覆盖范围可变时WSNs优化算法

无线传感器网络（WSNs）具有放置方便,自组织,移动性强等特点,为了保证安全生产,可以将网络中的节点放置在煤矿井下狭长工作面上,用来对大型设备的工作情况和环境参数进行监测以提供实时信息。基于上述环境,提出了监测区域可变的覆盖算法,在B样条数学理论和Matlab工具的支持下做了算法仿真。结果表明：通常情况下覆盖度随着节点通信半径的增大而增加;合理选择第一个簇首会达到较高的覆盖度;合适的节点密度不但可以有效覆盖监测区域,还可延长网络寿命。     

基于RSSI距离修正的WSNs定位算法

针对无线传感器网络(WSNs)易受外界因素影响,导致三边定位的锚圆不能相交的情况,提出了一种接收信号强度指示(RSSI)距离修正定位算法。通过对锚圆半径进行修正,形成3个锚圆相交的区域,然后用加权定位法对未知节点进行准确定位。仿真和实验结果表明:在6 m×10 m的区域范围内,该算法的平均定位误差为0.62 m,和其他定位方法相比,有更好的定位精度。     

基于地形改正的无线传感器网络DV-Hop定位算法

对山坡地形的无线传感器网络三维DV-Hop定位算法进行了研究.通过理论与仿真对比分析,在山坡地形应用典型三维DV-Hop算法,定位误差很大,且不稳定,不能满足实际应用需求.提出了基于地形改正的三维DV-Hop算法,节点定位精度高,相对无线射程的平均均方差30％左右,且基本不受山坡倾角、通信半径与锚节点比例的影响,完全可以满足实际应用的需求.     

基于WSNs的压缩频谱感知稀疏基设计

研究了现有利用无线传感器网络(WSNs)进行区域压缩频谱感知的算法,针对其运算量大,准确性有限的问题,设计了以子带基作为稀疏基进行压缩感知(CS)的算法;证明了子带基作为稀疏基的正交性和完备性;同时计算表明子带基满足重构的约束等距条件.仿真结果显示:以子带基进行重构可以准确给出频谱的占用位置和幅值,比传统的边缘检测算法提高了压缩频谱感知的准确性和鲁棒性,同时具有更高的压缩比,运算量小,适于在WSNs中实施.     

基于感知数据时域特征的WSNs故障被动诊断方法

由于大规模无线传感器网络的动态拓扑性及资源受限,无线传感网的故障诊断成为该领域内的一个难点。现有的诊断方法消耗大量通信带宽和节点资源,给资源有限的网络带来繁重的负担。本文提出一种利用感知数据时域特征来检测故障以及对故障进行分类的被动诊断方法（TDSD）。首先运用一维离散Gabor变换对感知数据进行特征提取与分析,进而结合SOM神经网络对数据进行诊断与分类,判断当前网络状态并找出故障原因。实验结果表明,与其它方法相比,此方法具有网络通信负担小、诊断准确率高及分类效果好等优点,对节点故障和网络故障诊断都具有较高的诊断精度。     

Time-of-Flight sensor calibration for accurate range sensing

Over the past years Time-of-Flight (ToF) sensors have become a considerable alternative to conventional distance sensing techniques like laser scanners or image based stereo-vision. Due to the ability to provide full-range distance information at high frame-rates, ToF sensors achieve a significant impact onto current research areas like online object recognition, collision prevention or scene and object reconstruction. Nevertheless, ToF-cameras like the Photonic Mixer Device (PMD) still exhibit a number of error sources that affect the accuracy of measured distance information. For this reason, major error sources for ToF-cameras will be discussed, along with a new calibration approach that combines intrinsic, distance as well as a reflectivity related error calibration in an overall, easy to use system and thus significantly reduces the number of necessary reference images. The main contribution, in this context, is a new intensity-based calibration model that requires less input data compared to other models and thus significantly contributes to the reduction of calibration data.     

Approximate algorithms for static and continuous range queries in mobile navigation

For many years, spatial range search has been applied to computational geometry and multimedia problems to find interest objects within a given radius. Range search query has traditionally been used to return all objects within a given radius. However, having all objects is not necessary, especially when there are already enough objects closer to the query point. Furthermore, expanding the radius may give users better results, especially when there are a lot of objects just outside the search boundary. Therefore, in this paper, we focus on approximate range search, where the query results are approximate, rather than exact. We propose approximate static range search (ARS) which combines two approaches, namely (i) lowerbound approximate range search, and (ii) upperbound approximate range search. Using ARS, we are able to deliver a better performance, together with low false hit and reasonable false miss. We also extend ARS in the context of a continuous query setting, in which the query moves. This is particularly important in spatial databases as a mobile user who invokes the query is moving. In terms of continuous range search, the intention is to find split points—the locations where the query results will be updated. Accordingly, we propose two methods for approximate continuous range search, namely (i) range search minimization, and (ii) split points minimization. Our performance evaluation which compares our methods with the traditional continuous range search shows that our methods considerably reduce the number of split points, thereby improving overall performance.     

