On optimal monitor placement for localizing node failures via network tomography

We investigate the problem of placing monitors to localize node failures in a communication network from binary states (normal/failed) of end-to-end paths, under the assumption that a path is in normal state if and only if it contains no failed nodes. To uniquely localize failed nodes, the measurement paths must show different symptoms (path states) under different failure events. Our goal is to deploy the minimum set of monitors to satisfy this condition for a given probing mechanism. We consider three families of probing mechanisms, according to whether measurement paths are (i) arbitrarily controllable, (ii) controllable but cycle-free, or (iii) uncontrollable (i.e., determined by the default routing protocol). We first establish theoretical conditions that characterize network-wide failure identifiability through a per-node identifiability measure that can be efficiently evaluated for the above three probing mechanisms. Leveraging these results, we develop a generic monitor placement algorithm, applicable under any probing mechanism, that incrementally selects monitors to optimize the per-node measure. The proposed algorithm is shown to be optimal for probing mechanism (i), and provides upper and lower bounds on the minimum number of monitors required by the other probing mechanisms. In the special case of single-node failures, we develop an improved monitor placement algorithm that is optimal for probing mechanism (ii) and has linear time complexity. Using these algorithms, we study the impact of the probing mechanism on the number of monitors required for uniquely localizing node failures. Our results based on real network topologies show that although more complicated to implement, probing mechanisms that allow monitors to control measurement paths substantially reduce the required number of monitors.     

基于多边限定的室内定位方法设计与系统实现

随着基于位置服务（Location Based Services，LBS）的发展与智能移动设备的普及，室内定位算法与系统受到了广泛研究与关注。为提高室内定位精度、增强系统鲁棒性，提出了基于多边限定的fingerprint定位方法。基于Wi-Fi RSSI（Received Signal Strength Indication）信号处理建立离线fingerprint数据库；通过对拟合距离-RSSI函数分析，提出了多边限定的方法确定一个最佳参考点（Reference Point，RP）集合，缩小在线定位阶段的搜索范围。在此基础上，再利用fingerprint定位方法进行定位。此外，实现了基于提出方法的室内定位系统原型用于算法性能评估。通过大量真实场景实验分析、验证了相较于传统fingerprint方法，基于多边限定的fingerprint定位方法能有效提高室内定位精度，增强系统鲁棒性。     

基于人工智能算法的投诉用户质差小区定位及预测的方法研究

目前LTE及GSM的质差小区投诉定位主要是依靠用户投诉的地址回放分析附近小区是否存在性能、告警上的裂变,如无则安排人员现场测试信号强度、干扰等。为了解决自动定位投诉质差小区,创新性提出以数据驱动来实现业务规则自动化,充分考虑用户投诉的时间和问题、网络交互信令、无线性能指标和告警等海量数据,运用人工智能技术中的逻辑回归及决策树算法训练出“2G语音、VoLTE语音及数据业务的投诉质差小区定界模型、质差小区预测模型及问题网元”,更快速,更智能,更准确输出语音及数据业务投诉原因、质差小区及质差原因,并对潜在质差小区进行预测,对无线规划、基站建设、故障处理、网络改造等方面的工作也具有积极指导意义。     

On the use of calibration sensors in source localization using TDOA and FDOA measurements

The accuracy of a source location estimate is very sensitive to the presence of the random noise in the known sensor positions. This paper investigates the use of calibration sensors, each of which is capable of broadcasting calibration signals to other sensors as well as receiving the signals from the source and other calibration sensors, to reduce the loss in the source localization accuracy due to uncertainties in sensor positions. We begin the study with deriving the Cramer–Rao lower bound (CRLB) for source localization using time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measurements when a single calibration sensor is available. The obtained CRLB result is then extended to the more general case with multiple calibration sensors. The performance improvement due to the use of calibration sensors is established analytically. We then propose a closed-form algorithm that can explore efficiently the calibration sensors to improve the source localization accuracy when the sensor positions are subject to random errors. We prove analytically that the newly developed localization method attains the CRLB accuracy under some mild approximations. Simulations verify the theoretical developments.     

