基于汉明距离的无线传感器网络密钥预分配方案*

无线传感器网络自身的特征,如网络规模庞大,动态的拓扑结构,有限的计算、通信和存储能力等,使得传统的密钥分配和管理机制无法直接应用。基于汉明距离提出了一种新的适用于无线传感器网络的密钥预分配方案。该方案将对称密钥系统和非对称密钥系统结合起来,并借助汉明距离的概念在无线传感器网络中实现了密钥的分配和管理。与随机密钥预分配方案相比,本方案在健壮性和安全性方面具有一定的优势,其计算和存储开销也不大,具有一定的实用性。     

Secure architecture to circumvent collision using RSSI measurement in WSN: a cross layer design approach

Multimedia Tools and Applications - ‘Wireless sensor networks’ (WSNs) follow layered architecture for the fruitful and reliable working of distributed WSNs. The region of WSN is...     

无线传感器网络中利用改进型SDN-WISE协议的大数据处理方法

针对无线传感器网络（WSN）中数据量大、处理成本高的问题,提出了一种基于软件定义网络（SDN）和多播数据流路由算法的大数据方法,在WSN中实现大数据技术并计算其通信成本。首先,将MapReduce接口动态加载到WSN的适合节点中。然后,通过在控制器和传感器节点上改进SDN-WISE协议,在网络内实现map和reduce功能。最后,采用多播路由算法找到网络中MapReduce的最优部署方案,以确保部署方案的通信成本最小。通过操作系统Contiki集成上述功能和组件,在WSN中实现提出的方法并计算相应成本。在不同限制条件下的WSN中实现大数据处理实验,实验结果表明,该方法具有较好的经济性。     

3D posture estimation using geodesic distance maps

This paper presents a novel technique for three-dimensional (3D) human motion capture using a set of two non-calibrated cameras. The user’s five extremities (head, hands and feet) are extracted, labeled and tracked after silhouette segmentation. As they are the minimal number of points that can be used in order to enable whole body gestural interaction, we will henceforth refer to these features as crucial points. Features are subsequently labelled using 3D triangulation and inter-image tracking. The crucial point candidates are defined as the local maxima of the geodesic distance with respect to the center of gravity of the actor region that lie on the silhouette boundary. Due to its low computational complexity, the system can run at real-time paces on standard personal computers, with an average error rate range between 4% and 9% in realistic situations, depending on the context and segmentation quality.

An attribute-based lightweight cloud data access control using hypergraph structure

The Journal of Supercomputing - Cloud file storage systems are the current trend of enterprises and also of individual users. Due to the malicious or unauthorized users, file sharing among the...     

基于马氏距离的缺失值填充算法

提出了一种基于马氏距离的填充算法来估计基因表达数据集中的缺失数据。该算法通过基因之间的马氏距离来选择最近邻居基因,并将已得到的估计值应用到后续的估计过程中,然后采用信息论中熵值的概念计算最近邻居的加权系数,得到缺失数据的填充值。实验结果证明了该算法具有有效性,其性能优于其他基于最近邻居法的缺失值处理算法。     

Comparison of content-based music recommendation using different distance estimation methods

With rapid growth in the online music market, music recommendation has become an active research area. In most current approaches, content-based recommendation methods play an important role. Estimation of similarity between music content is the key to these approaches. A distance formula is used to calculate the music distance measure, and music recommendations are provided based on this measure. However, people have their own unique tastes in music. This paper proposes a method to calculate a personalized distance measure between different pieces of music based on user preferences. These methods utilize a randomized algorithm, a genetic algorithm, and genetic programming. The first two methods are based on Euclidean distance calculation, where the weight of each music feature in the distance calculation approximates user perception. The third method is not limited to Euclidean distance calculation. It generates a more complex distance function to estimate a user’s music preferences. Experiments were conducted to compare the distance functions calculated by the three methods, and to compare and evaluate their performance in music recommendation.     

轻量级大数据运算系统Helius

针对Spark数据集不可变,以及Java虚拟机（JVM）依赖环境引起的代码执行、内存管理、数据序列化/反序列化等开销过多的不足,采用C/C++语言,设计并实现了一种轻量级的大数据运算系统--Helius。Helius支持Spark的基本操作,同时允许数据集整体修改;同时,Helius利用C/C++优化内存管理和网络传输,并采用stateless worker机制简化分布式计算平台的容错恢复过程。实验结果显示：5次迭代中,Helius运行PageRank算法的时间仅为Spark的25.12%~53.14%,运行TPCH Q6的时间仅为Spark的57.37%;在PageRank迭代1次的基础上,运行在Helius系统下时,master节点IP接收和发送数据量约为运行于Spark系统的40%和15%,而且200 s的运行过程中,Helius占用的总内存约为Spark的25%。实验结果与分析表明,与Spark相比,Helius具有节约内存、不需要序列化和反序列化、减少网络交互以及容错简单等优点。     

Efficient scale estimation methods using lightweight deep convolutional neural networks for visual tracking

Neural Computing and Applications - In recent years, visual tracking methods that are based on discriminative correlation filters (DCFs) have been very promising. However, most of these methods...     

