Search Results (129,143)

For deformed wheel-based staircase-climbing robots, the accuracy of staircase step geometry perception and scene mapping are critical factors in determining whether the robot can successfully ascend the stairs and continue its task. Currently, while there are LiDAR-based algorithms that focus either on step geometry detection or scene mapping, few comprehensive algorithms exist that address both step geometry perception and scene mapping for staircases. Moreover, significant errors in step geometry estimation and low mapping accuracy can hinder the ability of deformed wheel-based mobile robots to climb stairs, negatively impacting the efficiency and success rate of task execution. To solve the above problems, we propose an effective LiDAR-Inertial-based point cloud detection method for staircases. Firstly, we preprocess the staircase point cloud, mainly using the Statistical Outlier Removal algorithm to effectively remove the outliers in the staircase scene and combine the vertical angular resolution and spatial geometric relationship of LiDAR to realize the ground segmentation in the staircase scene. Then, we perform post-processing based on the point cloud map obtained from LiDAR SLAM, extract the staircase point cloud and project and fit the staircase point cloud by Ceres optimizer, and solve the dimensional information such as depth and height of the staircase by combining with the mean filtering method. Finally, we fully validate the effectiveness of the method proposed in this paper by conducting multiple sets of SLAM and size detection experiments in real different staircase scenarios. Full article

Background/Objectives: Previous studies have examined the role of working memory in cognitive tasks such as syntactic, semantic, and phonological processing, thereby contributing to our understanding of linguistic information management and retrieval. However, the real-time processing of phonological information—particularly in relation to suprasegmental features like tone, where its contour represents a time-varying signal—remains a relatively underexplored area within the framework of Information Processing Theory (IPT). This study aimed to address this gap by investigating the real-time processing of similar tonal information by native Cantonese speakers, thereby providing a deeper understanding of how IPT applies to auditory processing. Methods: Specifically, this study combined assessments of cognitive functions, an AX discrimination task, and electroencephalography (EEG) to investigate the discrimination results and real-time processing characteristics of native Macau Cantonese speakers perceiving three pairs of similar tones. Results: The behavioral results confirmed the completed merging of T2–T5 in Macau Cantonese, and the ongoing merging of T3–T6 and T4–T6, with perceptual merging rates of 45.46% and 27.28%, respectively. Mismatch negativity (MMN) results from the passive oddball experiment revealed distinct temporal processing patterns for the three tone pairs. Cognitive functions, particularly attention and working memory, significantly influenced tone discrimination, with more pronounced effects observed in the mean amplitude of MMN during T4–T6 discrimination. Differences in MMN peak latency between T3–T6 and T4–T6 further suggested the use of different perceptual strategies for these contour-related tones. Specifically, the T3–T6 pair can be perceived through early signal input, whereas the perception of T4–T6 relies on constant signal input. Conclusions: This distinction in cognitive resource allocation may explain the different merging rates of the two tone pairs. This study, by focusing on the perceptual difficulty of tone pairs and employing EEG techniques, revealed the temporal processing of similar tones by native speakers, providing new insights into tone phoneme processing and speech variation. Full article

Traditional tactile brain–computer interfaces (BCIs), particularly those based on steady-state somatosensory–evoked potentials, face challenges such as lower accuracy, reduced bit rates, and the need for spatially distant stimulation points. In contrast, using transient electrical stimuli offers a promising alternative for generating tactile BCI control signals: somatosensory event-related potentials (sERPs). This study aimed to optimize the performance of a novel electrotactile BCI by employing advanced feature extraction and machine learning techniques on sERP signals for the classification of users’ selective tactile attention. The experimental protocol involved ten healthy subjects performing a tactile attention task, with EEG signals recorded from five EEG channels over the sensory–motor cortex. We employed sequential forward selection (SFS) of features from temporal sERP waveforms of all EEG channels. We systematically tested classification performance using machine learning algorithms, including logistic regression, k-nearest neighbors, support vector machines, random forests, and artificial neural networks. We explored the effects of the number of stimuli required to obtain sERP features for classification and their influence on accuracy and information transfer rate. Our approach indicated significant improvements in classification accuracy compared to previous studies. We demonstrated that the number of stimuli for sERP generation can be reduced while increasing the information transfer rate without a statistically significant decrease in classification accuracy. In the case of the support vector machine classifier, we achieved a mean accuracy over 90% for 10 electrical stimuli, while for 6 stimuli, the accuracy decreased by less than 7%, and the information transfer rate increased by 60%. This research advances methods for tactile BCI control based on event-related potentials. This work is significant since tactile stimulation is an understudied modality for BCI control, and electrically induced sERPs are the least studied control signals in reactive BCIs. Exploring and optimizing the parameters of sERP elicitation, as well as feature extraction and classification methods, is crucial for addressing the accuracy versus speed trade-off in various assistive BCI applications where the tactile modality may have added value. Full article

