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We consider wireless system consisting of secondary users equipped with cognitive radios that attempt to access radio spectrum that is not being used by the primary licensed users. To avoid causing harmful interference to the primary users, the secondary users perform spectrum sensing to determine spectrum hole opportunities for channel access. Due to multipath fading, users in the network experience...
This paper proposes a novel framework called distributed compressed video sensing (DISCOS) - a solution for distributed video coding (DVC) based on the recently emerging compressed sensing theory. The DISCOS framework compressively samples each video frame independently at the encoder. However, it recovers video frames jointly at the decoder by exploiting an interframe sparsity model and by performing...
We propose a novel layered compressed sensing (CS) approach for robust transmission of video signals over packet loss channels. In our proposed method, the encoder consists of a base layer and an enhancement layer. The base layer is a conventionally encoded bitstream and transmitted without any error protection. The additional enhancement layer is a stream of compressed measurements taken across slices...
Video-on-demand service in wireless networks is one important step to achieving the goal of providing video services anywhere anytime. Typically, carrier mobile networks are used to deliver videos wirelessly. Since every video stream comes from the base station, regardless of what bandwidth sharing techniques are being utilized, the media stream system is still limited by the network capacity of the...
We consider the problem of supporting continuous spatial queries in a new and challenging network environment, namely a Mobile Peer-To-Peer (MP2P) network. Specifically, we propose a distributed technique, called ExtRange, that can efficiently answer continuous moving range queries in a MP2P network. The two key ideas in ExtRange are extended range and safe period. For each query, ExtRange extends...
Structurally random matrices (SRM) are first proposed in as fast and highly efficient measurement operators for large scale compressed sensing applications. Motivated by the bridge between compressed sensing and the Johnson-Lindenstrauss lemma, this paper introduces a related application of SRMs regarding to realizing a fast and highly efficient embedding. In particular, it shows that a SRM is also...
Minimizing the rank of a matrix X over certain constraints arises in diverse areas such as machine learning, control system and is known to be computationally NP-hard. In this paper, a new simple and efficient algorithm for solving this rank minimization problem with linear constraints is proposed. By using gradient projection method to optimize S while consecutively updating matrices U and V (where...
The problem of affine rank minimization seeks to find the minimum rank matrix that satisfies a set of linear equality constraints. Generally, since affine rank minimization is NP-hard, a popular heuristic method is to minimize the nuclear norm that is a sum of singular values of the matrix variable. A recent intriguing paper shows that if the linear transform that defines the set of equality constraints...
This paper proposes a novel framework called Distributed Compressed Video Sensing (DISCOS) - a solution for Distributed Video Coding (DVC) based on the compressed sensing (CS) theory. The DISCOS framework compressively samples each video frame independently at the encoder and recovers video frames jointly at the decoder by exploiting an interframe sparsity model and by performing sparse recovery with...
This paper presents a novel iterative greedy reconstruction algorithm for practical compressed sensing (CS), called the sparsity adaptive matching pursuit (SAMP). Compared with other state-of-the-art greedy algorithms, the most innovative feature of the SAMP is its capability of signal reconstruction without prior information of the sparsity. This makes it a promising candidate for many practical...
This paper presents a novel framework of fast and efficient compressive sampling based on the new concept of structurally random matrices. The proposed framework provides four important features, (i) It is universal with a variety of sparse signals, (ii) The number of measurements required for exact reconstruction is nearly optimal, (iii) It has very low complexity and fast computation based on block...
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