Search Results (13,012)

Synthetic Aperture Radar (SAR) onboard satellites provides high-resolution Earth imaging independent of weather conditions. SAR data are acquired by an aircraft or satellite and sent to a ground station to be processed. However, for novel applications requiring real-time analysis and decisions, onboard processing is necessary to escape the limited downlink bandwidth and latency. One such application is real-time target recognition, which has emerged as a decisive operation in areas such as defense and surveillance. In recent years, deep learning models have improved the accuracy of target recognition algorithms. However, these are based on optical image processing and are computation and memory expensive, which requires not only processing the SAR pulse data but also optimized models and architectures for efficient deployment in onboard computers. This paper presents a fast and accurate target recognition system directly on raw SAR data using a neural network model. This network receives and processes SAR echo data for fast processing, alleviating the computationally expensive DSP image generation algorithms such as Backprojection and RangeDoppler. Thus, this allows the use of simpler and faster models, while maintaining accuracy. The system was designed, optimized, and tested on low-cost embedded devices with low size, weight ,and energy requirements (Khadas VIM3 and Raspberry Pi 5). Results demonstrate that the proposed solution achieves a target classification accuracy for the MSTAR dataset close to 100% in less than 1.5 ms and 5.5 W of power. Full article

In the current scenario of universal accessibility of data, organisations face highly complex challenges related to integrating and processing diverse sets of data in order to meet their analytical needs. This review paper analyses traditional and innovative methods used for data storage and integration, with particular focus on their implications for scalability, consistency, and interoperability within an analytical ecosystem. In particular, it contributes a cross-layer taxonomy linking integration mechanisms (schema matching, entity resolution, and semantic enrichment) to storage/query substrates (row/column stores, NoSQL, lakehouse, and federation), together with comparative tables and figures that synthesise trade-offs and performance/governance levers. Through schema mapping solutions addressing the challenges brought about by structural heterogeneity, storage architectures varying from traditional storage solutions all the way to cloud storage solutions, and ETL pipeline integration using federated query processors, the research provides specific attention for the application of metadata management, with a focus on semantic enrichment using ontologies and lineage management to enable end-to-end traceability and governance. It also covers performance hotspots and caching techniques, along with consistency trade-offs arising out of distributed systems. Empirical case studies from real applications in enterprise lakehouses, scientific exploration activities, and public governance applications serve to invoke this review. Following this work is the possibility of future directions in convergent analytical platforms with support for multiple workloads, along with metadata-centric orchestration with provisions for AI-based integration. Combining technological advancement with practical considerations results in an enabling resource for researchers and practitioners seeking the creation of fault-tolerant, reliable, and future-ready data infrastructure. This review is primarily aimed at researchers, system architects, and advanced practitioners who design and evaluate heterogeneous analytical platforms. It also offers value to graduate students by serving as a structured overview of contemporary methods, thereby bridging academic knowledge with industrial practice. Full article

This paper presents a radiation-hardened 4-bit flash analog-to-digital converter (ADC) implemented in a 22 nm fully depleted silicon-on-insulator (FD-SOI) process for high-reliability applications in radiation environments. To improve single-event upsets (SEU) tolerance, the design introduces a compact fault-tolerant logic scheme based on Dual Modular Redundancy (DMR), offering reliability comparable to Triple Modular Redundancy (TMR) while using two storage nodes instead of three, and a simple XOR-based check in place of a majority voter. A distributed sampling architecture mitigates SEU vulnerabilities in the input path, while thin-oxide devices are used in analog-critical circuits to enhance total ionizing dose (TID) resilience. Post-layout simulations demonstrate SEU detection within 200 ps and correction within ~600 ps. The ADC achieves an active area of 0.089 mm², power consumption below 30 µW, and provides a scalable solution for radiation-tolerant data acquisition in aerospace and other high-reliability systems. Full article

