Search Results (124)

The continuous growth of traffic in metro networks is increasing the need for cost-effective, scalable, and power-efficient optical solutions. Filterless optical networks (FONs) have emerged as a promising architecture for metro-aggregation and metro-access domains, thanks to their low complexity and reliance on passive optical components. However, their inherent broadcast nature introduces key challenges, including spectrum waste, limited power equalization, and significant noise accumulation, particularly when coherent pluggable transceivers are employed. This work provides a detailed assessment of FON performance using state-of-the-art multi-source agreement (MSA)-compliant coherent modules, evaluating both point-to-point (P2P) and digital subcarrier multiplexing (DSCM)-based point-to-multipoint (P2MP) architectures. A novel optical amplifier (OA) optimization algorithm is proposed to balance expressed and added signal power in FON, accounting for optical power saturation effects and optical performance degradation due to limited power at the receiver input. The analysis highlights the substantial impact of transmitter out-of-band (OB) noise in FONs and its detrimental accumulation during multi-channel colorless aggregation, which can limit network capacity. In scenarios with lower capacity requirements, P2MP architectures demonstrate superior performance, benefiting from reduced insertion loss and lower OB noise accumulation while offering enhanced scalability compared with P2P solutions. Overall, the study highlights that FONs combined with coherent pluggables can support cost-efficient and scalable metro solutions, provided that OB noise, power imbalance, and amplifier operation are properly addressed through optimized design strategies. Full article

Techno-economic analysis (TEA) plays a vital role in assessing the feasibility and scalability of emerging technologies, especially in the context of innovation and development. Central to any effective TEA is a reliable and detailed model of capital and operational costs. This paper reports the development of such a model for optical networks in the framework of the SEASON project, aimed at supporting a broad spectrum of techno-economic evaluations. The model is constructed using publicly available data and expert insights from project participants. Its generalizable design allows it to be used both within the SEASON project and as a reference for other studies. By harmonizing assumptions and cost parameters, the model fosters consistency across different analyses. It includes cost and power consumption data for a wide range of commercially available optical network components (including transceivers for point-to-multipoint communications), introduces a statistical framework for estimating values for emerging technologies, and provides a cost model for multiband-doped fiber amplifiers. To demonstrate its practical relevance, the paper applies the model to two case studies: an evaluation of how the cost of various multiband node architectures scales with network traffic in meshed topologies and a comparison of different transport solutions to carry fronthaul flows in the radio access network. Full article

Intelligent vehicle networks powered by machine learning, AI and blockchain are transforming various sectors beyond transportation. In this context, being able to remote drive a vehicle is key for enhancing autonomous driving systems. After deploying end-to-end teleoperated driving systems under 5G networks, the need to address complex challenges in other critical areas arises. These challenges belong to different technologies that need to be integrated in this particular system: video transmission and visualization technologies, artificial intelligence techniques, and network optimization features, incorporating haptic devices and critical data security. This article explores how these technologies can enhance the teleoperated driving activity experiences already executed in real-life environments by analyzing the quality of the video which is transmitted over the network, exploring its correlation with the current state-of-the-art AI object detection algorithms, analyzing the extended reality and digital twin paradigms, obtaining the maximum possible performance of forthcoming 6G networks and proposing decentralized security schema for ensuring the privacy and safety of the end-users of teleoperated driving infrastructures. An integrated set of conclusions and recommendations will be given to outline the future teleoperated driving systems design in the forthcoming years. Full article

In light of the recent advances in hollow-core fiber (HCF) design and manufacturing, wide-scale deployments of this fiber type to realize next-generation optical transport networks may become viable in the foreseeable future, with benefits in terms of lower latency and improved capacity/reach. Nevertheless, several uncertainties remain regarding the properties of HCF that can be manufactured at scale, as well as the specifications of optical amplifiers developed to leverage the negligible low linearity of this fiber type. This work evaluates the performance of HCFs considering a wide range of potential fiber and amplifier parameters and compares them with traditional standard single-mode fiber (SSMF) and pure-silica-core fiber (PSCF). The resulting analysis allows us to determine, at a system and network level, the combination of fiber and amplifier parameters that will allow HCF to become a competitive transmission medium for next-generation optical transport networks. Full article

The S11D mining complex in Brazil, situated in Pará state, extracts 20 million tons of iron each quarter. Connecting via a standard 802.11b/g/n wireless network is crucial for mine operations across vast distances. A local team employs a network monitoring tool called the Ekahau Site Survey to guarantee the proper functioning of the network. However, due to the harsh terrain and the dangerous nature of S11D operations, this tool fails to gather data from all points of interest, resulting in interpolated maps that may not accurately represent the network’s overall quality. In this work, we propose a platform that can be attached to mobile machines during operations to automatically collect network parameters, such as channelization, RSSI, latency, packet loss, and bandwidth, without requiring human intervention. Using these network data, we generate an RSSI map using Kriging, which the local team can use. Comparison tests conducted in the laboratory and the field demonstrate that the platform performs similarly to Ekahau in capturing network parameters, ensuring its use in day-to-day operations for mapping. Full article

