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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.

The idea defended in this paper consists in finding, at any time and everywhere, the arrangement of containers within a composite container. The digital image of the real arrangement obtained defines its digital twin. This image evolves at the same time as its real twin. It can be used throughout the logistics chain during loading/unloading phases in hubs, to check the completeness of a load, to find the particular position of a container, etc. This digital twin is obtained through the collection of neighborhood information from the sensor nodes embedded on each container. This embedded solution allows accessibility to this information everywhere. This proximity information and the instrumentation of the containers define new types of constraints and a new version of a packing problem. We propose here a model integrating them. This model is implemented and tested on different test cases, and numerical results are provided. These show that, under certain conditions that will be presented, it is possible to obtain the digital twin of the real arrangement.

Underground Mining (UM) is a hostile industry that generally requires a wireless communication system as a cross-cutting axis for its optimal operation. Therefore, in the last five years, it has been shown that, in addition to radio-frequency-based communication links, wireless optical communications, such as Visible Light Communication (VLC), can be applied to UM environments. The application of VLC systems in underground mines, known as UM-VLC, must take into account the unique physical features of underground mines. Among the physical phenomena found in underground mines, the most important ones are the positioning of optical transmitters and receivers, irregular walls, shadowing, and a typical phenomenon found in tunnels known as scattering, which is caused by the atmosphere and dust particles. Consequently, it is necessary to use proper dust particle distribution models consistent with these scenarios to describe the scattering phenomenon in a coherent way in order to design realistic UM-VLC systems with better performance. Therefore, in this article, we present an in-depth study of the interaction of optical links with dust particles suspended in the UM environment and the atmosphere. In addition, we analytically derived a hemispherical 3D dust particle distribution model, along with its main statistical parameters. This analysis allows to develop a more realistic scattering channel component and presents an enhanced UM-VLC channel model. The performance of the proposed UM-VLC system is evaluated using computational numerical simulations following the IEEE 802.1.5.7 standard in terms of Channel Impulse Response (CIR), received power, Signal-to-Noise-Ratio (SNR), Root Mean Square (RMS) delay spread, and Bit Error Rate (BER). The results demonstrate that the hemispherical dust particle distribution model is more accurate and realistic in terms of the metrics evaluated compared to other models found in the literature. Furthermore, the performance of the UM-VLC system is negatively affected when the number of dust particles suspended in the environment increases.

In this manuscript we propose a hybrid Visible Light Communication and Radio Frequency (VLC-RF) scheme for the implementation of a portable Phaser Measurement Unit (PMU) for deep underground tunnels. Through computer simulations and laboratory measurements we are capable of providing Coordinated Universal Time (UTC) to the PMUs, as well as high accuracy positioning in a Global Positioning System (GPS) denied environment. The estimated PMU position, time stamp, and electrical power system measurements are sent to a central monitoring station using a radio frequency uplink with a data rate of hundreds of Kbps. Simulations and experimental measurements show that the proposed scheme can be used to control a large number of VLC-RF PMU devices inside a tunnel. The tests demonstrate the viability of the hybrid prototype, which will improve performance compared to commercial PMUs that lack these features.

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.

Evaluation of seismic signals is one of the most important research topics on Volcanology. Volcanoes have daily activity; therefore, high speed evaluation of recorded signals is a challenge for improving the study of the natural phenomena occurring inside these natural formations. The aim of this paper is the evaluation (denoising, localization and classification) and analysis of Llaima volcano activities, one of the most actives volcanoes in South America. Different already proposed methods, such as, Butterworth, Spectral Subtraction (SS) and Wiener Filter (WF) are compared to the proposed Modified Spectral Subtraction (MSS) and Modified Wiener Filter (MWF) to find the best method for denoising the volcano signals. Then, event localization based on received signals of volcano is performed. In this step, Time Delay Estimation (TDE)-based method is used on data acquired from 3 mechanical sensors located in the volcano area. The proposed method is used to estimate the area for event location. The proposed denoising methods make the starting point for the event more evident to increase the localization accuracy for events where the starting point is difficult to find. In the last step, a method based on the novel DNN technique is proposed to classify the three main events occurring in the Llaima volcano (TR (Tremor), LP (Long Period) and VT (Volcano Tectonic)).

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.

Radar sensors operating in the mmWave frequency range face challenges when used as indoor perception and imaging devices, primarily due to noise and multipath signal distortions. These distortions often impair the sensors' ability to accurately perceive and image the indoor environment. Nevertheless, this sensor offers distinct advantages over camera and LiDAR sensors. This encompasses the estimation of object reflectivity, known as radar cross-section (RCS), and the ability to penetrate through objects that are thin or have low reflectivity. This results in a 'through-the-wall' sensing capability. Due to the aforementioned disadvantages, most research in the field of imaging radar tends to exclude indoor areas. We introduce a machine learning-based mmWave MIMO FMCW imaging radar object classifier designed to identify small, hand-sized objects in indoor settings, utilizing only radar IQ samples as input. This system achieves 97-99 % accuracy on our test set and maintains approximately 50 % accuracy even under challenging conditions, such as increased background noise and occlusion of sample objects, without the need for adjusting training or pre-processing. This demonstrates the robustness of our approach and offers insights into what needs to be improved in the future to achieve generalization and very high accuracy even in the presence of significant indoor perturbations.

Understanding surface material properties is crucial for enhancing indoor robot perception and indoor digital twinning. However, not all sensor modalities typically employed for this task are capable of reliably capturing detailed surface material characteristics. By analyzing the reflected RF signal from a mmWave radar sensor, it is possible to extract information about the reflective material and its composition from a certain surface. We introduce a mmWave MIMO FMCW radar-based surface material classifier SMCNet, employing a complex-valued Convolutional Neural Network (CNN) and complex radar IQ signal input for classifying indoor surface materials. While current radar-based material estimation approaches rely on a fixed sensing distance and constrained setups, our approach incorporates a setup with multiple sensing distances. We trained SMCNet using data from three distinct distances and subsequently tested it on these distances, as well as on two more unseen distances. We reached an overall accuracy of 99.12-99.53 % on our test set. Notably, range FFT pre-processing improved accuracy on unknown distances from 25.25 % to 58.81 % without re-training.