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  • Articles  (1,093)
  • Institute of Electrical and Electronics Engineers (IEEE)  (1,093)
  • Molecular Diversity Preservation International
  • 2015-2019  (1,093)
  • IEEE Signal Processing Letters  (1,093)
  • 1267
  • 1
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    Detection of Glottal Activity Using Different Attributes of Source Information (2015)
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    Publication Date: 2015-08-04
    Description: The major activity during speech production is glottal activity and is earlier detected using strength of excitation (SoE). This work uses the normalized autocorrelation peak strength (NAPS) and higher order statistics (HOS) as additional features for detecting glottal activity. The three features, namely, SoE, NAPS, and HOS, are, respectively indicators of different attributes of glottal activity, namely, energy, periodicity, and asymmetrical nature of the resulting source signal. The effectiveness of these features is analyzed using the differential electroglottograph signal, zero-frequency filtered signal, and integrated linear prediction residual, as representatives of source signal. The combination of glottal activity information from the three features outperforms any single of them, demonstrating different information represented by each of these features.
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  • 2
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    Robust Texture Classification by Aggregating Pixel-Based LBP Statistics (2015)
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    Publication Date: 2015-08-04
    Description: This letter addresses the texture classification problem through a pixel-based local binary pattern (LBP) statistics aggregation mechanism. Real-world texture images often present challenges for classification algorithms in terms of intra-class variability due, among others, to variable illumination. The LBP operator, a state-of-the-art texture descriptor, possesses key properties for tackling real-world texture images: discriminative power and invariance against monotonic gray level changes. We propose a novel texture classification approach that increases the robustness of LBP-based methods with respect to any type of intra-class variations. The method locally characterizes each pixel with an LBP code histogram and globally computes the label of a textured image by aggregating pixel labels through a voting process. Our approach can be in principle applied to any LBP version, as it focuses on how statistics are computed from LBP codes. We show that the proposed pixel-based approach improves upon traditional LBP block-based approaches in terms of classification accuracy by up to 5.1 p.p. on the public Outex database for the classic LBP with various neighborhoods as well as for various LBP extensions.
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  • 3
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    Trigonometric Interpolation Kernel to Construct Deformable Shapes for User-Interactive Applications (2015)
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    Publication Date: 2015-08-04
    Description: We present a new trigonometric basis function that is capable of perfectly reproducing circles, spheres and ellipsoids while at the same time being interpolatory. Such basis functions have the advantage that they allow to construct shapes through a sequence of control points that lie on their contour (2-D) or surface (3-D) which facilitates user-interaction, especially in 3-D. Our piecewise exponential basis function has finite support, which enables local control for shape modification. We derive and prove all the necessary properties of the kernel to represent shapes that can be smoothly deformed and show how idealized shapes such as ellipses and spheres can be constructed.
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  • 4
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    On the Average Directivity Factor Attainable With a Beamformer Incorporating Null Constraints (2015)
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    Publication Date: 2015-08-07
    Description: The directivity factor (DF) of a beamformer describes its spatial selectivity and ability to suppress diffuse noise which arrives from all directions. For a given array constellation, it is possible to select beamforming weights which maximize the DF for a particular look-direction, while enforcing nulls for a set of undesired directions. In general, the resulting DF is dependent upon the specific look- and null directions. Using the same array, one may apply a different set of weights designed for any other feasible set of look- and null directions. In this contribution, we show that when the optimal DF is averaged over all look directions, the result equals the number of sensors minus the number of null constraints. This result holds regardless of the positions and spatial responses of the individual sensors and regardless of the null directions. The result generalizes to more complex wave-propagation domains (e.g., reverberation).
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  • 5
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    Robust Design of Transmit Waveform and Receive Filter For Colocated MIMO Radar (2015)
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    Publication Date: 2015-08-04
    Description: We consider the problem of angle-robust joint transmit waveform and receive filter design for colocated multiple-input multiple-output (MIMO) radar, in the presence of signal-dependent interferences. The design problem is cast as a max-min optimization problem to maximize the worst-case output signal-to-interference-plus-noise-ratio (SINR) with respect to the unknown angle of the target of interest. Based on rank-one relaxation and semi-definite programming (SDP) representation of a nonnegative trigonometric polynomial, a cyclic optimization algorithm is proposed to tackle this problem. The effectiveness of the proposed method is illustrated via numerical examples.
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  • 6
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    Regularized Covariance Matrix Estimation via Empirical Bayes (2015)
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    Publication Date: 2015-08-07
    Description: An Empirical Bayes formalization of the regularized covariance estimation problem is proposed for (possibly high-dimensional, low-sample) normal variates. A simple iteration is provided to automatically adjust the shrinkage level, which provably converges to the maximum likelihood hyperparameter estimation for any choice of the starting point. The proposed approach is effective and can outperform both MSE-optimized diagonal loading and the Rao–Blackwell Leidot–Wolf estimator in terms of covariance-matrix-specific metrics.
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  • 7
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    A Low-Complexity Near-ML Differential Spatial Modulation Detector (2015)
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    Publication Date: 2015-06-06
    Description: Differential spatial modulation (DSM) is a newly-emerging differential scheme tailored to the spatial modulation technique, which selects only one among a group of antennas for transmission at any time instant. DSM, however, gives rise to prohibitive search complexity when the number of transmit antennas is large. In this letter, a low-complexity suboptimal detector is proposed for DSM. It is designed based on the maximum-likelihood criterion but takes more candidates for the antenna activation orders into account. The detection is performed in two steps: the first step is to confine the number of candidates for the modulated symbols to a small portion by exploiting the symmetry of the signal constellation; the second step is to select the most likely modulated symbols from the output of the first step according to the determined antenna activation order via a Viterbi-like algorithm. Analyses and simulations show that the proposed detector achieves near-optimal performance yet largely reduces the search complexity.
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  • 8
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    Similarity Validation Based Nonlocal Means Image Denoising (2015)
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    Publication Date: 2015-08-14
    Description: Nonlocal means is one of the well known and mostly used image denoising methods. The conventional nonlocal means approach uses weighted version of all patches in a search neighbourhood to denoise the center patch. However, this search neighbourhood can include some dissimilar patches. In this letter, we propose a pre-processing hard thresholding algorithm that eliminates those dissimilar patches. Consequently, the method improves the performance of nonlocal means. The threshold is calculated based on the distribution of distances of noisy similar patches. The method denoted by Similarity Validation Based Nonlocal Means (NLM-SVB) shows improvement in terms of PSNR and SSIM of the retrieved image in comparison with nonlocal means and some recent variations of nonlocal means.
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  • 9
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    Time–Frequency Filtering Based on Spectrogram Zeros (2015)
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    Publication Date: 2015-08-11
    Description: For a proper choice of the analysis window, a short-time Fourier transform is known to be completely characterized by its zeros, which coincide with those of the associated spectrogram. A simplified representation of the time-frequency structure of a signal can therefore be given by the Delaunay triangulation attached to spectrogram zeros. In the case of multicomponent nonstationary signals embedded in white Gaussian noise, it turns out that each time–frequency domain attached to a given component can be viewed as the union of adjacent Delaunay triangles whose edge length is an outlier as compared to the distribution in noise-only regions. Identifying such domains offers a new way of disentangling the different components in the time–frequency plane, as well as of reconstructing the corresponding waveforms.
