The sparse unmixing of hyperspectral data has attracted much attention in recent years because it does not need to estimate the number of endmembers nor consider the lack of pure pixels in a given hyperspectral scene. However, the high mutual coherence of spectral libraries strongly affects the practicality of sparse unmixing. The collaborative sparse unmixing via variable splitting and augmented Lagrangian (CLSUnSAL) algorithm is a classic sparse unmixing algorithm that performs better than other sparse unmixing methods. In this paper, we propose a CLSUnSAL-based hyperspectral unmixing method based on dictionary pruning and reweighted sparse regression. First, the algorithm identifies a subset of the original library elements using a dictionary pruning strategy. Second, we present a weighted sparse regression algorithm based on CLSUnSAL to further enhance the sparsity of endmember spectra in a given library. Third, we apply the weighted sparse regression algorithm on the pruned spectral library. The effectiveness of the proposed algorithm is demonstrated on both simulated and real hyperspectral datasets. For simulated data cubes (DC1, DC2 and DC3), the number of the pruned spectral library elements is reduced by at least 94% and the runtime of the proposed algorithm is less than 10% of that of CLSUnSAL. For simulated DC4 and DC5, the runtime of the proposed algorithm is less than 15% of that of CLSUnSAL. For the real hyperspectral datasets, the pruned spectral library successfully reduces the original dictionary size by 76% and the runtime of the proposed algorithm is 11.21% of that of CLSUnSAL. These experimental results show that our proposed algorithm not only substantially improves the accuracy of unmixing solutions but is also much faster than some other state-of-the-art sparse unmixing algorithms.

Image quality depends on the randomness of the k-space signal under-sampling in compressed sensing MRI (CS-MRI), especially for two-dimensional image acquisition. We investigate the feasibility of non-random signal under-sampling CS-MRI to stabilize the quality of reconstructed images and avoid arbitrariness in sampling point selection. Regular signal under-sampling for the phase-encoding direction is adopted, in which sampling points are chosen at equal intervals for the phase-encoding direction while varying the sampling density. Curvelet transform was adopted to remove the aliasing artifacts due to regular signal under-sampling. To increase the incoherence between the measurement matrix and the sparsifying transform function, the scale of the curvelet transform was varied in each iterative image reconstruction step. We evaluated the obtained images by the peak-signal-to-noise ratio and root mean squared error in localized 3×3 pixel regions. Simulation studies and experiments showed that the signal-to-noise ratio and the structural similarity index of reconstructed images were comparable to standard random under-sampling CS. This study demonstrated the feasibility of non-random under-sampling based CS by using the multi-scale curvelet transform as a sparsifying transform function. The technique may help to stabilize the obtained image quality in CS-MRI.

Miller [9] proposed a linear-time algorithm for computing small separators for 2-connected planar graphs. We explain his algorithm and present a way to modify it to a space efficient version. Our algorithm can be regarded as a log-space reduction from the separator construction to the breadth first search tree construction.

This letter presents a computational complexity reduction technique for space diversity based spectrum sensing when the number of receive antennas is greater than three (NR≥3 where NR is the number of receive antenna). The received signals are combined with phase inversion so as to not attenuate the combined signal, and a statistic for signal detection is computed from the combined signal. Because the computation of only one statistic is required regardless of the number of receive antenna, the complexity can be reduced. Numerical examples and simple analysis verify the effectiveness of the presented technique.

This paper improves our previously proposed semi-blind uplink interference suppression scheme for multicell multiuser massive MIMO systems by incorporating the beamspace approach. The constant modulus algorithm (CMA), a known blind adaptive array scheme, can fully exploit the degree of freedom (DoF) offered by massive antenna arrays to suppress inter-user interference (IUI) and inter-cell interference (ICI). Unfortunately, CMA wastes a lot of the benefit of DoF for null-steering even when the number of incoming signal is fewer than that of receiving antenna elements. Our new proposal introduces the beamspace method which degenerates the number of array input for CMA from element-space to beamspace. It can control DoF expended for subsequent interference suppression by CMA. Optimizing the array beamforming gain and null-steering ability, can further improve the output signal-to-interference and noise power ratio (SINR). Computer simulation confirmed that our new proposal reduced the required number of data symbols by 34.6%. In addition, the 5th percentile SINR was also improved by 14.3dB.