Multi-source remote sensing image semantic segmentation can provide more detailed feature attribute information, making it an important research field for remote sensing intelligent interpretation. However, due to the complexity of remote sensing scenes and the feature redundancy caused by multi-source fusion, multi-source remote sensing semantic segmentation still faces some challenges. In this paper, we propose a multi-source remote sensing semantic segmentation method based on differential feature attention fusion (DFAFNet) to alleviate the problems of difficult multi-source discriminant feature extraction and the poor quality of decoder feature reconstruction. Specifically, we achieve effective fusion of multi-source remote sensing features through a differential feature fusion module and unsupervised adversarial loss. Additionally, we improve decoded feature reconstruction without introducing additional parameters by employing an attention-guided upsampling strategy. Experimental results show that our method achieved 2.8% and 2.0% mean intersection over union (mIoU) score improvements compared with the competitive baseline algorithm on the available US3D and ISPRS Potsdam datasets, respectively. Full article

Multi-source heterogeneous information fusion based on the Global Navigation Satellite System (GNSS)/Inertial Navigation System (INS)/odometer is an important technical means to solve the problem of navigation and positioning in complex environments. The measurement noise of the GNSS/INS/odometer integrated navigation system is complex and non-stationary; it approximates a Gaussian distribution in an open-sky environment, and it has heavy-tailed properties in the GNSS challenging environment. This work models the measurement noise and one-step prediction as the Gaussian and Student’s t mixture distribution to adjust to different scenarios. The mixture distribution is formulated as the hierarchical Gaussian form by introducing Bernoulli random variables, and the corresponding hierarchical Gaussian state-space model is constructed. Then, the mixing probability of Gaussian and Student’s t distributions could adjust adaptively according to the real-time kinematic solution state. Based on the novel distribution, a robust variational Bayesian Kalman filter is proposed. Finally, two vehicle test cases conducted in GNSS-friendly and challenging environments demonstrate that the proposed robust Kalman filter with the Gaussian–Student’s t mixture distribution can better model heavy-tailed non-Gaussian noise. In challenging environments, the proposed algorithm has position root mean square (RMS) errors of 0.80 m, 0.62 m, and 0.65 m in the north, east, and down directions, respectively. With the assistance of inertial sensors, the positioning gap caused by GNSS outages has been compensated. During seven periods of 60 s simulated GNSS data outages, the RMS position errors in the north, east, and down directions were 0.75 m, 0.30 m, and 0.20 m, respectively. Full article

Climate change has increased the risk of snow drought, which is associated with a deficit in snowfall and snowpack. The objectives of this research are to improve drought identification in a warming climate by developing a new snow-based hydroclimatic aggregate drought index (SHADI) and to assess the impacts of snowpack and snowmelt in drought analyses. To derive the SHADI, an R-mode principal component analysis is performed on precipitation, snowpack, surface runoff, and soil water storage. Then, a joint probability distribution function of drought frequencies and drought classes, conditional expectation, and k-means clustering are used to categorize droughts. The SHADI was applied to the Red River of the North Basin (RRB), a typical cold climate region, to characterize droughts in a mostly dry period from 2003 to 2007. The SHADI was compared with the hydroclimatic aggregate drought index (HADI) and U.S. drought monitor (USDM) data. Cluster analysis was also utilized as a benchmark to compare the results of the HADI and SHADI. The SHADI showed better alignment with cluster analysis results than the HADI, closely matching the identified dry/wet conditions in the RRB. The major differences between the SHADI and HADI were observed in cold seasons and in transition periods (dry to wet or wet to dry). The derived variable threshold levels for different categories of drought based on the SHADI were close to, but different from, those of the HADI. The SHADI can be used for short-term lead prediction of droughts in cold climate regions and, in particular, can provide an early warning for drought in the warming climate. Full article