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  • 10
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    HOG-Dot: A Parallel Kernel-Based Gradient Extraction for Embedded Image Processing (2015)
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    Publication Date: 2015-08-11
    Description: In this letter, we propose HOG-Dot, a method for the direct computation of the polar image gradients coordinates from the pixels values. The proposed algorithm, to be used as the first step of the Histogram of Oriented Gradient (HOG) pipeline, approximates the exact gradient with its projection onto a versor chosen among the projection plane set. Instead of non linear computations, the HOG-Dot method exploits linear operations while introducing a bounded approximation error with respect to other HOG approaches, thus resulting a more suitable solution for embedded devices. Concerning the state of the art, it also achieves improved accuracy with the mathematical spatial gradient formulation .
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  • 11
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    Block Iterative Reweighted Algorithms for Super-Resolution of Spectrally Sparse Signals (2015)
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    Publication Date: 2015-09-25
    Description: We propose novel algorithms that enhance the performance of recovering unknown continuous-valued frequencies from undersampled signals. Our iterative reweighted frequency recovery algorithms employ the support knowledge gained from earlier steps of our algorithms as block prior information to enhance frequency recovery. Our methods improve the performance of the atomic norm minimization which is a useful heuristic in recovering continuous-valued frequency contents. Numerical results demonstrate that our block iterative reweighted methods provide both better recovery performance and faster speed than other known methods.
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  • 12
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    Decision Fusion for Image Quality Assessment using an Optimization Approach (2015)
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    Publication Date: 2015-11-24
    Description: The proliferation of electronic means of communication entails distortion of visual information carried by processed images. Therefore, automatic evaluation of image perceptual quality in a way that is consistent with human perception is important. In this letter, an approach to full-reference image quality assessment (IQA) is proposed. The perceptual quality of the image is evaluated using an aggregated decision of several IQA measures. An optimization problem of designing a decision fusion of 18 IQA measures is defined and solved using a genetic algorithm. Obtained fusion strategies are evaluated on widely used large image benchmarks and compared with 32 state-of-the-art IQA approaches. Results of comparison reveal that the proposed approach outperforms other competing techniques.
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  • 13
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    On the Properties of Cubic Metric for OFDM Signals (2015)
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    Publication Date: 2015-11-27
    Description: As a metric for amplitude fluctuation of orthogonal frequency division multiplexing (OFDM) signal, cubic metric (CM) has received an increasing attention because it is more closely related to the distortion induced by nonlinear devices than the well-known peak-to-average power ratio (PAPR). In this letter, the properties of CM of OFDM signal is investigated. First, asymptotic distribution of CM is derived. Second, it is verified that 1.7 times oversampling rate is good enough to capture the CM of continuous OFDM signals in terms of mean square error, which is also practically meaningful because the fast Fourier transform size is typically 1.7 times larger than the nominal bandwidth in the long-term evolution (LTE) cellular communication systems.
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  • 14
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    A Path-following Algorithm for Robust Point Matching (2015)
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    Publication Date: 2015-11-27
    Description: We propose a new point matching algorithm in this letter by minimizing a concave geometric matching cost function coming from the objective function of the robust point matching algorithm. Due to concavity of this function, naive optimization strategies such as gradient descent will fail. To address this problem, we use a path following strategy for optimization which works by adding a convex quadratic term to the objective function and then gradually transitioning from the state that there is only weight of the convex term to the state that there is only weight of the concave geometric matching cost term. Extensive experimental results demonstrate strong robustness of the method over several state-of-the-art methods and it also has good computational efficiency.
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  • 15
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    Robust Whisper Activity Detection Using Long-Term Log Energy Variation of Sub-Band Signal (2015)
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    Publication Date: 2015-06-13
    Description: The goal in the whisper activity detection (WAD) is to find the whispered speech segments in a given noisy recording of whispered speech. Since whispering lacks the periodic glottal excitation, it resembles an unvoiced speech. This noise-like nature of the whispered speech makes WAD a more challenging task compared to a typical voice activity detection (VAD) problem. In this paper, we propose a feature based on the long term variation of the logarithm of the short-time sub-band signal energy for WAD. We also propose an automatic sub-band selection algorithm to maximally discriminate noisy whisper from noise. Experiments with eight noise types in four different signal-to-noise ratio (SNR) conditions show that, for most of the noises, the performance of the proposed WAD scheme is significantly better than that of the existing VAD schemes and whisper detection schemes when used for WAD.
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  • 16
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    The NLMS Algorithm with Time-Variant Optimum Stepsize Derived from a Bayesian Network Perspective (2015)
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    Publication Date: 2015-06-13
    Description: In this letter, we derive a new stepsize adaptation for the normalized least mean square algorithm (NLMS) by describing the task of linear acoustic echo cancellation from a Bayesian network perspective. Similar to the well-known Kalman filter equations, we model the acoustic wave propagation from the loudspeaker to the microphone by a latent state vector and define a linear observation equation (to model the relation between the state vector and the observation) as well as a linear process equation (to model the temporal progress of the state vector). Based on additional assumptions on the statistics of the random variables in observation and process equation, we apply the expectation-maximization (EM) algorithm to derive an NLMS-like filter adaptation. By exploiting the conditional independence rules for Bayesian networks, we reveal that the resulting EM-NLMS algorithm has a stepsize update equivalent to the optimal-stepsize calculation proposed by Yamamoto and Kitayama in 1982, which has been adopted in many textbooks. As main difference, the instantaneous stepsize value is estimated in the M step of the EM algorithm (instead of being approximated by artificially extending the acoustic echo path). The EM-NLMS algorithm is experimentally verified for synthesized scenarios with both, white noise and male speech as input signal.
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  • 17
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    An Amplitude Preserving S-Transform for Seismic Data Attenuation Compensation (2016)
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    Publication Date: 2016-07-22
    Description: The S-transform (ST), as a time–frequency analysis tool, has been widely used, but the amplitude preserving property is a little poor near the boundary of the selected discrete signal. The reason lies that the summation of the product between the analytical window and the comprehensive window over the sliding step deviates from unity near the boundary in the discrete cases. In order to hold the amplitude preserving property for the discrete signal recovery analysis, an amplitude preserving S-transform (APST) is proposed based on a novel analytical window selection. First, lots of numerical tests are used to analyze the shortcomings of the ST near the boundary for the selected discrete signal and demonstrate the effectiveness and the validity of the proposed APST using the novel analytical window. After that, the proposed APST is used for seismic data attenuation compensation, during which the attenuation function is estimated based on the minimum phase assumption using a statistical variable-step hyperbolic smoothing method. Numerical examples on synthetic and field data demonstrate the validity of the proposed method using the seismogram and time–frequency spectrum comparisons. Besides, the proposed APST can be easily extended into a generalized ST which is more flexible compared with the ST, and it can also be used in seismology, remote sensing, and other related discrete signal analysis fields.
