Search Results (528)

In free space optical (FSO) communications, signals are affected by turbulence when transmitted through the atmosphere. Fluctuations in intensity caused by atmospheric turbulence lead to an increase in the bit error rate of FSO systems. Deep learning (DL), as a current research hotspot, offers a promising approach to improve the accuracy of signal detection. In this paper, we propose a signal detection method based on a bidirectional gated recurrent unit (BiGRU) neural network for FSO communications. The proposed detection method considers the temporal correlation of received signals due to the properties of the BiGRU neural network, which is not available in existing detection methods based on DL. In addition, the proposed detection method does not require channel state information (CSI) for channel estimation, unlike maximum likelihood (ML) detection technology with perfect CSI. Numerical results demonstrate that the proposed BiGRU-based detector achieves significant improvements in bit error rate (BER) performance compared with a multilayer perceptron (MLP)-based detector. Specifically, under weak turbulence conditions, the BiGRU-based detector achieves an approximate 2 dB signal-to-noise ratio (SNR) gain at a target BER of ${10}^{-6}$ compared to the MLP-based detector. Under moderate turbulence conditions, it achieves an approximate 6 dB SNR gain at the same target BER of ${10}^{-6}$. Under strong turbulence conditions, the proposed detector obtains a 6 dB SNR gain at a target BER of ${10}^{-4}$. Additionally, it outperforms conventional methods by more than one order of magnitude in BER under the same turbulence and SNR conditions. Full article

Across the Middle East, gated communities have become a defining feature of contemporary urban development, raising urgent questions about spatial inequality, public access, and social cohesion. This study examines the socio-spatial impacts of these developments by combining qualitative perceptions from regional expert interviews with in-depth analysis of two case studies in Jordan: Al Andalucía and Green Land. Drawing on semi-structured interviews with urban planners, architects, and policy experts from Egypt, Jordan, Saudi Arabia, and the United Arab Emirates, the study employs thematic analysis to investigate expert perspectives on gated communities as a regional planning phenomenon. Findings reveal four dominant themes: (1) gated communities intensify spatial fragmentation and disconnection from surrounding urban fabric; (2) private sector dominance leads to unregulated, market-driven development that weakens strategic urban planning; (3) the erosion of inclusive public space and social cohesion is widely perceived as a social cost; and (4) gated living is framed as an aspirational lifestyle associated with security, prestige, and socio-economic distinction. The article concludes by calling for more inclusive urban policies that balance private development with inclusive planning strategies to mitigate the long-term impacts of fragmentation and exclusivity in Middle Eastern cities. Full article

A Labrador Retriever (4-year-old, castrated male) with signs of fatigue was diagnosed with an atrial septal defect at his primary veterinary clinic. Due to the uncertainty of this diagnosis, he was referred to the Tokyo University of Agriculture and Technology Animal Medical Center for further investigation. Transthoracic echocardiography performed on arrival showed an irregular blood flow from the left atrium (LA) to the right atrium (RA), yet no opening was found in the septum. An electrocardiogram (ECG)-gated computed tomography (CT) exam revealed a communication between the coronary sinus (CS) and the LA, causing a shunt between the LA and the RA. A diagnosis of unroofed coronary sinus syndrome (UCSS) was made. The dog’s condition was stable and plans to keep observations were made. This is the first case of UCSS diagnosed with an ECG-gated CT exam. Full article

