Joint frame rate adaptation and object recognition model selection for stabilized unmanned aerial vehicle surveillance

We propose an adaptive unmanned aerial vehicle (UAV)-assisted object recognition algorithm for urban surveillance scenarios. For UAV-assisted surveillance, UAVs are equipped with learning-based object recognition models and can collect surveillance image data. However, owing to the limitations of UAVs regarding power and computational resources, adaptive control must be performed accordingly. Therefore, we introduce a self-adaptive control strategy to maximize the time-averaged recognition performance subject to stability through a formulation based on Lyapunov optimization. Results from performance evaluations on real-world data demonstrate that the proposed algorithm achieves the desired performance improvements.     

雷达辐射源信号分选研究进展

雷达辐射源信号分选是雷达信号侦察的关键技术之一,同时也是战场态势感知的重要环节。该文系统梳理了雷达辐射源信号分选的主流技术,从基于脉间调制特征、基于脉内调制特征、基于机器学习的雷达辐射源信号分选3个角度阐述了目前雷达辐射源信号分选工作的主要研究方向及进展,并重点阐释了基于深度神经网络、数据流聚类等最新分选技术的原理与特点。最后,对现有雷达辐射源信号分选技术的不足进行了总结并对未来趋势进行了预测。      

Extracting embedded messages using adaptive steganography based on optimal syndrome-trellis decoding paths

Privacy protection is the key to maintaining the Internet of Things (IoT) communication strategy. Steganography is an important way to achieve covert communication that protects user data privacy. Steganalysis technology is the key to checking steganography security, and its ultimate goal is to extract embedded messages. Existing methods cannot extract under known cover images. To this end, this paper proposes a method of extracting embedded messages under known cover images. First, the syndrome-trellis encoding process is analyzed. Second, a decoding path in the syndrome trellis is obtained by using the stego sequence and a certain parity-check matrix, while the embedding process is simulated using the cover sequence and parity-check matrix. Since the decoding path obtained by the stego sequence and the correct parity-check matrix is optimal and has the least distortion, comparing the path consistency can quickly filter the coding parameters to determine the correct matrices, and embedded messages can be extracted correctly. The proposed method does not need to embed all possible messages for the second time, improving coding parameter recognition significantly. The experimental results show that the proposed method can identify syndrome-trellis coding parameters in stego images embedded by adaptive steganography quickly to realize embedded message extraction.     

基于IVIF-VIKOR的雷达辐射源信号分选识别特征性能综合评价方法

为解决雷达辐射源信号分选识别特征评价不够客观和缺乏评价依据等问题,提出了一种基于区间模糊原理、模糊交叉熵和多准则折衷法的特征评价方法. 首先通过区间模糊原理建立信噪比分级评价模型,并基于汉明距离进行寻优得出信噪比权重;其次结合信噪比权重和区间直觉模糊加权平均算子将分级模型整合成群决策矩阵,使用熵最大化法计算属性权重;最后基于多准则折衷法框架,采取模糊交叉熵实现特征方案排序. 仿真实验结果表明,所提方法能够给出与实际仿真实验相一致的分选识别特征评价排序结果,并优于逼近理想点方法,验证了所提方法的可行性和有效性.     

增量式块主成分分析的焊缝图像特征提取算法北大核心CSCD

针对焊缝图像特征提取的实时性问题,该文提出一种增量式块主成分分析(incremental block principal component analysis,IBlockPCA)算法,用于焊缝特征主成分的提取。该算法先将焊缝表面图像分割成子图像块并对其进行重构,然后利用提出的IBlockPCA算法对局部块图像进行增量式特征提取,并采用KNN算法对提取的特征主成分进行分类识别;最后在焊缝数据集上进行了算法的性能对比。实验结果表明,该算法在收敛率、分类率及复杂度等方面均优于其他主成分分析(principal component analysis,PCA)算法,其分类识别率为97.5%,其平均处理速度可达50 frame/s,能够满足焊缝表面图像的实时性处理需求。     

