This article describes the design, implementation, and evaluation of an indoor localization system based on Received Signal Strength Indicator (RSSI) measurements in wireless sensor networks. While the majority of the literature uses the IEEE 802.15 standard for this type of system, all of the measurements in this study were performed using a test bench operating under the IEEE 802.11ax standard in the 2.4 GHz band. RSSI is widely used due to its simplicity and availability; however, its accuracy is limited by signal attenuation, electromagnetic interference, and environmental variability. To mitigate these limitations, the present work proposes the implementation of advanced techniques, including weighted averages and positioning algorithms such as Min–Max, Maximum Likelihood, and trilateration, aiming to achieve an accuracy of 2 m in controlled conditions. The design also included a specialized test bench to calculate the coordinates and estimate the location of unknown nodes using anchor node positioning. This approach combines the simplicity of RSSI with optimized algorithms, providing a robust and practical solution for indoor localization. The results validate the system’s effectiveness and highlight its potential for future applications in real-world environments, opening new possibilities for optimizing wireless sensor networks and addressing the current challenges in localization systems.

In some complex networks, nodes can continuously increase or decrease the number of their incoming and outgoing links. The World Wide Web is one such example, as webmasters can add or delete hyperlinks on web pages at any time. There are also networks where this can not happen. For instance, in citation networks of scientific articles, after an article has been published, it will start gaining incoming links as it is cited, but its outgoing links will remain unchanged. Although articles are published with a predetermined number of references, the distribution of their outgoing links follows a power law, as if they were the result of a preferential process. So, how can we explain that the number of references an author includes in a scientific article is not purely random? In this work, a growth model for citation networks is presented, proposing that the distribution of outgoing links can be shaped by the presence of communities.

Multiple input-multiple output (MIMO) is a key enabling technology for the next generation of wireless communication systems. However, one of the main challenges in the implementation of MIMO system is the complexity of the detectors when the number of antennas increases. This aspect will be crucial in the implementation of future massive MIMO systems. A flexible design can offer a convenient tradeoff between detection complexity and bit error rate (BER). Deep learning (DL) has emerged as an efficient method for solving optimization problems in different areas. In MIMO communication systems, neural networks can provide efficient and innovative solutions. This paper presents an efficient DL-based signal detection strategy for MIMO communication systems. More specifically, a preprocessing stage is added to label the input signals. The labeling scheme provides more information about the transmitted symbols for better training. Based on this strategy, two novel schemes are proposed and evaluated considering BER performance and detection complexity. The performance of the proposed schemes is compared with the conventional one-hot (OH) scheme and the optimal maximum likelihood (ML) criterion. The results show that the proposed OH per antenna (OHA) and direct symbol encoding (DSE) schemes reach a classification performance F1-score of 0.97. Both schemes present a lower complexity compared with the conventional OH and the ML schemes, used as references. On the other hand, the OHA and DSE schemes have losses of less than 1 dB and 2 dB in BER performance, respectively, compared to the OH scheme. The proposed strategy can be applied to adaptive systems where computational resources are limited.

Many real-life complex networks have in-degree and out-degree distributions that decay as apower-law. However, the few models that have been able to reproduce both of these properties,cannot reproduce the wide range of values found in real systems. Another limitation of thesemodels is that they add links from nodes which are created into the network, as well as betweennodes already present in this network. However, adding links between existing nodes is not acharacteristic available in all systems. This paper introduces a new complex network growthmodel that, without adding links between existing nodes is able to generate complex topologieswith in-degree and out-degree distributions that decay as a power-law. Moreover, in this growthmodel, the ratio at which links are created is greater than the ratio at which nodes are born, whichproduces an accelerated growth phenomenon that can be found in some real systems, like theInternet at the Autonomous System level.

Deep learning (DL) has emerged as a promising alternative for signal detection in multiple-input multiple-output (MIMO) wireless communication systems, achieving a performance close to the optimal maximum likelihood (ML) criterion. However, implementing DL algorithms for MIMO signal detection still demands a high computational cost. Recently, a DL-based architecture with reduced complexity was proposed for MIMO signal detection. More specifically, a preprocessing stage was added to label the input signals, resulting in a more efficient detection scheme. However, even with this reduction in complexity, the proposed detection schemes remain computationally demanding, and this cost grows sharply as the system scales to higher-order modulations and/or large antenna arrays. To further reduce the complexity of these schemes, this paper proposes a graphics processing unit (GPU)-accelerated procedure for DL-based MIMO signal detection. The algorithm is accelerated by using two strategies in a CUDA-enabled PyTorch implementation. First, we optimize the training, which tightens the bit error rate (BER) gap from 1.00 dB to approximately 0.20 dB for the One-Hot (OH) labeling and from 2.00 dB to approximately 0.90 dB for the One-Hot-per-Antenna (OHA) labeling. Additionally, we optimize the Monte Carlo simulation using seven strategies, reducing the end-to-end evaluation time by 34.7% on an NVIDIA RTX 4090 GPU. These results aim to support future projects in evaluating DL-based MIMO systems using shorter computing windows.

There is evidence that high levels of folic acid in human plasma may prevent SARS-CoV-2 infections or reduce its symptoms. However, the mechanisms that enable this inhibition are still unknown. This article proves, through molecular dynamics simulation, that folic acid metabolite 7,8-dihydrofolate (DHF) has high affinity to bind as ligands to the angiotensin-converting enzyme 2 (ACE2), and when this complex has been formed, the Wild type SARS-CoV-2 virus cannot bind to the ACE2 receptor, possibly inhibiting infection to the host cell. In contrast, the Beta and Delta variants of this same virus can join the ACE2 with high affinity even with the presence of DHF. This results lead to the conclusion that DHF may inhibit infection from the Wild type SARS-CoV-2 virus, but not its Beta and Delta variants. These results could explain the almost double increase in severe cases of COVID-19 due to the delta variant in pregnant women compared to the Wild type SARS-CoV-2.