The rising incidence of skin cancer necessitates scalable and accurate diagnostic tools. While dermoscopy-based systems have achieved expert-level performance, clinical smartphone images pose challenges due to variability in lighting, resolution, and artifacts. Recent advances in multimodal deep learning have shown promise, yet most approaches rely on simple feature concatenation or single-model classifiers, limiting their ability to capture complex cross-modal interactions. This study aims to bridge the diagnostic gap in resource-limited settings by developing a robust multimodal framework that synergizes clinical smartphone images with structured patient metadata for automated skin cancer classification. We propose a novel hybrid architecture integrating a Swin Transformer V2 (SwinV2-Tiny) for hierarchical visual feature extraction and a Denoising Autoencoder (DAE) with PCA for robust metadata embedding. These heterogeneous modalities are fused via a Gated Attention Mechanism that dynamically weighs feature importance across streams. Classification is performed by a Heterogeneous Meta-Stack Ensemble comprising CatBoost, LightGBM, XGBoost, and Logistic Regression, designed to maximize calibration and generalization across imbalanced classes. Evaluated on the PAD-UFES-20 dataset (2298 clinical smartphone images, six diagnostic classes), the proposed framework achieves state-of-the-art performance with a macro-averaged F1-score of 0.977, accuracy of 0.978, and an AUC of 0.990. It significantly outperforms unimodal baselines and existing multimodal methods, demonstrating superior sensitivity (0.974) and precision (0.981), particularly for underrepresented malignant classes like Melanoma (F1: 0.995) and Squamous Cell Carcinoma (F1: 0.960). The integration of clinical metadata with advanced visual embeddings via gated attention significantly enhances diagnostic reliability. Comprehensive ablation studies confirm the contribution of each architectural component. This framework offers a viable pathway for deploying high-precision, AI-driven dermatological screening tools on standard smartphone devices.

In this chapter, we present an extension of the rigorous analysis of the propagation of electromagnetic waves in magnetic transverse (TM) and transverse electric (TE) modes in a metallic circular waveguide partially filled with anisotropic metamaterial. In our analysis, the design of waveguide filters with uniaxial discontinuities is based on the determination of the higher-order modes, which have been analyzed and exploited. Below the cutoff frequency, the back backward waves can propagate in an anisotropic material. The numerical results with our MATLAB code for TM and TE modes were compared to theoretical predictions. Good agreements have been obtained. We analyzed a waveguide filters filled with partially anisotropic metamaterial using the mode matching (MM) technique based on the Scattering Matrix Approach (SMA), which, from the decomposition of the modal fields (TE and TM modes), are used to determine the dispersion matrix and thus the characterization of a discontinuity in waveguide. We extended the application of MM technique to the anisotropic material. By using modal analysis, our approach has considerably reduced the computation time compared to High Frequency Structure Simulator (HFSS) software.

Compact ultra-wideband (UWB) multiple-input multiple-output (MIMO) front-ends must stay matched over a wide continuum, reject licensed 5G interferers and remain uncorrelated at sub-wavelength spacing. This paper formulates coexistence as the inversion of a mask M = (N(f), I(f), rho-max, eta-min) and treats complementary split-ring resonators (CSRRs) and a split-ring resonator (SRR) fence as operators acting on electrically distinct regions. Sufficient conditions for a conditionally factorised inverse are derived (bounded interaction on the support of N(f), bounded cross-sensitivity and a well-conditioned rejection Jacobian), together with a first-order bound on the inversion error. The rejection factor is implemented by inverse regressors with a forward-consistency check and an out-of-domain detector (random forest and k-nearest neighbours, R-squared = 0.9825 and 0.9921 on unit-cell data); non-uniqueness is handled by selecting, among forward-consistent candidates, the geometry with the largest fabrication margin. While ten target specifications are queried across the C-band and WLAN windows, only the two fabricated unit cells have undergone dedicated full-wave and experimental validation, whereas the remaining eight are compared against their nearest training-set neighbours, leaving multi-target generalisation across the specification space only partially validated. A two-element FR4 prototype designed as an experimental testbed for the union of a C-band and a WLAN mask, with 10 $\mathrm{m}$$\mathrm{m}$ edge-to-edge spacing (0.07 wavelength at $2.1$$\mathrm{G}$$\mathrm{Hz}$ and 0.097 at the $2.9$$\mathrm{G}$$\mathrm{Hz}$ lower matched edge in full wave), is measured and scored against the mask. In measurement, frequency-domain coupling stays below $-20$ dB from 2.1 to about $14.8$$\mathrm{G}$$\mathrm{Hz}$ (full wave: 67% of the band). Rejection is the weak coordinate: the measured reflection of the two-port board peaks at about $-9.5$ dB in both stop-bands, against $-2.9$ and $-2.0$ dB in full wave, so the $-10$ dB criterion is met over about 44% of the combined mask, and the C-band mask is covered only from about $3.9$$\mathrm{G}$$\mathrm{Hz}$. This partial compliance exposes the effects of uncompensated unit-cell-to-aperture transfer offsets, stop-band bandwidth mismatch and FR4 Q-factor damping, showing that the prototype demonstrates the diagnostic scoring pipeline rather than a fully compliant hardware realisation of coexistence-mask inversion. The four-configuration test, carried out in full wave at the fabricated design, shows an interaction residual (0.61 in magnitude norm) far above the rejection-margin threshold (0.019), so condition (C1) is not met and factorised inversion remains a strictly conditional formulation that is not experimentally validated by the current prototype. Moreover, because pattern-based correlation is evaluated only from simulated patterns at six spot frequencies (ECC at most 0.015) without measured pattern validation, and because group delay and fidelity factor have not been assessed, wideband performance claims are restricted to continuous-wave frequency-domain matching and isolation. The changes needed to close this gap, the tolerance budget of both cells and the validation that remains open are stated explicitly, and modelled results are separated from measured ones.