ADSL传输技术概述

介绍ADSL传输技术使用情况及与ISDN等的比较。     

计算机维修模拟训练系统的设计与实现

探索了一种在多媒体环境下进行计算机及外部设备维修教学、训练的新方法 ,介绍了计算机维修模拟训练系统的设计与实现 ,给出了主要的设计思想、总体结构及关键技术。     

中药知母有效组分——知母皂甙元“滋阴”作用的研究

本工作以~(123)I-心得静结合分析法及环核苷酸RIA法观察了知母中提取得到的知母皂甙元对甲亢模型(模拟“阴虚”)βAR-cAMP系统的作和,结果表现,知母皂甙元明显降低甲亢小鼠脑β受体的RT值,显著降低甲亢小鼠β受体-cAMP系统的反应性,重现了知母水煎剂的下调作用。体外竞争抑制实验表明,知母皂甙元和知母皂甙元半琥珀酸都不能抑制~(125)I-心得静与β受体的结合。     

A new sorbent that simultaneously sequesters multiple classes ofpollutants from water: Surfactant modified zeolite

A zeolite (ZFA) was synthesized from coal fly ash and then modified using hexadecyltrimethylammonium. The surfactant modified ZFA (SMZFA) was evaluated for its versatility to sequester multiple classes of pollutants from water. The target pollutants include ionic inorganic pollutants (ammonium and phosphate), ionic organic pollutants (methylene blue and humic acid), ionizable organic pollutants (bisphenol A,-chlorophenol and phenol, with different pKa), and electrically neutral or-ganic pollutants (aniline, nitrobenzene, and naphthalene, with different hydrophobicity). The SMZFA showed high potential for the retention of ammonium and phosphate, being comparable with ZFA. While the negative charge in the internal pores of zeolite was responsible for the retention of ammonium, the oxides of CaO, Al2O3and Fe2O3in the non-zeolite fraction, which originated from coal fly ash but received modification during zeolite synthesis, accounted for the removal of phosphate. Results also showed that while ZFA had little affinity for humic acid, the ionizable, and the electrically neutral organic compounds, SMZFA exhibited greatly enhanced adsorption capacity. Thus, the surfactant modified external surface, which formed a bilayer micelle of zeolite, imparts a hydrophobic characteristic that enables the uptake of the organic pollutants. We showed that SMZFA is a promising versatile sorbent for water treatment.     

Acoustic emission from gas-filled coal under triaxial compression

Acoustic emission (AE) experiments have been performed on gas-saturated coal specimens under conventional triaxial compression. The AE characteristics were investigated for a methane gas flow through the coal specimen. One AE parameter, AE count, when normalized by the total count number was used to represent the damage evolution in the gassy coal. It is shown that this AE parameter is a reasonable indicator for damage occurring within the coal specimen since its envelope has almost the same shape as the complete stress–strain curve, except for a short time delay. In addition, the change in AE count is highly consistent with the change in coal permeability. Test results also show that methane containing coal emits a small number of AE events before entering the yield stage. AE activity gradually increases during the yield process up to the peak stress. The lowest permeability corresponds to the highest AE activity, implying failure will soon occur. An AE based constitutive model was constructed and the theoretical results agree well with those of experiments.     

