Search Results (1,388)

In physical spaces, pointing interactions cannot rely on cursors, rays, or virtual hands for feedback as in virtual environments; users must rely solely on their perception and experience to capture targets. Currently, research on modeling target distribution for pointing interactions in physical space is relatively sparse. Area division is typically simplistic, and theoretical models are lacking. To address this issue, we propose two models for target distribution in physical space-pointing interactions: the single-target pointing endpoint distribution model (ST-PEDM) and the multi-target pointing endpoint distribution model (MT-PEDM). Based on these models, we have developed a basic region partitioning algorithm (BRPA) and an enhanced region partitioning algorithm (ERPA). We conducted experiments with 15 participants (11 males, and four females) to validate the proposed distribution models and region partitioning algorithm. The results indicate that these target distribution models accurately describe the distribution areas of targets, and the region partitioning algorithm demonstrates high precision and efficiency in determining user intentions during pointing interactions. At target distances of 200 cm and 300 cm, the accuracy without any algorithm is 60.54% and 42.39%, respectively. Using the BRPA algorithm, the accuracy is 72.94% and 68.57%, while, with the ERPA algorithm, the accuracy reaches 84.11% and 82.74%, respectively. This technology can be utilized in interaction scenarios involving handheld pointing devices, such as handheld remote controls. Additionally, it can be applied to the rapid capture control and trajectory planning of drone swarms. Users can quickly and accurately capture and control target drones using pointing interaction technology, issue commands, and transmit data through smart glasses, thereby achieving effective drone control and trajectory planning. Full article

Climate change is driving regime shifts across ecosystems, exposing species to novel challenges of extreme weather, altered disturbances, food web disruptions, and habitat loss. For disturbance-dependent species like the endangered piping plover (Charadrius melodus), these shifts present both opportunities and risks. While most piping plover populations show net growth following storm-driven habitat creation, similar gains have not been documented in the Eastern Canadian breeding unit. In September 2022, post-tropical cyclone Fiona caused record coastal changes in this region, prompting our study of population and nesting responses within the central subunit of Prince Edward Island (PEI). Using satellite imagery and machine learning tools, we mapped storm-induced change in open sand habitat on PEI and compared nest outcomes across habitat conditions from 2020 to 2023. Open sand areas increased by 9–12 months post-storm, primarily through landward beach expansion. However, the following breeding season showed no change in abundance, minimal use of new habitats, and mixed nest success. Across study years, backshore zones, pure sand habitats, and sandspits/sandbars had lower apparent nest success, while washover zones, sparsely vegetated areas, and wider beaches had higher success. Following PTC Fiona, nest success on terminal spits declined sharply, dropping from 45–55% of nests hatched in pre-storm years to just 5%, partly due to increased flooding. This suggests reduced suitability, possibly from storm-induced changes to beach elevation or slope. Further analyses incorporating geomorphological and ecological data are needed to determine whether the availability of suitable habitat is limiting population growth. These findings highlight the importance of conserving and replicating critical habitat features to support piping plover recovery in vulnerable areas. Full article

Unmanned aerial vehicle (UAV) swarms have shown substantial potential to enhance operational efficiency and reduce strike costs, presenting extensive applications in modern urban warfare. However, achieving effective strike performance in complex urban environments remains challenging, particularly when considering three-dimensional obstacles and threat zones simultaneously, which can significantly degrade strike effectiveness. To address this challenge, this paper proposes a target strike strategy using the Electric Eel Foraging Optimization (EEFO) algorithm, a heuristic optimization method designed to ensure precise strikes in complex environments. The problem is formulated with specific constraints, modeling each UAV as an electric eel with random initial positions and velocities. This algorithm simulates the interaction, resting, hunting, and migrating behaviors of electric eels during their foraging process. During the interaction phase, UAVs engage in global exploration through communication and environmental sensing. The resting phase allows UAVs to temporarily hold their positions, preventing premature convergence to local optima. In the hunting phase, the swarm identifies and pursues optimal paths, while in the migration phase the UAVs transition to target areas, avoiding threats and obstacles while seeking safer routes. The algorithm enhances overall optimization capabilities by sharing information among surrounding individuals and promoting group cooperation, effectively planning flight paths and avoiding obstacles for precise strikes. The MATLAB(R2024b) simulation platform is used to compare the performance of five optimization algorithms—SO, SCA, WOA, MFO, and HHO—against the proposed Electric Eel Foraging Optimization (EEFO) algorithm for UAV swarm target strike missions. The experimental results demonstrate that in a sparse undefended environment, EEFO outperforms the other algorithms in terms of trajectory planning efficiency, stability, and minimal trajectory costs while also exhibiting faster convergence rates. In densely defended environments, EEFO not only achieves the optimal target strike trajectory but also shows superior performance in terms of convergence trends and trajectory cost reduction, along with the highest mission completion rate. These results highlight the effectiveness of EEFO in both sparse and complex defended scenarios, making it a promising approach for UAV swarm operations in dynamic urban environments. Full article

