Search Results (736)

Broadcast-style downlinks (e.g., PONs and satellites) expose physical waveforms despite transport-layer cryptography, motivating physical-layer encryption (PLE). Digital chaotic encryption is appealing for its noise-like spectra, sensitivity, and DSP-friendly implementation, but in low-CSPR KK-SSB systems, common embeddings disrupt minimum-phase requirements and raise PAPR/SSBI near 1 dB CSPR, while finite-precision effects can leak correlation after KK reconstruction. We bridge this gap by integrating 3D Lorenz-based PLE into our low-CSPR KK-SSB receiver. A KK-compatible embedding applies a Lorenz-driven XOR mapping to I/Q bitstreams before PAM4-to-16QAM modulation, preserving the minimum phase and avoiding spectral zeros. Co-design of chaotic strength and subband usage with the KK SSBI-suppression method maintains SSBI mitigation with negligible PAPR growth. We further adopt digitization settings and fractional-digit-parity-based key derivation to suppress short periods and remove key-revealing synchronization cues. Experiments demonstrate a 10⁹¹ key space without degrading transmission quality, enabling secure, key-concealed operation on shared downlinks and offering a practical path for chaotic PLE in near-minimum-CSPR SSB systems. Full article

Chloroplast codon usage bias (CUB) records both maternal phylogeny and selection intensity. Characterizing CUB in the synthetic cereal × Triticosecale and its Triticum and Secale parents is therefore a prerequisite for plastid-based engineering and for tracing the evolutionary consequences of recent allopolyploidy. Complete plastome sequences of five taxa—Triticum monococcum, T. turgidum, T. aestivum, Secale cereale and × Triticosecale sp.—were downloaded. Protein-coding genes were extracted to calculate overall GC, GC1–GC3, SCUO, RSCU, ENC-GC3s, neutrality, and PR2 plots. Optimal codons were defined as RSCU ≥ 1 and △RSCU ≥ 0.8. The results showed that the chloroplast genomes of these five species are low in GC content for the third base of codons, suggesting an end preference for A or U bases. The SCUO values ranged from 0.22 to 0.23, suggesting no significant codon usage bias. GC content was relatively low (38.78–39.16%), with the order GC1 > GC2 > GC3. RSCU analysis indicated that codons ending with A/T are more commonly used. Neutral mapping, ENC-GC3s, and the PR2 plot all showed that the preference of codon usage for the majority of functional genes was influenced by a combination of mutation and natural selection pressure, and the influence of natural selection was predominant. RSCU clustering recovers the expected maternal tree (Triticum clade + triticale). All optimal codons terminate with A or U, yielding identical plastid translation tables for the five species. Despite its recent hybrid origin, triticale plastid CUB is indistinguishable from its wheat maternal ancestor and is governed mainly by selection. The compiled optimal codon set provides an immediate reference for chloroplast transformation and for dissecting selection relaxation in newly synthesized triticale combinations. Full article

Understanding the thermal environment of outdoor public spaces is critical for climate-responsive architectural design, evidence-based urban science, and data-driven smart city planning. Thermal comfort shapes both individual decision-making and collective behavioural patterns, offering valuable insights for designing spaces that support year-round vitality. This study investigates the relationship between thermal conditions and crowd behaviour in severe cold regions by combining behavioural mapping with on-site environmental measurements. Results show that in high-temperature conditions, spatial distribution is primarily influenced by sunlight and shade, whereas at low temperatures, sunlight has minimal effect on space use. Attendance, duration of stay, and activity intensity follow quadratic relationships with the Universal Thermal Climate Index (UTCI), with optimal values at 29 °C, 26 °C, and 27 °C, respectively. Walking speed is inversely correlated with UTCI, with the fastest speeds observed under cold discomfort, reflecting rapid departure from space. Sitting behaviour peaks at 21 °C UTCI and declines to nearly zero when UTCI is below 10 °C. A comparative analysis between Harbin and other regions reveals significant deviations from temperate zone patterns and greater similarity to subtropical behavioural responses. A key contribution of this study is the introduction of the spatial usage rate model and the foot vote method, two novel, observation-based tools that allow for the objective estimation of thermal comfort without relying solely on subjective surveys. These methods offer architects, planners, and smart city practitioners a powerful evidence-based framework to evaluate and optimise outdoor thermal performance, ultimately enhancing usability, adaptability, and public engagement in cold-climate cities. Full article

