SiGe (Ge-dot) heterojunction phototransistors for efficient light detection at 1.3–1.55 μm

The aim of this work is to develop a Si/SiGe HBT-type phototransistor with several Ge dot layers incorporated in the collector, in order to obtain improved light detectivity at 1.3–1.55 μm. The MBE grown HBT detectors are of n–p–n type and based on a multilayer structure containing 10 Ge-dot layers (8 ML in each layer, separated by 60 nm Si spacer) in the base-collector junction. The transistors were processed for normal incidence or with waveguide geometry where the light is coupled through the edge of the sample. The measured breakdown voltage, BV_ceo, was about 6 V. Compared to a p–i–n reference photodiode with the same dot layer structure, photoconductivity measurements show that the responsivity is improved by a factor of 60 for normal incidence at 1.3 μm. When the light is coupled through the edge of the device, the detectivity is even further enhanced. The measured photo-responsivity is more than 100 and 5 mA/W at 1.3 and 1.55 μm, respectively.     

A new step size rule for the superiorization method and its application in computerized tomography

Numerical Algorithms - In this paper, we consider a regularized least squares problem subject to convex constraints. Our algorithm is based on the superiorization technique, equipped with a new...     

The equivariant homotopy type of <Emphasis Type="Italic">G</Emphasis>-<Emphasis Type="Italic">ANR</Emphasis>’s for compact group actions

We prove that if G is a compact Hausdorff group then every G-ANR has the G-homotopy type of a G-CW complex. This is applied to extend the James–Segal G-homotopy equivalence theorem to the case of arbitrary compact group actions. The first author was supported in part by grant U42563-F from CONACYT (Mexico).     

A stationary iterative pseudoinverse algorithm

Iterative methods applied to the normal equationsA ^T Ax=A ^T b are sometimes used for solving large sparse linear least squares problems. However, when the matrix is rank-deficient many methods, although convergent, fail to produce the unique solution of minimal Euclidean norm. Examples of such methods are the Jacobi and SOR methods as well as the preconditioned conjugate gradient algorithm. We analyze here an iterative scheme that overcomes this difficulty for the case of stationary iterative methods. The scheme combines two stationary iterative methods. The first method produces any least squares solution whereas the second produces the minimum norm solution to a consistent system. This work was supported by the Swedish Research Council for Engineering Sciences, TFR.     

A method of iterative data refinement and its applications

Iterative data refinement (IDR) is a general procedure for producing a sequence of estimates of the data that would be collected by a measuring device which is idealized to a certain extent, starting from the data that are collected by an actual measuring device. Following a discussion of the fundamentals of IDR, we present a number of previously published procedures which are special cases of it. We concentrate on examples from medical imaging. In particular, we discuss beam hardening correction in x-ray computerized tomography, attenuation correction in emission computerized tomography, and compensation for missing data in reconstruction from projections. We also show that a standard method of numerical mathematics (the parallel chord method) as well as a whole family of constrained iterative restoration algorithms are special cases of IDR. Thus IDR provides a common framework within which a number of originally different looking procedures are presented and discussed. We also present a result of theoretical nature concerning the initial behavior of IDR.     

Convergence analysis for column-action methods in image reconstruction

Column-oriented versions of algebraic iterative methods are interesting alternatives to their row-version counterparts: they converge to a least squares solution, and they provide a basis for saving computational work by skipping small updates. In this paper we consider the case of noise-free data. We present a convergence analysis of the column algorithms, we discuss two techniques (loping and flagging) for reducing the work, and we establish some convergence results for methods that utilize these techniques. The performance of the algorithms is illustrated with numerical examples from computed tomography.     

A multiprojection algorithm using Bregman projections in a product space

Generalized distances give rise to generalized projections into convex sets. An important question is whether or not one can use within the same projection algorithm different types of such generalized projections. This question has practical consequences in the area of signal detection and image recovery in situations that can be formulated mathematically as a convex feasibility problem. Using an extension of Pierra's product space formalism, we show here that a multiprojection algorithm converges. Our algorithm is fully simultaneous, i.e., it uses in each iterative stepall sets of the convex feasibility problem. Different multiprojection algorithms can be derived from our algorithmic scheme by a judicious choice of the Bregman functions which govern the process. As a by-product of our investigation we also obtain blockiterative schemes for certain kinds of linearly constraned optimization problems.     

On some methods for entropy maximization and matrix scaling

We describe and survey in this paper iterative algorithms for solving the discrete maximum entropy problem with linear equality constraints. This problem has applications e.g. in image reconstruction from projections, transportation planning, and matrix scaling. In particular we study local convergence and asymptotic rate of convergence as a function of the iteration parameter. For the trip distribution problem in transportation planning and the equivalent problem of scaling a positive matrix to achieve a priori given row and column sums, it is shown how the iteration parameters can be chosen in an optimal way. We also consider the related problem of finding a matrix X, diagonally similar to a given matrix, such that corresponding row and column norms in X are all equal. Reports of some numerical tests are given.     

Algorithms for confluent Vandermonde systems

Two compact algorithms are developed for solving systems of linear equationsV x=b andV ^T a=f, whereV=V( ₀, ₁, ..., _n ) is a confluent Vandermonde matrix of Hermite type. The solution is obtained by one forward and one backward vector recursion, starting with the right hand side. The total amount of storage is only 2n. The number of arithmetic operations needed isO(n ²) and compares favourably with other proposed methods.     

A block‐preconditioner for a special regularized least‐squares problem

We consider a linear system of the form A₁x₁ + A₂x₂ + η=b₁. The vector ηconsists of independent and identically distributed random variables all with mean zero. The unknowns are split into two groups x₁ and x₂. It is assumed that AA₁ has full rank and is easy to invert. In this model, usually there are more unknowns than observations and the resulting linear system is most often consistent having an infinite number of solutions. Hence, some constraint on the parameter vector x is needed. One possibility is to avoid rapid variation in, e.g. the parameters x₂. This can be accomplished by regularizing using a matrix A₃, which is a discretization of some norm (e.g. a Sobolev space norm). We formulate the problem as a partially regularized least‐squares problem and use the conjugate gradient method for its solution. Using the special structure of the problem we suggest and analyse block‐preconditioners of Schur compliment type. We demonstrate their effectiveness in some numerical tests. The test examples are taken from an application in modelling of substance transport in rivers. Copyright © 2007 John Wiley & Sons, Ltd.