The conventional covariance matrix technique based subspace methods, such as the 2-D Capon algorithm and computationally efficient ESPRIT-type algorithms, are invalid with a single snapshot in a bistatic MIMO radar. A novel matrix pencil method is proposed for the direction of departures (DODs) and direction of arrivals estimation (DOAs) estimation. The proposed method constructs an enhanced matrix from the direct sampled data, and then utilizes the matrix pencil approach to estimate DOAs and DODs, which are paired automatically. The proposed method is able to provide favorable and unambiguous angle estimation performance with a single snapshot. Simulation results are presented to verify the effectiveness of the proposed method.

A method for efficiently estimating the time-varying spectra of nonstationary autoregressive (AR) signals is derived using an indefinite matrix-based sliding window fast linear prediction (ISWFLP). In the linear prediction, the indefinite matrix plays a very important role in sliding an exponentially weighted finite-length window over the prediction error samples. The resulting ISWFLP algorithm successively estimates the time-varying AR parameters of order N at a computational complexity of O(N) per sample. The performance of the AR parameter estimation is superior to the performances of the conventional techniques, including the Yule-Walker, covariance, and Burg methods. Consequently, the ISWFLP-based AR spectral estimation method is able to rapidly track variations in the frequency components with a high resolution and at a low computational cost. The effectiveness of the proposed method is demonstrated by the spectral analysis results of a sinusoidal signal and a speech signal.

In the shadow theory, a new description and a physical mean at a low grazing limit of incidence on gratings in the two dimensional scattering problem have been discussed. In this paper, by applying the shadow theory to the three dimensional problem of multilayered dielectric periodic gratings, we formulate the oblique primary excitation and introduce the scattering factors through our analytical method, by use of the matrix eigenvalues. In terms of the scattering factors, the diffraction efficiencies are defined for propagating and evanescent waves with linearly and circularly polarized incident waves. Numerical examples show that when an incident angle becomes low grazing, only specular reflection occurs with the reflection coefficient -1, regardless of the incident polarization. It is newly found that in a circularly polarized incidence case, the same circularly polarized wave as the incident wave is specularly reflected at a low grazing limit.

The effective bandwidth of the dynamic random-access memory (DRAM) for the alternate row-wise/column-wise matrix access (AR/CMA) mode, which is a basic characteristic in scientific and engineering applications, is very low. Therefore, we propose the window memory layout scheme (WMLS), which is a matrix layout scheme that does not require transposition, for AR/CMA applications. This scheme maps one row of a logical matrix into a rectangular memory window of the DRAM to balance the bandwidth of the row- and column-wise matrix access and to increase the DRAM IO bandwidth. The optimal window configuration is theoretically analyzed to minimize the total number of no-data-visit operations of the DRAM. Different WMLS implementationsare presented according to the memory structure of field-programmable gata array (FPGA), CPU, and GPU platforms. Experimental results show that the proposed WMLS can significantly improve DRAM bandwidth for AR/CMA applications. achieved speedup factors of 1.6× and 2.0× are achieved for the general-purpose CPU and GPU platforms, respectively. For the FPGA platform, the WMLS DRAM controller is custom. The maximum bandwidth for the AR/CMA mode reaches 5.94 GB/s, which is a 73.6% improvement compared with that of the traditional row-wise access mode. Finally, we apply WMLS scheme for Chirp Scaling SAR application, comparing with the traditional access approach, the maximum speedup factors of 4.73X, 1.33X and 1.56X can be achieved for FPGA, CPU and GPU platform, respectively.

Text categorization, especially short text categorization, is a difficult and challenging task since the text data is sparse and multidimensional. In traditional text classification methods, document texts are represented with “Bag of Words (BOW)” text representation schema, which is based on word co-occurrence and has many limitations. In this paper, we mapped document texts to Wikipedia concepts and used the Wikipedia-concept-based document representation method to take the place of traditional BOW model for text classification. In order to overcome the weakness of ignoring the semantic relationships among terms in document representation model and utilize rich semantic knowledge in Wikipedia, we constructed a semantic matrix to enrich Wikipedia-concept-based document representation. Experimental evaluation on five real datasets of long and short text shows that our approach outperforms the traditional BOW method.