Human action recognition (HAR) based on WiFi channel state information (CSI) has attracted growing attention due to its contactless, privacy-preserving, and cost-effective nature. Recent studies have reported promising results by leveraging deep learning and image-based representations of CSI. However, methodological flaws in experimental protocols, particularly improper dataset partitioning, can lead to data leakage and significantly overestimate model performance. In this paper, we critically analyze a recently published WiFi-CSI-based HAR approach that converts CSI measurements into images and applies deep learning for classification. We show that the original evaluation relied on random data splitting without subject separation, causing substantial data leakage and inflated results. To address this, we reimplemented the method using subject-independent partitioning, which provides a realistic assessment of generalization ability. Furthermore, we conduct a quantitative study of post-training quantization under both correct and flawed partitioning strategies, revealing that methodological errors can conceal the true performance degradation of compressed models. Our findings demonstrate that evaluation protocols strongly influence reported outcomes, not only for baseline models but also for engineering decisions regarding model optimization and deployment. Based on these insights, we provide guidelines for designing robust experimental protocols in WiFi-CSI-based HAR to ensure methodological integrity and reproducibility. Full article

The growing demand for real-time video streaming in power-constrained embedded systems, such as drone navigation and remote surveillance, requires encoding solutions that prioritize low latency. In these applications, even small delays in video transmission can impair the operator’s ability to react in time, leading to instability in closed-loop control systems. To mitigate this, encoding must be lightweight and designed so that streaming can start as soon as possible, ideally even while frames are still being processed, thereby ensuring continuous and responsive operation. This paper presents the design of a hardware implementation of the Logarithmic Hop Encoding (LHE) algorithm on a Field-Programmable Gate Array (FPGA). The proposed architecture is deeply pipelined and parallelized to achieve sub-frame latency. It employs adaptive compression by dividing frames into regions of interest and uses a quantized differential system to minimize data transmission. Our design achieves an encoding latency of between 1.87 ms and 2.1 ms with a power consumption of only 2.7 W when implemented on an FPGA clocked at 150 MHz. Compared to a parallel GPU implementation of the same algorithm, this represents a 6.6-fold reduction in latency at approximately half the power consumption. These results show that FPGA-based LHE is a highly effective solution for low-latency, real-time video applications and establish a robust foundation for its deployment in embedded systems. Full article

Workplace accidents in high-risk environments remain a major safety concern, particularly when workers’ visual and auditory channels are overloaded. Haptic feedback offers a promising alternative for alerting individuals to unseen dangers and enhancing situational awareness. Motivated by challenges commonly observed in construction, this study investigates haptic alerting strategies applicable across dynamic, attentionally demanding contexts. We present two empirical experiments exploring how wearable vibration cues can inform users about approaching objects outside their field of view. The first experiment evaluated variations of pattern-based vibrations to simulate motion and examined the relationship between signal parameters and perceived urgency. A negative correlation between urgency and pulse duration emerged, identifying a key design factor. The second experiment conducted a novel comparison of pattern-based and location-based haptic alerts in a complex virtual environment, with tasks designed to simulate cognitive engagement with work processes. Results indicate that location-based alerts were more efficient for hazard detection. These findings offer insights into the design of effective user-centred haptic-based safety systems and provide a foundation for future development and deployment in real-world settings. This work contributes a generalisable step toward wearable alerting technologies for safety-critical occupations, including but not limited to construction. Full article

We consider the problem of data poisoning attack detection in a federated learning (FL) setup with differential privacy (DP). Local DP in FL ensures that privacy leakage caused by shared gradients is controlled by adding randomness to the process. We are interested in studying the effect of the Gaussian mechanism in the detection of different data poisoning attacks. As the additive noise from DP could hide poisonous data, the effectiveness of detection algorithms should be analyzed. We present two poisonous data detection algorithms and one malicious client identification algorithm. For the latter, we show that the effect of DP noise decreases as the size of the neural network increases. We further demonstrate this effect alongside the performance of these algorithms on three publicly available datasets. Full article

The orthogonal time frequency space (OTFS) is a modulation designed to transmit data in high Doppler channels where the usage of the orthogonal frequency division multiplexing (OFDM) is challenging. The random padded OTFS (RP-OTFS) modulation, introduced recently, is an OTFS-like waveform optimized for more precise estimation of channel state information (CSI) and, in the case of integrated sensing and communication (ISAC), for radar detection as well. One of the main drawbacks of the RP-OTFS is the high level of interference between carriers (the inter-carrier interference—ICI) of Doppler-delay (DD) grid. In the article, we optimize the RP-OTFS waveform in terms of reducing the level of pilot-to-data interference and also offer a way to reduce the data carrier interference. The reduction in the pilot-to-data interference is achieved due to the introduction of the following: (1) redistributing interferences along the DD grid, and (2) special DD grid configuration. In turn, the reduction in data carrier interference is achieved by extrapolating the estimate of channel state information. The proposed approach allows us to reduce the influence of the interference component and, as a result, to improve the probability of correct demodulation in the ISAC RP-OTFS system. Various DD grid configurations for different use cases from a radar point of view are considered in the article. The questions of choosing appropriate values of the DD grid parameters depending on the operating environment are also discussed here. In simulations, the ICI-reduced RP-OTFS is compared with its predecessor, the regular RP-OTFS, and classical modulations: OFDM and zero-padded OTFS, and benefits of its usage are shown: lower bit error rate (BER) of the transmission and higher detection probability of the radar detection. Full article

