A Brief Introduction on Joint Radar and Communication Systems

Joint radar and communication(JRC) technology is gradually becoming an essential approach to alleviating spectral congestion. Radar and communications systems were designed with common spectral and hardware resources to reduce size, improve performance, reduce cost, and decongest the spectrum. Various approaches have been proposed to achieve the coexistence of radar and communication systems. This paper mainly focuses on the research directions of radar communication coexistence(RCC) and dual-function radar communication systems(DFRC) in JRC technology. We summarize and analyze the existing research problems in the JRC era. According to the characteristics and advantages of JRC technology, we highlight several potentials in military and commercial applications.     

基于日志分析的增量数据捕获方法研究

通过扫描数据库日志文件可以捕获变化数据,但已有研究仅适应某种特定类型的数据库管理系统并且缺乏对冗余信息的消除。针对上述不足,提出一个基于日志分析的增量数据检测及其净效应处理通用模型,描述增量检测过程中的通用处理步骤,即日志抽取、日志分析和净效应处理3个模块。通过实验对净效应处理速度、冗余数据压缩率、网络传输速度等因素进行分析,结果表明,净效应处理可以有效地减少数据的网络传输时间和更新时间,提高运行效率。     

Designing Camera Networks by Convex Quadratic Programming

In this paper, we study the problem of automatic camera placement for computer graphics and computer vision applications. We extend the problem formulations of previous work by proposing a novel way to incorporate visibility constraints and camera‐to‐camera relationships. For example, the placement solution can be encouraged to have cameras that image the same important locations from different viewing directions, which can enable reconstruction and surveillance tasks to perform better. We show that the general camera placement problem can be formulated mathematically as a convex binary quadratic program (BQP) under linear constraints. Moreover, we propose an optimization strategy with a favorable trade‐off between speed and solution quality. Our solution is almost as fast as a greedy treatment of the problem, but the quality is significantly higher, so much so that it is comparable to exact solutions that take orders of magnitude more computation time. Because it is computationally attractive, our method also allows users to explore the space of solutions for variations in input parameters. To evaluate its effectiveness, we show a range of 3D results on real‐world floorplans (garage, hotel, mall, and airport).     

Highly Conductive MXene/PEDOT:PSS-Integrated Poly(N-Isopropylacrylamide) Hydrogels for Bioinspired Somatosensory Soft Actuators

Sophisticated sensing and actuation capabilities of many living organisms in nature have inspired scientists to develop biomimetic somatosensory soft robots. Herein, the design and fabrication of homogeneous and highly conductive hydrogels for bioinspired somatosensory soft actuators are reported. The conductive hydrogels are synthesized by in situ copolymerization of conductive surface-functionalized MXene/Poly(3,4-ethylenedioxythiophene)/poly(styrenesulfonate) ink with thermoresponsive poly(N-isopropylacrylamide) hydrogels. The resulting hydrogels are found to exhibit high conductivity (11.76 S m⁻¹), strain sensitivity (GF of 9.93), broad working strain range (≈560% strain), and high stability after over 300 loading–unloading cycles at 100% strain. Importantly, shape-programmable somatosensory hydrogel actuators with rapid response, light-driven remote control, and self-sensing capability are developed by chemically integrating the conductive hydrogels with a structurally colored polymer. As the proof-of-concept illustration, structurally colored hydrogel actuators are applied for devising light-driven programmable shape-morphing of an artificial octopus, an artificial fish, and a soft gripper that can simultaneously monitor their own motions via real-time resistance variation. This work is expected to offer new insights into the design of advanced somatosensory materials with self-sensing and actuation capabilities, and pave an avenue for the development of soft-matter-based self-regulatory intelligence via built-in feedback control that is of paramount significance for intelligent soft robotics and automated machines.     