To address global semantic loss, local detail blurring, and spatial–semantic conflict during image restoration under adverse weather conditions, we propose an image restoration network that integrates Mamba with Transformer architectures. We first design a Vision-Mamba–Transformer (VMT) module that combines the long-range dependency modeling of Vision Mamba with the global contextual reasoning of Transformers, facilitating the joint modeling of global structures and local details, thus mitigating information loss and detail blurring during restoration. Second, we introduce an Adaptive Content Guidance (ACG) module that employs dynamic gating and spatial–channel attention to enable effective inter-layer feature fusion, thereby enhancing cross-layer semantic consistency. Finally, we embed the VMT and ACG modules into a U-Net backbone, achieving efficient integration of multi-scale feature modeling and cross-layer fusion, significantly improving reconstruction quality under complex weather conditions. The experimental results show that on Snow100K-S/L, VMT-Net improves PSNR over the baseline by approximately 0.89 dB and 0.36 dB, with SSIM gains of about 0.91% and 0.11%, respectively. On Outdoor-Rain and Raindrop, it performs similarly to the baseline and exhibits superior detail recovery in real-world scenes. Overall, the method demonstrates robustness and strong detail restoration across diverse adverse-weather conditions. Full article

This study presents the design and preliminary validation of a novel portable, wireless, and wearable sensing system—The Breast Impact Monitoring System (BIMS)—for female athletes, developed to monitor and quantify localised mechanical impacts to the breast during high-intensity sporting activity. The platform addresses a critical gap in sports biomechanics by enabling, for the first time, objective measurement of breast forces in both controlled mechanical impact testing and preliminary on-body tackling trials for female athletes. Its application extends to advancing understanding of sports-related breast injuries, informing prevention strategies, and assessing the effectiveness of protective equipment. The BIMS leverages an array of 16 thin-film Force Sensitive Resistors (FSRs) and employs a dual-core microcontroller architecture to manage the trade-off between wireless constraints and high-speed data fidelity, successfully achieving uninterrupted acquisition at 856 Hz for each channel. The system was rigorously validated against a reference instrument using a commercial Force Plate and a custom mechanical drop rig, demonstrating high accuracy with a calibration model ($R^2=0.9988$). Preliminary wearable testing confirmed the system’s capability to detect and spatially map high localised impact forces, including peak forces up to 550 N (across an area diameter of 20 mm), during preliminary rugby tackling activities. By offering a practical and scalable solution for capturing previously inaccessible data, this system establishes a foundation for future research into athlete welfare and long-term breast health. Full article

Accurate prediction of the rate of penetration (ROP) in deep and ultra-deep wells remains a major challenge due to complex downhole conditions and limited real-time data. To address the issues of physical inconsistency and weak generalization in conventional da-ta-driven approaches, this study proposes a mechanism-guided and attention-enhanced deep learning framework. In this framework, drilling physical principles such as energy balance are reformulated into differentiable constraint terms and directly incorporated in-to the loss function of deep neural networks, ensuring that model predictions strictly ad-here to drilling physics. Meanwhile, attention mechanisms are integrated to improve feature selection and temporal modeling: for tree-based models, we investigate their implicit attention to key parameters such as weight on bit (WOB) and torque; for sequential models, we design attention-enhanced architectures (e.g., LSTM and GRU) to capture long-term dependencies among drilling parameters. Validation on 49,284 samples from 11 deep and ultra-deep wells in China (depth range: 1226–8639 m) demonstrates that the synergy between mechanism constraints and attention mechanisms substantially improves ROP prediction accuracy. In blind-well tests, the proposed method achieves a mean absolute percentage error (MAPE) of 9.47% and an R² of 0.93, significantly outperforming traditional methods under complex deep-well conditions. This study provides reliable intelligent decision support for optimizing deep and ultra-deep well drilling operations. By improving prediction accuracy and enabling real-time anomaly detection, it enhances operational safety and efficiency while reducing drilling risks. The proposed approach offers high practical value for field applications and supports the intelligent development of the oil and gas industry. Full article