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  • 18
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    Fast Phase Retrieval for High Dimensions: A Block-Based Approach (2016)
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    Publication Date: 2016-07-22
    Description: This paper addresses fundamental scaling issues that hinder phase retrieval (PR) in high dimensions. We show that, if the measurement matrix can be put into a generalized block-diagonal form, a large PR problem can be solved on separate blocks, at the cost of a few extra global measurements to merge the partial results. We illustrate this principle using two distinct PR methods, and discuss different design trade-offs. Experimental results indicate that this block-based PR framework can reduce computational cost and memory requirements by several orders of magnitude.
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  • 19
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    Joint Antenna Selection and Energy-Efficient Beamforming Design (2016)
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    Publication Date: 2016-07-22
    Description: Wireless networks face the challenge of increasing energy consumption while satisfying the unprecedented demand for higher data rates. Energy-efficient transmission has been regarded as a key technology for the next-generation wireless system. Meanwhile, to reduce the cost, in practice, a base station usually has less radio chains than the antennas, which makes antenna selection an appealing transmission strategy. This letter addresses the problem of joint optimization of energy-efficient beamforming and antenna selection for downlink multiuser systems. The nonconvexity arising from both the nonlinear fractional programming and the $ell _{0}$ -(quasi)norm presents the main difficulty in solving the joint optimization problem. Nevertheless, we develop an effective algorithm to address this problem. Numerical results are given to validate the effectiveness and the performance of the developed algorithm.
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  • 20
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    Kaczmarz Method for Solving Quadratic Equations (2016)
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    Publication Date: 2016-07-26
    Description: Estimating low-rank positive-semidefinite (PSD) matrices from symmetric rank-one measurements is of great importance in many applications, such as high-dimensional data processing, quantum state tomography, and phase retrieval. When the rank is known a priori , this problem can be regarded as solving a system of quadratic equations of a low-dimensional subspace. The authors develop a fast iterative algorithm based on an adaptation of the Kaczmarz method, which is traditionally used for solving overdetermined linear systems. In particular, the authors characterize the dynamics of the algorithm when the measurement vectors are composed of standard Gaussian entries in the online setting. Numerical simulations demonstrate the compelling performance of the proposed algorithm.
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  • 21
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    Dynamic Hand Gesture Recognition With Leap Motion Controller (2016)
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    Publication Date: 2016-07-29
    Description: Dynamic hand gesture recognition is a crucial but challenging task in the pattern recognition and computer vision communities. In this paper, we propose a novel feature vector which is suitable for representing dynamic hand gestures, and presents a satisfactory solution to recognizing dynamic hand gestures with a Leap Motion controller (LMC) only. These have not been reported in other papers. The feature vector with depth information is computed and fed into the Hidden Conditional Neural Field (HCNF) classifier to recognize dynamic hand gestures. The systematic framework of the proposed method includes two main steps: feature extraction and classification with the HCNF classifier. The proposed method is evaluated on two dynamic hand gesture datasets with frames acquired with a LMC. The recognition accuracy is 89.5% for the LeapMotion-Gesture3D dataset and 95.0% for the Handicraft-Gesture dataset. Experimental results show that the proposed method is suitable for certain dynamic hand gesture recognition tasks.
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  • 22
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    Linear Precoder Design for MIMO-ISI Broadcasting Channels Under Channel Shortening Detection (2016)
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    Publication Date: 2016-07-29
    Description: We consider optimal precoder design for multiuser multiple-input multiple-output broadcasting channels in single-carrier systems. Instead of linear detection, we assume that the advanced nonlinear channel shortening detectors are utilized at the receivers. Such a scenario is challenging for precoder design as the uplink–downlink duality is inapplicable. The target of our linear precoder design is to maximize the sum of the achievable information rate (sum-AIR), with AIR of each user being explicitly derived. We analyze such a precoder design in general, and provide an efficient per-user based optimization algorithm for the design of block-diagonalization precoder.
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  • 23
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    Turbo Equalization Using Partial Gaussian Approximation (2016)
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    Publication Date: 2016-08-05
    Description: This letter deals with turbo equalization for coded data transmission over intersymbol interference (ISI) channels. We propose a message-passing algorithm that uses the expectation propagation rule to convert messages passed from the demodulator and decoder to the equalizer and computes messages returned by the equalizer by using a partial Gaussian approximation (PGA). We exploit the specific structure of the ISI channel model to compute the latter messages from the beliefs obtained using a Kalman smoother/equalizer. Doing so leads to a significant complexity reduction compared to the initial PGA implementation. Results from Monte Carlo simulations show that the proposed approach leads to a significant performance improvement compared to state-of-the-art turbo equalizers and allows for trading performance with complexity.
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    Post-Segmentation Swallowing Accelerometry Signal Trimming and False Positive Reduction (2016)
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    Publication Date: 2016-08-05
    Description: Dual-axis swallowing accelerometry has been proposed as a method for quantifying swallowing function. Acceleration signals in anterior–posterior and superior–inferior anatomical directions are processed and automatically segmented. However, the latter is often too liberal, admitting pre- and post-swallowing activity while also giving rise to non-swallow segments. These segmentation shortcomings adversely affect feature extraction and ultimately classification of swallowing function. In this paper, we propose a kernel density estimation-based algorithm to adaptively trim the swallow segments, and energy and noise-floor algorithms to reduce the number of false positive swallow segments. The balance between false positive reduction and loss of true positives can be adjusted according to algorithmic thresholds. Dramatic reductions ( $-$ 85.4%) in false positives can be achieved with a moderate loss of true positives ( $-$ 15.1%).
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  • 25
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    Improving Action Recognition Using Collaborative Representation of Local Depth Map Feature (2016)
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    Publication Date: 2016-08-05
    Description: Based on depth information, this letter introduces a new local depth map feature describing local spatiotemporal details of human motion and a collaborative representation for classification with regularized least squares. By extracting a multilayered depth motion feature and then applying a multiscale Histograms of Oriented Gradient (HOG) descriptor to it, the proposed feature characterizes the local temporal change of human motion and the local spatial structure (appearance) of an action. Instead of class-specific dictionary, the test action sample is represented collaboratively by the common shared dictionary. Moreover, we present an analytical solution of collaborative representation, which is independent of the query and can be precalculated as a projection matrix, leading to low computational cost in recognition. The evaluations on MSRAction3D and MSRGesture3D datasets demonstrate its effectiveness.
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    Moving Target's HRRP Synthesis With Sparse Frequency-Stepped Chirp Signal via Atomic Norm Minimization (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-08-05
    Description: Compressive sensing (CS) has been introduced into inverse synthetic aperture radar (ISAR) imaging with partial measurements. However, in the case of transmitting sparse frequency-stepped chirp signal (FSCS), the CS-based method will produce an irregular range cell migration (IRCM) problem in the recovered high-resolution range profiles (HRRPs). The IRCM is induced by the basis mismatch problem in CS, and it will degrade the ISAR image. To obviate the IRCM, an atomic norm minimization (ANM) method is proposed in this letter. By reformulating the ANM as a semidefinite program (SDP), the echo with full FSCS can be recovered using off-the-shelf SDP solvers. Thus, HRRPs without IRCM can be achieved via the conventional inverse fast Fourier transform. As a result, an improved ISAR image will be obtained. Real data results demonstrate the advantages of the proposed method over the CS-based and matrix completion-based methods.
