Feasibility of tomography with unknown view angles

In the standard two-dimensional (2-D) parallel beam tomographic formulation, it is generally assumed that the angles at which the projections were acquired are known. We have previously demonstrated, however, that under fairly mild conditions these view angles can be uniquely recovered from the projections themselves. We address the question of reliability of such solutions to the angle recovery problem using moments of the projections. We demonstrate that under mild conditions, the angle recovery problem has unique solutions and is stable with respect to perturbations in the data. Furthermore, we determine the Cramer-Rao lower bounds on the variance of the estimates of the angles when the projection are corrupted by additive Gaussian noise. We also treat the case in which each projection is shifted by some unknown amount which must be jointly estimated with the view angles. Motivated by the stability results and relatively small values of the error bounds, we construct a simple algorithm to approximate the ML estimator and demonstrate that the problem can be feasibly solved in the presence of noise. Simulations using this simple estimator on a variety of phantoms show excellent performance at low to moderate noise levels, essentially achieving the Cramer-Rao bounds.     

Uniqueness of tomography with unknown view angles

In the standard two-dimensional (2-D) parallel beam tomographic formulation, it is assumed that the angles at which the projections were acquired are known. In certain situations, however, these angles are known only approximately (as in the case of magnetic resonance imaging (MRI) of a moving patient), or are completely unknown. The latter occurs in a three-dimensional (3-D) version of the problem in the electron microscopy-based imaging of viral particles. We address the problem of determining the view angles directly from the projection data itself in the 2-D parallel beam case. We prove the surprising result that under some fairly mild conditions, the view angles are uniquely determined by the projection data. We present conditions for the unique recovery of these view angles based on the Helgasson-Ludwig consistency conditions for the Radon transform, we also show that when the projections are shifted by some random amount which must be jointly estimated with the view angles, unique recovery of both the shifts and view angles is possible.     

多基站数据聚合无线传感器网络中的最大生命期地理位置路由

将地理位置路由应用于多基站数据聚合无线传感器网络以最大化网络生命期.为减少传输开销并避免路由环路,提出了归属基站集合降势法,采用线性规划方法优化网络生命期.提出了基于地理位置的多基站剖分法,采用次梯度方法设计了分布式算法.通过仿真实验验证了所提算法的性能.     

Ultrasound imaging and identification of microcalcification clusters by correlation of scatter from multiple angles

Various types of malignant and benign breast tumors are associated with clusters of calcifications with grain sizes 0.1 to a few mm spread out over volumes of a few cc. A series of phantoms containing calcium carbonate grains embedded in a gelatin mixture were made and the ultrasound scattering patterns were measured with 2.25 MHz transducers. Scatterings from the calcifications were distinguished from the larger reflections from tissue interfaces by computer correlation of the signals obtained from transducers placed at three different angles. An automatic gain control detection system was developed for the purpose of amplifying the signal to the right level for the computer correlation and to compensate for the attenuation of the ultrasound in the tissue.     

Image reconstruction from multiple frames of sparse data

High-resolution image reconstruction from sparse data is an important problem in sensor array imaging (SAI). The reconstructed images from such a data are poorly resolved. However, there may exist possibilities of making multiple measurements, for example, collecting many frames of data in a dynamic scene situation where there is relative motion between the object and the receiver. We discuss here a method of reconstructing good-quality images from multiple frames of sparse data obtained from a simulated dynamic scene situation. This method, based on projection onto convex sets (POCS), not only restricts the solution set by satisfying the constraints in the multiple measurements but also reconstructs a high-resolution image.     

Radiation from conical horns with large flare angles

Radiation patterns of conical horns with large flare angles excited in theTE_{11}mode are calculated from a simple solution for horn modes using the vector diffraction formula. Radiation patterns are expressed in closed form when the half-flare angle is less than30deg. Patterns obtained by experiment show close agreement with calculated results.     