South Asia’s worsening air pollution crisis represents one of the most urgent public health and environmental challenges of the 21st century. Nearly two billion people—over one-quarter of the global population—reside in this region, where air quality levels routinely exceed World Health Organization (WHO) guidelines by factors of 10 to 15. This has translated into an unprecedented health burden, with approximately two million premature deaths annually, widespread chronic respiratory and cardiovascular disease, and rising economic losses. According to recent World Bank estimates, welfare losses amount to over 5% of regional GDP, a figure far exceeding the projected costs of coordinated mitigation. Despite this, South Asia continues to lack a binding regional framework capable of addressing its shared airshed. Existing cooperative efforts—such as the Malé Declaration on Control and Prevention of Air Pollution (1998)—have provided a useful platform for dialog and pilot monitoring, but they remain voluntary, under-resourced, and insufficient to manage the transboundary nature of the crisis. National-level programs, including India’s National Clean Air Programme (NCAP), Bangladesh’s National Air Quality Management Plan (NAQMP), and Nepal’s National Air Quality Management Action Plan (AQMAP), demonstrate domestic commitment but are constrained by fragmentation, limited financing, and lack of regional integration. This gap represents the central knowledge and governance challenge that prompted the present commentary. To address it, we propose a dual-track architecture designed to institutionalize binding regional cooperation. Track A would establish a United Nations-anchored South Asian Transboundary Air Pollution Protocol, under the auspices of the United Nations Environment Programme, the World Health Organization (WHO), and the United Nations Economic and Social Commission for Asia and the Pacific (UNESCAP). This protocol would codify legally enforceable emission standards, compliance committees, financial mechanisms, and harmonized monitoring. Track B would establish a South Asian Association for Regional Cooperation (SAARC) Prime Ministers’ Council on Air Quality (SPMCAQ) to provide political leadership, align domestic implementation, and authorize rapid responses to cross-border haze events. Lessons from the Indian Ocean Experiment, the ASEAN Agreement on Transboundary Haze Pollution, and Europe’s Convention on Long-Range Transboundary Air Pollution demonstrate that legally binding agreements combined with high-level political ownership can achieve durable reductions in pollution despite geopolitical tensions. By situating South Asia within these global precedents, the proposed framework provides a pragmatic, enforceable, and politically resilient pathway to protect health, reduce economic losses, and deliver cleaner air for nearly one-quarter of humanity. Full article

Building Information Modeling (BIM) is a transformative tool in Sustainable Design (SD), providing measurable benefits for efficiency, collaboration, and performance in architectural, engineering, and construction (AEC) practices. Despite its growing presence in academic curricula, a gap persists between students’ recognition of BIM’s sustainability potential and their confidence or ability to apply these concepts in real-world practice. This study examines students’ understanding and perceptions of BIM and Sustainable Design education, offering insights for enhancing curriculum integration and pedagogical strategies. The objectives are to: (1) assess students’ current understanding of BIM and Sustainable Design; (2) identify gaps and misconceptions in applying BIM to sustainability; (3) evaluate the effectiveness of existing teaching methods and curricula to inform future improvements; and (4) explore the alignment between students’ theoretical knowledge and practical abilities in using BIM for Sustainable Design. The research methodology includes a comprehensive literature review and a survey of 213 students from architecture and construction management programs. Results reveal that while most students recognize the value of BIM for early-stage sustainable design analysis, many lack confidence in their practical skills, highlighting a perception–practice gap. The paper examines current educational practices, identifies curriculum shortcomings, and proposes strategies, such as integrated, hands-on learning experiences, to better align academic instruction with industry needs. Distinct from previous studies that focused primarily on single-discipline or software-based training, this research provides an empirical, cross-program analysis of students’ perception–practice gaps and offers curriculum-level insights for sustainability-driven practice. These findings provide practical recommendations for enhancing BIM and sustainability education, thereby better preparing students to meet the demands of the evolving AEC sector. Full article