WSNs中基于期望网络覆盖和分簇压缩感知的数据收集方案

为提高无线传感器网络数据收集精确度、降低网络能耗和改善数据包丢失情况下数据收集算法的鲁棒性,提出一种基于期望网络覆盖和分簇压缩感知的数据收集方案.首先设计期望网络覆盖优化算法,给出节点调度策略,实现对“特殊”区域重点观测和降低节点能耗的目的;然后通过分析网络分簇与节点部署之间的关系,设计弱相关性观测矩阵,降低数据包丢失对数据收集的影响;最后引入群居蜘蛛优化算法以提高汇聚节点处CS数据重构精度.仿真结果表明,与其他数据收集算法相比,所提出方案数据重构误差降低了约23.5{%     

联合智能优化和分簇CS的WSNs稀疏数据采集

为提高无线传感器网络（Wireless Sensor Networks,WSNs）数据处理效率和降低网络能耗,提出了一种基于自适应智能优化和分簇压缩感知的WSNs稀疏数据采集方案。首先,建立分簇WSNs稀疏数据通信模型,通过定量分析节点密度与网络数据通信总跳数的关系,给出网络自适应分簇结果,并采用簇内观测矩阵测量数据获取和簇间多跳通信方式完成WSNs压缩感知数据采集;其次,采用StOMP算法进行稀疏信号重构,针对网络节点数据包丢失等链路不可靠情况,引入相关性矩阵变换策略,以降低错误数据传输对数据重构的影响,针对数据稀疏度未知特性和StOMP算法参数配置难的缺陷,将一种新型自适应智能优化（Improved Adaptive Intelligent Optimization algorithm,IAIO）算法应用于CS重构算法中,在理论分析IAIO全局寻优能力的基础上,实现对稀疏数据的可靠重构。最后,仿真结果表明,该方案能够实现稀疏信号的精确重构,而且降低了网络通信总量,提高了网络生存时间。     

Practical sea-water algorithms for fundamental bio-optical and remote sensing quantities

Spectral in-water measurements of the downward irradiance, E _d and the upward irradiance, E _u are sufficient in order to calculate the following spectral quantities: the irradiance ratio R, the two scalar irradiances E _o and E _od, the two average cosines μ and μ _d, the light absorption coefficient a, the backscattering coefficient b _b and the vertical attenuation coefficients for the above-mentioned downward and upward irradiances. The algorithms are valid for horizontally stratified and optically deep sea waters.     

Applying soft computing for remote sensing data composite algorithms

Remote sensing of the earth’s surface using satellite-mounted sensor data is one of the most important methods for global environmental monitoring today. However, when using satellite sensor data, clouds in the atmosphere can interfere with the remote sensing, and specific land points may not be correctly monitored on any given day. In order to overcome this problem, a common alternative is to use multiple day composite data. Multiple day composite data use several consecutive days’ remote sensing data, and choose the most accurate data within the temporal dataset for the same land point. This allows the creation of a more complete dataset by patching together data which have had no cloud interference during a specified time period in order to create a clearer, more usable dataset. In this article, we propose the application of soft computing, namely fuzzy logic, in order to select the clearest data in the temporal interval to use for the composite data. Moderate resolution remote sensing data of areas in Japan were used for the evaluation, and the results were compared with previous composite methods.     

Participant selection algorithms for large-scale mobile crowd sensing environment

Microsystem Technologies - Mobile crowd sensing (MCS) is an emerging sensing platform that concedes mobile users to efficiently collect data and share information with the MCS service providers....     

Genetic algorithms and bundle adjustment for the enhancement of 3D reconstruction

Multimedia Tools and Applications - In this paper, we present a new technique of tridimensional reconstruction from a sequence of uncalibrated stereo images taken with cameras having varying...     

