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Metasurfaces, the ultrathin, 2D version of metamaterials, have recently attracted a surge of attention for their capability to manipulate electromagnetic waves. Recent advances in reconfigurable and programmable metasurfaces have greatly extended their scope and reach into practical applications. Such functional sheet materials can have enormous impact on imaging, communication, and sensing applications, serving as artificial skins that shape electromagnetic fields. Motivated by these opportunities, this progress report provides a review of the recent advances in tunable and reconfigurable metasurfaces, highlighting the current challenges and outlining directions for future research. To better trace the historical evolution of tunable metasurfaces, a classification into globally and locally tunable metasurfaces is first provided along with the different physical addressing mechanisms utilized. Subsequently, coding metasurfaces, a particular class of locally tunable metasurfaces in which each unit cell can acquire discrete response states, is surveyed, since it is naturally suited to programmatic control. Finally, a new research direction of software‐defined metasurfaces is described, which attempts to push metasurfaces toward unprecedented levels of functionality by harnessing the opportunities offered by their software interface as well as their inter‐ and intranetwork connectivity and establish them in real‐world applications.

This work proposes the use of Permutation Entropy (PE), a measure of time-series complexity, to characterize electroencephalogram (EEG) signals recorded during sleep. Such a measure could provide information concerning the different sleep stages and, thus, be utilized as an additional aid to obtain sleep staging information. PE has been estimated for artifact-free 30s segments from more than 80 hours of EEG records obtained from 16 subjects during all-night recordings, from which the mean PE for each sleep stage was obtained. It was found that different sleep stages are characterized by significantly different PE values, which track the physiological changes in the complexity of the EEG signals observed at the different sleep stages. This finding encourages the use of PE as an additional aide to either visual or automated sleep staging.

BackgroundGeneral anesthesia is a reversible state of unconsciousness and depression of reflexes to afferent stimuli induced by administration of a “cocktail” of chemical agents. The multi-component nature of general anesthesia complicates the identification of the precise mechanisms by which anesthetics disrupt consciousness. Devices that monitor the depth of anesthesia are an important aide for the anesthetist. This paper investigates the use of effective connectivity measures from human electrical brain activity as a means of discriminating between ‘awake’ and ‘anesthetized’ state during induction and recovery of consciousness under general anesthesia.Methodology/Principal FindingsGranger Causality (GC), a linear measure of effective connectivity, is utilized in automated classification of ‘awake’ versus ‘anesthetized’ state using Linear Discriminant Analysis and Support Vector Machines (with linear and non-linear kernel). Based on our investigations, the most characteristic change of GC observed between the two states is the sharp increase of GC from frontal to posterior regions when the subject was anesthetized, and reversal at recovery of consciousness. Features derived from the GC estimates resulted in classification of ‘awake’ and ‘anesthetized’ states in 21 patients with maximum average accuracies of 0.98 and 0.95, during loss and recovery of consciousness respectively. The differences in linear and non-linear classification are not statistically significant, implying that GC features are linearly separable, eliminating the need for a complex and computationally expensive non-linear classifier. In addition, the observed GC patterns are particularly interesting in terms of a physiological interpretation of the disruption of consciousness by anesthetics. Bidirectional interaction or strong unidirectional interaction in the presence of a common input as captured by GC are most likely related to mechanisms of information flow in cortical circuits.Conclusions/SignificanceGC-based features could be utilized effectively in a device for monitoring depth of anesthesia during surgery.

Metamaterials and their two-dimensional analogues, metasurfaces, have recently attracted enormous attention because of their powerful control over electromagnetic (EM) waves from microwave to visible range. Moreover, by introducing explicit control of its sub-wavelength unit cells, a metamaterial can become programmable. Programmable metamaterials may not only host multiple EM functionalities that can be chosen or combined through simple software directives, but also be provided with means to adapt to the environment or communicate with other metamaterials, thereby enabling a myriad of applications in sensing, imaging, or communications. The realization of such a software-driven cyber-physical vision comes, however, at the cost of significant hardware requirements. In this paper, recent progress in the field of programmable metasurfaces is reviewed, cutting across layers from the application down to the device and technology levels. The main aim is to present the current status, main benefits, and key challenges of this thriving research area with a tutorial spirit and from a hardware perspective.

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