Smoke detection is a practical approach for early identification of wildfires and mitigating hazards that affect ecosystems, infrastructure, property, and the community. The existing deep learning (DL) object detection methods (e.g., Detection Transformer (DETR)) have demonstrated significant potential for early awareness of these events. However, their precision is influenced by the low visual salience of smoke and the reliability of the annotation, and collecting real-world and reliable datasets with precise annotations is a labor-intensive and time-consuming process. To address this challenge, we propose a weakly supervised Transformer-based approach with a teacher–student architecture designed explicitly for smoke detection while reducing the need for extensive labeling efforts. In the proposed approach, an expert model serves as the teacher, guiding the student model to learn from a variety of data annotations, including bounding boxes, point labels, and unlabeled images. This adaptability reduces the dependency on exhaustive manual annotation. The proposed approach integrates a Deformable-DETR backbone with a modified loss function to enhance the detection pipeline by improving spatial reasoning, supporting multi-scale feature learning, and facilitating a deeper understanding of the global context. The experimental results demonstrate performance comparable to, and in some cases exceeding, that of fully supervised models, including DETR and YOLOv8. Moreover, this study expands the existing datasets to offer a more comprehensive resource for the research community. Full article

Building archetypes are essential for advancing architectural theory and energy modeling, providing a foundation for scalable assessments of building performance and sustainability worldwide. In Saudi Arabia, educational buildings, especially those in public universities, are predominantly constructed using standardized and repetitive design templates, such as courtyard and prototype models, which have significant implications for energy efficiency, indoor environmental quality, and sustainability outcomes. Despite their prevalence, there is a notable lack of systematic research on the classification and distribution of these archetypes within the Saudi context, particularly regarding their impact on energy consumption and sustainable campus planning. This study addresses this gap by systematically collecting and analyzing data from 29 public universities across Saudi Arabia, employing GIS mapping to document building characteristics including age, region, urban context, masterplan typology, and architectural design. A cumulative weighting factor was applied to quantify the representativeness of archetypes, while chi-square tests and effect size metrics assessed the statistical concentration and significance of observed patterns. The results reveal a pronounced dominance of a small number of archetypes, especially standardized courtyard and identical design models, across the national stock, with the top 10% of archetype ranks accounting for the majority of buildings. This high degree of standardization enables efficient modeling, benchmarking, and targeted energy interventions, while also highlighting the need for greater contextual adaptation in future campus planning. While this study does not directly simulate building energy performance, it establishes a national-scale typological foundation that can support future simulation, benchmarking, and policy design. The developed GIS-based framework primarily serves managerial and planning objectives, offering a standardized reference for facility management, retrofitting prioritization, and strategic energy-efficiency planning in Saudi public universities. Full article

The gravity forward modeling algorithm is a compute-intensive method and is widely used in scientific computing, particularly in geophysics, to predict the impact of subsurface structures on surface gravity fields. Traditional implementations rely on CPUs, where performance gains are mainly achieved through algorithmic optimization. With the rise of domain-specific architectures, FPGA offers a promising platform for acceleration, but faces challenges such as limited programmability and the high cost of nonlinear function implementation. This work proposes an FPGA-based co-processor to accelerate gravity forward modeling. A RISC-V core is integrated with a custom instruction set targeting key computation steps. Tasks are dynamically scheduled and executed on eight fully pipeline processing units, achieving high parallelism while retaining programmability. To address nonlinear operations, we introduce a piecewise linear approximation method optimized via stochastic gradient descent (SGD), significantly reducing resource usage and latency. The design is implemented on the AMD UltraScale+ ZCU102 FPGA (Advanced Micro Devices, Inc. (AMD), Santa Clara, CA, United States) and evaluated across several forward modeling scenarios. At 250 MHz, the system achieves up to 179× speedup over an Intel Xeon 5218R CPU (Intel Corporation, Santa Clara, CA, United States) and improves energy efficiency by 2040×. To the best of our knowledge, this is the first FPGA-based gravity forward modeling accelerate design. Full article

