Search Results (674)

Over the past two decades, Light Detection and Ranging (LiDAR) technology has evolved from early National Aeronautics and Space Administration (NASA)-led airborne laser altimetry into commercially mature systems that now underpin vegetation remote sensing across scales. Continuous advancements in laser engineering, signal processing, and complementary technologies—such as Inertial Measurement Units (IMU) and Global Navigation Satellite Systems (GNSS)—have yielded compact, cost-effective, and highly sophisticated LiDAR sensors. Concurrently, innovations in carrier platforms, including uncrewed aerial systems (UAS), mobile laser scanning (MLS), Simultaneous Localization and Mapping (SLAM) frameworks, have expanded LiDAR’s observational capacity from plot- to global-scale applications in forestry, precision agriculture, ecological monitoring, Above Ground Biomass (AGB) modeling, and wildfire science. This review synthesizes LiDAR’s cross-domain capabilities for the following: (a) quantifying vegetation structure, function, and compositional dynamics; (b) recent sensor developments encompassing ALS discrete-return (ALS_D) and ALS full-waveform (ALS_FW), photon-counting LiDAR (PCL), emerging multispectral LiDAR (MSL), and hyperspectral LiDAR (HSL) systems; and (c) state-of-the-art data processing and fusion workflows integrating optical and radar datasets. The synthesis demonstrates that many LiDAR-derived vegetation metrics are inherently transferable across domains when interpreted within a unified structural framework. The review further highlights the growing role of artificial-intelligence (AI)-driven approaches for segmentation, classification, and multitemporal analysis, enabling scalable assessments of vegetation dynamics at unprecedented spatial and temporal extents. By consolidating historical developments, current methodological advances, and emerging research directions, this review establishes a comprehensive state-of-the-art perspective on LiDAR’s transformative role and future potential in monitoring and modeling Earth’s vegetated ecosystems. Full article

Edge detection is a key building block in real-time image-processing applications such as drone-based infrastructure inspection, autonomous navigation, and remote sensing. However, its computational cost remains a challenge for resource-constrained embedded systems. This work presents a hardware-accelerated edge detection architecture based on a homogeneous 2D-mesh Network-on-Chip (NoC) integrating systolic arrays to efficiently perform the convolution operations required by the Sobel filter. The proposed architecture was first developed and validated as a 3 × 3 mesh prototype on FPGA (Xilinx Zynq-7000, Zynq-7010, XC7Z010-CLG400A, Zybo board, utilizing 26,112 LUTs, 24,851 flip-flops, and 162 DSP blocks), achieving a throughput of 8.8 Gb/s with a power consumption of 0.79 W at 100 MHz. Building upon this validated prototype, a reduced 2 × 2 node cluster with 14-bit word width was subsequently synthesized at the physical level as a proof-of-concept using the OpenLane RTL-to-GDSII open-source flow targeting the SkyWater 130 nm PDK (sky130A). Post-layout analysis confirms the manufacturability of the design, with a total power consumption of 378 mW and compliance with timing constraints, demonstrating the feasibility of mapping the proposed architecture to silicon and its suitability for drone-based infrastructure monitoring applications. Full article

Ghost imaging (GI) offers a robust framework for remote sensing under degraded visibility conditions. However, atmospheric scattering in phenomena such as fog introduces significant noise and signal attenuation, thereby limiting its efficacy. Inspired by the selective attention mechanisms of biological visual systems, this study introduces a novel deep learning (DL) architecture that embeds a self-attention mechanism to enhance GI reconstruction in foggy environments. The proposed approach mimics neural processes by modeling both local and global dependencies within one-dimensional bucket measurements, enabling superior recovery of image details and structural coherence even at reduced sampling rates. Extensive simulations on the Modified National Institute of Standards and Technology (MNIST) and a custom Human-Horse dataset demonstrate that our bio-inspired model outperforms conventional GI and convolutional neural network-based methods. Specifically, it achieves Peak Signal-to-Noise Ratio (PSNR) values between 24.5–25.5$\mathrm{dB}/\mathrm{m}$ and Structural Similarity Index Measure (SSIM) values of approximately 0.8 under high scattering conditions ($\beta \ge 3.0 \mathrm{dB}/\mathrm{m}$) and moderate sampling ratios ($N \ge 50\%$). A comparative analysis confirms the critical role of the self-attention module, providing high-quality image reconstruction over baseline techniques. The model also maintains computational efficiency, with inference times under $0.12 \mathrm{s}$, supporting real-time applications. This work establishes a new benchmark for bio-inspired computational imaging, with significant potential for environmental monitoring, autonomous navigation and defense systems operating in adverse weather. Full article

