Crowd counting has received extensive attention in the field of computer vision, and methods based on deep convolutional neural networks (CNNs) have made great progress in this task. However, challenges such as scale variation, nonuniform distribution, complex background, and occlusion in crowded scenes hinder the performance of these networks in crowd counting. In order to overcome these challenges, this article proposes a multiscale spatial guidance perception aggregation network (MGANet) to achieve efficient and accurate crowd counting. MGANet consists of three parts: multiscale feature extraction network (MFEN), spatial guidance network (SGN), and attention fusion network (AFN). Specifically, to alleviate the scale variation problem in crowded scenes, MFEN is introduced to enhance the scale adaptability and effectively capture multiscale features in scenes with drastic scale variation. To address the challenges of nonuniform distribution and complex background in population, an SGN is proposed. The SGN includes two parts: the spatial context network (SCN) and the guidance perception network (GPN). SCN is used to capture the detailed semantic information between the multiscale feature positions extracted by MFEN, and improve the ability of deep structured information exploration. At the same time, the dependence relationship between the spatial remote context is established to enhance the receptive field. GPN is used to enhance the information exchange between channels and guide the network to select appropriate multiscale features and spatial context semantic features. AFN is used to adaptively measure the importance of the above different features, and obtain accurate and effective feature representations from them. In addition, this article proposes a novel region-adaptive loss function, which optimizes the regions with large recognition errors in the image, and alleviates the inconsistency between the training target and the evaluation metric. In order to evaluate the performance of the proposed method, extensive experiments were carried out on challenging benchmarks including ShanghaiTech Part A and Part B, UCF-CC-50, UCF-QNRF, and JHU-CROWD $++$ . Experimental results show that the proposed method has good performance on all four datasets. Especially on ShanghaiTech Part A and Part B, CUCF-QNRF, and JHU-CROWD $++$ datasets, compared with the state-of-the-art methods, our proposed method achieves superior recognition performance and better robustness.

The recognition of non-line-of-sight (NLOS) state is a prerequisite for alleviating NLOS errors and is crucial to ensure the accuracy of positioning. Recent studies only identify the line-of-sight (LOS) state and the NLOS state, but ignore the contribution of occlusion categories to spatial information perception. This paper proposes a bidirectional search algorithm based on maximum correlation, minimum redundancy, and minimum computational cost (BS-mRMRMC). The optimal channel impulse response (CIR) feature set, which can identify NLOS and LOS states well, as well as the blocking categories, are determined by setting the constraint thresholds of both the maximum evaluation index, and the computational cost. The identification of blocking categories provides more effective information for the indoor space perception of ultra-wide band (UWB). Based on the vector projection method, the hierarchical structure of decision tree support vector machine (DT-SVM) is designed to verify the recognition accuracy of each category. Experiments show that the proposed algorithm has an average recognition accuracy of 96.7% for each occlusion category, which is better than those of the other three algorithms based on the same number of CIR signal characteristics of UWB.

An improved path planning algorithm based on dynamic window approach (DWA) is proposed to optimize real-time paths for agents moving in complex environments with plenty of static and/or dynamic obstacles, path constraints, and performance constraints. In order to improve the accuracy of the evaluation function and the adaptability of the agents to different obstacle environments, the weight of the evaluation function is ameliorated by designing adaptive laws in this article. Meanwhile, aiming at the defect of local minima in DWA algorithm, a low-speed steering strategy is presented to help the robot escape the U-shaped trap. Especially, in the case of dynamic obstacle environment, a predictive scheme is carried out for the agents to reach the destination safely by fitting the trajectory of the dynamic obstacles to calculate their possible future positions, and then setting these positions as virtual obstacles. By comparison, some simulations and experiments are illustrated to show that the proposed algorithms are superior to the DWA algorithm in terms of running time, path length, and security to a large extent.

In recent years, few-shot semantic segmentation of strip steel surface defects (S³D) has posed significant challenges, primarily due to the wide diversity of scenarios and the inherent complexity of defects. However, existing semantic segmentation methods have not effectively addressed these issues. In this paper, we propose a few-shot segmentation method called the Multiscale Adaptive Prototype Transformer Network (MAPTNet), which aims to integrate multi-scale feature aggregation and enhance the adaptability of defect detection across diverse and complex defect scenarios. Specifically, we introduce an Adaptive Prototype Transformer (APT) module to locate defects better and generate more refined feature representations. This module effectively explores the relationships between support and query sets and transforms them into optimal prototypes. Additionally, through hierarchical feature fusion to capture more detailed and contextually relevant S³D information, our MAPTNet improves the discriminability of defects at multiple feature scales. To further strengthen the learning process across different feature layers, our MAPTNet incorporates a deep dual supervision mechanism, facilitating effective optimization at both intermediate and final stages. Extensive experiments on the FSSD-12, Surface Defects-4i, and ESDIs-SOD datasets demonstrate that our network achieves state-of-the-art performance, significantly outperforming existing approaches. The code is available at https://github.com/hhhjjc/MAPTNet.

In rotary ultrasonic machining (RUM), the utiliza-tion of contactless energy transfer (CET) theory addresses the limitations imposed by traditional contact power supply systems on rotational speed, however, it also introduces the challenge of inadequate transmission performance. Specifically, impedance variations of oscillator pose challenges for the contactless energy transfer system (CETS). These challenges include maintaining high transmission efficiency and desirable impedance charac-teristics, which can impact the performance of system and the quality of workpiece. To address the issue, this paper proposes the implementation of a bi-lateral compensation network using an LC-CL structure to against the low output power and poor anti-interference capabilities of series-series (SS) topology. Addi-tionally, a variable loads parameter optimization (VLPO) model, based on the fluctuations of equivalent dynamic component parameters during actual machining, is proposed. This model integrates an efficiency evaluation function aimed at achieving higher transmission efficiency and reducing fluctuations, as well as evaluation functions for the quality factor, admittance circle radius, and bandwidth to enhance impedance characteristics. Through the implementation of VLPO, the system impedance characteristics of LC-CL are significantly improved in compar-ison to SS and carrying capacity is effectively enhanced. The effectiveness of this scheme is validated through the construction of CETS experimental platform.