Topological phase has received considerable attention in recent decades. One of the crucial factors to determine the phase is symmetry. Such a concept involves mathematical, geometrical, and physical meanings, which displays many fascinating phases in Hermitian and non-Hermitian systems. In this paper, we first briefly review the symmetry-related topological phases in Hermitian and non-Hermitian systems. The study in this section focuses on the topological phase itself, not the realizations therein. Then, we present a thorough review of the observations about these symmetry-related topological phenomena in classical platforms. Accompanied by the rise of quantum technology, the combination of symmetry-related topological phase and quantum technology leads to an additional new avenue, in which quantum information tasks can be accomplished better. Finally, we provide comments about future research into symmetry-related topological phases. Full article

Many reptiles actively regulate their body temperature. During thermoregulation, they suffer evaporative water loss (EWL). Since evaporation increases with temperature, EWL could limit the activity of ectotherms when water is not available. In this study, we compared the preferred body temperatures (T_p) and EWL of two lacertid lizard species, Darevskia praticola and Podarcis muralis, at the western edge of D. praticola’s range, where they live in syntopy. We hypothesized that D. praticola, a species that inhabits forested and humid environments, would have a higher EWL than the more widespread P. muralis. Our results show that D. praticola prefers lower temperatures (mean T_p = 28.1 °C) than P. muralis (mean T_p = 30.6 °C). Despite the differences in their thermal preferences, both species showed similar total EWL (2.76% for D. praticola and 2.67% for P. muralis), although their daily patterns of water loss differed. Our results suggest that D. praticola has developed mechanisms to control water loss and that its lower thermal preference may be due to both historical factors and local adaptations. These results contribute to the understanding of how environmental factors influence the physiology of lizards, which in turn has implications for predicting the effects of climate change on species distribution. Full article

In addition to the mechanical properties and barrier performance, one of the key properties of flexible films used in food packaging is the resistance of their gas barrier layer to buckling and bending. Testing the gas barrier before and after mechanical stress is time-consuming and resource-intensive, but important to assure a certain gas barrier during the whole life time of the package until food consumption. The aim of this study was, on the one hand, to identify the most significant influencing factors of a composite lacquer formulation and coating on its buckling resistance and, on the other hand, to show a fast and efficient method to identify defects occurring during buckling. The influence of mechanical stress was simulated via Gelbo-Flex treatment, and the samples were examined and evaluated before and after using light microscopy. The evaluation was verified with scanning electron microscopy (SEM) and helium barrier measurement. Polyethylene terephthalate (PET) and polyethylene (PE) films were coated with composite barrier lacquers made of polyvinyl alcohol (PVA) and montmorillonite (MMT) and the wet coating layer thickness (20;40;80;130$\mathrm{m}$) and the composition of the coating were changed. It was found that thin coatings are more resistant to buckling than thick coatings. It was also shown that a higher proportion of MMT in the coating layer leads to a better gas barrier, but poorer buckling resistance. Additionally, it was found that soft PE films are already subjected to high stresses during the coating process, which means that barrier coatings do not build up ideally. However, the barrier-coated soft film withstood mechanical stress better and lost less barrier by a lower factor than the counterpart on the basis of PET. To conclude, the evaluation of the buckling resistance with microscopy offers an efficient method during lacquer development; however, the final decision on the right lacquer composition is dependent on many factors. Full article

