Progress in Microgrid (MG) research has evolved the MG concept from classical, purely MG power networks to more advanced power and communications networks. The communications infrastructure helps control and manage the unreliable power outputs that most standard power generation elements of the MG (e.g., wind turbines and photo-voltaic panels) deliver. Although communication technologies do offer certain advantages for sensing and control, they generate other complications due to packet loss and packet latency, among other transmission impairments. In this work, we discuss the impact of communications on MG performance, establishing the requirements of data exchanges and system response in the three levels of a hierarchical control approach: primary, secondary, and tertiary. With a focus on the secondary level — responsible for ensuring the restoration of electrical parameters — we identify standards, networking protocols, and communication technologies relevant for the interoperability of MGs and clusters of MGs, including both modes of operation: isolated and grid-connected. We review theoretical approaches and practical implementations that consider the effects of the communications network on the general performance of the MG. Moreover, we undertake an experimental analysis of the influence of wired and wireless communication networks on MG performance, revealing the importance of designing future smart control solutions more robust to communication degradation, especially if wireless technologies are integrated to provide scalable deployments. Aspects such as resilience, security, and interoperability are also shown to require continuing efforts in research and practical applications.

Underground mining is expected to lead the mining industry in the near future. Due to the advance of electronic devices and communications technologies, new safety regulations must be accomplished in order to operate underground mines safely and optimally. In 2006, the US government updated safety policies and introduced the PUBLIC LAW109-236. As a result, underground tracking is nowadays mandatory for underground mine operations. This new regulation establishes that current, or immediately pre-accident location of all underground personnel must be delivered over ground. Despite the recent advances of tracking and positioning systems for indoor environments, underground mines are a unique scenario which poses different and highly demanding constraints for current technologies. Throughout this manuscript an extensive survey on underground mines’ positioning technologies is provided. The main characteristics of current localization systems in underground mines are analysed for their implementation from a user’s requirements point of view. Furthermore, the main constraints and opportunities of the existing indoor positioning systems to deliver underground tracking are presented. Finally, future research directions are highlighted.

The expectation for communication systems beyond 5G/6G is to provide high reliability, high throughput, low latency, and high energy efficiency services. The integration between systems based on radio frequency (RF) and visible light communication (VLC) promises the design of hybrid systems capable of addressing and largely satisfying these requirements. Hybrid network design enables complementary cooperation without interference between the two technologies, thereby increasing the overall system data rate, improving load balancing, and reducing non-coverage areas. VLC/RF hybrid networks can offer reliable and efficient communication solutions for Internet of Things (IoT) applications such as smart lighting, location-based services, home automation, smart healthcare, and industrial IoT. Therefore, hybrid VLC/RF networks are key technologies for next-generation communication systems. In this paper, a comprehensive state-of-the-art study of hybrid VLC/RF networks is carried out, divided into four areas. First, indoor scenarios are studied considering lighting requirements, hybrid channel models, load balancing, resource allocation, and hybrid network topologies. Second, the characteristics and implementation of these hybrid networks in outdoor scenarios with adverse conditions are analyzed. Third, we address the main applications of hybrid VLC/RF networks in technological, economic, and socio-environmental domains. Finally, we outline the main challenges and future research lines of hybrid VLC/RF networks.

This paper proposes two solutions based on angle diversity receivers (ADRs) to mitigate inter-cell interference (ICI) in underground mining visible light communication (VLC) systems, one of them is a novel approach. A realistic VLC system based on two underground mining scenarios, termed as mining roadway and mine working face, is developed and modeled. A channel model based on the direct component in line-of-sight (LoS) and reflections of non-line-of-sight (NLoS) links is considered, as well as thermal and shot noises. The design and mathematical models of a pyramid distribution and a new hemi-dodecahedral distribution are addressed in detail. The performances of these approaches, accompanied by signal combining schemes, are evaluated with the baseline of a single photo-diode in reception. Results show that the minimum lighting standards established in both scenarios are met. As expected, the root-mean-square delay spread decreases as the distance between the transmitters and receivers increases. Furthermore, the hemi-dodecahedron ADR in conjunction with the maximum ratio combining (MRC) scheme, presents the best performance in the evaluated VLC system, with a maximum user data rate of 250 Mbps in mining roadway and 120 Mbps in mine working face, received energy per bit/noise power of 32 dB and 23 dB, respectively, when the bit error rate corresponds to ${10}^{-4}$ , and finally, values of 120 dB in mining roadway and 118 dB in mine working face for signal-to-interference-plus-noise ratio are observed in a cumulative distribution function.

Epilepsy affects millions worldwide, making timely seizure detection crucial for effective treatment and enhanced well-being. Electroencephalogram (EEG) analysis offers a non-intrusive solution, but its visual interpretation is prone to errors and requires a lot of time. Many existing works focus solely on achieving competitive levels of accuracy without considering processing speed or the computational complexity of their models. This study aimed to develop an automated technique for identifying epileptic seizures in EEG data through analysis methods. The efforts have been primarily focused on achieving high accuracy results by operating exclusively within a narrow frequency band of the signal, while also aiming to minimize computational complexity. In this article, a new automated approach is presented for seizure detection by combining signal processing and machine learning techniques. The proposed method comprises four stages: (1) Preprocessing: Savitzky–Golay filter to remove the background noise. (2) Decomposition: discrete wavelet transform (DWT) to extract spontaneous alpha and beta frequency bands. (3) Feature extraction: six features (mean, standard deviation, skewness, kurtosis, energy, and entropy) are computed for each frequency band. (4) Classification: a support vector machine (SVM) method classifies signals as normal or containing a seizure. The method was assessed using two publicly available EEG datasets. For the alpha band, the highest achieved accuracy was 92.82%, and for the beta band it was 90.55%, which demonstrates adequate capability in both bands for accurate seizure detection. Furthermore, the obtained low computational cost suggests a potentially valuable application in real-time assessment scenarios. The obtained results indicate its capacity as a valuable instrument for diagnosing epilepsy and monitoring patients. Further research is necessary for clinical validation and potential real-time deployment.

A new visible light communication (VLC) system is proposed for localization in underground mining. Existent systems, such as global positioning system (GPS) and systems based on mobile communication, are generally not useful in underground mining. The proposed system is based on a three-dimensional trilateration VLC localization scheme. This articles offers an evaluation of the proposed system in different evaluation scenarios in terms of the average localization error. The proposed algorithm localizes the source with an average localization estimation error of less than (16.4 cm), based on the source location. The average error is (3.5 cm) for subjects that are very close to the light-emitting-diode (LEDs).The obtained results show the superiority of the proposed method in comparison with traditional short range radio frequency technologies such as RFID, Wi-Fi and Zigbee, making it a feasible system for localizing objects in underground mining.