Cumulus cloud base height estimation from high spatial resolutionLandsat data: a Hough transform approach

This study develops a semiautomated methodology for estimating cumulus cloud base heights using high-spatial-resolution Landsat multispectral scanner data. The approach employs a variety of image processing techniques to match cloud edges with their corresponding shadow edges. Cloud base height is then estimated by computing the separation between the corresponding generalized Hough transform reference points. Sixteen subregions, each 30 km×30 km in size, are selected for four Landsat scenes. Standard deviations of cloud base height within each of the subregion range from about 100 m to 150 m. Differences between cloud base heights computed using the Hough transform and a manual verification technique are small. It is estimated that cloud base height accuracies of 50-70 m may be possible using HIRIS and ASTER instruments in the Earth Observation Satellite (EOS) Global Climate Change program     

Learning algorithms for a class of neurofuzzy network and application

A class of neurofuzzy networks and a constructive, competition-based learning procedure is introduced. Given a set of training data, the learning procedure automatically adjusts the input space portion to cover the whole space and finds membership functions parameters for each input variable. The network processes data following fuzzy reasoning principles and, due to its structure, it is dual to a rule-based fuzzy inference system. The neurofuzzy model is used to forecast seasonal streamflow, a key step to plan and operate hydroelectric power plants and to price energy. A database of average monthly inflows of three Brazilian hydroelectric plants located at different river basins was used as source of training and test data. The performance of the neurofuzzy network is compared with period regression, a standard approach used by the electric power industry to forecast streamflows. Comparisons with multilayer perceptron, radial basis network and adaptive neural-fuzzy inference system are also included. The results show that the neurofuzzy network provides better one-step-ahead streamflow forecasting, with forecasting errors significantly lower than the other approaches.     

SYBASE数据库的自动管理

介绍数据系统管理员的工作内容,自动管理的意义和如何实现SYBAWSE数据库的自动管理。     

Retrieval of cloud base heights from passive microwave and cloudtop temperature data

Water vapor profiling algorithms that treat liquid clouds explicitly yield a cloud base height as a byproduct. A single case of a water vapor profile retrieval using a combination of the SSM/T-2 on the DMSP satellite and cloud parameters from the AVHRR on the NOAA satellite retrieved a reasonable cloud base. While hardly definitive, this case is suggestive. The authors examine the cloud base signal in a combination of the SSM/1 and SSM/T-2 on the DMSP satellite from a theoretical point of view. It is shown that the signal is strong enough for a useful retrieval only over the ocean. For low altitudes, a cloud top temperature (CTT) constraint, as could be provided from an infrared radiometer, is required. While difficult with the DMSP-NOAA satellite combination, this has become much easier with the recent launch of NOAA-K with the AMSU-B and AVHRR. It is shown that the signal is acceptable over the relevant range of cloud liquid water content values. To achieve useful results, some local tuning of the algorithm will be necessary. This tuning could take the form of water vapor profile covariance matrices, climatological estimates of the cloud liquid water density, or purely empirical methods. Broken and multilayer clouds provide additional complications to the problem     

多试飞数据流文件的融合处理

为了向型号科研提供一个高质量、高精度的统一时间基准的数据信息,根据型号试飞需求,将来自多个相同或不同类型的数据流文件的参数数据进行融合处理。介绍线形插值、时间逼近及采样保持的数据融合算法,比较分析相关算法不同情况下的有效性,为试飞数据流文件的融合处理提供了依据。     

Digital filters for attenuating interference arriving from a widerange of angles

Digital filters for the offline multichannel processing of the signals recorded by an array of sensors are derived with the objective of extracting a desired signal arriving from a known direction. Both the desired signal and coherent interference have arbitrary waveforms. Since the direction of arrival of the interference is unknown, the array response to it is treated as a random variable, and the expected value of its squared magnitude is taken as a minimization criterion in order to attenuate the coherent interference. The output power spectrum in response to the additive random sensor noise is taken as a second criterion. A convex combination of both criteria is minimized subject to an all-pass condition for the desired signal. Simulation results are presented showing the tradeoff between attenuating interference and random noise, the effect of the angular separation between the directions of arrival of the desired signal and interference and the effect of the number of sensors of the array on the output of the array filter     