Stereo-camera-based object detection using fuzzy color histograms and a fuzzy classifier with depth and shape estimations

This paper proposes a new method of detecting an object containing multiple colors with non-homogeneous distributions in complex backgrounds and subsequently estimating the depth and shape of the object using a stereo camera. To extract features for object detection, this paper proposes fuzzy color histograms (FCHs) based on the self-splitting clustering (SSC) of the hue-saturation (HS) color space. For each scanning window in a pyramid of scaled images, the FCH is obtained by accumulating the fuzzy degrees of all of the pixels belonging to each cluster. The FCH is fed to a fuzzy classifier to detect an object in the left image captured by the stereo camera. To find the matched object region in the right image, the left and right images are first segmented using the SSC-partitioned HS space. The depth of the object is then found by performing stereo matching on the segmented images. To find the shape of the object, a disparity map is built using the estimated object depth to automatically determine the stereo matching window size and disparity search range. Finally, the shape of the object is segmented from the disparity map. The experimental results of the detection of different objects with depth and shape estimations are used to verify the performance of the proposed method. Comparisons with different detection and disparity map construction methods are performed to demonstrate the advantage of the proposed method.     

第三代核主泵屏蔽电机的关键部件金属材料国产化综述

材料的国产化是设备国产化的基础,第三代核主泵屏蔽电机部分兲键材料在国内均为首次制造,能否实现国产化将会成为制约主泵电机国产化的瓶颈问题。为推迚主泵电机兲键材料的国产化迚程,同时满足后续项目的需求,依托国家科技重大专项,对部分兲键材料的国产化研究迚行了专项立项研究,取得了突破性的迚展。通过与联合单位的合作,迚行了材料试制及试验研究,实现了屏蔽套、电机轴,飞轮护环,高强度支撑筒及推力盘等兲键部件材料的国产化制造,为核主泵电机的国产化奠定了材料基础。本文主要论述了国家重大专项立项研究的兲键部件金属材料国产化情况。     

Energy-efficient WiFi scanning for localization

WiFi radio signals are commonly used for the localization of mobile devices. However, frequent scanning of WiFi reduces user experiences because it consumes significant energy on mobile devices. In this paper, we propose an energy-efficient WiFi scan system for localization. The proposed system preserves the quality of service of each application and minimizes the energy consumption of WiFi scanning. The main idea is that the system adaptively controls the dwell time of beacon-listening in given locations. We predict the number of scanned access points (APs) according to locations and optimize the dwell time of beacon-listening to obtain the minimum number of scanned APs. The evaluation shows that the proposed system saves the energy consumption of WiFi scans by 33.6% and 45.7%, according to the number of scanned APs, while not decreasing the accuracy of localization in indoor and outdoor scenarios.     

Enhanced radio tomographic imaging with heterogeneous Bayesian compressive sensing

This work explores the novel use of Bayesian compressive sensing (BCS) in radio tomographic imaging (RTI), which aims at addressing the performance degradation of shadow fade imaging due to multipath interferences, through the sophisticated efforts on enhancing BCS with the capability of heterogeneous-noise-variance learning. Our contribution is twofold. Firstly, we incorporate a hierarchical model of heterogeneous noise variances into sparse Bayesian learning, which can contribute to the enhancement of BCS in terms of noise-variance awareness. Then, under our enhanced BCS (namely heterogeneous BCS) framework, we develop two learning algorithms for the RTI reconstruction. Theoretical analysis will show the potential advantages of using our heterogeneous BCS in mitigating the effect of multipath interferences, as well as in improving the RTI performance with our learning algorithms. Finally, the experimental results in the context of device-free localization and tracking are reported to demonstrate the effectiveness of the proposed approach.