This paper presents a problem definition and feasibility check for an algorithm to select a connection point in an existing fiber-optical access network topology that can be used to connect a new site, the planned location, via an E-band millimeter-wave radio link. The newly added fixed wireless access connections must meet end-to-end network requirements for availability, latency, and bandwidth. To accommodate highly dynamic service traffic patterns, requirements are defined with a suitable time granularity. Similarly, dynamic changes in available network capacity affect end-to-end availability, latency, and bandwidth. The proposed algorithm is designed to handle these dynamic changes both in the service requirements and in the available resources. Full article

Sustainability is vital to engineering education, requiring future engineers to integrate technological advancements with environmental responsibility. This study explores the relationship between sustainability awareness, pro-environmental behavior, and environmental attitudes among engineering students at Shaqra University, Saudi Arabia. The findings indicate that, while students possess moderate sustainability awareness, their engagement in eco-friendly actions remains limited, despite expressing positive environmental attitudes. Civil Engineering students and those in later academic years show higher awareness, emphasizing the role of departmental focus and academic progression. Correlation analysis reveals a strong link between awareness and behavior (r = 0.628 and p < 0.001), yet multiple regression suggests that neither academic year nor department uniquely predicts sustainable actions once awareness is accounted for. Moreover, while pro-environmental attitudes correlate with behavior in bivariate analysis, their impact diminishes in regression, suggesting that positive environmental values do not necessarily translate into consistent green habits. ANOVA results confirm higher awareness among Civil Engineering students, though differences in sustainable behavior are subtle. These findings highlight the need for curricular reforms that integrate sustainability through experiential learning, bridging the gap between awareness and real-world actions. This study supports Saudi Arabia’s Vision 2030 by promoting environmental awareness and aligning education with labor market needs. It offers tools to help stakeholders and policymakers develop competitive, future-ready generations. Full article

We present a thorough machine-learning framework based on real-time state-of-polarization (SOP) monitoring for robust anomaly identification in optical fiber networks. We exploit SOP data under three different threat scenarios: (i) malicious or critical vibration events, (ii) overlapping mechanical disturbances, and (iii) malicious fiber tapping (eavesdropping). We used various supervised machine learning techniques like k-Nearest Neighbor (k-NN), random forest, extreme gradient boosting (XGBoost), and decision trees to classify different vibration events. We also assessed the framework’s resilience to background interference by superimposing sinusoidal noise at different frequencies and examining its effects on the polarization signatures. This analysis provides insight into how subsurface installations, subject to ambient vibrations, affect detection fidelity. This highlights the sensitivity to which external interference affects polarization fingerprints. Crucially, it demonstrates the system’s capacity to discern and alert on malicious vibration events even in the presence of environmental noise. However, we focus on the necessity of noise-mitigation techniques in real-world implementations while providing a potent, real-time mechanism for multi-threat recognition in the fiber networks. Full article

This study presents a comparative analysis of several multimodal large language models (LLMs) for no-reference image quality assessment, with a particular focus on images containing authentic distortions. We evaluate three models developed by OpenAI and three models from Claude.AI, comparing their performance in estimating image quality without reference images. Our results demonstrate that these LLMs outperform traditional methods based on hand-crafted features. However, more advanced deep learning models, especially those based on deep convolutional networks, surpass LLMs in performance. Notably, we make a unique contribution by publishing the processed outputs of the LLMs, providing a transparent and direct comparison of their quality assessments based solely on the predicted quality scores. This work underscores the potential of multimodal LLMs in image quality evaluation, while also highlighting the continuing advantages of specialized deep learning approaches. Full article

WiFi channel state information (CSI) has emerged as a promising modality for human action recognition due to its non-invasive nature and robustness in diverse environments. However, most existing methods process CSI channels collectively, potentially overlooking valuable channel-specific information. In this study, we propose a novel architecture, DF-CNN, which treats CSI channels separately and integrates their outputs using a decision fusion (DF) strategy. Extensive experiments demonstrate that DF-CNN significantly outperforms traditional approaches, achieving state-of-the-art performance. We also provide a comprehensive analysis of individual and combined CSI channel evaluations, showcasing the effectiveness of our method. This work establishes the importance of separate channel processing in CSI-based human action recognition and sets a new benchmark for the field. Full article