically derive an upper-bound for their computational burden in terms of number of FLoating-point OPerations (FLOPs). [ABSTRACT FROM AUTHOR]

The importance of robust audio speech processing has rapidly increased in the latest years, as the number of smart and connected devices is growing. This effect is strongly related to the Internet of things framework, introducing concepts such as connected vehicles and future smart cities. Context-aware applications are fundamental in this evolving environment, enabling smart and custom-tailored services for a variety of users. The use of on-board speaker recognition (SR) systems can play a key role in enhancing the customization of in-vehicle applications, by identifying the actual users and personalizing services based on their identity. Driven by this motivation, in this paper we present a performance study of an SR system, designed to face typical challenging conditions of an in-vehicle environment. We propose the design of a robust speaker identification algorithm embedding a smart preprocessing method based on voice activity detection, which can effectively reduce the influence of noise and distance on classification. Results show that our solution is able to efficiently improve the correct classification rate, even in the case of distant audio acquisition and in a variety of noisy environments. [ABSTRACT FROM AUTHOR]

The diffusion of Device-to-Device (D2D) communications opens the door to exploit the contributions of multiple Mobile Devices (MDs) to accomplish collaborative tasks. In this paper a speaker recognition algorithm for MDs based on a multiple-observations approach is presented. We propose various fusion and clustering algorithms aimed at efficiently exploiting data coming from MDs. Numerical results show that in many cases our multiple-observation approach is able to significantly improve the accuracy of the considered speaker recognition algorithm. [ABSTRACT FROM AUTHOR]

Heart failure is a chronic disease that alternates intense and weak phases and requires repeated and frequent hospital treatments. The use of automatic instruments for a remote and ubiquitous monitoring of biological parameters relevant to heart failure pathophysiology offers new perspectives to improve the patients' quality of life and the efficacy of the treatment. The platform described in this paper represents an implementation of an automatic remote monitoring tool where smartphones play a crucial role. They are not employed just to communicate through traditional client-server applications and they are not only the hub of information sent by sensors, but they also act as intelligent processors and autonomous sensors of the patients' motion through a high-accuracy activity recognition algorithm. The proposed platform combines the evolution of health systems and the consequent needs of modern telemedicine with the current context-aware capability of recent smartphones, obtained by implementing specific algorithmic solutions, and with the anything, anytime, and anywhere communication capability of the pervasive communications paradigm. Performance evaluation focuses on the accuracy of the activity recognition estimation, on its applicability to real life, and on the data exchange between smartphones and medical server, allowing to envisage the potential of the designed platform. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Objectives: The ADNEX (Assessment of Different NEoplasias in the adneXa) model was developed using parameters collected by experienced (level III) ultrasound examiners. Our primary aim was to externally validate the ADNEX model. Then, the discriminatory performance of ADNEX was compared with the two-step strategy and subjective assessment by an experienced ultrasound operator.Methods: Between February 2013 and January 2017, all patients who were scheduled for surgery for an adnexal mass at the Sant'Anna Hospital in Turin were enrolled in this study. Preoperative transvaginal sonography was performed, and the two-step strategy was applied for triage of the adnexal mass. Two ultrasound examiners, IOTA certified, applied the ADNEX model to all the collected masses based on the ultrasound reports. Finally, an experienced operator assigned the subjective assessment based on recorded ultrasound images. The discrimination and calibration performance of ADNEX were evaluated. The AUC was calculated for the basic discrimination between benign and malignant tumours. In addition, AUCs were computed for each pair of tumour types using the conditional risk method.Results: A total of 577 patients were included in the analysis: the overall prevalence of malignancy was 25 %. With ADNEX, the AUC to differentiate between benign and malignant masses was 0.9111 (95 % CI 0. 8788-0.9389). At risk cut-offs of 1%, 10 % and 30 %, sensitivities were 100 %, 89.6 % and 79.2 %, respectively, and specificities were 2.8 %, 76.2 % and 89.6 %, respectively. Discrimination between benign and stage II-IV tumours was good (AUC 0.935). The model had the most difficulties discriminating between borderline and stage I tumours (AUC 0.666), and between stages II-IV invasive and secondary metastatic tumours (AUC 0.736). The polytomous discrimination index (PDI) was 0.61 for ADNEX, whereas PDI for random performance would be 0.25. ADNEX proved to be equally or more accurate than the subjective assessment or the two-step strategy in the discrimination between benign and malignant adnexal masses.Conclusions: the ADNEX model could probably be successfully applied when an expert examiner is not available and, therefore both a subjective assessment and the two-step strategy cannot be performed. [ABSTRACT FROM AUTHOR]