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Magnetic biomaterials are multifunctional tools currently under investigation as theranostic platforms for biomedical applications. They can be implanted in bone tissue after bone cancer resection to perform local interstitial hyperthermia treatment. Given the requirements of high quality treatment, the hyperthermia therapy should be performed monitoring the system temperature, to avoid hot spots and control the treatment outcome. It is known that the magnetic properties of such implants vary with temperature. It is hypotesized that the treatment dynamic could be monitored using a microwave monitoring system. The variation of the electromagnetic properties of the biological tissues and the magnetic implant during the therapy would result in a different propagation of the microwave signal. This work investigates the feasibility of using microwaves to non-invasively monitor hyperthermia treatments with a simplified monodimensional propagation model. The forward problem is solved to identify the working frequencies, the matching medium properties and study several candidate materials. By using the numerical solutions from nonlinear and multiphysics simulations of the bone tumor hyperthermia treatment using magnetic scaffolds, the microwave signal propagation dynamic is studied. From our feasibility analysis, we found that it is possible to correlate the average tumor temperature with significant (~20 dB) variations in the transmission coefficient during a typical interstitial hyperthermia session using magnetic scaffolds. Our work brings together, for the first time, the electromagnetic material properties, the physio-pathology and physics of the hyperthermia treatment and the microwave propagation problem, thus paving the route for the development of an innovative theranostic system.

In this paper, a microwave imaging approach based on artificial neural networks (ANNs) for neck tumor detection is presented. The aim of this technique is to retrieve the geometric and dielectric properties of the neck to identify the possible presence of tumors, starting from scattered electric field data. A fully-connected neural network is developed to test the feasibility of the proposed approach. Moreover, a numerical model including the main features of a cross section of the neck is specifically designed in order to create a suitable training dataset. Subsequently, for the optimization of the ANN architecture and performance evaluation, a numerical analysis is conducted. A set of simulated cases, based on realistic neck phantoms, is tested to evaluate the robustness of the network. Preliminary results show the possibility to identify and locate neck tumors.

The nondestructive characterization of cylindrical objects is needed in many fields, such as medical diagnostics, tree trunk inspection, or concrete column testing. In this study, the radar equation of Lambot et al. is combined with cylindrical Green’s functions to fully model and invert ultra-wideband (UWB) ground-penetrating radar (GPR) data and retrieve the properties of cylindrical objects. Inversion is carried out using a lookup table (LUT) approach followed by local optimization to ensure retrieval of the global minimum of the objective function. Numerical experiments were conducted to analyze the capabilities of the developed inversion procedure to estimate the radius, permittivity, and conductivity of the cylinders. The full-wave model was validated in laboratory conditions on metallic and plastic pipes of different sizes. The adopted radar system consists of a lightweight vector network analyzer (VNA) connected to a single transmitting and receiving horn antenna. The numerical experiments highlighted the complexity of the inverse problem, mainly originating from the multiple propagation modes within cylindrical objects. The laboratory measurements demonstrated the accuracy of the forward modeling and reconstructions in far-field conditions.

A microwave imaging approach for the diagnosis of neck diseases is proposed in this paper. Specifically, the case study of cervical myelopathy has been considered. The developed technique aims at retrieving the distributions of the dielectric properties of a cross section of the neck, from which information about the spinal canal size can be extracted. To this end, a new hybrid inversion procedure exploiting a qualitative delay-and-sum method together with a conjugate-gradient-like scheme developed in Lebesgue spaces has been used. The feasibility of the approach has been assessed by means of a prototype of measurement system equipped with ten bowtie-like antennas and using a simplified phantom. Preliminary experimental results are reported.

Microwave heating offers a lot of advantages compared to conventional heating methods in the chemical reactions field due to its positive effects on reaction time and selectivity. Dielectric properties, and in particular permittivity, of substances and mixtures, are important for the optimization of microwave heating processes, notwithstanding this, specific databases are poor and far from being complete, and in the scientific literature very little data regarding these properties can be found. In this work, impedance measurements were carried out using a specially designed system to get the real and imaginary parts of the dielectric constant. The apparatus was tested in the estimation of permittivity of water&ndash, ethanol and water&ndash, NaCl mixtures, varying their composition to obtain a wide range of permittivity values. The results were compared to literature data and fitted with available literature models to verify the correspondence between them, finding that permittivity dependence on mixture composition can be effectively described by the models.

