NextGEM: Next-Generation Integrated Sensing and Analytical System for Monitoring and Assessing Radiofrequency Electromagnetic Field Exposure and Health.

The evolution of emerging technologies that use Radio Frequency Electromagnetic Field (RF-EMF) has increased the interest of the scientific community and society regarding the possible adverse effects on human health and the environment. This article provides NextGEM's vision to assure safety for EU citizens when employing existing and future EMF-based telecommunication technologies. This is accomplished by generating relevant knowledge that ascertains appropriate prevention and control/actuation actions regarding RF-EMF exposure in residential, public, and occupational settings. Fulfilling this vision, NextGEM commits to the need for a healthy living and working environment under safe RF-EMF exposure conditions that can be trusted by people and be in line with the regulations and laws developed by public authorities. NextGEM provides a framework for generating health-relevant scientific knowledge and data on new scenarios of exposure to RF-EMF in multiple frequency bands and developing and validating tools for evidence-based risk assessment. Finally, NextGEM's Innovation and Knowledge Hub (NIKH) will offer a standardized way for European regulatory authorities and the scientific community to store and assess project outcomes and provide access to findable, accessible, interoperable, and reusable (FAIR) data.

A deterministic compressive sensing model for bat biosonar.

The big brown bat (Eptesicus fuscus) uses frequency modulated (FM) echolocation calls to accurately estimate range and resolve closely spaced objects in clutter and noise. They resolve glints spaced down to 2 µs in time delay which surpasses what traditional signal processing techniques can achieve using the same echolocation call. The Matched Filter (MF) attains 10-12 µs resolution while the Inverse Filter (IF) achieves higher resolution at the cost of significantly degraded detection performance. Recent work by Fontaine and Peremans [J. Acoustic. Soc. Am. 125, 3052-3059 (2009)] demonstrated that a sparse representation of bat echolocation calls coupled with a decimating sensing method facilitates distinguishing closely spaced objects over realistic SNRs. Their work raises the intriguing question of whether sensing approaches structured more like a mammalian auditory system contains the necessary information for the hyper-resolution observed in behavioral tests. This research estimates sparse echo signatures using a gammatone filterbank decimation sensing method which loosely models the processing of the bat's auditory system. The decimated filterbank outputs are processed with [script-l](1) minimization. Simulations demonstrate that this model maintains higher resolution than the MF and significantly better detection performance than the IF for SNRs of 5-45 dB while undersampling the return signal by a factor of six.