We present a new approach for sparse Cholesky factorization on a heterogeneous platform with a graphics processing unit (GPU). The sparse Cholesky factorization is one of the core algorithms of numerous computing applications. We tuned the supernode data structure and used a parallelization method for GPU tasks to increase GPU utilization. Results show that our approach substantially reduces computational time.

This paper presents a novel scale-rotation invariant generative model (SRIGM) and a kernel sparse representation classification (KSRC) method for scene categorization. Recently the sparse representation classification (SRC) methods have been highly successful in a number of image processing tasks. Despite its popularity, the SRC framework lucks the abilities to handle multi-class data with high inter-class similarity or high intra-class variation. The kernel random coordinate descent (KRCD) algorithm is proposed for 1 minimization in the kernel space under the KSRC framework. It allows the proposed method to obtain satisfactory classification accuracy when inter-class similarity is high. The training samples are partitioned in multiple scales and rotated in different resolutions to create a generative model that is invariant to scale and rotation changes. This model enables the KSRC framework to overcome the high intra-class variation problem for scene categorization. The experimental results show the proposed method obtains more stable performances than other existing state-of-art scene categorization methods.

This survey provides a brief introduction to compressed sensing as well as several major algorithms to solve it and its various applications to communications systems. We firstly review linear simultaneous equations as ill-posed inverse problems, since the idea of compressed sensing could be best understood in the context of the linear equations. Then, we consider the problem of compressed sensing as an underdetermined linear system with a prior information that the true solution is sparse, and explain the sparse signal recovery based on 1 optimization, which plays the central role in compressed sensing, with some intuitive explanations on the optimization problem. Moreover, we introduce some important properties of the sensing matrix in order to establish the guarantee of the exact recovery of sparse signals from the underdetermined system. After summarizing several major algorithms to obtain a sparse solution focusing on the 1 optimization and the greedy approaches, we introduce applications of compressed sensing to communications systems, such as wireless channel estimation, wireless sensor network, network tomography, cognitive radio, array signal processing, multiple access scheme, and networked control.

In this paper, we propose a new method for wideband spectrum sensing using compressed measurements of the received wideband signal; we can directly separate information of the sub-channels and perform detection in each. Wideband spectrum sensing empowers us to rapidly access the vacant sub-channels in high utilization regime. Regarding the fact that at each time instant some sub-channels are vacant, the received signal is sparse in some bases. Then we could apply the Compressive Sensing (CS) algorithms and take the compressed measurements. On the other hand, the primary user signals in different sub-channels could have different modulation types; therefore, the signal in each sub-channel is chosen among a signal space. Knowing these signal spaces, the secondary user could separate information of different sub-channels employing the compressed measurements. We perform filtering and detection based on these compressed measurements; this decreases the computational complexity of the wideband spectrum sensing. In addition, we model the received wideband signal as a vector which has a block-sparse representation on a basis consisting of all sub-channel bases whose elements occur in clusters. Based on this feature of the received signal, we propose another wideband spectrum sensing method with lower computational complexity. In order to evaluate the performance of the proposed method, we employ the Monte-Carlo simulation. According to simulations if the compression rate is selected appropriately according to the CS theorems and the problem model, the detection performance of our method leads to the performance of the ideal filter bank-based method, which uses the ideal and impractical narrow band filters.

Solving image recovery problems requires the use of some efficient regularizations based on a priori information with respect to the unknown original image. Naturally, we can assume that an image is modeled as the sum of smooth, edge, and texture components. To obtain a high quality recovered image, appropriate regularizations for each individual component are required. In this paper, we propose a novel image recovery technique which performs decomposition and recovery simultaneously. We formulate image recovery as a nonsmooth convex optimization problem and design an iterative scheme based on the alternating direction method of multipliers (ADMM) for approximating its global minimizer efficiently. Experimental results reveal that the proposed image recovery technique outperforms a state-of-the-art method.

