大数据技术在住房信息系统中的应用

阐述在建设住房信息系统过程中,如何利用成熟技术进行大数据库建设,保证住房监测分析、住房公积金监管、住房保障监管等系统运行。详细阐述创新性整合关系型数据库技术和大数据技术,构建一整套适应地域分布范围广、数据环境复杂、对生产系统影响小的住房信息系统的技术解决方案。     

测量方差自适应的多传感器数据融合算法

针对测量方差预先设定对滤波精度的影响,提出了一种基于时空综合分析在线估算测量方差的方法,以充分利用多个传感器单次采样数据和单个传感器多次采样数据中蕴含的冗余和互补信息。改善了由于被测目标所处环境的先验信息匮乏而造成的建模误差问题。同时根据滤波误差协方差阵具有表示滤波精度的特性,实现系统中各传感器权系数动态合理的分配。最终给出了一种基于测量方差自适应的多传感器数据融合算法。通过蒙特卡罗仿真验证了该算法的有效性。     

Novel Digital Nonvolatile Memory Devices Based on Semiconducting Polymer Thin Films

Recent increases in the demand for mobile devices have stimulated the development of nonvolatile memory devices with high performance. In this Communication, we describe the fabrication of low‐cost, high‐performance, digital nonvolatile memory devices based on semiconducting polymers, poly(o‐anthranilic acid) and poly(o‐anthranilic acid‐co‐aniline). These memory devices have ground‐breaking and novel current–voltage switching characteristics. The devices are switchable in a very low voltage range (which is much less than those of all other devices reported so far) with a very high ON/OFF current ratio (which is on the order of 10⁵). The low critical voltages have the advantage for nonvolatile memory device applications of low operation voltages and hence low power consumption. With this very low power consumption, the devices demonstrate in air ambient to have very stable ON‐ and OFF‐states without any degradation for a very long time (which has been confirmed up to one year so far) and to be repeatedly written, read and erased. Our study proposes that the ON/OFF switching of the devices is mainly governed by a filament mechanism. The high ON/OFF switching ratio and stability of these devices, as well as their repeatable writing, reading and erasing capability with low power consumption, opens up the possibility of the mass production of high performance digital nonvolatile polymer memory devices with low cost. Further, these devices promise to revolutionize microelectronics by providing extremely inexpensive, lightweight, and versatile components that can be printed onto plastics, glasses or metal foils.     

An analytical drain current model for graded channel cylindrical/surrounding gate MOSFET

In the present paper, a comprehensive drain current model incorporating various effects such as drain-induced barrier lowering (DIBL), channel length modulation and impact ionization has been developed for graded channel cylindrical/surrounding gate MOSFET (GC CGT/SGT) and the expressions for transconductance and drain conductance have been obtained. It is shown that GC design leads to drain current enhancement, reduced output conductance and improved breakdown voltage. The effectiveness of GC design was examined by comparing uniformly doped (UD) devices with GC devices of various L₁/L₂ ratios and doping concentrations and it was found that GC devices offer superior characteristics as compared to the UD devices. The results so obtained have been compared with those obtained from 3D device simulator ATLAS and are found to be in good agreement.     

一种新的红外图像自适应增强算法

针对红外热图像普遍存在的目标与背景对比度差、边缘模糊等特点,提出了一种新的红外图像自适应增强算法.通过自适应地选择平台阈值,对红外图像进行平台直方图均衡化,有效抑制了背景的过度增强,避免目标灰度级的过多合并,提升了目标的灰度空间,获得了很好的增强效果.实验结果表明,本算法优于直方图均衡化算法,可有效地突出红外目标.     

Joint image-to-image translation with denoising using enhanced generative adversarial networks

Impressive progress has been made recently in image-to-image translation using generative adversarial networks (GANs). However, existing methods often fail in translating source images with noise to target domain. To address this problem, we joint image-to-image translation with image denoising and propose an enhanced generative adversarial network (EGAN). In particular, built upon pix2pix, we introduce residual blocks in the generator network to capture deeper multi-level information between source and target image distribution. Moreover, a perceptual loss is proposed to enhance the performance of image-to-image translation. As demonstrated through extensive experiments, our proposed EGAN can alleviate effects of noise in source images, and outperform other state-of-the-art methods significantly. Furthermore, we experimentally indicate that the proposed EGAN is also effective when applied to image denoising.     

A low light natural image statistical model for joint contrast enhancement and denoising

We study the problem of joint low light image contrast enhancement and denoising using a statistical approach. The low light natural image in the band pass domain is modeled by statistically relating a Gaussian scale mixture model for the pristine image, to the low light image, through a detail loss coefficient and Gaussian noise. The detail loss coefficient is statistically described using a posterior distribution with respect to its estimate based on a prior contrast enhancement algorithm. We then design our low light enhancement and denoising (LLEAD) method by computing the minimum mean squared error estimate of the pristine image band pass coefficients. We create the Indian Institute of Science low light image dataset of well-lit and low light image pairs to learn the model parameters and evaluate our enhancement method. We show through extensive experiments on multiple datasets that our method helps better enhance the contrast while simultaneously controlling the noise when compared to other state of the art joint contrast enhancement and denoising methods.     

Learn from the past – sequentially one-to-one video deblurring network

With the growing availability of hand-held cameras in recent years, more and more images and videos are taken at any time and any place. However, they usually suffer from undesirable blur due to camera shake or object motion in the scene. In recent years, a few modern video deblurring methods are proposed and achieve impressive performance. However, they are still not suitable for practical applications as high computational cost or using future information as input. To address the issues, we propose a sequentially one-to-one video deblurring network (SOON) which can deblur effectively without any future information. It transfers both spatial and temporal information to the next frame by utilizing the recurrent architecture. In addition, we design a novel Spatio-Temporal Attention module to nudge the network to focus on the meaningful and essential features in the past. Extensive experiments demonstrate that the proposed method outperforms the state-of-the-art deblurring methods, both quantitatively and qualitatively, on various challenging real-world deblurring datasets. Moreover, as our method deblurs in an online manner and is potentially real-time, it is more suitable for practical applications.     

A fast and efficient color image enhancement method based on fuzzy-logic and histogram

A new fuzzy logic and histogram based algorithm for enhancing low contrast color images has been proposed here. The method is computationally fast compared to conventional and other advanced enhancement techniques. It is based on two important parameters M and K, where M is the average intensity value of the image, calculated from the histogram and K is the contrast intensification parameter. The given RGB image is converted into HSV color space to preserve the chromatic information contained in the original image. To enhance the image, only the V component is stretched under the control of the parameters M and K. The proposed method has been compared with conventional contrast enhancement techniques as well as with advanced algorithms. All the above techniques were based on the principle of transforming the skewed histogram of the original image into a uniform histogram. The performance of the different contrast enhancement algorithms are evaluated based on the visual quality, Tenengrad, CII and the computational time. The inter comparison of different techniques was carried out on different low contrast color images. Based on the performance analysis, we advocate that our proposed Fuzzy Logic method is well suited for contrast enhancement of low contrast color images.     

流量经营时代的大数据处理技术应用研究

移动互联网的快速发展及流量收入占比的快速攀升,促使流量经营成为运营商战略转型的重点。运营商传统数据存储与处理技术无论是从成本、性能还是扩展性等方面都受到了巨大挑战。探讨了流量经营时代运营商引入大数据处理技术的必要性与具体实施方案,并通过实践验证大数据技术带来的效益,以此指导运营商实施大数据解决方案。