基于压缩感知的高速移动环境中OFDM信道估计

针对目前高速移动信道中通信质量较差,为降低高速移动环境中信道估计误差,建立了以Winner Ⅱ信道为基础的高速信道OFDM系统仿真模型,通过压缩感知技术对移动信道进行估计,并与传统的信道估计方法进行比较.仿真结果表明,压缩感知技术能大幅度提高高速移动环境中的OFDM系统信道估计精度.     

Source parameter estimation in inhomogeneous volume conductors ofarbitrary shape

In this paper it is demonstrated that the use of a direct matrix inverse in the solution of the forward problem in volume conduction problems greatly facilitates the application of standard, nonlinear parameter estimation procedures for finding the strength as well as the location of current sources inside an inhomogeneous volume conductor of arbitrary shape from potential measurements at the outer surface (inverse procedure). This, in turn, facilitates the inclusion of a priori constraints. Where possible, the performance of the method is compared to that of the Gabor-Nelson method. Applications are in the fields of bioelectricity (e.g., electrocardiography and electroencephalography).     

Spatial spectrum estimation in a coherent signal environment using an array in motion

Spatial spectrum estimation utilizing an array in motion is here investigated for dealing with coherent arrivals in a multiple signal environment. The effect of estimating the spatial correlations while the array is moving is studied in terms of the decorrelation it produces, the change it causes in the eigenvalues of the correlation matrix, and the improvements obtained in the measured spectrum. Cases of both fixed and varying angle of arrival are investigated, The former arises with distant sources and will, with a sufficiently long estimation interval, emulate uncorrelated sources and given correspondingly sharp spectra. The latter arises with nearby sources and will allow them to be distinguished but will be attended by spectral shift and broadening, and loss of resolution. It is shown that meaningful estimates of the arrival angles can nevertheless be made. Specific illustrations are worked out using a seven-element, sparse, nonuniformly spaced linear array utilizing the well-known superresolution spectral estimators-the maximum likelihood (ML) method, the method of linear prediction (LP), and the method of multiple signal classification (MUSIC).     

A multiwindow method for spectrum estimation and sinusoid detectionin an array environment

In many applications (radar, communication, plasma physics) the signal of interest is sinusoidal and is hidden in nonwhite noise. The multiwindow method of spectrum estimation gives a constant false alarm test for the presence of a sinusoid in a time series if the noise can be assumed Gaussian. The authors generalize the method to an array and study the resulting test. Expressions for the probabilities of detection and of false alarm are obtained analytically, and receiver operation characteristic curves are computed for a particular scenario under different conditions     

智能温室环境感知系统设计

随着传感器技术和物联网技术的发展,采用现代传感器以及物联网技术进行无线传输信息,对于农业温室大棚环境监测具有一定的应用价值。本文以单片机STC15F2K60S2为核心,通过将光照强度传感器、温湿度传感器、CO2传感器整合在一个系统中,来实现对温室大棚的监测和报警,并将数据通过无线传输方式传送到监控室。通过对环境的监测与控制来提高作物产率和经济效益。     

Asymmetric coupled lines in an inhomogeneous medium

Up-to-date, asymmetric coupled lines in an inhomogeneous medium have been dealt with either theoretically or by making use of certain properties obtained in practice. According to certain formulas, these properties are only valid for extremely special geometrical configurations. However, we realised from the general theory, by performing numerical applications, that these properties possess an unexpected application domain: they are, in particular, valid for practically all experimental manipulations.     

Location sensing and privacy in a context-aware computing environment

This article presents and evaluates the performance of a location sensing algorithm developed and demonstrated at Carnegie Mellon University. We compare our model with various others based on different architectures and software paradigms. We show comparative results in accuracy, the complexity of training, total power consumption, and suitability to users. Our method reduces training complexity by a factor of eight over previous algorithms, and yields noticeably better accuracy. The algorithm uses less power than previous models, and offers a more secure privacy model.     

Rank-defective millimeter-wave channel estimation based on subspace-compressive sensing

Millimeter-wave communication (mmWC) is considered as one of the pioneer candidates for 5G indoor and outdoor systems in E-band. To subdue the channel propagation characteristics in this band, high dimensional antenna arrays need to be deployed at both the base station (BS) and mobile sets (MS). Unlike the conventional MIMO systems, Millimeter-wave (mmW) systems lay away to employ the power predatory equipment such as ADC or RF chain in each branch of MIMO system because of hardware constraints. Such systems leverage to the hybrid precoding (combining) architecture for downlink deployment. Because there is a large array at the transceiver, it is impossible to estimate the channel by conventional methods. This paper develops a new algorithm to estimate the mmW channel by exploiting the sparse nature of the channel. The main contribution is the representation of a sparse channel model and the exploitation of a modified approach based on Multiple Measurement Vector (MMV) greedy sparse framework and subspace method of Multiple Signal Classification (MUSIC) which work together to recover the indices of non-zero elements of an unknown channel matrix when the rank of the channel matrix is defected. In practical rank-defective channels, MUSIC fails, and we need to propose new extended MUSIC approaches based on subspace enhancement to compensate the limitation of MUSIC. Simulation results indicate that our proposed extended MUSIC algorithms will have proper performances and moderate computational speeds, and that they are even able to work in channels with an unknown sparsity level.     

