Model Selection for Unsupervised Learning of Visual Context

This study addresses the problem of choosing the most suitable probabilistic model selection criterion for unsupervised learning of visual context of a dynamic scene using mixture models. A rectified Bayesian Information Criterion (BICr) and a Completed Likelihood Akaike’s Information Criterion (CL-AIC) are formulated to estimate the optimal model order (complexity) for a given visual scene. Both criteria are designed to overcome poor model selection by existing popular criteria when the data sample size varies from small to large and the true mixture distribution kernel functions differ from the assumed ones. Extensive experiments on learning visual context for dynamic scene modelling are carried out to demonstrate the effectiveness of BICr and CL-AIC, compared to that of existing popular model selection criteria including BIC, AIC and Integrated Completed Likelihood (ICL). Our study suggests that for learning visual context using a mixture model, BICr is the most appropriate criterion given sparse data, while CL-AIC should be chosen given moderate or large data sample sizes.     

Does decision quality (always) increase with the size of information samples? Some vicissitudes in applying the law of large numbers.

Adaptive decision making requires that contingencies between decision options and their relative assets be assessed accurately and quickly. The present research addresses the challenging notion that contingencies may be more visible from small than from large samples of observations. An algorithmic account for such a seemingly paradoxical effect is offered within a satisficing-choice framework. Accordingly, a choice is only made when the sample contingency describing the relative evaluation of the 2 options exceeds a critical threshold. Small samples, because of the high dispersion of their sampling distribution, facilitate above-threshold contingencies. Across a broad range of parameters, the resulting small-sample advantage in terms of hits is stronger than their disadvantage in false alarms. Computer simulations and experiments support the model predictions. The relative advantage of small samples is most apparent when information loss is low, when the threshold is high relative to the ecological contingency, and when the sampling process is self-truncated. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

扬声器系统时域结构系数的仿真

从扬声器系统的等效类比线路出发,得出了阻抗特性Z(jω)的表达形式及其曲线。采用全面最小二乘法识别扬声器系统的时域函数的全套系数,从而测出扬声器系统的低频特性参数。以计算机仿真技术为基础,通过MATLAB和EWB软件进行系统模拟分析仿真,得到扬声器系统的阶跃响应、阻抗曲线等特性。     

几种涉密网主机系统保护及审计技术的比较

论文比较详细地阐述了涉密网主机系统所面临的安全隐患,提出了涉密网主机安全防护所要解决的几个技术问题,并就可能实现的技术方法进行了探讨和比较。     

SIMPLIS环境中开关电源集成电路控制器模型的开发及应用

介绍现代开关电源行业仿真工作的意义和现状,以及专用仿真软件包SIM etrix/SIMPLIS的优点和不足。通过实例给出SIMPLIS环境中集成电路控制芯片的建模过程,并就其中的关键技术予以简单讨论。     

浅谈计算软件的开发

软件开发重要的是 :确定编程语言 ,代码设计 ,程序设计 ,软件试验 ,人员培训等步骤     

变换域数字水印处理技术

数字水印是信息安全领域的一项新技术,给出了相对明确的定义。着重分析了变换域数字水印系统的特征及常见攻击,给出了一些相应的对策。最后指出变换域水印的重要问题,即频率的选择问题。     

Discrete Wavelet Transform: Architectures, Design and Performance Issues

Due to the demand for real time wavelet processors in applications such as video compression [1], Internet communications compression [2], object recognition [3], and numerical analysis, many architectures for the Discrete Wavelet Transform (DWT) systems have been proposed. This paper surveys the different approaches to designing DWT architectures. The types of architectures depend on whether the application is 1-D, 2-D, or 3-D, as well as the style of architecture: systolic, semi-systolic, folded, digit-serial, etc. This paper presents an overview and evaluation of the architectures based on the criteria of latency, control, area, memory, and number of multipliers and adders. This paper will give the reader an indication of the advantages and disadvantages of each design.     

新一代深度张力面板

新一代深度张力面板不仅具有基本的深度、张力采集、处理、显示功能,同时还具有多种报警及通讯功能.其最大的特点是采用了液晶显示屏和触摸屏,使得信息的显示更加丰富、直观和醒目.同时,触摸屏的采用,省去了传统的按钮、开关及键盘操作模式,使操作更加方便简洁、自然.该面板可以单独工作,也可以配备于测井、录井地面系统中使用.