In compressive sensing theory (CS), the restricted isometry property (RIP) is commonly used for the measurement matrix to guarantee the reliable recovery of sparse signals from linear measurements. Although many works have indicated that random matrices with excellent recovery performance satisfy the RIP with high probability, Toeplitz-structured matrices arise naturally in real scenarios, such as applications of linear time-invariant systems. Thus, the corresponding measurement matrix can be modeled as a Toeplitz (partial) structured matrix instead of a completely random matrix. The structure characteristics introduce coherence and cause the performance degradation of the measurement matrix. To enhance the recovery performance of the Toeplitz structured measurement matrix in multichannel convolution source separation, an efficient construction of measurement matrix is presented, referred to as sparse random block-banded Toeplitz matrix (SRBT). The sparse signal is pre-randomized by locally scrambling its sample locations. Then, the signal is subsampled using the sparse random banded matrix. Finally, the mixing measurements are obtained. Based on the analysis of eigenvalues, the theoretical results indicate that the SRBT matrix satisfies the RIP with high probability. Simulation results show that the SRBT matrix almost matches the recovery performance of random matrices. Compared with the existing banded block Toeplitz matrix, SRBT significantly improves the probability of successful recovery. Additionally, SRBT has the advantages of low storage requirements and fast computation in reconstruction.

Joint transmit and receive antenna selection (JTRAS) for transceive spatial modulation (TRSM) is investigated in this paper. A couple of low-complexity and efficient JTRAS algorithms are proposed to improve the reliability of TRSM systems by maximizing the minimum Euclidean distance (ED) among all received signals. Specifically, the QR decomposition based ED-JTRAS achieves near-optimal error performance with a moderate complexity reduction as compared to the optimal ED-JTRAS method. The singular value decomposition based ED-JTRAS achieves sub-optimal error performance with a significant complexity reduction. Simulation results show that the proposed methods remarkably improve the system reliability in both uncorrelated and spatially correlated Rayleigh fading channels, as compared to the conventional norm based JTRAS method.

Accurate traffic flow prediction is the precondition for many applications in Intelligent Transportation Systems, such as traffic control and route guidance. Traditional data driven traffic flow prediction models tend to ignore traffic self-features (e.g., periodicities), and commonly suffer from the shifts brought by various complex factors (e.g., weather and holidays). These would reduce the precision and robustness of the prediction models. To tackle this problem, in this paper, we propose a CNN-based multi-feature predictive model (MF-CNN) that collectively predicts network-scale traffic flow with multiple spatiotemporal features and external factors (weather and holidays). Specifically, we classify traffic self-features into temporal continuity as short-term feature, daily periodicity and weekly periodicity as long-term features, then map them to three two-dimensional spaces, which each one is composed of time and space, represented by two-dimensional matrices. The high-level spatiotemporal features learned by CNNs from the matrices with different time lags are further fused with external factors by a logistic regression layer to derive the final prediction. Experimental results indicate that the MF-CNN model considering multi-features improves the predictive performance compared to five baseline models, and achieves the trade-off between accuracy and efficiency.

The large computational complexity makes stereo matching a big challenge in real-time application scenario. The problem of stereo matching in a video sequence is slightly different with that in a still image because there exists temporal correlation among video frames. However, no existing method considered temporal consistency of disparity for algorithm acceleration. In this work, we proposed a scheme called the dynamic disparity range (DDR) to optimize matching cost calculation and cost aggregation steps by narrowing disparity searching range, and a scheme called temporal cost aggregation path to optimize the cost aggregation step. Based on the schemes, we proposed the DDR-SGM and the DDR-MCCNN algorithms for the stereo matching in video sequences. Evaluation results showed that the proposed algorithms significantly reduced the computational complexity with only very slight loss of accuracy. We proved that the proposed optimizations for the stereo matching are effective and the temporal consistency in stereo video is highly useful for either improving accuracy or reducing computational complexity.

Micro-expression is one type of special facial expressions and usually occurs when people try to hide their true emotions. Therefore, recognizing micro-expressions has potential values in lots of applications, e.g., lie detection. In this letter, we focus on such a meaningful topic and investigate how to make full advantage of the color information provided by the micro-expression samples to deal with the micro-expression recognition (MER) problem. To this end, we propose a novel method called color space fusion learning (CSFL) model to fuse the spatiotemporal features extracted in different color space such that the fused spatiotemporal features would be better at describing micro-expressions. To verify the effectiveness of the proposed CSFL method, extensive MER experiments on a widely-used spatiotemporal micro-expression database SMIC is conducted. The experimental results show that the CSFL can significantly improve the performance of spatiotemporal features in coping with MER tasks.

Cell phones with GPS function as well as GPS loggers are widely used and we can easily obtain users' geographic information. Now classifying the measured GPS positions into indoor/outdoor positions is one of the major challenges. In this letter, we propose a robust indoor/outdoor detection method based on sparse GPS measured positions utilizing machine learning. Given a set of clusters of measured positions whose center position shows the user's estimated stayed position, we calculate the feature values composed of: positioning accuracy, spatial features, and temporal feature of measured positions included in every cluster. Then a random forest classifier learns these feature values of the known data set. Finally, we classify the unknown clusters of measured positions into indoor/outdoor clusters using the learned random forest classifier. The experiments demonstrate that our proposed method realizes the maximum F1 measure of 1.000, which classifies measured positions into indoor/outdoor ones with almost no errors.

