Search Results (1,751)

The novel framework of an integrated aeronautical communication and radar system (IACRS) to realize spectrum sharing is investigated. A non-orthogonal multiple access (NOMA)-motivated multi-input–multi-output (MIMO) scheme is proposed for the dual-function system, which is able to detect multiple aircraft while simultaneously transmitting dedicated messages. Specifically, NOMA-inspired technology is utilized to enable dual-spectrum sharing. The superposition of communication and radar signals is facilitated in the power domain. Successive interference cancellation (SIC) is employed at the receiver to effectively mitigate inter-function interference. Subsequently, the regularity of the three-dimensional flight track and attitude is exploited to model the air-to-ground (A2G) MIMO channel. Based on this framework, a joint optimization problem is formulated to maximize the weighted achievable sum rate and the sensing signal–clutter–noise ratio (SCNR) while satisfying the rate requirements for message transmission and ensuring the radar detection threshold. An alternative optimization (AO) algorithm is proposed to solve the non-convex problem with highly coupled variables. The original problem is decoupled into two manageable subproblems: transmit beamforming of the ground base station combined with power allocation and receiver beamforming at the aircraft. The penalty-based approach and the successive rank-one constraint relaxation (SROCR) method are developed for iteratively handling the non-convex rank-one constraints in subproblems. Numerical simulations demonstrate that the proposed IACRS framework significantly outperforms benchmark schemes. Full article

This paper introduces a comprehensive and publicly accessible data set from an experimental study on an independent cart system powered by linear motors. The primary objective is to advance research in machine health monitoring, predictive maintenance, and stochastic modeling by providing the first data set of its kind. Vibration signals were collected using sensors placed along the track, alongside key system variables such as cart position, following error, speed, and set current. Experiments were conducted under a wide range of operating conditions, including different fault types, fault severities, cart speeds, and fault orientations, for both single-cart and multi-cart configurations. The data set captures the relationship between vibration signatures, system variables, and fault characteristics across diverse speed profiles. The data set includes inner race (IR) and outer race (OR) faults in both the top and bottom bearings, with fault severities of 0.25 mm, 0.5 mm, 1.0 mm, and 1.5 mm in width. Eight different types of experiments were performed, classified based on the number of carts used, the section of the guide rail traversed, and the type of movement exhibited. Each experiment was conducted at two distinct nominal speeds of 1000 mm/s and 2000 mm/s, with acquisition durations ranging from 30 s to 2 min. Many experiments included multiple realizations to ensure statistical reliability. Data were recorded at a sampling frequency of 50 kHz with a resolution of 24 bits. For single-cart experiments, 5 system variables were captured, while for three-cart experiments, 15 system variables were recorded along with nine vibration channels. The total data set is approximately 400 GB, offering an extensive resource for data-driven research. Independent cart systems present unique challenges such as non-synchronous operation, speed reversals, and modularity, with each cart containing multiple bearings. In industrial applications where hundreds of carts may operate simultaneously, monitoring a large number of bearings becomes highly complex, making fault identification and localization particularly difficult. Unlike conventional rotary systems, where bearings are fixed around a rotating shaft, independent cart systems involve bearings that both rotate and translate along the track. This fundamental difference makes existing data sets and methodologies inadequate, emphasizing the need for specialized research. By addressing this gap, this work provides a critical resource for benchmarking and developing novel algorithms for fault diagnosis, signal processing, and machine learning in industrial transport applications. The outcomes of this study lay the foundation for future research in the condition monitoring of linear motor-driven transport systems. Full article

