A feasibility study of 5G positioning with current cellular network deployment.

This research examines the feasibility of using synchronization signals broadcasted by currently deployed fifth generation (5G) cellular networks to determine the position of a static receiver. The main focus lies on the analysis of synchronization among the base stations of a real 5G network in Milan, Italy, as this has a major impact on the accuracy of localization based on time of arrival measurements. Understanding such properties, indeed, is fundamental to characterize the clock drifts and implement compensation strategies as well as to identify the direct communication beam. The paper shows how the clock errors, i.e., inaccurate synchronization, among 5G base stations exhibit a significant bias, which is detrimental for precise cellular positioning. By compensating the synchronization errors of devices' clocks, we demonstrate that it is in principle possible to localize a static user with an accuracy of approximately 8-10 m in non-obstructed visibility conditions, for urban and rural scenarios, using the deployed 5G network operating at 3.68 GHz and relying on broadcast signals as defined by 5G Release 15 standard. This work has been funded by the European Space Agency (ESA) Navigation Innovation and Support Program (NAVISP) Element 2 pillar which aims at improving the competitiveness of the industry of the participating States in the global Positioning, Navigation and Timing (PNT) market.

A Self-Calibrating Localization Solution for Sport Applications with UWB Technology.

This study addressed the problem of localization in an ultrawide-band (UWB) network, where the positions of both the access points and the tags needed to be estimated. We considered a fully wireless UWB localization system, comprising both software and hardware, featuring easy plug-and-play usability for the consumer, primarily targeting sport and leisure applications. Anchor self-localization was addressed by two-way ranging, also embedding a Gauss-Newton algorithm for the estimation and compensation of antenna delays, and a modified isolation forest algorithm working with low-dimensional set of measurements for outlier identification and removal. This approach avoids time-consuming calibration procedures, and it enables accurate tag localization by the multilateration of time difference of arrival measurements. For the assessment of performance and the comparison of different algorithms, we considered an experimental campaign with data gathered by a proprietary UWB localization system.

The (a)typical burden of COVID-19 pandemic scenario in Autism Spectrum Disorder.

Psychological and mental health consequences of large-scale anti-contagion policies are assuming strong relevance in the COVID-19 pandemic. We proposed a specific focus on a large sample of children with Autism Spectrum Disorder (ASD), developing an ad hoc instrument to investigate changes occurred in specific (sub-)domains during a period of national lockdown (Italy). Our questionnaire, named AutiStress, is both context-specific (being set in the COVID-19 pandemic scenario) and condition-specific (being structured taking into account the autistic functioning peculiarities in the paediatric age). An age- and gender-matched group of neurotypical (TD) controls was also provided. As expected, the severe lockdown policies had a general negative impact both on ASD and TD children, reflecting the obvious burden of the pandemic situation. However, our findings also indicate that children with ASD experienced more positive changes than TD ones. Noteworthy, we report a thought-provoking double dissociation in the context-specific predictor (i.e., accessibility to private outdoor spaces), indicating that it impacts differently on the two groups. Focusing on the ASD group, results suggest a condition-specific impact of the COVID-19 pandemic on core autistic (sub-)domains. Taken together, our data call for a multi-layered, context- and condition-specific analysis of the pandemic burden beyond any oversimplification.

EM Model-Based Device-Free Localization of Multiple Bodies.

In this paper, we discuss the problem of device-free localization and tracking, considering multiple bodies moving inside an area monitored by a wireless network. The presence and motion of non-instrumented subjects leave a specific footprint on the received Radio-Frequency (RF) signals by affecting the Received Signal Strength (RSS) in a way that strongly depends on people location. The paper targets specifically the modelling of the effects on the electromagnetic (EM) field, and the related inference methods. A multiple-body diffraction model is exploited to predict the impact of these bodies on the RSS field, i.e., the multi-body-induced shadowing, in the form of an extra attenuation w.r.t. the reference scenario where no targets are inside the monitored area. Unlike almost all methods available in the literature, that assume multi-body-induced shadowing to sum linearly with the number of people co-present in the monitored area, the proposed model describes also the EM effects caused by their mutual interactions. As a relevant case study, the proposed EM model is exploited to predict and evaluate the effects due to two co-located bodies inside the monitored area. The proposed real-time localization and tracking method, exploiting both average and deviation of the RSS perturbations due to the two subjects, is compared against others techniques available in the literature. Finally, some results, based on experimental RF data collected in a representative indoor environment, are presented and discussed.

Neurotypical individuals fail to understand action vitality form in children with autism spectrum disorder.

