Normalized Efficient Routing Protocol for WSN

WSNs （wireless sensor networks） consist of thousands of tiny nodes having the capability of sensing, computation, and wireless communications. Unfortunately these devices are limited energy devices, that is means we must save energy as much as possible, to increase network life time as long as possible. In this paper we introduce NEER--normalized energy efficient routing protocol that increases network life time through switching between AODV protocol that depends on request-reply routing, and MRPC that depends on residual battery in routing.     

基于灰度阈值法的煤岩界面识别研究

针对煤岩界面识别存在准确性低的问题,以颜色区分度较高的某工作面煤岩图像为例,采用边缘检测法和灰度阈值法分别对该图像进行了煤岩分割处理,结果表明,当煤岩颜色的灰度差别较为明显时,基于灰度阈值法的煤岩界面识别方案具有较高的识别准确性。     

一种基于自动显微镜的X光图片快速图像拼接算法

为提高工业无损探伤中对X光图片的检测效率,提出用自动数字显微镜替代肉眼对X光图片进行自动检测,并通过研究自动显微镜图像采样方式和X光图像的特点,提出一种改进的基于空间像素灰度相关的快速图像拼接算法:以前一幅采样图像矩阵最后一列的中点为基点建立匹配矩阵,设立阈值,并通过相关性匹配找到下一采样图像中的匹配点,然后进行多重循环校验来验证匹配点的准确性。仿真实验证明,该算法不仅精确性高,而且运算量小,具有很高的运算效率和采样效率。     

一种自适应阈值曲波图像去噪算法

利用曲波变换进行图像去噪能取得较好的效果,但是现有的曲波去噪没有充分利用图像曲波系数的特点,容易过分扼杀图像的细节信息.本文提出了一种基于阈值自适应的曲波图像去噪算法,利用图像经过曲波变换后的能量分布特性选取阈值,以更好地保护图像细节.实验结果表明,该算法可以将每一尺度上的信号与噪声在最大程度上分离,有效去除了高斯白噪声,进一步提高了峰值信噪比,更好地实现了去除噪声与保护图像细节之间的平衡.     

一种提高模糊人脸图片识别率的方法

介绍了一个通过人脸检测程序对模糊人脸图片进行识别的方法：先对图片修改其灰度阈值,以使Haar能够对模糊人脸信息进行快速分辨,再使用0pencv人脸检测程序对模糊人脸图片进行检测。实验结果表明,该方法能够提高对模糊人脸的识别率。     

基于嵌入式系统的图像对象分割

随着计算机科学和微电子技术的迅速发展,以计算机技术、集成电路技术和软件技术为核心的嵌入式系统的信息处理能力迅速提升,为基于嵌入式系统的图像信息处理提供了硬件基础。本文以ARM920T结构的S3C2410处理器为核心,构建了一个嵌入式图像处理平台,在嵌入式Linux操作系统的支撑下,实现了嵌入式图像的采集、图像灰度化和基于迭代法的图像对象分割,实验证明本系统是切实可行,为进一步的研究提供了基础。     

Enhancing the accuracy of WLAN-based location determination systems using predicted orientation information

Indoor location determination has emerged as a significant research topic due to the wide-spread deployment of wireless local area networks (WLANs) and the demand for context-aware services inside buildings. However, prediction accuracy remains a primary issue surrounding the practicality of WLAN-based location determination systems. This study proposes a novel scheme that utilizes mobile user orientation information to improve prediction accuracy. Theoretically, if the precise orientation of a user can be identified, then the location determination system can predict that user’s location with a high degree of accuracy by using the training data of this specific-orientation. In reality, a mobile user’s orientation can be estimated only by comparing variations in received signal strength; and nevertheless the predicted orientation may be incorrect. Incorrect orientation information causes the accuracy of the entire system to decrease. Therefore, this study presents an accumulated orientation strength algorithm which can utilize uncertain estimated orientation information to improve prediction accuracy. Implementation of this system is based on the Bayesian model, and the experimental results indeed show the effectiveness of our proposed approach.     

An asynchronous recurrent linear threshold network approach to solving the traveling salesman problem

In this paper, an approach to solving the classical Traveling Salesman Problem (TSP) using a recurrent network of linear threshold (LT) neurons is proposed. It maps the classical TSP onto a single-layered recurrent neural network by embedding the constraints of the problem directly into the dynamics of the network. The proposed method differs from the classical Hopfield network in the update of state dynamics as well as the use of network activation function. Furthermore, parameter settings for the proposed network are obtained using a genetic algorithm, which ensure a stable convergence of the network for different problems. Simulation results illustrate that the proposed network performs better than the classical Hopfield network for optimization.     

Optimized scenario for rainfall forecasting using genetic algorithm coupled with artificial neural network

Rainfall forecasting plays many important role in water resources studies such as river training works and design of flood warning systems. Recent advancement in artificial intelligence and in particular techniques aimed at converting input to output for highly nonlinear, non-convex and dimensionalized processes such as rainfall field, provide an alternative approach for developing rainfall forecasting model. Artificial neural networks (ANNs), which perform a nonlinear mapping between inputs and outputs, are such a technique. Current literatures on artificial neural networks show that the selection of network architecture and its efficient training procedure are major obstacles for their daily usage. In this paper, feed-forward type networks will be developed to simulate the rainfall field and a so-called back propagation (BP) algorithm coupled with genetic algorithm (GA) will be used to train and optimize the networks. The technique will be implemented to forecast rainfall for a number of times using rainfall hyetograph of recording rain gauges in the Upper Parramatta catchment in the western suburbs of Sydney, Australia. Results of the study showed the structuring of ANN network with the input parameter selection, when coupled with GA, performed better compared to similar work of using ANN alone.     

鸟嘌呤和8-羟基脱氧鸟嘌呤核苷在聚甘氨酸修饰电极上的电化学行为及其同时测定

在优化的实验条件下,利用电化学方法制备了甘氨酸修饰电极,对修饰膜的电活性进行了表征.用循环伏安法研究了鸟嘌呤(G)和8一羟基脱氧鸟嘌呤核苷(8-OH-dG)在聚甘氨酸修饰电极上的电化学行为,并建立了对两者进行分别检测和同时检测的分析方法.实验结果表明,聚甘氨酸修饰电极可以增强鸟嘌呤和8-羟基脱氧鸟嘌呤核苷在电极表面的吸附,并且可以加快鸟嘌呤和8-羟基脱氧鸟嘌呤核苷在电极表面的电子传输,使两种电活性物质在聚甘氨酸修饰电极上的电化学信号明显增大,检测灵敏度大大提高,并且该修饰电极具有良好的稳定性和重现性.可用于鸟嘌呤和8-羟基脱氧鸟嘌呤核昔的分别和同时检测.