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    Ambiguity Optimization for Frequency-Hopping Waveforms in MIMO Radars With Arbitrary Antenna Separations (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-08-05
    Description: Ambiguity functions are important design tools that characterize the response of radar detectors to mismatched targets. Recently, the authors proposed a new definition of the ambiguity function for radars that perform non-coherent processing. In this paper, this new ambiguity function definition is utilized to optimize frequency hopping waveforms for MIMO radars with arbitrary antenna separations. To this end, ambiguity function expressions for a general MIMO radar with arbitrary antenna separation are derived, and further simplified for frequency-hopping waveforms. Next, a scalar cost function is proposed that expresses the desirability of the ambiguity function associated with any frequency-hopping waveform. Simulation results demonstrate that this cost function can be optimized via heuristic optimization techniques to yield improvements in ambiguity performance.
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    A Tensor-Driven Temporal Correlation Model for Video Sequence Classification (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-08-05
    Description: The task of video sequence classification plays a critical role in the development of computer vision. Considering this fact, this letter proposes a novel tensor decomposition method called tensor-driven temporal correlation in which general tensors are used as input for video sequence classification. Because distortion and redundancy may exist in the tensor representations of video sequences, we project the original tensor into subspaces spanned by spatial basis matrices in the proposed formulation. Moreover, to better preserve the temporal smoothness between consecutive slices of the tensor, the basis matrices are jointly learned by introducing an autoregressive model. An experiment on the commonly used Cambridge hand-gesture database demonstrates that our proposed method reaches convergence within a small number of iterations during the training stage and achieves promising results compared with state-of-the-art methods.
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    A Novel Power Allocation Scheme Under Outage Constraints in NOMA Systems (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-08-05
    Description: In this letter, we study a downlink non-orthogonal multiple access (NOMA) transmission system, where only the average channel state information (CSI) is available at the transmitter. Two criteria in terms of transmit power and user fairness for NOMA systems are used to formulate two optimization problems, subjected to outage probabilistic constraints and the optimal decoding order. We first investigate the optimal decoding order when the transmitter knows only the average CSI, and then, we develop the optimal power allocation schemes in closed form by employing the feature of the NOMA principle for the two problems. Furthermore, the power difference between NOMA systems and OMA systems under outage constraints is obtained.
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    Active Learning for Regression With Correlation Matching and Labeling Error Suppression (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-07-12
    Description: In this letter, we develop an active learning algorithm to optimize the selection of training data for robust linear regression. This algorithm selects training data based on the principle of correlation matching between the training dataset and the overall data pool. Considering the inevitable and potentially heavy human labeling errors, we model the probability of labeling errors based on the item response theory (IRT) and develop data screening techniques to control the error sparsity. Compressive sensing theory is then exploited for human labeling error suppression. This algorithm is robust even in the case of short training dataset with nonsparse labeling errors. Its performance is verified by simulations with both artificial data and real benchmark data. Experiments are also conducted to demonstrate the validity of the IRT-based human labeling error model and the superior performance of the algorithm in practical applications.
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    Composition of Binary Compressed Sensing Matrices (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-07-12
    Description: In the recent past, various methods have been proposed to construct deterministic compressed sensing (CS) matrices. Of interest has been the construction of binary sensing matrices as they are useful for multiplierless and faster dimensionality reduction. In most of these binary constructions, the matrix size depends on primes or their powers. In this study, we propose a composition rule which exploits sparsity and block structure of existing binary CS matrices to construct matrices of general size. We also show that these matrices satisfy optimal theoretical guarantees and have similar density compared to matrices obtained using Kronecker product. Simulation work shows that the synthesized matrices provide comparable results against Gaussian random matrices.
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    Moving Target Localization in Multistatic Sonar by Differential Delays and Doppler Shifts (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-07-19
    Description: A moving target creates the Doppler effect on the transmitted signal, which can be exploited to improve the target localization accuracy in multistatic sonar that normally utilizes differential delay time measurements only. In this letter, we first examine the contribution of Doppler measurements via the Cr $acute{text{a}}$ mer–Rao lower bound (CRLB) study, and then develop an algebraic closed-form solution for the moving target localization problem. The proposed algorithm is shown in both theory and simulation to be able to reach the CRLB performance under Gaussian noise, when the measurement error is small.
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    Coincidence of Maximal Invariants for Two Adaptive Radar Detection Problems (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-07-29
    Description: This letter deals with adaptive radar detection of targets embedded in Gaussian clutter plus range-distributed subspace-structured jamming by exploiting the invariance theory. The class of invariant detectors which ensure the constant false alarm rate property has been characterized by designing the maximal invariant statistic for the studied detection problem. The achievement of this letter is the coincidence of the obtained maximal invariant with that derived assuming range-concentrated jamming.
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    Generalized Signal Utility for LMMSE Signal Estimation With Application to Greedy Quantization in Wireless Sensor Networks (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-07-29
    Description: The ability to efficiently assess and track the utility of each sensor signal is crucial to reduce the energy consumption in a wireless sensor network (WSN), e.g., by putting the sensors with low utility to sleep. Methods to track the sensor signal utility have been described for several multichannel signal estimation methods. For linear minimum mean squared error (LMMSE) estimation, the utility of a sensor signal is defined as the predicted increase in the minimum mean squared error when the sensor would be shut down. However, rather than making such a binary decision, more flexible energy-saving methods could be considered where a sensor changes internal parameters such as, e.g., the number of bits per sample, which results in noise injection in the transmitted sensor signal. We propose a generalization of the original definition of sensor signal utility to include this effect, and we show that it can be efficiently computed and tracked at hardly any computational cost compared to the already available LMMSE estimator. In addition, we illustrate how it can be used to assign a number of bits to each sensor with a greedy approach. Simulation results show that a greedy assignment based on the proposed generalized utility leads to improved results compared to the original utility measure.
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    A Rotation-Aided Arctangent Phase Discriminator With One-Bit Quantization (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-08-05
    Description: In this letter, we present a rotation-aided arctangent phase discriminator (RaAPD) with one-bit analog-to-digital conversion. Different from the existing digital phase discriminator (DPD) and noise-balanced digital phase discriminator (NB-DPD), the proposed RaAPD can achieve higher accuracy and better noise robust features through utilizing an extra rotation channel in the arctangent phase discriminator (APD). Experimental results show that RaAPD achieves 98.3%, 79.3%, and 79.4% reduction in terms of the average root-mean-square error of phase estimation with the signal noise ratio range [−20 dB, 20 dB] and a 16.384-MHz sampling frequency comparing to DPD, NB-DPD, and APD, respectively.
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    An Affine Motion Model for Removing Rolling Shutter Distortions (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-08-05
    Description: Rolling shutter distortions degrade the quality of videos captured by hand-held cameras. This letter proposes an affine motion model for removing rolling shutter distortions. The model represents the image motion during image capture as a sequence of affine transformations and computes the composition of these affine transformations precisely. Because an affine transformation can be represented by a neural network with one layer of linear neurons, the motion model can be represented by a multilayer neural network of linear neurons. Thus, the backpropagation algorithm can be used to improve the efficiency of the optimization process that estimates the model parameters. The proposed model is calibration-free. It is more general than other rolling shutter motion models because it only assumes that the velocity of the image during image acquisition is piecewise constant. Experimental results demonstrate that the model is more accurate than two state-of-the-art models and that the model parameters can be estimated efficiently.
