共享交通的时空轨迹检索与群体发现

为解决共享交通下的共乘用户群体发现效率低、准确率不高问题，依据R-树原理建立GeoOD-Tree索引，并在此基础上提出以最大化共乘率为目标的群体发现策略。首先，对原始时空轨迹数据进行特征提取与标定处理，挖掘有效出行起讫点（OD）轨迹；其次，针对用户起讫点轨迹的特征，建立GeoOD-Tree索引进行有效的存储管理；最后，给出以最大化共乘行程为目标的群体发现模型，并运用K最近邻（KNN）查询对搜索空间剪枝压缩，提高群体发现效率。采用西安市近12000辆出租车营运轨迹数据，选取动态时间规整（DTW）等典型算法与所提算法在查询效率与准确率上进行性能对比分析。与DTW算法相比，所提算法的准确率提高了10.12%，查询效率提高了约15倍。实验结果表明提出的群体发现策略能有效提高共乘用户群体发现的准确率和效率，可有效提升共乘出行方式的出行率。     

一种北斗联合低轨星座的导航增强方法研究

单纯依靠北斗导航系统提供定位导航授时服务,存在卫星信号落地功率低、易受遮蔽和阻断的不足。针对此问题,研究了一种北斗联合低轨星座实现导航增强的系统架构;提出了一种新的适应严重遮蔽或干扰条件下,基于到达时间(TOA)和到达频率(FOA)联合观测的定位解算算法;对高中低轨混合星座条件下的覆盖特性和精度衰减因子进行了仿真分析。分析结果表明,用户可见星数平均增加了64.2%,位置精度衰减因子(PDOP)平均改善了28.7%。研究结果可为下一代北斗的论证设计与研制建设提供参考借鉴。     

An adaptive image steganographic scheme based on Noise Visibility Function and an optimal chaotic based encryption method

Steganography is the science of hiding secret message in an appropriate digital multimedia in such a way that the existence of the embedded message should be invisible to anyone apart from the sender or the intended recipient. This paper presents an irreversible scheme for hiding a secret image in the cover image that is able to improve both the visual quality and the security of the stego-image while still providing a large embedding capacity. This is achieved by a hybrid steganography scheme incorporates Noise Visibility Function (NVF) and an optimal chaotic based encryption scheme. In the embedding process, first to reduce the image distortion and to increase the embedding capacity, the payload of each region of the cover image is determined dynamically according to NVF. NVF analyzes the local image properties to identify the complex areas where more secret bits should be embedded. This ensures to maintain a high visual quality of the stego-image as well as a large embedding capacity. Second, the security of the secret image is brought about by an optimal chaotic based encryption scheme to transform the secret image into an encrypted image. Third, the optimal chaotic based encryption scheme is achieved by using a hybrid optimization of Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) which is allowing us to find an optimal secret key. The optimal secret key is able to encrypt the secret image so as the rate of changes after embedding process be decreased which results in increasing the quality of the stego-image. In the extracting process, the secret image can be extracted from the stego-image losslessly without referring to the original cover image. The experimental results confirm that the proposed scheme not only has the ability to achieve a good trade-off between the payload and the stego-image quality, but also can resist against the statistics and image processing attacks.     

Dynamic probe of ZnTe(110) surface by scanning tunneling microscopy

The reconstructed surface structure of the II–VI semiconductor ZnTe (110), which is a promising material in the research field of semiconductor spintronics, was studied by scanning tunneling microscopy/spectroscopy (STM/STS). First, the surface states formed by reconstruction by the charge transfer of dangling bond electrons from cationic Zn to anionic Te atoms, which are similar to those of IV and III–V semiconductors, were confirmed in real space. Secondly, oscillation in tunneling current between binary states, which is considered to reflect a conformational change in the topmost Zn–Te structure between the reconstructed and bulk-like ideal structures, was directly observed by STM. Third, using the technique of charge injection, a surface atomic structure was successfully fabricated, suggesting the possibility of atomic-scale manipulation of this widely applicable surface of ZnTe.     