Microwave imaging is acquiring a growing importance in several biomedical applications, such as breast and brain stroke diagnosis and monitoring. In this work, a preliminary feasibility analysis concerning the application of such a technique to the monitoring of cervical myelopathy is reported. In particular, suitable working conditions are defined on the basis of a simplified multilayer model of the neck and a first inversion result, aimed at assessing the possibility of retrieving the spinal cord size, is shown.

Magnetic resonance imaging (MRI) has the clinical potential of helping diagnosis in providing to doctors structural and functional information of several neurological disorders. In this study, we proposed a new method based on the elaboration of MR-Images and functional Magnetic Resonace Images (fMRI), combined with the explotation of Mini Mental Score Examination (MMSE) to discriminate Alzheimer’s Disease (AD) by control subjects using Support Vector Machine (SVM) classification. 69 subjects from the Alzheimer’s disease Neuroimaging Initiative (ADNI) open database, 33 AD patients and 36 healthy controls (HC), were analyzed. The use of a unimodal approach led to unsatisfactory results, whereas the multimodal approach, i.e., the combination of MRI, fMRI, and MMSE features, provided an accuracy of 95.65%, a specificity of 97.22%, and a sensibility of 93.39%.

This work copes with the design and implementation of a wireless sensors network architecture to automatically and continuously monitor, for the first time, the manufacturing process of Sardinian Carasau bread. The case of a traditional bakery company facing the challenge of the Food-Industry 4.0 competitiveness is investigated. The process was analyzed to identify the most relevant variables to be monitored during the product manufacturing. Then, a heterogeneous, multi-tier wireless sensors network was designed and realized to allow the real-time control and the data collection during the critical steps of dough production, sheeting, cutting and leavening. Commercial on-the-shelf and cost-effective integrated electronics were employed, making the proposed approach of interest for many practical cases. Finally, a user-friendly interface was provided to enhance the understanding, control and to favor the process monitoring. With the wireless senors network (WSN) we designed, it is possible to monitor environmental parameters (temperature, relative humidity, gas concentrations), cinematic quantities of the belts, and, through a dedicated image processing system, the morphological characteristics of the bread before the baking. The functioning of the WSN was demonstrated and a statistical analysis was performed on the variables monitored during different seasons.

The ubiquitous nature and great potential of Wireless Sensors Network has not yet been fully exploited in automotive applications. This work deals with the choice of the cost-effective hardware required to face the challenges and issues proposed by the new trend in the development of intelligent transportation systems. With this aim, a preliminary WSN architecture is proposed. Several commercially available open-source platforms are compared and the Raspberry Pi stood out as a suitable and viable solution. The sensing layer is designed with two goals. Firstly, accelerometric, temperature, and relative humidity sensors were integrated on a dedicated PCB to test if mechanical or environmental stresses during bus rides could be harmful to the device or to its performances. The physical quantities are monitored automatically to alert the driver, thus improving the quality of service. Then, the rationale and functioning of the management and service layer is presented. The proposed cost-effective WSN node was employed and tested to transmit messages and videos, while investigating if any quantitative relationship exists between these operations and the environmental and operative conditions experienced by the hardware.

MRI can favor clinical diagnosis providing morphological and functional information of several neurological disorders. This paper deals with the problem of exploiting both data, in a combined way, to develop a tool able to support clinicians in the study and diagnosis of Alzheimer&rsquo, s Disease (AD). In this work, 69 subjects from the ADNI open database, 33 AD patients and 36 healthy controls, were analyzed. The possible existence of a relationship between brain structure modifications and altered functions between patients and healthy controls was investigated performing a correlation analysis on brain volume, calculated from the MRI image, the clustering coefficient, derived from fRMI acquisitions, and the Mini Mental Score Examination (MMSE). A statistically-significant correlation was found only in four ROIs after Bonferroni&rsquo, s correction. The correlation analysis alone was still not sufficient to provide a reliable and powerful clinical tool in AD diagnosis however. Therefore, a machine learning strategy was studied by training a set of support vector machine classifiers comparing different features. The use of a unimodal approach led to unsatisfactory results, whereas the multimodal approach, i.e., the synergistic combination of MRI, fMRI, and MMSE features, resulted in an accuracy of 95.65%, a specificity of 97.22%, and a sensibility of 93.93%.