Owing to changes in the spatial position of Autonomous Aerial Vehicle (AAV) aerial images and limited platform resources, most existing AAV aerial image detection models have low accuracy, and it is difficult to achieve a good balance between detection performance and lightweight. To solve the above problems, an object detection model of AAV aerial images based on YOLOv8s, called BSDS-YOLOv8s, is proposed. In the proposed model the Dynamic Head (DyHead) is used to replace the detection head of YOLOv8s firstly, which can improve the spatial perception ability of the detection head of the model. Second, to improve the detection performance of DyHead and make the model lightweight, a new feature pyramid network (SDI-MBiFPN) in the Neck of YOLOv8s is proposed, which contains a Multiscale Bidirectional Feature Pyramid Network (BiFPN), called MBiFPN, and a feature fusion method based on the redesigned Semantic and Detail Fusion (SDI). Finally, a method that integrates Soft Non-Maximum Suppression (Soft-NMS) with Generalized Intersection over Union (GIoU), called GIoU-Soft-NMS, is proposed to enhance the model’s post-processing capability and reduce the missed and false detection rates, thereby further improving the detection accuracy of the model. The experimental results showed that the mAP0.5 and mAP0.5:0.95 of BSDS-YOLOv8s reached 49.6% and 34.1%, respectively, on the VisDrone2019 dataset. This is an improvement of 9.2 and 9.8 percentage points over YOLOv8s, respectively. The number of parameters and the floating point operations (FLOPs) were 8.03M and 26.6G, 27.8% and 7.6% less than those of YOLOv8s, respectively.

To improve the performance of two-dimensional direction-of-arrival (DOA) estimation of signals, a three-dimensional sparse array structure and its coupled tensor decomposition-based DOA estimation method was proposed. By utilizing the second-order statistics of the signals from these subarrays, a virtual three-dimensional cross array was constructed, where the element spacing in the z-axis direction was sparse. Analysis showed that when N32+5N22+3 (where N was an odd number greater than 2) physical elements were used in the array, the corresponding virtual array possessed an aperture of (N3+2N-1)d in the x-axis and y-axis directions and (N3+3N)d in the z-axis direction. To fully exploit the large array aperture of the virtual array for enhancing DOA estimation performance and eliminating phase ambiguity caused by the element spacing greater than d in the z-axis direction, a method using coupled tensor decomposition for resolving phase ambiguity and achieving DOA estimation was developed. Theoretical analysis and simulation results demonstrate that, when the number of physical elements used in arrays is identical, the proposed method can yield a better estimation performance than existing three-dimensional arrays, because it has a larger array aperture.

Addressing the shortcomings of the Sparrow Search Algorithm (SSA), such as low accuracy of convergence and tendency of falling into local optimum, a Multi-strategy Integrated Sparrow Search Algorithm (MISSA) is proposed. In this method, by improving the black-winged kite algorithm and applying it to the producer’s position update formula, an improved search strategy (ISS) is firstly proposed to enhance search ability. Secondly, a new strategy inspired by the Coot algorithm, called the group follow strategy (GFS), is proposed to improve the ability to jump out of the local optimum. Finally, a proposed random opposition-based learning strategy (ROBLS) is applied to the population after each iteration to enhance its diversity. To verify MISSA’s effectiveness, extensive testing is conducted on 24 benchmark functions as well as CEC 2017 functions. The experimental results, complemented by Wilcoxon rank-sum tests, conclusively demonstrate that MISSA outperforms SSA and other advanced optimization algorithms, exhibiting superior overall performance.

In view of the phenomenon of detail loss and edge blurring in color images collected underwater, noise reduction is mostly ignored in underwater image enhancement research. In this paper, an improved image enhancement method based on Retinex algorithm is proposed. First, the original underwater image is transferred from RGB channels to HSI color space, and the saturation component is adaptively linearly stretched by gamma correction, The improved Retinex decomposition network based on homomorphic filtering is used to decompose and enhance the Lightness component separately. Eventually, the fused image is transferred from HSI to RGB color space, and the processed image is output. According to the experimental results, this algorithm can not only eliminate the halo artifacts, but also improve the brightness and contrast of the image, restore the original details of the image, and effectively remove the image noise. It greatly improves the visual effect and objective evaluation results of images.

