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  • Articles  (684)
  • Institute of Electrical and Electronics Engineers (IEEE)  (684)
  • 2010-2014  (684)
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  • IEEE Signal Processing Letters  (684)
  • 1267
  • 1
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    Efficient Saliency-Model-Guided Visual Co-Saliency Detection (2014)
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    Publication Date: 2014-11-08
    Description: This letter proposes a novel framework to detect common salient objects in a group of images automatically and efficiently. Different from most existing co-saliency models which directly redesign algorithms for multiple images, the saliency model for a single image is fully exploited under the proposed framework to guide the co-saliency detection. Given single image saliency maps, a two-stage guided detection pipeline led by queries is proposed to obtain the guided saliency maps of the image set through a ranking scheme. Then the guided saliency maps generated by different queries are fused in a way that takes advantages of both averaging and multiplication. The proposed model makes existing saliency models work well in co-saliency scenarios. Experimental results on two benchmark databases demonstrate that the proposed framework outperforms the state-of-the-art models in terms of both accuracy and efficiency.
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  • 2
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    Bivariate Empirical Mode Decomposition for Cognitive Radar Scene Analysis (2014)
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    Publication Date: 2014-11-08
    Description: A method based on the Bivariate Empirical Mode Decomposition (BEMD) is addressed to facilitate radar scene analysis for cognitive radar, building and expanding upon a previous contribution. The method exploits the response of BEMD to the fractional Gaussian character of coherent sea clutter returns. Second–order properties of the intrinsic mode functions are used to form a null hypothesis, which indicates the absence of target(s) if accepted. Extensive experiments on real–world radar data show that the proposed radar scene analysis procedure leads to significantly enhanced statistical separability for target+clutter and clutter–alone data. The results are judged from an information–theoretic perspective using the Kullback–Leibler distance as well as by visual inspection.
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  • 3
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    Simple Features for Separating CPFSK from QAM and PSK Modulations (2014)
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    Publication Date: 2014-11-08
    Description: In this letter we propose simple and robust features to distinguish continuous-phase frequency shift keying from quadrature amplitude modulation and phase shift keying modulations. The features are based on sample mean and sample variance of the imaginary part of the product of two consecutive complex signal values. Root raised cosine pulses are used to generate the linearly modulated signals. Support vector machines are employed to distinguish the signals. One benefit of using support vector machines is that it requires very few realizations for training. Moreover, no a priori information is required about carrier amplitude, carrier phase, carrier offset, symbol rate, pulse shape, initial symbol phase (timing offset) and channel impulse response. Effectiveness of the features and signal separation by support vector machines is tested by observing the joint effects of additive white Gaussian noise, carrier offset, lack of symbol and sampling synchronization, and either fast or slow fading. In the course of doing that, the proposed classifier is compared to the wavelet based classifier, equipped by support vector machines.
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  • 4
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    Information Theoretic Analysis of OFDM/OQAM with Utilized Intrinsic Interference (2014)
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    Publication Date: 2014-11-08
    Description: In this paper, the capacity of OFDM/OQAM with isotropic orthogonal transfer algorithm (IOTA) pulse shaping is evaluated through information theoretic analysis. In the conventional OFDM systems the insertion of a cyclic prefix (CP) decreases the system’s spectral efficiency. As an alternative to OFDM, filter bank based multicarrier systems adopt proper pulse shaping with good time and frequency localisation properties to avoid interference and maintain orthogonality in real field among sub-carriers without the use of CP. We evaluate the spectral efficiency of OFDM/OQAM systems with IOTA pulse shaping in comparison with conventional OFDM/QAM systems, and our analytical model is further extended in order to gain insights into the effect of utilizing the intrinsic interference on the performance of our system. Furthermore, the spectral efficiency of OFDM/OQAM systems is analyzed when the effect of inter-symbol and inter-carrier interference is considered.
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  • 5
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    Fast Iterative Hard Thresholding for Compressed Sensing (2014)
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    Publication Date: 2014-11-08
    Description: Algebraic Pursuit (ALPS) is an effective class of iterative hard thresholding algorithms for compressed sensing, with 1-ALPS(2) being the most computationally efficient variant of ALPS. We present a proof of convergence, using restricted isometry constants, for 1-ALPS(2) as well as the recently introduced Fast Iterative Hard Thresholding (FIHT). Large scale empirical testing shows FIHT is superior to 1-ALPS(2) in terms of both the sizes of the problems that are recoverable and overall computational time.
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  • 6
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    Asymptotic Analysis of Distributed Bayesian Detection with Byzantine Data (2014)
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    Publication Date: 2014-11-08
    Description: In this letter, we consider the problem of distributed Bayesian detection in the presence of Byzantine data. The problem of distributed detection is formulated as a binary hypothesis test at the fusion center (FC) based on 1-bit data sent by the sensors. Adopting Chernoff information as our performance metric, we study the detection performance of the system under Byzantine attack in the asymptotic regime. The expression for minimum attacking power required by the Byzantines to blind the FC is obtained. More specifically, we show that above a certain fraction of Byzantine attackers in the network, the detection scheme becomes completely incapable of utilizing the sensor data for detection. When the fraction of Byzantines is not sufficient to blind the FC, we also provide closed form expressions for the optimal attacking strategies for the Byzantines that most degrade the detection performance.
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  • 7
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    To Listen or Not: Distributed Detection with Asynchronous Transmissions (2014)
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    Publication Date: 2014-11-08
    Description: This letter examines a variation of the canonical distributed detection system: a sensor may overhear other sensors’ transmissions and thus may choose to refine its output in the hope of achieving a better detection performance. We show that while this is indeed possible for the fixed sample size test, asymptotically (in the number of samples) there is no performance gain, as measured by the Kullback-Leibler distance achievable at the fusion center, provided that the observations are conditionally independent. For conditionally dependent observations, however, we demonstrate that asymptotic detection performance may indeed be improved when overhearing is utilized.
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  • 8
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    Phase Estimation in Single Channel Speech Enhancement Using Phase Decomposition (2014)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2014-11-08
    Description: Conventional speech enhancement methods typically utilize the noisy phase spectrum for signal reconstruction. This letter presents a novel method to estimate the clean speech phase spectrum, given the noisy speech observation in single-channel speech enhancement. The proposed method relies on the phase decomposition of the instantaneous noisy phase spectrum followed by temporal smoothing in order to reduce the large variance of noisy phase, and consequently reconstructs an enhanced instantaneous phase spectrum for signal reconstruction. The effectiveness of the proposed method is evaluated in two ways: phase enhancement-only and by quantifying the additional improvement on top of the conventional amplitude enhancement scheme where noisy phase is often used in signal reconstruction. The instrumental metrics predict a consistent improvement in perceived speech quality and speech intelligibility when the noisy phase is enhanced using the proposed phase estimation method.
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  • 9
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    Sparse Phase Retrieval from Short-Time Fourier Measurements (2014)
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    Publication Date: 2014-11-08
    Description: We consider the classical 1D phase retrieval problem. In order to overcome the difficulties associated with phase retrieval from measurements of the Fourier magnitude, we treat recovery from the magnitude of the short-time Fourier transform (STFT). We first show that the redundancy offered by the STFT enables unique recovery for arbitrary nonvanishing inputs, under mild conditions. An efficient algorithm for recovery of a sparse input from the STFT magnitude is then suggested, based on an adaptation of the recently proposed GESPAR algorithm. We demonstrate through simulations that using the STFT leads to improved performance over recovery from the oversampled Fourier magnitude with the same number of measurements.
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  • 10
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    Comments on “Near-Field Source Localization via Symmetric Subarrays” (2014)
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    Publication Date: 2014-11-08
    Description: In the aforementioned letter, the authors indicate that with a $2M + 1$ sensor uniform linear array (ULA), up to $2M - 1$ sources can actually be localized by the proposed algorithm. In this comment, however, we prove that the algorithm will no longer be valid if the number of sources exceeds $M$ . A numerical simulation is performed to verify this conclusion.
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  • 11
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    Efficient Histogram PMHT Via Single Target Chip Processing (2014)
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    Publication Date: 2014-11-05
    Description: The Histogram Probabilistic Multi-Hypothesis Tracker is a multi-target track before detect method with low computational complexity. However, for high resolution sensors with millions of pixels, a naive implementation can be too slow. Single target chip processing is proposed as an approximate implementation scheme that dramatically reduces computation cost without significant performance degradation. Simulated examples are used to quantify the improvements achieved.
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  • 12
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    An Adaptive Motion Model for Person Tracking with Instantaneous Head-Pose Features (2014)
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    Publication Date: 2014-11-05
    Description: This letter presents novel behaviour-based tracking of people in low-resolution using instantaneous priors mediated by head-pose. We extend the Kalman Filter to adaptively combine motion information with an instantaneous prior belief about where the person will go based on where they are currently looking. We apply this new method to pedestrian surveillance, using automatically-derived head pose estimates, although the theory is not limited to head-pose priors. We perform a statistical analysis of pedestrian gazing behaviour and demonstrate tracking performance on a set of simulated and real pedestrian observations. We show that by using instantaneous ‘intentional’ priors our algorithm significantly outperforms a standard Kalman Filter on comprehensive test data.
