Search Results (1,727)

Strawberry greenhouse cultivation is increasingly supported by sensing technologies, artificial intelligence (AI), and decision-support infrastructure, but their horticultural value depends on whether heterogeneous measurements can be translated into biologically meaningful crop states and practical management decisions. This review synthesizes strawberry phenotyping, multimodal sensing, AI-based crop-state interpretation, and supervised agentic coordination as a phenotyping-to-action framework for greenhouse strawberry cultivation. The reviewed studies show substantial progress in measuring and interpreting vegetative, reproductive, fruit-quality, stress-related, and environmental crop states through imaging, spectral, environmental, root-zone, and modeling approaches. However, much of the literature still emphasizes measurement accuracy, model performance, or infrastructure capability, whereas fewer studies validate whether AI-derived outputs improve crop response, management decisions, workflow, resource use, or production outcomes. The review therefore distinguishes sensing technologies for data acquisition and measurement from AI-based methods for interpretation and prediction, and examines how crop-state information can be connected to practical greenhouse decision making. It also compares established decision technologies, including expert systems, model predictive control, digital twins, and closed-loop coordination, with supervised agentic coordination as bounded decision-support concepts rather than as evidence of unrestricted autonomous control. Future work should emphasize phenotype-to-action validation, domain-aware benchmarking, and supervised deployment studies that connect model outputs with decision rules, crop outcomes, operational constraints, and grower oversight. By grounding sensing technologies and AI-based interpretation methods in crop-response validation, strawberry greenhouse systems can progress toward supervised, crop-state-driven decision support. Full article

Background/Objectives: Optical coherence tomography (OCT) and OCT angiography (OCT-A) provide detailed measurements of retinal structure and vasculature; however, age-related differences in how these parameters correlate with one another remain poorly understood. We hypothesized that vascular–structural integration in the macula is more pronounced in adults than in children. Our aim was to characterize correlation patterns in pediatric and adult populations to inform the development of age-specific clinical interpretation guidelines. Methods: This prospective cross-sectional observational study enrolled 37 healthy children (age 1–17 years) and 28 healthy adults (age 18–65 years). Eyes with ocular or systemic conditions affecting the retina or prior intraocular surgery were excluded. Standardized OCT and OCT-A acquisition protocols provided structural and vascular measures. Univariable correlation analyses applied a stringent threshold (p < 0.001) to identify robust associations. Significant univariable results were entered into multivariable regression models adjusting for age, gender, intraocular pressure, and axial length. A Group-wise Linkage Proportion quantified the percentage of potential significant correlations among eight predefined anatomical parameter groups. Results: Ninety univariable correlations met p < 0.001. Fourteen correlations were shared across age groups, notably foveal avascular zone metrics and vessel density, showing very large negative correlations (r = −0.70 to −0.87). The pediatric cohort displayed 40 unique correlations, primarily linking optic nerve head flow indices to retinal nerve fiber layer thickness. Adults exhibited 36 unique correlations, dominated by macular vascular–thickness coupling concentrated in the parafoveal region. After multivariable adjustment, 52 of 90 associations remained significant. Adult-specific associations lost significance more frequently (58%) than pediatric-specific associations (43%), whereas correlations shared across both groups showed complete stability (100%). The Group-wise Linkage Proportion indicated pronounced macular vascular–structural coupling in adults (48.4%) versus near absence in children (1.2%). Conclusions: Retinal parameter correlation patterns show fundamental differences between pediatric and adult eyes. While optic nerve head-macular thickness relationships remain consistent across ages, adults exhibit mature, localized integration of macular vascular and structural parameters absent in children. These findings suggest that pediatric and adult OCT/OCT-A measurements may benefit from separate reference standards, although prospective validation is required before clinical implementation. Full article

While infrastructure asset management increasingly relies on high-resolution drone imagery, existing workflows suffer from fragmented information management and dependence on costly local processing infrastructure. This paper addresses these limitations by using a cloud-native spatial intelligence hub that converts raw inspection imagery into an interactive and queryable three-dimensional information layer. The system integrates a timeout-resilient orchestration layer for photogrammetry pipelines, a multi-user three-dimensional environment for collaborative review, and a PostGIS-backed spatial database that stores defects as georeferenced anchors. We further introduce a spatial anchoring workflow mapping three-dimensional interactions to world coordinates, retrieving context-relevant images via frustum-based visibility scoring. Evaluated on real inspection datasets, the serverless architecture achieved end-to-end reconstruction in under one hour with sub-25 ms query latency. Results indicate that acquisition geometry, particularly oblique convergent viewpoints, is a stronger predictor of reconstruction complexity than image count. This work establishes a reproducible reference architecture, enabling a transition from file-centric documentation to traceable, spatially indexed evidence management for infrastructure Digital Twins. Full article