Through the more available acoustic information or the polarization information provided, vector sensor arrays outperform the scalar sensor arrays in accuracy of localization. However, the cost of a vector sensor array is higher than that of a scalar sensor array. To reduce the cost of a two-dimensional (2-D) vector sensor array, a hybrid T-shaped sensor array consisting of two orthogonal uniform linear arrays (ULAs) is proposed, where one ULA is composed of acoustic vector sensors and the other is composed of scalar sensors. By utilizing the cross-correlation tensor between the received signals from the two ULAs, two virtual uniform rectangular arrays (URAs) of acoustic vector sensors are obtained, and they can be combined into a larger URA. It is shown that a larger acoustic vector sensor URA with M-2+1 degrees of freedom (DOFs) can be obtained from the specially designed T-shaped array with M acoustic vector sensors and 2M scalar sensors. Furthermore, by means of the proposed tensor model for the larger URA, the inter-sensor spacing can be allowed to exceed greatly a half-wavelength. Accordingly, the proposed method can achieve both a high DOF and a large array aperture. Simulation results show that the proposed method has a better performance in 2-D direction-of-arrival estimation than some existing methods under the same array cost.

将最小冗余线阵结构从一维扩展至二维得到二维冗余阵列,同时各阵元使用声矢量传感器,从而形成二维声矢量传感器阵列.然后利用张量运算对该阵列信号的协方差进行处理得到一个虚拟的声矢量传感器均匀面阵,且虚拟阵元数远多于原物理阵元数即虚拟阵列具有高自由度.最后利用张量分解从虚拟阵列信号数据张量中估计出信号波达角.理论分析和仿真实验结果表明,新阵列及其处理方法具有更高的自由度、更优的信号波达角估计性能.

A new direction-of-arrival（DOA） estimation method is proposed for uncorrelated and coherent mixed signals in the background of colored noise. Firstly, the covariance matrix of the mixed signals is analyzed and processed to eliminate the colored noise. On this basis, the DOA of uncorrelated signals is first estimated by the multiple signal classification（MUSIC） method or the estimation of signal parameters via rotational invariance techniques（ESPRIT） method. Then a new covariance matrix containing only coherent signals without rank deficit is constructed by using the improved spatial difference method. Finally, the DOA of coherent signals is estimated by MUSIC or ESPRIT. The proposed method outperform related methods in estimating DOA of mixed signals, especially coherent signals. Simulation results show the effectiveness of the proposed algorithm.

In recent years, semantic segmentation has been continuously developing, but there are still problems such as incomplete image understanding, incomplete contextual information extraction, imbalanced data samples, and uneven object distribution. A semantic segmentation algorithm based on an improved Dynamic Multi scale Network (DMNet) is proposed to address the current issues. An attention mechanism is added to the decoder to make the network pay more attention to the target to be detected; Using online difficult sample mining to improve the problem of sample imbalance in the dataset; Utilize the CrossEntropyLoss+Lovasz SoftmaxLoss joint loss function and the Adam with decoupled weight Decay (AdamW) optimizer to improve network training effectiveness and segmentation accuracy. The algorithm achieves 85.01% mIoU and 90.30% mAcc, effectively optimizing the model structure and improving segmentation accuracy, thus providing a certain technical foundation and application reference for semantic understanding of maritime channel scenes.

如何提高学生的学习自主性是翻转课堂教学过程中需要解决的关键问题。本文以《数据库原理》课程为例，分别从课前准备、课中教学和课后推送三个教学环节，介绍了如何通过案例教学、分组教学、讨论式教学、启发式教学和项目驱动式教学多种教学方法，并结合其它辅助教学手段以提高学生的学习自主性。教学实践表明，这种多教学方法融合的方式能够激发学生的学习兴趣，提高了学习自主性，有效提升了翻转课堂的教学效果。

有噪张量秩的估计,一直以来都是张量处理领域的重点和难点问题之一.现有大多数估计方法都是针对无噪情况下的张量提出的,而一旦张量含有噪声这些方法则失效.有噪张量的秩尤其是高阶有噪张量的秩较难准确获取,往往需要通过反复实验来判定.借助典范多元张量分解的联合对角化矩阵实现,并结合阵列信号处理领域中的信源信号数的估计方法,提出了一种新的有噪张量秩的估计方法.仿真结果验证了新方法的有效性.