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    Object Recognition With an Elastic Net-Regularized Hierarchical MAX Model of the Visual Cortex (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-07-08
    Description: The human visual cortex has evolved to determine efficiently objects from within a scene. Hierarchical MAX (HMAX) is an object recognition model which has been inspired by the visual cortex, and sparse coding, which is a characteristic of neurons in the visual cortex, was previously integrated into the HMAX model for improved performance. In this study, in order to further enhance recognition accuracy, we have developed an elastic net-regularized dictionary learning approach for use in the HMAX model. We term this the En-HMAX model. With the En-HMAX model, we can exploit the sparsity-grouping tradeoff, such that correlated but informative features are preserved for object classification. Results show that the En-MAX model outperforms the original HMAX model in recognizing unseen objects by $sim$ 40 $%$ as well as the two special cases of the HMAX model, i.e., the least absolute shrinkage and selection operator (LASSO)-HMAX ( $sim$ 19 $%$ ) and Ridge-HMAX ( $sim$ 9 $%$ ) models.
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    Optimal Rayleigh Approximation of the K-Distribution via the Kullback–Leibler Divergence (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-07-08
    Description: Understanding the validity of distributional approximations is important in radar signal processing. This is certainly exemplified in the context of target detection with X-band maritime surveillance radar. This is due to the fact that if the underlying clutter amplitude model can be assumed to be Rayleigh distributed, then there is a large class of detection processes with the constant false alarm rate property that can be applied. This paper examines the Rayleigh approximation of the K-distribution, since the latter is a popular model in X-band maritime surveillance radar. With an application of ideas from information theory, and in particular the Kullback–Leibler divergence, it is possible to derive the optimal Rayleigh approximation for any given K-distribution. Consequently bounds are derived to measure this approximation. These bounds reveal a necessary interaction between the K-distribution parameters to achieve a good Rayleigh approximation. Some numerical results are included to provide a practical assessment of the approximation.
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    Color Image Denoising via Cross-Channel Texture Transferring (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-07-08
    Description: Image denoising can reduce the perturbation inevitably generated during image signal acquisition and its subsequent processing. While the utilization of nonlocal properties can enhance the performance of the state-of-the-art denoising methods, a heavy computational burden is incurred especially for color images. Inspired by the high correlation in the texture information over color channels, for a reduction of the computational burden, this letter proposes denoising the luma channel first, and then, performing a patch-wise linear prediction to transfer the texture information of the denoised luma channel to the other two channels. The texture transferring is adapted to local characteristic (i.e., variance of the local patches) for a reduction of color smearing caused by large prediction error especially along edges. Experimental results confirm that the proposed method achieves performance improvement over the state-of-the-art color image denoising methods only at a slightly increased complexity of single-channel denoising.
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    EEF: Exponentially Embedded Families With Class-Specific Features for Classification (2016)
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    Publication Date: 2016-06-21
    Description: In this paper, we present a novel exponentially embedded families (EEF) based classification method, in which the probability density function (PDF) on raw data is estimated from the PDF on features. With the PDF construction, we show that class-specific features can be used in the proposed classification method, instead of a common feature subset for all classes as used in conventional approaches. We apply the proposed EEF classifier for text categorization as a case study and derive an optimal Bayesian classification rule with class-specific feature selection based on the Information Gain score. The promising performance on real-life data sets demonstrates the effectiveness of the proposed approach and indicates its wide potential applications.
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    Semidefinite Relaxations for MIMO Transmissions With High-Order QAM Constellations (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-06-21
    Description: In this letter, simple semidefinite relaxations are proposed for the detection of high-order quadrature amplitude modulations in multiple-input–multiple-output systems. The detector is based on the addition of several convex and concave quadratic inequality constraints into the maximum-likelihood detector before relaxing the problem into a convex one. Combined with a randomized sampling procedure, it is shown that the performance and worst-case computational complexity of the proposed approach makes it very competitive against existing detectors in large problem sizes.
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    Fold-based Kolmogorov–Smirnov Modulation Classifier (2016)
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    Publication Date: 2016-06-24
    Description: Modulation classification is crucial in applications such as electronic warfare and interference cancellation. In this letter, a novel feature-based Kolmogorov–Smirnov classifier is proposed for the identification of the modulation formats. The received signal is first preprocessed with a folding operation that helps identify the modulation formats based on their different axes of symmetry. Simulation results show that the performance of the proposed classifier is close to that of the optimal likelihood-based classifier, while its robustness to noise uncertainty is improved and its computational complexity is reduced compared to that of the optimal likelihood-based classifier.
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    Feature Selection Embedded Subspace Clustering (2016)
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    Publication Date: 2016-06-24
    Description: We propose a new subspace clustering method that integrates feature selection into subspace clustering. Rather than using all features to construct a low-rank representation of the data, we find such a representation using only relevant features, which helps in revealing more accurate data relationships. Two variants are proposed by using both convex and nonconvex rank approximations. Extensive experimental results confirm the effectiveness of the proposed method and models.
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    Correlation Detection for Adaptive Feedback Cancellation in Hearing Aids (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-06-21
    Description: Acoustic feedback is a well-known phenomenon in hearing aids. Under certain conditions, it causes the so-called howling effect, which is highly annoying for the hearing aid user and limits the maximum amplification of the hearing aid. The standard adaptive feedback cancellation algorithms suffer from a biased adaptation if the input signal is spectrally colored or tonal, as it is for speech and music signals. Due to this bias distortion artifacts (entrainment) are generated. In this letter, we present a method to detect tonal, high correlated parts of the input signal. In particular, the method is able to distinguish between correlation resulting from the input signal and from feedback path changes. A subband feedback cancellation system which applies decorrelation methods is the basis for the proposed method. Additionally, we suggest to use the correlation detection to increase the performance of the mentioned feedback cancellation system. The performance is measured by preventing entrainment and reacting to feedback path changes.
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    Sparse Signal Detection for Space Shift Keying Using the Monte Carlo EM Algorithm (2016)
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    Publication Date: 2016-06-21
    Description: In order to detect space shift keying (SSK) signals, we propose to use the Monte Carlo (MC) expectation maximization (EM) algorithm in this paper. First, we formulate the EM algorithm for the maximum likelihood detection of SSK signals. Then, we derive a Gibbs sampler to approximate the E-step. Through simulation results, we can see that the performance of the derived MCEM algorithm is better than that of the orthogonal matching pursuit algorithm, which was adopted to detect SSK signals with low complexity by Yu et al.
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    Spatial Steganalysis Using Contrast of Residuals (2016)
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    Publication Date: 2016-06-24
    Description: This letter proposes a novel scheme for spatial steganalysis based on contrast of residuals (CoR). After selecting complex blocks from an uncompressed image by a fluctuation function, the residuals are calculated from the selected blocks and the whole image after applying diverse filters. The CoR is represented as an angle and the norm of residuals is considered as the corresponding weight of angle, which is used as the new steganalysis feature. In the proposed scheme, no quantization and truncation is required and the effective information of long-range dependencies among pixels is kept properly. Also, the dimensionality of feature is linear with the number of residuals. The accuracy of proposed scheme is evaluated on HUGO and WOW algorithms, and the experimental results show that the proposed CoR feature has superior performance at low embedding rate with lower dimensionality.
