双站被动多普勒测速

基于双基站侦查系统对于有源空中运动目标,提出了一种被动测速方法.该方法利用双站与目标间的几何位置关系和相对速度引起的多普勒频差信息构建动态方程,求解目标速度.通过几组真实的参数详细分析了测速精度与测频精度、测角精度的关系.仿真结果证明该方法在较大的方位角范围内有较高的测速精度,且对于测频精度和测角精度的敏感性不高.     

多普勒雷达安装偏差及测速精度的估计与补偿

研究飞行器导航定位精度优化问题,由于固定天线多普勒雷达、航姿系统及导航系统等测速存在误差,引起系统导航误差.为了减小测速误差,提高导航精度,提出了一种采用安装偏差与测速精度估计的补偿方法.分析了多普勒雷达的工作原理及误差的影响因素,建立了误差模型,然后利用卡尔曼滤波器,结合误差模型,将安装偏差、测速精度作为状态变量进行了估计,最后根据估计值进行了补偿.仿真结果表明,在载机起飞、降落、平飞等飞行状态下,改进方法能准确地对安装偏差、测速精度进行估计,减小了测速误差,提高了载机在陆地及海洋上飞行的导航精度.     

Iterative regularization method for lidar remote sensing

In this paper we present an inversion algorithm for ill-posed problems arising in atmospheric remote sensing. The proposed method is an iterative Runge-Kutta type regularization method. Those methods are better well known for solving differential equations. We adapted them for solving inverse ill-posed problems. The numerical performances of the algorithm are studied by means of simulations concerning the retrieval of aerosol particle size distributions from lidar observations.     

Airborne lidar bathymetry

Abstract

Through an extensive literature review the results of previous lidar investigations in Australia, Canada, Sweden, the U.S.A. and West Germany have been evaluated. Based on these findings estimates are given for the anticipated depth capability, measurement accuracy and operational constraints for a laser system in U.K. waters. Consideration is also given to the possibility of deploying a depth-sounding lidar for non-bathymetric purposes such as depth-resolved turbidity mapping.     

Inter-comparison of wind profiles in the tropical boundary layer remotely sensed from GPS radiosonde and Doppler wind lidar

Winds play a very important role in the dynamics of the lower atmosphere, and there is a need to obtain vertical distribution of winds at high spatio-temporal resolution for various observational and modelling applications. Profiles of wind speed and direction obtained at two tropical Indian stations using a Doppler wind lidar during the Indian southwest monsoon season were inter-compared with those obtained simultaneously from GPS upper-air sounding (radiosonde). Mean wind speeds at Mahbubnagar (16.73° N, 77.98° E, 445 m above mean sea level) compare well in magnitude for the entire height range from 100 m to 2000 m. The mean difference in wind speed between the two techniques ranged from ?0.81 m s^?1 to +0.41 m s^?1, and the standard deviation of wind speed differences ranged between 1.03 m s^?1 and 1.95 m s^?1. Wind direction by both techniques compared well up to about 1200 m height and then deviated slightly from each other at heights above, with a standard deviation in difference of 19°–48°. At Pune (18^○32′ N, 73^○51′ E, 559 m above mean sea level), wind speed by both techniques matched well throughout the altitude range, but with a constant difference of about 1 m s^?1. The root mean square deviation in wind speed ranged from 1.0 to 1.6 m s^?1 and that in wind direction from 20° to 45°. The bias and spread in both wind speed and direction for the two stations were computed and are discussed. The study shows that the inter-comparison of wind profiles obtained by the two independent techniques is very good under conditions of low wind speeds, and they show larger deviation when wind speeds are large, probably due the drift of the radiosonde balloon away from the location.     

An efficient concept-based retrieval model for enhancing text retrieval quality

Most of the common techniques in text retrieval are based on the statistical analysis terms (words or phrases). Statistical analysis of term frequency captures the importance of the term within a document only. Thus, to achieve a more accurate analysis, the underlying model should indicate terms that capture the semantics of text. In this case, the model can capture terms that represent the concepts of the sentence, which leads to discovering the topic of the document. In this paper, a new concept-based retrieval model is introduced. The proposed concept-based retrieval model consists of conceptual ontological graph (COG) representation and concept-based weighting scheme. The COG representation captures the semantic structure of each term within a sentence. Then, all the terms are placed in the COG representation according to their contribution to the meaning of the sentence. The concept-based weighting analyzes terms at the sentence and document levels. This is different from the classical approach of analyzing terms at the document level only. The weighted terms are then ranked, and the top concepts are used to build a concept-based document index for text retrieval. The concept-based retrieval model can effectively discriminate between unimportant terms with respect to sentence semantics and terms which represent the concepts that capture the sentence meaning. Experiments using the proposed concept-based retrieval model on different data sets in text retrieval are conducted. The experiments provide comparison between traditional approaches and the concept-based retrieval model obtained by the combined approach of the conceptual ontological graph and the concept-based weighting scheme. The evaluation of results is performed using three quality measures, the preference measure (bpref), precision at 10 documents retrieved (P(10)) and the mean uninterpolated average precision (MAP). All of these quality measures are improved when the newly developed concept-based retrieval model is used, confirming that such model enhances the quality of text retrieval.     