Any defects of sociality in individuals diagnosed with autism spectrum disorder (ASD) are standardly explained in terms of those individuals' putative impairments in a variety of cognitive functions. Recently, however, the need for a bidirectional approach to social interaction has been emphasized. Such an approach highlights differences in basic ways of acting between ASD and neurotypical individuals which would prevent them from understanding each other. Here we pursue this approach by focusing on basic action features reflecting the agent's mood and affective states. These are action features Stern named "vitality forms," and which are widely assumed to substantiate core social interactions [D. N. Stern, The Interpersonal World of the Infant (1985); D. N. Stern, Forms of Vitality Exploring Dynamic Experience in Psychology, Arts, Psychotherapy, and Development (2010)]. Previously we demonstrated that, although ASD and typically developing (TD) children alike differentiate vitality forms when performing actions, ASD children express them in a way that is motorically dissimilar to TD children. To assess whether this motor dissimilarity may have consequences for vitality form recognition, we asked neurotypical participants to identify the vitality form of different types of action performed by ASD or TD children. We found that participants exhibited remarkable inaccuracy in identifying ASD children's vitality forms. Interestingly, their performance did not benefit from information feedback. This indicates that how people act matters for understanding others and for being understood by them. Because vitality forms pervade every aspect of daily life, our findings promise to open the way to a deeper comprehension of the bidirectional difficulties for both ASD and neurotypical individuals in interacting with one another.

Sensor-Aided V2X Beam Tracking for Connected Automated Driving: Distributed Architecture and Processing Algorithms.

This paper focuses on ultra-reliable low-latency Vehicle-to-Anything (V2X) communications able to meet the extreme requirements of high Levels of Automation (LoA) use cases. We introduce a system architecture and processing algorithms for the alignment of highly collimated V2X beams based either on millimeter-Wave (mmW) or Free-Space Optics (FSO). Beam-based V2X communications mainly suffer from blockage and pointing misalignment issues. This work focuses on the latter case, which is addressed by proposing a V2X architecture that enables a sensor-aided beam-tracking strategy to counteract the detrimental effect of vibrations and tilting dynamics. A parallel low-rate, low-latency, and reliable control link, in fact, is used to exchange data on vehicle kinematics (i.e., position and orientation) that assists the beam-pointing along the line-of-sight between V2X transceivers (i.e., the dominant multipath component for mmW, or the direct link for FSO). This link can be based on sub-6 GHz V2X communication, as in 5G frequency range 1 (FR1). Performance assessments are carried out to validate the robustness of the proposed methodology in coping with misalignment induced by vehicle dynamics. Numerical results show that highly directional mmW and/or FSO communications are promising candidates for massive data-rate vehicular communications even in high mobility scenarios.

A Cooperative Machine Learning Approach for Pedestrian Navigation in Indoor IoT.

This paper presents a system based on pedestrian dead reckoning (PDR) for localization of networked mobile users, which relies only on sensors embedded in the devices and device- to-device connectivity. The user trajectory is reconstructed by measuring step by step the user displacements. Though step length can be estimated rather accurately, heading evaluation is extremely problematic in indoor environments. Magnetometer is typically used, however measurements are strongly perturbed. To improve the location accuracy, this paper proposes a novel cooperative system to estimate the direction of motion based on a machine learning approach for perturbation detection and filtering, combined with a consensus algorithm for performance augmentation by cooperative data fusion at multiple devices. A first algorithm filters out perturbed magnetometer measurements based on a-priori information on the Earth's magnetic field. A second algorithm aggregates groups of users walking in the same direction, while a third one combines the measurements of the aggregated users in a distributed way to extract a more accurate heading estimate. To the best of our knowledge, this is the first approach that combines machine learning with consensus algorithms for cooperative PDR. Compared to other methods in the literature, the method has the advantage of being infrastructure-free, fully distributed and robust to sensor failures thanks to the pre-filtering of perturbed measurements. Extensive indoor experiments show that the heading error is highly reduced by the proposed approach thus leading to noticeable enhancements in localization performance.

People Counting by Dense WiFi MIMO Networks: Channel Features and Machine Learning Algorithms.

Subject counting systems are extensively used in ambient intelligence applications, such as smart home, smart building and smart retail scenarios. In this paper, we investigate the problem of transforming an unmodified WiFi radio infrastructure into a flexible sensing system for passive subject counting. We first introduce the multi-dimensional channel features that capture the subject presence. Then, we compare Bayesian and neural network based machine learning tools specialized for subject discrimination and counting. Ensemble classification is used to leverage space-frequency diversity and combine learning tools trained with different channel features. A combination of multiple models is shown to improve the counting accuracy. System design is based on a dense network of WiFi devices equipped with multiple antennas. Experimental validation is conducted in an indoor space featuring up to five moving people. Real-time computing and practical solutions for cloud migration are also considered. The proposed approach for passive counting gives detection results with 99% average accuracy.