Semantic communication based on deep learning technology extracts the meaning of the transmitted source to achieve spectrum resource savings and enhance anti-interference capabilities. The black-box nature of deep learning models increases the difficulty for eavesdroppers in intercepting information, thereby significantly improving transmission security under normal circumstances. However, eavesdroppers may utilize model theft techniques to acquire models that have similar functionality and performance to the victim’s model. Moreover, even if all users are legitimate, the private information of a specific legitimate user should not be accessible to other users, and this issue also requires attention. As the lowest layer of wireless communication, the physical layer has been proven to enhance the security performance of communication systems by leveraging the randomness of physical channels. Unlike traditional communication, which transmits bit streams, semantic communication transmits semantic streams. Therefore, this paper converts semantic streams into bit streams to analyze the security performance of semantic communication using traditional communication metrics. Specifically, this paper assumes that the performance of the eavesdropper’s stolen model is consistent with that of the original model (although this assumption is impossible) and conducts a derivation analysis of the Secrecy Outage Probability (SOP). Full article

Recently, the concept of cell-free massive multiple-input multiple-output (CF-mMIMO) has been implemented in low-Earth-orbit (LEO) constellations to enhance energy efficiency. However, signal distortion caused by nonlinear power amplifiers (NPAs) significantly degrades the performance of LEO satellite communication (SATCOM) links. In this paper, we propose a serving cluster scheme and a hybrid precoding framework for CF-mMIMO-assisted LEO SATCOM systems, aiming to suppress the impact of NPAs while reducing onboard hardware complexity and power consumption. Specifically, we first develop a user-centric access point clustering strategy that incorporates elevation constraints and channel fading. Then, we formulate a precoding optimization problem to maximize energy efficiency by treating the product of tightly coupled analog and digital matrices as a single fully digital precoder. The proposed distortion-aware precoding design is achieved by integrating the weighted minimum mean square error (WMMSE) approach with a gradient descent method enhanced by Nesterov’s accelerated momentum. Furthermore, an efficient hybrid precoding method based on alternating minimization is proposed to address the matrix decomposition challenge. Simulation results validate the effectiveness of our proposed user-centric serving clustering algorithm and the WMMSE-based precoding algorithm in CF-mMIMO-assisted LEO SATCOM systems. Full article

Reconfigurable Intelligent Surfaces (RISs) and Non-Orthogonal Multiple Access (NOMA) have emerged as key technologies for next-generation (6G) wireless networks, attracting significant attention from researchers. As an advanced extension of RISs, Simultaneously Transmitting and Reflecting Reconfigurable Intelligent Surfaces (STAR-RISs) offer superior geometric and functional symmetry due to their capability to simultaneously reflect and transmit signals, thereby achieving full 360° spatial coverage. This symmetry not only ensures balanced energy distribution between the Transmission (T) and Reflection (R) regions but also facilitates interference cancellation through phase alignment. Furthermore, in NOMA networks, the symmetric allocation of power coefficients and the tunable transmission and reflection coefficients of STAR-RIS elements aligns with the principle of resource fairness in multi-user systems, which is crucial for maintaining fairness under asymmetric channel conditions. In this study, key factors, such as interference sources and distance effects, are considered in order to conduct a detailed analysis of the performance of STAR-RIS-assisted NOMA wireless education networks under multiple interference devices. Specifically, a comprehensive analysis of the Signal-to-Interference-plus-Noise Ratio (SINR) for both near-end and far-end devices is conducted, considering various scenarios, such as whether or not the direct communication link exists between the base station and the near-end device, and whether or not the near-end device is affected by interference. Based on these analyses, closed-form approximate expressions for the outage probabilities of the near-end and far-end devices, as well as the closed-form approximation for the system’s Spectral Efficiency (SE), are derived. Notably, the Gamma distribution is used to approximate the square of the composite channel amplitude between the base station and the near-end device, effectively reducing computational complexity. Finally, simulation results validate the accuracy of our analytical results. Both numerical and simulation results show that adjusting the base station’s power allocation, and the transmission and reflection coefficients of the STAR-RIS, can effectively mitigate the impact of interference devices on the near-end device and enhance the communication performance of receiving devices. Additionally, increasing the number of STAR-RIS elements can effectively improve the overall performance of the near-end device, far-end device, and the entire system. Full article