基于人工蜂群算法的基站天线方位角与下倾角自优化方法

针对3GPP中提出的SON(self-organizing network,自组织网络)覆盖自优化问题,提出一种基于人工蜂群算法的基站天线方位角与下倾角同时优化方法。首先,基站根据用户设备上报的MR(measurement report,测量报告)数据确定待优化区域,并建立以基站天线对待优化区域的平均增益为目标函数的优化模型;其次,利用人工蜂群算法求解该目标函数,并得到基站天线方位角与下倾角的最优解;最后,将基站天线方位角与下倾角调整至最佳值,实现基站根据用户设备位置的覆盖自优化。通过系统建模、仿真与外场实验,以用户设备接收的RSRP(reference signal receiving power,参考信号接收功率)为指标,利用人工蜂群算法的计算方法较未优化的初始参数提升6.81 dB,较依靠人工经验的判别方法提升4.35 dB。实验结果证明,提出的自优化方法可根据用户位置分布精准及时地对基站天线方位角与下倾角进行调整,提升用户对信号强度的感知。     

Identification of a class of multivariable systems from impulse response data: Theory and computational algorithm

A theoretical and algorithmic framework is proposed for the identification of rational transfer function matrices of a class of discrete-time multivariable systems. The proposed technique obtains an optimal approximation from the given (possibly noisy) measuredimpulse response data. It is assumed that the measured impulse response data corresponds to a system with a strictly proper transfer function matrix. The impulse response fitting error criterion is theoretically decoupled into a purely linear problem for estimating the optimal numerators and a nonlinear problem for the optimal denominators. Based on the proposed theoretical basis, an efficient computational algorithm is developed and illustrated with several examples. Research supported by AFOSR-89-0291 and by WRDC/WPAFB grant F33615-88-C-3605.     

Algorithm design for a class of base station location problems in sensor networks

Base station placement has significant impact on sensor network performance. Despite its significance, results on this problem remain limited, particularly theoretical results that can provide performance guarantee. This paper proposes a set of procedure to design (1− ε) approximation algorithms for base station placement problems under any desired small error bound ε > 0. It offers a general framework to transform infinite search space to a finite-element search space with performance guarantee. We apply this procedure to solve two practical problems. In the first problem where the objective is to maximize network lifetime, an approximation algorithm designed through this procedure offers 1/ε² complexity reduction when compared to a state-of-the-art algorithm. This represents the best known result to this problem. In the second problem, we apply the design procedure to address base station placement problem when the optimization objective is to maximize network capacity. Our (1− ε) approximation algorithm is the first theoretical result on this problem. Yi Shi received his B.S. degree from University of Science and Technology of China, Hefei, China, in 1998, a M.S. degree from Institute of Software, Chinese Academy of Science, Beijing, China, in 2001, and a second M.S. degree from Virginia Tech, Blacksburg, VA, in 2003, all in computer science. He is currently working toward his Ph.D. degree in electrical and computer engineering at Virginia Tech. While in undergraduate, he was a recipient of Meritorious Award in International Mathematical Contest in Modeling and 1997 and 1998, respectively. His current research focuses on algorithms and optimizations for wireless sensor networks, wireless ad hoc networks, UWB-based networks, and SDR-based networks. His work has appeared in journals and highly selective international conferences (ACM Mobicom, ACM Mobihoc, and IEEE Infocom). Y. Thomas Hou received the B.E. degree from the City College of New York in 1991, the M.S. degree from Columbia University in 1993, and the Ph.D. degree from Polytechnic University, Brooklyn, New York, in 1998, all in Electrical Engineering. Since Fall 2002, he has been an Assistant Professor at Virginia Tech, the Bradley Department of Electrical and Computer Engineering, Blacksburg, VA. His current research interests are radio resource (spectrum) management and networking for software-defined radio wireless networks, optimization and algorithm design for wireless ad hoc and sensor networks, and video communications over dynamic ad hoc networks. From 1997 to 2002, Dr. Hou was a Researcher at Fujitsu Laboratories of America, Sunnyvale, CA, where he worked on scalable architectures, protocols, and implementations for differentiated services Internet, service overlay networking, video streaming, and network bandwidth allocation policies and distributed flow control algorithms. Prof. Hou is a recipient of an Office of Naval Research (ONR) Young Investigator Award (2003) and a National Science Foundation (NSF) CAREER Award (2004). He is a Co-Chair of Technical Program Committee of the Second International Conference on Cognitive Radio Oriented Wireless Networks and Communications (CROWNCOM 2007), Orlando, FL, August 1–3, 2007. He also was the Chair of the First IEEE Workshop on Networking Technologies for Software Defined Radio Networks, September 25, 2006, Reston, VA. Prof. Hou holds two U.S. patents and has three more pending. Alon Efrat earned his Bachelor in Applied Mathematics from the Technion (Israel’s Institute of Technology) in 1991, his Master in Computer Science from the Technion in 1993, and his Ph.D in Computer Science from Tel-Aviv University in 1998. During 1998–2000 he was a Post Doctorate Research Associate at the Computer Science Department of Stanford University, and at IBM Almaden Research Center. Since 2000, he is an assistant professor at the Computer Science Department of the University of Arizona. His main research areas are Computational Geometry, and its applications to sensor networks and medical imaging.     

