We extend ideas from compressive sensing to a structured sparsity model related to fusion frames. We present theoretical results concerning the recovery of sparse signals in a fusion frame from undersampled measurements. We provide both nonuniform and uniform recovery guarantees. The novelty of our work is to exploit an incoherence property of the fusion frame which allows us to reduce the numb...

background: surveillance of the canine reservoir is highly important to help control of visceral leishmaniasis in human. it is therefore imperative to improve and develop new tools reliable, easy to use, and cheap for the diagnosis of canine leishmani asis. k39 sub recombinant antigen of leishmania infantum was expressed in prokaryotic system and evaluated for sero-diagnosis of canine visceral ...

Transmitted data may be corrupted by both noise and data loss. Grassmannian frames are in some sense optimal representations of data transmitted over a noisy channel that may lose some of the transmitted coefficients. Fusion frame (or frame of subspaces) theory is a new area that has potential to be applied to problems in such fields as distributed sensing and parallel processing. Grassmannian ...

This paper studies the design of data fusion algorithm for asynchronous system with integer times sampling. Firstly, the multisensor asynchronous samplings is mapped to the basic axis, accordingly a sampling sequence of single sensor can be taken. Secondly, aiming at the sensor with the densest sampling points, the modified parallel filtering is given. Afterwards, the sequential filtering fusio...

Fusion frames consist of a sequence of subspaces from a Hilbert space and corresponding positive weights so that the sum of weighted orthogonal projections onto these subspaces is an invertible operator on the space. Given a spectrum for a desired fusion frame operator and dimensions for subspaces, one existing method for creating unitweight fusion frames with these properties is the flexible a...

Fusion frames are collection of subspaces which provide a redundant representation of signal spaces. They generalize classical frames by replacing frame vectors with frame subspaces. This paper considers the sparse recovery of a signal from a fusion frame. We use a block sparsity model for fusion frames and then show that sparse signals under this model can be compressively sampled and reconstr...

David Kroetsch Master of Applied Science University of Waterloo September 2007 Multi-frame Measurement Fusion for State Estimation Simultaneous Localization and Mapping (SLAM) is a technique used by robots and autonomous vehicles to build up a map while operating in an unknown environment, while at the same time keeping track of a current position estimate. This process is not as straightforwar...

Multiple kernel clustering aims to seek an appropriate combination of base kernels mine inherent non-linear information for optimal clustering. Late fusion algorithms generate partitions independently and integrate them in the following procedure, improving overall efficiency. However, separate partition generation leads inadequate negotiation with procedure a great loss beneficial correspondin...

Abstract Fusion frames are widely studied for their applications in recovering signals from large data. These proved to be very useful many areas, example, wireless sensor networks. In this paper, we discuss a generalization of fusion frames, K -fusion frames. provide decompositions Hilbert space into atomic subspaces with respect bounded linear operator. This article studies various kinds prop...

Of the numerous endogenous retroviral elements that are present in the human genome, the abundant HERV-K family is distinct because several members are transcriptionally active and coding for biologically active proteins. A detailed phylogeny of the HERV-K family based on the partial sequence of the reverse transcriptase (RT) gene revealed a high incidence of an intact RT open reading frame wit...