放射性物质沾染检测仪的研制

介绍了一种放射性物质沾染检测仪的结构和功能,该仪器具有探测灵敏,结构简单,便携灵活等特点,可以在现场迅速对人身上的放射性物质沾染情况进行检测,对保护现场工作人员的身体安全,防止放射性物资污染扩散有着十分重要的意义。     

Advanced data acquisition system for SEVAN

Huge magnetic clouds of plasma emitted by the Sun dominate intense geomagnetic storm occurrences and simultaneously they are correlated with variations of spectra of particles and nuclei in the interplanetary space, ranging from subtermal solar wind ions till GeV energy galactic cosmic rays. For a reliable and fast forecast of Space Weather world-wide networks of particle detectors are operated at different latitudes, longitudes, and altitudes. Based on a new type of hybrid particle detector developed in the context of the International Heliophysical Year (IHY 2007) at Aragats Space Environmental Center (ASEC) we start to prepare hardware and software for the first sites of Space Environmental Viewing and Analysis Network (SEVAN). In the paper the architecture of the newly developed data acquisition system for SEVAN is presented. We plan to run the SEVAN network under one-and-the-same data acquisition system, enabling fast integration of data for on-line analysis of Solar Flare Events. An Advanced Data Acquisition System (ADAS) is designed as a distributed network of uniform components connected by Web Services. Its main component is Unified Readout and Control Server (URCS) which controls the underlying electronics by means of detector specific drivers and makes a preliminary analysis of the on-line data. The lower level components of URCS are implemented in C and a fast binary representation is used for the data exchange with electronics. However, after preprocessing, the data are converted to a self-describing hybrid XML/Binary format. To achieve better reliability all URCS are running on embedded computers without disk and fans to avoid the limited lifetime of moving mechanical parts. The data storage is carried out by means of high performance servers working in parallel to provide data security. These servers are periodically inquiring the data from all URCS and storing it in a MySQL database. The implementation of the control interface is based on high level web standards and, therefore, all properties of the system can be remotely managed and monitored by the operators using web browsers. The advanced data acquisition system at ASEC in Armenia was started in November, 2006. The reliability of the multi-client service was proven by continuously monitoring neutral and charged cosmic ray particles. Seven particle monitors are located at 2000 and 3200 m above sea level at a distance of 40 and 60 km from the main data server.     

BOC信号自适应门限判决捕获方法

BOC信号是一种分离频谱的调制方式,与传统的导航扩频信号相比,具有更强的抗干扰能力和更高的测量精度。但BOC信号自相关函数具有多峰值的特点,如何从接收端找出最大峰值并正确恢复BOC信号是技术难点。本文给出了一种BOC信号的捕获方案和具体的实现算法。     

The analysis of affections to the cold space calibration source of ChangE-1 payload Microwave Detector

ChangE-1 (CE-1) will be the first satellite of China in lunar orbit. Its Microwave Detector makes real-time and periodical calibration at high and low temperature points. The low brightness temperature calibration source will be provided by calibration antenna which is pointing towards the cold space.     

量子级联技术研究进展：从光源到探测

量子级联技术基于多量子阱或超晶格结构中的子带跃迁和共振隧穿理论,既可以产生光源,又可以探测光信号,是量子级联激光器（QCL）和量子级联探测器（QCD）的理论基石,在检测、遥感、通信、雷达等领域具有广泛的应用前景。经过最近三十年的研究,量子级联技术在基础研究、产品性能以及应用系统研发和场景试验方面都取得了重大进展。本文首先简要介绍了量子级联技术的原理和发展历史,随后阐述了量子级联器件子带能级结构和电子输运动力学计算思路,接着重点综述了量子级联技术的研究进展,包括中远红外高功率QCL、中远红外宽调谐QCL、太赫兹QCL、高性能QCD,以及QCL和QCD的单芯片光子集成方面的内容,最后介绍了QCL和QCD的产品与应用情况。     

基于视觉的行人引领移动机器人导航方法研究

移动机器人通过跟随领航员以实现导航是一种便捷的导航方式。针对行人引领导航中的领航员定位问题,提出了一种基于视觉的行人引领导航方法。该方法利用卡尔曼滤波器预测领航员的位置和尺度,并基于深度神经网络的行人检测器提供的结果更新滤波器的状态。为了关联检测结果和卡尔曼滤波器预测结果,提出了2个指标用于衡量两者之间的关联性。其中,为了提高在多个行人中辨认领航员的可靠性,创新性地引入了一个孪生神经网络,使用该网络全连接层提取的特征作为候选人的特征描述子,并通过计算特征之间的余弦距离来验证检测器检测到的行人身份。此外,当卡尔曼滤波器跟踪领航员失败时,综合考虑检测结果和孪生网络的判断结果重新初始化卡尔曼滤波器,以实现持续的领航员定位。视频实验和物理机器人实验验证了所提出的方法的有效性和可靠性。     

天基导弹紫外预警及紫外预警探测仪

文章简单介绍了国外导弹紫外预警的研究现状,分析了紫外预警的关键要素。     

飞机燃油箱漏油故障的检查和处理

容易产生燃油渗漏的主要部位是飞机燃油箱的结构搭接缝、对接缝、接头部位、紧固件以及放油口和加油口等位置。查找漏源是至关重要的，然后采取相应的措施，修理油箱、紧固螺拴、重涂封胶，以保证航班正点和安全。     

Alpha Particle X-Ray Spectrometer (APXS) on-board Chandrayaan-2 rover

Alpha Particle X-ray Spectrometer (APXS) payload configuration for Chandrayaan-2 rover has been completed recently and fabrication of mechanical assembly, PCB layout design and fabrication are in progress. Here we present the design and performance evaluation of various subsystems developed for APXS payload. The low energy threshold of <1 keV and the energy resolution of ∼150 eV at 5.9 keV, for the Silicon Drift Detector (SDD), as measured from the developed APXS electronics is comparable to the standard spectrometers available off-the-shelf. We have also carried out experiments for measuring fluorescent X-ray spectrum from various standard samples from the USGS catalog irradiated by the laboratory X-ray source ²⁴¹Am with 1 mCi activity. It is shown that intensities of various characteristic X-ray lines are well correlated with the respective elemental concentrations.     

Multi-block SSD based on small object detection for UAV railway scene surveillance

A method of multi-block Single Shot MultiBox Detector (SSD) based on small object detection is proposed to the railway scene of unmanned aerial vehicle surveillance. To address the limitation of small object detection, a multi-block SSD mechanism, which consists of three steps, is designed. First, the original input images are segmented into several overlapped patches. Second, each patch is separately fed into an SSD to detect the objects. Third, the patches are merged together through two stages. In the first stage, the truncated object of the sub-layer detection result is spliced. In the second stage, a sub-layer suppression and filtering algorithm applying the concept of non-maximum suppression is utilized to remove the overlapped boxes of sub-layers. The boxes that are not detected in the main-layer are retained. In addition, no sufficient labeled training samples of railway circumstance are available, thereby hindering the deployment of SSD. A two-stage training strategy leveraging to transfer learning is adopted to solve this issue. The deep learning model is preliminarily trained using labeled data of numerous auxiliaries, and then it is refined using only a few samples of railway scene. A railway spot in China, which is easily damaged by landslides, is investigated as a case study. Experimental results show that the proposed multi-block SSD method produces an overall accuracy of 96.6% and obtains an improvement of up to 9.2% compared with the traditional SSD.