This paper proposes a low-complexity concatenated (LCC) soft-in soft-out (SISO) detector for spreading OFDM systems. The LCC SISO detector uses the turbo principle to compute the extrinsic information of the optimal maximum a priori probability (MAP) SISO detector with extremely low complexity. To develop the LCC SISO detector, we first partition the spreading matrix into some concatenated sparse matrices separated by interleavers. Then, we use the turbo principle to concatenate some SISO detectors, which are separated by de-interleavers or interleavers. Each SISO detector computes the soft information for each sparse matrix. By exchanging the soft information between the SISO detectors, we find the extrinsic information of the MAP SISO detector with extremely low complexity. Simulation results show that using the LCC SISO detector produces a near-optimal performance for both uncoded and coded spreading OFDM systems. In addition, by using the LCC SISO detector, the spreading OFDM system significantly improves the BER of the conventional OFDM system.

This letter proposes a simple heuristic to identify the discrete-time switched autoregressive exogenous (SARX) systems. The goal of the identification is to identify the switching sequence and the system parameters of all submodels simultaneously. In this letter the SARX system identification problem is formulated as the l0 norm minimization problem, and an iterative algorithm is proposed by applying the reweighted least squares technique. Although the proposed algorithm is heuristic, the numerical examples show its efficiency and robustness for noise.

A facial components based facial expression recognition algorithm with sparse representation classifier is proposed. Sparse representation classifier is based on sparse representation and computed by L1-norm minimization problem on facial components. The features of “important” training samples are selected to represent test sample. Furthermore, fuzzy integral is utilized to fuse individual classifiers for facial components. Experiments for frontal views and partially occluded facial images show that this method is efficient and robust to partial occlusion on facial images.

We investigate a sparsely encoded Hopfield model with unit replacement by using a statistical mechanical method called self-consistent signal-to-noise analysis. We theoretically obtain a relation between the storage capacity and the number of replacement units for each sparseness a. Moreover, we compare the unit replacement model with the forgetting model in terms of the network storage capacity. The results show that the unit replacement model has a finite value of the optimal sparseness on an open interval 0 (1/2 coding) < a < 1 (the limit of sparseness) to maximize the storage capacity for a large number of replacement units, although the forgetting model does not.

This letter proposes a new image colorization algorithm based on the sparse optimization. Introducing some assumptions, a problem of recovering a color image from a grayscale image with the small number of known color pixels is formulated as a mixed l0/l1 norm minimization, and an iterative reweighted least squares (IRLS) algorithm is proposed. Numerical examples show that the proposed algorithm colorizes the grayscale image efficiently.

In this letter, a new method is proposed to solve the direction-of-arrivals (DOAs) estimation problem of coherently distributed sources based on the block-sparse signal model of compressed sensing (CS) and the convex optimization theory. We make use of a certain number of point sources and the CS array architecture to establish the compressive version of the discrete model of coherently distributed sources. The central DOA and the angular spread can be estimated simultaneously by solving a convex optimization problem which employs a joint norm constraint. As a result we can avoid the two-dimensional search used in conventional algorithms. Furthermore, the multiple-measurement-vectors (MMV) scenario is also considered to achieve robust estimation. The effectiveness of our method is confirmed by simulation results.

A flexible and computationally efficient method for spectral analysis of sinusoidal signals using the Basis Pursuit De-Noising (BPDN) is proposed. This method estimates a slotted Auto-Correlation Function (ACF) and computes the spectrum as the sparse representation of the ACF in a dictionary of cosine functions. Simulation results illustrate flexibility and effectiveness of the proposed method.

This paper presents a novel algorithm to generate homogeneous superpixels from Markov random walks. We exploit Markov clustering (MCL) as the methodology, a generic graph clustering method based on stochastic flow circulation. In particular, we introduce a graph pruning strategy called compact pruning in order to capture intrinsic local image structure. The resulting superpixels are homogeneous, i.e. uniform in size and compact in shape. The original MCL algorithm does not scale well to a graph of an image due to the square computation of the Markov matrix which is necessary for circulating the flow. The proposed pruning scheme has the advantages of faster computation, smaller memory footprint, and straightforward parallel implementation. Through comparisons with other recent techniques, we show that the proposed algorithm achieves state-of-the-art performance.

