Multistage integration model for human egomotion perception

Human computational vision models that attempt to account for the dynamic perception of egomotion and relative depth typically assume a common three-stage process: first, compute the optical flow field based on the dynamically changing image; second, estimate the egomotion states based on the flow; and third, estimate the relative depth/shape based on the egomotion states and possibly on a model of the viewed surface. We propose a model more in line with recent work in human vision, employing multistage integration. Here the dynamic image is first processed to generate spatial and temporal image gradients that drive a mutually interconnected state estimator and depth/shape estimator. The state estimator uses the image gradient information in combination with a depth/shape estimate of the viewed surface and an assumed model of the viewer's dynamics to generate current state estimates; in tandem, the depth/shape estimator uses the image gradient information in combination with the viewer's state estimate and assumed shape model to generate current depth/shape estimates. In this paper, we describe the model and compare model predictions with empirical data.     

The Plasma Environment of Comet 67P/Churyumov-Gerasimenko Throughout the Rosetta Main Mission

The plasma environment of comet 67P/Churyumov-Gerasimenko, the Rosetta mission target comet, is explored over a range of heliocentric distances throughout the mission: 3.25 AU (Rosetta instruments on), 2.7 AU (Lander down), 2.0 AU, and 1.3 AU (perihelion). Because of the large range of gas production rates, we have used both a fluid-based magnetohydrodynamic (MHD) model as well as a semi-kinetic hybrid particle model to study the plasma distribution. We describe the variation in plasma environs over the mission as well as the differences between the two modeling approaches under different conditions. In addition, we present results from a field aligned, two-stream transport electron model of the suprathermal electron flux when the comet is near perihelion.     

Optimal motion planning for dual-spacecraft interferometry

We address the question of design and optimal control of a class of dual-spacecraft interferometric imaging formations. The first main contribution is that we combine two ideas introduced separately in the literature and propose a maneuver that offers improved imaging performance. We then formulate an optimal control problem to minimize fuel consumption and maximize image quality by minimizing the relative speed, which is proportional to the signal-to-noise ratio (SNR) of the reconstructed image. We show that the necessary conditions are also sufficient and that the resulting optimal control is unique. Finally, we apply a continuation method to solve for the unique optimal trajectory.     

The Pre-CME Sun

The coronal mass ejection (CME) phenomenon occurs in closed magnetic field regions on the Sun such as active regions, filament regions, transequatorial interconnection regions, and complexes involving a combination of these. This chapter describes the current knowledge on these closed field structures and how they lead to CMEs. After describing the specific magnetic structures observed in the CME source region, we compare the substructures of CMEs to what is observed before eruption. Evolution of the closed magnetic structures in response to various photospheric motions over different time scales (convection, differential rotation, meridional circulation) somehow leads to the eruption. We describe this pre-eruption evolution and attempt to link them to the observed features of CMEs. Small-scale energetic signatures in the form of electron acceleration (signified by nonthermal radio bursts at metric wavelengths) and plasma heating (observed as compact soft X-ray brightening) may be indicative of impending CMEs. We survey these pre-eruptive energy releases using observations taken before and during the eruption of several CMEs. Finally, we discuss how the observations can be converted into useful inputs to numerical models that can describe the CME initiation.     

Detection of small objects in clutter using a GA-RBF neural network

Detection of small objects in a radar or satellite image is an important problem with many applications. Due to a recent discovery that sea clutter, the electromagnetic wave backscatter from a sea surface, is chaotic rather than purely random, computational intelligence techniques such as neural networks have been applied to reconstruct the chaotic dynamic of sea clutter. The reconstructed sea clutter dynamical system which usually takes the form of a nonlinear predictor does not only provide a model of the sea scattering phenomenon, but it can also be used to detect the existence of small targets such as fishing boats and small fragments of icebergs by observing abrupt changes in the prediction error. We applied a genetic algorithm (GA) to obtain an optimal reconstruction of sea clutter dynamic based on a radial basis function (RBF) neural network. This GA-RBF uses a hybrid approach that employes a GA to search for the optimum values of the following RBF parameters: centers, variance, and number of hidden nodes, and uses the least square method to determine the weights. It is shown here that if the functional form of an unknown nonlinear dynamical system can be represented exactly using an RBF net (i.e., no approximation error), this GA-RBF approach can reconstruct the exact dynamic from its time series measurements. In addition to the improved accuracy in modeling sea clutter dynamic, the GA-RBF is also shown to enhance the detectability of small objects embedded in the sea. Using real-life radar data that are collected in the east coast of Canada by two different radar systems: a ground-based radar and a satellite equipped with synthetic aperture radar (SAR), we show that the GA-RBF network is a reliable detector for small surface targets in various sea conditions and is practical for real-life search and rescue, navigation, and surveillance applications     

Design of a Correlator for Real-Time Video Comparisons

The problem of locating a reference image within a larger image using a correlation technique is discussed. Although the particular application discussed is that of locating a reference image obtained from one video sensor or a photograph, within the larger field of view obtained from a second video sensor in real time (i.e., at the TV field rate), the results are general and useful for a number of applications. The tradeoffs necessary to obtain real time correlat are discussed and their effect on correlation accuracy is given.     

无穷观问题的研究(Ⅲ)——‘每一''与‘所有''

本组系列论文（I）－（Ⅴ）从数学和认识论的角度系统地研究了无穷观问题的历史发展和现状，确立了无穷观背景世界的三分法原则，指出了两种穷观相互排斥的局限性，形成了统一两种无穷于同一框架中的基本观点，并建立了一个统一实无限与潜无限于同一框架中的公理集合论系统APAS。     

华东地区围护结构节能研究

根据南京市的典型年逐时气象参数，对于该市的某一座高层建筑物，应用自编的逐时动态负荷模拟程序，输入围护结构（窗户、墙体和屋顶）相关的物理参数及其与周围环境的关系（地点及朝向等），计算围护结构冬季采暖期散热量及夏季制冷得热量。结果表明，采用双层塑窗代替单层塑窗，使用节能墙体代替传统墙体能够使得冬季围护结构传散热量减少51％，夏季围护结构总得热量减少46％。因此，这一结果为华东地区建筑节能技术提供了理论依据。     

金属载体上线天线阻抗与辐射特性的矩量法研究

用线网格模拟金属载体表面，应用矩量法求出天线及载体网格上的电流分布，据此研究在该金属载体影响下的天线特性，在计算阻抗矩阵元素时，利用了非对称正弦偶极子辐射场的精确表达式，并采用了直接复数积分，简化了计算公式，本文给出了几种金属载体上线天线的阻抗与辐射特性的计算结果，它们与有关文献上的数据或实验结果一致，最后计算了一种舰船简单模型上两根线天线的远区辐射场及它们的自阻抗与互阻抗。     

基于BP网络的热工过程模型辨识方法

主要研究了人工神经网络在辨识火电厂热工过程模型中的应用，利用Visual C^ 语言构造BP神经网络,提出了把BP网络权值转换为传递函数的方法，针对火电厂常见的热工过程，不用人为加入特殊的激励信号，只利用现场生产中自然存在的扰动信号进行辨识试验，试验结果准确可靠。