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This work is motivated by a distributed compressed sensing (DCS) scenario where multiple sensors independently perform compressed sensing and the sparse signals share a common support. The heterogeneous case is considered where the numbers of measurements and the noise levels at different sensors may be different. To analyse the performance, we focus on a probability model for sparse signals and use...
In this paper, we address the frame-level dependent bit allocation (DBA) problem in hybrid video coding. In most existing methods, the DBA solution is achieved at the expense of high, sometimes even unbearable, computational complexity because of the multipass coding involved. Motivated by this, we propose a model-based approach as an attempt to solve this problem analytically. Leveraging the predictive...
In this paper, we propose an adaptive truncate k-bit re-configurable approximation (aTra) method which can achieve similar video coding efficiency as the original transform but substitute all low efficient multiplication by conformed shifting and addition operations, lifting the utilization of the hardware implementation and making simple hardware implementation and high efficiency pipeline design...
One of the main computational challenges in digital fingerprinting systems is the complexity of colluder identification. Inspired by compressive sensing approaches for support recovery of sparse vectors, we propose a novel list-decoding approach for partial colluder identification. We also derive formulas for the minimum codelength required for identifying a nonzero fraction of colluders based on...
We propose a new compressive sensing scheme, based on codes of graphs, that allows for joint design of sensing matrices and low complexity reconstruction algorithms. The compressive sensing matrices can be shown to offer asymptotically optimal performance when used in combination with OMP methods. For more elaborate greedy reconstruction schemes, we propose a new family of list decoding and multiple-basis...
We propose a new iterative algorithm, termed subspace pursuit (SP), for decoding of weighted Euclidean superimposed codes (WESCs). WESCs allow for unique identification of small subsets of codewords based on their superposition, and therefore can be viewed as a specialization of compressive sensing schemes. Motivated by various algorithms for compressive sensing reconstruction, we propose the SP algorithm...
We introduce a new family of codes, termed weighted Euclidean superimposed codes (WESCs). This family generalizes the class of Euclidean superimposed codes, used in multiuser identification systems. WESCs allow for discriminating bounded, integer-valued linear combinations of real-valued codewords, and can therefore also be seen as a specialization of compressed sensing schemes. We present lower and...
We address the problem of bounding the achievable rates of a new class of superimposed codes, termed weighted Euclidean superimposed codes (WESCs). WESCs generalize traditional Euclidean superimposed codes in so far that they allow for distinguishing bounded, integer-valued linear combinations of codewords. They can also be viewed as a bridge between superimposed coding and compressive sensing. In...
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