Assessing the completeness of an underwater 3D reconstruction on-site is crucial as it allows for rescheduling acquisitions, which capture missing data during a mission, avoiding additional costs of a subsequent mission. This assessment needs to rely on a dense point cloud since a sparse cloud lacks detail and a triangulated model can hide gaps. The challenge is to generate a dense cloud with field-deployable tools. Traditional dense reconstruction methods can take several dozen hours on low-capacity systems like laptops or embedded units. To speed up this process, we propose building the dense cloud incrementally within an SfM framework while incorporating data redundancy management to eliminate recalculations and filtering already-processed data. The method evaluates overlap area limits and computes depths by propagating the matching around SeaPoints—the keypoints we design for identifying reliable areas regardless of the quality of the processed underwater images. This produces local partial dense clouds, which are aggregated into a common frame via the SfM pipeline to produce the global dense cloud. Compared to the production of complete dense local clouds, this approach reduces the computation time by about 70% while maintaining a comparable final density. The underlying prospect of this work is to enable real-time completeness estimation directly on board, allowing for the dynamic re-planning of the acquisition trajectory. Full article

Malaria remains a significant public health problem in India. Although temperature influences Anopheline mosquito feeding intervals, population density, and longevity, the reproductive potential of the Plasmodium parasite and rainfall influence the availability of larval habitats, and evidence to correlate the impact of climatic factors on the incidence of malaria is sparse. Understanding the influence of climatic factors on malaria transmission will help us predict the future spread and intensification of the disease. The present study aimed to determine the impact of temporal trend of climatic factors such as annual average maximum, minimum, mean temperature, and rainfall on the annual incidence of malaria cases in India for a period of 61 years from 1961 to 2021 and relative humidity for a period of 41 years from 1981 to 2021. Two different analyses were performed. In the first analysis, the annual incidence of malaria and meteorological parameters such as annual maximum, minimum, and mean temperature, annual rainfall, and relative humidity were plotted separately in the graph to see if the temporal trend of climatic factors had any coherence or influence over the annual incidence of malaria cases. In the second analysis, a scatter plot was used to determine the relationship of the incidence of malaria in response to associated climatic factors. The incidence of malaria per million population was also calculated. In the first analysis, the annual malaria cases showed a negative correlation of varying degrees with relative humidity, minimum, maximum, and mean temperature, except rainfall, which showed a positive correlation. In the second analysis, the scatter plot showed that the rainfall had a positive correlation with malaria cases, and the rest of the climatic factors, such as temperature and humidity, had negative correlations of varying degrees. Out of the total 61 years studied, in 29 years, malaria cases increased more than 1000 square root counts when the minimum temperature was at 18–19 °C; counts also increased over a period of 33 years when the maximum temperature was 30–31 °C, over 37 years when the mean temperature was 24–25 °C, over 20 years when the rainfall was in the range of 100–120, and over a period of 29 years when the relative humidity was at 55–65%. While the rainfall showed a strong positive correlation with the annual incidence of malaria cases, the temperature and relative humidity showed negative correlations of various degrees. The increasing temperature may push the boundaries of malaria towards higher altitude and northern sub-tropical areas from the southern peninsular region. Although scanty rainfall reduces the transmission, increases in the same would increase the malaria incidence in India. Full article

Over the past twenty years, camera networks have become increasingly popular. In response to various demands imposed on these networks, several coverage models have been developed in the scientific literature, such as area, trap, barrier, and target coverage. In this paper, a new type of coverage task, the Maximum Target Coverage with k-Barrier Coverage (MTCBC-k) problem, is defined. Here, the goal is to cover as many moving targets as possible from time step to time step while continuously maintaining k-barrier coverage over the region of interest (ROI). This approach is different from independently solving the two tasks and then merging the results. An Integer Linear Programming (ILP) formulation for the MTCBC-k problem is presented. Additionally, two types of camera clustering methods have been developed. This approach allows for solving smaller ILPs within clusters, and combining their solutions. Furthermore, a polynomial-time greedy algorithm has been introduced as an alternative to solve the MTCBC-k problem. An example was also provided of how the aforementioned methods can be modified to handle a more realistic scenario, where only the targets detected by the cameras are known, rather than all the targets within the ROI. The simulations were run with both dense and sparse camera placements, convincingly supporting the usefulness of the clustering and greedy methods. Full article

