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In fluorescence optical tomography, many works in the literature focus on the linear reconstruction problem to obtain the fluorescent yield or the linearized reconstruction problem to obtain the absorption coefficient. The nonlinear reconstruction problem, to reconstruct the fluorophore absorption coefficient, is of interest in imaging studies as it presents the possibility of better reconstructions owing to a more appropriate model. Accurate and computationally efficient forward models are also critical in the reconstruction process. The SPN approximation to the radiative transfer equation (RTE) is gaining importance for tomographic reconstructions owing to its computational advantages over the full RTE while being more accurate and applicable than the commonly used diffusion approximation. This paper presents Gauss-Newton-based fully nonlinear reconstruction for the SP3 approximated fluorescence optical tomography problem with respect to shape as well as the conventional finite-element method-based representations. The contribution of this paper is the Frechet derivative calculations for this problem and demonstration of reconstructions in both representations. For the shape reconstructions, radial-basis-function represented level-set-based shape representations are used. We present reconstructions for tumor-mimicking test objects in scattering and absorption dominant settings, respectively, for moderately noisy data sets in order to demonstrate the viability of the formulation. Comparisons are presented between the nonlinear and linearized reconstruction schemes in an element wise setting to illustrate the benefits of using the former especially for absorption dominant media.

The dynamic reconstruction problem in tomographic imaging is encountered in several applications, such as species determination, the study of blood flow through arteries/veins, motion compensation in medical imaging, and process tomography. The reconstruction method of choice is the Kalman filter and its variants, which, however, are faced by issues of filter tuning. In addition, since the time-propagation models of physical parameters are typically very complex, most of the time, a random walk model is considered. For geometric deformations, affine models are typically used. In our work, with the objectives of minimizing tuning issues and reconstructing time-varying geometrically deforming features of interest with affine in addition to pointwise-normal scaling motions, a novel level-set-based reconstruction scheme for ray tomography is proposed for shape and electromagnetic parameters using a regularized Gauss-Newton-filter-based scheme. We use an implicit Hermite-interpolation-based radial basis function representation of the zero level set corresponding to the boundary curve. Another important contribution of the paper is an evaluation of the shape-related Frechet derivatives that does not need to evaluate the pointwise Jacobian (the ray-path matrix in our ray-tomography problem). Numerical results validating the formulation are presented for a straight ray-based tomographic reconstruction. To the best of our knowledge, this paper presents the first tomographic reconstruction results in these settings. (C) 2014 Optical Society of America

In a reconstruction problem for subsurface tomography (modeled by the Helmholtz equation), we formulate a novel reconstruction scheme for shape and electromagnetic parameters from scattered field data, based upon an implicit Hermite interpolation based radial basis function (RBF) representation of the boundary curve. An object's boundary is defined implicitly as the zero level set of an RBF fitted to boundary parameters comprising the locations of few points on the curve (the RBF centers) and the normal vectors at those points. The electromagnetic parameter reconstructed is the normalized (w.r.t. the squared ambient wave number) difference of the squared wave numbers between the object and the ambient half-space. The objective functional w.r.t. boundary and electromagnetic parameters is set up and required Frechet derivatives are calculated. Reconstructions using a damped Tikhonov regularized Gauss Newton scheme for this almost rank-deficient problem are presented for 2D test cases of subsurface landmine-like dielectric single and double-phantom objects under noisy data conditions. The double phantom example demonstrates the capability of our present scheme to separate out the two objects starting from an initial single-object estimate. The present implicit-representation scheme thus enjoys the advantages (and conceptually overcomes the respective disadvantages) of current implicit and explicit representation approaches by allowing for topological changes of the boundary curve, while having few unknowns respectively. In addition, the Hermite interpolation based RBF representation is a powerful method to represent shapes in three dimensions, thus conceptually paving the way for the algorithm to be used in 3D.