RISC-V has emerged as a compelling alternative to proprietary instruction set architectures, distinguished by its openness, extensibility, and modularity. As the ecosystem matures, attention has turned to building confidential computing foundations, notably Trusted Execution Environments (TEEs) and secure enclaves, to support sensitive workloads. These efforts explore a variety of design directions, yet reveal important trade-offs. Some approaches achieve strong isolation guarantees, but fall short in scalability or broad adoption. Others introduce defenses, such as memory protection or side-channel resistance, although often with significant performance costs that limit deployment in constrained systems. Lightweight enclaves address embedded contexts, but lack the advanced security features demanded by complex applications. In addition, early-stage development, complex programming models, and limited real-world validation hinder their usability. This survey reviews the current landscape of RISC-V TEEs and secure enclaves, analyzing their architectural principles, strengths, and weaknesses. To the best of our knowledge, this is the first work to present such a consolidated view. Finally, we highlight open challenges and research opportunities, aiming toward establishing a cohesive and trustworthy RISC-V trusted computing ecosystem. Full article

Prefabricated stations utilizing digital modeling techniques demonstrate significant advantages over traditional cast-in-place methods, including improved dimensional accuracy, reduced environmental impact, and minimized material waste. To maximize these benefits, this study develops a digital twin platform for prefabricated assembled superimposed stations through the integration of Digital Twin Scene–Entity–Relationship–Incident–Control (SERIC) modeling with IoT technology. The platform adopts a “1+5+N” architecture that implements model-data separation, lightweight processing, and model-data association for SERIC model management, while IoT-enabled data acquisition facilitates lifecycle data sharing. By integrating BIM models, engineering data, and IoT sensor inputs, the platform employs multi-source analytics to monitor construction progress, enhance safety surveillance, ensure quality control, and optimize designs. Implementation at Jinan Metro Line 8’s prefabricated underground station confirms the SERIC-IoT digital twin’s efficacy in advancing sustainable, high-quality rail transit development. Results demonstrate the platform’s capacity to improve construction efficiency and operational management, aligning with urban rail objectives prioritizing sustainability and technological innovation. This study establishes that integrating SERIC modeling with IoT in digital twin frameworks offers a robust approach to modernizing prefabricated station construction, with scalable applications for future smart transit infrastructure. Full article

Plant viral infections continue to pose a significant and ongoing threat to global food security, especially in the context of climatic instability and intensive agricultural practices. The CRISPR/Cas system has emerged as a powerful tool for developing virus-resistant crops by enabling precise modifications to viral genomes or plant susceptibility factors. Nonetheless, the efficacy and dependability of CRISPR-based antiviral approaches are limited by challenges in guide RNA design, off-target effects, insufficiently annotated datasets, and the intricate biological dynamics of plant–virus interactions. This paper summarizes the latest advancements in the incorporation of artificial intelligence (AI) methodologies, including machine learning and deep learning algorithms, into the CRISPR design and optimization framework. It examines how convolutional and recurrent neural networks, transformer architectures, and generative models like AlphaFold2, RoseTTAFold, and ESMFold can be used to predict protein structures, score sgRNAs, and model host–virus interactions. AI-enhanced methods have been proven to improve target specificity, Cas protein performance, and in silico validation. This paper aims to establish a foundation for next-generation genome editing strategies against plant viruses and promote the adoption of AI-powered CRISPR technologies in sustainable agriculture. Full article

Due to a complex of factors, visually impaired people are facing difficulties and increased risks during fire emergencies and evacuations from different types of buildings. Even if a lot of studies have been conducted to improve the mobility and autonomy of people with visual impairment during emergency evacuation processes, these offer only partial solutions, especially in the presence of uncertainties characteristic of fire evolution. Aiming for a more comprehensive approach to the safe evacuation of people with visual impairments, this paper proposes a support system that integrates innovative aspects related to the architecture of the application, modeling and simulation methods, and experimental realization. The system is decentralized, capable of anticipating possible fire extensions and determining, in real-time, new corresponding evacuation routes. The overall design complies with the standard norms in emergency situations. Two models, one developed in Stateflow and the other based on Delay Time Petri Nets (DTPN), were constructed to describe the dynamic behavior of the system in the presence of unexpected events that can change the initial recommended evacuation path. To test the functionality and efficiency of the proposed system, the conditions created by potential fire sources were simulated as a part of realistic scenarios. Tests were conducted with visually impaired people. Simulation and prototype testing showed that the presented system can improve evacuation times, achieving a measurable gain compared to scenarios where there is no information regarding fire evolution. Full article