Stacked intelligent metasurfaces (SIMs) represent a key enabler for next-generation wireless networks, offering beamforming gains while significantly reducing the number of radio-frequency chains. In conventional space-only (S-only) SIM architectures, the rate of reconfigurability of the SIM is equal to the inverse of the channel coherence time. This paper investigates a novel beamforming strategy for massive downlink connectivity using a randomized space-time (ST) SIM. In addition to conventional S-only metasurface layers, the proposed design integrates an ST metasurface layer at the input stage of the SIM that introduces random variations over each channel coherence interval. These artificial time variations enable opportunistic user scheduling and exploitation of multiuser diversity under slow channel dynamics. To mitigate the prohibitive overhead associated with full channel state information at the transmitter (CSIT), we propose a partial-CSIT-based beamforming scheme that leverages randomized steering vectors and limited user-side feedback based on signal quality measurements. Numerical results demonstrate that the proposed ST-SIM architecture achieves satisfactory sum-rate performance while significantly reducing CSIT acquisition and feedback overhead, thereby enabling scalable downlink connectivity in dense networks.

Amplitude, phase, frequency, and polarization states of electromagnetic (EM) waves can be dynamically manipulated by means of artificially engineered planar materials, composed of sub-wavelength meta-atoms, which are typically referred to as metasurfaces. In wireless communications, metasurfaces can be configured to judiciously modify the EM propagation environment in such a way to achieve different network objectives in a digitally programmable way. In such a context, metasurfaces are typically referred to as reconfigurable intelligent surfaces (RISs). Until now, researchers in wireless communications have mainly focused their attention on slowly time-varying designs of RISs, where the spatial-phase gradient across the RIS is varied at the rate equal to the inverse of the channel coherence time. Additional degrees of freedom for controlling EM waves can be gained by applying a time modulation to the reflection response of RISs during the channel coherence time interval, thereby attaining rapidly time-varying RISs. In this paper, we develop a general framework where a downlink multiuser transmission over single-input single-output slow fading channels is assisted by a digitally controlled rapidly time-varying RIS. We show that reconfiguring the RIS at a rate greater than the inverse of the channel coherence time might be beneficial from a communication perspective depending on the considered network utility function and the available channel state information at the transmitter (CSIT). The conclusions of our analysis in terms of system design guidelines are as follows: (i) if the network utility function is the sum-rate time-averaged network capacity, without any constraint on fair resource allocation, and full CSIT is available, it is unnecessary to change the electronic properties of the RIS within the channel coherence time interval; (ii) if partial CSIT is assumed only, a rapidly time-varying randomized RIS allows to achieve a suitable balance between sum-rate time-averaged capacity and user fairness, especially for a sufficiently large number of users; (iii) regardless of the available amount of CSIT, the design of rapid temporal variations across the RIS is instrumental for developing scheduling algorithms aimed at maximizing the network capacity subject to fairness constraints.

This paper deals with a steady state load-shedding strategy considering the effect of uncertain data on generation supplies and loads. Uncertainty data are modeled by fuzzy numbers and a genetic algorithm is used to solve the load-shedding problem. The method is applied on two test case networks. Results prove additional enhancements for improving continuity of service in power systems during emergencies.

The increasing diffusion of distributed generation plants in recent years highlights problems concerning voltage regulation in medium voltage (MV) radial distribution networks. Among various possible control techniques able to regulate voltage profiles, intelligent systems based ones seems to be very promising. In particular, fuzzy control techniques are very interesting for a wide range of applicative fields like power distribution systems control, allowing regulation of voltage profiles handling uncertainty/vagueness and imprecise information. This paper presents a decentralized fuzzy based control technique finalized to realize a local regulation of voltage profiles at buses where wind power generators are connected, in order to avoid their disconnection. Validation of the proposed control system has been carried out by simulations conducted on a real MV Italian radial distribution system.

Nowadays, in Europe tunnel safety is a problem of great interest. Recent tragic events have led community and national politicians to start a process of legislation harmonization planning investments toward new technologies designed to improve tunnel safety. The purpose of this paper is to present an innovative system aiming at monitoring and steering vehicular flows nearby tunnels with high accidents risk rate. Based on the integration of IC, video, agent and soft computing technologies, the system's mission is to reduce the risk of accidents which occur inside tunnel through the adaptive generation of speed limits and information to the users approximating tunnels. Strict real-time requirements of the applicative context impose our goal being achieved through a parallel hierarchical fuzzy controller (HFC) implemented by means of a fuzzy control agents network (FCAN) allowing the system to gain higher performances. The fuzzy controller is divided into several sub controllers conceived separately. Some of these, adopt in turn a hierarchical prioritized structure.

In this paper, we present a new technique for determining the surface profile of a moderately rough interface between air and a homogeneous dielectric half-space. Based on sparsely sampled step-frequency ground penetrating radar measurements, the proposed inversion scheme uses a quasi-ray Gaussian beam fast forward model, coupled with a low-order parameterization of the surface profile in terms of B-splines. The profile estimation problem is posed as a parameter optimization problem, which is solved using a multiresolution continuation method via frequency hopping. Numerical experiments establish that the algorithm is efficient and yields accurate reconstructions throughout most of the illuminated region even in noisy environments, losing accuracy only in regions with very weak illumination.