利用小波变换理论对土壤裂缝小世界网络进行了多尺度分解.研究结果表明,两种土壤裂缝小世界网络的复杂性并不能掩埋整体连接的稀疏性,可通过裂缝网络的大尺度的低维子集来表现.在不同的尺度上观察到土壤裂缝的形态可看做不同时间段上的发展过程.裂缝怎样由"小"到"大"的,对研究土壤裂缝的发展过程提供连续的可视化过程.

For the co-prime planar array (CPPA) consisting of two sparse uniform rectangular array (URA),a new processing method based on tensor algebra was proposed to enhance the degrees of freedom (DoF).By dividing each URA into some overlapping subarrays,the received signals of two URAs were expressed as two tensors.And then the cross-correlation between such two tensors was processed into a received signal tensor of the virtual array.Analysis show that by the new method,the CPPA with 2 2L -1 physical elements can be transformed into a virtual sparse non-uniform planar array with (L+1) 4 16 elements.For the virtual array,the tensor decomposition-based approach for estimating the two-dimensional (2-D) direction of arrival (DoA) of the incident signal is also proposed,which means 2-D spectral peak searching is avoided.Compared with the co-prime planar signal processing methods reported in the literature,the proposed method can increase the DoF from L 2to (L+1) 4 16 +1 ,and has the better performance of the 2-D DoA estimation and lower computational complexity.Simulation results demonstrate the efficiency of the proposed method.

提出了一类二维互质矢量传感器阵列及其张量处理方法,以提高阵列自由度及信号波达角(Direction of ar-rival,DOA)估计性能.新阵列并非是二维互质标量传感器阵列的简单扩展,而是针对新阵列的高维信号数据,提出了一种新的基于张量代数理论的建模和处理方法.分析表明:针对一个具有4M2+N2-1(其中M和N互为质数)个矢量传感器(阵元)的二维互质阵列,利用其接收信号的高维二阶统计量,可将该阵列转换成一个具有(MN+M+N-1)2个虚拟矢量传感器(阵元)的均匀矩形阵列(Uniform Rectangular Array,URA).为充分利用增加的阵元数来提高阵列的可辨识性和信号的DOA估计精度,还给出了该URA对应的张量模型及处理方法,并最终借助张量分解实现了信号DOA及极化参数估计.仿真实验证明了新方法的有效性.

It is well known that for constant modulus (i.e., magnitude) signals the famous constant modulus (CM) blind equalisation algorithm implemented in a fractionally spaced equaliser can present a zero steady-state mean square error (MSE), which means completely eliminating the distortions introduced in transmitting signals through channels. But for non-constant modulus signals it suffers a large steady-state MSE. In order to overcome this defect, a segment cost function according to the CM criterion is suggested. The distinctive feature of the segment cost function is that the equalised signals (of the non-constant modulus signals) are divided into three segments to form a ⊓ shape where the ideal signals have a constant modulus instead of non-constant. And then a new blind equalisation algorithm seeks to minimise this segment cost function by applying a stochastic gradient method is proposed. When employing the proposed algorithm to equalise the 4-PAM or 16-QAM non-constant modulus signals, just as using the CM blind equalisation algorithm to equalise the 4-QAM constant modulus signal, a zero steady-state MSE can be obtained, which is derived. Compared to the classical bind equalisation algorithms, such as CMA, MMA, MCMA, or CMA+SDD, the proposed algorithm yields improved performance, especially for higher SNR.

Prior literature on a two-level supply chain has mainly focused on the game between one manufacturer and one supplier. Exploring group game behavior in a green supply chain (GSC), our research develops and studies a sequential GSC game model consisting of a single manufacturer and three suppliers based on the characteristics of the textile and apparel industry clusters. In our GSC model, the manufacturer is the leader of the supply chain and the suppliers are either homogeneous or complementary. Through equilibrium analysis, we identify critical conditions that influence the behavior of the manufacturer and suppliers to improve the green investment in the supply chain. Our study provides a theoretical basis and a decision-making reference for promoting the cooperation in GSCs and improving the performance of the government’s environmental policies.