La_(1-x)Mg_xNi_(2.5)Co_(0.5)(x=0～0.4)储氢合金的制备及电化学氢化性能

本文以感应熔炼法制备的La_(1-x)Mg_xNi_(2.5)Co_(0.5)(x=0~0.4)储氢合金作为研究对象,探究了熔炼过程中合金成分的损耗情况。通过对合金质量损耗的填补,并结合AES-ICP等测试手段,比较精确地制备出了目标合金。根据合金充放电前后的X射线衍射(XRD)测试结果,发现合金的氢化物随着Mg含量的增高而逐渐由非晶态转化成晶态,晶态氢化物由合金的α相转化成氢化物的β相。合金氢化物的晶胞参数a和c均显著增大,晶胞体积也有较大膨胀,但增大幅度却随着Mg含量的增加而逐渐减小。在0.1MPa干燥Ar保护条件下,填补0.9%的La和10.0%的Mg所制备的La_(0.7)Mg_(0.3)Ni_(2.5)Co_(0.5)合金不仅具有良好的电化学氢化性能和较大的放电容量,而且能够有效抑制合金的氢致非晶化。     

Unsupervised Learning of Probabilistic Object Models (POMs) for Object Classification, Segmentation, and Recognition Using Knowledge Propagation

We present a method to learn probabilistic object models (POMs) with minimal supervision, which exploit different visual cues and perform tasks such as classification, segmentation, and recognition. We formulate this as a structure induction and learning task and our strategy is to learn and combine elementary POMs that make use of complementary image cues. We describe a novel structure induction procedure, which uses knowledge propagation to enable POMs to provide information to other POMs and “teach them” (which greatly reduces the amount of supervision required for training and speeds up the inference). In particular, we learn a POM-IP defined on Interest Points using weak supervision [1], [2] and use this to train a POM-mask, defined on regional features, which yields a combined POM that performs segmentation/localization. This combined model can be used to train POM-edgelets, defined on edgelets, which gives a full POM with improved performance on classification. We give detailed experimental analysis on large data sets for classification and segmentation with comparison to other methods. Inference takes five seconds while learning takes approximately four hours. In addition, we show that the full POM is invariant to scale and rotation of the object (for learning and inference) and can learn hybrid objects classes (i.e., when there are several objects and the identity of the object in each image is unknown). Finally, we show that POMs can be used to match between different objects of the same category, and hence, enable objects recognition.     

Recursive segmentation and recognition templates for image parsing

In this paper, we propose a Hierarchical Image Model (HIM) which parses images to perform segmentation and object recognition. The HIM represents the image recursively by segmentation and recognition templates at multiple levels of the hierarchy. This has advantages for representation, inference, and learning. First, the HIM has a coarse-to-fine representation which is capable of capturing long-range dependency and exploiting different levels of contextual information (similar to how natural language models represent sentence structure in terms of hierarchical representations such as verb and noun phrases). Second, the structure of the HIM allows us to design a rapid inference algorithm, based on dynamic programming, which yields the first polynomial time algorithm for image labeling. Third, we learn the HIM efficiently using machine learning methods from a labeled data set. We demonstrate that the HIM is comparable with the state-of-the-art methods by evaluation on the challenging public MSRC and PASCAL VOC 2007 image data sets.     

虚拟展览馆系统中冲突检测的实现

冲突检测是虚拟环境系统中一个重要的基本功能模块。也是给用户提供真实感的重要手段之一。然而，它的实现往往伴随着大量的资源开销。该文介绍了虚拟展览馆系统中采用的基本冲突检测算法。以及作出的改进；并阐述了在此基础上构造的一个基于层次场景的冲突检测功能模块。提出了一个基于场景的连续冲突检测方法。实验结果表明虚拟展览馆系统中的冲突检测功能部件能较为理想地检测出场景中三维物体移动中的冲突情况。     

具有部分n~+浮空埋层的高压SJ-LDMOS器件新结构

为了抑制衬底辅助耗尽(SAD)效应并提高超结器件击穿电压,提出一种具有部分n+浮空层SJ-LDMOS新结构。n+浮空等位埋层能够调制器件横向电场,使得partial n+-floating SJ-LDMOS比传统SJ-LDMOS具有更加均匀的电场分布。通过三维仿真软件对新器件结构分析,与传统SJ-LDMOS进行比较。仿真结果表明,具有部分n+浮空层SJ-LDMOS结构的器件能将器件的击穿电压从138V提高到302V,且比导通电阻也从33.6mΩ·cm2降低到11.6mΩ·cm2,获得一个较为理想的低导通电阻高压功率器件。