The evolution of the winemaking industry towards intelligent and digitalized systems is crucial for precision winemaking and ensuring product safety. In this context, the Internet of Things (IoT) provides a key strategy for real-time monitoring and data management throughout the winemaking process. However, comprehensive multi-parameter IoT-based monitoring and time-series prediction of physicochemical parameters during storage are currently lacking, limiting the ability to assess storage conditions and provide early warning of quality deterioration. To address these gaps, a multi-parameter IoT monitoring system was designed and developed to track conductivity, dissolved oxygen, and temperature in real time. Data were transmitted via a 4th-generation (4G) mobile communication module to the TLINK cloud platform for storage and visualization. An 80-day storage experiment confirmed the system’s reliability for long-term monitoring, and analysis of parameter trends demonstrated its effectiveness in assessing storage conditions and wine quality evolution. Furthermore, Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Temporal Convolutional Network (TCN) models, and Autoregressive Integrated Moving Average (ARIMA) were implemented to predict physicochemical parameter trends. The TCN model achieved the highest predictive performance, with coefficients of determination (R²) of 0.955, 0.968, and 0.971 for conductivity, dissolved oxygen, and temperature, respectively, while LSTM and GRU showed comparable results. These results demonstrate that integrating IoT-based multi-parameter monitoring with deep learning time-series prediction enables real-time detection of abnormal storage and quality deterioration, providing a novel and practical framework for early warning throughout the wine storage process. Full article

For most, if not all, AI-accelerated hardware, communication with the agent is expensive and heavily bottlenecks the hardware performance. This omnipresent hardware restriction is also found in neuromorphic computing: a novel style of computing that involves deploying spiking neural networks to specialized hardware to achieve low size, weight, and power (SWaP) compute. In neuromorphic computing, spike trains, times, and values are used to communicate information to, from, and within the spiking neural network. Input data, in order to be presented to a spiking neural network, must first be encoded as spikes. After processing the data, spikes are communicated by the network that represent some classification or decision that must be processed by decoder logic. In this paper, we first present principles for interconverting between spike trains, times, and values using custom-designed spiking subnetworks. Specifically, we present seven networks that encompass the 15 conversion scenarios between these encodings. We then perform three case studies where we either custom design a novel network or augment existing neural networks with these conversion subnetworks to vastly improve their communication performance with the outside world. We employ a classic space vs. time tradeoff by pushing spike data encoding and decoding techniques into the network mesh (increasing space) in order to minimize intra- and extranetwork communication time. This results in a classification inference speedup of 23× and a control inference speedup of 4.3× on field-programmable gate array hardware. Full article

This study addresses the impact of pandemics, economic limitations, and physical constraints on physical pilgrimage by proposing and evaluating a culturally sensitive, ritual-oriented virtual Boudhanath Stupa environment. Using user-centered design (UCD) and Behavior-Driven Development (BDD), the project created interactive ritual nodes on a Minecraft–VR platform, combining spatial configuration, symbolic elements, and exploratory freedom to move beyond static representation toward participatory engagement. A mixed-methods evaluation with 50 participants from diverse backgrounds and 2 Tibetan Buddhist experts showed positive feedback for aesthetic experience (M = 4.36) and user control (M = 4.62). Despite its non-photorealistic style, the environment was able to evoke a strong sense of presence and was recognized by experts as a “digital Dharma gate” suitable for younger audiences and those unable to travel to sacred sites. Limitations include a small sample size, a short evaluation period, and a lack of social interaction features. Future development will enhance guidance and feedback, expand narratives, support community co-creation, and introduce multi-user functions, providing a scalable framework for virtual religious cultural heritage. Full article

In response to the problem of decreased collaborative control performance in underwater unmanned vehicles (UUVs) with communication packet loss, a GRU-KF method for multi-UUV control that integrates a gated recurrent unit (GRU) and a Kalman filter (KF) is proposed. First, a UUV feedback linearization model and a current model are established, and a multi-UUV controller-based leader–follower method is designed, using a neural network-based radial basis function (RBF) to counteract the uncertainty effects in the model. For scenarios involving packet loss in multi-UUV collaborative communication, the GRU network extracts historical temporal features to enhance the system’s adaptability to communication uncertainties, while the KF performs state estimation and error correction. The simulation results show that, compared to compensation by the GRU network, the proposed method significantly reduces the jitter level and convergence time of errors, enabling the formation to exhibit good robustness and accuracy in communication packet loss scenarios. Full article