Wearable technologies offer practical opportunities for assistive communication and educational support in introductory sign language learning. This paper presents an IoT-enabled smart glove for deterministic static sign language recognition over a bounded vocabulary of 15 isolated static gestures, comprising digits (0–9) and five vowel handshapes (A, E, I, O, U). The system is intended for foundational static gesture and posture practice and is not designed or validated for dynamic gestures, coarticulated signing, continuous sign language recognition, or sentence-level translation. The prototype integrates five 2.2-inch (55.9 mm) resistive flex sensors and an MPU6050 3-axis accelerometer, performs acquisition, exponential moving average filtering, user-specific calibration, normalization, and deterministic classification on a NodeMCU ESP32 board, and transmits selected processed variables to Arduino Cloud through MQTT for remote monitoring. A 10 s calibration routine maps user-specific open-hand and closed-fist responses into normalized flex-sensor ranges, allowing the same deterministic rule structure to operate across participants without model retraining. Experimental evaluation with 10 healthy adult participants aged 20–41 years (mean age: 27 years), all familiar with sign language and all providing written informed consent, produced a balanced dataset of 1500 labeled steady-state sensor vectors. The class-averaged recognition rate was 92.8%, and leave-one-subject-out validation produced a subject-wise accuracy of $92.80\pm 2.03\%$, with individual participant accuracies ranging from 90.00% to 96.00%. The local embedded processing pipeline required less than 2 ms per cycle, the complete path including MQTT visualization produced approximately 150 ms end-to-end latency, and the device operated for up to 14 h using a 3.7 V, 1000 mAh Li-Po battery. The results indicate that calibrated deterministic sensor fusion can provide a low-cost, low-latency, edge-executed solution for bounded static sign-language gesture learning tasks while maintaining stable short-term subject-wise performance under controlled experimental conditions. Full article

Accurate channel state information is essential for the performance of modern wireless communication systems. Conventional channel estimation typically relies on uplink Sounding Reference Signals (SRSs), which can introduce considerable overhead and power consumption, particularly in high-mobility or resource-constrained scenarios. To alleviate this burden, this paper explores an alternative approach that leverages sensing channel information to assist communication channel reconstruction. A shared cluster concept is introduced to capture the correlation between sensing and communication channels, and a sharing probability function is derived through statistical analysis of ray tracing simulation data across multiple scenarios. The shared cluster parameters extracted from the sensing channels are integrated into a cluster-based channel modeling framework to reconstruct the downlink communication channel. A deterministic simulation platform is developed using the Sionna ray tracing library, and the K-Power-Means algorithm is employed for multipath clustering. Simulation results demonstrate that the reconstructed channel closely matches the original channel in terms of the power delay profile and the root mean square delay spread, with mean values of 84.16 ns and 73.52 ns, respectively. The proposed method offers a promising supplementary approach for channel acquisition in scenarios where frequent SRS transmission is undesirable, and provides insights for future sensing-assisted communication system design. Full article

This study addresses the limitation of single-factor environmental assessment in autonomous sweet potato farming under open-field conditions. An IoT-based sweet potato growth environment monitoring and comprehensive evaluation system was developed by integrating an STM32-based acquisition terminal, multi-sensor data collection, Narrow Band Internet of Things (NB-IoT) transmission, and cloud-based visualization. Five key environmental variables, namely soil temperature, soil moisture, soil available nitrogen, photosynthetically active radiation (PAR), and CO₂, were continuously monitored. To improve the evaluation of heterogeneous and uncertain environmental information, a multi-factor environmental quality assessment method combining fuzzy membership functions and an improved D-S evidence theory was proposed. Field experiments were conducted in Danzhou, Hainan, China, and 600 valid synchronized samples were obtained for analysis. The results showed that most samples were classified as Suitable (63.5%), followed by Normal (30.8%) and Poor (5.7%), with a mean comprehensive environmental score of 0.802. Among the monitored variables, PAR and soil temperature showed relatively high adaptive weights, indicating their important roles in environmental quality discrimination. Furthermore, the comprehensive environmental evaluation result exhibited a significant positive correlation with sweet potato yield (r = 0.6501, p = 2.3724 × 10⁻⁷³), demonstrating good explanatory ability for yield variation. The proposed system provides an effective technical framework for real-time environmental monitoring, quantitative suitability evaluation, and precision management in autonomous sweet potato farming. Full article

