The radio spectrum is characterized by a noticeable variability, which impairs performance and determinism of every wireless communication technology. To counteract this aspect, mechanisms like Minstrel are customarily employed in real Wi-Fi devices, and the adoption of machine learning for optimization is envisaged in next-generation Wi-Fi 8. All these approaches require communication quality to be monitored at runtime. In this paper, the effectiveness of simple techniques based on moving averages to estimate wireless link quality is analyzed, to assess their advantages and weaknesses. Results can be used, e.g., as a baseline when studying how artificial intelligence can be employed to mitigate unpredictability of wireless networks by providing reliable estimates about current spectrum conditions.

Evaluating preimplantation frozen donor kidney biopsies is essential to determine the quality of kidney allografts. However, this task is hindered by interobserver variability and lack of reproducibility between pathologists due to differences in expertise levels and common artifacts in frozen preparations. While deep learning aids kidney structure segmentation on permanent sections, its application to frozen biopsies remains limited, particularly for complex structures. To address this, we developed SegRenal, an AI-driven model designed to segment key kidney compartments, including glomeruli (sclerotic and non-sclerotic), arteries, and the extent of interstitial scarring. SegRenal uses three deep learning architectures-DenseNet, UNet, and ResNet-trained on a comprehensive dataset of whole slide images (WSIs) from frozen kidney biopsies. The model was evaluated on both binary and multiclass segmentation tasks, with DenseNet demonstrating superior performance. Notably, the multiclass approach improved tissue differentiation compared to binary segmentation, particularly for complex structures. In terms of quantification, the pre-trained DenseNet model demonstrated robust performance across critical kidney structures. The model achieved a recall of 99.81% for glomeruli and 94.4% for sclerotic glomeruli. For arteries, the model reached a recall of 100%, detecting all arteries, though it generated some false positives due to the similarity of other structures like arterioles. Additionally, the model performed well in quantifying IFTA, accurately predicting the extent of fibrosis across the dataset. The inclusion of data from multiple scanners enhanced the model's generalization and robustness. By automating the quantification of these structures, SegRenal improves the accuracy and efficiency of kidney biopsy assessments, supporting more informed decision-making in kidney organ transplantation.

Probabilistic Neural Contextualization introduces a groundbreaking approach that bridges probabilistic reasoning with neural architectures to redefine contextual knowledge retrieval in computational models. By integrating dynamic contextualization mechanisms, it achieves enhanced adaptability and precision in managing uncertainty across diverse linguistic domains. The novel architecture incorporates advanced probabilistic inference layers, ensuring that outputs are consistently aligned with the complex demands of varied contextual scenarios. Comprehensive evaluations demonstrated marked improvements in key performance metrics, including perplexity and F1-score, validating the model's robustness and scalability. Experimental results further revealed its computational efficiency, maintaining performance consistency even under significant data variability and noise. Through seamless integration with open-source frameworks, the proposed methodology demonstrates its practical viability for large-scale applications, addressing critical challenges in knowledge-driven processing tasks. The study highlights a transformative advancement in contextual modeling, paving the way for more reliable, efficient, and intelligent computational systems capable of operating in complex and dynamic environments.

Multi-Link Operation (MLO) in Wi-Fi 7 is expected to tangibly boost throughput while lowering transmission latency at the same time. This is very relevant in industrial scenarios and makes MLO suitable, e.g., to support seamless device mobility. Benefits depend on the ability of multi-link devices to select at run-time the best link, among the available ones, in order to maximize both communication performance and reliability. In this paper an experimental platform is proposed, with the aim of leveraging commercial hardware and open source software, and easing prototyping and evaluation of MLO techniques. The platform has been employed to analyze the transmission quality of two pairs of non-overlapping channels, and in particular to assess whether or not adequate diversity is provided, so that those channels can be exploited to improve reliability. Results point out that correlation between different links is, in most cases, limited, which makes MLO a valuable approach.

Unmanned aerial vehicle (UAV) communications can be leveraged for efficient data collection from widely distributed sensors in wireless sensor networks (WSNs). Integrating federated learning (FL) with UAV-enabled WSNs allows decentralized and privacy-preserving training of machine learning models, enabling different Internet-of-things applications. Achieving energy efficiency for such FL-based systems is important due to the limited battery capacities of both UAVs and sensors but it remains an open challenge to the best of our knowledge. To fill this research gap, we study the joint design of UAV trajectory, data transmission, and computational resource allocation and formulate it as an optimization problem aiming to minimize the total weighted energy consumed by sensors and UAVs. In order to solve the underlying difficult mixed-integer nonlinear optimization problem, we employ the alternating optimization approach, where we sequentially solve different sub-problems in each iteration until convergence. We show that the data transmission and computational resource allocation sub-problems can be transformed into linear integer and convex optimization problems, respectively. Meanwhile, the successive convex approximation method is employed to tackle the UAV trajectory control sub-problem. Via extensive simulations, we demonstrate the effectiveness of our approach, showcasing significant energy savings and improved performance compared to other benchmarks. We also analyze the impacts of key parameters and the trade-off between learning performance and energy consumption via numerical studies.