Intra-fractional respiratory management represents one of the greatest challenges of modern cancer radiotherapy (RT), as significant breathing-induced lesion motion might affect target coverage and organs at risk (OARs) sparing, jeopardizing oncological and toxicity outcomes. The detrimental effects on dosage of uncompensated organ motion are exacerbated in RT with charged particles (e.g., protons and carbon ions), due to their higher ballistic selectivity. The simplest strategies to counteract this phenomenon are the use of larger treatment margins and reductions in or control of respiration (e.g., by means of compression belts, breath hold). Gating and tracking, which synchronize beam delivery with the respiratory signal, also represent widely adopted solutions. When tracking the tumor itself or surrogates, invasive procedures (e.g., marker implantation), an unnecessary imaging dose (e.g., in X-ray-based fluoroscopy), or expensive equipment (e.g., magnetic resonance imaging, MRI) is usually required. When chest and abdomen excursions are measured to infer internal tumor displacement, the additional devices needed to perform this task, such as pressure sensors or surface cameras, present inherent limitations that can impair the procedure itself. In this context, radars have intrigued the radiation oncology community, being inexpensive, non-invasive, contactless, and insensitive to obstacles. Even if real-world clinical implementation is still lagging behind, there is a growing body of research unraveling the potential of these devices in this field. The purpose of this narrative review is to provide an overview of the studies that have delved into the potential of radar-based technologies for motion-adaptive photon and particle RT applications. Full article

Chaotic systems, characterized by extreme sensitivity to initial conditions and complex dynamical behaviors, exhibit significant potential for applications in various fields. Effective control of chaotic system synchronization is particularly crucial in sensor-related applications. This paper proposes a preset-time fuzzy zeroing neural network (PTCFZNN) model based on Takagi–Sugeno fuzzy control to achieve chaotic synchronization in aperiodic parameter exciting chaotic systems. The designed PTCFZNN model accurately handles the complex dynamic variations inherent in chaotic systems, overcoming the challenges posed by aperiodic parameter excitation to achieve synchronization. Additionally, field-programmable gate array (FPGA) verification experiments successfully implemented the PTCFZNN-based chaotic system synchronization control on hardware platforms, confirming its feasibility for practical engineering applications. Furthermore, experimental studies on chaos-masking communication applications of the PTCFZNN-based chaotic system synchronization further validate its effectiveness in enhancing communication confidentiality and anti-jamming capability, highlighting its important application value for securing sensor data transmission. Full article

Background: Hepatitis presents a major health and economic challenge in Saudi Arabia, necessitating insight into its epidemiology, risk factors, and control measures. This review aims to synthesize current evidence on the epidemiology, risk factors, and prevention strategies for viral hepatitis in Saudi Arabia. It evaluates the effectiveness of existing interventions and proposes data-driven approaches to advance national hepatitis elimination goals. Methods: This study reviewed data from 2016 to 2024, sourced from PubMed, Google Scholar, ResearchGate, and ScienceDirect, focusing on hepatitis epidemiology and prevention in Saudi Arabia. Studies relevant to Saudi-specific trends and prevention strategies were included. Results: Saudi Arabia has achieved significant reductions in viral hepatitis prevalence, notably HBV (1.3%) due to universal infant vaccination (98% coverage), and HCV (0.124%) through the Saudi National Hepatitis Program (SNHP), which provides free DAAs (95% cure rate) and has screened 5 million people. However, challenges persist: HAV susceptibility is rising in adults (seroprevalence 33.1%), HDV affects 7.7% of HBV patients, and key risk factors include socioeconomic disparities (higher HAV/HEV in rural/low-income areas), intravenous drug use (30–50% of HCV cases), unsafe medical/cultural practices (e.g., Hijama), and limited healthcare access for migrants/rural populations. While interventions like water sanitation initiatives (58% HAV decline) and prenatal screening are effective, advancing elimination goals requires addressing gaps in HDV/HEV surveillance, outdated seroprevalence data, equitable treatment access (35% lower in rural areas), stigma reduction, and targeted strategies for high-risk groups to meet WHO 2030 targets. Conclusions: Saudi Arabia has made significant progress in hepatitis control through vaccination and public health efforts, but challenges persist. Strengthening healthcare systems, improving community engagement, and ensuring equitable access are key to sustaining elimination efforts. Full article

