LiDAR has emerged as one of the most pivotal sensors in the field of navigation, owing to its expansive measurement range, high resolution, and adeptness in capturing intricate scene details. This ...significance is particularly pronounced in challenging navigation scenarios where GNSS signals encounter interference, such as within urban canyons and indoor environments. However, the copious volume of point cloud data poses a challenge, rendering traditional iterative closest point (ICP) methods inadequate in meeting real-time odometry requirements. Consequently, many algorithms have turned to feature extraction approaches. Nonetheless, with the advent of diverse scanning mode LiDARs, there arises a necessity to devise unique methods tailored to these sensors to facilitate algorithm migration. To address this challenge, we propose a weighted point-to-plane matching strategy that focuses on local details without relying on feature extraction. This improved approach mitigates the impact of imperfect plane fitting on localization accuracy. Moreover, we present a classification optimization method based on the normal vectors of planes to further refine algorithmic efficiency. Finally, we devise a tightly coupled LiDAR-inertial odometry system founded upon optimization schemes. Notably, we pioneer the derivation of an online gravity estimation method from the perspective of S2 manifold optimization, effectively minimizing the influence of gravity estimation errors introduced during the initialization phase on localization accuracy. The efficacy of the proposed method was validated through experimentation employing various LiDAR sensors. The outcomes of indoor and outdoor experiments substantiate its capability to furnish real-time and precise localization and mapping results.
Abstract
Recent observations have revealed two types of decayless transverse oscillations in short coronal loops: one with short periods scaling with loop lengths, and the other with longer periods ...that exhibit a peak at around 5 minutes in the period distribution. To understand such a difference in period, we work in the framework of ideal MHD and model a short coronal loop embedded in an atmosphere with density stratification from the chromosphere to the corona. An inclined p-mode-like driver with a period of 5 minutes is launched at one loop footpoint. It is discovered that two types of decayless transverse oscillations can be excited in the loop. We interpret the 5 minutes periodicity as being directly driven by the footpoint driver, while the others, with periods of several tens of seconds, are regarded as kink eigenmodes of different harmonics. Therefore, our simulation shows that both types of decayless oscillations found in observations can be excited by p-modes in one short coronal loop. This study extends our understanding of ubiquitous decayless transverse oscillations in the corona. Furthermore, it suggests that p-modes could be an important energy source for coronal heating by driving decayless transverse oscillations.
The trade-off between system performance and energy efficiency (service time) is critical for battery-based embedded systems. Most of the previous work focuses on saving energy in a deterministic way ...by taking the average or worst scenario into account. However, such deterministic approaches usually are inappropriate in modeling energy consumption because of uncertainties in conditional instructions on processors and time-varying external environments (e.g., fluctuant network bandwidth and different user inputs). By adopting a probabilistic approach, this paper proposes a model and a set of algorithms to address the Processor and Voltage Assignment with Probability (PVAP) problem of data-dependent aperiodic tasks in real-time embedded systems, ensuring that all the tasks can be done under the time constraint with a guaranteed probability. We adopt a task DAG (Directed Acyclic Graph) to model the PVAP problem. We first use a processor scheduling algorithm to map the task DAG onto a set of voltage-variable processors, and then use our dynamic programming algorithm to assign a proper voltage to each task. Finally, to escape from local optima, a local search with restarts searches the optimal solution from candidate solutions by updating the objective function, until the stop criteria are reached or a time bound is elapsed. The experimental results demonstrate that for probability 1.0, our approach yields slightly better results than the well-known algorithms like ASAP/ALAP (As Soon As Possible/As Late As Possible) and ILP (Integer Linear Programming) with/without DVS (Dynamic Voltage Scaling). However, for probabilities 0.8 and 0.9, our approach significantly outperforms those algorithms (maximum improvement of 50.3 percent).
Abstract
Alfvénic motions are ubiquitous in the solar atmosphere and their observed properties are closely linked to those of photospheric p-modes. However, it is still unclear how a predominantly ...acoustic wave driver can produce these transverse oscillations in the magnetically dominated solar corona. In this study we conduct a 3D ideal MHD numerical simulation to model a straight, expanding coronal loop in a gravitationally stratified solar atmosphere which includes a transition region and chromosphere. We implement a driver locally at one foot-point corresponding to an acoustic–gravity wave which is inclined by
θ
= 15° with respect to the vertical axis of the magnetic structure and is similar to a vertical driver incident on an inclined loop. We show that transverse motions are produced in the magnetic loop, which displace the axis of the waveguide due to the breaking of azimuthal symmetry, and study the resulting modes in the theoretical framework of a magnetic cylinder model. By conducting an azimuthal Fourier analysis of the perturbed velocity signals, the contribution from different cylindrical modes is obtained. Furthermore, the perturbed vorticity is computed to demonstrate how the transverse motions manifest themselves throughout the whole non-uniform space. Finally we present some physical properties of the Alfvénic perturbations and present transverse motions with velocity amplitudes in the range 0.2–0.75 km s
−1
which exhibit two distinct oscillation regimes corresponding to 42 and 364 s, where the latter value is close to the period of the p-mode driver in the simulation.
