Fast monostatic GPR modeling

We propose the exploding-reflector method to simulate a monostatic survey with a single simulation. The exploding reflector, used in seismic modeling, is adapted for ground-penetrating radar (GPR) modeling by using the analogy between acoustic and electromagnetic waves. The method can be used with ray tracing to obtain the location of the interfaces and estimate the properties of the medium on the basis of the traveltimes and reflection amplitudes. In particular, these can provide a better estimation of the conductivity and geometrical details. The modeling methodology is complemented with the use of the plane-wave method. The technique is illustrated with GPR data from an excavated tomb of the nineteenth century.

Semisupervised Dimensionality Reduction and Classification Through Virtual Label Regression

Semisupervised dimensionality reduction has been attracting much attention as it not only utilizes both labeled and unlabeled data simultaneously, but also works well in the situation of out-of-sample. This paper proposes an effective approach of semisupervised dimensionality reduction through label propagation and label regression. Different from previous efforts, the new approach propagates the label information from labeled to unlabeled data with a well-designed mechanism of random walks, in which outliers are effectively detected and the obtained virtual labels of unlabeled data can be well encoded in a weighted regression model. These virtual labels are thereafter regressed with a linear model to calculate the projection matrix for dimensionality reduction. By this means, when the manifold or the clustering assumption of data is satisfied, the labels of labeled data can be correctly propagated to the unlabeled data; and thus, the proposed approach utilizes the labeled and the unlabeled data more effectively than previous work. Experimental results are carried out upon several databases, and the advantage of the new approach is well demonstrated.

Incremental pairwise discriminant analysis based visual tracking

The distinguishment between the object appearance and the background is the useful cues available for visual tracking in which the discriminant analysis is widely applied However due to the diversity of the background observation there are not adequate negative samples from the background which usually lead the discriminant method to tracking failure Thus a natural solution is to construct an object-background pair constrained by the spatial structure which could not only reduce the neg-sample number but also make full use of the background information surrounding the object However this Idea is threatened by the variant of both the object appearance and the spatial-constrained background observation especially when the background shifts as the moving of the object Thus an Incremental pairwise discriminant subspace is constructed in this paper to delineate the variant of the distinguishment In order to maintain the correct the ability of correctly describing the subspace we enforce two novel constraints for the optimal adaptation (1) pairwise data discriminant constraint and (2) subspace smoothness The experimental results demonstrate that the proposed approach can alleviate adaptation drift and achieve better visual tracking results for a large variety of nonstationary scenes (C) 2010 Elsevier B V All rights reserved

Fast Haar Transform Based Feature Extraction for Face Representation and Recognition

Subspace learning is the process of finding a proper feature subspace and then projecting high-dimensional data onto the learned low-dimensional subspace. The projection operation requires many floating-point multiplications and additions, which makes the projection process computationally expensive. To tackle this problem, this paper proposes two simple-but-effective fast subspace learning and image projection methods, fast Haar transform (FHT) based principal component analysis and FHT based spectral regression discriminant analysis. The advantages of these two methods result from employing both the FHT for subspace learning and the integral vector for feature extraction. Experimental results on three face databases demonstrated their effectiveness and efficiency.

L1-Norm-Based 2DPCA

In this paper, we first present a simple but effective L1-norm-based two-dimensional principal component analysis (2DPCA). Traditional L2-norm-based least squares criterion is sensitive to outliers, while the newly proposed L1-norm 2DPCA is robust. Experimental results demonstrate its advantages.