A new tensor-based approach, which is capable of significantly increasing the degrees of freedom (DOF) of an L-shaped array consisting of two orthogonal uniform linear arrays (ULAs), is proposed. By dividing each ULA into some overlapping subarrays and then combining their received signals into a data tensor, a new cross-correlation tensor between the two data tensors from the two ULAs is built. Analyses show that such a cross-correlation tensor can be transformed into an equivalent received data matrix of a virtual uniform rectangular array (URA). Under the constraint of a fixed actual number of physical sensors, the optimal number of the subarrays is found by maximizing the DOF of the URA. It is shown that a virtual URA with approximately 0.34(M + 1)2 DOF can be obtained from an L-shaped array with 2M physical sensors. To exploit the increased DOF for the two dimensional (2-D) DOA estimation without the multidimensional search, a parallel factor (PARAFAC) model of the URA is provided so that the PARAFAC decomposition can be utilized to do the 2-D DOA estimation effectively. Simulation results demonstrate that the proposed method can yield a better estimation performance and resolve more sources than some computationally efficient methods reported in literature.

In this paper, a novel parallel factor (PARAFAC) model for processing the nested vector-sensor array is proposed. It is first shown that a nested vector-sensor array can be divided into multiple nested scalar-sensor subarrays. By means of the autocorrelation matrices of the measurements of these subarrays and the cross-correlation matrices among them, it is then demonstrated that these subarrays can be transformed into virtual scalar-sensor uniform linear arrays (ULAs). When the measurement matrices of these scalar-sensor ULAs are combined to form a third-order tensor, a novel PARAFAC model is obtained, which corresponds to a longer vector-sensor ULA and includes all of the measurements of the difference co-array constructed from the original nested vector-sensor array. Analyses show that the proposed PARAFAC model can fully use all of the measurements of the difference co-array, instead of its partial measurements as the reported models do in literature. It implies that all of the measurements of the difference co-array can be fully exploited to do the 2-D direction of arrival (DOA) and polarization parameter estimation effectively by a PARAFAC decomposition method so that both the better estimation performance and slightly improved identifiability are achieved. Simulation results confirm the efficiency of the proposed model.

It is well known that the constant modulus algorithm (CMA) presents a large steady-state mean-square error (MSE) for high-order quadrature amplitude modulation (QAM) signals. In this paper, we propose a low-complexity hybrid adaptive blind equalization algorithm, which augments the CMA error function with a novel constellation matched error (CME) term. The most attractive advantage of the proposed algorithm is that it is computationally simpler than concurrent CMA and soft decision-directed (SDD) scheme (CMA+SDD), and modified CMA (MCMA), while the approximation of steady-state MSE of the proposed algorithm is same with CMA+SDD, and lower than MCMA. Extensive simulations demonstrate the performance of the proposed algorithm.

A new complex exponential function is defined to map the real and imaginary parts of a classic 1 6-QAM nonconstant modulus signal to the unit circle,respectively.With the relationship between the real and imaginary parts of the new constellation after mapping,a new cost function is built and then a new blind equalization algorithm is proposed.The proposed algorithm can converge the steady-state mean square error (MSE)to zero under a noiseless environment,which is different from the famous constant modulus algorithm (CMA)and some CMA-based improved algorithms.In addition,we provide the theoretical analysis of the proposed algorithm.Simulation results demonstrate that the proposed algorithm has the lower steady-state MSE than CMA and some CMA-based improved algorithms.

A printed arc coupler has been proposed for avoiding and suppressing the spurious mode in the permittivity measurement cavity. Geometric parameters such as radius of arc (r), the central angle of the arc (θ), and the height of the board (t) are varied and their effects are investigated. Due to the central symmetry design of the coupler, the cavity has a clear TE011 mode, when r, θ, and t modify the resonance magnitude of the system. Simulation and experimental results show reasonable agreement. The error introduced due to coupling will be reduced. This method has the advantages of simple setting of the apparatus and fast tuning of the coupling.