In a drug delivery system, the physicochemical properties of the polymeric matrix have a positive impact on the bioavailability of poorly water-soluble drugs. In this work, monolithic F1 fibers and ...coaxial F2 fibers were successfully prepared using polyvinylpyrrolidone as the main polymer matrix for drug loading and the poorly water-soluble curcumin (Cur) as a model drug. The hydrophobic poly (3-hydroxybutyric acid-co-3-hydroxyvaleric acid) (PHBV) was designed as a blank layer to change the hydrophilicity of the fiber and restrain the drug dissolution rate. The curved linear morphology without beads of F1 fibers and the straight linear morphology with few spindles of F2 fibers were characterized using field-emission environmental scanning electron microscopy. The amorphous forms of the drug and its good compatibility with polymeric matrix were verified by X-ray diffraction and attenuated total reflectance Fourier transformed infrared spectroscopy. Surface wettability and drug dissolution data showed that the weaker hydrophilicity F2 fibers (31.42° ± 3.07°) had 24 h for Cur dissolution, which was much longer than the better hydrophilic F1 fibers (15.31° ± 2.79°) that dissolved the drug in 4 h.
Background
Recent studies have found that microRNAs (miRNAs) play a critical role in development and progression of intervertebral disc degeneration. In the present study, we examined the role of ...miR-185 in nucleus pulposus cell behavior in vitro and the histological changes of intervertebral disc tissue in intervertebral disc degeneration rat models in vivo.
Methods
Intervertebral disc degeneration models were developed in Sprague-Dawley rats. Intervertebral disc tissue was collected for histological evaluation after miR-185 agomir/agomir transduction. Next, nucleus pulposus tissues were collected from lumbar intervertebral discs to isolate nucleus pulposus cells, which were treated with miR-185 mimic/inhibitor and an inhibitor of the Wnt signaling pathway to assess cell viability and apoptosis.
Results
We observed a high expression of Galectin-3 in nucleus pulposus cells of rats with intervertebral disc degeneration. Bioinformatics prediction and dual-luciferase reporter assay confirmed that miR-185 specifically binds to and negatively regulates Galectin-3. Furthermore, we found that miR-185 inhibition resulted in increased expression of Galectin-3, pro-autophagy factors (LC3 and Beclin-1), and pro-apoptosis factors (caspase-3 and Bax), along with the activation of the Wnt/β-catenin signaling pathway. Moreover, the gain- and loss-of-function studies suggested that miR-185 overexpression promoted cell viability and inhibited nucleus pulposus cell apoptosis and autophagy via inactivation of the Wnt/β-catenin signaling pathway. Moreover, miR-185 agomir alleviated the histological changes observed in intervertebral disc tissues in intervertebral disc degeneration rats, which helped us validate the results observed in vitro.
Conclusions
Overexpression of miR-185 promotes nucleus pulposus cell viability and reduces the histological changes observed in intervertebral disc tissues in rats with intervertebral disc degeneration via inactivation of the Wnt/β-catenin signaling pathway and Galectin-3 inhibition. Our findings also highlight the potential of miR-185 as a promising novel therapeutic target to prevent and control intervertebral disc degeneration.
As an important part of modern marine monitoring systems, ship target identification has important significance in maintaining marine rights and monitoring maritime traffic. With the development of ...artificial intelligence technology, image detection and recognition based on deep learning methods have become the most popular and practical method. In this paper, two deep learning algorithms, the Mask R-CNN algorithm and the Faster R-CNN algorithm, are used to build ship target feature extraction and recognition models based on deep convolutional neural networks. The established models were compared and analyzed to verify the feasibility of target detection algorithms. In this study, 5748 remote sensing maps were selected as the dataset for experiments, and two algorithms were used to classify and extract warships and civilian ships. Experiments showed that for the accuracy of ship identification, Mask R-CNN and Faster R-CNN reached 95.21% and 92.76%, respectively. These results demonstrated that the Mask R-CNN algorithm achieves pixel-level segmentation. Compared with the Faster R-CNN algorithm, the obtained target detection effect is more accurate, and the performance in target detection and classification is better, which reflects the great advantage of pixel-level recognition.
Abstract
We model a coronal loop as a 3D magnetic cylinder in a realistic solar atmosphere that extends from the chromosphere to the corona. Kink oscillations, believed to be ubiquitous in the solar ...corona, are launched in the loop. Heating is expected owing to the dissipation of wave energy at small structures that develop from the Kelvin–Helmholtz instability induced by kink oscillations. Increases in temperature and internal energy can be observed in the coronal counterpart of the driven loop. With the presence of thermal conduction, chromospheric evaporation can also be seen. Although the volume-averaged temperature and density changes seem slight (∼4% relative to a nondriven loop), the enthalpy flow from the lower atmosphere redistributes the density and temperature in the vertical direction, thus enhancing the dissipation of wave energy in the corona. The efficient heating in the coronal counterpart of the loop can complement the thermal conductive losses shown in the current model and thus maintain the internal energy in the corona.
The visual SLAM system requires precise localization. To obtain consistent feature matching results, visual features acquired by neural networks are being increasingly used to replace traditional ...manual features in situations with weak texture, motion blur, or repeated patterns. However, to improve the level of accuracy, most deep learning enhanced SLAM systems, which have a decreased efficiency. In this paper, we propose Coarse TRVO, a visual odometry system that uses deep learning for feature matching. The deep learning network uses a CNN and transformer structures to provide dense high-quality end-to-end matches for a pair of images, even under indistinctive settings with low-texture regions or repeating patterns occupying the majority of the field of view. Meanwhile, we made the proposed model compatible with NVIDIA TensorRT runtime to boost the performance of the algorithm. After obtaining the matching point pairs, the camera pose is solved in an optimized way by minimizing the re-projection error of the feature points. Experiments based on multiple data sets and real environments show that Coarse TRVO achieves a higher robustness and relative positioning accuracy in comparison with the current mainstream visual SLAM system.
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