We introduce the concept of metamaterial analog computing, based on suitably designed metamaterial blocks that can perform mathematical operations (such as spatial differentiation, integration, or convolution) on the profile of an impinging wave as it propagates through these blocks. Two approaches are presented to achieve such functionality: (i) subwavelength structured metascreens combined with graded-index waveguides and (ii) multilayered slabs designed to achieve a desired spatial Green's function. Both techniques offer the possibility of miniaturized, potentially integrable, wave-based computing systems that are thinner than conventional lens-based optical signal and data processors by several orders of magnitude.

The interest in terahertz photometric and imaging measurements has motivated the development of bandpass resonant filters to be coupled to multiple-pixel devices such as bolometer arrays. Resonant grids are relatively simple to fabricate, exhibiting high transmission at the central frequency, a narrow bandpass, and good rejection of the side frequencies of the spectrum. We have fabricated filters centered at different frequencies between 0.4 and 10 THz, using photolithography and electroforming techniques. Transmission measurements have shown center frequencies and bandwidths close to the design predictions. The performance of the filters was found not to be critically dependent on small physical deformations in the mesh, becoming more noticeable at higher frequencies (i.e., for smaller physical sizes). Wider bandwidths, needed to attain higher sensitivities in the continuum, were obtained by changing the design parameters for filters at 2 and 3 THz.

Terahertz frequencies experiments has motivated the development of new sources, detectors and optical components. Here we will present a review of THz bandpass filters ranging from 0.4 to 10 THz. We also demonstrate our fabrication process, simulations and experimental results.

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Traffic Sign Recognition (TSR) is one of the many utilities made possible by embedded systems with internet connections. Through the usage of vehicular cameras, it’s possible to capture and classify traffic signs in real time with Artificial Intelligence (AI), more specifically, Convolutional Neural Networks (CNNs) based techniques. This article discusses the implementation of such TSR systems, and the building process of datasets for AI training. Such datasets include a brand new class to be used in TSR, vegetation occlusion. The results show that this approach is useful in making traffic sign maintenance faster since this application turns vehicles into moving sensors in that context. Leaning on the proposed technique, identified irregularities in traffic signs can be reported to a responsible body so they will eventually be fixed, contributing to a safer traffic environment. This paper also discusses the usage and performance of different YOLO models according to our case studies.

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