Antenna gain is an important metric for most modern communication systems. It can be determined several ways, but most commonly by producing an incident plane wave using a reference antenna, and measuring the received power at the antenna of interest. By measuring the received power several wavelengths away in an isolated environment, like an anechoic chamber, the antenna gain can be determined. This and other similar methods work well for frequencies above 2 GHz, but lower frequency measurements can be logistically challenging and expensive due to the large facilities required, as well as the lack of readily available broadband absorber materials. This work presents a new method for determining antenna gain using a transmission line based near field S-parameter measurement in a waveguide. To provide evidence for the proposed method, two monopole antennas are modeled over an infinite ground plane using full wave electromagnetics, and both are experimentally measured within a waveguide. It was found that there is very good agreement between the model and measurement, providing evidence of the validity of the method.

The next generation of Wi-Fi is meant to achieve ultra-high reliability for wireless communication. Several approaches are available to this extent, some of which are being considered for inclusion in standards specifications, including coordination of access points to reduce interference. In this paper, we propose a centralized architecture based on digital twins, called WiTwin, with the aim of supporting wireless stations in selecting the optimal association according to a set of parameters. Unlike prior works, we assume that Wi-Fi 7 features like multi-link operation (MLO) are available. Moreover, one of the main goals of this architecture is to preserve communication quality in the presence of mobility, by helping stations to perform reassociation at the right time and in the best way.

In this study, we investigate the effects of Adversarial Neural Network Training (ANNT) on the robustness and effectiveness of Brain-to-Brain Communication (B2B-C) systems using Steady-State Visually Evoked Potentials (SSVEP) EEG data. We utilized a combined Convolutional Neural Network-Temporal Convolutional Network (CNN-TCN) architecture to classify the data and assessed the system's resistance to various adversarial strategies, including Fast Gradient Sign Method (FGSM), Projected Gradient Descent (PGD), Basic Iterative Method (BIM), Carlini & Wagner (C&W), and Momentum Iterative Method (MIM). By analyzing publicly accessible datasets, specifically Lee2019_SSVEP and Nakanishi2015, we observed significant enhancements in both accuracy and AUC metrics when ANNT was applied. Specifically, the Lee2019_SSVEP dataset exhibited a 24% increase in accuracy and a 0.23-point improvement in AUC, while the Nakanishi2015 dataset demonstrated improvements of 9% and 0.07 points, respectively. Our results indicate that PGD posed the greatest challenge to the model, significantly reducing accuracy and AUC across various scenarios, whereas FGSM was the least impactful. These findings highlight ANNT's potential in fortifying the security and stability of B2B-C systems against diverse adversarial conditions.

This study proposes an innovative hierarchical binary classification approach based on scalp EEG data to predict comatose patients' Glasgow Outcome Scale (GOS).The dataset consists of continuous EEG recordings from 13 comatose patients classified into three GOS groups: GOS 1 (death), GOS 3 (severe disability), and GOS 5 (good recovery). Traditional machine learning (ML) and deep learning (DL) models, though effective in some instances, struggle to generalize across diverse patient populations, mainly when evaluated with leave-one-subject-out (LOSO) cross-validation. We introduce a two-step binary classification approach to address these challenges, utilizing Convolutional Neural Networks (CNNs) with spectrogram features for one classifier and Random Forest (RF) with Approximate Entropy (ApEn) for the other. By simplifying the multi-class classification task into two binary decisions, we achieve perfect classification performance for GOS 1 and GOS 5, while GOS 3 is inferred by exclusion. Importantly, we found that just 2 minutes of EEG data per subject was sufficient to achieve this level of performance, offering significant implications for clinical applications where data is often limited. Even considering the small dataset, our results prove that our method performs better than traditional approaches, providing a scalable, efficient solution for coma outcome prediction.