A nonlinear Helmholtz-equation-modeled electromagnetic tomographic reconstruction problem is solved for the object boundary and inhomogeneity parameters in a damped Tikhonov-regularized Gauss-Newton (DTRGN) solution framework. In this paper, the object is represented in a suitable global basis, whereas the boundary is expressed as the zero level set of a signed-distance function. For an explicit parameterized boundary-representation-based reconstruction scheme, analytical Jacobian and Hessian calculations are made to express the changes in scattered field values w.r.t. changes in the inhomogeneity parameters and the control points in a spline representation of the object boundary, via the use of a level-set representation of the object. Even though, in this paper, a homogeneous dielectric is considered and a spline representation has been used to represent the boundary, the formulation can be used for a general global basis representation of the inhomogeneity as well as arbitrary parameterizations of the boundary, and is generalizable to three dimensions. Reconstruction results are presented for test cases of landminelike dielectric objects embedded in the ground under noisy data conditions. To confirm convergence and, at times, to know which of the obtained iterates are closer to the actual unknown solution, using a perturbation theory framework, a local (Hessian-based) convergence analysis is applied to the DTRGN scheme for the reconstruction, yielding estimates of convergence rates in the residual and parameter spaces.

Compared to unicast traffic, multicast is not protected by any ARQ mechanism in 802.11 networks: collisions with other multicast and unicast transmissions are not detected and senders will not adapt to the contention situation by backing off. This results in an unreliable service for multicast transmissions. We propose early multicast collision detection (EMCD), an algorithm with the purpose of increasing the reliability of multicast transmissions in the MAC layer of an IEEE 802.11 network. A multicast sender using it will introduce an early pause in a transmission, perform a clear channel assessment (CCA), and if a collision is detected abort the transmission after a fixed time and schedule a retransmission. This allows for detecting collisions with both multicast and unicast transmissions but also adapting to the contention situation. A probabilistic analysis is provided showing that EMCD is more efficient than ordinary multicast and can be made even more efficient by tuning parameters.

Mobile TV is a new interesting area in the telecommunication industry. The technology for sending live video to mobile clients is characterized by relatively low CPU processing power, low network resources, and low display resolution. In this paper we discuss a solution to all of these problems by using application layer multicasting. This can significantly reduce the needed bitrate and required computing resources for each client. At the same time the received video quality is increased. Several different methods for splitting the video into substreams are discussed. Simulations for the local wireless ad-hoc network are performed. A system for application layer multicasting using layered H.264 is also presented.

Connectivity in ad-hoc networks is a fundamental, but to a large extend still unsolved problem. In this paper we consider the connectivity problem when a number of nodes are uniformly distributed within a unit square. We limit our problem to the one-hop and two-hop connectivity. For the one-hop connectivity we find the exact analytically solution. For the two-hop connectivity we find the lower and upper bound for connectivity.

In this paper we investigate important issue for real-time video over wireless ad-hoc networks on different layers. Many error control methods for this approach use multiple streams and multipath routing. Thus the new proactive, link-state routing protocol have been developed, where the protocol finds the available route in the network and also it will not cause any interruption in the video traffic between the source and the destination. The open source MPEG-4 is also implemented to get the efficient video quality for the picture.

In solving the nonlinear least-squares problem of minimizing parallel to f(x)parallel to(2)(2), difficulties arise with standard approaches, such as the Levenberg-Marquardt approach, when the Jacobian of f is rank-deficient or very ill-conditioned at the solution. To handle this difficulty, we study a special class of least-squares problems that are uniformly rank-deficient, i.e., the Jacobian of f has the same deficient rank in the neighborhood of a solution. For such problems, the solution is not locally unique. We present two solution tecniques: (i) finding a minimum-norm solution to the basic problem, (ii) using a Tikhonov regularization. Optimality conditions and algorithms are given for both of these strategies. Asymptotical convergence properties of the algorithms are derived and confirmed by numerical experiments. Extensions of the presented ideas make it possible to solve more general nonlinear least-squares problems in which the Jacobian of f at the solution is rank-deficient or ill-conditioned.

Two implementations of the bundle adjustment problem were applied to a subset of the Zurich City Hall reference data set. One implementation used the standard Euler angle parameterisation of the rotation matrix. The second implementation used all nine elements of the rotation matrix as unknowns and six functional constraints. The second formulation was constructed to reduce the non-linearity of the optimisation problem. The hypothesis was that a lower degree of non-linearity would lead to faster convergence. Furthermore, each implementation could optionally use the line search damping technique known from optimisation theory. The algorithms were used to solve the relative orientation problem for a varying number of homologous points from 33 different camera pairs. The results show that the constrained formulation has marginally better convergence properties, with or without damping. However, damping alone halves the number of convergence failures at a minor computational cost. The conclusion is that except to avoid the singularities associated with the Euler angles, the preferred use of the constrained formulation remains an open question. However, the results strongly suggest that the line search damping technique should be included in standard implementations of the bundle adjustment algorithm.