The Yangtze River Economic Belt is a crucial driver of China’s economy, and its shoreline is a strategic, finite resource vital for ecological security, flood control, navigation, and socioeconomic development. However, intensive development has resulted in functional conflicts and ecological degradation, underscoring the need for accurate identification and suitability assessment of shoreline functions. Conventional methods, which predominantly rely on land use data and remote sensing imagery, are often limited in their ability to capture dynamic changes in large river systems. This study introduces an integrated framework combining macro-level “Three-Zone Space” (urban, agricultural, ecological) theory with micro-level Point of Interest (POI) data to rapidly identify shoreline functions along the Yichang section of the Yangtze River. We further developed a multi-criteria evaluation system incorporating ecological, production, developmental, and risk constraints, utilizing a combined AHP-Entropy weight method to assess suitability. The results reveal a clear upstream-downstream gradient: ecological functions dominate upstream, while agricultural and urban functions increase downstream. POI data enabled refined classification into five functional types, revealing that ecological conservation shorelines are extensively distributed upstream, port and urban development shorelines concentrate in downstream nodal zones, and agricultural production shorelines are widespread yet exhibit a spatial mismatch with suitability scores. The comprehensive evaluation identified high-suitability units, primarily in downstream urban cores with superior development conditions and lower risks, whereas low-suitability units are constrained by high geological hazards and poor infrastructure. These findings provide a scientific basis for differentiated shoreline management strategies. The proposed framework offers a transferable approach for the sustainable planning of major river corridors, offering insights applicable to similar contexts. Full article

Vehicle remote driving services are increasingly used in urban settings. Yet, vehicle-operator communication time delays may pose a challenge for teleoperators in maintaining safety and efficiency. The purpose of this study was to examine whether Predictive Displays (PDs), which show the vehicle’s predicted real-time position, improve performance, safety, and mental workload under moderate time delays typical of 4G/5G networks. Twenty-nine participants drove a simulated urban route containing pedestrian crossings, overtaking, gap acceptance, and traffic light challenges under three conditions: 50 ms delay (baseline), 150 ms delay without PD, and 150 ms delay with PD. We analyzed the counts of crashes and navigation errors, task completion times, and the probability and intensity of braking and steering events, as well as self-reports of workload and usability. Results indicate that though descriptive trends indicated slightly sharper steering and braking under the 150 ms time delay conditions, the 150 ms time delay did not significantly degrade performance or increase workload compared with the 50 ms baseline. In addition, the PD neither improved performance nor reduced workload. Overall, participants demonstrated tolerance to typical 4G/5G network time delays, leaving little room for improvement rendering the necessitating of PDs. Full article

Magnetic microrobots have emerged as a promising platform for drug delivery in recent years. By enabling remotely controlled motion and precise navigation under external magnetic fields, these systems offer new solutions to overcome the limitations of traditional drug delivery nanocarriers, such as inadequate tissue penetration and heterogeneous biodistribution. Over the past few years, significant advancements have been made in the structural design of magnetic microrobots, as well as in drug loading techniques and stimuli-responsive drug release mechanisms, thereby demonstrating distinct advantages in enhancing therapeutic efficacy and targeting precision. This review provides a comprehensive overview of magnetic drug delivery microrobots, which are categorised into biomimetic structural, bio-templated and advanced material-based types, and introduces their differences in propulsion efficiency and biocompatibility. Additionally, drug loading and release strategies are summarised, including physical adsorption, covalent coupling, encapsulation, and multistimuli-responsive mechanisms such as pH, enzyme activity and thermal triggers. Overall, these advancements highlight the significant potential of magnetic microrobots in targeted drug delivery and emphasise the key challenges in their clinical translation, such as biological safety, large-scale production and precise targeted navigation within complex biological environments. Full article