Modern GPUs have evolved to become a more general processor capable of executing scientific and engineering computations. It provides a highly parallel computing environment due to its large number of computing cores, which are suitable for numerous data parallel arithmetic computations, particularly linear algebra operations. The matrix-vector multiplication is one of the most important dense linear algebraic operations. It is applied to a diverse set of applications in many fields and must therefore be fully optimized to achieve a high-performance. In this paper, we proposed a novel auto-tuning method for matrix-vector multiplication on GPUs, where the number of assigned threads that are used to compute one element of the result vector can be auto-tuned according to the size of matrix. On the Nvidia's GPU GTX 650 with the most recent Kepler architecture, we developed an auto-tuner that can automatically select the optimal number of assigned threads for calculation. Based on the auto-tuner's result, we developed a versatile generic matrix-vector multiplication kernel with the CUDA programming model. A series of experiments on different shapes and sizes of matrices were conducted for comparing the performance of our kernel with that of the kernels from CUBLAS 5.0, MAGMA 1.3 and a warp method. The experiments results show that the performance of our matrix-vector multiplication kernel is close to the optimal behavior with increasing of the size of the matrix and has very little dependency on the shape of the matrix, which is a significant improvement compared to the other three kernels that exhibit unstable performance behavior for different shapes of matrices.

In this paper, we consider the optimization of measurement matrix in Compressed Sensing (CS) framework. Based on the boundary constraint, we propose a novel algorithm to make the “mutual coherence” approach a lower bound. This algorithm is implemented by using an iterative strategy. In each iteration, a neighborhood interval of the maximal off-diagonal entry in the Gram matrix is scaled down with the same shrinkage factor, and then a lower mutual coherence between the measurement matrix and sparsifying matrix is obtained. After many iterations, the magnitudes of most of off-diagonal entries approach the lower bound. The proposed optimization algorithm demonstrates better performance compared with other typical optimization methods, such as t-averaged mutual coherence. In addition, the effectiveness of CS can be used for the compression of complex synthetic aperture radar (SAR) image is verified, and experimental results using simulated data and real field data corroborate this claim.

Intruder detection method by utilizing a time variation of Multiple Input Multiple Output (MIMO) channel (MIMO Sensor) has been proposed. Although the channel capacity on the MIMO transmission is severely degraded in time variant channels, we can take advantage of this feature in MIMO Sensor applications. We have already demonstrated the effectiveness of 2×2 MIMO sensor using 2.4GHz band at a small room (Size is 50m2). In this paper, we compare the detection probability of SIMO/MIMO sensors when the number of channel responses are same between SIMO/MIMO sensors: The numbers of transmit and receive antennas are 1 and 4 (SIMO), it is clarified that 2 and 2 (MIMO). The measurement was carried out at the room with the size of 140m2. From the measured results, 2×2 MIMO sensor obtains the same or higher detection probability compared to 4×1 SIMO sensor regardless of the measured location.

In this letter, we consider the global exponential stabilization problem by output feedback for a class of nonlinear systems. Along with a newly proposed matrix inequality condition, the proposed control method has improved flexibility in dealing with nonlinearity, over the existing methods. Analysis and examples are given to illustrate the improved features of our control method.

Phase-nonreciprocal ε-negative and CRLH metamaterials are analyzed using a new approach in which field analysis and transmission line model are combined. The examined one-dimensional nonreciprocal metamaterials are composed of a ferrite-embedded microstrip line periodically loaded with shunt stubs. In the present approach, the phase constant nonreciprocity is analytically estimated and formulated under the assumption of operating frequency far above the ferromagnetic resonant frequency. The present approach gives a good explanation to the phenomenon in terms of ferromagnetic properties of the ferrite and asymmetric geometry of the metamaterial structure, showing a good agreement with numerical simulations and experiment.