Over the last decade, L-band synthetic aperture radar (SAR) satellite data has become more widely available globally, providing new opportunities for biodiversity and ecosystem services (BES) monitoring. To better understand these opportunities, we conducted a systematic scoping review of articles that utilized L-band synthetic aperture radar (SAR) satellite data for BES monitoring. We found that the data have mainly been analyzed using image classification and regression methods, with classification methods attempting to understand how the extent, spatial distribution, and/or changes in different types of land use/land cover affect BES, and regression methods attempting to generate spatially explicit maps of important BES-related indicators like species richness or vegetation above-ground biomass. Random forest classification and regression algorithms, in particular, were used frequently and found to be promising in many recent studies. Deep learning algorithms, while also promising, have seen relatively little usage thus far. PALSAR-1/-2 annual mosaic data was by far the most frequently used dataset. Although free, this data is limited by its low temporal resolution. To help overcome this and other limitations of the existing L-band SAR datasets, 64% of studies combined them with other types of remote sensing data (most commonly, optical multispectral data). Study sites were mainly subnational in scale and located in countries with high species richness. Future research opportunities include investigating the benefits of new free, high temporal resolution L-band SAR datasets (e.g., PALSAR-2 ScanSAR data) and the potential of combining L-band SAR with new sources of SAR data (e.g., P-band SAR data from the “Biomass” satellite) and further exploring the potential of deep learning techniques. Full article

You Only Look Once (YOLO) is a convolutional neural network-based object detection algorithm widely used in real-time vision applications. However, its high computational demand leads to significant power consumption and cost when deployed in graphics processing units. Field-programmable gate arrays offer a low-power alternative. However, their efficient implementation requires architecture-level optimization tailored to limited device resources. This study presents an optimized YOLOv2 accelerator for the Zynq-7000 system-on-chip (SoC). The design employs 16-bit integer quantization, a filter reuse structure, an input feature map reuse scheme using a line buffer, and tiling parameter optimization for the convolution and max pooling layers to maximize resource efficiency. In addition, a stall-based control mechanism is introduced to prevent structural hazards in the pipeline. The proposed accelerator was implemented on the Zynq-7000 SoC board, and a system-level evaluation confirmed a negligible accuracy drop of only 0.2% compared with the 32-bit floating-point baseline. Compared with previous YOLO accelerators on the same SoC, the design achieved up to 26% and 15% reductions in flip-flop and digital signal processor usage, respectively. This result demonstrates feasible deployment on XC7Z020 with DSP 57.27% and FF 16.55% utilization. Full article

To meet the increasing demands of energy efficiency and real-time performance in autonomous driving systems, this paper presents a lightweight and robust LiDAR SLAM framework designed with power-aware considerations. The proposed system introduces three core innovations. First, it replaces traditional ordered point cloud indexing with a 2D range image projection, significantly reducing memory usage and enabling efficient feature extraction with curvature-based criteria. Second, a multi-stage outlier rejection mechanism is employed to enhance feature robustness by adaptively filtering occluded and noisy points. Third, we propose a dynamically filtered local mapping strategy that adjusts keyframe density in real time, ensuring geometric constraint sufficiency while minimizing redundant computation. These components collectively contribute to a SLAM system that achieves high localization accuracy with reduced computational load and energy consumption. Experimental results on representative autonomous driving datasets demonstrate that our method outperforms existing approaches in both efficiency and robustness, making it well-suited for deployment in low-power and real-time scenarios within intelligent transportation systems. Full article