Vulnerabilities in local food systems revealed by the COVID-19 pandemic were especially evident in highly tourism-dependent islands. This underscores the crucial role of agriculture in ensuring socio-ecological resilience, food security, and livelihood options in these contexts. Yet despite renewed policy attention, sustaining local farming in remote island settings continues to face numerous challenges. Amid growing recognition of the ways in which collective imagination shapes (and constrains) sustainability transformations, this paper applies the conceptual lens of imaginaries to examine agricultural futures in the Galápagos Islands and to explore the question of why agriculture remains marginal, despite widespread acknowledgement that supporting sustainable farming is central to the archipelago’s long-term sustainability. Through reflexive thematic analysis of policy documents, grey literature, and semi-structured interviews, the paper shows how imaginative spaces of possibility around food futures in Galápagos are conditioned by the powerful entanglement of hegemonic conservationist imaginaries with touristic imaginaries of an uninhabited wilderness. Within this contested terrain, five overlapping and co-constituting imaginaries of agriculture are distinguished, oriented variously around conservation priorities, technocratic planning, entrepreneurial growth, traditional livelihoods, and agroecological transformation. The analysis highlights how these imaginaries mobilize contrasting logics of support and mechanisms of change and illustrates how they complicate simplistic binaries, for example, between pristine and human-managed ecosystems, or between technological and holistic approaches to farming. The paper underscores the importance of paying critical attention to imaginaries of agriculture in order to navigate pathways toward more sustainable and resilient food systems in ecologically fragile island contexts. Full article

Background and Objectives: Mobile Eye Units (MEUs) have emerged as practical innovations to overcome geographic, financial, and systemic obstacles to eye care. Although numerous programs operate across the United States and Canada, a narrative review describing their structure, implementation and services, remain limited. This narrative review examines various MEUs models in the United States and Canada, using real-world examples to highlight each model’s structure, services, populations served, and key benefits and limitations. Methods: We performed a narrative review of peer-reviewed and gray literature published from 1990 to August 2025, identifying mobile eye units in the United States and Canada. Programs were grouped into four operational models based on services, equipment, and implementation characteristics. Ophthalmology residency program websites in the United States were also reviewed to assess academic involvement in mobile outreach. Results: We identified four operational MEU models: Fully Equipped Mobile Units (FEMUs), Semi-Mobile Outreach Units (SMOUs), School-Based Vision Mobile Units (SBVMUs), and Hybrid Teleophthalmology Units (HTOUs). FEMUs provide comprehensive on-site diagnostic capabilities but require substantial financial and logistical resources. SMOUs are lower-cost and flexible but offer more limited diagnostics. SBVMUs facilitate early detection in children and reduce school-based access barriers but depend on school coordination. HTOUs expand specialist interpretation through remote imaging, although their success relies on reliable digital infrastructure. Across all models, follow-up and continuity of care remain major implementation challenges. Approximately 21% of U.S. ophthalmology residency programs publicly report involvement in mobile outreach. Conclusions: MEUs play a critical role in reducing geographic and structural barriers to eye care for underserved populations across United States and Canada. However, limited outcome reporting, particularly regarding follow-up rates and continuity of care, hinders broader assessment of their effectiveness. Strengthening the integration of MEUs with patient navigators, integrated electronic health record, insurance support and support of local health networks is essential for improving long-term sustainability and impact. Full article

Precise lagoon bathymetry remains scarcely available for most tropical islands despite its importance for navigation, resource assessment, spatial planning, and numerical hydrodynamic modeling. Hydrodynamic models are increasingly used for instance to understand the ecological connectivity between marine populations of interest. Island remoteness and shallow waters complicate in situ bathymetric surveys, which are substantially costly. A multisource strategy using historical point sounding, multibeam surveys and well calibrated satellite-derived bathymetry (SDB) can offer the possibility to map entirely extensive and geomorphologically complex lagoons. The process is illustrated here for the rugose complex lagoon of Gambier Islands in French Polynesia. The targeted bathymetry product was designed to be used in priority for numerical larval dispersal modeling at 100 m spatial resolution. Spatial gaps in in situ data were filed with Sentinel-2 satellite images processed with the Iterative Multi-Band Ratio method that provided an accurate bathymetric model (1.42 m Mean Absolute Error in the 0–15 m depth range). Processing was optimized here, considering the specifications and the constraints related to the targeted hydrodynamic modeling application. In the near future, a similar product, possibly at higher spatial resolution, could improve spatial planning zoning scenarios and resource-restocking programs. For tropical island countries and for French Polynesia, in particular, the needs for lagoon hydrodynamic models remain high and solutions could benefit from such multisource coverage to fill the bathymetry gaps. Full article

