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.

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.

The rapid expansion of Internet of Things (IoT) networks has significantly increased security vulnerabilities, exposing critical infrastructures to sophisticated cyberattacks. Traditional Intrusion Detection Systems, based mainly on signature matching and predefined rules, present limitations in identifying emerging threats and distributed attacks due to their inability to analyze complex interactions within IoT networks. To address this problem, this study proposes a graph-based intrusion detection model using Graph Neural Networks (GNNs), leveraging a dynamic representation of IoT network traffic. In this model, devices are represented as nodes and communications as weighted edges, integrating features such as communication frequency, transmitted data volume, and protocol type. The proposed method was evaluated using a customized dataset from a simulated IoT network to reflect real-world attack scenarios, including Denial of Service, Spoofing, and Man-in-the-Middle. Experimental results demonstrate that our GNN-based model significantly outperforms traditional machine learning methods, achieving an F1-Score of 0.95 and an AUC-ROC of 0.98, compared to values between 0.84 and 0.91 for Support Vector Machines and Random Forests. Furthermore, the system reduces the false positive rate by 40% compared to signature-based IDS, improving its applicability in operational environments. The model also proved scalable, maintaining an inference time of 2.5 ms per sample on graphs of up to 10,000 nodes, making it viable for real-time deployment. These findings confirm that graph-based anomaly detection is a promising approach for securing large-scale IoT infrastructures, providing increased precision, adaptability, and robustness against emerging cyber threats.

Localization in hospitals can be valuable in improving different services in medical environments. In this sense, an accurate location system in this environment requires adequately enabling communication technology. However, widely adopted technologies such as Wireless Fidelity (WiFi), Bluetooth, and Radio Frequency Identification (RFID) are considered poorly suited to enable hospital localization due to their inherent drawbacks, including high implementation costs, poor signal strength, imprecise estimates, and potential interference with medical devices. The increasing expenses associated with the implementation and maintenance of these technologies, along with their limited accuracy in dynamic hospital environments, underscore the pressing need for alternative solutions. In this context, it becomes imperative to explore and present novel approaches that not only avoid these challenges but also offer more cost effective, accurate, and interference-resistant connectivity to achieve precise localization within the complex and sensitive hospital environment. In the quest to achieve adequate localization accuracy, this article strategically focuses on leveraging Visible Light Communication (VLC) as a fundamental technology to address the specific demands of hospital environments to achieve the precise localization and tracking of life-saving equipment. The proposed system leverages existing lighting infrastructure and utilizes three transmitting LEDs with different wavelengths. The Received Signal Strength (RSS) is used at the receiver, and a trilateration algorithm is employed to determine the distances between the receiver and each LED to achieve precise localization. The accuracy of the localization is further enhanced by integrating a trilateration algorithm with the sophisticated Particle Swarm Optimization (PSO) algorithm. The proposed method improves the localization accuracy, for example, at a height of 1 m, from a 11.7 cm error without PSO to 0.5 cm with the PSO algorithm. This enhanced accuracy is very important to meet the need for precise equipment location in dynamic and challenging hospital environments to meet the demand for life-saving equipment. Furthermore, the performance of the proposed localization algorithm is compared with conventional positioning methods, which denotes improvements in terms of the localization error and position estimation.

In this paper, OFDM-based techniques designed for Intensity Modulation/Direct Detection (IMDD) modulation applied to Visible Light Communications (VLC) systems are evaluated. The evaluated techniques are Asymmetrically Clipped Optical OFDM (ACO-OFDM), Direct Current (DC) Biased Optical OFDM (DCO-OFDM), and Asymmetrically Clipped DC Biased Optical OFDM (ADO-OFDM). These techniques are evaluated and compared for different types of M-QAM modulation in terms of Bit Error Rate (BER). It is shown that ACO-OFDM, in general terms, presents a better performance compared to the conventional DCO-OFDM and ADO-OFDM schemes.