Optical fingerprinting to protect data: a proposal

To counter the increasing problem with optical media piracy, the author proposes Thor, a copy protection mechanism that incorporates both optical fingerprinting and cryptographic techniques to secure disc data. Thor presents a possible new direction in the battle against piracy. Its acceptability, and the consequent realization of this concept, depends on how determined manufacturers are to defeat digital theft. A determined pirate can break most security mechanisms, but this proposal aims to discourage piracy by offering an innovative security technique     

Accurate smartphone indoor positioning using a WSN infrastructure and non-invasive audio for TDoA estimation

In this paper we propose a reliable acoustic indoor positioning system fully compatible with a conventional smartphone. The proposed system takes advantage of the smartphone audio I/O and its processing capabilities to perform acoustic ranging in the audio band using non-invasive audio signals and it has been developed having in mind applications that require high accuracy, such as augmented or virtual reality, gaming or audio guiding applications. The system works in a distributed operation mode, i.e. each smartphone is able to obtain its own position using only acoustic signals. In order to support the positioning system, a Wireless Sensor Network (WSN) of synchronized acoustic beacons is proposed. To keep the infrastructure in sync we developed an Automatic Time Synchronization and Syntonization (ATSS) protocol that ensures a sync offset error below 5 μs. Using an improved Time Difference of Arrival (TDoA) estimation approach (that takes advantage of the beacon signals’ periodicity) and by performing Non-Line-of-Sight (NLoS) mitigation, we were able to obtain very stable and accurate position estimates with an absolute error of less than 10 cm in 95% of the cases and a mean absolute standard deviation of 2.2 cm for a position refresh period of 350 ms.     

Center of gravity height real-time estimation for lightweight vehicles using tire instant effective radius

Center of gravity (CG) height is an important parameter for lightweight vehicles (LWVs). Because of the inherently smaller weight and size, a LWV's CG height is more easily affected by loading conditions compared with conventional vehicles. This paper proposes a novel tire instant effective radius (TIER) method for real-time estimation of the CG height for LWVs. The method utilizes the mathematical correlation between the tire vertical load transfer that is proportional to the CG height, and the TIER variation. A Kalman filter based estimator is designed to simultaneously identify the vehicle CG height as well as the unknown nominal tire effective radius. To verify the performance of the proposed estimator, simulation results are first provided for several vehicles with different CG heights, and then road test results obtained on a lightweight electric ground vehicle (EGV) equipped with an advanced measurement system are given. Both simulation and experimental results show that the developed estimator is capable of providing an accurate estimation of the vehicle CG height in real-time.     

Soil-moisture estimation from TerraSAR-X data using neural networks

Early prediction of natural disasters like floods and landslides is essential for reasons of public safety. This can be attained by processing Synthetic-Aperture Radar (SAR) images and retrieving soil-moisture parameters. In this article, TerraSAR-X product images are investigated in combination with a water-cloud model based on the Shi semi-empirical model to determine the accuracy of soil-moisture parameter retrieval. SAR images were captured between January 2008 and September 2010 in the vicinity of the city Maribor, Slovenia, at different incidence angles. The water-cloud model provides acceptable estimated soil-moisture parameters at bare or scarcely vegetated soil areas. However, this model is too sensitive to speckle noise; therefore, a pre-processing step for speckle-noise reduction is carried out. Afterwards, self-organizing neural networks (SOM) are used to segment the areas at which the performance of this model is poor, and at the same time neural networks are also used for a more accurate approximation of model parameters’ values. Ground-truth is measured using the Pico64 sensor located on the field, simultaneously with capturing SAR images, in order to enable the comparison and validation of the obtained results. Experimental results show that the proposed method outperforms the water-cloud model accuracy over all incidence angles.     

Shadow detection and building-height estimation using IKONOS data

The spectral confusion between shadow and water (or other dark surfaces) often results in suboptimal urban classification performances, especially from high-resolution satellite imagery (e.g. IKONOS). A classification method was developed to incorporate spatial indices of image objects to improve the shadow/water detection. A number of spatial indices, such as size, shape and spatial neighbour of image objects, were characterized to differentiate water and shadow objects. This generated superior shadow/water detection performance compared to a traditional per-field Extraction and Classification of Homogeneous Objects (ECHO) classification method. The user's accuracies for shadow and water classes were increased to 88% and 92%, compared to 80% and 76% obtained from the traditional ECHO classification approach. Furthermore, an automated approach was developed for shadow-length and corresponding building-height estimation. The accuracy assessment suggested good results for very high buildings, especially for isolated high-rise buildings.     