The use of airborne lidar for orchard tree inventory

The tree inventory in orchards is of great interest for orchard management and for government insurance plans. However, the conventional inventory is time‐consuming and expensive. Here a remote sensing method is introduced for orchard inventory. Airborne LIDAR (light detection and ranging) data were employed to obtain tree topography, and multispectral images were used as a reference. LIDAR vector data were converted to raster data for tree crown delineating purpose and in order to be easily superimposed on multispectral data in the same database. A tree crown delineation model was developed using a tree height image derived from the difference between canopy and ground LIDAR altitudes. The number of trees was computed from the delineation model. Spatially separated trees were precisely counted by fine definition of their crowns. For larger trees, although they have irregular crown form, like multi‐tops, holes in the centre or overlapped branches, the model developed in this study provided reliable results for crown delineation.     

Real-time coherent stylization for augmented reality

This paper presents a new stylized augmented reality (AR) framework which can generate line drawing and abstracted shading styles. In comparison with the state-of-art work, our framework can significantly improve both the visual immersion of a single frame and the temporal coherence of augmented video streams in real time. In our framework, we first render virtual objects over the input camera images and then uniformly process the combined contents with stylization techniques. For generating line drawing stylization, we first propose a specially designed shading method to render the virtual objects, and then use an adapted Flow-based anisotropic Difference-of-Gaussion (FDoG) filter to yield the high-quality line drawing effect. For generating the abstracted stylization, a focus-guided diffusion filter and a soft color quantization operator are sequentially applied to the augmented image, and then the processed result is combined with the detected edges to produce the final abstraction effect. The presented algorithms are all sympathetic to highly parallel processing, allowing a real-time performance on contemporary graphics hardware.     

A coherent query language for XML

Text search engines are inadequate for indexing and searching XML documents because they ignore metadata and aggregation structure implicit in the XML documents. On the other hand, the query languages supported by specialized XML search engines are very complex. In this paper, we present a simple yet flexible query language, and develop its semantics to enable intuitively appealing extraction of relevant fragments of information while simultaneously falling back on retrieval through plain text search if necessary. Our approach combines and generalizes several available techniques to obtain precise and coherent results.

Stylized strokes for coherent line drawings

Computational Visual Media - Temporal coherence is one of the central challenges for rendering a stylized line. It is especially difficult for stylized contours of coarse meshes or non-uniformly...     

Semi-supervised ranking for document retrieval

Ranking functions are an important component of information retrieval systems. Recently there has been a surge of research in the field of “learning to rank”, which aims at using labeled training data and machine learning algorithms to construct reliable ranking functions. Machine learning methods such as neural networks, support vector machines, and least squares have been successfully applied to ranking problems, and some are already being deployed in commercial search engines.Despite these successes, most algorithms to date construct ranking functions in a supervised learning setting, which assume that relevance labels are provided by human annotators prior to training the ranking function. Such methods may perform poorly when human relevance judgments are not available for a wide range of queries. In this paper, we examine whether additional unlabeled data, which is easy to obtain, can be used to improve supervised algorithms. In particular, we investigate the transductive setting, where the unlabeled data is equivalent to the test data.We propose a simple yet flexible transductive meta-algorithm: the key idea is to adapt the training procedure to each test list after observing the documents that need to be ranked. We investigate two instantiations of this general framework: The Feature Generation approach is based on discovering more salient features from the unlabeled test data and training a ranker on this test-dependent feature-set. The importance weighting approach is based on ideas in the domain adaptation literature, and works by re-weighting the training data to match the statistics of each test list. We demonstrate that both approaches improve over supervised algorithms on the TREC and OHSUMED tasks from the LETOR dataset.     

Matrix for retrieval and sorting

A conception of matrix which is a rectangular cellular array is provided. Application of the matrix for data sorting, retrieval and inverted file implementation are presented. Retrieval time does not depend on the size of the set being searched, and the sorting time is directly proportional to the number of ordered elements. Simulation of the matrix is reported and remarks on possibilities or relational data base implementation in the matrix are included.     

Citation-based retrieval for scholarly publications

Scholarly publications are available online and in digital libraries, but existing search engines are mostly ineffective for these publications. The proposed publication retrieval system is based on Kohonen's self-organizing map and offers fast retrieval speeds and high precision in terms of relevance.     

Content-based retrieval for trademark registration

With ever increasing number of registered trademarks, the task of trademark office is becoming increasingly difficult to ensure the uniqueness of all trademarks registered. Trademarks are complex patterns consisting of various image and text patterns, called device-mark and word-in-mark respectively. Due to the diversity and complexity of image patterns occurring in trademarks, due to multi-lingual word-in-mark, there is no very successful computerized operating trademark registration system. We have tackled key technical issues: multiple feature extraction methods to capture the shape, similarity of multi-lingual word-in-mark, matching device mark interpretation using fuzzy thesaurus, and fusion of multiple feature measures for conflict trademark retrieval. A prototype System for Trademark Archival and Registration (STAR) has been developed. The initial test run has been conducted using 3000 trademarks, and the results have shown satisfaction to trademark officers and specialists.     