Early identification of thermal and electrical anomalies in grid-connected photovoltaic (PV) systems is becoming increasingly important to reduce energy losses, limit power quality (PQ) degradation, and avoid excessive operating stress on power electronic converters. Conventional electrical monitoring methods can provide overall performance information, but they are generally unable to detect and localize early-stage defects occurring at module or cell level. In this context, the present study proposes an integrated diagnostic framework that combines non-destructive infrared thermography (IRT) with advanced electrical signal processing techniques for PV condition monitoring. The proposed approach correlates thermographic information, capable of revealing defects such as hotspots, cell cracks, and bypass diode failures, with high-frequency electrical signal analysis based on frequency-domain and time–frequency methods, together with deep learning-driven thermographic segmentation. By associating thermal acquisitions with electrical PQ indicators, the framework enables the early detection of physical defects linked to inefficient Maximum Power Point Tracking (MPPT) operation and progressive degradation of PV system performance. The methodology was experimentally validated on a grid-connected photovoltaic installation under different fault conditions, including hotspots, bypass diode anomalies, and localized overheating effects, demonstrating the potential of the proposed approach for predictive maintenance and intelligent PV monitoring applications. The obtained results indicate that the proposed framework improves the reliability of photovoltaic fault detection by combining thermographic inspection with advanced electrical signal analysis and AI-based defect interpretation, thus supporting predictive maintenance strategies in smart PV infrastructures. The proposed approach demonstrates image segmentation capabilities, as evidenced by a precision (PA) of 96.88%, a mean IoU (mIoU) of 77.83% and a macro F1-score of 87.47%. The proposed framework maintained reduced computational requirements compatible with real-time monitoring applications. Full article

Animal tuberculosis in cattle (TB) has been controlled in many European countries through long-standing eradication programs, yet sporadic breakdowns continue to occur in officially tuberculosis-free (OTF) areas, challenging the sustainability of disease freedom. This study aimed to identify and quantify herd-level and area-level risk factors associated with TB occurrence in Italian OTF regions in order to support risk-based surveillance strategies. A national longitudinal open-cohort study was conducted using data from the Italian Veterinary Information System, including approximately 300,000 herd–year observations from 2022 to 2025. The outcome was the occurrence of at least one TB breakdown per herd–year, analyzed using a discrete-time hazard modeling approach based on a binomial generalized linear mixed model with province-level random effects. The incidence of TB remained very low but increased over time, and significant spatial clustering was observed. Higher TB risk was associated with larger herd size, a previous history of TB, non-OTF herd status, proximity to recent breakdowns, number of animals purchased, transhumance practices, and a shorter time since acquisition of OTF status at provincial level. These findings highlight that, even in disease-free contexts, TB risk is heterogeneous and driven by identifiable factors, supporting the refinement of targeted, risk-based surveillance to maintain OTF status over time. Full article

Image sensors produce high-dimensional visual data for classification algorithms. Deep Neural Networks (DNNs) achieve high accuracy but require large labeled datasets and computational and energy resources, limiting their use in embedded systems. Active Learning (ALrn) can reduce labeling effort by selecting samples based on informativeness scores, but it remains computationally expensive, especially for high-dimensional images. This work presents a hardware-accelerated approach for the instance selection stage based on a query strategy in uncertainty-based ALrn for image classification using a novel in-line top-k selection algorithm that avoids conventional sorting and reduces memory and computational requirements. The algorithm is implemented on an Xilinx ZYNQ-7000 System on Chip (SoC) using a Field Programmable Gate Array (FPGA)-based accelerator operating at 110 MHz, interfacing with an embedded Advanced RISC Machine (ARM) processor for data acquisition and communication via the Python Productivity for Zynq (PYNQ) framework. Experiments on diverse multiclass datasets demonstrate correctness within an ALrn setting, showing negligible performance deviation in the learning curves compared to software baselines. The accelerator achieves speedup of 231.7× and 22.9× over software baseline and optimized software implementation of the proposed algorithm, respectively, in query-strategy computation while consuming only 0.473 W, substantially lower than conventional Central Processing Unit (CPU)- and Graphics Processing Unit (GPU)-based platforms. These results demonstrate the efficiency and extensibility of the proposed accelerator across alternative ALrn designs and hardware platforms, where the computational cost of instance selection scales with the size of the unlabeled pool. Full article