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  • 13
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    Bayesian Compressive Sensing Using Normal Product Priors (2014)
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    Publication Date: 2014-11-05
    Description: In this letter, we introduce a new sparsity-promoting prior, namely, the “normal product” prior, and develop an efficient algorithm for sparse signal recovery under the Bayesian framework. The normal product distribution is the distribution of a product of two normally distributed variables with zero means and possibly different variances. Like other sparsity-encouraging distributions such as the Student’s $t$ -distribution, the normal product distribution has a sharp peak at the origin, which makes it a suitable prior to encourage sparse solutions. A two-stage normal product-based hierarchical model is proposed. We resort to the variational Bayesian (VB) method to perform the inference. Simulations are conducted to illustrate the effectiveness of our proposed algorithm as compared with other state-of-the-art compressed sensing algorithms.
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  • 14
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    Discriminative Clustering and Feature Selection for Brain MRI Segmentation (2014)
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    Publication Date: 2014-11-05
    Description: Automatic segmentation of brain tissues from MRI is of great importance for clinical application and scientific research. Recent advancements in supervoxel-level analysis enable robust segmentation of brain tissues by exploring the inherent information among multiple features extracted on the supervoxels. Within this prevalent framework, the difficulties still remain in clustering uncertainties imposed by the heterogeneity of tissues and the redundancy of the MRI features. To cope with the aforementioned two challenges, we propose a robust discriminative segmentation method from the view of information theoretic learning. The prominent goal of the method is to simultaneously select the informative feature and to reduce the uncertainties of supervoxel assignment for discriminative brain tissue segmentation. Experiments on two brain MRI datasets verified the effectiveness and efficiency of the proposed approach.
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  • 15
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    Visual Tracking via Structure Constrained Grouping (2014)
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    Publication Date: 2014-12-06
    Description: This letter introduces a novel two-pass structural grouping algorithm and casts visual tracking as foreground superpixels grouping problem. In the first step, pairwise superpixel grouping is conducted in four orientations. Grouping prototypes containing the prior information of foreground and background are generated to determine whether any pair of neighboring superpixels should be grouped together. In the second step, superpixels selected by the first step are grouped into a single region which serves as the object region. The proposed grouping method has two benefits over the state-of-the-art ones. First, pairwise grouping is independently conducted in four orientations, which exploits the local structure of the foregound/backgroud and facilitates a more robust grouping process. Second, rather than considering the similarity of two neighboring superpixels, the grouping process is performed via accounting for the prior information of the object and the background, which is more suitable for visual tracking. Many experiments on challenging video clips demonstrate that our method achieves good performance than the state-of-the-art trackers in a wide range of tracking scenarios.
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  • 16
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    Local Topographic Shape Patterns for Texture Description (2014)
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    Publication Date: 2014-12-06
    Description: This letter introduces a new image descriptor named Local Topographic Shape Pattern (LTSP) for texture description by relying on complete shape-based image representation. Firstly, a texture image is decomposed into a tree of shapes, i.e., topographic map, to obtain a multi-scale and contrast-invariant representation. Secondly, the shape of each node in the tree is handled with four shape patterns which are proposed as spatial codes of the shape. Finally, statistical histograms of shape patterns are used as texture descriptors. Contrast experiments of the proposed method show satisfactory performance in terms of both texture retrieval and classification tasks when applied to the Brodatz and UMD texture datasets.
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  • 17
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    On the Performance of Secure Full-Duplex Relaying under Composite Fading Channels (2014)
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    Publication Date: 2014-12-06
    Description: We assume a full-duplex (FD) cooperative network subject to hostile attacks and undergoing composite fading channels. We focus on two scenarios: a) the transmitter has full CSI, for which we derive closed-form expressions for the average secrecy rate; and b) the transmitter only knows the CSI of the legitimate nodes, for which we obtain closed-form expressions for the secrecy outage probability. We show that secure FD relaying is feasible, even under strong self-interference and in the presence of sophisticated multiple antenna eavesdropper.
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  • 18
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    Higher-Order-Statistics-Based Fractal Dimension for Noisy Bowel Sound Detection (2014)
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    Publication Date: 2014-12-06
    Description: Bowel sounds is an important physiological parameter of distinguishing the gastrointestinal motility dysfunction. Auscultation of bowel sounds provides a noninvasive way for clinical diagnosis, but it is also easily affected by environmental noise. In this study, a novel higher-order-statistics (HOS)-based fractal dimension algorithm was proposed for detecting noisy bowel sounds. By using the nature of preserving non-Gaussianity for higher order statistics technique, the proposed method can effectively detect bowel sounds under different noise conditions, and its performance is insensitive to the change of noise type and noise level.
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  • 19
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    Two Efficient Algorithms for Approximately Orthogonal Nonnegative Matrix Factorization (2014)
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    Publication Date: 2014-12-06
    Description: Nonnegative matrix factorization (NMF) with orthogonality constraints is quite important due to its close relation with the K-means clustering. While existing algorithms for orthogonal NMF impose strict orthogonality constraints, in this letter we propose a penalty method with the aim of performing approximately orthogonal NMF, together with two efficient algorithms respectively based on the Hierarchical Alternating Least Squares (HALS) and the Accelerated Proximate Gradient (APG) approaches. Experimental evidence was provided to show their high efficiency and flexibility by using synthetic and real-world data.
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  • 20
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    Bi-DCT: DCT-based Local Binary Descriptor for Dense Stereo Matching (2014)
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    Publication Date: 2014-12-06
    Description: In this letter, we present a novel DCT-based local binary descriptor for the dense matching of multiple view stereo (MVS). Recently, although much progress has been made in the field of MVS, a key component of which, i.e., dense matching, is still a challenging task because it has two difficult issues: robust matching over non-salient regions (e.g., lines and textureless regions) and fast matching of a large number of pixels. To deal with these issues effectively, in the proposed dense descriptor, 2D DCT-based local features are utilized to achieve high discriminative power even for the non-salient regions. A binary representation is adopted to increase the matching performance as well as accelerate the matching speed via the Hamming distance. In addition, the discriminability of binarized vectors is further improved by a space-frequency pooling scheme. Through extensive experiments on the benchmark datasets for MVS, we demonstrate the superiority of the proposed descriptor over the state-of-the-art descriptors in terms of accuracy and efficiency.
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  • 21
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    Joint Power Splitting and Antenna Selection in Energy Harvesting Relay Channels (2014)
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    Publication Date: 2014-12-06
    Description: The simultaneous wireless transfer of information and power with the help of a relay equipped with multiple antennas is considered in this letter, where a “harvest-and-forward” strategy is proposed. In particular, the relay harvests energy and obtains information from the source with the radio-frequent signals by jointly using the antenna selection (AS) and power splitting (PS) techniques, and then the processed information is amplified and forwarded to the destination relying on the harvested energy. This letter jointly optimizes AS and PS to maximize the achievable rate for the proposed strategy. Considering that the joint optimization is according to the non-convex problem, a two-stage procedure is proposed to determine the optimal ratio of received signal power split for energy harvesting, and the optimized antenna set engaged in information forwarding. Simulation results confirm the accuracy of the two-stage procedure, and demonstrate that the proposed “harvest-and-forward” strategy outperforms the conventional amplify-and-forward (AF) relaying and the direct transmission.
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  • 22
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    On the Behavior of EMD and MEMD in Presence of Symmetric $alpha $ -Stable Noise (2014)
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    Publication Date: 2014-12-06
    Description: Empirical Mode Decomposition (EMD) and its extended versions such as Multivariate EMD (MEMD) are data-driven techniques that represent nonlinear and non-stationary data as a sum of a finite zero-mean AM-FM components referred to as Intrinsic Mode Functions (IMFs). The aim of this work is to analyze the behavior of EMD and MEMD in stochastic situations involving non-Gaussian noise, more precisely, we examine the case of Symmetric $alpha $ -Stable ( ${rm S}alpha{rm S} $ ) noise. We report numerical experiments supporting the claim that both EMD and MEMD act, essentially, as filter banks on each channel of the input signal in the case of ${rm S}alpha {rm S}$ noise. Reported results show that, unlike EMD, MEMD has the ability to align common frequency modes across multiple channels in same index IMFs. Further, simulations show that, contrary to EMD, for MEMD the stability property is well satisfied for the modes of lower indices and this result is exploited for the estimation of the stability index of the ${rm S}alpha{rm S}$ input signal.
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  • 23
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    Non-Local Means Image Denoising With a Soft Threshold (2014)
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    Publication Date: 2014-12-06
    Description: Non-local means (NLM) are typically biased by the accumulation of small weights associated with dissimilar patches, especially at image edges. Hence, we propose to null the small weights with a soft threshold to reduce this accumulation. We call this method the NLM filter with a soft threshold (NLM-ST). Its Stein’s unbiased risk estimate (SURE) approaches the true mean square error; thus, we can linearly aggregate multiple NLM-STs of Monte-Carlo-generated parameters by minimizing SURE to surpass the performance limit of single NLM-ST, which is referred to as the Monte-Carlo-based linear aggregation (MCLA). Finally, we employ a simple moving average filter to smooth the MCLA image sequence to further improve the denoising performance and stability. Experiments indicate that the NLM-ST outperforms the classic patchwise NLM and three other well-known recent variants in terms of the peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and visual quality. Furthermore, its PSNR is higher than that of BM3D for certain images.