Robust and efficient ridge-based palmprint matching

During the past decade, many efforts have been made to use palmprints as a biometric modality. However, most of the existing palmprint recognition systems are based on encoding and matching creases, which are not as reliable as ridges. This affects the use of palmprints in large-scale person identification applications where the biometric modality needs to be distinctive as well as insensitive to changes in age and skin conditions. Recently, several ridge-based palmprint matching algorithms have been proposed to fill the gap. Major contributions of these systems include reliable orientation field estimation in the presence of creases and the use of multiple features in matching, while the matching algorithms adopted in these systems simply follow the matching algorithms for fingerprints. However, palmprints differ from fingerprints in several aspects: 1) Palmprints are much larger and thus contain a large number of minutiae, 2) palms are more deformable than fingertips, and 3) the quality and discrimination power of different regions in palmprints vary significantly. As a result, these matchers are unable to appropriately handle the distortion and noise, despite heavy computational cost. Motivated by the matching strategies of human palmprint experts, we developed a novel palmprint recognition system. The main contributions are as follows: 1) Statistics of major features in palmprints are quantitatively studied, 2) a segment-based matching and fusion algorithm is proposed to deal with the skin distortion and the varying discrimination power of different palmprint regions, and 3) to reduce the computational complexity, an orientation field-based registration algorithm is designed for registering the palmprints into the same coordinate system before matching and a cascade filter is built to reject the nonmated gallery palmprints in early stage. The proposed matcher is tested by matching 840 query palmprints against a gallery set of 13,736 palmprints. Experimental results show that the proposed matcher outperforms the existing matchers a lot both in matching accuracy and speed.     

Tracking mobile users in wireless networks via semi-supervised colocalization

Recent years have witnessed the growing popularity of sensor and sensor-network technologies, supporting important practical applications. One of the fundamental issues is how to accurately locate a user with few labeled data in a wireless sensor network, where a major difficulty arises from the need to label large quantities of user location data, which in turn requires knowledge about the locations of signal transmitters or access points. To solve this problem, we have developed a novel machine learning-based approach that combines collaborative filtering with graph-based semi-supervised learning to learn both mobile users' locations and the locations of access points. Our framework exploits both labeled and unlabeled data from mobile devices and access points. In our two-phase solution, we first build a manifold-based model from a batch of labeled and unlabeled data in an offline training phase and then use a weighted k-nearest-neighbor method to localize a mobile client in an online localization phase. We extend the two-phase colocalization to an online and incremental model that can deal with labeled and unlabeled data that come sequentially and adapt to environmental changes. Finally, we embed an action model to the framework such that additional kinds of sensor signals can be utilized to further boost the performance of mobile tracking. Compared to other state-of-the-art systems, our framework has been shown to be more accurate while requiring less calibration effort in our experiments performed on three different testbeds.     

Identification of Breast Cancer Prognosis Markers via Integrative Analysis

In breast cancer research, it is of great interest to identify genomic markers associated with prognosis. Multiple gene profiling studies have been conducted for such a purpose. Genomic markers identified from the analysis of single datasets often do not have satisfactory reproducibility. Among the multiple possible reasons, the most important one is the small sample sizes of individual studies. A cost-effective solution is to pool data from multiple comparable studies and conduct integrative analysis. In this study, we collect four breast cancer prognosis studies with gene expression measurements. We describe the relationship between prognosis and gene expressions using the accelerated failure time (AFT) models. We adopt a 2-norm group bridge penalization approach for marker identification. This integrative analysis approach can effectively identify markers with consistent effects across multiple datasets and naturally accommodate the heterogeneity among studies. Statistical and simulation studies demonstrate satisfactory performance of this approach. Breast cancer prognosis markers identified using this approach have sound biological implications and satisfactory prediction performance.     

Design of a modified repetitive-control system based on a continuous–discrete 2D model

This paper concerns the problem of designing a modified repetitive-control system for a class of strictly proper plants. Repetitive control involves two types of actions: control and learning; but the insertion of a low-pass filter in a modified repetitive controller, which is employed to guarantee the stability of the system, mixes the two actions together. In this paper, a continuous–discrete two-dimensional model is first constructed. Next, the continuity of repetitive control and Lyapunov stability theory are applied to the model to establish two linear-matrix-inequality (LMI) based sufficient stability conditions, one for the design of the cutoff angular frequency and one for the design of the feedback gains. The features of these conditions are exploited to develop an iterative algorithm that searches for the best combination of the maximum cutoff angular frequency of the low-pass filter and the feedback gains. A numerical example illustrates the design procedure and demonstrates the validity of the method.     

Preface

Recently, there has been a dramatic increase in research on data mining on social networks and social media. Theubiquitous nature of Web-enabled devices, including desktops,