As the modulation rate in high-speed optical communication systems continues to increase and modulation formats become increasingly complex, conventional electrical-domain sampling techniques, limited by the “electronic bottleneck,” are unable to meet the time-domain analysis requirements of optical vector signals with bandwidths exceeding 100 GHz. In this paper, a system based on linear optical sampling (LOS) is implemented for time-domain analysis of high-speed polarization-division-multiplexed (PDM) optical vector signals. An unbalanced input method is proposed to ensure the integrity of the sampling clock when the power of the signal under test is zero; a resampling method combined with soft integration is proposed to replace the conventional peak detection method, improving the accuracy of sampling point position and amplitude information extraction; and an adaptive frequency offset estimation algorithm is proposed to compensate for the continuously varying frequency offset caused by the use of low-repetition-rate sampling pulses. We constructed a signal acquisition system for optical vector signal measurement based on LOS. Using the above methods, the eye diagrams and constellation diagrams of 50 Gbaud PDM-QPSK (quadrature phase-shift keying), PDM-16QAM (quadrature amplitude modulation), and PDM-32QAM signals are successfully measured, and related parameters, including error vector magnitude (EVM) and signal-to-noise ratio (SNR), are calculated. The experimental results show that the proposed system achieves quasi-real-time measurement of 500 Gbps optical vector signals, and the measured performance parameters are on the same order of magnitude as those obtained from a commercial high-speed oscilloscope. Full article

This paper investigates the optimal control problem of time-delay backward doubly stochastic systems under partial information. Partial information widely exists in practical control systems due to monitoring constraints, communication delays, and data acquisition costs. Combined with inherent system time delays, it greatly complicates state estimation and decision-making, which requires research. A new type of anticipated backward doubly stochastic differential equations is introduced to describe the system dynamics. Using stochastic analysis and the variational methods, the corresponding maximum principle for optimal control is derived. Furthermore, a verification theorem is established that provides rigorous sufficient optimality conditions: any admissible control satisfying the necessary conditions, along with reasonable convexity assumptions, indeed optimizes the cost functional, thereby bridging the gap between necessary and sufficient optimality criteria. As an application, we solve a time-delay linear-quadratic optimal control problem and obtain explicit analytical expressions; the results demonstrate the validity of the established theoretical framework. Full article

Robotic sorting-and-filling tasks in flexible manufacturing require robots to reproduce specified in-box arrangements while adapting to variations in container poses, object availability, sensing conditions, and external interventions. This paper proposes a box-centric one-shot teaching framework for robotic packing tasks with relative pose constraints. In the teaching stage, a human operator demonstrates the desired packing layout only once. The system uses reference-prompted SAM-based contour refinement to extract box and in-box object contours, object categories, quantities, and relative position and orientation constraints. These constraints are then converted from pixel-plane measurements into box-local pose constraints, forming a reusable box-centric packing template that preserves both translational and angular layout information. During execution, the recorded template is transferred to detected box instances with different global poses, and executable pick-and-place commands are generated through a task-level perception-to-command pipeline. A mechanism for continuous assignment and state updates is further introduced to maintain residual target slots, update object-to-slot allocation, and report missing or redundant objects across execution rounds. Single-box template transfer experiments achieved mean placement errors of 7.16 mm and 7.57 mm for two recorded templates, while representative post-execution images further showed that the relative object orientations were visually preserved with respect to the taught template footprints. Multi-box experiments demonstrated that unfinished residual slots could be preserved and completed after scene updates without re-teaching. Additional validation with different container types and object shapes showed the feasibility of extending the framework beyond cube-only cases. Ablation tests under nine exposure settings further showed that SAM refinement improved template-acquisition robustness compared with the previous recognition method. These results verify that the proposed framework enables one-shot template acquisition, box-centric layout transfer, relative pose preservation, and persistent task-level execution for constrained robotic packing tasks. Full article