Depth completion is widely employed in Simultaneous Localization and Mapping (SLAM) and Structure from Motion (SfM), which are of great significance to the development of autonomous driving. Recently, the methods based on the fusion of vision transformer (ViT) and convolution have brought the accuracy to a new level. However, there are still two shortcomings that need to be solved. On the one hand, for the poor performance of ViT in details, this paper proposes a semi-convolutional vision transformer to optimize local continuity and designs a geometric perception module to learn the positional correlation and geometric features of sparse points in three-dimensional space to perceive the geometric structures in depth maps for optimizing the recovery of edges and transparent areas. On the other hand, previous methods implement single-stage fusion to directly concatenate or add the outputs of ViT and convolution, resulting in incomplete fusion of the two, especially in complex outdoor scenes, which will generate lots of outliers and ripples. This paper proposes a novel double-stage fusion strategy, applying learnable confidence after self-attention to flexibly learn the weight of local features. Our network achieves state-of-the-art (SoTA) performance with the NYU-Depth-v2 Dataset and the KITTI Depth Completion Dataset. It is worth mentioning that the root mean square error (RMSE) of our model on the NYU-Depth-v2 Dataset is 87.9 mm, which is currently the best among all algorithms. At the end of the article, we also verified the generalization ability in real road scenes. Full article

Satellite-based precipitation products (SPPs) are essential for climate monitoring, especially in regions with sparse observational data. This study compares the performance of the latest version (V07B) and its predecessor (V06B) of the Integrated Multi-satellitE Retrievals for GPM (IMERG) across South America and the adjacent oceans. It focuses on evaluating their accuracy under different precipitation regimes in Brazil using 22 years of IMERG Final data (2000–2021), aggregated into seasonal totals (summer, autumn, winter, and spring). The observations used for the evaluation were organized into 0.1° × 0.1° grid points to match IMERG’s spatial resolution. The analysis was restricted to grid points containing at least one rain gauge, and in cases where multiple gauges were present within a grid point the average value was used. The evaluation metrics included the Root Mean Square Error (RMSE) and categorical indices. The results reveal that while both versions effectively capture major precipitation systems such as the mesoscale convective system (MCS), South Atlantic Convergence Zone (SACZ), and Intertropical Convergence Zone (ITCZ), significant discrepancies emerge in high-rainfall areas, particularly over oceans and tropical zones. Over the continent, however, these discrepancies are reduced due to the correction of observations in the final version of IMERG. A comprehensive analysis of the RMSE across Brazil, both as a whole and within the five analyzed regions, without differentiating precipitation classes, demonstrates that version V07B effectively reduces errors compared to version V06B. The analysis of statistical indices across Brazil’s five regions highlights distinct performance patterns between IMERG versions V06B and V07B, driven by regional and seasonal precipitation characteristics. V07B demonstrates a superior performance, particularly in regions with intense rainfall (R1, R2, and R5), showing a reduced RMSE and improved categorical indices. These advancements are linked to V07B’s reduced overestimation in cold-top cloud regions, although both versions consistently overestimate at rain/no-rain thresholds and for light rainfall. However, in regions prone to underestimation, such as the interior of the Northeastern region (R3) during winter, and the northeastern coast (R4) during winter and spring, V07B exacerbates these issues, highlighting challenges in accurately estimating precipitation from warm-top cloud systems. This study concludes that while V07B exhibits notable advancements, further enhancements are needed to improve accuracy in underperforming regions, specifically those influenced by warm-cloud precipitation systems. Full article

The cost of indoor mapping methods based on three-dimensional (3D) LiDAR can be relatively high, and they lack environmental color information, thereby limiting their application scenarios. This study presents an innovative, low-cost, omnidirectional 3D color LiDAR mapping system for indoor environments. The system consists of two two-dimensional (2D) LiDARs, six monocular cameras, and a servo motor. The point clouds are fused with imagery using a pixel-spatial dual-constrained depth gradient adaptive regularization (PS-DGAR) algorithm to produce dense 3D color point clouds. During fusion, the point cloud is reconstructed inversely based on the predicted pixel depth values, compensating for areas of sparse spatial features. For indoor scene reconstruction, a globally consistent alignment algorithm based on particle filter and iterative closest point (PF-ICP) is proposed, which incorporates adjacent frame registration and global pose optimization to reduce mapping errors. Experimental results demonstrate that the proposed density enhancement method achieves an average error of 1.5 cm, significantly improving the density and geometric integrity of sparse point clouds. The registration algorithm achieves a root mean square error (RMSE) of 0.0217 and a runtime of less than 4 s, both of which outperform traditional iterative closest point (ICP) variants. Furthermore, the proposed low-cost omnidirectional 3D color LiDAR mapping system demonstrates superior measurement accuracy in indoor environments. Full article