Aiming to address the issue of multi-user dynamic spectrum access in an opportunistic mode in cognitive radio networks leading to low sum throughput, we propose a multi-user opportunistic spectrum access method based on multi-head self-attention and multi-agent deep reinforcement learning. First, an optimization model for joint channel selection and power control in multi-user systems is constructed based on centralized training with a decentralized execution framework. In the training phase, the decision-making policy is optimized using global information, while in the execution phase, each agent makes decisions according to its observations. Meanwhile, a multi-constraint dynamic proportional reward function is designed to guide the agent in selecting more rational actions by refining the constraints and dynamically adjusting the reward proportion. Furthermore, a multi-head self-attention mechanism is incorporated into the critic network to dynamically allocate attention weights to different users, thereby enhancing the ability of the network to estimate the joint action value. Finally, the proposed method is evaluated in terms of convergence, throughput, and dynamic performance. Simulation results demonstrate that the proposed method significantly improves the sum throughput of secondary users in opportunistic spectrum access. Full article

Popular Large Language Models (LLMs) access uses website browsing and also faces website fingerprinting attacks. Website fingerprinting attacks have increasingly threatened website users to the leakage of browsing privacy. In addition to the often-used network traffic analysis, interrupt tracing exploits the microarchitectural side channels to be a new compromising method and assists website fingerprinting attacks on non-LLM websites with up to 96.6% classification accuracy. More importantly, our observations show that LLM website access performs inherent defense and decreases the attack classification accuracy to 6.5%. This resistance highlights the need to develop new website fingerprinting attacks for LLM websites. Therefore, we propose a fine-grained LLM-oriented website fingerprinting attack via fusing interrupt trace and network traffic (LLM-WFIN) to identify the browsing website and the content type accurately. A prior-fusion-based one-stage classifier and post-fusion-based two-stage classifier are trained to enhance website fingerprinting attacks. The comprehensive results and ablation study on 25 popular LLM websites and varying machine learning methods demonstrate that LLM-WFIN using post-fusion achieves 97.2% attack classification accuracy with no defense and outperforms prior-fusion with 81.6% attack classification accuracy with effective defenses. Full article

Background/Objectives: Sensorineural hearing loss (HL) is a highly prevalent chronic health condition. It can be managed through hearing care, including the use of hearing aids (HAs). Still, a majority of individuals with HL remain undiagnosed or untreated. Virtual care delivery may support uptake and adherence to interventions. In blended care, individuals can choose interchangeably between in-person and virtual services. This study aimed to investigate how real-world individuals accessed blended hearing care (through in-person, virtual, or hybrid services), the amount of support they received, and their satisfaction with services and products. Methods: An exploratory, retrospective analysis was performed on longitudinal observational data collected through Australia’s longest-running blended hearing care model. A total of 25,058 appointment records were available, matched to HA purchase records and clinical notes where possible, as well as 916 satisfaction ratings. Results: The majority of individuals attended in-person appointments (75%); 25% were virtual or hybrid appointments. The number of appointments attended depended on how HAs were purchased (in-person, virtually, or hybrid), but all modalities were complemented by ample unscheduled email and telephone support. Of those who purchased HAs repeatedly, 49% changed preferred sales channel (in-person versus virtual) over time. Satisfaction ratings were highest for virtual services. Conclusions: This first report of real-world, longitudinal evidence on blended hearing care showed strong attendance of in-person appointments, while hybrid services—including informal; unscheduled support—may have responded to individuals’ changing needs and preferences over time. The findings offer practice-based evidence for blended care models and recommendations for further research. Full article

