A WKNN-based approach for NB-IoT sensors localization

With the recent introduction of NarrowBand Internet of Things (NB-IoT) technology in the 4th and 5th generations of mobile radio networks, the mobile communications context opens up significantly to the world of sensors. By means of NB-IoT, the mobile systems within 3GPP standardization introduce the peculiar functions of sensor networks, thus making it possible to satisfy very specific requirements with respect to those which characterize traditional mobile telecommunications. Among the functions of interest for sensor networks, the possibility of locating the positions of the sensors without an increase in costs and energy consumption of the sensor nodes is of utmost interest. The present work describes a procedure for locating the NB-IoT nodes based on the quality of radio signals received by the mobile terminals, which therefore does not require further hardware implementations on board the nodes. This procedure, based on the RF fingerprinting technique and on machine learning processing, has been tested experimentally and has achieved interesting performances.     

A novel dictionary-based approach for missing sample recovery of signals in manifold

Recovery of signal with randomly positioned missing samples is a difficult or impossible task. The aim of this paper is to accurately recover missing samples if prior information on domain of sparsity is known. This paper proposes a novel approach for recovery of signals lying in low-dimensional sub-manifold, embedded in high-dimensional signal space and heavily corrupted by arbitrarily positioned missing samples. The proposed simple and efficient algorithm is based on global manifold model and can recover the corrupted signal from the limited available samples without affecting the remaining samples. The proposed method is applicable to any type of data and can be extensively used in a wide variety of data processing techniques. Experimental results prove that the proposed method outperforms the counterparts without much computational complexity.     

Indoor localization via -graph regularized semi-supervised manifold learning

In this paper,a new l1-graph regularized semi-supervised manifold learning(LRSML) method is proposed for indoor localization.Due to noise corruption and non-linearity of received signal strength(RSS),traditional approaches always fail to deliver accurate positioning results.The l1-graph is constructed by sparse representation of each sample with respect to remaining samples.Noise factor is considered in the construction process of l1-graph,leading to more robustness compared to traditional k-nearest-neighbor graph(KNN-graph).The KNN-graph construction is supervised,while the l1-graph is assumed to be unsupervised without harnessing any data label information and uncovers the underlying sparse relationship of each data.Combining KNN-graph and l1-graph,both labeled and unlabeled information are utilized,so the LRSML method has the potential to convey more discriminative information compared to conventional methods.To overcome the non-linearity of RSS,kernel-based manifold learning method(K-LRSML) is employed through mapping the original signal data to a higher dimension Hilbert space.The efficiency and superiority of LRSML over current state of art methods are verified with extensive experiments on real data.     

基于单变量信号的混沌同步变结构控制

提出一种基于单变量实现混沌同步的变结构控制方法。该非线性控制策略的有效性通过对Lorenz混沌系统在理论分析和数字仿真两方面得到了证明。     

A deconvolution approach to source localization

Due to its low resolution the conventional beamformer (CBF) has generally been neglected in the recent literature on array processing and considerable attention devoted to the high-resolution (HR) techniques. Applying a deconvolution approach to the output of the CBF, the authors obtain a source localization procedure whose performances are comparable to those of the most efficient HR techniques. The scheme, moreover, also furnishes estimates of the source powers and comprises a test that detects the number of sources present. It can indeed be seen as a means to obtain maximum likelihood estimates (MLE) when the CBF-output is taken as the observed data     

空指针异常的自动故障定位方法

提出一种空指针异常自动定位方法。该方法首先结合程序的静态分析技术,利用程序运行时的堆栈信息指导程序切片,然后对得到的切片进行空指针分析及别名分析,得出引发空指针异常的可疑语句集合,最终给出错误定位报告。实验结果表明,所提方法虽然因收集堆栈信息和别名分析增加了时间开销,但是利用堆栈信息可以缩小问题搜索空间,别名分析可以发现隐含的值传递过程,从而克服单独使用静态方法分析结果引起误报和漏报的缺陷,使最终的错误定位结果更精确。     

A state-space modeling approach for localization of focal current sources from MEG

State-space modeling is a promising approach for current source reconstruction from magnetoencephalography (MEG) because it constrains the spatiotemporal behavior of inverse solutions in a flexible manner. However, state-space model-based source localization research remains underdeveloped; extraction of spatially focal current sources and handling of the high dimensionality of the distributed source model remain problematic. In this study, we propose a novel state-space model-based method that resolves these problems, extending our previous source localization method to include a temporal constraint by state-space modeling. To enable focal current reconstruction, we account for spatially inhomogeneous temporal dynamics by introducing dynamics model parameters that differ for each cortical position. The model parameters and the intensity of the current sources are jointly estimated according to a bayesian framework. We circumvent the high dimensionality of the problem by assuming prior distributions of the model parameters to reduce the sensitivity to unmodeled components, and by adopting variational bayesian inference to reduce the computational cost. Through simulation experiments and application to real MEG data, we have confirmed that our proposed method successfully reconstructs focal current activities, which evolve with their temporal dynamics.     