The scope of the research work described in this article involved identifying the effects of the COVID-19 pandemic on the urban public transport system in a medium-sized city and its adjacent metropolitan area, using as reference information the number of tickets effectively sold in order to determine the fluctuation in the volume of passengers on the different bus lines before, during and after the pandemic. At the methodological level, a combined approach was employed, involving, on the one hand, the collection of open access public data from institutional repositories and information provided by the government and, on the other hand, network analysis and graphical mapping using GIS tools. The results obtained at the micro level (individualised study of each urban bus line) reveal a significant decrease in the number of passengers during the pandemic, showing the effect of mobility restrictions and the fear of contagion. However, a gradual recovery in post-pandemic demand has been observed, highlighting a large variability in recovery patterns between different bus lines. Such a situation could be attributable to several factors, such as the socio-demographic characteristics of the areas served, the frequency of the service, connectivity with other modes of transport and users’ perception of the quality of the service. At the macro level (comparison between urban and interurban transport), lines with higher demand prior to the pandemic have shown greater resilience and faster recovery. However, urban transport has experienced a more uniform and accelerated recuperation than interurban transport, with significant percentage differences in the years analysed. This disparity could be explained by the greater dependence of inhabitants on urban transport for their daily trips, due to its greater frequency and geographical coverage. Interurban transport, on the other hand, shows a more fluctuating demand and a lower dependence of users. Finally, the lack of previous research focused on the impact of the pandemic in sparsely populated rural areas restricts the ability to establish a solid frame of reference and generalise the results of this study. The authors consider that more detailed future research, including a comparative analysis of different alternative transport modes in inter-urban settings and considering a broader set of socio-demographic variables of passengers, is needed to better understand mobility dynamics in these areas and their evolution in the context of the pandemic. Full article

This study examined the effect of atmospheric, topographic, and Bidirectional Reflectance Distribution Function (BRDF) corrections of Sentinel-2 images implemented in Google Earth Engine (GEE) for use in land cover classification. The study was carried out in an area of complex orography in northern Spain and made use of the Spanish National Forest Inventory plots and other systematically located plots to cover non-forest classes. A total of 2991 photo-interpreted ground plots and 15 Sentinel-2 images, acquired in summer at a spatial resolution of 10–20 m per pixel, were used for this purpose. The overall goal was to determine the optimal level of image correction in GEE for subsequent use in time series analysis of images for accurate forest cover classification. Particular attention was given to the classification of cover by the major commercial forest species: Eucalyptus globulus, Eucalyptus nitens, Pinus pinaster, and Pinus radiata. The Second Simulation of the Satellite Signal in the Solar Spectrum (Py6S) algorithm, used for atmospheric correction, provided the best compromise between execution time and image size, in comparison with other algorithms such as Sentinel-2 Level 2A Processor (Sen2Cor) and Sensor Invariant Atmospheric Correction (SIAC). To correct the topographic effect, we tested the modified Sun-canopy-sensor topographic correction (SCS + C) algorithm with digital elevation models (DEMs) of three different spatial resolutions (90, 30, and 10 m per pixel). The combination of Py6S, the SCS + C algorithm and the high-spatial resolution DEM (10 m per pixel) yielded the greatest precision, which demonstrated the need to match the pixel size of the image and the spatial resolution of the DEM used for topographic correction. We used the Ross-Thick/Li-Sparse-Reciprocal BRDF to correct the variation in reflectivity captured by the sensor. The BRDF corrections did not significantly improve the accuracy of the land cover classification with the Sentinel-2 images acquired in summer; however, we retained this correction for subsequent time series analysis of the images, as we expected it to be of much greater importance in images with larger solar incidence angles. Our final proposed dataset, with image correction for atmospheric (Py6S), topographic (SCS + C), and BRDF (Ross-Thick/Li-Sparse-Reciprocal BRDF) effects and a DEM of spatial resolution 10 m per pixel, yielded better goodness-of-fit statistics than other datasets available in the GEE catalogue. The Sentinel-2 images currently available in GEE are therefore not the most accurate for constructing land cover classification maps in areas with complex orography, such as northern Spain. Full article

