Performance of cellular packet CDMA in an integrated voice/data network

This paper presents a simulation-based study of cellular packet CDMA systems operating in an integrated voice/data traffic scenario. Spread-spectrum CDMA provides a suitable framework for resource-shared packet transport capable of combining isochronous (voice, ISDN) and bursty data services. In this work, a general network model for cellular packet CDMA with mixed voice/data traffic is described and used to evaluate the capacity/performance impact of several key system parameters. First, the effect of spreading factor (N) and forward error correction (FEC) rate are studied, confirming earlier work indicating a weak dependence onN and a well-defined optimum code rate in the range of 0.5–0.7 (with BCH coding). Next, the effect of propagation loss coefficient () on network capacity is investigated over a range of possible assumptions for, including both constant and distance-dependent models. The results show that system capacity depends strongly on, varying by as much as a factor of 2 over the range of parameters considered. For a given distance-dependent assumption, performance results are also obtained for different cell sizes in order to understand the overall spatial reuse efficiency achievable in different cellular and microcellular scenarios. This is followed by an investigation of traffic source model effects: first the capacity improvement from voice activity detection VAD) is presented, showing the expected 21 gains. Results for varying proportions of voice and data traffic intensities indicate that the operating efficiency does not change significantly as the proportion of bursty data relative to voice is varied.     

基于新媒体技术的运营管理模式

针对微博、微信、微视频等新媒体及移动互联网的快速发展给运营商带来的巨大冲击,分析了运营商目前在新媒体领域遇到的困境及应对新媒体挑战的思路,提出了新媒体分析技术的方法和步骤,举例阐述了新媒体技术在营销支撑分析中的应用,从运营管理的角度介绍了运营管理和投诉管理的功能实现,并给出了基于新媒体运营管理功能的系统建设方案及系统实施效果.     

基于地理位置的社交网络信息数据类别分析

移动社交网络的应用已经成为是近几年来应用的热点。现有的基于地理位置的社交网络信息数据有自身的特点,分别论述了基于地理位置的社交网络的信息来源,几种基于地理位置的社交网络所形成的数据集以及其数据类型。     

面向对象数据库在压裂措施管理系统中的应用

简要分析了面向对象数据库技术产生的必然性,着重介绍一种面向对象数据库技术——对象关系型数据库,包括它的内容和特点;以及基于实际研发项目全面阐述了压裂措施管理系统中面向对象数据库的统一建模,并对其实现数据共享提供基础性的统一数据结构的支持。     

Adversarial Data Hiding in Digital Images

In recent studies of generative adversarial networks (GAN), researchers have attempted to combine adversarial perturbation with data hiding in order to protect the privacy and authenticity of the host image simultaneously. However, most of the studied approaches can only achieve adversarial perturbation through a visible watermark; the quality of the host image is low, and the concealment of data hiding cannot be achieved. In this work, we propose a true data hiding method with adversarial effect for generating high-quality covers. Based on GAN, the data hiding area is selected precisely by limiting the modification strength in order to preserve the fidelity of the image. We devise a genetic algorithm that can explore decision boundaries in an artificially constrained search space to improve the attack effect as well as construct aggressive covert adversarial samples by detecting “sensitive pixels” in ordinary samples to place discontinuous perturbations. The results reveal that the stego-image has good visual quality and attack effect. To the best of our knowledge, this is the first attempt to use covert data hiding to generate adversarial samples based on GAN.     

EGFAFS: A Novel Feature Selection Algorithm Based on Explosion Gravitation Field Algorithm

Feature selection (FS) is a vital step in data mining and machine learning, especially for analyzing the data in high-dimensional feature space. Gene expression data usually consist of a few samples characterized by high-dimensional feature space. As a result, they are not suitable to be processed by simple methods, such as the filter-based method. In this study, we propose a novel feature selection algorithm based on the Explosion Gravitation Field Algorithm, called EGFAFS. To reduce the dimensions of the feature space to acceptable dimensions, we constructed a recommended feature pool by a series of Random Forests based on the Gini index. Furthermore, by paying more attention to the features in the recommended feature pool, we can find the best subset more efficiently. To verify the performance of EGFAFS for FS, we tested EGFAFS on eight gene expression datasets compared with four heuristic-based FS methods (GA, PSO, SA, and DE) and four other FS methods (Boruta, HSICLasso, DNN-FS, and EGSG). The results show that EGFAFS has better performance for FS on gene expression data in terms of evaluation metrics, having more than the other eight FS algorithms. The genes selected by EGFAGS play an essential role in the differential co-expression network and some biological functions further demonstrate the success of EGFAFS for solving FS problems on gene expression data.     

Deep Image Steganography Using Transformer and Recursive Permutation

Image steganography, which usually hides a small image (hidden image or secret image) in a large image (carrier) so that the crackers cannot feel the existence of the hidden image in the carrier, has become a hot topic in the community of image security. Recent deep-learning techniques have promoted image steganography to a new stage. To improve the performance of steganography, this paper proposes a novel scheme that uses the Transformer for feature extraction in steganography. In addition, an image encryption algorithm using recursive permutation is proposed to further enhance the security of secret images. We conduct extensive experiments to demonstrate the effectiveness of the proposed scheme. We reveal that the Transformer is superior to the compared state-of-the-art deep-learning models in feature extraction for steganography. In addition, the proposed image encryption algorithm has good attributes for image security, which further enhances the performance of the proposed scheme of steganography.     

关联规则增量式更新的改进算法

关联规则是数据挖掘中的重要研究内容之一，对在事务数据库不变，只对最小支持度和最小可信度进行改变的情况下，针对关联规则的维护问题，设计了一个增量式更新的改进算法AIUA。     

基于NPP-VIIRS夜间灯光数据的山西省GDP空间化模拟

选取山西省为研究对象,以美国国家极轨合作仪件-可见红外成像辐射计套件（NPP-VIIRS）夜间灯光数据、GDP统计数据等为数据源,构建GDP空间化拟合模型,建立山西省GDP密度图,据此研究山西省经济的空间差异性。通过对NPP-VIIRS夜间灯光数据的空间化处理,提取灯光指数,并将其与GDP进行回归拟合,建立最佳回归模型,得到GDP密度拟合图;利用县级GDP数据进行线性纠正,从而提高GDP的模拟精度。结果表明：（1）NPP-VIIRS夜间灯光数据与GDP的相关性较高,可用于山西省GDP模拟;（2）与GDP分区建模相比,GDP整体建模的精度更高;（3）山西省GDP的空间分布整体呈由城市中心逐渐向周边辐射的特点,构成GDP过渡带。     

关于电信企业决策支持系统的建设意见

阐述了当前电信企业各项计算机管理系统的现状 ,指出存在的不足 ,提出开发建设新的电信决策支持系统的意义 ;介绍电信决策支持系统的体系结构和所涉及的新技术、新概念 ,并指出了电信决策支持系统建设的难点 ,较详细地介绍了电信决策支持系统所实现的主要功能。