Natural resonance extraction from multiple data sets using agenetic algorithm

The paper presents a scheme to extract the natural frequencies of a radar target from multiple data sets using a genetic algorithm technique (GAT). This technique prevents the extracted damping coefficients from being positive, as often happens with low signal-to-noise ratio data, by restricting the search parameters of the damping coefficients to be negative values. The GAT does not require an initial guess, unlike the constrained E-pulse technique (CET). The results from the GAT compare well with the CET (with much less effort required) and also with such established techniques as the pencil-of-functions method and the nonlinear least-squares technique     

The statistical data fork: a class of broad-band multichannelswitches

The circuit switching concept of trunk grouping has been introduced into the broadband packet switch. This trunk grouping concept exploits the very high bandwidth offered by lightwave technology with electronic switching technology operating at substantially slower speeds. A key component of this concept is the multichannel switch, which is a device capable of supporting routing requirements at the trunk group level. The statistical data fork (SDF), which is a multichannel switch enjoying an extremely simple control structure, is introduced here. It is based on the concepts of deflection routing and error correcting codes. It is shown that by properly designing the SDF, an extremely reliable and fault tolerant broadband switch can be obtained     

Consolidation of common parameters from multiple fits in dynamicPET data analysis

In dynamic positron emission tomography (PET) data analysis, regions of interest (ROIs) are analyzed by fitting a parametric model to the time-activity curve acquired after a radio-labeled tracer has been introduced into the patient's bloodstream. This procedure can be carried out for multiple ROIs and/or multiple injections of the same or a different radiopharmaceutical. The approach presented here takes advantage of prior knowledge that some of the parameters of those multiple fits are the same. Reduction of the total number of parameters to be estimated results in smaller statistical uncertainty for all parameter estimates, especially those common to multiple fits     

Radiation from a ferrite-filled rectangular waveguide with multiple slits

Radiation from a ferrite-filled waveguide with multiple thick slits is investigated. The Fourier transform and mode matching are utilized to obtain a rigorous solution. The effect of the ferrite linewidth is included to account for the magnetic loss of ferrite. The maximum beam steering ratio 4.33/spl deg//100 oe is achieved with the magnetic bias field ranging from 1000 to 1400 oe. The ferrite-filled rectangular waveguide with nine slits is fabricated and its radiation patterns are measured at X-band. Our computational results agree with measurement data.