Compressive sensing(CS) is an emerging research field that has applications in signal processing, error correction, medical imaging, seismology, and many more other areas. CS promises to efficiently reconstruct a sparse signal vector via a much smaller number of linear measurements than its dimension. In order to improve CS reconstruction performance, this paper present a novel reconstruction g...

In this paper we consider the orthogonal cooperative broadcast channel with a common message. The downlink and cooperation channels are modelled by erasure channels and assumed to be orthogonal. We derive an achievable rate for this channel by using a combination of decode-and-forward and estimate-and-forward and considering both symmetric and asymmetric cooperation. These schemes are based on ...

Subset selection and regularisation are two well known techniques which can improve the generalisation performance of nonparametric linear regression estimators, such as radial basis function networks. This paper examines regularised forward selection (RFS) { a combination of forward subset selection and zero-order regularisation. An eecient implementation of RFS into which either delete-1 or g...

In order to quantify the reliability of NDE systems, large amounts of experiments are performed to develop a probability of detection (POD) curve for the system. These POD studies require a substantial amount of experimentation which can sometimes be cost prohibitive. To expedite the process of developing these curves, highly precise numerical models are used in conjunction with NDE sensors to ...

In this paper, we develop robust methods for subset selection based on the minimization of diversity measures. A Bayesian framework is used to account for noise in the data and a maximum a posteriori (MAP) estimation procedure leads to an iterative procedure which is a regularized version of the FOCal Underdetermined System Solver (FOCUSS) algorithm. The convergence of the regularized FOCUSS al...

We outline a range of criteria for evaluating model selection approaches that have been used in the literature. Focusing on three key criteria, we evaluate automatically selecting the relevant variables in an econometric model from a large candidate set. General-tospecific selection is outlined for a regression model in orthogonal variables, where only one decision is required to select, irresp...

Orthogonal, parallel and independent, systems are one key foundation for synthetic biology. The synthesis of orthogonal systems that are uncoupled from evolutionary constraints, and selectively abstracted from cellular regulation, is an emerging approach to making biology more amenable to engineering. Here, we combine orthogonal transcription by T7 RNA polymerase and translation by orthogonal r...

Identity transformations, used as skip-connections in residual networks, directly connect convolutional layers close to the input and those close to the output in deep neural networks, improving information flow and thus easing the training. In this paper, we introduce two alternative linear transforms, orthogonal transformation and idempotent transformation. According to the definition and pro...

A novel construction of compactly supported orthogonal scaling functions and wavelets with spline functions is presented in this paper. Let [Formula: see text] be the center B-spline of order n, except for the case of order one, we know [Formula: see text] is not orthogonal. But by the formula of orthonormalization procedure, we can construct an orthogonal scaling function corresponding to [For...

in this paper, we propose space-time block codes based on constituent intersectant orthogonal designs. after channel model is formulated, we studied the space-time block codes from constituent intersectant orthogonal designs. at last we made simulations for the space-time block codes from complex orthogonal design and the space-time block codes from constituent intersectant orthogonal design to...