With the accumulation of synthetic aperture radar (SAR) altimetry data and advancements in retracking algorithms, the improved along-track spatial resolution and signal-to-noise ratio have significantly enhanced the availability and precision of sea surface height (SSH) measurements, particularly in challenging environments such as coastal areas, ocean currents, and polar regions. These improvements have refined the accuracy and reliability of mean sea surface (MSS) models, which in turn have enhanced the precision of mean dynamic topography (MDT) and geostrophic current models. However, in-depth research is required to quantify the specific contributions of SAR altimetry to these critical regions and their impacts on the MSS, MDT, and geostrophic currents. Given that DTU21MSS (Technical University of Denmark MSS 2021) incorporates a substantial amount of SAR altimetry data, this study utilized independent Sentinel-3A altimetric observations to evaluate the signal improvements of DTU21MSS compared with DTU15MSS, with a focus on its performance in polar, coastal, and current regions. In addition, a least-squares-based approach was employed to assess the impact of the improved MSS model on the deduced MDT and geostrophic current signals. The numerical results revealed that DTU21MSS achieved an accuracy improvement of ~8% within 20 km offshore compared with DTU15MSS. In the polar regions within 100 km offshore, DTU21MSS exhibited a maximum signal enhancement of ~0.1 m, with overall improvements of 10–20%. The DTU21MSS-derived MDT solution demonstrates better consistency with validation data, reducing the standard deviation of misfits from 0.058 m to 0.054 m. Signal enhancements of maximumly 0.1 m were observed in the polar regions and the Mediterranean/Red Sea. Furthermore, improvements in the MSS and its error information could directly enhance the deduced MDT models, highlighting its foundational role in precise oceanographic modeling. Full article

Freezing precipitation and the resultant ice glaze can have catastrophic impacts on urban infrastructure, the environment, forests, and various industries, including transportation, energy, and agriculture. In this study, we develop and evaluate regional algorithms for detecting freezing precipitations in the Far East, utilizing the ERA5 reanalysis dataset from the European Centre for Medium-Range Weather Forecasts, along with standard meteorological observations for 20 cold seasons (September–May) from 2004 to 2024. We propose modified diagnostic algorithms based on vertical atmospheric temperature and humidity profiles, as well as near-surface characteristics. Additionally, we apply a majority voting ensemble (MVE) technique to integrate outputs from multiple algorithms, thereby enhancing classification accuracy. Evaluation of detection skills shows significant improvements over the original method developed at the Finnish Meteorological Institute and the ERA5 precipitation-type product. The MVE-based method demonstrates optimal verification statistics. Furthermore, the modified algorithms validly reproduce the spatially averaged inter-annual variability of freezing precipitation activity in both continental (mean correlation of 0.93) and island (correlation of 0.54) regions. Overall, our findings offer a more effective and valuable tool for operational activities and climatological assessments in the Far East. Full article

Object detection in remotely sensed satellite pictures is fundamental in many fields such as biophysical and environmental monitoring. While deep learning algorithms are constantly evolving, they have been mostly implemented and tested on popular ground-taken photos. This paper critically evaluates and compares a suite of advanced object detection algorithms customized for the task of identifying aircraft within satellite imagery. The goal is to enable researchers to choose efficiently from algorithms that are trainable and usable in real time on a deep learning infrastructure with moderate requirements. Using the large HRPlanesV2 dataset, together with rigorous validation with the GDIT dataset, this research encompasses an array of methodologies including YOLO versions 5, 8, and 10, Faster RCNN, CenterNet, RetinaNet, RTMDet, DETR, and grounding DINO, all trained from scratch. This exhaustive training and validation study reveals YOLOv5 as the pre-eminent model for the specific case of identifying airplanes from remote sensing data, showcasing high precision and adaptability across diverse imaging conditions. This research highlights the nuanced performance landscapes of these algorithms, with YOLOv5 emerging as a robust solution for aerial object detection, underlining its importance through superior mean average precision, recall, and intersection over union scores. The findings described here underscore the fundamental role of algorithm selection aligned with the specific demands of satellite imagery analysis and extend a comprehensive framework to evaluate model efficacy. This aims to foster exploration and innovation in the realm of remote sensing object detection, paving the way for improved satellite imagery applications. Full article