Viscous-force-dominated tensile deformation behavior of oriented polyethylene

High-density polyethylene with shish-kebab structure, prepared by a melt extrusion drawing, was employed to investigate the effect of the well-defined lamellar orientation on the deformation characteristics under uniaxial tensile deformation along the drawing direction. This was done by investigating the true stress-true strain dependencies at different strain rates, recovery properties, and stress relaxation measurements. Measurements were complemented by recording in-situ wide-angle X-ray scattering patterns during the deformation process. The oriented samples showed not only a higher modulus, but different from analogous isotropic samples, a homogeneous deformation without necking. The true strain associated with the onset of fibrillation was determined. Because of the preorientation, it is shifted to 0.3, which is below the value 0.6 of the isotropic counterpart. The main finding is a strong enhancement of the Viscous force, as was revealed by stress relaxation experiments; the viscous force takes up 70% of the total stress. The presence of shish-kebabs, i.e., interconnected lamellae in a stack, seems to be responsible for the high viscous force in the oriented samples. The absence of necking has to be ascribed to the high viscous force.

青藏高原东部长周期大地电磁测深研究

As a complement to conventional MT, Long-period Magnetotellurics (LMT) has been developed at low frequency for soundings of deep electric structures. Eastern Himalayan Syntaxis (EHS) and surrounding area is a key place for the study of dynamics of the uplift of Tibetan plateau. Experiments in the pioneer studies for EHS3D project showed that the study area shares an unusual low resistive crust and upper mantle. Conventional MT could not provide sufficient information about the deep structures of the study area that requested long period MT measurement to be complemented. This thesis presents the LMT studies in eastern Tibet along the EHS3D-3 Profile from Xiachayu to Yushu including data acquisition, processing, inversion and interpretation. The effective period of the measured LMT signals extend from 10s up to 30000s for the duration more than one week measurement. The resulting model shows that the LMT sounding coincides with the MT data in overlapped periods. Especially the induction arrows and tippers derived from LMT data provide more information about the base of the conductors beneath the plateau with higher resolution. Anomalous induction coefficients and 2-D model suggest extensive conductive bodies beneath Lhasa block and Qiangtang terrain which would be a possible evidence for partial melt and fluids at depth.

地震反射系数成像方法研究

Reflectivity sequences extraction is a key part of impedance inversion in seismic exploration. Although many valid inversion methods exist, with crosswell seismic data, the frequency brand of seismic data can not be broadened to satisfy the practical need. It is an urgent problem to be solved. Pre-stack depth migration which developed in these years becomes more and more robust in the exploration. It is a powerful technology of imaging to the geological object with complex structure and its final result is reflectivity imaging. Based on the reflectivity imaging of crosswell seismic data and wave equation, this paper completed such works as follows: Completes the workflow of blind deconvolution, Cauchy criteria is used to regulate the inversion(sparse inversion). Also the precondition conjugate gradient(PCG) based on Krylov subspace is combined with to decrease the computation, improves the speed, and the transition matrix is not necessary anymore be positive and symmetric. This method is used to the high frequency recovery of crosswell seismic section and the result is satisfactory. Application of rotation transform and viterbi algorithm in the preprocess of equation prestack depth migration. In equation prestack depth migration, the grid of seismic dataset is required to be regular. Due to the influence of complex terrain and fold, the acquisition geometry sometimes becomes irregular. At the same time, to avoid the aliasing produced by the sparse sample along the on-line, interpolation should be done between tracks. In this paper, I use the rotation transform to make on-line run parallel with the coordinate, and also use the viterbi algorithm to complete the automatic picking of events, the result is satisfactory. 1. Imaging is a key part of pre-stack depth migration besides extrapolation. Imaging condition can influence the final result of reflectivity sequences imaging greatly however accurate the extrapolation operator is. The author does migration of Marmousi under different imaging conditions. And analyzes these methods according to the results. The results of computation show that imaging condition which stabilize source wave field and the least-squares estimation imaging condition in this paper are better than the conventional correlation imaging condition. The traditional pattern of "distributed computing and mass decision" is wisely adopted in the field of seismic data processing and becoming an obstacle of the promoting of the enterprise management level. Thus at the end of this paper, a systemic solution scheme, which employs the mode of "distributed computing - centralized storage - instant release", is brought forward, based on the combination of C/S and B/S release models. The architecture of the solution, the corresponding web technology and the client software are introduced. The application shows that the validity of this scheme.