Autonomous mowers’ navigation pattern plays a crucial role in turfgrass quality, influencing both esthetic and functional performance. However, despite extensive research on mowing efficiency, the effects of different navigation patterns on turfgrass damage and visual quality remain inadequately investigated. This study aimed to evaluate the impact of three different autonomous mower navigation patterns (random, vertical, and chessboard) on operational performance and the effect of trampling activity on turfgrass. Each pattern was tested in terms of data on the number of passages, distance traveled (m), number of intersections and the percentage of area mowed using a remote sensing system and an updated custom-built software. Green coverage percentage was assessed weekly using image analysis (Canopeo app) to evaluate the turfgrass green coverage. The green coverage percentage, together with the number of passages, is analyzed and correlated. The random pattern generated the highest number of passages and intersections, leading to lower average green coverage (64%) compared with the chessboard (80%) and vertical (81%) patterns. Data of the green coverage percentage in the function of the average number of passages recorded using the custom-built software for each pattern fit the asymptotic regression model. The effective number of passages to reach 60% green cover (EP60) was 56.26, 87.30, and 155.32 for random, vertical, and chessboard, respectively. The model could be integrated into DSS, useful for the end user in turf management in order to maintain a high quality. Future studies should extend this approach to other species and environmental conditions, integrating the effective dose (in terms of passages) method for smart mowing management. Full article

A remote operator (RO) of Maritime Autonomous Surface Ships (MASSs) is required to respond to the effects of external forces, such as tidal currents, and ensure safe, efficient, and sustainable navigation. However, previous studies primarily focus on the physical movement changes of the ship caused by tidal currents, with limited research addressing the impact of external forces on ship maneuverability and steering response. Therefore, analysis of an RO’s steering competence and identification features for training is important. In the context of sustainable maritime operations and navigation, the purpose of this study is to analyze the competence of ROs in steering ships under the effects of tidal currents and to identify priority training features as a foundational framework for future applications to MASS remote operation training. Twenty third-year cadets at Mokpo National Maritime University participated in simulator experiments designed to analyze steering competence in the presence and absence of tidal currents in a controlled environment. The experimental results showed the difference in steering performance considering the effect of tidal currents, and machine learning algorithms were used to identify priority training features. Machine learning analysis ranked Altering to ROT zero time (ART) and Maximum port ROT (MRT) as the two most influential steering features among the four identified variables, consistently showing the highest importance scores across all models. This simulator-based study identifies tidal current response steering features as a foundational framework for RO training and competence evaluation, which may inform the design of future MASS remote operation training programs after further validation. Full article

The management of age-related macular degeneration (AMD) presents a significant challenge attributable to high disease heterogeneity. Patient realization of symptoms is poor and it is urgent to treat before permanent anatomic damage results in vision loss. This is true for the initial conversion from non-exudative intermediate AMD (iAMD) to exudative AMD (nAMD), and for the recurrence of nAMD undergoing treatment. Starting from the essential requirements that any practical solution needs to fulfill, we will reflect on how persistent navigation towards innovative solutions during a 25-year journey yielded significant advances towards improvements in personalized care. An early insight was that the acute nature of AMD progression requires frequent monitoring and therefore diagnostic testing should be performed at the patient’s home. Four key requirements were identified: (1) A tele-connected home device with acceptable diagnostic performance and a supportive patient user interface, both hardware and software. (2) Automated analytics capabilities that can process large volumes of data. (3) Efficient remote patient engagement and support through a digital healthcare provider. (4) A low-cost medical system that enables digital healthcare delivery through appropriate compensation for both the monitoring provider and the prescribing physician services. We reviewed the published literature accompanying first the development of Preferential Hyperacuity Perimetry (PHP) for monitoring iAMD, followed by Spectral Domain Optical Coherence Tomography (SD-OCT) for monitoring nAMD. Emphasis was given to the review of the validation of the core technologies, the regulatory process, and real-world studies, and how they led to the release of commercial services that are covered by Medicare in the USA. We concluded that while during the first quarter of the 21st century, the two main pillars of management of AMD were anti-VEGF intravitreal injections and in-office OCT, the addition of home-monitoring-based digital health services can become the third pillar. Full article