Fuzzy approach for image retrieval

Current technology allows the acquisition, transmission, storing, and manipulation of large collections of images. Content-based information retrieval is now a widely investigated issue that aims at allowing users of multimedia information systems to retrieve images coherent with a sample image. A way to achieve this goal is the automatic computation of features such as color, texture, and shape and the use of these features as query terms. Feature extraction is a crucial part of any such system. Current methods for feature extraction suffer from two main problems: firstly, many methods do not retain any spatial information, and secondly, the problem of invariance with respect to standard transformation is still unsolved. In this paper, we describe some results of a study on similarity evaluation in image retrieval using shape, texture, and color as content features. Images are retrieved based on similarity of features, where features of the query specification are compared with features of the image database to determine which images match similarly with given features. In this paper, we propose an effective method for image representation which utilizes fuzzy features. The text was submitted by the author in English. Ryszard S. Choraś is Professor of Computer Science in the Department of Telecommunications and EE of University of Technology and Agriculture, Bydgoszcz, Poland. He also holds a courtesy appointment with the Faculty of Mathematics, Technology, and Natural Sciences of Kazimierz Wielki University, Bydgoszcz and the College of Computer Science, Lódz, Poland. His research interests include image signal compression and coding, computer vision, and multimedia data transmission. He received his M.S. degree in Electrical Engineering from Electronics from the Technical University of Wroclaw, Poland in 1973, and his Ph.D. degree in Electronics from Technical University of Wroclaw, Poland, in 1980, and D.Sc. (Habilitation degree) in Computer Science from Warsaw Technical University, Poland, in 1993. Until 1973–1976 he was a member of the research staff at the Institute of Mathematical Machines Silesian Division, Gliwice, working on graphics hardware and human visual perception. In 1976, he joined University of Technology and Agriculture, Bydgoszcz, Poland, first as an Assistant, then as a Professor of Computer Science at the Department of Telecommunications and EE. From 1994 to 1996, he was also Professor of Computer Sciences of the Zielona Góra University, Poland. He has served as the Chairman of the Communication Switching Division and as Chief of the Image Processing and Recognition Group. Until 1996–2002 he was the Vice Rector of University of Technology and Agriculture, Bydgoszcz. Prof. Choraś has an expertise in EU Programs and National Programs, e.g., he was coordinator of EU Program CME-02060, EU Program on Continuous Education and Technology Transfer, and coordinator of national programs in IST and multimedia in e-learning. Prof. Choraś has authored two monographs, and over 130 book chapters, journal articles, and conference papers in the area of image processing. Professor Choraś is a member of the editorial boards of “Machine Vision and Graphics.” He is the editor-in-chief of “Image Processing and Communications Journal.” He has served on numerous conference committees, e.g., Visualization, Imaging, and Image Processing (VIIP), IASTED International Conference on Signal Processing, Pattern Recognition and Applications, International Conference on Computer Vision and Graphics, ICINCO International Conference on Informatics in Control, Automation and Robotics, ICETE International Conference on E-business and Telecommunication Networks, and CORES International Conference on Computer Recognition Systems, and many others. Prof Choraś is a member of the IASTED, WSEAS, various Committees of the Polish Academy of Sciences, TPO. When not working on academic ventures, Professor Choraś likes to relax with activities such as walking, tennis, and swimming.     

Best-match retrieval for structured images

Propose a methodology for fast best-match retrieval of structured images. A triangle inequality property for the tree-distance introduced by Oflazer (1997) is proven. This property is, in turn, applied to obtain a saturation algorithm of the trie used to store the database of the collection of pictures. The new approach can be considered as a substantial optimization of Oflazer's technique and can be applied to the retrieval of homogeneous hierarchically structured objects of any kind. The new technique inscribes itself in the number of distance-based search strategies and it is of interest for the indexing and maintenance of large collections of historical and pictorial data. We demonstrate the proposed approach on an example and report data about the speed-up that it introduces in query processing. Direct comparison with an MVP-trees algorithm is also presented     

Efficient storage and retrieval of probabilistic latent semantic information for information retrieval

Probabilistic latent semantic analysis (PLSA) is a method for computing term and document relationships from a document set. The probabilistic latent semantic index (PLSI) has been used to store PLSA information, but unfortunately the PLSI uses excessive storage space relative to a simple term frequency index, which causes lengthy query times. To overcome the storage and speed problems of PLSI, we introduce the probabilistic latent semantic thesaurus (PLST); an efficient and effective method of storing the PLSA information. We show that through methods such as document thresholding and term pruning, we are able to maintain the high precision results found using PLSA while using a very small percent (0.15%) of the storage space of PLSI.