In sparsity-based optimization problems for two dimensional (2-D) direction-of-arrival (DOA) estimation using L-shaped nested arrays, one of the major issues is computational complexity. A 2-D DOA estimation algorithm is proposed based on reconsitution sparse Bayesian learning (RSBL) and cross covariance matrix decomposition. A single measurement vector (SMV) model is obtained by the difference coarray corresponding to one-dimensional nested array. Through spatial smoothing, the signal measurement vector is transformed into a multiple measurement vector (MMV) matrix. The signal matrix is separated by singular values decomposition (SVD) of the matrix. Using this method, the dimensionality of the sensing matrix and data size can be reduced. The sparse Bayesian learning algorithm is used to estimate one-dimensional angles. By using the one-dimensional angle estimations, the steering vector matrix is reconstructed. The cross covariance matrix of two dimensions is decomposed and transformed. Then the closed expression of the steering vector matrix of another dimension is derived, and the angles are estimated. Automatic pairing can be achieved in two dimensions. Through the proposed algorithm, the 2-D search problem is transformed into a one-dimensional search problem and a matrix transformation problem. Simulations show that the proposed algorithm has better angle estimation accuracy than the traditional two-dimensional direction finding algorithm at low signal-to-noise ratio and few samples.

The spectrum sculpting precoder (SSP) is a precoding scheme for sidelobe suppression of frequency division multiplexing (OFDM) signals. It can form deep spectral notches at chosen frequencies and is suitable for cognitive radio systems. However, the SSP degrades the error rate as the number of notched frequencies increases. Orthogonal precoding that improves the SSP can achieve both spectrum notching and the ideal error rate, but its computational complexity is very high since the precoder matrix is large in size. This paper proposes an effective and equivalent decomposition of the precoder matrix by QR-decomposition in order to reduce the computational complexity of orthogonal precoding. Numerical experiments show that the proposed method can drastically reduce the computational complexity with no performance degradation.

In this paper, we propose an efficient regularized zero-forcing (RZF) precoding method that has lower hardware resource requirements and produces a shorter delay to the first transmitted symbol compared with truncated polynomial expansion (TPE) that is based on Neumann series in massive multiple-input multiple-output (MIMO) systems. The proposed precoding scheme, named matrix decomposition-polynomial expansion (MDPE), essentially applies a matrix decomposition algorithm based on polynomial expansion to significantly reduce full matrix multiplication computational complexity. Accordingly, it is suitable for real-time hardware implementations and high-mobility scenarios. Furthermore, the proposed method provides a simple expression that links the optimization coefficients to the ratio of BS/UTs antennas (β). This approach can speed-up the convergence to the matrix inverse by a matrix polynomial with small terms and further reduce computation costs. Simulation results show that the MDPE scheme can rapidly approximate the performance of the full precision RZF and optimal TPE algorithm, while adaptively selecting matrix polynomial terms in accordance with the different β and SNR situations. It thereby obtains a high average achievable rate of the UTs under power allocation.

Prediction of seasonal infectious disease spread is traditionally dealt with as a function of time. Typical methods are time series analysis such as ARIMA (autoregressive, integrated, and moving average) or ANN (artificial neural networks). However, if we regard the time series data as the matrix form, e.g., consisting of yearly magnitude in row and weekly trend in column, we may expect to use a different method (matrix approach) to predict the disease spread when seasonality is dominant. The MD (matrix decomposition) method is the one method which is used in recommendation systems. The other is the IRT (item response theory) used in ability evaluation systems. In this paper, we apply these two methods to predict the disease spread in the case of infectious gastroenteritis caused by norovirus in Japan, and compare the results obtained by using two conventional methods in forecasting, ARIMA and ANN. We have found that the matrix approach is simple and useful in prediction for the seasonal infectious disease spread.

Chronic liver disease is a major worldwide health problem. Diagnosis and staging of chronic liver diseases is an important issue. In this paper, we propose a quantitative method of analyzing local morphological changes for accurate and practical computer-aided diagnosis of cirrhosis. Our method is based on sparse and low-rank matrix decomposition, since the matrix of the liver shapes can be decomposed into two parts: a low-rank matrix, which can be considered similar to that of a normal liver, and a sparse error term that represents the local deformation. Compared with the previous global morphological analysis strategy based on the statistical shape model (SSM), our proposed method improves the accuracy of both normal and abnormal classifications. We also propose using the norm of the sparse error term as a simple measure for classification as normal or abnormal. The experimental results of the proposed method are better than those of the state-of-the-art SSM-based methods.

Visually saliency detection provides an alternative methodology to image description in many applications such as adaptive content delivery and image retrieval. One of the main aims of visual attention in computer vision is to detect and segment the salient regions in an image. In this paper, we employ matrix decomposition to detect salient object in nature images. To efficiently eliminate high contrast noise regions in the background, we integrate global context information into saliency detection. Therefore, the most salient region can be easily selected as the one which is globally most isolated. The proposed approach intrinsically provides an alternative methodology to model attention with low implementation complexity. Experiments show that our approach achieves much better performance than that from the existing state-of-art methods.

Combined input-crosspoint buffered (CICB) packet switches have been of research interest in the last few years because of their high performance. These switches provide higher performance than input-buffered (IB) packet switches while requiring the crosspoint buffers run at the same speed as that of the input buffers in IB switches. Recently, it has been shown that CICB switches with one-cell crosspoint buffers, virtual output queues, and simple input and output arbitrations, provide 100% throughput under uniform traffic. However, it is of general interest to know the maximum throughput that a CICB switch, with no speedup, can provide under admissible traffic. This paper analyzes the throughput performance of a CICB switch beyond uniform traffic patterns and shows that a CICB switch with one-cell crosspoint buffers can provide 100% throughput under admissible traffic while using no speedup.

In this paper, we propose a transformation technique for the multiplications of one variable with multiple constants, which are frequently seen in the various applications of signal processing, image processing, and so forth. The method is based on the exploration of common subexpressions among constants and reduces the number of shifts, additions, and subtractions to implement linear computations with hardware. Our method searches for regularity among elements of a linear transform using matrix decomposition and generates a reduced data-flow graph which preserves the full regularity. We show experimental results obtained using Discrete Cosine Transform (DCT) and Fast Fourier Transform (FFT) and illustrate the effectiveness of the method.