DWDM用增益平坦EDFA研究

本文阐述了ＤＷＤＭ系统对ＥＤＦＡ的要求和ＥＤＦＡ为达到这些要求的几种实现方法。并给出了ＤＷＤＭ用ＥＤＦＡ级联和增益锁定的实验结果。     

D-fibre filter for erbium gain spectrum flattening

A novel type of filter based on D-fibre with an overlay grating is proposed. The fabrication and characterisation of the device are described and its use in an erbium doped fibre amplifier to obtain a flatter gain spectrum is discussed.<>     

Neural-based approach for localization of sensors in indoor environment

Location of wireless sensor nodes is an important piece of information for many applications. There are many algorithms present in literature based on Received Signal Strength (RSSI) to estimate the location. However the radio signal propagation is easily influenced by diffraction, reflection and scattering. Therefore algorithms purely based on RSSI may not accurately predict the position of the node. In the present work, an algorithm for estimating the position of mobile nodes is proposed which is based on a combination of Received Signal Strength (RSSI) and Link Quality Indicator (LQI). Artificial Neural Networks are used to establish the relationship between the location of the mobile node and the experimentally obtained values of RSSI and LQI. Two different algorithms namely, Bayesian Regularization and Gradient Descent are used to develop the neural network model. Proposed algorithms improve the localization accuracy and perform better than other state-of-the-art algorithms.     

An approach to define parameters for localization of thunderstorms

The basic topic of this paper is the localization of the thunderstorm center. It concentrates on the development of a method to analytically determine the range, the geometric factor, and the angular parameters. An electrical dipole of arbitrary spatial orientation is used as model of the discharge between a cloud and the Earth.     

A model-based, semi-global segmentation approach for automatic 3-D point landmark localization in neuroimages

The existing differential approaches for localization of 3-D anatomic point landmarks in 3-D images are sensitive to noise and usually extract numerous spurious landmarks. The parametric model-based approaches are not practically usable for localization of landmarks that can not be modeled by simple parametric forms. Some dedicated methods using anatomic knowledge to identify particular landmarks are not general enough to cope with other landmarks. In this paper, we propose a model-based, semi-global segmentation approach to automatically localize 3-D point landmarks in neuroimages. To localize a landmark, the semi-global segmentation (meaning the segmentation of a part of the studied structure in a certain neighborhood of the landmark) is first achieved by an active surface model, and then the landmark is localized by analyzing the segmented part only. The joint use of global model-to-image registration, semi-global structure registration, active surface-based segmentation, and point-anchored surface registration makes our method robust to noise and shape variation. To evaluate the method, we apply it to the localization of ventricular landmarks including curvature extrema, centerline intersections, and terminal points. Experiments with 48 clinical and 18 simulated magnetic resonance (MR) volumetric images show that the proposed approach is able to localize these landmarks with an average accuracy of 1 mm (i.e., at the level of image resolution). We also illustrate the use of the proposed approach to cortical landmark identification and discuss its potential applications ranging from computer-aided radiology and surgery to atlas registration with scans.      

基于最小包含圆的无线传感器网络定位算法

提出一种新的无需测距定位算法--基于最小包含圆的定位(SECL).该算法根据目标周围的锚节点所决定的最小包含圆来估计目标位置.基于最小包含圆的算法考虑的不是坐标系中所有锚节点位置的平均值,而是覆盖所有锚节点区域的几何中心,能够有效地控制锚节点分布不均匀给定位带来的负面影响.仿真结果显示,相对于质心算法平均定位精度能提高10%以上.SECL在锚节点拓扑不均匀情况下,精度提升更高.     