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    Window-Based Three-Dimensional Aggregation for Stereo Matching (2016)
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    Publication Date: 2016-06-24
    Description: This paper presents a window-based three-dimens-ional (3-D) aggregation technique, which can approximate the surfaces of all kinds of objects, in stereo matching. The 3-D aggregation, which means to aggregate in 3-D surfaces, is implemented by decomposing the adaptive support window into horizontal segments; we allow the disparity to change smoothly in or between segments. Compared to traditional local stereo methods, the 3-D aggregation greatly improves the accuracy of results in slanted surfaces and occlusion areas while keeping excellent performance near depth discontinuities. We also propose an acceleration method that surprisingly improves the accuracy at the same time. The evaluation experiments confirm our achievements.
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    Structural Optimization and Online Evolutionary Learning for Spoken Dialog Management (2016)
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    Publication Date: 2016-06-24
    Description: Designing dialog management (DM) policies that are robust to environmental noises is a nontrivial task. Approaches based on reinforcement learning (RL) are popular in academia and have been empirically shown to exhibit much better performance than handcrafted policies. However, the policies trained using RL are mostly incomprehensible, thus limiting the deployments for commercial applications. Policy optimization using genetic algorithm (GA) is a relatively new approach to spoken DM. The most notable advantage of this approach is that the trained policies can be directly interpreted by human experts. In this letter, we make several contributions to the GA-based framework. First, a structural policy learning procedure is presented. Second, a new fitness estimation method based on fitted policy evaluation is proposed. Finally, combining with these methods, an online evolutionary policy learning algorithm is designed which is much more data efficient than direct policy search using Monte Carlo simulations. These proposed approaches are empirically evaluated and compared with several state-of-the-art methods in a simulated environment. The experiments show favorable results for our approach.
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    Partitioned Alternating Least Squares Technique for Canonical Polyadic Tensor Decomposition (2016)
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    Publication Date: 2016-06-24
    Description: Canonical polyadic decomposition (CPD), also known as parallel factor analysis, is a representation of a given tensor as a sum of rank-one components. Traditional method for accomplishing CPD is the alternating least squares (ALS) algorithm. Convergence of ALS is known to be slow, especially when some factor matrices of the tensor contain nearly collinear columns. We propose a novel variant of this technique, in which the factor matrices are partitioned into blocks, and each iteration jointly updates blocks of different factor matrices. Each partial optimization is quadratic and can be done in closed form. The algorithm alternates between different random partitionings of the matrices. As a result, a faster convergence is achieved. Another improvement can be obtained when the method is combined with the enhanced line search of Rajih et al. Complexity per iteration is between those of the ALS and the Levenberg–Marquardt (damped Gauss–Newton) method. It is important, however, that the idea of alternating quadratic optimization with partitioned factor matrices is general and can be applied to other variants of the tensor decomposition problems, e.g., when non-Gaussian additive noise is considered.
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    Person Re-identification by Exploiting Spatio-Temporal Cues and Multi-view Metric Learning (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-06-24
    Description: In this letter, we introduce a new spatio-temporal feature, namely optical flow energy image (OFEI), for video-based person re-identification. OFEI aims to exploit spatio-temporally stable regions across frames, which can capture discriminative cues such as human body parts and carry-on stuffs. Furthermore, we propose a novel matching method, denoted by multi-view relevance metric learning with list-wise constraints (mvRMLLC), to integrate the spatio-temporal (i.e., OFEI) and appearance features. Unlike previous works, mvRMLLC assumes that multiple features are generated from different views with distinct data distributions, while their similarities should be globally consistent. Multiple similarity metrics are then learned and fused by maximizing their global consistency and simultaneously allowing local discrepancies. Extensive experiments on two benchmarks demonstrate that OFEI outperforms the state-of-the-art spatio-temporal features, and mvRMLLC could further enhance the overall performance significantly.
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    Saliency Detection for Stereoscopic Images Based on Depth Confidence Analysis and Multiple Cues Fusion (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-05-06
    Description: Stereoscopic perception is an important part of human visual system that allows the brain to perceive depth. However, depth information has not been well explored in existing saliency detection models. In this letter, a novel saliency detection method for stereoscopic images is proposed. First, we propose a measure to evaluate the reliability of depth map, and use it to reduce the influence of poor depth map on saliency detection. Then, the input image is represented as a graph, and the depth information is introduced into graph construction. After that, a new definition of compactness using color and depth cues is put forward to compute the compactness saliency map. In order to compensate the detection errors of compactness saliency when the salient regions have similar appearances with background, foreground saliency map is calculated based on depth-refined foreground seeds’ selection (DRSS) mechanism and multiple cues contrast. Finally, these two saliency maps are integrated into a final saliency map through weighted-sum method according to their importance. Experiments on two publicly available stereo data sets demonstrate that the proposed method performs better than other ten state-of-the-art approaches.
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    Generalized Brillinger-Like Transforms (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-05-06
    Description: We propose novel transforms of stochastic vectors, called the generalized Brillinger transforms (GBT1 and GBT2), which are generalizations of the Brillinger transform (BT). The GBT1 extends the BT to the cases when the covariance matrix and the weighting matrix are singular, and moreover, the weighting matrix is not necessarily symmetric. We show that the GBT1 may computationally be preferable over another related optimal technique, the generic Karhunen–Loève transform (GKLT). The GBT2 generalizes the GBT1 to provide, under the condition we impose, better associated accuracy than that of the GBT1. It is achieved because of the increase in a number of parameters to optimize compared to that in the GBT1.
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    Novel Pilot Position Detection for SC-FDE Systems With Frequency Domain Pilot Multiplexing Technique (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-05-27
    Description: Frequency domain pilot multiplexing technique (FDPMT) has recently emerged as an appealing technique for channel estimation in single-carrier frequency domain equalization systems as it achieves high spectral efficiency at the expense of tiny signal distortion. In FDPMT, pilot positions are dynamically selected to achieve a relatively low level of signal distortion, rendering pilot position detection (PPD) indispensable at the receiver. In this letter, by exploiting both the prior known pilots and the statistical information of the data tones, we propose a novel PPD scheme based on the maximum-likelihood criterion. Simulation results show that our proposed PPD scheme significantly improves the PPD accuracy and outperforms the existing counterparts in terms of bit error rate.
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    An Accurate Method for Frequency Estimation of a Real Sinusoid (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-05-27
    Description: It is well known that the positive- and negative-frequency components of a real sinusoid spectrally interact with each other; thus, introducing bias in frequency estimation based on the periodogram maximization. We propose to filter out the negative-frequency component. To that end, a coarse frequency estimation is obtained using the windowing approach, known to reduce the estimation bias, and then used to filter out the negative-frequency component via modulation and discrete Fourier transform bin excision approach. Fine estimation is performed using accurate frequency estimators, developed for complex sinusoids, on the filtered signal. The proposed method is characterized by the $ O(Nlog _2N)$ complexity in terms of additions/multiplications and the $ O(N)$ complexity in terms of sine/cosine operations and comparisons. Moreover, it achieves the Cramér–Rao lower bound and is not sensitive to sinusoid frequency and initial phase, thus, outperforming the state-of-the-art methods.
