We present an initial experimental validation of a microwave tomography (MWT) prototype for brain stroke detection and classification using the distorted Born iterative method, two-step iterative shrinkage thresholding (DBIM-TwIST) algorithm. The validation study consists of first preparing and characterizing gel phantoms which mimic the structure and the dielectric properties of a simplified brain model with a haemorrhagic or ischemic stroke target. Then, we measure the S-parameters of the phantoms in our experimental prototype and process the scattered signals from 0.5 to 2.5 GHz using the DBIM-TwIST algorithm to estimate the dielectric properties of the reconstruction domain. Our results demonstrate that we are able to detect the stroke target in scenarios where the initial guess of the inverse problem is only an approximation of the true experimental phantom. Moreover, the prototype can differentiate between haemorrhagic and ischemic strokes based on the estimation of their dielectric properties.

A novel technique for imaging based on ultrawideband (UWB) microwave signals is introduced. Specifically, the procedure deals with the Huygens Principle (HP); using HP to forward-propagate the waves removes the need to solve inverse problems and, consequently, no matrix generation/inversion is required. Together with its simplicity, the methodology permits the capture of contrast-the extent to which different media can be discriminated in the final image. Moreover, UWB allows all the information in the frequency domain to be utilized by combining the information from the individual frequencies to construct a consistent image. It follows that the methodology can identify the presence and location of significant scatterers inside a volume. Validation of the technique through both simulations and measurements on cylinder and spheres with inclusions has been performed. Application of the proposed technique to medical imaging is envisaged.

We present the European research project GHOST, (Safe-guarding home IoT environments with personalised real-time risk control), which challenges the traditional cyber security solutions for the IoT by proposing a novel reference architecture that is embedded in an adequately adapted smart home network gateway, and designed to be vendor-independent. GHOST proposes to lead a paradigm shift in consumer cyber security by coupling usable security with transparency and behavioural engineering.