The antenna-elements that make up a radio interferometer form a spatial filter that defines the main characteristics of the instrument, such as the resolution of the synthesized beam and the sampled spatial frequencies together with their density. For this reason, it is important to define the observation goals for the interferometer to then determine the location of the elements using an optimization algorithm. In this work, we implement an existing gradient based optimization method but extend its capabilities to (i) allow long-track observation optimization, and (ii) optimize array distributions when more elements are added, called stage optimization. Then, we apply our extended optimization algorithm to determine the distribution of the planned Argentinean Multipurpose Interferometer Array, based on the desired mean declination and mean observation time length. Finally, we evaluate the effects of different elevations for the elements of the optimized distribution in the terrain where the interferometer is planned to be built.

Static random access memory (SRAM) plays a vital role in the overall power consumption of system-on-chip (SoC), particularly in the portable applications. Supply voltage overscaling is one of the most commonly used design methods to reduce the power consumption of SRAM, which degrades the speed and stability. This paper proposes a high-speed/stability 10T SRAM (HS10T) cell with high read stability and write ability for low-voltage operation. A discharge transistor and the power-gating structure are used in SRAM to improve the write ability and reduce write delay with relatively low supply voltage. Meanwhile, the read-decoupling structure can improve the read static noise margin (RSNM) by separating the reading circuit from the writing circuit. The simulation results show that the delay and power-delay-product (PDP) of the proposed SRAM cell are reduced by 86.22% and 82.56% respectively compared to the latest similar work. Additionally, the average write margin (WM) of the HS10T SRAM cell has also been improved by 4.14% while ensuring high energy efficiency.

Recently, in-car monitoring has emerged as a promising technology for detecting early-stage abnormal status of the driver and providing timely alerts to prevent traffic accidents. Although training models with multimodal data enhances the reliability of abnormal status detection, the scarcity of labeled data and the imbalance of class distribution impede the extraction of critical abnormal state features, significantly deteriorating training performance. Furthermore, missing modalities due to environment and hardware limitations further exacerbate the challenge of abnormal status identification. More importantly, monitoring abnormal health conditions of passengers, particularly in elderly care, is of paramount importance but remains underexplored. To address these challenges, we introduce our IC3M, an efficient camera-rotation-based multimodal framework for monitoring both driver and passengers in a car. Our IC3M comprises two key modules: an adaptive threshold pseudo-labeling strategy and a missing modality reconstruction. The former customizes pseudo-labeling thresholds for different classes based on the class distribution, generating class-balanced pseudo labels to guide model training effectively, while the latter leverages crossmodality relationships learned from limited labels to accurately recover missing modalities by distribution transferring from available modalities. Extensive experimental results demonstrate that IC3M outperforms state-of-the-art benchmarks in accuracy, precision, and recall while exhibiting superior robustness under limited labeled data and severe missing modality.

With the growing adoption of network function virtualization, telco core network elements and network functions will increasingly be designed and deployed as cloud-native application instances. To ensure the efficient use of virtualised resources and meet diverse requirements for quality of services a resource scaling algorithm is used to scale the the number of application instances up or down depending on variations in offered traffic from customers. Most of the observed performance metrics for a service are a function of the current customer traffic and the current number of application instances providing the service. The ubiquitous use of Kubernetes, the popular open-source framework for deployment and management of cloud-native functions, has resulted in variants of the Kubernetes Horizontal Pod Autoscaling (HPA) algorithm being widely used to change the number of application instances providing network functions as traffic demands vary. This change is done by determining whether a selected performance metric of interest is outside a range set by two input parameters (the desired metric value and the tolerance parameter). In this paper, we invesitigate the characteristics of the HPA algorithms and prove that there are only a finite number of intervals for its tolerance parametere. Further any choice of the tolerance parameter from each interval leads to similar computational decisions on the recommended number of application instances. As a consequence, the number of parameter setting choices is finite due to the rule that the desired metric value can only be an integer in specific ranges. Additionally, we investigate the use of HPA for scaling application instances that provide session-based services and establish lower and the upper bounds for performance of the HPA scaling algorithms in this scenario. Our contributions can help operators find appropriate parameter settings efficiently-administrators of Kubernetes clusters only need to select parameters from a limited and finite number of choices (instead of infinite) for scaling cloud-native applications.