Multiobjective PSO based adaption of neural network topology for pixel classification in satellite imagery

The proposed work involves the multiobjective PSO based adaption of optimal neural network topology for the classification of multispectral satellite images. It is per pixel supervised classification using spectral bands (original feature space). This paper also presents a thorough experimental analysis to investigate the behavior of neural network classifier for given problem. Based on 1050 number of experiments, we conclude that following two critical issues needs to be addressed: (1) selection of most discriminative spectral bands and (2) determination of optimal number of nodes in hidden layer. We propose new methodology based on multiobjective particle swarm optimization (MOPSO) technique to determine discriminative spectral bands and the number of hidden layer node simultaneously. The accuracy with neural network structure thus obtained is compared with that of traditional classifiers like MLC and Euclidean classifier. The performance of proposed classifier is evaluated quantitatively using Xie-Beni and β indexes. The result shows the superiority of the proposed method to the conventional one.     

Iterative equalization combining algorithm in intersymbol interference channel

To realize joint optimization of spatial diversity and equalization combining in the intersymbol interference (ISI) channel, an iterative equalization combining algorithm is proposed. The proposed algorithm uses the coefficients of Turbo equalization to calculate the combination weights without estimating the signal to noise ratio in each diversity branch. The equalized symbols from different diversity branches are combined, and the extrinsic information output from the decoder is fed back to the equalizers, so as to exchange soft information between the equalizers and the decoder. The performance of the proposed algorithm is analyzed using the extrinsic information transfer (EXIT) chart and verified by simulations. Results show that our approach fully exploits time domain information from the multipath channel and spatial domain information from multi receiving antennas, which efficiently improve the performance of the receiver in the severe ISI channel.     

Validation of performance of real-time kinematic PPP. A possible tool for deformation monitoring

Structural failures (bridge or building collapses) and geohazards (landslides, ground subsidence or earthquakes) are worldwide problems that often lead to significant economic and loss of life. Monitoring the deformation of both natural phenomena and man-made structures is a major key to assessing structural dynamic responses. Actually, this monitoring process is under real-time demand for developing warning and alert systems.One of the most used techniques for real-time deformation monitoring is the Global Navigation Satellite System (GNSS) real-time procedure, where the relative positioning approach, using a well-known reference station, has been applied.This study was conducted to evaluate the actual quality of the real-time kinematic Precise Point Positioning (PPP) GNSS solution for deformation monitoring, where it can be concluded that a promise tool is under development and should be taken into account on actual and near future real-time deformation monitoring studies and applications.     

Viability of intertwined supply networks: extending the supply chain resilience angles towards survivability. A position paper motivated by COVID-19 outbreak

An intertwined supply network (ISN) is an entirety of interconnected supply chains (SC) which, in their integrity secure the provision of society and markets with goods and services. The ISNs are open systems with structural dynamics since the firms may exhibit multiple behaviours by changing the buyer-supplier roles in interconnected or even competing SCs. From the positions of resilience, the ISNs as a whole provide services to society (e.g. food service, mobility service or communication service) which are required to ensure a long-term survival. The analysis of survivability at the level of ISN requires a consideration at a large scale as resilience of individual SCs. The recent example of coronavirus COVID-19 outbreak clearly shows the necessity of this new perspective. Our study introduces a new angle in SC resilience research when a resistance to extraordinary disruptions needs to be considered at the scale of viability. We elaborate on the integrity of the ISN and viability. The contribution of our position study lies in a conceptualisation of a novel decision-making environment of ISN viability. We illustrate the viability formation through a dynamic game-theoretic modelling of a biological system that resembles the ISN. We discuss some future research areas.     

基于RFID标签阵列的睡眠期间呼吸量连续监测系统

睡眠期间连续且准确的呼吸量监测有助于推断用户的睡眠阶段以及提供一些慢性疾病的线索。现有工作主要针对呼吸频率进行感知和监测,缺乏对呼吸量进行连续监测的手段。针对上述问题提出了一种基于商用无线射频识别(RFID)标签的无线感知用户睡眠期间呼吸量的系统——RF-SLEEP。RF-SLEEP通过阅读器连续收集附着在胸部表面的标签阵列返回的相位值及时间戳数据,计算出呼吸引起的胸部不同点的位移量,基于广义回归神经网络(GRNN)构建胸部不同点的位移量与呼吸量之间的关系模型,从而实现对用户睡眠期间呼吸量的评估。RF-SLEEP通过在用户肩膀处附着双参考标签,消除用户睡眠期间翻转身体对胸部位移计算造成的误差。实验结果表明,RFSLEEP对不同用户睡眠期间的呼吸量连续监测的平均精确度为92.49%。