基于视觉传达的后继帧视频图像目标跟踪仿真

现阶段所采用的跟踪方法对后继帧视频图像目标跟踪存在跟踪效果不理想、跟踪效率较低等问题。提出基于视觉传达的后继帧视频图像目标跟踪方法。利用图像差分方法获取视频图像运动目标可能出现的区域,并对这个区域视频图像目标进行运动估计,采用形态学方法来降低聚类区域的数量,得到后继帧视频图像目标区域;采用均值漂移法估计后继帧视图像核概率密度,对后继帧视频图像进行分割处理,找出后继帧视频图像目标区域最显著的特征,通过迭代运算找到目标位置,实现目标跟踪。实验结果表明,所提算法具有较好的后继帧视频图像跟踪效果、并且跟踪效率较高,具有一定的应用价值。     

粮仓环境监测系统设计

随着科学技术的发展,粮食行业信息化发展得到了强有力的科技支撑。为满足粮库整体信息化建设的要求,打破"信息孤岛"现状,实现资源整合、数据共享,为大数据分析和数据应用提供数据基础,设计一款能够实时采集粮仓内温湿度、氧气、二氧化碳气体浓度、粮堆高度的数据集成系统。系统采用分层分布式结构,由数据集中器和多种传感器模块组成,数据集中器与各传感器模块间通过RS485总线进行数据通讯,并由数据集中器通过网络专线传输汇总的环境监测数据,通过专用软件进行数据处理和挖掘,从而实现粮仓环境监测和预警等功能。     

以任意高维纠缠态为量子信道的受控隐形传态

目的是利用高维量子纠缠态为量子信道，讨论未知单粒子态的受控隐形传输问题。以三维量子纠缠态为信道，提出一个二维任意单粒子态的受控隐形传输协议。提出了以任意[d]-维量子纠缠态为量子信道，[t]-维任意单粒子态的隐形传输协议[(t相似文献   

一种新的应用于5G中的自适应波束赋形优化算法

5G standard uses millimeter wave band to realize communication. In order to cover more reasonable in 5G communication system, beamforming technology must be adopted. To speed up the convergence, a novel algorithm based on the SWO criterion is proposed which can be applied in millimeter wave communication. The simulation results show that compared with other beamform- ing algorithms, the proposed algorithm can achieve higher convergence speed and is more realizable.     

Transfer learning between crop types for semantic segmentation of crops versus weeds in precision agriculture

Agricultural robots rely on semantic segmentation for distinguishing between crops and weeds to perform selective treatments and increase yield and crop health while reducing the amount of chemicals used. Deep‐learning approaches have recently achieved both excellent classification performance and real‐time execution. However, these techniques also rely on a large amount of training data, requiring a substantial labeling effort, both of which are scarce in precision agriculture. Additional design efforts are required to achieve commercially viable performance levels under varying environmental conditions and crop growth stages. In this paper, we explore the role of knowledge transfer between deep‐learning‐based classifiers for different crop types, with the goal of reducing the retraining time and labeling efforts required for a new crop. We examine the classification performance on three datasets with different crop types and containing a variety of weeds and compare the performance and retraining efforts required when using data labeled at pixel level with partially labeled data obtained through a less time‐consuming procedure of annotating the segmentation output. We show that transfer learning between different crop types is possible and reduces training times for up to 80%. Furthermore, we show that even when the data used for retraining are imperfectly annotated, the classification performance is within 2% of that of networks trained with laboriously annotated pixel‐precision data.     

Applied data science in patient-centric healthcare: Adaptive analytic systems for empowering physicians and patients

We define the emerging research field of applied data science as the knowledge discovery process in which analytic systems are designed and evaluated to improve the daily practices of domain experts. We investigate adaptive analytic systems as a novel research perspective of the three intertwining aspects within the knowledge discovery process in healthcare: domain and data understanding for physician- and patient-centric healthcare, data preprocessing and modelling using natural language processing and (big) data analytic techniques, and model evaluation and knowledge deployment through information infrastructures. We align these knowledge discovery aspects with the design science research steps of problem investigation, treatment design, and treatment validation, respectively. We note that the adaptive component in healthcare system prototypes may translate to data-driven personalisation aspects including personalised medicine. We explore how applied data science for patient-centric healthcare can thus empower physicians and patients to more effectively and efficiently improve healthcare. We propose meta-algorithmic modelling as a solution-oriented design science research framework in alignment with the knowledge discovery process to address the three key dilemmas in the emerging “post-algorithmic era” of data science: depth versus breadth, selection versus configuration, and accuracy versus transparency.     

HSDN‐GRA: A hybrid software‐defined networking‐based geographic routing protocol with multi‐agent approach

We propose in this paper a Hybrid Software‐Defined Networking‐based Geographical Routing Protocol (HSDN‐GRA) with a clustering approach. It takes into account three different criteria to select the best relay to send data: (1) the contact duration between vehicles, (2) the available load of each vehicle, (3) and the log of encountered communication errors embedded in each cluster head. The multi‐criteria strategy allows the selection of the most reliable vehicles by avoiding communication problems and ensuring connection availability. Once the hybrid control plane has found out the next eligible neighbor, the data plane will be in charge of dividing and sending data. To validate our approach, HSDN‐GRA has been modeled and implemented in JADE, a multi‐agent platform, to be compared to other multi‐agent based protocols. Simulation results show that HSDN‐GRA achieves good performance with respect to the average routing overhead, the packet drop rate, and the throughput.     

宽带毫米波大规模MIMO系统中低复杂度波束域信道估计

针对宽带毫米波大规模MIMO系统信道估计精度低及实现复杂度较高的问题，在传统支撑检测方案的基础上提出一种基于Gauss-Seidel方法的串行支撑检测（GS-SSD）方案。该方案不使用共同支撑假设，参考串行干扰删除，将整体信道估计问题分解为一系列子问题，每个子问题仅考虑一个信道成分。同时，利用Gauss-Seidel方法近似高复杂度的矩阵求逆。仿真结果表明，相比于基于串行支撑检测（SSD）的方案， GS-SSD方案在将求逆复乘数降低一个数量级的同时可以取得接近SSD方案的信道估计性能。