An efficient DOA estimation method in multipath environment

An efficient direction of arrival (DOA) estimation method is proposed with uniform linear array (ULA) in multipath environment. By applying a transformation, we can convert a constructed complex matrix to a real one, and then utilize the real matrix combined with our proposed criterion to estimate the DOAs of uncorrelated signals. Afterwards, the contributions of uncorrelated signals are eliminated, and then several new matrices without the information of uncorrelated signals are constructed to resolve the remaining coherent signals. The proposed estimation method overcomes the shortcomings of the existing methods and has satisfactory performance. Simulation results confirm the theoretical analysis and show the effectiveness of the proposed method.     

Compress sensing algorithm for estimation of signals in sensor networks

Wireless Networks - In this research, we present a data recovery scheme for wireless sensor networks. In some sensor networks, each node must be able to recover the complete information of the...     

大数据环境下的Web网络风险估计

针对传统大数据评估过程中的Web网络风险评估结果不精准并且速度较慢的问题,提出一种大数据环境下的Web网络风险估计方法。采用特定的风险评估因子进行有效的评估,避免了传统方法中评定项目繁杂、计算量大等因素造成的评估不准的问题。评估过程中使用了特定的评估模型,把传统的加权平均的风险估计方法转变成为非线性映射评估方法,这样能够更加准确的进行风险评估。为了验证设计的大数据环境下的Web网络风险估计的有效性,设计了对比仿真实验。实验结果表明,设计的大数据环境下的Web网络风险估计方法能够有效地解决风险评估过程中的评估结果不准确问题。     

快变信道环境下OFDM频谱感知算法

针对基于慢变信道假设的认知无线电不适合高速移动场景的问题,提出一种基于广义似然比的OFDM频谱感知算法。利用OFDM信号自身相关性得到OFDM检测统计量,通过研究其统计分布特性得到OFDM广义似然比检测统计量,构建快变信道检测模型进行频谱感知。在原算法的基础上,用检测窗口的宽度近似替代检测统计量的方差,得到广义似然比的改进算法。仿真结果表明,两种感知算法在低信噪比、快变信道环境中都具有很高的认知度,而且改进算法对噪声和干扰还具有很好的鲁棒性。     

An inhomogeneous thermal block model of man for the electromagnetic environment

An inhomogeneous thermal block model of man with 476 cubical cells has been developed. It incorporates inhomogeneous blood flow rates, metabolic heat production, specific heats, and thermal conductivities, obtained by volume-weighted averaging of the values for eleven different tissue types which constitute the human body. The transient heat conduction equation for the thermal model includes the effects of internal metabolic heat generation, electromagnetic energy deposition, heat conduction through the tissues, convective heat transfer by blood, evaporative heat dissipation by sweating and insensible perspiration, respiratory heat loss, and heat transfer to the environment by radiation and convection. The standard implicit finite difference technique has been utilized to solve the three-dimensional heat conduction equation.     

Decentralized nonlinear adaptive control of an HVAC system

This paper presents a new decentralized nonlinear adaptive controller (DNAC) for a heating, ventilating, and air conditioning (HVAC) system capable of maintaining comfortable conditions under varying thermal loads. In this scheme, an HVAC system is considered to be two subsystems and controlled independently. The interactions between the two subsystems are treated as deterministic types of uncertain disturbances and their magnitudes are supposed to be bounded by absolute value. The decentralized nonlinear adaptive controller (DNAC) consists of an inner loop and an outer loop. The inner loop is a single-input fuzzy logic controller (FLC), which is used as the feedback controller to overcome random instant disturbances. The outer loop is a Fourier integral-based control, which is used as the frequency-domain adaptive compensator to overcome steady, lasting uncertain disturbances. The global DNAC controller ensures that the system output vector tracks a desired trajectory vector within the system bandwidth and that the tracking error vector converges uniformly to a zero vector. The simulated experimental results on the HVAC system show that the performance is dramatically improved.     