Understanding the intricate relationship between composition, processing conditions, and material properties is essential for optimizing Cu-based alloys. Machine learning offers a powerful tool for decoding these complex interactions, enabling more efficient alloy design. This work introduces a comprehensive machine learning framework aimed at accurately predicting key properties such as hardness and electrical conductivity of low-alloyed Cu-based alloys. By integrating various input parameters, including chemical composition and thermo-mechanical processing parameters, the study develops and validates multiple machine learning models, including Multi-Layer Perceptron with Production-Aware Deep Architecture (MLP-PADA), Deep Feedforward Network with Multi-Regularization Framework (DFF-MRF), Feedforward Network with Self-Adaptive Optimization (FFN-SAO), and Feedforward Network with Materials Mapping (FFN-TMM). On a held-out test set, DFF-MRF achieved the best generalization (R²_test = 0.9066; RMSE_test = 5.3644), followed by MLP-PADA (R²_test = 0.8953; RMSE_test = 5.7080) and FFN-TMM (R²_test = 0.8914; RMSE_test = 5.8126), with FFN-SAO slightly lower (R²_test = 0.8709). Additionally, a computational performance analysis was conducted to evaluate inference time, memory usage, energy consumption, and batch scalability across all models. Feature importance analysis was conducted, revealing that aging temperature, Cr, and aging duration were the most influential factors for hardness. In contrast, aging duration, aging temperature, solution treatment temperature, and Cu played key roles in electrical conductivity. The results demonstrate the effectiveness of these advanced machine learning models in predicting critical material properties, offering insightful advancements for materials science research. This study introduces the first controlled, statistically validated, multi-model benchmark that integrates composition and thermo-mechanical processing with deployment-grade profiling for property prediction of low-alloyed Cu alloys. Full article

Ensuring hygiene compliance in regulated environments—such as food processing facilities, hospitals, and public indoor spaces—requires reliable detection of personal protective equipment (PPE) usage, including gloves, face masks, and hairnets. Manual inspection is labor-intensive and unsuitable for continuous, real-time enforcement. This study benchmarks three lightweight object detection models—YOLOv8n, YOLOv10n, and YOLOv12n—for automated PPE compliance monitoring using a large curated dataset of over 31,000 annotated images. The dataset spans seven classes representing both compliant and non-compliant conditions: glove, no_glove, mask, no_mask, incorrect_mask, hairnet, and no_hairnet. All evaluations were conducted using both detection accuracy metrics (mAP@50, mAP@50–95, precision, recall) and deployment-relevant efficiency metrics (inference speed, model size, GFLOPs). Among the three models, YOLOv10n achieved the highest mAP@50 (85.7%) while maintaining competitive efficiency, indicating strong suitability for resource-constrained IoT-integrated deployments. YOLOv8n provided the highest localization accuracy at stricter thresholds (mAP@50–95), while YOLOv12n favored ultra-lightweight operation at the cost of reduced accuracy. The results provide practical guidance for selecting nano-scale detection models in real-time hygiene compliance systems and contribute a reproducible, deployment-aware evaluation framework for computer vision in hygiene-critical settings. Full article