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  • 24
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    Fast Decomposition of Large Nonnegative Tensors (2014)
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    Publication Date: 2014-12-06
    Description: In signal processing, tensor decompositions have gained in popularity this last decade. In the meantime, the volume of data to be processed has drastically increased. This calls for novel methods to handle Big Data tensors. Since most of these huge data are issued from physical measurements, which are intrinsically real nonnegative, being able to compress nonnegative tensors has become mandatory. Following recent works on HOSVD compression for Big Data, we detail solutions to decompose a nonnegative tensor into decomposable terms in a compressed domain.
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  • 25
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    The Significant Gains From Optimally Processed Multiple Signals of Opportunity and Multiple Receive Stations in Passive Radar (2014)
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    Publication Date: 2014-01-11
    Description: Target position and velocity estimation using a passive radar with multiple signals of opportunity and multiple receive stations is investigated. The maximum likelihood (ML) estimate of the unknown position and velocity vector of a target is presented. Formulas bounding the best possible mean square error are provided, via the Cramer-Rao lower bound, for any unbiased estimator of target position and velocity. The model assumes a single target, a single receive antenna at each receive station, spatially and temporally white Gaussian clutter-plus-noise, and uncorrelated reflection coefficients. To describe the best possible performance, it is assumed that the signals of opportunity are estimated perfectly from the direct path reception. For a specific example where the signals of opportunity come from the Global System for Mobile (GSM) communication system, the optimum possible estimation performance is presented using numerical examples. It is shown that it is possible to obtain large performance gains through using multiple signals of opportunity and multiple receive stations.
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  • 26
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    Maximum Phase Modeling for Sparse Linear Prediction of Speech (2014)
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    Publication Date: 2014-01-11
    Description: Linear prediction (LP) is an ubiquitous analysis method in speech processing. Various studies have focused on sparse LP algorithms by introducing sparsity constraints into the LP framework. Sparse LP has been shown to be effective in several issues related to speech modeling and coding. However, all existing approaches assume the speech signal to be minimum-phase. Because speech is known to be mixed-phase, the resulting residual signal contains a persistent maximum-phase component. The aim of this paper is to propose a novel technique which incorporates a modeling of the maximum-phase contribution of speech, and can be applied to any filter representation. The proposed method is shown to significantly increase the sparsity of the LP residual signal and to be effective in two illustrative applications: speech polarity detection and excitation modeling.
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    Online Discriminative Structured Output SVM Learning for Multi-Target Tracking (2014)
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    Publication Date: 2014-01-11
    Description: In this letter, we propose an online discriminative learning method for feature combination during multi-target tracking. Previous works utilize offline learned weights for fusion of multiple features, which is not always effective for different tracking contexts. Our work aims to update the weights adaptively in online tracking. We formulate the feature combination problem in data association using structured output SVM, and solve it by online learning algorithm. The constraints of discriminative appearance affinity are integrated to discriminate positive associations from disturbing ones, which makes association more reliable. By comparison with five state-of-the-art methods, our proposed online tracking approach outperforms the other online methods, and is competitive with the global optimal ones.
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    Scalable and Compact Representation for Motion Capture Data Using Tensor Decomposition (2014)
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    Publication Date: 2014-01-18
    Description: Motion capture (mocap) technology is widely used in movie and game industries. Compact representation of the mocap data is critical to efficient storage and transmission. In this letter, we propose a novel tensor decomposition based scheme for compact and progressive representation of the mocap data. Our method segments and stacks the mocap sequence locally, and generates a 3rd-order tensor, which has strong correlation within and across slices of the tensor. Then, our method iteratively applies tensor decomposition in a multi-layer structure to explore the correlation characteristic. Experimental results demonstrate that the proposed scheme significantly outperforms existing algorithms in terms of scalability and storage requirement.
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    Strong Impossibility Results for Sparse Signal Processing (2014)
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    Publication Date: 2014-01-18
    Description: This letter derives strong impossibility results for several sparse signal processing problems. It is shown that regardless of the allowed error probability in identifying the salient support set (as long as this probability is below one), the required number of measurements is almost the same as that required for the error probability to be arbitrarily small. Our proof technique involves the use of the blowing-up lemma and can be applied to diverse problems from noisy group testing to graphical model selection as long as the observations are discrete.
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    Adaptive Compressed Sensing via Minimizing Cramer–Rao Bound (2014)
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    Publication Date: 2014-01-22
    Description: This letter considers the problem of observation strategy design for compressed sensing. An adaptive method, based on Cramer–Rao bound minimization, is proposed to design the sensing matrix. Simulation results demonstrate that the adaptively constructed sensing matrix can lead to much lower recovery errors than those of traditional Gaussian matrices and some existing adaptive approaches.
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    Adaptive Widely Linear Reduced-Rank Beamforming Based on Joint Iterative Optimization (2014)
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    Publication Date: 2014-01-22
    Description: We propose a reduced-rank beamformer based on the rank- $D$ Joint Iterative Optimization (JIO) of the modified Widely Linear Constrained Minimum Variance (WLCMV) problem for non-circular signals. The novel WLCMV-JIO scheme takes advantage of both the Widely Linear (WL) processing and the reduced-rank concept, outperforming its linear counterpart as well as the full-rank WL beamformer. We develop an augmented recursive least squares algorithm and present an improved structured version with a much more efficient implementation. It is shown that the improved adaptive scheme achieves the best convergence performance among all the considered methods with a low computational complexity.
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    Revealing the Traces of Median Filtering Using High-Order Local Ternary Patterns (2014)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2014-01-22
    Description: Recently, detecting the traces introduced by the content-preserving image manipulations has received a great deal of attention from forensic analyzers. It is well known that the median filter is a widely used nonlinear denoising operator. Therefore, the detection of median filtering is of important realistic significance in image forensics. In this letter, a novel local texture operator, named the second-order local ternary pattern (LTP), is proposed for median filtering detection. The proposed local texture operator encodes the local derivative direction variations by using a 3-valued coding function and is capable of effectively capturing the changes of local texture caused by median filtering. In addition, kernel principal component analysis (KPCA) is exploited to reduce the dimensionality of the proposed feature set, making the computational cost manageable. The experiment results have shown that the proposed scheme performs better than several state-of-the-art approaches investigated.
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    Rate-adaptive Compact Fisher Codes for Mobile Visual Search (2014)
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    Publication Date: 2014-01-11
    Description: Extraction and transmission of compact descriptors are of great importance for next-generation mobile visual search applications. Existing visual descriptor techniques mainly compress visual features into compact codes of fixed bit rate, which is not adaptive to the bandwidth fluctuation in wireless environment. In this letter, we propose a Rate-adaptive Compact Fisher Codes (RCFC) to produce a bit rate scalable image signature. In particular, RCFC supports fast matching of descriptors based on Hamming distance; meanwhile, low memory footprint is offered. Extensive evaluation over benchmark databases shows that RCFC significantly outperforms the state-of-the-art and provides a promising descriptor scalability in terms of bit rates versus desired search performance.
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    3-D Face Recognition Using Curvelet Local Features (2014)
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    Publication Date: 2014-01-11
    Description: In this letter, we present a robust single modality feature-based algorithm for 3-D face recognition. The proposed algorithm exploits Curvelet transform not only to detect salient points on the face but also to build multi-scale local surface descriptors that can capture highly distinctive rotation/displacement invariant local features around the detected keypoints. This approach is shown to provide robust and accurate recognition under varying illumination conditions and facial expressions. Using the well-known and challenging FRGC v2 dataset, we report a superior performance compared to other algorithms, with a 97.83% verification rate for probes with all facial expressions.
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    Transceiver Design for Hybrid One-Way and Two-Way Relay Networks (2014)
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    Publication Date: 2014-01-15
    Description: Due to a variety of service types required by different users, unidirectional and bidirectional data transmissions may coexist in a relay network. In this letter, we consider such a hybrid one-way and two-way relay network, where both unidirectional and bidirectional data transmissions are delivered simultaneously between different user pairs via a common multi-antenna relay node. We first develop a relaying scheme that provides inter-pair interference free transmissions for all users. A near optimal power allocation method at the relay is then proposed to maximize the system sum rate. Numerical results show that our relaying scheme significantly outperforms the traditional zero-forcing based scheme.
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    Spectrally Efficient Alamouti Code Structure in Asynchronous Cooperative Systems (2014)
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    Publication Date: 2014-03-14
    Description: A relay communication system with two amplify and forward (AF) relays under flat fading channel conditions is considered where the signals received from the relays are not necessarily time aligned. We propose a new time-reversal (TR)-based scheme providing an Alamouti code structure which needs a smaller overhead in transmitting every pair of data blocks in comparison with the existing schemes and, as a result, increases the transmission rate significantly (as much as 20%) in exchange for a small performance loss. The scheme is particularly useful when the delay between the two relay signals is large, e.g., in typical underwater acoustic (UWA) channels.
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    Topic Language Model Adaption for Recognition of Homologous Offline Handwritten Chinese Text Image (2014)
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    Publication Date: 2014-03-14
    Description: As the content of a full text page usually focuses on a specific topic, a topic language model adaption method is proposed to improve the recognition performance of homologous offline handwritten Chinese text image. Firstly, the text images are recognized with a character based bi-gram language model. Secondly, the topic of the text image is matched adaptively. Finally, the text image is recognized again with the best matched topic language model. To obtain a tradeoff between the recognition performance and computational complexity, a restricted topic language model adaption method is further presented. The methods have been evaluated on 100 offline Chinese text images. Compared to the general language model, the topic language model adaption has reduced the relative error rate by 11.94%. The restricted topic language model has lessened the running time by 19.22% at the cost of losing 0.35% of the accuracy.
