This paper illustrates how the penetration of electromagnetic waves in lossy media strongly depends on the waveform and not only on the media involved. In particular, the so-called inhomogeneous plane waves are compared against homogeneous plane waves illustrating how the first ones can generate deep penetration effects. Moreover, the paper provides examples showing how such waves may be practically generated. The approach taken here is analytical and it concentrates on the deep penetration conditions obtained by means of incident inhomogeneous plane waves incoming from a lossless medium and impinging on a lossy medium. Both conditions and constraints that the waveforms need to possess to achieve deep penetration are analysed. Some results are finally validated through numerical computations. The theory presented here is of interest in view of a practical implementation of the deep penetration effect.

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Abstract-Recent studies highlighted deep-penetration properties of inhomogeneous waves at the interface between a lossless and a lossy medium. Such waves can be generated by means of radiating structures known as Leaky-Wave Antennas (LWAs). Here, a different approach is proposed based on the use of a lossy prism capable to generate an inhomogeneous wave when illuminated by a homogeneous wave. The lossy prism is conceived and designed thinking of GroundPenetrating Radar (GPR). The results achieved by the lossy prism will be compared with those obtained by means of a previously designed LWA that was created with the identical objective. The approach of this paper is purely theoretical, and it aims at providing basic ideas and preliminary results useful for an innovative LWA design.

Skin cancer (SC) is one of the most prevalent cancers worldwide. Clinical evaluation of skin lesions is necessary to assess the characteristics of the disease; however, it is limited by long timelines and variety in interpretation. As early and accurate diagnosis of SC is crucial to increase patient survival rates, machine-learning (ML) and deep-learning (DL) approaches have been developed to overcome these issues and support dermatologists. We present a systematic literature review of recent research on the use of machine learning to classify skin lesions with the aim of providing a solid starting point for researchers beginning to work in this area. A search was conducted in several electronic databases by applying inclusion/exclusion filters and for this review, only those documents that clearly and completely described the procedures performed and reported the results obtained were selected. Sixty-eight articles were selected, of which the majority use DL approaches, in particular convolutional neural networks (CNN), while a smaller portion rely on ML techniques or hybrid ML/DL approaches for skin cancer detection and classification. Many ML and DL methods show high performance as classifiers of skin lesions. The promising results obtained to date bode well for the not-too-distant inclusion of these techniques in clinical practice.

The manuscript reviews the current literature on scattering applications of RADAR (Radio Detecting And Ranging) in remote sensing and diagnostics. This paper gives prime features for a variety of RADAR applications ranging from forest and climate monitoring to weather forecast, sea status, planetary information, and mapping of natural disasters such as the ones caused by earthquakes. Both the fundamental parameters involved in scattering mechanisms of RADAR applications and the factors affecting RADAR performances are also discussed.