Background/Objectives: Understanding health information-seeking behavior is critical in providing effective interventions and improving quality of life for patients, especially those facing complex diagnoses like cancer. The purpose of this study is to understand rural–urban differences in trust levels for various information sources and how trust may differ by cancer status (no cancer, newly diagnosed, survived for six and more years). Methods: We examined 5775 responses from the 2022 Health Information National Trends Survey^®. Using the component analysis, eight sources of information were classified into three domains: structured (doctor, government, scientist, and charity), less structured (family and religion), and semi-structured (health system and social media). Respondents answered questions on a scale of 1–4. Weighted linear regression models were constructed to examine trust level in three domains by rural residency and cancer status, while adjusting for demographic and socioeconomic status. Results: Urban patients reported higher trust in more structured sources of information (2.999 > 2.873, p = 0.005) whereas rural counterparts reported higher trust in less structured sources of information (2.241 > 2.153, p = 0.012). After adjusting for covariates, urban respondents with cancer are more likely to trust doctors (Coeff. = 0.163, p < 0.001) than those without cancer. Rural respondents with cancer are less likely to trust charities (Coeff. = −0.357, p < 0.01) and scientists (Coeff. = −0.374, p < 0.05) than rural respondents without cancer. Conclusions: Newly diagnosed cancer patients in rural areas are less likely to trust structured sources of information even after adjusting for all covariates. Additional studies about misinformation and disinformation being channeled through less structured sources of information are needed to prevent any delay in care among cancer patients, especially rural patients who are more likely to access these sources of information. Full article

Background: Vast spatial mobility changes happened globally during the COVID-19 pandemic, profoundly affecting older adults’ well-being and active aging experience. This study aims to examine how the virtual environment and cyberspace, in conjunction with the physical and social neighbourhood environments, influence outdoor activities and spatial mobility for older immigrants. Methods: Four online focus groups were conducted with 25 older Chinese immigrants aged 65 and over in the Greater Toronto Area, Canada. The focus groups explored coping strategies during the pandemic and spatial mobility patterns related to different activity types such as grocery shopping, leisure activities and physical exercises, social and familial activities, and healthcare. Qualitative thematic analysis was conducted guided by the neighbourhood and health theoretical framework. Results: The overall engagement of older Chinese immigrants in various types of outdoor activities declined drastically and the spatial mobility pattern was complex. This change was shaped largely by the intersecting physical/built (e.g., residential conditions, access to public spaces), social (e.g., social support, interpersonal cohesion) and virtual (e.g., online communities and internet-based resources) environmental factors, as well as individual risk perceptions towards COVID-19 and public health interventions during the pandemic. Conclusions: Virtual environment emerged as an important domain that compensates for the heavily reduced spatial mobility of the group during the pandemic. It functioned as a vital channel for older Chinese immigrants to sustain the necessary leisure, social, and healthcare-related activities and maintain well-being during the pandemic. The study provides implications for addressing neighbourhood-level factors in policymaking and implementing initiatives to enhance active ageing experience of older Chinese immigrants. Full article

Given the critical requirements for both speed and accuracy in facial expression recognition, this paper presents a novel deep-learning architecture named Fast Central Consistency Attention (FCCA). With FasterNet-s as its backbone network, FCCA is designed to recognize facial expressions. Firstly, we leverage partial convolution to extract features from specific channels, thereby reducing frequent memory access and substantially boosting training speed. Secondly, we enhance recognition accuracy by introducing an additional pointwise convolution on the partial features, focusing on the central facial position using weighted mechanisms. Lastly, we integrate flip consistency loss to tackle uncertainty challenges inherent in facial expression recognition (FER) tasks, further improving the overall model performance. Our approach yielded superior results: we achieved recognition accuracies of 91.30% on RAF-DB and 65.51% on AffectNet datasets, along with 56.61% UAR and 69.66% WAR on the DFEW dataset. The FCCA method has demonstrated state-of-the-art performance across multiple datasets, underscoring its robustness and capability for generalization. Full article