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    Threshold-Independent QRS Detection Using the Dynamic Plosion Index (2014)
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    Publication Date: 2014-03-14
    Description: Detection of QRS serves as a first step in many automated ECG analysis techniques. Motivated by the strong similarities between the signal structures of an ECG signal and the integrated linear prediction residual (ILPR) of voiced speech, an algorithm proposed earlier for epoch detection from ILPR is extended to the problem of QRS detection. The ECG signal is pre-processed by high-pass filtering to remove the baseline wandering and by half-wave rectification to reduce the ambiguities. The initial estimates of the QRS are iteratively obtained using a non-linear temporal feature, named the dynamic plosion index suitable for detection of transients in a signal. These estimates are further refined to obtain a higher temporal accuracy. Unlike most of the high performance algorithms, this technique does not make use of any threshold or differencing operation. The proposed algorithm is validated on the MIT-BIH database using the standard metrics and its performance is found to be comparable to the state-of-the-art algorithms, despite its threshold independence and simple decision logic.
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    New Results on Fractional QCQP with Applications to Radar Steering Direction Estimation (2014)
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    Publication Date: 2014-05-07
    Description: This letter considers constrained steering direction estimation in the presence of additive Gaussian disturbance. The uncertainty region is modeled through double-sided quadratic constraints (up to three) and the Maximum Likelihood (ML) criterion is adopted to get the direction estimator. It is shown that the considered formulation leads to a fractional Quadratically Constrained Quadratic Program (QCQP) whose solution can be computed in polynomial time via semidefinite programming relaxation, Charnes-Cooper transformation, and suitable rank-one decomposition tools. At the analysis stage, with reference to a specific constraint set, the performance of the devised estimator is compared with the constrained Cramer Rao lower Bound (CRB).
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    Effective Estimation of Image Rotation Angle Using Spectral Method (2014)
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    Publication Date: 2014-05-07
    Description: In this letter, we propose a blind and effective method to estimate the image rotation angle. It operates by exploiting the hidden periodicities in the rotated image using the 2-D spectrum of its second order statistics. The expected positions of rotation-related peaks as well as their trajectory paths in the 2-D spectrum are first derived. Based on the peaks found around those paths, the rotation angle is then estimated using a much simpler formula than those of the state-of-the-art 2-D method. Through both theoretical analyses and experimental results on a large set of natural images, the proposed scheme is demonstrated to give more accurate estimate than previous methods.
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    A Reed-Solomon Code Based Measurement Matrix with Small Coherence (2014)
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    Publication Date: 2014-04-30
    Description: In this letter, we construct a class of deterministic measurement matrices which are asymptotically optimal. For this purpose, we first apply the tensor product over the Reed Solomon (R-S) generator matrix to produce a new one; then employing this generator matrix, we construct a measurement matrix. If the R-S code is defined on ${BBF_q}$ , then the resulting measurement matrices are of dimensions ${q^2} times {q^3}$ , where $q$ is an arbitrary prime power and its coherence would be ${1 over q}$ , which is desirable for compressive sampling. We also illustrate the effectiveness of our proposed matrices in compressed sensing with some simulation examples.
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    A Lossless Tagged Visual Cryptography Scheme (2014)
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    Publication Date: 2014-04-30
    Description: As one of the most efficient multi-secret visual cryptography (MVC) schemes, the tagged visual cryptography (TVC) is capable of hiding tag images into randomly selected shares. However, the encoding processes of TVC and other MVC schemes bring distortion to shares, which definitely lowers the visual quality of the decoded secret image. This letter proposes an extended TVC scheme, named as lossless TVC (LTVC). Specifically, “lossless” means that the proposed LTVC scheme encodes the tag image without affecting the rebuilt secret image, i.e., the decoded secret image of LTVC has the same visual quality with that of the conventional VC scheme . Moreover, we propose the probabilistic LTVC (P-LTVC) to solve the potential security problem of LTVC. Finally, the superiority of the proposed scheme is experimentally verified.
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    Low Complexity Equalization of HCM Systems with DPFFT Demodulation over Doubly-Selective Channels (2014)
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    Publication Date: 2014-04-30
    Description: To mitigate the inter-carrier interference (ICI) of doubly-selective (DS) fading channels, we consider a hybrid carrier modulation (HCM) system employing the discrete partial fast Fourier transform (DPFFT) demodulation and the banded minimum mean square error (MMSE) equalization in this letter. We first provide the discrete form of partial FFT demodulation, then apply the banded MMSE equalization to suppress the residual interference at the receiver. The proposed algorithm has been demonstrated, via numerical simulations, to be its superior over the single carrier modulation (SCM) system and circularly prefixed orthogonal frequency division multiplexing (OFDM) system over a typical DS channel. Moreover, it represents a good trade-off between computational complexity and performance.
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    An Asymmetric Matching Method for a Robust Binary Audio Fingerprinting (2014)
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    Publication Date: 2014-04-30
    Description: Audio fingerprinting has been successfully applied for the problems associated with the protection of intellectual property, management of large database and indexation of content. For a reliable fingerprinting system, improving fingerprint matching accuracy is crucial. In this letter, we try to improve a binary audio fingerprint matching performance by utilizing auxiliary information which is discarded while extracting fingerprint bits. Since it is not feasible to store all the auxiliary information in the fingerprint DB, we only utilize the auxiliary information obtained while extracting fingerprints from the input unknown audio. We call the proposed method an asymmetric fingerprint matching, since the auxiliary information is available only in one side. Experimental results show that the proposed asymmetric matching method is effective in improving fingerprint matching performance over the conventional Hamming distance.
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    Dynamically-Sampled Bivariate Empirical Mode Decomposition (2014)
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    Publication Date: 2014-04-30
    Description: A novel scheme for selecting projection vectors in bivariate empirical mode decomposition (BEMD) is proposed in order to enable accurate signal decomposition at lower computational complexity. Unlike existing algorithms which use a static uniform scheme for the distribution of projection vectors, the proposed scheme examines local curvature in multidimensional spaces to produce a data-adaptive set of direction vectors for taking signal projections. This is achieved by aligning the density of projection vectors according to the empirical distributions of angles where the signal exhibits highest local dynamics. We show that the proposed methodology outperforms the existing schemes for a small number of signal projections. The proposed algorithm is verified via illustrative simulations demonstrating accurate local mean estimation and mode extraction.
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    Fast Synopsis for Moving Objects Using Compressed Video (2014)
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    Publication Date: 2014-04-30
    Description: With the increasing volume of video data, how to analyze and browse video in a fast and effective way has become an urgent problem in applications. This letter proposes a novel video synopsis method in compressed domain for browsing video captured by static cameras. Synopsis video is a video abstraction, which displays moving objects from different periods simultaneously on the primary background contents of original video. To overcome the low efficiency of traditional video synopsis for compressed video, our method presents a new graph cut algorithm to extract objects tubes and meanwhile gives a fast solution to minimize energy function in compressed domain. Experimental results in H.264 video have demonstrated the high-efficiency of this new video synopsis scheme for massive video browsing.
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    Adaptive Detection of Point-Like Targets in the Presence of Homogeneous Clutter and Subspace Interference (2014)
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    Publication Date: 2014-04-30
    Description: In this letter, we devise an adaptive decision scheme for point-like targets capable of handling the joint presence of homogeneous clutter and structured interference in the primary and secondary data. To this end, we resort to a design procedure based on the method of sieves: the usual generalized likelihood ratio test (GLRT) is modified constraining the unknown parameters to belong to a suitable subset of the original space ensuring unique solutions for the involved optimizations. Remarkably, the proposed receiver possesses the constant false alarm rate (CFAR) property with respect to the unknown covariance matrix of the unstructured interference. At the analysis stage, closed-form expressions for the false alarm and detection probabilities are derived.
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    Gaussian-Sum Cubature Kalman Filter with Improved Robustness for Bearings-only Tracking (2014)
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    Publication Date: 2014-03-05
    Description: This letter presents a Gaussian-sum cubature Kalman filter with improved robustness compared to the original algorithm proposed by the authors in , which demonstrated excellent accuracy and efficiency for the bearings-only tracking problem. Modifications are made in the splitting and merging procedure of the Gaussian components in the algorithm. Simulation results confirm the improved robustness of the modified algorithm against the choice of threshold level for the splitting procedure.
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    A Lower Bound for the Fisher Information Measure (2014)
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    Publication Date: 2014-04-26
    Description: The problem how to approximately determine the value of the Fisher information measure for a general parametric probabilistic system is considered. Having available the first and second moment of the system output in a parametric form, it is shown that the information measure can be bounded from below through a replacement of the original system by a Gaussian system with equivalent moments. The presented technique is applied to a system of practical importance and the potential quality of the bound is demonstrated.