In dynamic and information-rich contexts, systems must be capable of making instantaneous, context-aware decisions. Such scenarios require optimization methods that are both fast and flexible. This paper introduces an innovative hardware-based intelligent optimization framework, deployed on FPGAs, designed to support autonomous decisions in real-time systems. In contrast to conventional methods based on a single chaotic map, our scheme brings together six separate chaotic generators in simultaneous operation, orchestrated by an adaptive voting system based on past results. The system, in conjunction with the Secretary Bird Optimization Algorithm (SBOA), constantly adjusts its optimization approach according to the changing profile of the objective function. This delivers first-rate, timely solutions with improved convergence, resistance to local minima, and a high degree of adaptability to a variety of decision-making contexts. Simulations carried out on reference standards and engineering problems have demonstrated the scalability, responsiveness, and efficiency of the proposed model. These characteristics make it particularly suitable for use in embedded intelligence applications in sectors such as intelligent production, robotics, and IoT-based infrastructures. The suggested solution was tested using post-synthesis simulations on Vivado 2022.2 and experimented on three concrete engineering challenges: welded beam design, pressure equipment design, and tension/compression spring refinement. In each situation, the adaptive selection process dynamically determined the most suitable chaotic map, such as the logistics map for the Welded Beam Design Problem (WBDP) and the Tent map for the Pressure Vessel Design Problem (PVDP). This led to ideal results that exceed both conventional static methods and recent references in the literature. The post-synthesis results on the Nexys 4 DDR (Artix-7 XC7A100T, Digilent Inc., Pullman, WA, USA) show that the initial Q16.16 implementation exceeded the device resources (128% LUTs and 100% DSPs), whereas the optimized Q4.8 representation achieved feasible deployment with 80% LUT utilization, 72% DSP usage, and 3% FF occupancy. This adjustment reduced resource consumption by more than 25% while maintaining sufficient computational accuracy. Full article

Indoor positioning remains a recurrent and significant challenge in research. Unlike outdoor environments, where the Global Positioning System (GPS) provides reliable location information, indoor scenarios lack direct line-of-sight to satellites or cellular towers, rendering GPS inoperative and requiring alternative positioning techniques. Despite numerous approaches, indoor contexts with resource limitations, energy constraints, or physical restrictions continue to suffer from unreliable localization. Many existing methods employ a fixed number of reference anchors, which sets a hard balance between localization accuracy and energy consumption, forcing designers to choose between precise location data and battery life. As a response to this challenge, this paper proposes an energy-aware indoor positioning strategy based on Mobile Ad Hoc Networks (MANETs). The core principle is a self-adaptive control loop that continuously monitors the network’s positioning accuracy. Based on this real-time feedback, the system dynamically adjusts the number of active anchors, increasing them only when accuracy degrades and reducing them to save energy once stability is achieved. The method dynamically estimates relative coordinates by analyzing node encounters and contact durations, from which relative distances are inferred. Generalized Multidimensional Scaling (GMDS) is applied to construct a relative spatial map of the network, which is then transformed into absolute coordinates using reference nodes, known as anchors. The proposal is evaluated in a realistic simulated indoor MANET, assessing positioning accuracy, adaptation dynamics, anchor sensitivity, and energy usage. Results show that the adaptive mechanism achieves higher accuracy than fixed-anchor configurations in most cases, while significantly reducing the average number of required anchors and their associated energy footprint. This makes it suitable for infrastructure-poor, resource-constrained indoor environments where both accuracy and energy efficiency are critical. Full article

Cloud-native applications have significantly advanced the development and scalability of online services through the use of microservices and modular architectures. However, achieving adaptability, resilience, and efficient performance management within cloud environments remains a key challenge. This work systematically reviews 111 publications from the last eight years on self-adaptive cloud design and operations patterns, classifying them by objectives, control scope, decision-making approach, automation level, and validation methods. Our analysis reveals that performance optimization dominates research goals, followed by cost reduction and security enhancement, with availability and reliability underexplored. Reactive feedback loops prevail, while proactive approaches—often leveraging machine learning—are increasingly applied to predictive resource provisioning and application management. Resource-oriented adaptation strategies are common, but direct application-level reconfiguration remains scarce, representing a promising research gap. We further catalog tools, platforms, and more than 30 publicly accessible datasets used in validation, and that dataset usage is fragmented without a de facto standard. Finally, we map the research findings on a generic application and system-level design for self-adaptive applications, including a proposal for a federated learning approach for SaaS application Agents. This blueprint aims to guide future work toward more intelligent, context-aware cloud automation. Full article