Polarimetric modeling and parameter estimation with applications toremote sensing

Develops and analyzes two parametric models in which electromagnetic plane waves carrying polarimetric information are received. The first model considers estimation of the polarimetric response of a surface by measuring the reflections of actively generated waves. The second considers estimation of the polarization of passively generated waves. Both models have applications to remote sensing. The authors propose a natural parametrization of the distribution of the received signal. Using the Cramer-Rao bound, they characterize the best possible accuracy of unbiased estimators of these parameters. Simple estimators are given. Both models are fitted into a common framework and compared     

Distribution agnostic Bayesian compressive sensing with incremental support estimation

In most compressive sensing algorithms, such as L1-optimization and greedy family techniques, the only a priori information utilized in the reconstruction procedure is the sparsity information. Meanwhile, there exists another family of techniques based on the Bayesian strategy, which considers comprehensive a priori statistical knowledge of the sparse data. This feature resulted in more increased attention to this category of algorithms. One member of the Bayesian-based family of compressive sensing reconstruction algorithms is the support Agnostic Bayesian Matching Pursuit, which is agnostic to support distribution.However, its high computation complexity in determining the set of dominant supports makes this algorithm unfeasible for practical applications such as wireless sensor networks (WSNs). Due to the special conditions of WSNs, consists of limited-power sensors, developed algorithms for them must have the least possible amount of computations. Given this, in this paper, we propose a Bayesian-based method with incremental support detection for distributed sparse signal recovery, which considerably reduces computational complexity. In the proposed method, in a network of sensors, sparse signal reconstruction from noisy measurements is done distributively and in the form of incremental cooperation. So, the number of required computations will be significantly reduced, which will result in a fast approach. The computer simulations show the superior performance of the proposed incremental Bayesian recovery method.

     

Harmonic generation by an electromagnetic wave in an inhomogeneous isotropic plasma

The generation of harmonic waves by a plane electromagnetic wave normally incident on a collision-free plasma with a linear density profile is considered. The solution for the primary wave in the plasma region is first obtained, and nonlinear polarization currents for the higher harmonics are expressed in terms of the lower harmonic fields by a perturbation method. The analysis shows that the plasma excites a longitudinally oscillating second harmonic electric field which is totally confined inside the plasma and has a singularity. The third harmonic wave excited in the plasma, however, radiates back into free space, although the effect is negligibly small in a quasi-homogeneous limit. The amplitude of this radiated field oscillates as a function ofomega^{3}, and the electron density gradient has little effect on it.     

Channel estimation based on distributed compressed sensing in amplify-and-forward relay networks

In orthogonal frequency-division multiplexing(OFDM)amplify-and-forward(AF)relay networks,in order to exploit diversity gains over frequency-selective fading channels,the receiver needs to acquire the knowledge of channel state information(CSI).In this article,based on the recent methodology of distributed compressed sensing(DCS),a novel channel estimation scheme is proposed.The joint sparsity model 2(JSM-2)in DCS theory and simultaneous orthogonal matching pursuit(SOMP)are both introduced to improve the estimation performance and increase the spectral efficiency.Simulation results show that compared with current compressed sensing(CS)methods,the estimation error of our scheme is reduced dramatically in high SNR region while the pilot number is still kept small.     

A unified framework for MAP estimation in remote sensing image segmentation

A complete framework is proposed for applying the maximum a posteriori (MAP) estimation principle in remote sensing image segmentation. The MAP principle provides an estimate for the segmented image by maximizing the posterior probabilities of the classes defined in the image. The posterior probability can be represented as the product of the class conditional probability (CCP) and the class prior probability (CPP). In this paper, novel supervised algorithms for the CCP and the CPP estimations are proposed which are appropriate for remote sensing images where the estimation process might to be done in high-dimensional spaces. For the CCP, a supervised algorithm which uses the support vector machines (SVM) density estimation approach is proposed. This algorithm uses a novel learning procedure, derived from the main field theory, which avoids the (hard) quadratic optimization problem arising from the traditional formulation of the SVM density estimation. For the CPP estimation, Markov random field (MRF) is a common choice which incorporates contextual and geometrical information in the estimation process. Instead of using predefined values for the parameters of the MRF, an analytical algorithm is proposed which automatically identifies the values of the MRF parameters. The proposed framework is built in an iterative setup which refines the estimated image to get the optimum solution. Experiments using both synthetic and real remote sensing data (multispectral and hyperspectral) show the powerful performance of the proposed framework. The results show that the proposed density estimation algorithm outperforms other algorithms for remote sensing data over a wide range of spectral dimensions. The MRF modeling raises the segmentation accuracy by up to 10% in remote sensing images.