基于众包的嵌套流形匹配室内定位方法

随着普适应用的兴起,室内定位变得越来越重要。传统的基于指纹的定位方法需要现场勘测,所需时间及工作量巨大,且需实时更新,以适应室内变化,这大大限制了其应用范围。采用众包形式进行室内信息采集,并记录其在室内的大量路径信息,利用嵌套在路径中的低维流形一致性进行地理位置匹配,以建立位置指纹库。通过高斯粒子滤波器对传感器数据进行去噪,进而解决步长差异问题。定位时,根据用户位置的连续性和路径信息筛选出合理的近邻点,继而实现精确定位。在84m²的会议室进行实验,在不需要现场勘测的情况下,所提方法可达到与传统方法可比的定位精度。该方法可以实时适应环境变化,在2周甚至1个月之后,其定位准确性优于传统定位方法。     

NFI: a neuro-fuzzy inference method for transductive reasoning

This paper introduces a novel neural fuzzy inference method-NFI for transductive reasoning systems. NFI develops further some ideas from DENFIS-dynamic neuro-fuzzy inference systems for both online and offline time series prediction tasks. While inductive reasoning is concerned with the development of a model (a function) to approximate data in the whole problem space (induction), and consecutively-using this model to predict output values for a new input vector (deduction), in transductive reasoning systems a local model is developed for every new input vector, based on some closest to this vector data from an existing database (also generated from an existing model). NFI is compared with both inductive connectionist systems (e.g., MLP, DENFIS) and transductive reasoning systems (e.g., K-NN) on three case study prediction/identification problems. The first one is a prediction task on Mackey Glass time series; the second one is a classification on Iris data; and the last one is a real medical decision support problem of estimating the level of renal function of a patient, based on measured clinical parameters for the purpose of their personalised treatment. The case studies have demonstrated better accuracy obtained with the use of the NFI transductive reasoning in comparison with the inductive reasoning systems.     

Medical image enhancement algorithm based on NSCT and the improved fuzzy contrast

In order to solve the problem of noise amplification, low contrast and image distortion in the process of medical image enhancement, a new algorithm is proposed which combines NSCT (nonsubsampled contourlet transform) and improved fuzzy contrast. The image is decomposed by NSCT. Firstly, linear enhancement method is used in low frequency coefficients; secondly the improved adaptive threshold function is used to deal with the high frequency coefficients. Finally, the improved fuzzy contrast is used to enhance the global contrast and the Laplace operator is used to enhance the details of the medical images. Experimental results show that the proposed algorithm can improve the image visual effects, remove the noise and enhance the details of medical images. © 2015 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 25, 7–14, 2015     

A Preliminary Study on Negative Correlation Learning via Correlation-Corrected Data (NCCD)

This letter presents a novel cooperative neural network ensemble learning method based on Negative Correlation learning. It enables easy integration of various network models and reduces communication bandwidth significantly for effective parallel speedup. Comparison with the best Negative Correlation learning method reported demonstrates comparable performance at significantly reduced communication overhead.     

Spiking Neural Networks: Background,Recent Development and the NeuCube Architecture

Neural Processing Letters - This paper reviews recent developments in the still-off-the-mainstream information and data processing area of spiking neural networks (SNN)—the third generation...     

基于NSCT和改进模糊的遥感图像增强方法

为了解决遥感图像处理过程中噪声放大和图像失真现象,提出了一种结合NSCT和改进模糊对比度的图像增强方法。通过NSCT变换把图像分解成低频子带和若干个高频子带;对低频子带进行线性增强,并利用改进的阈值函数对高频子带进行去噪;随后采用改进的模糊对比度来调整NSCT反变换的系数,以提高图像的整体对比度。实验结果表明,提出的算法的客观指标明显优于其他对比算法,并且视觉效果也得到很大改善。     

结合位图切割和区域合并的彩色图像分割

就经典分水岭图像分割算法中存在的过分割问题，提出一种结合位图切割和区域合并的彩色图像分割算法。对原始彩色图像通过空域梯度算子求其梯度图像，并利用位图切割重建梯度图像；对新梯度图像进行分水岭预分割；对预分割图像基于异质性最小原则进行区域合并，并获得最终分割结果。相比于现有的同类方法，该算法引入位图切割，抑制噪声对分割结果的影响，在边缘模糊处分割准确，得到符合人类视觉的较小分割区域数目，同时在运行效率上提高。     

混合的SAR图像变化检测算法

为尽可能多地消除遥感图像变化检测过程中“伪变化”信息的影响,获得比较客观的感兴趣区域变化检测结果,针对遥感图像中SAR图像的特点,提出一种混合的SAR图像变化检测算法。对已配准好的图像进行Frost滤波,用邻域比值的方法构造差异图,对得到的差异图进行非下采样轮廓波变换（NSCT）,对变换得到的高频子带和低频子带分别处理,用模糊C均值（FCM）聚类算法得到变化检测的结果。实验结果表明,该算法模型很好地保留了图像变化区域的细节,提高了变化检测准确性。     

Functionally reconfigurable general purpose parallel machines and some image processing and pattern recognition applications

Functionally reconfigurable general purpose parallel machines (FRPM) could be reconfigured during the operation from SIMD to MIMD mode or vice versa (first aspect) and from one interconnection network to another according to the data storing order (second aspect). General purpose machines are considered in order to obtain an arbitrary data exchange between the processing elements they are built of. A model for describing such interconnection networks is presented. A full-information exchange network in introduced which is reconfigurable in a programming way to tree-, matrix-, cube-, linear-neighbourhood and FFT-network. Some schemes for constructing SIMD/MIMD reconfigurable machines are given. The usefullness of using FRMP for image processing and pattern recognition is discussed.