Unmanned Aerial Vehicle (UAV)-based pavement distress detection offers efficient and safe advantages. However, obstructions from road vehicles and the slender shape of cracks in UAV images challenge accuracy. To address this, this study established specific flight parameters, proposed the Historical Best Matching Image (HBMI) approach for data loss due to obstructions, and created the UAV-Crack500 dataset with 500 finely annotated crack images. Three algorithms with different loss functions were investigated, finding that the U-Net network combined with our Completely Asymmetric Loss (CAL) achieved the best performance, resolving the issue of class imbalance. Morphological analysis of the semantically segmented images provided precise crack morphology features. In complex scenarios, errors in features like crack area, length, mean width, and maximum width remained within 16%. This study establishes a comprehensive UAV-based pavement distress detection system, overcoming obstructions for accurate assessment.

Accurate multi-parameter perception of pavement traffic load state is a prerequisite for smart highway construction, which is essential in improving the level of road intelligence and realising road information management. This research was based on the piezoelectric effect. Piezoelectric perception devices and strips were prepared and placed in asphalt mixture specimens and test sections, respectively. Through laboratory tests and outdoor vehicle loading tests, piezoelectric signals were collected by the data collector. The signal output peak value, peak area, and other parameters of piezoelectric sensing devices under different test conditions were analyzed. The perceptive calculation method of driving speed, axle number, wheelbase, and total vehicle weight was proposed. The perceptual calculation formula of vehicle load, including temperature, vehicle speed, and signal output parameters, was established and improved. The results show that the peak value of the output signal has a high correlation with test temperature, load, and loading frequency. Also, the signal of piezoelectric material under wheel loading has good stability; the perception accuracy of driving speed can reach more than 95%, the perception accuracy of vehicle axle number was 97.3%, the perception accuracy of vehicle wheelbase was 92.99%, and the perception accuracy of total vehicle weight was 95.19%. Piezoelectric devices have high accuracy in the perception of traffic load state.

Applying convolution methods to domains that lack regular underlying structures is a challenging task for 3D vision. Existing methods require the manual design of feature representations suitable for the task or full-voxel-level analysis, which is memory intensive. In this paper, we propose a novel feature extraction method to facilitate 3D nonrigid shape analysis. Our approach, called 3D-MConv, extends convolution operations from regular grids to irregular mesh sets by parametrizing a series of convolutional templates and adopts a novel local perspective to ensure that the algorithm is more invariant against global isometric deformation and articulation. We carefully design the convolutional template as a polynomial function that flexibly represents the local shape. An unsupervised learning method is adopted to learn the convolutional template function. By using the convolution operation and the movement of the template on the model surface, we can obtain the distribution of the typical template shapes. We combine this distribution feature with the spatial co-occurrence information of typical template shapes modelled by Markov chains to form a high-level descriptor of a 3D model. The support vector machine method is used to classify the nonrigid 3D objects. Experiments on SHREC10 and SHREC15 demonstrate that 3D-MConv achieves state-of-the-art accuracy on standard benchmarks.

This paper considers an ambient backscatter communication network in which a full-duplex access point (FAP) simultaneously transmits downlink orthogonal frequency division multiplexing signals to its legacy user (LU) and receives uplink signals backscattered from multiple backscatter devices (BDs) in a time-division-multiple-access manner. To maximize the system throughput and ensure fairness, we aim to maximize the minimum throughput among all BDs by jointly optimizing the backscatter time and reflection coefficients of the BDs, and the FAP's subcarrier power allocation, subject to the LU's throughput constraint, the BDs' harvested-energy constraints, and other practical constraints. For the case with a single BD, we obtain closed-form solutions and propose an efficient algorithm by using the Lagrange duality method. For the general case with multiple BDs, we propose an iterative algorithm by leveraging the block coordinated decent and successive convex optimization techniques. In addition, we study the throughput region which characterizes the Pareto-optimal throughput tradeoffs among all BDs. Finally, extensive simulation results show that the proposed joint design achieves significant throughput gain as compared to the benchmark schemes.

This paper considers an ambient backscatter communication (AmBC) network in which a full-duplex access point (FAP) simultaneously transmits downlink orthogonal frequency division multiplexing (OFDM) signals to its legacy user (LU) and receives uplink signals backscattered from multiple wireless-powered backscatter devices (BDs) in a time-division-multiple-access manner. To maximize the system throughput and ensure fairness, we aim to maximize the minimum throughput among all BDs by jointly optimizing the backscatter time and reflection coefficients of the BDs, and the FAP's subcarrier power allocation, subject to the LU's throughput constraint, the BDs' harvested-energy constraints, and other practical constraints. However, the formulated problem is non-trivial to solve in general, since the variables are mutually coupled and result in non-convex constraints. We thus propose an iterative algorithm by leveraging the block coordinated decent and successive convex optimization techniques. We further show the convergence performances of the proposed algorithm and analyze its complexity. Finally, extensive simulation results show that the proposed joint design achieves significant throughput gain as compared to the benchmark schemes.