A Simultaneous Extraction of Context and Community from pervasive signals using nested Dirichlet process

Understanding user contexts and group structures plays a central role in pervasive computing. These contexts and community structures are complex to mine from data collected in the wild due to the unprecedented growth of data, noise, uncertainties and complexities. Typical existing approaches would first extract the latent patterns to explain human dynamics or behaviors and then use them as a way to consistently formulate numerical representations for community detection, often via a clustering method. While being able to capture high-order and complex representations, these two steps are performed separately. More importantly, they face a fundamental difficulty in determining the correct number of latent patterns and communities. This paper presents an approach that seamlessly addresses these challenges to simultaneously discover latent patterns and communities in a unified Bayesian nonparametric framework. Our Simultaneous Extraction of Context and Community (SECC) model roots in the nested Dirichlet process theory which allows a nested structure to be built to summarize data at multiple levels. We demonstrate our framework on five datasets where the advantages of the proposed approach are validated.     

在1899.703nm波长下木材含水率光谱检测方法研究

为实现木材含水率实时、快速、连续测量,研究了基于近红外光谱技术实时测量木材含水率.实验中采用中心波长位于1899.703 nm的工业蝶形封装的分布式反馈激光器(DFL)作为光源,选择以杨木含水率作为被测参数,搭建了近红外水分检测仪的软硬件系统.因为木材是强散射介质,因此,采用修正后的朗伯-比尔定律.对杨木含水率进行实验测量并数据拟合,木材含水率真值与激光测量木材含水率输出功率值在一定范围内可以拟合为线性关系,相关系数在0.90以上,木材含水率测量值与真值变化趋势一致,二者绝对误差小于0.15％.实验结果表明:利用近红外技术可实现木材含水率检测.     

利用频谱特性鉴别运动模糊方向

点扩展函数(PSF)的精确估计是运动模糊图像恢复的关键。匀速直线运动模糊的PSF参数主要由模糊角度（方向）和模糊尺度两个参数组成,然而模糊角度的估计又是重中之重。针对R Lokhande等人提出的霍夫变换的运动模糊方向估计法进行改进,通过增加边缘检测等预处理步骤,利用霍夫变换检测直线,并利用改进的霍夫变换峰值提取方法来估计角度值。实验表明,该方法能够得到比原方法更精确的角度值,并且具有抗噪能力强、鉴别精度高的优点。     

Detection of femoral artery occlusion from spectral density of Doppler signals using the artificial neural network

This research is concentrated on the diagnosis of occlusion disease through the analysis of femoral artery Doppler signals with the help of Artificial Neural Network (ANN). Doppler femoral artery signals belong to occlusion patient and healthy subjects were recorded. Afterwards, power spectral densities (PSD) of these signals were obtained using Welch method and Autoregressive (AR) modeling. Multilayer feed forward ANN trained with a Levenberg Marquart (LM) backpropagation algorithm was implemented to these PSD. The designed classification structure has about 98% sensitivity, 97–100% specifity and correct classification is calculated to be 98–99% (for AR modeling and Welch method respectively). The end results are classified as healthy and diseased. Testing results were found to be compliant with the expected results that are derived from the physician's direct diagnosis. The end benefit would be to assist the physician to make the final decision without hesitation.     

浅谈网络信息技术在企业中的应用

本文分析了在现代企业中使用现代化的信息技术和网络技术的必要性,通过对企业本身实际需求的分析,从企业的组织结构、营销管理和人力资源管理的角度分析了网络信息技术在企业管理中的应用情况。     

Detection and diagnosis of plant-wide oscillations from industrial data using the spectral envelope method

Plant-wide oscillations are common in many processes. Their effects propagate to many units and may impact the overall process performance. It is important to detect and diagnose the cause of such oscillations in order to rectify the situation. This paper proposes a new procedure to detect and diagnose plant-wide oscillations using routine operating data. A technique called spectral envelope is used to detect oscillations. The variables that have common oscillations are identified and categorized accurately by a statistical hypothesis test. A new index called the oscillation contribution index (OCI) is proposed to isolate the key variables as the potential root cause candidates of the common oscillation(s). Two industrial case studies are presented to demonstrate the utility and practicality of the proposed procedure.     

A novel method of blind signal detection using the distribution of the bin values of the power spectrum density and the moving average

Signal detection in additive white Gaussian noise (AWGN) is one of the long-term developments driving the evolution of many different fields of science and technology, with important applications in telecommunications, medicine and astronomy. In this paper, we propose a novel method of blind signal detection that does not require knowledge of the noise variance. This method uses the distribution of the bin values of the power spectrum density of the received signal and the moving average (MAV). The simulation results for radio pulses show that the spectrum sensing performance is significantly improved under the proposed scheme compared to that of known blind signal detection methods.     

第二小方向导数信息熵的兴趣点检测

目的 为了提高兴趣点检测的定位准确性和对噪声的鲁棒性,提出利用图像轮廓线及其邻域内像素点方向导数信息熵检测兴趣点的方法.方法 首先利用多方向Gabor虚部滤波器提取图像灰度变化信息得到第二小方向导数.然后利用Canny边缘检测器提取边缘映射,并填补断裂边缘映射提取边缘轮廓线.最后求解图像边缘轮廓线及其邻域内像素点对应的第二小方向导数所对应的信息熵归一化值并作为新的兴趣点测度.和直接由灰度变化信息及分析边缘轮廓形状或曲率提取兴趣点的方法相比,本文算法结合了两种算法的思想,利用轮廓线上及其邻域内的像素点梯度方向信息熵值作为兴趣点测度.同时不同于同质及边缘区域的梯度方向变化,兴趣点处的梯度方向变化信息呈现各向异性的特性,利用兴趣点第二小方向导数(第一小方向导数可能为零)对应的信息熵值作为新的兴趣点测度可提高算法的定位准确性.结果 通过对检测图像进行仿射变换和加入高斯噪声处理后,分别利用Harris算子、CSS算子、He&Yung算子和本文算法提取图像兴趣点,并比较各算法在仿射变换和高斯噪声情况下检测到的兴趣点的平均重复率和定位误差两个性能指标的平均值.其中本文算法的性能指标平均值为1.625,远高于Harris(3.25)、He&Yung(2.625)和CSS(2.5)三大兴趣点检测算子.结论 通过与典型的3种算法相对比,本文算法具有较好的平均重复率及噪声鲁棒性,尤其是图像在外界干扰的旋转变换和尺度变换下对兴趣点的定位性有着更好的检测性能.     

Reexamining the impact of information technology investment on productivity using regression tree and multivariate adaptive regression splines (MARS)

As health care costs increased significantly in the 1990s, investments in information technology (IT) in the health care industry have also increased continuously in order to improve the quality of patient care and to respond to government pressure to reduce costs. Several studies have investigated the impact of IT on productivity with mixed conclusions. In this paper, we revisit this issue and re-examine the impact of investments in IT on hospital productivity using two data mining techniques, which allowed us to explore interactions between the input variables as well as conditional impacts. The results of our study indicated that the relationship between IT investment and productivity is very complex. We found that the impact of IT investment is not uniform and the rate of IT impact varies contingent on the amounts invested in the IT Stock, Non-IT Labor, Non-IT Capital, and possibly time.