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    Cross-Frequency rs-fMRI Network Connectivity Patterns Manifest Differently for Schizophrenia Patients and Healthy Controls (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-07-15
    Description: Patterns of resting state fMRI functional network connectivity in schizophrenia patients have been shown to differ markedly from those of healthy controls. While some studies have explored connectivity within fixed frequency bands, the question of network phase synchrony across disparate frequency bands, or cross-frequency connectivity , has remained surprisingly underexplored. Computational modeling at the neuronal scale however has long acknowledged the existence of coupled fast and slow subsystems. Here, we present preliminary evidence that cross-frequency coupling exists at the network level, that it patterns in meaningful ways over functional domains, and that this patterning differs between the healthy population and individuals with diagnosed schizophrenia.
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    Variance Stabilization for Noisy+Estimate Combination in Iterative Poisson Denoising (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-07-15
    Description: We denoise Poisson images with an iterative algorithm that progressively improves the effectiveness of variance-stabilizing transformations (VST) for Gaussian denoising filters. At each iteration, a combination of the Poisson observations with the denoised estimate from the previous iteration is treated as scaled Poisson data and filtered through a VST scheme. Due to the slight mismatch between a true scaled Poisson distribution and this combination, a special exact unbiased inverse is designed. We present an implementation of this approach based on the BM3D Gaussian denoising filter. With a computational cost at worst twice that of the noniterative scheme, the proposed algorithm provides significantly better quality, particularly at low signal-to-noise ratio, outperforming much costlier state-of-the-art alternatives.
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    Secrecy Outage Analysis for SIMO Underlay Cognitive Radio Networks over Generalized- $K$ Fading Channels (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-07-15
    Description: In this letter, we consider a single-input multiple-output cognitive wiretap system over generalized- $K$ channels, where the eavesdropper overhears the transmission from the secondary transmitter (ST) to the legitimate receiver. Both the primary user and the ST are equipped with a single antenna, whereas the legitimate and the eavesdropper receivers are equipped with multiple antennas. The close-form expression for the secrecy outage probability is derived. Simulations are presented to validate the accuracy of our proposed analytical results.
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    Optimal Sparsity Tradeoff in $ell _0$ -NLMS Algorithm (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-07-15
    Description: The $ell _0$ -normalized least mean squares ( $ell _0$ -NLMS) is arguably the reference gradient adaptive algorithm for sparse system estimation. However, alike all sparse gradient adaptive algorithms, the $ell _0$ -NLMS performance is sensitive to the (adequate) selection of the tradeoff parameter. Highlighted in this letter, the existence of two convergence modes, linked to the negligible and to the significant taps, paves the way for the convergence analysis, which results in a set of nonlinear (quadratic) convergence equations. Therefrom, the minimization of the steady-state misalignment concludes in the optimal tradeoff, which happens to relate to the NLMS step size, filter length, plant sparsity, and noise level in an extremely compact fashion. Exhaustive simulation experiments show strong agreement between the analytical predictions and the empirical performance.
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    Intelligent Condition Diagnosis Method for Rotating Machinery Based on Probability Density and Discriminant Analyses (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-07-15
    Description: This letter puts forward a method for intelligent condition diagnosis of rotating machinery using the probability density analysis and the canonical discriminant analysis (CDA) comprising the following steps. First, the noise is cancelled by statistics filter (SF), and the probability density functions (PDFs) of the vibration signals measured in each state are determined. Second, the segment values of the PDFs of the vibration signals are calculated and the integrated symptom parameters (ISPs) are combined using CDA. Third, Mahalanobis distances between the ISPs are introduced to identify the machine state. Moreover, the selecting discrimination index is optimized according to the accuracy rate of the identification. The efficacy of this novel method was confirmed by the results of the condition diagnosis for a centrifugal blower.
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    The Forward Procedure for HSMMs based on Expected Duration (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-07-15
    Description: For dynamic models, the forward procedure is used to calculate the probability of an observation sequence for a given model. For hidden semiMarkov models (HSMMs), the calculation can be approximated by keeping a track of the expected state duration instead of the distribution. The update equation for the expected duration proposed by Azimi et al. [1] is, however, wrong. The experiments presented by Azimi et al. [1] did not reveal the error, since for the presented cases, the state duration does not play a role in the probabilities. We propose a better equation for updating the expected duration. It nevertheless remains an approximation for calculating the probability of observation sequences. We analyze the assumptions to show under which conditions the approximation errors become important. Experiments show that the approximation is only reasonable for left-to-right HSMMs. As we focus on a specific sub class of HSMMs, we derive specialized equations from the general form for the exact calculation of the forward variable.
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    A Block Minorization–Maximization Algorithm for Heteroscedastic Regression (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-07-15
    Description: The computation of the maximum likelihood (ML) estimator for heteroscedastic regression models is considered. The traditional Newton algorithms for the problem require matrix multiplications and inversions, which are bottlenecks in modern Big Data contexts. A new Big Data-appropriate minorization–maximization (MM) algorithm is considered for the computation of the ML estimator. The MM algorithm is proved to generate monotonically increasing sequences of likelihood values and to be convergent to a stationary point of the log-likelihood function. A distributed and parallel implementation of the MM algorithm is presented, and the MM algorithm is shown to have differing time complexity to the Newton algorithm. Simulation studies demonstrate that the MM algorithm improves upon the computation time of the Newton algorithm in some practical scenarios where the number of observations is large.
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    Wavelet Speech Enhancement Based on Nonnegative Matrix Factorization (2016)
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    Publication Date: 2016-07-15
    Description: For the state-of-the-art speech enhancement (SE) techniques, a spectrogram is usually preferred than the respective time-domain raw data, since it reveals more compact presentation together with conspicuous temporal information over a long time span. However, two problems can cause distortions in the conventional nonnegative matrix factorization (NMF)-based SE algorithms. One is related to the overlap-and-add operation used in the short-time Fourier transform (STFT)-based signal reconstruction, and the other is concerned with directly using the phase of the noisy speech as that of the enhanced speech in signal reconstruction. These two problems can cause information loss or discontinuity when comparing the clean signal with the reconstructed signal. To solve these two problems, we propose a novel SE method that adopts discrete wavelet packet transform (DWPT) and NMF. In brief, the DWPT is first applied to split a time-domain speech signal into a series of subband signals. Then, we exploit NMF to highlight the speech component for each subband. These enhanced subband signals are joined together via the inverse DWPT to reconstruct a noise-reduced signal in time domain. We evaluate the proposed DWPT-NMF-based SE method on the Mandarin hearing in noise test (MHINT) task. Experimental results show that this new method effectively enhances speech quality and intelligibility and outperforms the conventional STFT-NMF-based SE system.