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    Variable Tap-Length LMS Algorithm Based on Adaptive Parameters for TDL Structure Adaption (2014)
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    Publication Date: 2014-04-26
    Description: A variable tap-length LMS algorithm based on adaptive parameters for tapped-delay-line (TDL) structure adaption is proposed in this paper. The objective of this work is to overcome some drawbacks of previous variable tap-length adaptive filter algorithms, including slow convergence speed and high sensitivity to noise. Novel tap-length iteration equation based on adaptive parameters and arctangent-limited value is proposed. The values of adaptive parameters can automatically change according to the filter state, which not only improves the convergence speed but also reduces the steady-state error. The arctangent-limited value can effectively reduce the instantaneous estimated error, which enhances the robustness to noise. Computer simulations are conducted to verify the performance of proposed algorithm.
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    Uncensored Indoor Positioning (2014)
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    Publication Date: 2014-04-26
    Description: We present a new method to accommodate the unobserved access points in indoor positioning systems that use the Received Signal Strength (RSS). We formulate a probabilistic framework, and derive the estimation bounds that quantify the advantage of incorporating unobserved data. Further, we describe a realization for the maximum-likelihood estimator and establish its effectiveness in a typical indoor positioning scenario.
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    A Parametric Model for Describing the Correlation Between Single Color Images and Depth Maps (2014)
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    Publication Date: 2014-04-26
    Description: This letter introduces a new approach for modeling the correlation between a single color image and its depth map with a set of parameters. The proposed model treats the color image as a set of patches and describes the correlation with a kernel function in a non-linear mapping space. We also present how to estimate the model parameters from sampled color image patches as well as the corresponding depth values. The proposed approach is tested on different color images and experimental results are comparable to the state-of-the-art, which demonstrates the power of the proposed method. Furthermore, we validate the efficiency of the proposed parametric model by evaluating each of its component, including the filters optimization, the choice of the patches and the kernel function.
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    Discrete Anamorphic Transform for Image Compression (2014)
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    Publication Date: 2014-04-26
    Description: To deal with the exponential increase of digital data, new compression technologies are needed for more efficient representation of information. We introduce a physics-based transform that enables image compression by increasing the spatial coherency. We also present the Stretched Modulation Distribution, a new density function that provides the recipe for the proposed image compression. Experimental results show pre-compression using our method can improve the performance of JPEG 2000 format.
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    Application of Full-Duplex Wireless Technique into Secure MIMO Communication: Achievable Secrecy Rate based Optimization (2014)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2014-04-26
    Description: This letter considers the secure MIMO transmission in a wireless environment, in which one transmitter (Alice), one receiver (Bob) and one eavesdropper (Eve) are involved. Apart from the artificial noise (AN) generated by Alice, Bob can also exploit his remaining antenna resources to emit AN to further impair Eve’s channel. Such kind of AN can be cancelled by Bob himself by applying the Full-Duplex wireless communication technique. A computable secrecy rate is obtained for this model over a Rayleigh-fading eavesdropping channel. In order to maximize the secrecy rate, a joint optimization scheme is proposed to assign the TX/RX antennas for Bob and to design the beamforming and power allocation for Alice’s information and AN signal. Simulation is carried out to illustrate the process of the proposed optimization scheme.
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    Noise Interpolation for Unique Word OFDM (2014)
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    Publication Date: 2014-04-26
    Description: Unique word-orthogonal frequency division multiplexing (UW-OFDM) is known to feature an excellent bit error ratio performance when compared to conventional OFDM using cyclic prefixes (CP). In recent papers classical and Bayesian linear data estimators as well as the non-linear sphere decoding (SD) approach have been investigated in the UW-OFDM context. In general, the remaining error samples after a linear data estimation in UW-OFDM are correlated. In this work, noise interpolation (NI) is proposed, which uses preliminary data decisions together with Wiener interpolation to exploit these correlations. It turns out that the selection of samples to be used for NI is crucial. With the sample selection policy suggested in this work, NI clearly outperforms LMMSE data estimation, and due to its lower complexity compared to SD, it can be regarded as an attractive compromise for UW-OFDM reception.
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    Orthogonal Wavelet Filters with Minimum RMS Bandwidth (2014)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2014-04-26
    Description: For a time-limited sequence, the Root-Mean-Square (RMS) bandwidth is the normalized second moment of the spectrum. The RMS bandwidth is a useful and analytically tractable measure of the frequency localization of a discrete-time filter. In this work the design of orthogonal wavelet filter banks with a prescribed number of Vanishing Moment (VM) and having a minimum RMS bandwidth is considered. It is shown that the design problem can be cast as a convex optimization problem for which efficient algorithms and software for its solution exist.
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    Efficient Sparse Banded Acoustic Models for Speech Recognition (2014)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2014-01-25
    Description: We propose sparse banded acoustic models to significantly improve the recognition accuracy and reduce the computational cost of speech recognition systems. The sparse banded models are trained using a weighted lasso regularization. In addition, we propose new feature orders to reduce the bandwidth of sparse banded models in order to speed up computation. Experimental results on the Wall Street Journal data set show that sparse banded models significantly outperform diagonal and full covariance models by 9.5% and 15.1% relatively. Sparse banded models also run the fastest. The advantages of sparse banded models are also demonstrated on the collected Cantonese data set.
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    Modified Large Margin Nearest Neighbor Metric Learning for Regression (2014)
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    Publication Date: 2014-01-25
    Description: The main objective of this letter is to formulate a new approach of learning a Mahalanobis distance metric for nearest neighbor regression from a training sample set. We propose a modified version of the large margin nearest neighbor metric learning method to deal with regression problems. As an application, the prediction of post-operative trunk 3-D shapes in scoliosis surgery using nearest neighbor regression is described. Accuracy of the proposed method is quantitatively evaluated through experiments on real medical data.
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    Hierarchical Data Association Framework with Occlusion Handling for Multiple Targets Tracking (2014)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2014-01-25
    Description: The problem of tracking multiple targets in video is addressed, and a novel hierarchical data association framework with occlusion handling is presented. The association hierarchy first divides the detections, targets, and candidates into several disjoint branches, then progressively associates the detections to the targets and candidates within each branch, and finally initializes the targets through candidate upgrade. Furthermore, the depth disorder inference and targets motion pairing prediction are introduced to explicitly tackle target-target and target-environment occlusions, respectively. Experiments verify that our method improves the runtime performance significantly while keeping competitive tracking accuracy and precision compared with several state-of-the-art methods.
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    On Even-Period Binary Z-Complementary Pairs with Large ZCZs (2014)
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    Publication Date: 2014-01-25
    Description: For an even-period binary Z-complementary pair (EB-ZCP), if it is not a Golay complementary pair (GCP), we show that $Zleq N-2$ , where $N$ and $Z$ denote the sequence length and the zero correlation zone (ZCZ) width, respectively. This result partially answers the Fan-Yuan-Tu conjecture in 2007. In addition, we present a construction of EB-ZCPs with large ZCZ widths, where $N=2^{m+1}+{2^m}$ and $Z=2^{m+1}$ . Interestingly, each of the proposed EB-ZCPs features zero out-of-phase aperiodic auto-correlation sums except for the time-shift of $pm2^{m+1}$ , thus displaying a very close correlation property to that of GCPs.
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    Large Vocabulary Continuous Speech Recognition With Reservoir-Based Acoustic Models (2014)
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    Publication Date: 2014-02-01
    Description: Thanks to research in neural network based acoustic modeling, progress in Large Vocabulary Continuous Speech Recognition (LVCSR) seems to have gained momentum recently. In search for further progress, the present letter investigates Reservoir Computing (RC) as an alternative new paradigm for acoustic modeling. RC unifies the appealing dynamical modeling capacity of a Recurrent Neural Network (RNN) with the simplicity and robustness of linear regression as a model for training the weights of that network. In previous work, an RC-HMM hybrid yielding very good phone recognition accuracy on TIMIT could be designed, but no proof was offered yet that this success would also transfer to LVCSR. This letter describes the development of an RC-HMM hybrid that provides good recognition on the Wall Street Journal benchmark. For the WSJ0 5k word task, word error rates of 6.2% (bigram language model) and 3.9% (trigram) are obtained on the Nov-92 evaluation set. Given that RC-based acoustic modeling is a fairly new approach, these results open up promising perspectives.
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    An Efficient Multichannel Linear Prediction-Based Blind Equalization Algorithm in Near Common Zeros Condition (2014)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2014-02-01
    Description: This letter proposes an efficient multichannel acoustic channel equalization method under insufficient channel diversity conditions. To overcome an ill-posed problem caused by near common zeros (NCZs) conditions between different channels, a regularization method that restricts the filter norm has been investigated. However, direct application of this method to the linear-predictive multi-input equalization (LIME) method is not effective. To address this situation, this letter puts forth a novel method to disregard the erroneous term of the LIME solution matrix and to increase forced channel diversity (FCD). The accuracy of the proposed equalization filter is compared to that of the conventional regularization method. Experimental results confirm that the NCZs problem can be solved by adopting the proposed methods.
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    Scale and Rotation Invariant Texture Classification Using Covariate Shift Methodology (2014)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2014-02-01
    Description: In this letter, we propose to tackle rotation and scale variance in texture classification at the machine learning level. This is achieved by using image descriptors that interpret these variations as shifts in the feature vector. We model these variations as a covariate shift in the data. This shift is then reduced by minimising the Kullback–Leibler divergence between the true and estimated distributions using importance weights (IW). These IWs are used in support vector machines (SVMs) to formulate the IW-SVMs. The experimental results show that IW-SVMs exhibit good invariance characteristics and outperform other state-of-the-art classification methods. The proposed methodology gives a generic solution that can be applied to any texture descriptor that models the transformations as a shift in the feature vector.
