No abstract

Recently, multidimensional signal reconstruction using a low number of measurements is of great interest. Therefore, an effective sampling scheme which should acquire the most information of signal using a low number of measurements is required. In this paper, we study a novel cube-based method for sampling and reconstruction of multidimensional signals. First, inspired by the block-based compressive sensing (BCS), we divide a group of pictures (GoP) in a video sequence into cubes. By this way, we can easily store the measurement matrix and also easily can generate the sparsifying basis. The reconstruction process also can be done in parallel. Second, along with the Kronecker structure of the sampling matrix, we design a weight matrix based on the human visuality system, i.e. perceptually. We will also benefit from different weighted ℓ1-minimization methods for reconstruction. Furthermore, conventional methods for BCS consider an equal number of samples for all blocks. However, the sparsity order of blocks in natural images could be different and, therefore, a various number of samples could be required for their reconstruction. Motivated by this point, we will adaptively allocate the samples for each cube in a video sequence. Our aim is to show that our simple linear sampling approach can be competitive with the other state-of-the-art methods.

Efficient and reliable spectrum sensing is extremely significant, especially in the presence of noise uncertainty in low SNR environment below which conventional detectors fail to be robust. In this letter, by development of a sequential probability ratio test (SPRT) for the fuzzy hypothesis testing (FHT), we propose a novel cooperative sequential detector to deal with the effect of noise power uncertainty. In this approach, for every measurement, FHT is computed by each cognitive radio. Subsequently, fusion center (FC) sequentially accumulates these fuzzy test statistics and decides about the sensing time. Simulation results are illustrated to show the effectiveness and robustness of the proposed sequential FHT detector. The significant reduction in sample complexity is demonstrated for our scheme in comparison with energy detector, sequential crisp hypothesis testing detector, and fixed sample size FHT detector.

This paper investigates the problem of interference minimization which restricts the secondary users (SUs) quality of service (QoS) while coexisting the primary users (PUs), using distributed beamforming for a bidirectional cognitive relay network. We consider a network which consists of two secondary transceivers and K cognitive relay nodes and a primary network with a transmitter and receiver, all equipped with single-antenna. For effective use of spectrum we propose a two-step two-way relaying for cognitive relay networks. Our aim is to design the beamforming coefficients for a bidirectional cognitive relay network through interference minimization approach subject to two constraints on each transceiver QoS which is solved using convex optimization. Our simulation results demonstrate that our distributed relay beamforming scheme improve network performance significantly so that the interference power is decreased by increasing the number of relay nodes while QoS of the secondary network is guaranteed.

We propose an algorithm to uncover the intrinsic low-rank component of a high-dimensional, graph-smooth and grossly-corrupted dataset, under the situations that the underlying graph is unknown. Based on a model with a low-rank component plus a sparse perturbation, and an initial graph estimation, our proposed algorithm simultaneously learns the low-rank component and refines the graph. The refined graph improves the effectiveness of the graph smoothness constraint and increases the accuracy of the low-rank estimation. We derive the learning steps using ADMM. Our evaluations using synthetic and real brain imaging data in a supervised classification task demonstrate encouraging performance.