基于LRC与多样本扩充的指静脉识别方法北大核心CSCD

针对目前手指静脉识别由于训练样本不足引起图像识别率低的问题,提出基于线性回归分类(linear regression classification,LRC)与多样本扩充的指静脉识别方法。首先,利用矩阵变换生成原始图像的镜像,训练原始图像与镜像,增加指静脉图像中包含的有用信息;然后,基于LRC对测试和训练样本进行分类;最后,通过计算偏差得到最终分类结果,求出识别率。此外,设计了一种指静脉采集装置收集得到自建指静脉数据库。实验结果表明:所提算法在自建指静脉数据库、山东大学指静脉数据库、马来西亚理工大学指静脉数据库上的识别率分别达到98.93%、98.89%、99.67%,最低等误率为2.3888%。实验结果与其他传统和流行算法相比具有明显优势,拥有良好的实际应用价值。     

多尺度混合注意力胶囊网络的海洋鱼类识别

针对胶囊网络(capsule network,CapsNet)特 征提取结构单一和数据处理中参数量过大的问题,提出 多尺度混合注意力胶囊网络 模型。首先,在网络初始端添加不同尺度的卷积核来多角度提取 特征,并引 入混合注意力机制,通过聚焦更具分辨性的特征区域来降低复杂背景干扰。其次,采用局部 剪枝算法优 化动态路由,减少参数量,缩短模型训练时间。最后,在海洋鱼类数据集F4K(Fish4Knowled ge)上验证, 结果表明,与传统残差网络(residual network50,ResNet-50)、双线性网络(bilinear convo l utional neural network,B-CNN)、分层精简双线性注意力网络(spatial transformation netw ork and hierarchical compact bilinear pooling,STN-H-CBP)以及CapsNet模型相比,该算法 识别精度为98.65%,比ResNet-50模型提升 了5.92%;训练时间为2.2 h,相比于CapsNet 缩短了近40 min,验证了该算法的可行性。     

Saliency guided self-attention network for pedestrian attribute recognition in surveillance scenarios

Pedestrian attribute recognition is often considered as a multi-label image classification task. In order to make full use of attribute-related location information, a saliency guided sel-attention network ( SGSA-Net) was proposed to weakly supervise attribute localization, without annotations of attribute-related regions. Saliency priors were integrated into the spatial attention module ( SAM ). Meanwhile,channel-wise attention and spatial attention were introduced into the network. Moreover, a weighted binary cross-entropy loss ( WCEL) function was employed to handle the imbalance of training data. Extensive experiments on richly annotated pedestrian ( RAP) and pedestrian attribute ( PETA) datasets demonstrated that SGSA-Net outperformed other state-of-the-art methods.     

Recognition of guests bearing donor and acceptor hydrogen bonding groups by heteroditopic calix[4]arene receptors

The synthesis of new hosts specifically designed for the recognition of neutral guests bearing donor-acceptor hydrogen bonding groups is described. These hosts are characterized by the presence of two distinct binding region in close proximity: the rigid π-donor cavity and the H-bond donor N-methylene-N′-phenylureido group inserted onto the upper rim of the calix[4]arene skeleton. The binding abilities of these receptors were investigated toward a series of neutral ditopic organic molecules in CDCl₃ solution by ¹H NMR spectroscopy. The results obtained show that rigidity of the calix[4]arene apolar cavity is the control element in determining efficiency. In fact, compared with the more rigid 2, host 10, where the rigidity of the cone structure is maintained by hydrogen bonding of the OH of the lower rim, a decrease of efficiency of almost one order of magnitude was observed. The cooperative effect of the two binding region of host 2 was verified with different classes of ditopic guests. Good efficiency in the recognition of urea derivatives and dimethylsulfoxide was achieved.     

三维主体分子的合成及阴离子识别研究进展

生物功能体系的模拟是生物有机化学和超分子化学发展的前沿领域之一。本文简要论述新型大二环．大三环、笼型环番以及其它三维主体分子的合成及对阴离子选择性识别作用的研究新进展。