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    On the Randomized Kaczmarz Algorithm (2014)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2014-02-05
    Description: The Randomized Kaczmarz Algorithm is a randomized method which aims at solving a consistent system of over determined linear equations. This letter discusses how to find an optimized randomization scheme for this algorithm, which is related to the question raised by . Illustrative experiments are conducted to support the findings.
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    Track-to-Track Association for Biased Data Based on the Reference Topology Feature (2014)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2014-02-22
    Description: In this letter, we propose a novel track-to-track association (TTTA) algorithm based upon the reference topology (RET) feature, which is insensitive to sensor biases. A rigorous mathematical definition of RET is presented. The insensitivity of RET to sensor biases is analyzed theoretically. In order to construct the association cost matrix, we make use of the optimal subpattern assignment (OSPA) metric to measure the distance between two RETs. Simulation results demonstrate the advantages of the proposed algorithm.
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    The Marginal Enumeration Bayesian Cramér–Rao Bound for Jump Markov Systems (2014)
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    Publication Date: 2014-02-22
    Description: A marginal version of the enumeration Bayesian Cramér–Rao Bound (EBCRB) for jump Markov systems is proposed. It is shown that the proposed bound is at least as tight as EBCRB and the improvement stems from better handling of the nonlinearities. The new bound is illustrated to yield tighter results than BCRB and EBCRB on a benchmark example.
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    Persistent Homology of Delay Embeddings and its Application to Wheeze Detection (2014)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2014-02-22
    Description: We propose a new approach to detect and quantify the periodic structure of dynamical systems using topological methods. We propose to use delay-coordinate embedding as a tool to detect the presence of harmonic structures by using persistent homology for robust analysis of point clouds of delay-coordinate embeddings. To discover the proper delay, we propose an autocorrelation like (ACL) function of the signals, and apply the introduced topological approach to analyze breathing sound signals for wheeze detection. Experiments have been carried out to substantiate the capabilities of the proposed method.
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    Relay Beamforming for Amplify-and-Forward Multi-Antenna Relay Networks with Energy Harvesting Constraint (2014)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2014-02-22
    Description: For amplify-and-forward multi-antenna relay networks with energy harvesting (EH) constraint, we study the optimal relay beamforming problem which maximizes the achievable rate from source to information-decoding receiver subject to the transmit power constraint at relay and the EH constraint at EH receiver. Because of the EH constraint, the beamforming problem is not convex. We propose the optimal beamforming scheme by converting the beamforming problem into a convex semidefinite programming with the rank-one relaxation and Charnes-Cooper transformation. We also propose a suboptimal closed-form beamforming scheme. It is shown from simulations that when the maximum allowable relay transmit power to noise power ratio is high, the performance of proposed suboptimal scheme approaches that of the optimal scheme.
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    Cepstrum-Based Bandwidth Extension for Super-Wideband Coders (2014)
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    Publication Date: 2014-03-01
    Description: This letter proposes a bandwidth extension (BWE) method using the cepstral envelope coding and duplication of quantized wideband (WB) signals by means of analysis -by-synthesis (AbS) for super-wideband (SWB) coders. In the proposed method, a high frequency band is generated by utilizing the quantized cepstral coefficients extracted from the envelope and the quantized modified discrete cosine transform (MDCT) shape of the wideband signal. The proposed method is compared with the latest G.718 SWB codec and the experimental results show that the proposed method outperforms the baseline codec both in subjective listening tests and objective performance measures.
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    Extended Grouping of RFID Tags Based on Resolvable Transversal Designs (2014)
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    Publication Date: 2014-03-01
    Description: This letter presents a novel scheme for the design of extended grouping of radio-frequency identification (RFID) tags, based on resolvable transversal designs (RTDs). Extended grouping of RFID tags allows identifying missing objects without the requirement for accessing external systems. The original scheme relies on Gallager’s parity-check matrices and, as such, it cannot easily achieve designated decoding guarantees due to its pseudo-random nature. In view of the Gallager’s parity-check matrix form of incidence matrices of RTDs, this letter proposes extended grouping of RFID tags based on RTDs. The proposed scheme proves to provide designated decoding guarantees more easily than the original scheme. In addition, this letter introduces a simple method, termed group splitting (GS), to improve the performance of extended grouping of RFID tags. Theoretical and simulation results demonstrate that the proposed scheme with or without GS is an efficient approach for the design of extended grouping of RFID tags.
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    A Hybrid Descent Method for Optimal Sigmoid Filter Design (2014)
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    Publication Date: 2014-03-01
    Description: In this letter, a hybrid descent method is used to determine a set of filter parameters for a sigmoid filter which attempts to work under various SNR conditions. It overcomes the limitations of the current sigmoid filters that performs effectively only at a single SNR. Results show that significant improvement in terms of better speech qualities can be achieved by the proposed sigmoid filter when working under various SNR conditions.
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    Sparse Recovery by Means of Nonnegative Least Squares (2014)
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    Publication Date: 2014-03-01
    Description: This letter demonstrates that sparse recovery can be achieved by an ${ell _1}$ -minimization ersatz easily implemented using a conventional nonnegative least squares algorithm. A connection with orthogonal matching pursuit is also highlighted. The preliminary results call for more investigations on the potential of the method and on its relations to classical sparse recovery algorithms.
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    Source Identification Using Signal Characteristics in Controller Area Networks (2014)
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    Publication Date: 2014-02-08
    Description: The CAN (Controller Area Network) bus, i.e., the de facto standard for connecting ECUs inside cars, is increasingly becoming exposed to some of the most sophisticated security threats. Due to its broadcast nature and ID oriented communication, each node is sightless in regards to the source of the received messages and assuring source identification is an uneasy challenge. While recent research has focused on devising security in CAN networks by the use of cryptography at the protocol layer, such solutions are not always an alternative due to increased communication and computational overheads, not to mention backward compatibility issues. In this work we set steps for a distinct approach, namely, we try to take authentication up to unique physical characteristics of the frames that are placed by each node on the bus. For this we analyze the frames by taking measurements of the voltage, filtering the signal and examining mean square errors and convolutions in order to uniquely identify each potential sender. Our experimental results show that distinguishing between certain nodes is clearly possible and by clever choices of transceivers and frame IDs each message can be precisely linked to its sender.
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    Efficient One-Pass Decoding with NNLM for Speech Recognition (2014)
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    Publication Date: 2014-02-08
    Description: Neural network language model (NNLM) has achieved very good results in the field of speech recognition, machine translation, etc. Direct decoding with NNLM is challenging for the overwhelmingly heavy burden in complexity. Most of the previous work focused on rescoring the N-best list and lattice with NNLM in the second pass. In this work, several techniques are explored to directly incorporate the NNLM into the decoder of speech recognition. A novel training algorithm based on variance regularization is proposed to approximate the softmax-normalizing factor as a constant for fast evaluation. Also, the evaluation of NNLM is further speeded up via our advanced storage. Moreover, a simple cache-based strategy is explored to avoid redundant computations during the decoding process. To the authors’ knowledge, it is the first time to directly incorporate NNLM into decoding. We evaluate our proposed methods on an English-Switchboard phone-call speech-to-text task. Experimental results show that incorporating the NNLM into the decoder significantly reduces the word error rate (WER) by 1.5% and 1.4% absolutely on the Hub5’00-SWB and RT03S-FSH sets, respectively. Also, the decoding with NNLM is twice as fast as the baseline at the same word error rate.
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    A TR-MISI Serial Prefilter for Robustness to ISI and Noise in Indoor Wireless Communication System (2014)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2014-02-08
    Description: We propose a time reversal and minimum inter-symbol interference (TR-MISI) serial prefilter for indoor wireless communication system. Proposed TR-MISI serial prefilter minimizes inter-symbol interference (ISI) and makes received peak power be robust to noise. Since the TR prefilter does not minimize the ISI and the joint zero-forcing and TR prefilter does not guarantee the performance in low signal-to-noise ratio (SNR) environment, we consider these two facts in designing an optimization problem for proposed TR-MISI serial prefilter. We use the semidefinite relaxation to solve the optimization problem and get a near-optimal solution. The simulation results show that proposed TR-MISI serial prefilter improves the bit error rate performance at overall SNR.
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    Reverberant Audio Source Separation via Sparse and Low-Rank Modeling (2014)
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    Publication Date: 2014-02-12
    Description: The performance of audio source separation from underdetermined convolutive mixture assuming known mixing filters can be significantly improved by using an analysis sparse prior optimized by a reweighting ${ell_1}$ scheme and a wideband data-fidelity term, as demonstrated by a recent article. In this letter, we show that the performance can be improved even more significantly by exploiting a low-rank prior on the source spectrograms. We present a new algorithm to estimate the sources based on i) an analysis sparse prior, ii) a reweighting scheme so as to increase the sparsity, iii) a wideband data-fidelity term in a constrained form, and iv) a low-rank constraint on the source spectrograms. Evaluation on reverberant music mixtures shows that the resulting algorithm improves state-of-the-art methods by more than 2 dB of signal-to-distortion ratio.
