Search Results (11)

Space-Air-Ground Integrated Network (SAGIN), which is considered a network architecture with great development potential, exhibits significant cross-domain collaboration characteristics at present. However, most of the existing works ignore the matching and adaptability of differential tasks and heterogeneous resources, resulting in significantly inefficient task execution and undesirable network performance. As a consequence, we formulate a category attribute-oriented resource allocation and task offloading optimization problem with the aim of minimizing the overall scheduling cost. We first introduce a task–resource matching matrix to facilitate optimal task offloading policies with computation resources. In addition, virtual queues are constructed to take the impacts of randomized task arrival into account. To solve the optimization objective which jointly considers bandwidth allocation, transmission power control and task offloading decision effectively, we proposed a deep reinforcement learning (DRL) algorithm framework considering type matching. Simulation experiments demonstrate the effectiveness of our proposed algorithm as well as superior performance compared to others. Full article

To address the limitations of existing LiDAR–visual fusion methods in adequately accounting for map uncertainties induced by LiDAR measurement noise, this paper introduces a LiDAR–inertial–visual odometry framework leveraging mergeable probabilistic voxel mapping. The method innovatively employs probabilistic voxel models to characterize uncertainties in environmental geometric plane features and optimizes computational efficiency through a voxel merging strategy. Additionally, it integrates color information from cameras to further enhance localization accuracy. Specifically, in the LiDAR–inertial odometry (LIO) subsystem, a probabilistic voxel plane model is constructed for LiDAR point clouds to explicitly represent measurement noise uncertainty, thereby improving the accuracy and robustness of point cloud registration. A voxel merging strategy based on the union-find algorithm is introduced to merge coplanar voxel planes, reducing computational load. In the visual–inertial odometry (VIO) subsystem, image tracking points are generated through a global map projection, and outlier points are eliminated using a random sample consensus algorithm based on a dynamic Bayesian network. Finally, state estimation accuracy is enhanced by jointly optimizing frame-to-frame reprojection errors and frame-to-map RGB color errors. Experimental results demonstrate that the proposed method achieves root mean square errors (RMSEs) of absolute trajectory error at 0.478 m and 0.185 m on the M2DGR and NTU-VIRAL datasets, respectively, while attaining real-time performance with an average processing time of 39.19 ms per-frame on the NTU-VIRAL datasets. Compared to state-of-the-art approaches, our method exhibits significant improvements in both accuracy and computational efficiency. Full article

The optimization of film-unloading and film–soil separation components can effectively improve the residual film unloading rate and reduce impurity content. So, the DEM models of soil and residual film were established and the suspension and flow characteristics under fluid action were analyzed based on the CFD—DEM coupling simulation in this article. The matching parameters of the film-unloading and film-lifting device were optimized with the Box–Behnken test. When the wind velocity was between 1.65 and 10.54 $\mathrm{m}·{\mathrm{s}}^{-1}$, the film–soil separation effect was the best, with a film–impurity separation rate of 96.6%. The optimized parameter combination of the film-unloading device and film-lifting device is A = 9°, B = 40 mm, and C = 40 mm (A, B, and C represent the angle between the teeth and the normal of the air inlet, the minimum distance between the teeth and the air inlet, and the width of the air inlet, respectively). With the optimized parameter, the best film unloading effect is achieved, the minimum wind velocity of film unloading is 2.6 $\mathrm{m}·{\mathrm{s}}^{-1}$. This article provides theoretical and simulation methods for assessing the flow characteristics of flexible particles and parameter optimization of air suction devices, which is conducive to the high-purity recovery of residual film. Full article

In road engineering, road construction requires a large amount of natural aggregate; its substitution with recycled construction-solid-waste aggregate not only saves resources but also reduces the burden on the environment. The main components of construction solid waste are concrete blocks and brick slag; the breakability of the latter can affect the performance of mixed recycled aggregate, which hinders the use of construction solid waste in road engineering applications. To analyze the applicability of recycled construction-solid-waste aggregate containing brick slag aggregate in the subgrade layer, the effect of brick aggregate content on the CBR (California bearing ratio) and crushing value of mixed recycled aggregates was evaluated based on laboratory tests, and the field compaction quality of the recycled aggregates was analyzed. The results show that the 9.5–19 mm mixed recycled aggregate samples were crushed to a higher degree during the compaction process. A brick aggregate content less than 40% had little effect on the performance of mixed recycled construction-solid-waste aggregate. It is recommended to use a 22 t road roller for five passes (two weak vibrations + two strong vibrations + one weak vibration) at a speed of 3 km/h in the main compaction stage of the subgrade filling. Full article

The purpose of this article is to present some new nonlinear retarded integral inequalities which can be utilized to study the existence, stability, boundedness, uniqueness, and asymptotic behavior of solutions of nonlinear retarded integro-differential equations, and these inequalities can be used in the symmetrical properties of functions. These inequalities also generalize some former famous inequalities in the literature. Two examples as applications will be provided to demonstrate the strength of our inequalities in estimating the boundedness and global existence of the solution to initial value problems for nonlinear integro-differential equations and differential equations which can be seen in graphs. This research work will ensure opening new opportunities for studying nonlinear dynamic inequalities on a time-scale structure of a varying nature. Full article

The conditioning theory of the $\mathcal{ML}$-weighted least squares and $\mathcal{ML}$-weighted pseudoinverse problems is explored in this article. We begin by introducing three types of condition numbers for the $\mathcal{ML}$-weighted pseudoinverse problem: normwise, mixed, and componentwise, along with their explicit expressions. Utilizing the derivative of the $\mathcal{ML}$-weighted pseudoinverse problem, we then provide explicit condition number expressions for the solution of the $\mathcal{ML}$-weighted least squares problem. To ensure reliable estimation of these condition numbers, we employ the small-sample statistical condition estimation method for all three algorithms. The article concludes with numerical examples that highlight the results obtained. Full article