In this paper, a multi-temporal analysis of polarimetric synthetic aperture radar (Pol-SAR) data over the sandbank and oyster farm area is presented. Specifically, a four-component scattering model, being able to identify single bounce, double bounce, volume, and helix scattering power contributions, has been employed to retrieve information. Decomposition results of a time series RADARSAT Pol-SAR images acquired over the western Taiwan coast indicate that the coastal tide level plays a key role in the sandbank and oyster farm monitoring. At high tide levels, the underlying sandbank creates a shallow area with an increased roughness of the above sea surface, leading to an enhanced surface scattering power as compared to the ambient water. Contrarily, at low tide levels, the exposed sandbank appears to be a smooth scatterer, generating decreased backscattering power than the surrounding area. On the other hand, the double-bounce scattering power is shown to be highly correlated with the tide level in the oyster farms due to their vertical structures. This also demonstrates a promising potential of the four-component scattering power decomposition for coastal tide level monitoring applications.

This paper presents a voice conversion (VC) technique for noisy environments, where parallel exemplars are introduced to encode the source speech signal and synthesize the target speech signal. The parallel exemplars (dictionary) consist of the source exemplars and target exemplars, having the same texts uttered by the source and target speakers. The input source signal is decomposed into the source exemplars, noise exemplars and their weights (activities). Then, by using the weights of the source exemplars, the converted signal is constructed from the target exemplars. We carried out speaker conversion tasks using clean speech data and noise-added speech data. The effectiveness of this method was confirmed by comparing its effectiveness with that of a conventional Gaussian Mixture Model (GMM)-based method.

This paper introduces two schemes to put the decoding of the convolutional network code (CNC) into practice, which are named the Intermittent Packet Transmission Scheme (IPTS) and the Redundancy Packet Transmission Scheme (RPTS). According to the decoding formula of the sink nodes, we can see that, at the time k+δ in order to decode the source packet generated at time k, the sink node should know all the source packets generated before k-1. This is impractical. The two schemes we devised make it unnecessary. A construction algorithm is also given about the RPTS networks. For the two schemes, we analyze the strengths and weaknesses and point out their implemented condition.

A method to estimate sound source orientation in a reverberant room using a microphone array is proposed. We extend the conventional modeling of a room transfer function based on the image method in order to take into account the directivity of a sound source. With this extension, a transfer function between a sound source and a listener (or a microphone) is described by the superposition of transfer functions from each image source to the listener multiplied by the source directivity; thus, the sound source orientation can be estimated by analyzing how the image sources are distributed (power distribution of image sources) from observed signals. We applied eigenvalue analysis to the spatial correlation matrix of the microphone array observation to obtain the power distribution of image sources. Bsed on the assumption that the spatial correlation matrix for each set of source position and orientation is known a priori, the variation of the eigenspace can be modeled. By comparing the eigenspace of observed signals and that of pre-learned models, we estimated the sound source orientation. Through experiments using seven microphones, the sound source orientation was estimated with high accuracy by increasing the reverberation time of a room.

Parasitic antenna elements with tunable terminations can be used for interference suppression in multi-antenna systems without using the degrees of freedom. The authors have proposed a fast non-iterative algorithm for optimizing the termination conditions. However, this method cannot be used for suppressing the interference from unknown systems since it requires the channel state information. In this paper, a fast non-iterative algorithm based on the correlation matrix, which can be obtained even from unknown interference sources, is proposed for the multi-antenna system with parasitic antenna elements. The correlation matrix including both receiving and parasitic antennas can be estimated from a few observations of the signals even without receiving signals at the parasitic antenna. By using this correlation matrix, the power of the interference with the arbitrary termination conditions can be easily estimated. Therefore, the termination condition, which minimizes the interference power, can be calculated without knowledge of the channel state information or additional estimations. The results of a numerical analysis indicate that proposed method works well in suppressing the interference without the perfect channel state information.

