COVID-19 Contagion Risk Estimation Model for Indoor Environments.

COVID-19 is an infectious disease mainly transmitted through aerosol particles. Physical distancing can significantly reduce airborne transmission at a short range, but it is not a sufficient measure to avoid contagion. In recent months, health authorities have identified indoor spaces as possible sources of infection, mainly due to poor ventilation, making it necessary to take measures to improve indoor air quality. In this work, an accurate model for COVID-19 contagion risk estimation based on the Wells-Riley probabilistic approach for indoor environments is proposed and implemented as an Android mobile App. The implemented algorithm takes into account all relevant parameters, such as environmental conditions, age, kind of activities, and ventilation conditions, influencing the risk of contagion to provide the real-time probability of contagion with respect to the permanence time, the maximum allowed number of people for the specified area, the expected number of COVID-19 cases, and the required number of Air Changes per Hour. Alerts are provided to the user in the case of a high probability of contagion and CO2 concentration. Additionally, the app exploits a Bluetooth signal to estimate the distance to other devices, allowing the regulation of social distance between people. The results from the application of the model are provided and discussed for different scenarios, such as offices, restaurants, classrooms, and libraries, thus proving the effectiveness of the proposed tool, helping to reduce the spread of the virus still affecting the world population.

Machine Learning Approach to Quadratic Programming-Based Microwave Imaging for Breast Cancer Detection.

In this work, a novel technique is proposed that combines the Born iterative method, based on a quadratic programming approach, with convolutional neural networks to solve the ill-framed inverse problem coming from microwave imaging formulation in breast cancer detection. The aim is to accurately recover the permittivity of breast phantoms, these typically being strong dielectric scatterers, from the measured scattering data. Several tests were carried out, using a circular imaging configuration and breast models, to evaluate the performance of the proposed scheme, showing that the application of convolutional neural networks allows clinicians to considerably reduce the reconstruction time with an accuracy that exceeds 90% in all the performed validations.

Experimental Characterization of Spurious Signals in Magnetic Nanoparticles Enhanced Microwave Imaging of Cancer.

Magnetic nanoparticles enhanced microwave imaging relies on the capability of modulating the response of such nanocomponents at microwaves by means of a (low frequency) polarizing magnetic field. In medical imaging, this capability allows for the detection and imaging of tumors loaded with nanoparticles. As the useful signal is the one which arises from nanoparticles, it is crucial to remove sources of undesired disturbance to enable the diagnosis of early-stage tumors. In particular, spurious signals arise from instrumental drift, as well as from the unavoidable interaction between the polarizing field and the imaging system. In this paper, we experimentally assess and characterize such spurious effects in order to set the optimal working conditions for magnetic nanoparticles enhanced microwave imaging of cancer. To this end, simple test devices, which include all components typically comprised in a microwave imaging system, have been realized and exploited. The experiment's results allow us to derive design formulas and guidelines useful for limiting the impact of unwanted magnetic effects, as well as that relative to the instrumental drift on the signal generated by the magnetic nanoparticles-loaded tumor.

Software-Defined Doppler Radar Sensor for Human Breathing Detection.

Non-contact wireless sensing approaches have emerged in recent years, in order to enable novel enhanced developments in the framework of healthcare and biomedical scenarios. One of these technologically advanced solutions is given by software-defined radar platforms, a low-cost radar implementation, where all operations are implemented and easily changed via software. In the present paper, a software-defined radar implementation with Doppler elaboration features is presented, to be applied for the non-contact monitoring of human respiration signals. A quadrature receiver I/Q (In-phase/Quadrature) architecture is adopted in order to overcome the critical issues related to the occurrences of null detection points, while the phase-locked loop components included in the software defined radio transceiver are successfully exploited to guarantee the phase correlation between I/Q signal components. The proposed approach leads to a compact, low-cost, and flexible radar solution, whose application abilities may be simply changed via software, with no need for hardware modifications. Experimental results on a human target are discussed so as to demonstrate the feasibility of the proposed approach for vital signs detection.

Software Defined Doppler Radar as a Contactless Multipurpose Microwave Sensor for Vibrations Monitoring.

A vibration sensor based on the use of a Software-Defined Radio (SDR) platform is adopted in this work to provide a contactless and multipurpose solution for low-cost real-time vibrations monitoring. In order to test the vibration detection ability of the proposed non-contact method, a 1 GHz Doppler radar sensor is simulated and successfully assessed on targets at various distances, with various oscillation frequencies and amplitudes. Furthermore, an SDR Doppler platform is practically realized, and preliminary experimental validations on a device able to produce a harmonic motion are illustrated to prove the effectiveness of the proposed approach.

Compressed Sensing/Sparse-Recovery Approach for Improved Range Resolution in Narrow-Band Radar.

A compressed sensing/sparse-recovery procedure is adopted to obtain enhanced range resolution capability from the processing of data acquired with narrow-band SFCW radars. A mathematical formulation for the proposed approach is reported and validity limitations are fully discussed, by demonstrating the ability to identify a great number of targets, up to 20, in the range direction. Both numerical and experimental validations are presented, by assuming also noise conditions. The proposed method can be usefully applied for the accurate detection of parameters with very small variations, such as those involved in the monitoring of soil deformations or biological objects.

Spectral Methods for Response Enhancement of Microwave Resonant Sensors in Continuous Non-Invasive Blood Glucose Monitoring.

In this paper, the performance of three recent algorithms for the frequency-response enhancement of microwave resonant sensors are compared. The first one, a single-step algorithm, is based on a couple of direct-inverse Fourier transforms, giving a densely sampled response as a result. The second algorithm exploits an iterative procedure to progressively restricts the frequency response. The final one is based on the super-resolution MUSIC algorithm. The comparison is carried out through a Monte Carlo analysis. In particular, synthetic signals are firstly exploited to mimic the frequency response of a resonant microwave sensor. Then, experimental data collected from water-glucose solutions are adopted as validation test for potential applications in noninvasive blood-glucose monitoring.

Gel-Like Human Mimicking Phantoms: Realization Procedure, Dielectric Characterization and Experimental Validations on Microwave Wearable Body Sensors.

A simple and low-cost procedure for gel-like time-durable biological phantoms is presented in this work. Easily accessible materials are adopted, which are able to provide a flexible and controllable method to rapidly realize different kind of tissues. The proposed technique is applied to fabricate various tissue-mimicking phantoms, namely skin, muscle, blood and fat. Their effectiveness is first tested by performing dielectric characterization on a wide frequency range, from 500 MHz up to 5 GHz, and validating the measured dielectric parameters (dielectric constant and conductivity) by comparison with reference models in the literature. Then, a multi-layer phantom simulating the human arm is realized, and a wearable body sensor is adopted to prove the perfect agreement of the biometric response achieved in the presence of the fabricated phantom and that provided by a real human arm.