This paper is aimed at exploring and expanding the concept of mobility culture(s) (MC), with regard to its influence on public transportation (PT) usage share. Despite some factors being positively correlated with collective modes, the modal split is often skewed in favour of private or individual ones. To this end, a comprehensive analysis of 70 cities in Germany and Italy is conducted, employing geographically weighted regression (GWR) to assess the impact of some factors on the local share of PT. Factors examined include land use diversity, fare integration, service quality (measured as level of service), scheduling regularity and characteristics of the transit network maps. The findings of the study provide new perspectives on MC and suggest strategies for promoting sustainable and equitable transportation systems. Full article

As the core spatial carriers for teaching, research, and academic exchange, university campuses have long been central subjects in architectural design research. As a distinct phenotypic type of university campus, the grid-type campus has gradually gained academic attention due to its modular characteristics, horizontal expandability, and flexible organization—with advantages including improved spatial efficiency, enhanced interdisciplinary interaction, and stronger adaptability. In this study, a typological analysis was performed on 23 representative global grid-type campuses to explore their planning concepts and module selection criteria. Research data were collected from literature reviews, architectural drawings, and Google Maps (Web) satellite images and visualized and analyzed using Origin Pro 2021. Results show that campus module selection is primarily influenced by three factors: walking distance, functional requirements, and structural systems. At the master planning level, module selection aligns with the “five-minute walking radius” standard, and campus scale is generally controlled within 500 × 350 m. At the architectural level, functional needs determine that module sizes typically range from 50–90 m or 7.2–10 m. At the structural level, module ranges are usually 7–18 m, depending on usage requirements and structural systems. This study’s findings can provide theoretical support and practical references for the planning, design, and module selection of future grid-type university campuses. Full article

Background: Cochlear implants (CIs) serve diverse populations with hearing loss, but patients with single-sided deafness (SSD) often show lower post-implantation usage and satisfaction than bilateral CI users. This disparity may stem from their normal contralateral ear providing sufficient auditory input for many daily situations, reducing the perceived need for consistent CI use. Consequently, uniform screening and evaluations, typically designed for bilateral hearing loss, often fail to address SSD’s unique needs. Methods: This narrative review synthesizes the current literature to explore patient and device factors shaping CI integration, outcomes, and experience in SSD. It highlights implications for developing personalized care strategies distinct from those used in bilateral hearing loss. Results: SSD patients face unique challenges: reliance on compensatory behaviors and significant auditory processing difficulties like acoustic–electric mismatch and place–pitch discrepancy. Anatomical factors and deafness of duration also impact outcomes. Traditional measures are often insufficient due to ceiling effects. Music perception offers a sensitive metric and rehabilitation tool, while big data and machine learning show promise for predicting outcomes and tailoring interventions. Conclusions: Optimizing CI care for SSD necessitates a personalized approach across candidacy, counseling, and rehabilitation. Tailored strategies, including individualized frequency mapping, adaptive auditory training, advanced outcome metrics like music perception, and leveraging big data for precise, data-driven predictions, are crucial for improving consistent CI usage and overall patient satisfaction. Full article

Intelligent robotic systems frequently operate under stringent energy limitations, especially in complex and dynamic environments. To enhance both adaptability and reliability, this study introduces a semantic planning framework that integrates ontology-driven reasoning with energy awareness. The framework estimates energy consumption based on the platform-specific behavior of sensing, actuation, and computational modules while continuously updating place-level semantic representations using real-time execution data. These representations encode not only spatial and contextual semantics but also energy characteristics acquired from prior operational history. By embedding historical energy usage profiles into hierarchical semantic maps, this framework enables more efficient route planning and context-aware task assignment. A shared semantic layer facilitates coordinated planning for both single-robot and multi-robot systems, with the decisions informed by energy-centric knowledge. This approach remains hardware-independent and can be applied across diverse platforms, such as indoor service robots and ground-based autonomous vehicles. Experimental validation using a differential-drive mobile platform in a structured indoor setting demonstrates improvements in energy efficiency, the robustness of planning, and the quality of the task distribution. This framework effectively connects high-level symbolic reasoning with low-level energy behavior, providing a unified mechanism for energy-informed semantic decision-making. Full article