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    Computing Jacobian and Hessian of Estimators and Their Application to Risk Approximation (2014)
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    Publication Date: 2014-02-26
    Description: This letter gives formulas to compute the Jacobian and Hessian of an estimator that can be written as the maximum of a given scoring function, which includes the important cases of maximum likelihood (ML) and least squares (LS) estimation. We use the knowledge about these derivatives to compute two approximations of the estimator risk and show that the linear risk approximation of an ML estimator coincides with the Cramér–Rao bound for the case of a Gaussian signal model where the underlying loss function that is used for the risk computation is the squared error loss.
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    Time-Frequency Feature and AMS-GMM Mask for Acoustic Emotion Classification (2014)
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    Publication Date: 2014-03-25
    Description: In this letter, the pH time-frequency vocal source feature is proposed for multistyle emotion identification. A binary acoustic mask is also used to improve the emotion classification accuracy. Emotional and stress conditions from the Berlin Database of Emotional Speech (EMO-DB) and Speech under Simulated and Actual Stress (SUSAS) databases are investigated in the experiments. In terms of emotion identification rates, the pH outperforms the mel-frequency cepstral coefficients (MFCC) and a Teager-Energy-Operator (TEO) based feature. Moreover, the acoustic mask achieves accuracy improvement for both the MFCC and the pH feature.
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    Combination of Classification and Clustering Results with Label Propagation (2014)
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    Publication Date: 2014-03-25
    Description: This letter considers the combination of multiple classification and clustering results to improve the prediction accuracy. First, an object-similarity graph is constructed from multiple clustering results. The labels predicted by the classification models are then propagated on this graph to adaptively satisfy the smoothness of the prediction over the graph. The convex learning problem is efficiently solved by the label propagation algorithm. A semi-supervised extension is also provided to further improve the performance. Experiments on 11 tasks identify the validity of the proposed models.
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    A “Reciprocity” Property of the Unbiased Cramér–Rao Bound for Vector Parameter Estimation (2014)
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    Publication Date: 2014-03-25
    Description: In this letter, we consider Cramér–Rao bounds (CRBs) on the variance of unbiased vector parameter estimators. It is well known that the CRB for the estimation of a parameter $alpha$ , assuming that a second parameter $beta$ is unknown, is not smaller than the CRB computed assuming $beta$ known. The performance loss can be measured by the ratio between these two bounds. In this work, we derive a “reciprocity” property of the CRB for vector parameter estimation, which indicates that the performance loss in estimating $alpha$ when $beta$ is unknown equals the performance loss in estimating $beta$ with $alpha$ unknown. Though this property is of mainly theoretical interest, some examples are given which show that it can be useful also in practical applications.
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    Time-of-Flight Depth Camera Motion Blur Detection and Deblurring (2014)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2014-03-28
    Description: Recently, many consumer time-of-flight depth cameras have been introduced that provide direct 3-D geometry measurement of real world objects in real-time. However, these cameras suffer from motion blur artifact when there is any movement of camera or target object in the scene causing serious geometry measurement distortions. Unlike other noises, depth camera motion blur is difficult to eliminate using any existing image processing method due to the unique principle of time-of-flight depth calculation. In this letter, we propose a novel depth motion blur detection and deblurring method that can be applied for any ToF depth sensor. Our method utilizes the relations between different phase offsets observed at multiple time slots in ToF sensor. Experimental results show that the proposed method successfully detects motion blur regions and accurately eliminates them with minimal computational cost in real time.
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    Tongue Contour Reconstruction from Optical and Electrical Palatography (2014)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2014-03-28
    Description: Tongue shape reconstruction based on safe and convenient measurement techniques is of great interest for speech research and speech therapy. Two potentially useful and related measurement techniques for this purpose are electropalatography (EPG) and optopalatography (OPG). While EPG measures the time-varying contact pattern between the hard palate and the tongue, OPG measures distances between the two. Here, we examined the potential of EPG, OPG, and their combination for predicting the whole tongue contour using a multiple linear regression model. The model was trained and tested with tongue shapes and virtual sensor data obtained from Magnetic Resonance Images of sustained articulations of two speakers. When the model was trained and tested with the same speaker, the error of tongue contour reconstruction was significantly lower for predictions based on OPG data than for predictions based on EPG data. When the model was trained with one speaker and tested with the other, the error pattern was less consistent and the overall error was higher. Hence, especially OPG is well suited for tongue contour prediction, but an adaptation method is needed to transfer the model to a new speaker.
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    New Steady-State Analysis Results of Variable Step-Size LMS Algorithm With Different Noise Distributions (2014)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2014-03-28
    Description: The step-size in well-known variable step-size least mean square (VSSLMS) is updated as ${mu_{n + 1}} = alpha {mu_n} + gamma e_n^2$ with $0 〈 alpha 〈 1$ , $gamma 〉 0$ , and ${mu _{n + 1}}$ is set to ${mu _{min}}$ or ${mu _{max}}$ when it falls below or above these lower and upper bounds, respectively. It provides fast convergence at early stages of adaptation while ensuring small steady-state misalignment. This paper considers the steady-state performance of the VSSLMS in non-Gaussian noise environments. The contribution of the paper to the VSSLMS is threefold; (1) when $gamma ll 1 - alpha $ , the VSSLMS has low steady-state misalignment. (2) when $alpha ll 1$ , the VSSLMS achieves different steady-state misalignments for different noise distributions. (3) In theory, there are different optimal values $alpha $ for different noise distributions, i.e., 0.17 (Gaussian distribution), 0.21 (Student distribution), 0.38 (Laplace distribution), 0 (Binary and Uniform distributions). Analytical results are compared with simulations and are shown to agree well.
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    On the Complementarity of Phone Posterior Probabilities for Improved Speaker Recognition (2014)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2014-03-28
    Description: In this letter, we apply Phone Log-Likelihood Ratio (PLLR) features to the task of speaker recognition. PLLRs, which are computed on the phone posterior probabilities provided by phone decoders, convey acoustic-phonetic information in a sequence of frame-level vectors, and therefore can be easily plugged into traditional acoustic systems, just by replacing the Mel-Frequency Cepstral Coefficients (MFCC) or an alternate representation. To study the performance of the proposed features, MFCC-based and PLLR-based systems are trained under an i-vector-PLDA approach. Results on the NIST 2010 and 2012 Speaker Recognition Evaluation databases show that, despite yielding lower performance than the acoustic system, the system based on PLLR features does provide significant gains when both systems are fused, which reveals a complementarity among features, and provides a suitable and effective way of using higher level phonetic information in speaker recognition systems.
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    Consensus Bernoulli Filter for Distributed Detection and Tracking using Multi-Static Doppler Shifts (2014)
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    Publication Date: 2014-04-02
    Description: In this letter, we study the problem of distributed detection and tracking of a target over a network of separately located Doppler-shift sensors. For this challenging problem, we propose consensus Gaussian mixture - Bernoulli (CGM-Ber) filter. The simulation results prove the robust and effective performance of the proposed approach in a challenging tracking scenario.
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    A Non-Parametric Motion Model for Foreground Detection in Camera Jitter Scenes (2014)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2014-04-02
    Description: Foreground detection in video sequences is a fundamental step of extracting information in many visual surveillance applications. However, accurate foreground detection is obstructed by the vibration of cameras. In this letter, we present a non-parametric motion model for foreground detection in camera jitter scenes. More specifically, by studying the distinction between dynamic information of unstable background and that of moving foreground in camera jitter scenes, we find that the distribution of dynamic information of unstable background is relatively constant while that of moving foreground is uncertain. Inspired by this distinction, we propose to model the distribution of dynamic information of unstable background with a non-parametric estimation technique. Then a pixel is detected as foreground if its dynamic information is different from the reference model. Experimental results indicate that the proposed method achieves better performance in foreground detection in camera jitter scenes compared with several methods in the literature, especially those detecting foreground only with color distributions.
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    Multi-Focus Image Fusion Based on Spatial Frequency in Discrete Cosine Transform Domain (2014)
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    Publication Date: 2014-09-17
    Description: Multi-focus image fusion in wireless visual sensor networks (WVSN) is a process of fusing two or more images to obtain a new one which contains a more accurate description of the scene than any of the individual source images. In this letter, we propose an efficient algorithm to fuse multi-focus images or videos using discrete cosine transform (DCT) based standards in WVSN. The spatial frequencies of the corresponding blocks from source images are calculated as the contrast criteria, and the blocks with the larger spatial frequencies compose the DCT presentation of the output image. Experiments on plenty of pairs of multi-focus images coded in Joint Photographic Experts Group (JPEG) standard are conducted to evaluate the fusion performance. The results show that our fusion method improves the quality of the output image visually and outperforms the previous DCT based techniques and the state-of-art methods in terms of the objective evaluation.
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    NMF-based Target Source Separation Using Deep Neural Network (2014)
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    Publication Date: 2014-09-17
    Description: Non-negative matrix factorization (NMF) is one of the most well-known techniques that are applied to separate a desired source from mixture data. In the NMF framework, a collection of data is factorized into a basis matrix and an encoding matrix. The basis matrix for mixture data is usually constructed by augmenting the basis matrices for independent sources. However, target source separation with the concatenated basis matrix turns out to be problematic if there exists some overlap between the subspaces that the bases for the individual sources span. In this letter, we propose a novel approach to improve encoding vector estimation for target signal extraction. Estimating encoding vectors from the mixture data is viewed as a regression problem and a deep neural network (DNN) is used to learn the mapping between the mixture data and the corresponding encoding vectors. To demonstrate the performance of the proposed algorithm, experiments were conducted in the speech enhancement task. The experimental results show that the proposed algorithm outperforms the conventional encoding vector estimation scheme.