Comparative node selection‐based localization technique for wireless sensor networks: A bilateration approach

Wireless sensor networks find extensive applications, such as environmental and smart city monitoring, structural health, and target location. To be useful, most sensor data must be localized. We propose a node localization technique based on bilateration comparison (BACL) for dense networks, which considers two reference nodes to determine the unknown position of a third node. The mirror positions resulted from bilateration are resolved by comparing their coordinates with the coordinates of the reference nodes. Additionally, we use network clustering to further refine the location of the nodes. We show that BACL has several advantages over Energy Aware Co‐operative Localization (EACL) and Underwater Recursive Position Estimation (URPE): (1) BACL uses bilateration (needs only two reference nodes) instead of trilateration (that needs three reference nodes), (2) BACL needs reference (anchor) nodes only on the field periphery, and (3) BACL needs substantially less communication and computation. Through simulation, we show that BACL localization accuracy, as root mean square error, improves by 53% that of URPE and by 40% that of EACL. We also explore the BACL localization error when the anchor nodes are placed on one or multiple sides of a rectangular field, as a trade‐off between localization accuracy and network deployment effort. Best accuracy is achieved using anchors on all field sides, but we show that localization refinement using node clustering and anchor nodes only on one side of the field has comparable localization accuracy with anchor nodes on two sides but without clustering.     

A covariance fitting approach to parametric localization of multiple incoherently distributed sources

In this paper, a new algorithm for parametric localization of multiple incoherently distributed sources is presented. This algorithm is based on an approximation of the array covariance matrix using central and noncentral moments of the source angular power densities. Based on this approximation, a new computationally simple covariance fitting-based technique is proposed to estimate these moments. Then, the source parameters are obtained from the moment estimates. Compared with earlier algorithms, our technique has lower computational cost and obtains the parameter estimates in a closed form. In addition, it can be applied to scenarios with multiple sources that may have different angular power densities, while other known methods are not applicable to such scenarios.     

运动检测与定位的热释电红外传感新方法

针对人体目标运动检测与定位,提出了一种新型的热释电红外感知方法,采用递阶的体系结构对菲涅尔透镜的视场(FoV)进行空间调制。首先,对单个传感器节点上的多个热释电红外(PIR)传感器及其菲涅尔透镜的FoV进行环形调制编码,实现单自由度的FoV细分;然后,利用相邻的3个传感器节点相互协作定位。实验表明,该方法具有计算简单、鲁棒性好等优点。     

Source localization using a current-density minimization approach

Determining the location of cortical activity from electroencephalographic (EEG) data is important clinically. In this paper, a method is presented which uses the powerful optimization method of simulated annealing in conjunction with a finite-element-based model of the search domain for single-time slice solution of the EEG-inverse problem. The algorithm highlights a new objective function based on the current-density boundary integral associated with the finite-element formulation as the basis for parameter optimization. In two-dimensional experiments in a shallow tank containing saline, single dipoles are located within 2 mm. Simulations studying the algorithms response to structured noise are also presented. The new objective function is shown to take advantage of the natural framework associated with finite-elements and the results suggest that the approach is capable of resolving dipole locations in simulations and experiments.     

Communication on the Grassmann manifold: a geometric approach tothe noncoherent multiple-antenna channel

We study the capacity of multiple-antenna fading channels. We focus on the scenario where the fading coefficients vary quickly; thus an accurate estimation of the coefficients is generally not available to either the transmitter or the receiver. We use a noncoherent block fading model proposed by Marzetta and Hochwald (see ibid. vol.45, p.139-57, 1999). The model does not assume any channel side information at the receiver or at the transmitter, but assumes that the coefficients remain constant for a coherence interval of length T symbol periods. We compute the asymptotic capacity of this channel at high signal-to-noise ratio (SNR) in terms of the coherence time T, the number of transmit antennas M, and the number of receive antennas N. While the capacity gain of the coherent multiple antenna channel is min{M, N} bits per second per Hertz for every 3-dB increase in SNR, the corresponding gain for the noncoherent channel turns out to be M* (1 - M*/T) bits per second per Hertz, where M*=min{M, N, [T/2]}. The capacity expression has a geometric interpretation as sphere packing in the Grassmann manifold     

Passband flattening and broadening techniques for high spectralefficiency wavelength demultiplexers

Cancellation of the wavelength dependent image shift in demultiplexers, resulting in significant bandwidth increase, is described. Additional passband flattening using a Fabry-Perot filter is proposed. Spectral efficiencies greater than 90% can be achieved with a very low crosstalk