There is an ongoing search for a reliable and continuous method of noninvasive blood pressure (BP) tracking. In this study, we investigate the feasibility of utilizing seismocardiogram (SCG) signals, i.e., chest motion caused by cardiac activity, for this purpose. This research is novel in examining the temporal relationship between the SCG-measured isovolumic moment and the electrocardiogram (PEP_IM). Additionally, we compare these results with the traditionally measured pre-ejection period with the aortic opening marked as an endpoint (PEP_AO). The accuracy of the BP estimation was evaluated beat to beat against invasively measured arterial BP. Data were collected on separate days as eighteen sets from nine subjects undergoing a medical procedure with anesthesia. Results for PEP_IM showed a correlation of 0.67 ± 0.18 (p < 0.001), 0.66 ± 0.17 (p < 0.001), and 0.67 ± 0.17 (p < 0.001) when compared to systolic BP, diastolic BP, and mean arterial pressure (MAP), respectively. Corresponding results for PEP_AO were equal to 0.61 ± 0.22 (p < 0.001), 0.61 ± 0.21 (p < 0.001), and 0.62 ± 0.22 (p < 0.001). Values of PEP_IM were used to estimate MAP using two first-degree models, the linear regression model (achieved RMSE of 11.7 ± 4.0 mmHg) and extended model with HR (RMSE of 10.8 ± 4.2 mmHg), and two corresponding second-degree models (RMSE of 10.8 ± 3.7 mmHg and RMSE of 8.5 ± 3.4 mmHg for second-degree polynomial and second-degree extended, respectively). In the intrasubject testing of the second-degree model extended with HR based on PEP_IM values, the mean error of MAP estimation in three follow-up measurements was in the range of 7.5 to 10.5 mmHg, without recalibration. This study demonstrates the method’s potential for further research, particularly given that both proximal and distal pulses are measured in close proximity to the heart and cardiac output. This positioning may enhance the method’s capacity to more accurately reflect central blood pressure compared to peripheral measurements. 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

Black chokeberry (Aronia melanocarpa <Michx.> Elliot) represents a rich source of dietary polyphenols and other bioactive phytochemicals with pleiotropic beneficial cardiovascular effects. The present study was aimed at evaluating the ex vivo effects of two black chokeberry extracts (BChEs), obtained from either dry (DryAr) or frozen (FrozAr) berries, on oxidative stress and vascular function in mice aortic rings after incubation with angiotensin 2 (Ang 2), lipopolysaccharide (LPS) and glucose (GLUC) in order to mimic renin–angiotensin system activation, inflammation and hyperglycemia. The identification of phenolic compounds was performed by means of liquid chromatography with a diode array detector coupled with mass spectrometry using the electrospray ionization interface. The BChE obtained from the FrozAr was rich in cyanidin glucoside, rutin and caffeic acid, while the one obtained from the dried berries was rich in rutin, caffeic acid and chlorogenic acid. Mice aortas were dissected and acutely incubated (12 h) with Ang2 (100 nM), LPS (1 µg/mL) or GLUC (400 mg/dL) in the presence vs. absence of the two BChEs (1, 10, 50, 75, 100, 500 µg/mL). Subsequently, the tissues were used for the assessment of (i) hydrogen peroxide (H₂O₂) and superoxide production (using two methods, spectrophotometry and immunofluorescence), (ii) H₂O₂ scavenger effect and (iii) vascular reactivity (using the organ bath/myograph system). After exposure to Ang2, LPS or GLUC, both types of extracts decreased the H₂O₂ and superoxide levels in a concentration-dependent manner starting from either 50 µg/mL or 100 µg/mL. Also, in the highest concentrations (100 µg/mL, 150 µg/mL and 500 µg/mL), both extracts elicited a significant scavenger effect on H₂O₂ (similar to catalase, the classic H₂O₂ scavenger). Moreover, at 100 µg/mL, both extracts were able to significantly improve vascular relaxation in all stimulated aortic rings. In conclusion, in mice aortas, black chokeberry extracts in acute application elicited a concentration-dependent vasculo-protective effect through the reduction of oxidative stress and the alleviation of endothelial dysfunction in ex vivo conditions that mimic cardio-metabolic diseases. Full article