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    Convex 1-D Total Variation Denoising with Non-convex Regularization (2014)
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    Publication Date: 2014-09-03
    Description: Total variation (TV) denoising is an effective noise suppression method when the derivative of the underlying signal is known to be sparse. TV denoising is defined in terms of a convex optimization problem involving a quadratic data fidelity term and a convex regularization term. A non-convex regularizer can promote sparsity more strongly, but generally leads to a non-convex optimization problem with non-optimal local minima. This letter proposes the use of a non-convex regularizer constrained so that the total objective function to be minimized maintains its convexity. Conditions for a non-convex regularizer are given that ensure the total TV denoising objective function is convex. An efficient algorithm is given for the resulting problem.
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    Detecting Co-Salient Objects in Large Image Sets (2014)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2014-09-03
    Description: Co-salient object detection has attracted much more attention recently as it is useful for many problems in vision computing. However, most of existing methods emphasize detecting the common salient objects in a small group of images and the objects of interest in those images have clear borders with respect to the backgrounds. In this work, we propose a novel co-saliency detection method, which aims at discovering the common objects in a large and diverse image set composed of hundreds of images. First, we search a group of similar images for each image in the set. Our method is based on the overlapped groups. We handle each group with an unsupervised random forest to extract the rough contours of the common objects. Then a contrast-based measure is utilized to produce the saliency map for an individual image. For each image in the set, we collect all the maps from the groups that contain the image and fuse them together as the inter-saliency map for the image. The final co-saliency map is computed by combining the inter-saliency map with the single saliency map of this image. Experimental evaluation on an established large dataset demonstrates that our approach attains superior results and outperforms the state-of-the-art methods.
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    Robust Beamforming with Sidelobe Suppression for Impulsive Signals (2014)
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    Publication Date: 2014-10-04
    Description: Beamforming is a fundamental technique in array signal processing. Many existing approaches are based on second-order statistics. However, their performance degrades significantly due to outliers in the received signal. In this letter, we propose an outlier-resistant beamformer design criterion based on minimizing the expectation of the modulus of the array output with an ${ell _1}$ -regularization term being added for sidelobe suppression. By using the ${ell _1}$ -modulus of complex numbers instead of the standard modulus, the resulting optimization problem can be efficiently solved by a simple iterative algorithm or linear programming. Simulation results in the presence of impulsive signals are provided to demonstrate its robustness and accuracy compared to existing techniques.
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    Edge Perpendicular Binary Coding for USM Sharpening Detection (2014)
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    Publication Date: 2014-10-04
    Description: Unsharp masking (USM) sharpening is a basic technique for image manipulation and editing. In recent years, the detection of USM sharpening has attracted attention from image forensics point of view. After USM sharpening, overshoot artifacts, which shape image texture, are generated along image edges. By utilizing the special characteristic of the texture modification caused by the USM sharpening, a novel method called edge perpendicular binary coding is proposed in this letter to detect USM sharpening. Extensive experiments have been conducted to show the superiority of the proposed method over the existing methods.
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    Wideband Spectrum Sensing for Cognitive Radios: A Multistage Wiener Filter Perspective (2014)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2014-10-04
    Description: A wideband spectrum sensing scheme for cognitive radios is put forward in this letter. The proposed method operates over the total frequency bands simultaneously rather than a single band each time. It first estimates the number of occupied subbands, and then determines the accurate locations of occupied subbands as well as the vacant ones. Applying the ideas of multi stage Wiener filter to Gerschgorin disk estimator, this approach jointly makes the decision. In this way, the detection performance is enhanced because of capturing the signal information and suppressing the additive noise. Meanwhile, this approach avoids estimating the covariance matrix as well as the eigenvalue decomposition, and thus achieves a low computational complexity. Distinct from the conventional algorithms, neither noise power estimation nor prior knowledge of primary user signal is required, making the proposed algorithm robust to noise uncertainty and suitable for blind detection. Simulations under various conditions are presented to demonstrate the benefits of this approach and the results indicate that it surpasses the existing sensing algorithms.
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    Acoustic Echo Cancellation Using a Vector-Space-Based Adaptive Filtering Algorithm (2014)
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    Publication Date: 2014-10-04
    Description: A novel vector-space-based adaptive filtering (VAF) algorithm for acoustic echo cancellation (AEC) is presented. The proposed VAF algorithm can be divided into two phases: offline and online. In the offline phase, VAF constructs a vector space to incorporate the prior knowledge of adaptive filter coefficients from a wide range of different channel characteristics. Then, in the online phase, a mapping function is derived to estimate the adaptive filter for the testing condition using the constructed vector space. By using the vector space, VAF can effectively and efficiently estimate the parameters of the adaptive filter for the unknown testing condition. The experimental results for three designed AEC tasks demonstrate that VAF provides notably faster convergence rates compared to conventional adaptive filtering methods.
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    Statistical Modeling of Spectrum Sensing Energy in Multi-Hop Cognitive Radio Networks (2014)
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    Publication Date: 2014-10-04
    Description: The aim of this letter is to address the statistical modeling of the spectrum sensing energy consumption in cognitive radio networks. A Poisson point process has been shown to yield tractable and accurate results for the modeling of the interference in cognitive radio networks. We adopt this homogeneous stochastic process to develop an unified framework for deriving the energy consumption of the spectrum sensing in clustered cognitive radio networks. Furthermore, we extend the framework to multi-hop networks. The letter demonstrates that the spectrum sensing energy can be modeled as a Gamma-truncated distribution, as a function of the number of secondary users, their spatial density, and the number of hops of the cognitive radio network.
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    Texture Preserving Image Restoration with Dyadic Bounded Mean Oscillating Constraints (2014)
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    Publication Date: 2014-10-04
    Description: In this letter, we first propose an image restoration model with dyadic bounded mean oscillating (BMO) constraints to recover some clear texture from noisy data. The existence and uniqueness of the solution are discussed. The model is transformed into wavelet domain and then solved by dual Uzawa method. Each Lagrange multiplier of the discretized model corresponds to a certain scale of dyadic region of the image. Thus, the Lagrange multipliers are space adaptive and can control the extent of denoising over dyadic image regions. On the basis of this model, we propose a total variation (TV) based texture preserving denoising model. Finally, we display some numerical experiments to show the convergence of the algorithm and the validity of the proposed models.
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    Single-Image Super-Resolution Based on Compact KPCA Coding and Kernel Regression (2014)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2014-10-04
    Description: In this letter, we propose a novel approach for single-image super-resolution (SR). Our method is based on the idea of learning a dictionary which can capture the high-order statistics of high-resolution (HR) images. It is of central importance in image SR application, since the high-order statistics play a significant role in the reconstruction of HR image structure. Kernel principal component analysis (KPCA) is adopted to learn such a dictionary. A compact solution is adopted to reduce the time complexity of learning and testing for KPCA. Meanwhile, kernel ridge regression is employed to connect the input low-resolution (LR) image patches with the HR coding coefficients. Experimental results show that the proposed method is effective and efficient in comparison with state-of-art algorithms.
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    Image Guided Depth Map Upsampling using Anisotropic TV- ${L^2}$ (2014)
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    Publication Date: 2014-10-04
    Description: In this letter, we present a novel upsampling method to enhance the spatial resolution of depth maps. Given a low resolution depth map from a depth sensor and a registered high resolution intensity image from a camera, we formulate it as a convex optimization problem and solve it using the first-order primal-dual algorithm. The formulation combines an ${L^2}$ data term and an anisotropic total variation (TV) regularization term, thus it is more robust to noise and better preserves the fine details. Experimental results on Middlebury and real world datasets demonstrate that our method achieves favorable performance compared with state-of-the-art upsampling methods.
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    Local Binary Pattern Based Fast Digital Image Stabilization (2014)
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    Publication Date: 2014-10-04
    Description: In this letter, a fast digital image stabilization method based on local binary pattern (LBP) for real time applications is presented. The LBP approach utilized in this work enables efficient representation of the image frames in 1-bit depth resolution. A simple Boolean exclusive-OR (XOR) based matching criterion is employed to decide global motion vector between consecutive image frames. The constant velocity model based Kalman filtering is executed on the global motion vectors to obtain smoothed frame positions. Experiments show that the proposed approach provides comparable or better performance against the methods at the same category by requiring lower computational complexity.
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    Exact NLMS Algorithm with ${ell _p}$ -Norm Constraint (2014)
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    Publication Date: 2014-10-08
    Description: This letter presents the exact normalized least-mean-square (NLMS) algorithm for the ${ell _p}$ -norm-regularized square error, a popular choice for the identification of sparse systems corrupted by additive noise. The resulting exact ${ell _p}$ -NLMS algorithm manifests differences to the original one, such as an independent update for each weight, a new sparsity-promoting compensated update, and the guarantee of stable convergence for any configuration (regardless the choice of ${ell _p}$ norm and sparsity-tradeoff constant). Simulation results show that the exact ${ell _p}$ -NLMS is stable and it outperforms the original one, thus validating the optimality of the proposed methodology.
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