In this era of advanced communication technologies, many remote rural and hard-to-reach areas still lack Internet access due to technological, geographical, and economic challenges. The TV white space (TVWS) technology has proven to be effective and feasible in connecting these areas to Internet service in many parts of the world. The TVWS-based systems operate based on geolocation white space databases (WSDB) to protect the primary systems from harmful interference and thus there is a critical need to know the available and usable channels that can be used by the secondary white space devices (WSDs) in a specific geographic area. In this work, we developed a generalized and flexible graphical user interface (GUI) tool to evaluate the availability and usability of the TVWS channels and their noise levels at each geographic location within the analyzed area. The developed tool has many features and capabilities such as allowing the users to scan the TVWS spectrum for any geographic area in the world and any frequency band in the TVWS spectrum. Moreover, it allows the user to apply widely used terrain-based radio propagation models. It provides the flexibility to import the elevation terrain profile of any region with the desired spatial accuracy and resolution. In addition, various system parameters including those related to regulation rules can be modified in the tool. This tool exports to an external dataset file the output data of the available and usable TVWS channels and their noise levels and it also visualizes these data interactively.

This work investigates cellular millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems within the open radio access network (O-RAN) architecture, integrating the compatible spectrum, air interface, and networking entities of beyond fifth-generation wireless networks. To overcome O-RAN fronthaul (O-FH) load limitations and the short wavelength inherent in mmWave bands, we design a hybrid beamforming architecture with digital and analog beamformers generated at the O-RAN distributed unit and O-RAN radio unit, respectively. Using the information theory, we develop non-grid-of-beams analog beamformers to maximize the sum-spectral efficiency (SE) under constant-modulus constraints. For digital precoding, we apply a successive convex approximation method with second-order cone program procedures to maximize sum-SE, while addressing transmit power and limited O-FH load constraints, and ensuring user quality of service requirements. Sub-optimal digital combiners are also designed based on the inherent characteristics of the user side. However, the current optimization approach suffers from long execution times, posing challenges for near-real-time beamforming configurations. To address this issue, we propose an efficient deep learning (DL)-based digital precoding scheme with short execution time, low computational complexity, and high performance. Numerical results demonstrate that the proposed DL-based precoding scheme provides superior performance compared to benchmark schemes and offers scalability to massive MIMO configurations.

The escalating frequency and sophistication of cyber threats means the need of the development of more effective detection mechanisms. The Dynamic Entropy Analysis (DEA) framework introduces a novel approach to ransomware detection through real-time monitoring of entropy variations within system files and processes, enabling the identification of malicious activities with high accuracy. Comprehensive evaluation of the DEA framework demonstrated a detection accuracy of 94.5%, surpassing traditional signature-based and behavior-based methods. The framework's adaptability to emerging ransomware variants was evidenced by a consistent detection rate exceeding 90% over a six-month period. Scalability assessments revealed effective operation across networks ranging from 50 to 500 devices, with minimal impact on system performance, as indicated by consistent detection accuracy and acceptable resource utilization metrics. Detection latency analysis indicated prompt identification of ransomware activities, with detection occurring within 0.5 to 2.5 seconds across various samples. The modular design of the DEA framework facilitates seamless integration with existing security infrastructures, enhancing its feasibility for real-world deployment. These findings underscore the potential of the DEA framework to serve as a proactive and efficient solution in mitigating the impact of ransomware attacks across diverse computing environments.

Predicting the behavior of a wireless link in terms of, e.g., the frame delivery ratio, is a critical task for optimizing the performance of wireless industrial communication systems. This is because industrial applications are typically characterized by stringent dependability and end-to-end latency requirements, which are adversely affected by channel quality degradation. In this work, we studied two neural network models for Wi-Fi link quality prediction in dense indoor environments. Experimental results show that their accuracy outperforms conventional methods based on exponential moving averages, due to their ability to capture complex patterns about communications, including the effects of shadowing and multipath propagation, which are particularly pronounced in industrial scenarios. This highlights the potential of neural networks for predicting spectrum behavior in challenging operating conditions, and suggests that they can be exploited to improve determinism and dependability of wireless communications, fostering their adoption in the industry.

The increasing complexity and diversity of linguistic applications have highlighted the limitations of static and generalized mechanisms in adapting to the multifaceted structures of human language. Introducing a novel paradigm of dynamic hierarchical attention, the proposed methodology addresses these challenges through a multi-layered approach that prioritizes contextual relationships across varying levels of abstraction. The architecture dynamically recalibrates attention distributions, achieving significant improvements in contextual understanding, generative accuracy, and computational efficiency. Comprehensive evaluations demonstrated robust performance enhancements across multiple benchmarks, with reduced error rates, improved adversarial resilience, and superior semantic coverage. The framework's ability to process complex dependencies while maintaining scalability offers a meaningful advancement in the design and application of large-scale neural models. These contributions establish a new benchmark in the field, emphasizing the importance of integrating dynamic and hierarchical principles in future natural language processing research.