Background: Brain–computer interfaces (BCIs) based on motor imagery (MI) offer promising solutions for motor rehabilitation and communication. However, electroencephalography (EEG) signals are often characterized by low signal-to-noise ratios, strong non-stationarity, and significant inter-subject variability, which pose significant challenges for accurate decoding. Existing methods often struggle to simultaneously model the spatial interactions between EEG channels, the local fine-grained features within signals, and global semantic patterns. Methods: To address this, we propose the graph attention-based hierarchical temporal network (GAH-TNet), which integrates spatial graph attention modeling with hierarchical temporal feature encoding. Specifically, we design the graph attention temporal encoding block (GATE). The graph attention mechanism is used to model spatial dependencies between EEG channels and encode short-term temporal dynamic features. Subsequently, a hierarchical attention-guided deep temporal feature encoding block (HADTE) is introduced, which extracts local fine-grained and global long-term dependency features through two-stage attention and temporal convolution. Finally, a fully connected classifier is used to obtain the classification results. The proposed model is evaluated on two publicly available MI-EEG datasets. Results: Our method outperforms multiple existing state-of-the-art methods in classification accuracy. On the BCI IV 2a dataset, the average classification accuracy reaches 86.84%, and on BCI IV 2b, it reaches 89.15%. Ablation experiments validate the complementary roles of GATE and HADTE in modeling. Additionally, the model exhibits good generalization ability across subjects. Conclusions: This framework effectively captures the spatio-temporal dynamic characteristics and topological structure of MI-EEG signals. This hierarchical and interpretable framework provides a new approach for improving decoding performance in EEG motor imagery tasks. Full article

With the growing demand for secure image communication, effective encryption solutions are critical for safeguarding visual data from unauthorized access. The substitution box (S-box) in AES (Advanced Encryption Standard) is critical for ensuring nonlinearity and security. However, the static S-box used in AES is vulnerable to algebraic attacks, side-channel attacks, and so on. This study offers a novel Lorenz key and Chua key-based Reversible Substitution Box (LCK-SB) for image encryption, which takes advantage of the chaotic behavior of the Lorenz and Chua key systems to improve security due to nonlinear jumps and mixed chaotic behavior while maintaining optimal quantum cost, area, and power. The suggested method uses a hybrid Lorenz and Chua key generator to create a highly nonlinear and reversible S-box, which ensures strong confusion and diffusion features. The performance of the LCK-SB approach is examined on field-programmable gate array (FPGA) and application-specific integrated circuit (ASIC) platforms, and the findings show that quantum cost, delay, and power are decreased by 97%, 74.6%, and 35%, respectively. Furthermore, the formal security analysis shows that the suggested technique efficiently resists threats. The theoretical analysis and experimental assessment show that the suggested system is more secure for picture encryption, making it suitable for real-time and high-security applications. Full article

This study explores the integration of reduced graphene oxide (rGO) into an optoelectronic XOR logic gate to enhance BB84 protocol encryption in quantum communication systems. The research leverages the nonlinear optical properties of rGO, specifically its nonlinear refraction characteristics, in combination with a Michelson interferometer to implement an optoelectronic XOR gate. rGO samples were deposited using the Langmuir–Blodgett technique and characterized in morphology and structure. The optical setup utilized a frequency-modulated laser signal for the interferometer and a pulsed laser system that generates the quantum information carrier. This integration of quantum encryption with nonlinear optical materials offers enhanced security against classical attacks while providing adaptability for various applications from secure communications to quantum AI. Full article

