Improved learning of I2C distance and accelerating the neighborhood search for image classification

Image-to-class (I2C) distance is a novel measure for image classification and has successfully handled datasets with large intra-class variances. However, due to the lack of a training phase, the performance of this distance is easily affected by irrelevant local features that may hurt the classification accuracy. Besides, the success of this I2C distance relies heavily on the large number of local features in the training set, which requires expensive computation cost for classifying test images. On the other hand, if there are small number of local features in the training set, it may result in poor performance.In this paper, we propose a distance learning method to improve the classification accuracy of this I2C distance as well as two strategies for accelerating its NN search. We first propose a large margin optimization framework to learn the I2C distance function, which is modeled as a weighted combination of the distance from every local feature in an image to its nearest-neighbor (NN) in a candidate class. We learn these weights associated with local features in the training set by constraining the optimization such that the I2C distance from image to its belonging class should be less than that to any other class. We evaluate the proposed method on several publicly available image datasets and show that the performance of I2C distance for classification can significantly be improved by learning a weighted I2C distance function. To improve the computation cost, we also propose two methods based on spatial division and hubness score to accelerate the NN search, which is able to largely reduce the on-line testing time while still preserving or even achieving a better classification accuracy.     

Quantifying blood flow and wall shear stresses in the outflow tract of chick embryonic hearts

Wall shear stresses (WSS) exerted by blood flow on cardiac tissues modulate growth and development of the heart. To study the role of hemodynamic conditions on cardiac morphogenesis, here, we present a methodology that combines imaging and finite element modeling to quantify the in vivo blood flow dynamics and WSS in the cardiac outflow tract (OFT) of early chicken embryos (day 3 out of 21-day incubation period). We found a distinct blood flow field and heterogeneous distribution of WSS in the chicken embryonic heart OFT, which have physiological implications for OFT morphogenesis.     

A topology-preserving optimization algorithm for polycube mapping

We present an effective optimization framework to compute polycube mapping. Composed of a set of small cubes, a polycube well approximates the geometry of the free-form model yet possesses great regularity; therefore, it can serve as a nice parametric domain for free-form shape modeling and analysis. Generally, the more cubes are used to construct the polycube, the better the shape can be approximated and parameterized with less distortion. However, corner points of a polycube domain are singularities of this parametric representation, so a polycube domain having too many corners is undesirable. We develop an iterative algorithm to seek for the optimal polycube domain and mapping, with the constraint on using a restricted number of cubes (therefore restricted number of corner points). We also use our polycube mapping framework to compute an optimal common polycube domain for multiple objects simultaneously for lowly distorted consistent parameterization.     

CCS-MAC: Exploiting the overheard data for compression in wireless sensor networks

Both the overhearing and overhearing avoidance in a densely distributed sensor network may inevitably incur considerable power consumption. In this paper we propose a so-called CCS-MAC (collaborative compression strategy-based MAC) MAC protocol which facilitates to exploit those overheard data that is treated useless in traditional MAC protocols for the purpose of cost and energy savings. Particularly the CCS-MAC enables different sensor nodes to perform data compression cooperatively with regard to those overheard data, so that the redundancy of data prepared for the link layer transmission can be totally eliminated at the earliest. The problem of collaborative compression is analyzed and discussed along with a corresponding linear programming model formulated. Based on it a heuristic node-selection algorithm with a time complexity of (O(N²)) is proposed to the solve the linear programming problem. The node-selection algorithm is implemented in CCS-MAC at each sensor node in a distributed manner. The experiment results verify that the proposed CCS-MAC scheme can achieve a significant energy savings so as to prolong the lifetime of the sensor networks so far.     

A shape prior constraint for implicit active contours

We present a shape prior constraint to guide the evolution of implicit active contours. Our method includes three core techniques. Firstly, a rigid registration is introduced, using a line search method within a level set framework. The method automatically finds the time step for the iterative optimization processes. The order for finding the optimal translation, rotation and scale is derived experimentally. Secondly, a single reconstructed shape is created from a shape distribution of a previously acquired learning set. The reconstructed shape is applied to guide the active contour evolution. Thirdly, our method balances the impact of the shape prior versus the image guidance of the active contour. A mixed stopping condition is defined based on the stationarity of the evolving curve and the shape prior constraint. Our method is completely non-parametric and avoids taking linear combinations of non-linear signed distance functions, which would cause problems because distance functions are not closed under linear operations. Experimental results show that our method is able to extract the desired objects in several circumstances, namely when noise is present in the image, when the objects are in slightly different poses and when parts of the object are invisible in the image.     

对pSFLASH扰动公钥密码的一个实际攻击

通过对SFLASH的中心映射进行扰动,最近Wang等人提出了一个新的多变量公钥系统pSFLASH.pSFLASH的设计者认为,扰动后的中心映射可以破坏SFLASH公钥潜在的数学结构,从而抵抗针对SFLASH的差分代数攻击[2-3].然而对于以(T-1,U-1,β,γ)为私钥的任一pSFLASH实例,一定存在一个可逆仿射...     

高精度多径时延估计

提出了一种无线信道多径时延估计模型,将时廷估计问题转换为频谱估计问题,再利用具有高频谱分辨力的多重信号分类(MUSIC)算法和root MUSIC算法实现对信道多径时延的超分辨率估计,分析了基于单次测量数据进行时延估计的可行性.仿真并测试了MUSIC算法和root MUSIC算法应用于单次测量数据的性能.仿真结果表明,...     

卫星链路上的SR协议性能分析

选择重传ARQ协议兼顾了停等ARQ协议可靠性高、退后N ARQ协议传输能力强、应用广泛的优势.传统的选择重传ARQ协议模型没有考虑窗口大小对协议性能的影响,导致选择重传ARQ协议模型分析能力不足.为此,通过对选择重传ARQ协议的运行进行分析,建立了与选择重传ARQ协议运行一致的数学模型,并通过仿真验证了所建模型的合理性...     

基于ATmega128的通信基站智能节能窗控制系统设计

介绍了一种采用ATmega128单片机设计的通信基站智能节能窗控制系统,通过传感器对通信基站的内外环境变量进行实时取样,让节能窗与空调联动工作,从而实现节能效果。实际应用表明,该系统在通信基站、机房等建筑内能明显减少空调运行时间,降低能耗。     

TD-LTE系统中RACH信号检测的仿真与实现

提出了TD-LTE系统中RACH(随机接入)信号检测算法.该算法利用ZC (Zadoff- Chu)序列和FFT(快速傅里叶变换)特点,结合循环相关函数为eNodeB提供快速有效的随机接入.仿真结果表明,在不同的信道环境下,该检测算法都能较好地工作,具有快速稳定、易于实现等优点.