基于压缩感知理论的WSNs时序信号分段压缩算法

针对压缩感知理论(CS)应用在无线传感器网络中时序信号在传输过程存在压缩比率低、通信能耗高等问题,提出了一种时序信号分段压缩算法来解决在信号稀疏度未知及高稀疏度条件下,压缩感知数据重构算法中存在的重构效率低,重构精度差,影响网络生命周期的问题.该算法将采集数据中非零元素个数作为分段依据,通过减少段内非零元素组合数量来提高信号重构精度,同时利用了压缩感知理论特性实现了对信号的高压缩率.实验结果表明,在以混沌量子免疫克隆重构(Q-CSDR)算法为重构算法、在信号盲稀疏度及稀疏度高于40的条件下,能够以大于0.4的压缩比率对信号进行压缩,其重构信号的均方误差小于0.01,能够延长网络寿命2倍左右.     

Optimized algorithms for predictive range and KNN queries on moving objects

There have been many studies on management of moving objects recently. Most of them try to optimize the performance of predictive window queries. However, not much attention is paid to two other important query types: the predictive range query and the predictive k nearest neighbor query. In this article, we focus on these two types of queries. The novelty of our work mainly lies in the introduction of the Transformed Minkowski Sum, which can be used to determine whether a moving bounding rectangle intersects a moving circular query region. This enables us to use the traditional tree traversal algorithms to perform range and kNN searches. We theoretically show that our algorithms based on the Transformed Minkowski Sum are optimal in terms of the number of tree node accesses. We also experimentally verify the effectiveness of our technique and show that our algorithms outperform alternative approaches.     

Fuzzy clustering algorithms for unsupervised change detection in remote sensing images

In this paper, we propose a context-sensitive technique for unsupervised change detection in multitemporal remote sensing images. The technique is based on fuzzy clustering approach and takes care of spatial correlation between neighboring pixels of the difference image produced by comparing two images acquired on the same geographical area at different times. Since the ranges of pixel values of the difference image belonging to the two clusters (changed and unchanged) generally have overlap, fuzzy clustering techniques seem to be an appropriate and realistic choice to identify them (as we already know from pattern recognition literatures that fuzzy set can handle this type of situation very well). Two fuzzy clustering algorithms, namely fuzzy c-means (FCM) and Gustafson-Kessel clustering (GKC) algorithms have been used for this task in the proposed work. For clustering purpose various image features are extracted using the neighborhood information of pixels. Hybridization of FCM and GKC with two other optimization techniques, genetic algorithm (GA) and simulated annealing (SA), is made to further enhance the performance. To show the effectiveness of the proposed technique, experiments are conducted on two multispectral and multitemporal remote sensing images. A fuzzy cluster validity index (Xie-Beni) is used to quantitatively evaluate the performance. Results are compared with those of existing Markov random field (MRF) and neural network based algorithms and found to be superior. The proposed technique is less time consuming and unlike MRF does not require any a priori knowledge of distributions of changed and unchanged pixels.     

Terrestrial remote sensing science and algorithms planned for EOS/MODIS

The Moderate Resolution Imaging Spectroradiometer (MODIS) will be the primary daily global monitoring sensor on the NASA Earth Observing System (EOS) satellites, scheduled for launch on the EOS-AM platform in June 1998 and the EOS-PM platform in December 2000. MODIS is a 36 channel radiometer covering 0·415-14·235 μm wavelengths, with spatial resolution from 250 m to 1 km at nadir. MODIS will be the primary EOS sensor for providing data on terrestrial biospheric dynamics and process activity. This paper presents the suite of global land products currently planned for EOSDIS implementation, to be developed by the authors of this paper, the MODIS land team (MODLAND). These include spectral albedo, land cover, spectral vegetation indices, snow and ice cover, surface temperature and fire, and a number of biophysical variables that will allow computation of global carbon cycles, hydrologic balances and biogeochemistry of critical greenhouse gases. Additionally, the regular global coverage of these variables will allow accurate surface change detection, a fundamental determinant of global change.     

High-resolution image compression algorithms in remote sensing imaging

Digital image processing (DIP) has great application values in many fields, especially in remote sensing image processing, which represents the acquisition, enhancement, analysis, encoding, transmission, and storage of remote sensing images. With the development of chip technology and parallel computing technology, various digital image processing technologies have been successfully applied to satellite applications to help researchers exploit reliable information from remote-sensing images. However, the huge amount of images generated by ultra-high resolution optical remote sensing satellites put great pressure on existing transmission, storage, and processing technologies. Therefore, this paper proposes a spatio-temporal compression pipeline for remote sensing images based on lossy compression methods with ultra-high compression ratios to reduce the overhead required for the transmission and storage of remote sensing images while maintaining the quality of the compressed images. The experimental results show that the proposed method outperforms the classical image compression such as JPEG-2000.