Sparse representation based classification (SRC) has emerged as a new paradigm for solving face recognition problems. Further research found that the main limitation of SRC is the assumption of pixel-accurate alignment between the test image and the training set. A. Wagner used a series of linear programs that iteratively minimize the sparsity of the registration error. In this paper, we propose another face registration method called three-point positioning method. Experiments show that our proposed method achieves better performance.

In this paper, we propose a method for supervised single-channel speech separation through sparse decomposition using periodic signal models. The proposed separation method employs sparse decomposition, which decomposes a signal into a set of periodic signals under a sparsity penalty. In order to achieve separation through sparse decomposition, the decomposed periodic signals have to be assigned to the corresponding sources. For the assignment of the periodic signal, we introduce clustering using a K-means algorithm to group the decomposed periodic signals into as many clusters as the number of speakers. After the clustering, each cluster is assigned to its corresponding speaker using preliminarily learnt codebooks. Through separation experiments, we compare our method with MaxVQ, which performs separation on the frequency spectrum domain. The experimental results in terms of signal-to-distortion ratio show that the proposed sparse decomposition method is comparable to the frequency domain approach and has less computational costs for assignment of speech components.

In this paper, we propose an optimized virtual re-convergence system especially to reduce the visual fatigue caused by binocular stereoscopy. Our unique idea to reduce visual fatigue is to utilize the virtual re-convergence based on the optimized disparity-map that contains more depth information in the negative disparity area than in the positive area. Therefore, our system facilitates a unique search-range scheme, especially for negative disparity exploration. In addition, we used a dedicated method, using a so-called Global-Shift Value (GSV), which are the total shift values of each image in stereoscopy to converge a main object that can mostly affect visual fatigue. The experimental result, which is a subjective assessment by participants, shows that the proposed method makes stereoscopy significantly comfortable and attractive to view than existing methods.

In remote control, efficient compression or representation of control signals is essential to send them through rate-limited channels. For this purpose, we propose an approach of sparse control signal representation using the compressive sampling technique. The problem of obtaining sparse representation is formulated by cardinality-constrained 2 optimization of the control performance, which is reducible to 1-2 optimization. The low rate random sampling employed in the proposed method based on the compressive sampling, in addition to the fact that the 1-2 optimization can be effectively solved by a fast iteration method, enables us to generate the sparse control signal with reduced computational complexity, which is preferable in remote control systems where computation delays seriously degrade the performance. We give a theoretical result for control performance analysis based on the notion of restricted isometry property (RIP). An example is shown to illustrate the effectiveness of the proposed approach via numerical experiments.

We propose a Junction-Based Traffic Aware Routing (JTAR) protocol for Vehicular Ad Hoc Networks (VANETs) in sparse urban environments. A traffic aware optimum junction selection solution is adopted in packet-forwarding, and a metric named critical-segment is defined in recovery strategy. Simulation results show that JTAR can efficiently increase the packet delivery ratio and reduce the delivery delay.

Obtaining a compact representation of a large-size feature map built by mapper robots is a critical issue in recent mobile robotics. This “map compression” problem is explored from a novel perspective of dictionary-based data compression techniques in the paper. The primary contribution of the paper is the proposal of the dictionary-based map compression approach. A map compression system is presented by employing RANSAC map matching and sparse coding as building blocks. The effectiveness levels of the proposed techniques is investigated in terms of map compression ratio, compression speed, the retrieval performance of compressed/decompressed maps, as well as applications to the Kolmogorov complexity.

Tomo-SAR imaging with sparse baselines can be formulated as a sparse signal recovery problem, which suggests the use of the Compressive Sensing (CS) method. In this paper, a novel Tomo-SAR imaging approach based on Sparse Bayesian Learning (SBL) is presented to obtain super-resolution in elevation direction and is validated by simulation results.