Human activity recognition (HAR) using Wi-Fi channel state information (CSI) offers a privacy-preserving and contactless sensing modality suitable for smart homes, healthcare monitoring, and pervasive mobile Internet of Things (IoT) environments. However, existing CSI-based HAR approaches often suffer from computational inefficiency, high latency, and limited feasibility on resource-constrained embedded platforms. This work presents STAR (Sensing Technology for Activity Recognition), an edge AI-optimized framework that integrates lightweight temporal modeling, adaptive signal processing, and hardware-aware co-optimization to enable real-time, energy-efficient HAR on low-power embedded devices. STAR employs a streamlined three-layer Gated Recurrent Unit (GRU) architecture that reduces model parameters by 33% compared to conventional Long Short-Term Memory (LSTM) designs while maintaining strong temporal modeling capability. To enhance signal quality, STAR incorporates a multi-stage pre-processing pipeline consisting of median filtering, an eighth-order Butterworth low-pass filtering, and empirical mode decomposition (EMD) to denoise CSI amplitude measurements and extract stable spatial-temporal features. For on-device deployment, the system is implemented on a Rockchip RV1126 processor equipped with an embedded Neural Processing Unit (NPU) and interfaced with an ESP32-S3 CSI acquisition module. Experimental results demonstrate a mean recognition accuracy of 93.52% across seven activity classes and 99.11% for human-presence detection using a compact 97.6k-parameter model. INT8-quantized inference achieves a processing throughput of 33 MHz with only 8% CPU utilization, achieving a six-fold improvement in inference speed over CPU-based execution. With sub-second response latency and low power consumption, the system ensures real-time, privacy-preserving HAR, offering a practical, scalable solution for mobile and pervasive computing environments. Full article

High-resolution remote sensing is essential for information acquisition in smart agriculture, yet real-time processing remains a critical challenge. Although high-resolution imagery provides comprehensive data, its massive volume complicates efficient handling. Existing techniques are predominantly restricted to offline scenarios, conflicting with the practical requirements for online acquisition and transmission. To address these challenges, we propose an adaptive sparse coding method for agricultural remote sensing images that dynamically selects compression strategies based on image content. Using this approach, we developed an embedded terminal system for real-time agricultural data transmission over 5G networks. Experimental results show that at a 95% compression ratio, transmission time is reduced by over 90% compared with uncompressed images. The method also achieves high-fidelity reconstruction; the deviation rates for the normalized difference vegetation index (NDVI) and normalized difference red edge index (NDRE) remain below 5% even at a 97% compression ratio. This approach offers fast transmission, high compression efficiency, and strong reconstruction quality, making it suitable for field equipment such as unmanned aerial vehicles in real-time monitoring networks. Full article

The research aims to develop methodologies for the detailed characterization and spatial zoning of landfills as a means of assessing their environmental impact. The principal objective is to establish an integrated framework for evaluating landfill conditions through multisource data analysis, encompassing remote sensing, field investigations, and geochemical analyses. The proposed framework incorporates several critical components: satellite and UAV-based remote sensing, multispectral vegetation assessment, geochemical soil profiling, temporal and functional zoning, and morphodynamic evaluation. Research findings indicate substantial environmental pollution in the vicinity of landfill sites, at levels that exceed the natural self-purification capacity of surrounding ecosystems. This encompasses the contamination of all principal environmental components, including groundwater, surface water, soil, vegetation, and atmosphere. The key findings demonstrate that only a comprehensive environmental impact analysis, conducted in conjunction with detailed landfill zoning, yields a thorough understanding of the associated adverse effects. Remote sensing methodologies are shown to play a pivotal role in data acquisition and ongoing monitoring. The practical contribution of this study lies in the development of methodological frameworks for detailed landfill zoning, environmental impact assessment, monitoring, damage mitigation measures, and waste management optimisation. The results obtained have the potential to improve waste management systems, inform the development of effective monitoring protocols, and underpin strategies aimed at reducing the environmental footprint of landfills. Overall, this research advances scientific and technical knowledge in the field of waste management and contributes towards efforts to mitigate environmental impact—a matter of persistent concern given rising rates of waste generation and the increasingly constrained availability of suitable landfill capacity. Full article