This paper proposes an enhanced affinity graph (EA-graph) for image segmentation. Firstly, the original image is over-segmented to obtain several sets of superpixels with different scales, and the color and texture features of the superpixels are extracted. Then, the similarity relationship between neighborhood superpixels is used to construct the local affinity graph. Meanwhile, the global affinity graph is obtained by sparse reconstruction among all superpixels. The local affinity graph and global affinity graph are superimposed to obtain an enhanced affinity graph for eliminating the influences of noise and isolated regions in the image. Finally, a bipartite graph is introduced to express the affiliation between pixels and superpixels, and segmentation is performed using a spectral clustering algorithm. Experimental results on the Berkeley segmentation database demonstrate that our method achieves significantly better performance compared to state-of-the-art algorithms.

Data rate requirements, from consumer application to automotive and aerospace grew rapidly in the last years. This led to the development of a series of communication protocols (i.e. Ethernet, PCI-Express, RapidIO and SpaceFibre), which use more than one communication lane, both to speed up data rate and to increase link reliability. Some of these protocols, such as SpaceFibre, are able to detect real-time changes in the number of active lanes and to adapt the data flow appropriately, providing a flexible solution, robust to lane failures. This results in a real time varying data path in the lower layers of the data handling system. The aim of this paper is to propose the architecture of a hardware block capable of reading a fixed number of words from a host FIFO and shaping them on a real time variable number of words equal to the number of active lanes.

Facial micro-expression is momentary and subtle facial reactions, and it is still challenging to automatically recognize facial micro-expression with high accuracy in practical applications. Extracting spatiotemporal features from facial image sequences is essential for facial micro-expression recognition. In this paper, we employed 3D Convolutional Neural Networks (3D-CNNs) for self-learning feature extraction to represent facial micro-expression effectively, since the 3D-CNNs could well extract the spatiotemporal features from facial image sequences. Moreover, transfer learning was utilized to deal with the problem of insufficient samples in the facial micro-expression database. We primarily pre-trained the 3D-CNNs on normal facial expression database Oulu-CASIA by supervised learning, then the pre-trained model was effectively transferred to the target domain, which was the facial micro-expression recognition task. The proposed method was evaluated on two available facial micro-expression datasets, i.e. CASME II and SMIC-HS. We obtained the overall accuracy of 97.6% on CASME II, and 97.4% on SMIC, which were 3.4% and 1.6% higher than the 3D-CNNs model without transfer learning, respectively. And the experimental results demonstrated that our method achieved superior performance compared to state-of-the-art methods.

In this paper, we consider a group testing (GT) problem. We derive a lower bound on the probability of error for successful decoding of defected binary signals. To this end, we exploit Fano's inequality theorem in the information theory. We show that the probability of error is bounded as an entropy function, a density of a pooling matrix and a sparsity of a binary signal. We evaluate that for decoding of highly sparse signals, the pooling matrix is required to be dense. Conversely, if dense signals are needed to decode, the sparse pooling matrix should be designed to achieve the small probability of error.

The technology advancement of satellite instruments requires increasingly fast interconnection technologies, for which no standardised solution exists. SpaceFibre is the forthcoming protocol promising to overcome the limitation of its predecessor SpaceWire, offering data-rate higher than 1Gbps. However, while several implementations of the SpaceFibre IP already exist, its Network Layer is still at experimental level. This article describes the architecture of an implemented SpaceFibre Routing Switch and provides synthesis results for common FPGAs.

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.

This letter presents a comprehensive analytical framework for average pairwise error probability (PEP) of decode-and-forward cooperative network based on various distributed space-time block codes (DSTBCs) with antenna switching (DDF-AS) technique over quasi-static Rayleigh fading channels. Utilizing the analytical framework, exact and asymptotic PEP expressions can be effectively formulated, which are based on the Lauricella multiplicative hypergeometric function, when various DSTBCs are adopted for the DDF-AS system. The derived asymptotic PEP formulas and some numerical results enable us to verify that the DDF-AS scheme outperforms the conventional cooperative schemes in terms of error rate performance. Furthermore, the asymptotic PEP formulas can also provide explicit and useful insights into the full diversity transmission achieved by the DDF-AS system.

Sectors in the airspace are units of the air traffic control. For airspace traffic data consists of the location of each aircraft with timestamp, we propose an efficient method to identify the sector where each aircraft lies.

In this paper, we propose a periodic reactance time function for 2-element electronically steerable passive array radiator (ESPAR) antennas applicable to the receivers of both single-input multiple-output (SIMO) and multiple-input multiple-output (MIMO) systems with 2 outputs. Based on the proposed function, we evaluate the power patterns of the antenna for various distances between two antenna elements. Moreover, for the distances, we discuss the correlation properties and the strength of the two outputs to find the appropriate distance for the receiver. From the discussions, we can conclude that distances from 0.1 to 0.35 times the wavelength are effective in terms of receive diversity.