Control of downrange and crossrange motion of a descent module in a certain range of reentry angles with load factor constraints

Results concerning the terminal guidance algorithm at the reentry of the descent module into the Earth atmosphere are presented. This algorithm simultaneously eliminates predicted deviations in the downrange and crossrange directions. The restriction on the tolerable load factor n ?? 3 is taken into account. Three reentry angles are analyzed: the medium angle ?1.5°, the steep angle ?1.9°, and the flat angle ?1.2°. The motion of the descent module is considered in the disturbed Earth atmosphere represented by the TsNIIMash model. This model includes variations of the atmosphere density and the wind field. It is demonstrated that, depending on the reentry angle, the proposed algorithm guarantees the downrange maneuver zone in the range 1240?C1890 km and the crossrange maneuver zone of 210?C220 km on one side. The maximum guidance error at the altitude of 1 km does not exceed 0.8 km, and the average error is 0.1 km. The algorithm uses up to three bank angle reversals and uses the numerical prediction of the remaining part of the trajectory when choosing the guidance parameters. The concept of the automatic adjustment of the bank angle reference function depending on the entry angle and the prescribed distance to the target point in the maneuver zone is formulated.     

Interactive image segmentation based on level sets of probabilities

In this paper, we present a robust and accurate algorithm for interactive image segmentation. The level set method is clearly advantageous for image objects with a complex topology and fragmented appearance. Our method integrates discriminative classification models and distance transforms with the level set method to avoid local minima and better snap to true object boundaries. The level set function approximates a transformed version of pixelwise posterior probabilities of being part of a target object. The evolution of its zero level set is driven by three force terms, region force, edge field force, and curvature force. These forces are based on a probabilistic classifier and an unsigned distance transform of salient edges. We further propose a technique that improves the performance of both the probabilistic classifier and the level set method over multiple passes. It makes the final object segmentation less sensitive to user interactions. Experiments and comparisons demonstrate the effectiveness of our method.     

OpenGL ES 1.1 implementation based on OpenGL

In this paper, we present an efficient way of implementing OpenGL ES 1.1 3D graphics API library for the environments with hardware-supported OpenGL facility, typically as desktop PCs. Although OpenGL ES was started from the existing OpenGL features, it rapidly became the standard 3D graphics library customized for embedded systems through introducing fixed-point arithmetic operations, buffer management with fixed-point data type supports, completely new texture mapping functions and others. Currently, it is the official 3D graphics API for Google Android, Apple iPhone, Sony PlayStation3, etc. In this paper, we achieved improvements on the arithmetic operations for the fixed-point number representation, which is the most characteristic data type for OpenGL ES 1.1. For the conversion of fixed-point data types to the floating-point number representations for the underlying OpenGL, we show the way of efficient conversion processes even with satisfying OpenGL ES standard requirements. We also introduced a specialized memory management scheme to manage the converted data from the buffer containing fixed-point numbers. In the case of texture processing, the requirements in both standards are quite different, and thus we used completely new software-implementations. Our final implementation of OpenGL ES library provides all of more than 200 functions in the standard specification and passed its conformance test, to show its compliance with the standard. From the efficiency point of view, we measured its execution times for several OpenGL ES-specific application programs and achieved remarkable improvements.     

More than modelling and hiding: towards a comprehensive view of Web mining and privacy

Over the last decade, privacy has been widely recognised as one of the major problems of data collections in general and the Web in particular. This concerns specifically data arising from Web usage (such as querying or transacting) and social networking (characterised by rich self-profiling including relational information) and the inferences drawn from them. The data mining community has been very conscious of these issues and has addressed in particular the inference problems through various methods for “privacy-preserving data mining” and “privacy-preserving data publishing”. However, it appears that these approaches by themselves cannot effectively solve the privacy problems posed by mining. We argue that this is due to the underlying notions of privacy and of data mining, both of which are too narrow. Drawing on notions of privacy not only as hiding, but as control and negotiation, as well as on data mining not only as modelling, but as the whole cycle of knowledge discovery, we offer an alternative view. This is intended to be a comprehensive view of the privacy challenges as well as solution approaches along all phases of the knowledge discovery cycle. The paper thus combines a survey with an outline of an agenda for a comprehensive, interdisciplinary view of Web mining and privacy.     

Efficient compression and network adaptive video coding for distributed video surveillance

The availability of cheap network based video cameras and the prevalence of wireless networks has lead to a major thrust towards deployment of large scale Distributed Video Surveillance (DVS) systems. This has opened up an important area of research to deal with the issues involved in DVS system for efficient collection and transmission of large scale video streams from the cameras at the guarded sites, to the end users in possibly constrained network conditions. In this paper, we propose a framework based on content-based video classification and scalable compression scheme to provide a robust bandwidth efficient video transmission for DVS. The scheme builds on a Discrete Wavelet Transform (DWT) based Color-Set Partitioning for Hierarchical Trees (CSPIHT) coding to obtain a scalable bitstream. Wavelet domain segmentation and compression assists in development of a DVS architecture. The architecture includes a novel module for dynamic allocation of Network bandwidth based on the current available resources and constraints. Different frame constituents are optimally coded based on their relative significance, perceptual quality, and available estimate of network bandwidth. Experimental result over different video sequences and simulations for Network conditions demonstrate the efficient performance of the approach.     

Performance of TZCD-MBOK watermarking scheme in T-DMB systems

Recently, watermarking based data transmission techniques using terrestrial digital TV signal have been proposed since they are also cost-free and can overcome the limitations of GPS. However, in the previous watermarking based methods, the detection accuracy is low and additional data rate is too low. Thus, we propose the throughput enhancement method by employing the TZCD-MBOK watermarking technique in T-DMB system. By applying the proposed scheme to T-DMB, it allows additional data transmission for disaster broadcasting and improves efficiency of data transmission in shadow region and indoor to mobile environment through watermarking spread code. From the simulation results, we confirm the proposed watermarking scheme affected on the existing T-DMB signal. Moreover, it was also confirmed that the system capacity increases as the power of additional watermarking signal rose. The results of the paper can be applied to wireless multimedia digital broadcasting systems.     

Intelligent copyright protection system using a matching video retrieval algorithm

Many video service sites headed by YouTube know what content requires copyright protection. However, they lack a copyright protection system that automatically distinguishes whether uploaded videos contain legal or illegal content. Existing protection techniques use content-based retrieval methods that compare the features of video. However, if the video encoding has changed in resolution, bit-rate or codec, these techniques do not perform well. Thus, this paper proposes a novel video matching algorithm even if the type of encoding has changed. We also suggest an intelligent copyright protection system using the proposed algorithm. This can serve to automatically prevent the uploading of illegal content. The proposed method has represented the accuracy of 97% with searching algorithm in video-matching experiments and 98.62% with automation algorithm in copyright-protection experiments. Therefore, this system could form a core technology that identifies illegal content and automatically excludes access to illegal content by many video service sites.