For the Versatile Video Coding (VVC) standard, extensive research has been conducted on in-loop filtering to improve encoding efficiency. However, most methods use only spatial characteristics without exploiting the content correlation across multiple frames or fully utilizing the inter-channel relational information. In this paper, we introduce a novel three-stage Multi-frame Multi-channel In-loop Filtering (3-MMIF) method for VVC that improves the quality of each encoded frame by harnessing the correlations between adjacent frames and channels. Firstly, we establish a comprehensive database containing pairs of encoded and original frames across various scenes. Then, we select the nearest frames in the decode buffer as the reference frames for enhancing the quality of the current frame. Subsequently, we propose a three-stage in-loop filtering method that leverages spatio-temporal and inter-channel correlations. The three-stage method is grounded in the recently developed Residual Dense Network, benefiting from its enhanced generalization ability and feature reuse mechanism. Experimental results demonstrate that our 3-MMIF method, with the encoder’s standard filter tools activated, achieves 2.78%/4.87%/5.13% Bjøntegaard delta bit-rate (BD-Rate) reductions for the Y, U, and V channels over the VVC 17.0 codec for random access configuration on the standard test set, outperforming other VVC in-loop filter methods. Full article

Indonesia’s commitment to the early retirement of coal-fired power plants (CFPPs) underscores the urgent need to transition to renewable energy due to coal’s significant contribution to environmental degradation and rising CO₂ emissions. Despite this urgency, several challenges impede the widespread adoption of renewable energy, including disparities in energy access, inadequate policy implementation, unreliable government financing mechanisms, and lack of education and awareness, especially due to the current incorporation of hydrogen and nuclear energy. To overcome these barriers, a robust policy framework is essential, complemented by progressive policy enactment. This study examines Indonesia’s evolving energy landscape, highlighting key challenges and opportunities for the implementation of renewable energy. The findings emphasize that a comprehensive and integrated roadmap is critical to unlocking Indonesia’s renewable energy potential. The roadmap includes strengthening governance, fostering public–private collaborations, and securing diverse financing channels, while offering targeted incentives, such as tax breaks and financial benefits. Furthermore, conducting pre-feasibility studies and regional assessments for emerging energy sources, like hydrogen and nuclear power, is crucial to accurately evaluate potential risks and opportunities. By addressing gaps in regulatory framework and enforcing effective policy measures, Indonesia can facilitate public–private partnerships, promote technology transfer, and develop skilled workforce as an effort to transition into a sustainable and diversified energy future. Full article

This paper proposes a frame aggregation with a simple block acknowledgement (FASBA) mechanism to provide a strict QoS guarantee to life-saving emergency traffic in wireless local area networks. This work builds on our previous work on a multi-preemptive enhanced distributed channel access protocol called MP-EDCA. The main difference between FASBA and MP-EDCA is that MP-EDCA does not provide a strict QoS guarantee to life-saving emergency traffic (e.g., ambulance calls), especially in high-load conditions. Our proposed FASBA protocol solves the problems of achieving a strict QoS guarantee to life-saving emergency traffic. The strict QoS guarantee is achieved by aggregating multiple frames with a two-bit block acknowledgement for transmissions. FASBA assures guaranteed network services by reducing MAC overheads; consequently, it offers higher throughput, lower packet delays, and accommodates a larger number of life-saving emergency nodes during emergencies. The performance of the proposed FASBA is validated by Riverbed Modeler and MATLAB 2024a-based simulation. Results obtained show that the proposed FASBA offers about 30% lower delays, 17% higher throughput, and 60% lower retransmission attempts than MP-EDCA under high-traffic loads. Full article

The detection of synthetic speech has become a pressing challenge due to the potential societal risks posed by synthetic speech technologies. Existing methods primarily focus on either the time or frequency domain of speech, limiting their ability to generalize to new and diverse speech synthesis algorithms. In this work, we present a novel and scientifically grounded approach, the Dual-domain Fusion Network (DDFNet), which synergistically integrates features from both the time and frequency domains to capture complementary information. The architecture consists of two specialized single-domain feature extraction networks, each optimized for the unique characteristics of its respective domain, and a feature fusion network that effectively combines these features at a deep level. Moreover, we incorporate multi-task learning to simultaneously capture rich, multi-faceted representations, further enhancing the model’s generalization capability. Extensive experiments on the ASVspoof 2019 Logical Access corpus and ASVspoof 2021 tracks demonstrate that DDFNet achieves strong performance, maintaining competitive results despite the challenges posed by channel changes and compression coding, highlighting its robust generalization ability. Full article