In scenarios involving radiation such as decommissioning of nuclear disasters and operating nuclear power plants, it is necessary to perform tasks including maintenance, demolition, and inspection using robots in order to protect human workers from harm. LIDAR (LIght Detection And Ranging) sensors are used for many demanding real-time tasks in robotics such as obstacle avoidance, localisation, mapping, and navigation. Standard silicon-based electronics including LIDAR fail quickly in gamma radiation, however, high-radiation areas have a critical need for robotic maintenance to keep people safe. Sensors need to be developed, which can cope with this environment. A prototype including most required transmitter and receiver circuits is designed utilising components expected to provide up to (1 MGy) gamma radiation tolerance. Initial results testing the concepts of the laser transmission and detection in a lab environment shows reliable signal detection. Performance tests utilising multiple receivers show a linear relationship between receiver separation and measured time difference, allowing for the possibility of calibration of a sensor using the time difference between pulses. Future work (such as radiation testing trials) is discussed and defined. These results contribute to de-risking the feasibility of long-term deployment of LIDAR systems utilising these approaches into environments with high gamma dose rates, such as nuclear fission decommissioning, big science facilities such as the Large Hadron Collider, and remote maintenance systems used in future nuclear fusion power plants such as STEP and EU-DEMO. Full article

Unmanned surface vehicles (USVs) are equipped with numerous sensors, and data is transmitted through networks. Attackers may remotely compromise unmanned surface vehicle (USV) systems and commit criminal acts via networks, causing USVs to deviate from their intended course or pose a collision risk. Although significant research efforts have focused on developing attack detection and defense technologies for USVs, less attention has been paid to the offensive perspective—particularly the design and modeling of novel attack strategies. In this paper, we investigate the potential attack locations of USVs equipped with distributed navigation, guidance, and control modules. We propose a series of novel attack methods designed to compromise the navigation safety of USVs. These methods compromise the control system by blocking, modifying, and delaying navigation data to manipulate the navigation routes of USVs. Ultimately, the simulation results demonstrate the effectiveness of these attacks on the navigation of USVs. Full article

The integration of optical sensors and deep learning technologies in smart agriculture represents a critical intersection between technological innovation and agricultural economic sustainability, yet comprehensive assessments of their economic impact remain limited. This study applies a scientometric approach to 135 documents indexed in Scopus and Web of Science between January 2017 and June 2025, using Bibliometrix Bibliometrix (R package version 4.5.2), VOSviewer version 1.6.20, and Voyant Tools to examine publication trends, leading contributors, collaboration patterns, thematic structures, and reported economic outcomes. The analysis shows a strong upward trajectory with an estimated 66.48% annual increase in publications, identifying Xiukang Wang and Shaowen Wang as leading contributors among 791 authors from diverse institutions. Thematic analysis reveals three interconnected clusters: (i) precision agriculture and remote sensing as the sensing backbone; (ii) prediction and soil analysis as data-driven decision-support mechanisms; and (iii) vegetation indexes and productivity as measurement tools linking spectral information to yield and input use. Economic evidence includes high disease-detection accuracy (up to 95%), notable pesticide-use reductions (around 40%), improved autonomous-navigation precision (<6 cm error), and crop-detection performance exceeding 99%. However, adoption challenges persist, including technological heterogeneity, high implementation costs, limited model transferability, and varying levels of digital readiness across regions. Overall, the findings indicate that optical sensors and deep learning are transitioning from experimental applications to technologies with measurable economic impact, offering guidance for researchers, policymakers, technology developers, and agricultural producers seeking economically viable precision-agriculture solutions. Full article