The failure of the fight against obesity makes us turn to new goals in its treatment. Now, brown adipose tissue has attracted attention as a promising target for the treatment of obesity and associated metabolic disorders such as insulin resistance, dyslipidemia, and glucose tolerance disorders. Meanwhile, the expansion of our knowledge has led to awareness about two rather different subtypes: classic brown and beige (inducible brown) adipose tissue. These subtypes have different origin, differences in the expression of individual genes but also a lot in common. Both tissues are thermogenic, which means that, by increasing energy consumption, they can improve their balance with excess intake. Both tissues are activated in response to specific inducers (cold, beta-adrenergic receptor activation, certain food and drugs), but beige adipose tissue transdifferentiates back into white adipose tissue after the cessation of inducing action, while classic brown adipose tissue persists, but its activity decreases. In this review, we attempted to understand whether there are differences in the effects of different groups of thermogenesis-affecting drugs on these tissues. The analysis showed that this area of research is rather sparse and requires close attention in further studies. Full article

In this study, an innovative leak detection model based on Convolutional Graph Neural Networks (CGNNs) is proposed to enhance response speed during pipeline bursts and to improve detection accuracy. By integrating node features into pipe segment features, the model effectively combines CGNN with water distribution networks, achieving leak detection at the pipe segment level. Optimizing the receptive field and convolutional layers ensures high detection performance even with sparse monitoring device density. Applied to two representative water distribution networks in City H, China, the model was trained on synthetic leak data generated by EPANET simulations and validated using real-world leak events. The experimental results show that the model achieves 90.28% accuracy in high-density monitoring areas, and over 85% accuracy within three pipe segments of actual leaks in low-density areas (10%–20%). The impact of feature engineering on model performance is also analyzed and strategies are suggested for optimizing monitoring point placement, further improving detection efficiency. This research provides valuable technical support for the intelligent management of water distribution networks under resource-limited conditions. Full article

Data interpolation methods are important statistical analysis tools that can fill in data gaps and missing areas by predicting and estimating unknown data points, thereby improving the accuracy and credibility of data analysis and research. Different interpolation methods are widely used in related fields, but the error between different interpolation methods and their interpolation fusion optimization have a significant impact on the interpolation accuracy, which still deserves further exploration. This study is based on two different types of point data: PM_2.5 (PM_2.5 refers to particulate matter in the atmosphere with a diameter of 2.5 μm or less, also known as inhalable particles or fine particulate matter) in Xinyang City, Henan Province, and the elevation of typical gullies in Yuanmou County, Yunnan Province. Using relative difference coefficients and hotspot analysis methods, the differences in error characteristics among four interpolation methods, ordinary kriging (OK), universal kriging (UK), inverse distance weighted (IDW), and radial basis functions (RBFs), were compared, and the influence of interpolation fusion methods on the accuracy of interpolation results was explored. The results show that after interpolation of PM_2.5 concentration and gully elevation, the error difference between OK and UK is the smallest in both datasets. For PM_2.5 concentration data, IDW and UK interpolation errors have the largest difference; for elevation data, the differences between RBF and UK interpolation are the largest. The weighted fusion results show that the interpolation error accuracy of PM_2.5 concentration data with an interpolation point density of 0.009 points per square kilometer is improved, and the root mean square error (RMSE) after fusion is reduced from 0.374 μg/m³ to 0.004 μg/m³. However, the error accuracy of the elevation data of the gully with an interpolation point density of 0.76 points/m² did not improve significantly. This indicates that characteristics such as the density of the original data are important factors that affect the accuracy of interpolation. In the case of sparse interpolation points, it is possible to consider fusing the interpolation results with different error patterns to improve their accuracy. This study provides a new idea for improving the accuracy of interpolation errors. Full article

This paper presents a contribution to the state of the art in the design of tactile sensing algorithms that take advantage of the characteristics of generalized sparse matrix-vector multiplication to reduce the area, power consumption, and data storage required for real-time hardware implementation. This work also addresses the challenge of implementing the hardware to execute multiaxial contact-force estimation algorithms from a normal stress tactile sensor array on a field-programmable gate-array development platform, employing a high-level description approach. This paper describes the hardware implementation of the proposed sparse algorithm and that of an algorithm previously reported in the literature, comparing the results of both hardware implementations with the software results already validated. The calculation of force vectors on the proposed hardware required an average time of 58.68 ms, with an estimation error of 12.6% for normal forces and 7.7% for tangential forces on a 10 × 10 taxel tactile sensor array. Some advantages of the developed hardware are that it does not require additional memory elements, achieves a 4× reduction in processing elements compared to a non-sparse implementation, and meets the requirements of being generalizable, scalable, and efficient, allowing an expansion of the applications of normal stress sensors in low-power tactile systems. Full article