Accurately predicting the ionosphere’s Total Electron Content (TEC) is significant for ensuring the regular operation of satellite navigation and communication systems and space weather prediction. To further improve the accuracy of TEC prediction, this paper proposes a TEC prediction model based on the grid-optimized Gate Recurrent Unit (GRU). This model has the following main features: (1) it uses statistical learning methods to interpolate the missing data of TEC observations; (2) it constructs a sliding time window by using the multi-dimensional time series features of two types of solar activity indices to support modeling; (3) It adopts grid search combined with optimization of network depth, time step length, and other hyperparameters to significantly enhance the model’s ability to extract the characteristics of the ionospheric 11-year cycle and seasonal variations. Taking the EGLIN station as an example, the proposed model is verified. The experimental results show that the root mean square error of the GRU model during the period from 2019 to 2020 was 0.78 TECU, which was significantly lower than those of the CCIR, URSI, and statistical machine learning models. Compared with the other three models, the RMSE error of the GRU model was reduced by 72.73%, 72.64%, and 57.38%, respectively. The above research verifies the advantages of the proposed model in predicting TEC and provides a new idea for ionospheric modeling. Full article

The exponential growth of autonomous systems demands robust security mechanisms that can operate within the extreme constraints of real-time embedded environments. This paper introduces SMART DShot, a groundbreaking machine learning-enhanced framework that transforms the security landscape of unmanned aerial vehicle motor control systems through seamless integration of adaptive timing correction and real-time anomaly detection within Digital Shot (DShot) communication protocols. Our approach addresses critical vulnerabilities in Electronic Speed Controller (ESC) interfaces by deploying four synergistic algorithms—Kalman Filter Timing Correction (KFTC), Recursive Least Squares Timing Correction (RLSTC), Fuzzy Logic Timing Correction (FLTC), and Hybrid Adaptive Timing Correction (HATC)—each optimized for specific error characteristics and attack scenarios. Through comprehensive evaluation encompassing 32,000 Monte Carlo test iterations (500 per scenario × 16 scenarios × 4 algorithms) across 16 distinct operational scenarios and PolarFire SoC Field-Programmable Gate Array (FPGA) implementation, we demonstrate exceptional performance with 88.3% attack detection rate, only 2.3% false positive incidence, and substantial vulnerability mitigation reducing Common Vulnerability Scoring System (CVSS) severity from High (7.3) to Low (3.1). Hardware validation on PolarFire SoC confirms practical viability with minimal resource overhead (2.16% Look-Up Table utilization, 16.57 mW per channel) and deterministic sub-10 microsecond execution latency. The Hybrid Adaptive Timing Correction algorithm achieves 31.01% success rate (95% CI: [30.2%, 31.8%]), representing a 26.5% improvement over baseline approaches through intelligent meta-learning-based algorithm selection. Statistical validation using Analysis of Variance confirms significant performance differences (F(3,1996) = 30.30, p < 0.001) with large effect sizes (Cohen’s d up to 4.57), where 64.6% of algorithm comparisons showed large practical significance. SMART DShot establishes a paradigmatic shift from reactive to proactive embedded security, demonstrating that sophisticated artificial intelligence can operate effectively within microsecond-scale real-time constraints while providing comprehensive protection against timing manipulation, de-synchronization, burst interference, replay attacks, coordinated multi-channel attacks, and firmware-level compromises. This work provides essential foundations for trustworthy autonomous systems across critical domains including aerospace, automotive, industrial automation, and cyber–physical infrastructure. These results conclusively demonstrate that ML-enhanced motor control systems can achieve both superior security (88.3% attack detection rate with 2.3% false positives) and operational performance (31.01% timing correction success rate, 26.5% improvement over baseline) simultaneously, establishing SMART DShot as a practical, deployable solution for next-generation autonomous systems. Full article