This work uses the collocation approximation method to solve a specific type of backward stochastic Volterra integral equations (BSVIEs). Using Newton’s method, BSVIEs can be solved using block pulse functions and the corresponding stochastic operational matrix of integration. We present examples to illustrate the estimate analysis and to demonstrate the convergence of the two approximating sequences separately. To measure their accuracy, we compare the solutions with values of exact and approximative solutions at a few selected locations using a specified absolute error. We also propose an efficient method for solving a triangular linear algebraic problem using a single integral equation. To confirm the effectiveness of our method, we conduct numerical experiments with issues from real-world applications. Full article

Deep hashing has received a great deal of attraction in large-scale data analysis, due to its high efficiency and effectiveness. The performance of deep hashing models heavily relies on label information, which is very expensive to obtain. In this work, a novel end-to-end deep hashing model based on pseudo-labels for large-scale data without labels is proposed. The proposed hashing model consists of two major stages, where the first stage aims to obtain pseudo-labels based on deep features extracted by a pre-training deep convolution neural network. The second stage generates hash codes with high quality by the same neural network in the previous stage, coupled with an end-to-end hash layer, whose purpose is to encode data into a binary representation. Additionally, a quantization loss is introduced and interwound within these two stages. Evaluation experiments were conducted on two frequently-used image collections, CIFAR-10 and NUS-WIDE, with eight popular shallow and deep hashing models. The experimental results show the superiority of the proposed method in image retrieval. Full article

The conditioning theory of the generalized inverse $C_A^{‡}$ is considered in this article. First, we introduce three kinds of condition numbers for the generalized inverse $C_A^{‡}$, i.e., normwise, mixed and componentwise ones, and present their explicit expressions. Then, using the intermediate result, which is the derivative of $C_A^{‡}$, we can recover the explicit condition number expressions for the solution of the equality constrained indefinite least squares problem. Furthermore, using the augment system, we investigate the componentwise perturbation analysis of the solution and residual of the equality constrained indefinite least squares problem. To estimate these condition numbers with high reliability, we choose the probabilistic spectral norm estimator to devise the first algorithm and the small-sample statistical condition estimation method for the other two algorithms. In the end, the numerical examples illuminate the obtained results. Full article

Space-air-ground integrated networks (SAGIN) provide seamless global coverage and cross-domain interconnection for the ubiquitous users in heterogeneous networks, which greatly promote the rapid development of intelligent mobile devices and applications. However, for mobile devices with limited computation capability and energy budgets, it is still a serious challenge to meet the stringent delay and energy requirements of computation-intensive ubiquitous mobile applications. Therefore, in view of the significant success in ground mobile networks, the introduction of mobile edge computing (MEC) in SAGIN has become a promising technology to solve the challenge. By deploying computing, cache, and communication resources in the edge of mobile networks, SAGIN MEC provides both low latency, high bandwidth, and wide coverage, substantially improving the quality of services for mobile applications. There are still many unprecedented challenges, due to its high dynamic, heterogeneous and complex time-varying topology. Therefore, efficient MEC deployment, resource management, and scheduling optimization in SAGIN are of great significance. However, most existing surveys only focus on either the network architecture and system model, or the analysis of specific technologies of computation offloading, without a complete description of the key MEC technologies for SAGIN. Motivated by this, this paper first presents a SAGIN network system architecture and service framework, followed by the descriptions of its characteristics and advantages. Then, the MEC deployment, network resources, edge intelligence, optimization objectives and key algorithms in SAGIN are discussed in detail. Finally, potential problems and challenges of MEC in SAGIN are discussed for future work. Full article

The purinergic system plays an important role in pain transmission. Recent studies have suggested that activation of P2-purinergic receptors (P2Rs) may be involved in neuron-satellite glial cell (SGC) interactions in the dorsal root ganglia (DRG), but the details remain unclear. In DRG, P2X7R is selectively expressed in SGCs, which closely surround neurons, and is highly sensitive to 3’-O-(4-Benzoyl) benzoyl-ATP (BzATP). Using calcium imaging in intact mice to survey a large number of DRG neurons and SGCs, we examined how intra-ganglionic purinergic signaling initiated by BzATP affects neuronal activities in vivo. We developed GFAP-GCaMP6s and Pirt-GCaMP6s mice to express the genetically encoded calcium indicator GGCaM6s in SGCs and DRG neurons, respectively. The application of BzATP to the ganglion induced concentration-dependent activation of SGCs in GFAP-GCaMP6s mice. In Pirt-GCaMP6s mice, BzATP initially activated more large-size neurons than small-size ones. Both glial and neuronal responses to BzATP were blocked by A438079, a P2X7R-selective antagonist. Moreover, blockers to pannexin1 channels (probenecid) and P2X3R (A317491) also reduced the actions of BzATP, suggesting that P2X7R stimulation may induce the opening of pannexin1 channels, leading to paracrine ATP release, which could further excite neurons by acting on P2X3Rs. Importantly, BzATP increased the responses of small-size DRG neurons and wide-dynamic range spinal neurons to subsequent peripheral stimuli. Our findings suggest that intra-ganglionic purinergic signaling initiated by P2X7R activation could trigger SGC-neuron interaction in vivo and increase DRG neuron excitability. Full article