Single-packet attack can be tracked with logging-based IP traceback approaches, whereas DDoS attack can be tracked with marking-based approaches. However, both approaches have their limits. Logging-based approaches incur heavy overhead for packet-digest storage as well as time overhead for both path recording and recovery. Marking-based approaches incur little traceback overhead but are unable to track single packets. Simply deploying both approaches in the same network to deal with single-packet and DDoS attacks is not an efficient solution due to the heavy traceback overhead. Recent studies suggest that hybrid approaches are more efficient as they consume less router memory to store packet digests and require fewer attack packets to recover attack paths. Thus, the hybrid single packet traceback approach is more promising in efficiently tracking both single-packet and DDoS attacks. The major challenge lies in reducing storage and time overhead while maintaining single-packet traceback capability. We present in this paper a new hybrid approach to efficiently track single-packet attacks by designing a novel path fragment encoding scheme using the orthogonality of Walsh matrix and the degree distribution characteristic of router-level topologies. Compared to HIT (Hybrid IP Traceback), which, to the best of our knowledge, is the most efficient hybrid approach for single-packet traceback, our approach has three advantages. First, it reduces the overhead by 2/3 in both storage and time for recording packet paths. Second, the time overhead for recovering packet paths is also reduced by a calculatable amount. Finally, our approach generates no more than 2/3 of the false-positive paths generated by HIT.

In this letter, a property of the characteristic matrix of the Rotation Symmetric Boolean Functions (RSBFs) is characterized, and a sufficient and necessary condition for RSBFs being 1st correlation-immune (1-CI for simplicity) is obtained. This property is applied to construct resilient RSBFs of order 1 (1-resilient for simplicity) on pq variables, where p and q are both prime consistently in this letter. The results show that construction and counting of 1-resilient RSBFs on pq variables are equivalent to solving an equation system and counting the solutions. At last, the counting of all 1-resilient RSBFs on pq variables is also proposed.

Pulse coupled neural network (PCNN) is a new type of artificial neural network specific for image processing applications. It is a single layer, two dimensional network with neurons which have 1:1 correspondence to the pixels of an input image. It is convenient to process the intensities and spatial locations of image pixels simultaneously by applying a PCNN. Therefore, we propose a modified PCNN with anisotropic synaptic weight matrix for image edge detection from the aspect of intensity similarities of pixels to their neighborhoods. By applying the anisotropic synaptic weight matrix, the interconnections are only established between the central neuron and the neighboring neurons corresponding to pixels with similar intensity values in a 3 by 3 neighborhood. Neurons corresponding to edge pixels and non-edge pixels will receive different input signal from the neighboring neurons. By setting appropriate threshold conditions, image step edges can be detected effectively. Comparing with conventional PCNN based edge detection methods, the proposed modified PCNN is much easier to control, and the optimal result can be achieved instantly after all neurons pulsed. Furthermore, the proposed method is shown to be able to distinguish the isolated pixels from step edge pixels better than derivative edge detectors.

This paper presents the stability analysis for continuous-time Takagi-Sugeno fuzzy systems using a fuzzy Lyapunov function. The proposed fuzzy Lyapunov function involves the time derivatives of states to include new free matrices in the LMI stability conditions. These free matrices extend the solution space for Linear Matrix Inequalities (LMIs) problems. Numerical examples illustrate the effectiveness of the proposed methods.

Temporal Self-Similarity Matrix (SSM) based action recognition is one of the important approaches of single-person oriented action analysis in computer vision. In this study, we propose a new kind of SSM and a fast computation method. The computation method does not require time-consuming pre-processing to find bounding boxes of the human body, instead it processes difference images to obtain action patterns which can be done very quickly. The proposed SSM is experimentally confirmed to have high power/capacity to achieve a better classification performance than four typical kinds of SSMs.