Power estimation of digital data paths using HEAT

The major concerns of VLSI designers in the past were performance, area, reliability and cost. Power was only a secondary issue. In recent years, however, power, area, and speed have become equally important. There are many reasons for this new trend. Primarily, rapid advancement in semiconductor technology over the past decade has enabled designers to integrate many digital CMOS circuits on a single chip. However, the desirability of using these circuits in portable operations has necessitated the development of low-power technology. Portable applications range from desktop computers and audio-video based multimedia products to personal digital assistants and personal communicators. These systems demand both complex functionality and low power, which make their design challenging. The hierarchical energy analysis tool lets designers quickly estimate power consumption of various data-path architectures, enabling a power consumption comparison at a high level before the layout design is carried out     

Cpk index estimation using data transformation

Process capability indices (PCIs) are used in industry to assess percentages of nonconforming parts. An underlying assumption is that the output process measurements are distributed as normal random variables. When normal distributions are assumed, but different distributions are present - such as skew, heavy-tailed, and short-tailed distributions - the percentages of nonconforming parts are significantly different than the computed PCIs indicate. Data arising from nonnormal distributions can sometimes be transformed to conform to the normality assumption and the PCI's computed for the transformed data. In this paper, the effect of the transformation on the estimate of nonconforming parts is examined for three examples of nonnormal distributions - gamma, lognormal, and Weibull. The results of this experimental analysis suggest that data transformation can be useful for estimating an interval for C_pk values and the number of nonconforming parts.     

Position estimation using principal components of range data

This paper describes a new approach to mobile robot position estimation, based on principal component analysis of laser range data. An eigenspace is constructed from the principal components of a large number of range data sets. The structure of an environment, as seen by a range sensor, is represented as a family of surfaces in this space. Subsequent range data sets from the environment project as a point in this space. Associating this point to the family of surfaces gives a set of candidate positions and orientations (poses) for the sensor. These candidate poses correspond to positions and orientations in the environment which have similar range profiles. A Kalman filter can be used to select the most likely candidate pose based on coherence with small movements.

The first part of this paper describes how a relatively small number of depth profiles of an environment can be used to generate a complete eigenspace. This space is used to build a representation of the range scan profiles obtained from a regular grid of positions and orientations (poses). This representation has the form of a family of surface (a manifold). This representation converts the problem of associating a range profile to possible positions and orientations into a table lookup. As a side benefit, the method provides a simple means to detect obstacles in a range profile. The final section of the paper reviews the use of estimation theory to determine the correct pose hypothesis by tracking.     

Missing value estimation of microarray data using Sim-GAN

Knowledge and Information Systems - Microarray data analysis needs utmost care as it plays a significant role in cancer study. Due to the excessive complexity of the data extraction process, it...     

出租车数据的城市道路网路段通行时间估计方法

城市路段通行时间估计能够更好地运营和管理城市交通。针对包含起点-终点位置,行程时间和距离信息的GPS行程数据,提出了一种城市道路网短时通行时间的估计模型。首先将城市道路网按照交叉路口分解为多个路段,并基于k-最短路径搜索方法分析司机行进路线。然后针对每一个路段,提出了双车道通行时间多项式关联关系模型,既能提升道路网通行时间精细度,又能避免因训练数据不足导致的路网通行时间过拟合问题。最后以最小化行程期望时间和实际行程时间之间的均方误差为优化目标,拟合道路网通行时间。在纽约出租车数据集上的实验结果表明,所提模型及方法相对于传统单车道估计方法能够更准确地估计城市道路网路段的通行时间。     

眼球光心标定与距离修正的3维注视点估计

目的 在基于双目视线相交方法进行3维注视点估计的过程中,眼球光心3维坐标手工测量存在较大误差,且3维注视点估计结果在深度距离方向偏差较大。为此,提出了眼球光心标定与距离修正的方案对3维注视点估计模型进行改进。方法 首先,通过图像处理算法获取左右眼的PCCR（pupil center cornea reflection）矢量信息,并使用二阶多项式映射函数得到左、右眼的2维平面注视点;其次,通过眼球光心标定方法获取眼球光心的3维坐标,避免手工测量方法引入的误差;然后,结合平面注视点得到左、右眼的视线方向,计算视线交点得到初步的3维注视点;最后,针对结果在深度距离方向抖动较大的问题,使用深度方向数据滤波与Z平面截取修正法对3维注视点结果进行修正处理。结果 选择两个不同大小的空间测试,实验结果表明该方法在3050 cm的工作距离内,角度偏差0.7°,距离偏差17.8 mm,在50130 cm的工作距离内,角度偏差1.0°,距离偏差117.4 mm。与其他的3维注视点估计方法相比较,在同样的测试空间条件下,角度偏差和距离偏差均显著减小。结论 提出的眼球光心标定方法可以方便准确地获取眼球光心的3维坐标,避免手工测量方法带来的误差,对角度偏差的减小效果显著。提出的深度方向数据滤波与Z平面截取修正法可以有效抑制数据结果的抖动,对距离偏差的减小效果显著。