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    A Note on Topology-Transparent Scheduling via the Chinese Remainder Theorem (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-07-15
    Description: Topology-transparent scheduling (TTS) via the Chinese remainder theorem (CRT) has succeeded in providing guaranteed collision-free transmissions in each schedule without the need to know the maximum nodal degree of the graph representing connectivity of a mobile ad hoc network. Its main limitation is due to the restriction on the moduli imposed by the CRT. To address the shortcoming, this letter proposes an application of the general Chinese remainder theorem (GCRT) to TTS, which provides a unified framework for TTS that is developed via the CRT. The proposed GCRT-based scheme not only employs integer sequences to form the moduli, but also repeats the moduli to enhance TTS via the CRT. To determine how to repeat moduli in a systematic way, this letter formulates an integer programming problem, which is solved by the branch-and-bound technique. Numerical results are presented, demonstrating that the proposed GCRT-based scheme outperforms earlier works with much shorter schedule lengths.
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    Learning With Privacy in Consensus $+$ Obfuscation (2016)
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    Publication Date: 2016-07-22
    Description: We examine the interplay between learning and privacy over multiagent consensus networks. The learning objective of each individual agent consists of computing some global network statistic, and is accomplished by means of a consensus protocol. The privacy objective consists of preventing inference of the individual agents’ data from the information exchanged during the consensus stages, and is accomplished by adding some artificial noise to the observations (obfuscation). An analytical characterization of the learning and privacy performance is provided, with reference to a consensus perturbing and to a consensus-preserving obfuscation strategy.
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    Averaged Properties of the Residual Error in Sparse Signal Reconstruction (2016)
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    Publication Date: 2016-07-22
    Description: Greedy algorithms, like orthogonal matching pursuit (OMP), compressive sampling matching pursuit, and their modifications, are the popular methods of compressive sensing in sparse signal reconstruction. They are iterative in manner and often use a scaled value of the system noise energy as a reference threshold to decide when to stop the searching procedure. This letter presents an analytical analysis of the residual error and also of the difference of the successive residual errors in a general case of the greedy searching procedure, provided that the measurement error is an independent and identically distributed Gaussian stationary process. The obtained results show that the mean energy of the mentioned errors, just at the moment of perfect recovery of the signal support, depends only on the sparsity order and/or on the number of measurement samples. The results contributed to designing a practical stopping rule, which is a combination of those two errors. Numerical experiments with the new rule implemented in the OMP algorithm show that the number of executed iterations can be less spread around the true value of the signal sparsity in the mean sense. A quality of the sparse signal estimation is also preserved.
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    Cyclic Prefix-Based Universal Filtered Multicarrier System and Performance Analysis (2016)
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    Publication Date: 2016-07-29
    Description: Recently proposed universal filtered multicarrier (UFMC) system is not an orthogonal system in multipath channel environments and might cause significant performance loss. In this paper, the authors propose a cyclic prefix (CP) based UFMC system and first analyze the conditions for interference-free one-tap equalization in the absence of transceiver imperfections. Then the corresponding signal model and output signal-to-noise ratio expression are derived. In the presence of carrier frequency offset, timing offset, and insufficient CP length, the authors establish an analytical system model as a summation of desired signal, intersymbol interference, intercarrier interference, and noise. New channel equalization algorithms are proposed based on the derived analytical signal model. Numerical results show that the derived model matches the simulation results precisely, and the proposed equalization algorithms improve the UFMC system performance in terms of bit error rate.
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    A Case Study in Low-Complexity ECG Signal Encoding: How Compressing is Compressed Sensing? (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-05-16
    Description: When transmission or storage costs are an issue, lossy data compression enters the processing chain of resource-constrained sensor nodes. However, their limited computational power imposes the use of encoding strategies based on a small number of digital computations. In this case study, we propose the use of an embodiment of compressed sensing as a lossy digital signal compression, whose encoding stage only requires a number of fixed-point accumulations that is linear in the dimension of the encoded signal. We support this design with some evidence that for the task of compressing ECG signals, the simplicity of this scheme is well-balanced by its achieved code rates when its performances are compared against those of conventional signal compression techniques.
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    Performance Improvement of Average Based Spatial Filters through Multilevel Preprocessing using Wavelets (2015)
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    Publication Date: 2015-05-02
    Description: Image denoising filters intended to remove Gaussian noise, principally exploit a procedure called spatial averaging. Quite a lot of averaging approaches have been developed and numerous fall in the class of either pixel-based or patch-based or diffusion-based approach. While the designed filters get rid of the noise, the high frequency information will also be degraded, as the filters fit into a nature of integration. To preserve the high frequency information and hence the denoising performance, we propose a preprocessing filter designed in the wavelet domain, can be placed prior to the given existing spatial domain averaging filter. The proposed filter enhances high frequency information of given noisy image and obviously this enhanced information will also be degraded at some extent by the subsequent spatial domain filters. Accordingly, proposed preprocessing filter and existing average based spatial domain filter on a whole gives improved denoising performance. Simulation experiments have been conducted and it is proved that the proposed preprocessing filter certainly improves the denoising results of existing standard spatial domain filtering such as Anisotropic filtering, Bilateral filtering, Non local means filtering and recently proposed Probabilistic non local means filtering in terms of peak signal to noise ratio (PSNR) and structural similarity index (SSIM).
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    Extracting Major Lines by Recruiting Zero-Threshold Canny Edge Links along Sobel Highlights (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-05-02
    Description: A method of extracting major lines from a 2D image is presented. The novelty lies in that major lines are recruited from the maximally generated yet well-thinned zero threshold Canny edge links based on the, so called, Sobel highlights as the guide for recruitment. The Sobel highlights introduced here represent the scores accumulated at individual pixels that measure their significance of forming line segments along Sobel edge orientations. The proposed method offers several advantages over conventional ones: it is 1) more powerful to extract cursive lines, in particular, with a larger curvature, 2) more effective to represent a line as a whole with less intermittent discontinuities, and 3) more straightforward to use as it relies on no sensitive edge thresholding. Experimentations verify that the proposed method outperforms LSD, a top performance major line detector currently available, by about 20% in the coverage of ground truth major lines as well as in the coverage per major line: an intermittent discontinuity indicator. The extraction cycle time is about 54 msec using the Intel i7-2600 CPU and 4G RAM under Window 7.
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    Secure Transmission in Cooperative Networks with Weak Eavesdroppers (2015)
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    Publication Date: 2015-05-02
    Description: In this letter, we propose a scheme to improve the secrecy rate of cooperative networks using Analog Network Coding (ANC). ANC mixes the signals in the air; the desired signal is then separated out, from the mixed signals, at the legitimate receiver using techniques like self interference subtraction and signal nulling, thereby achieving better secrecy rates. Assuming global channel state information, memoryless adversaries and the decode-and-forward strategy, we seek to maximize the average secrecy rate between the source and the destination, subject to an overall power budget. Then, exploiting the structure of the optimization problem, we compute its optimal solution. Finally, we use numerical evaluations to compare our scheme with the conventional approaches.
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    Compressed Sensing with Non-Gaussian Noise and Partial Support Information (2015)
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    Publication Date: 2015-05-06
    Description: We study the problem of recovering sparse and compressible signals using a weighted ${ell _p}$ minimization with $0 〈 p leq 1$ from noisy compressed sensing measurements when part of the support is known a priori. To better model different types of non-Gaussian (bounded) noise, the minimization program is subject to a data-fidelity constraint expressed as the ${ell _q}(2 leq q 〈 infty)$ norm of the residual error. We show theoretically that the reconstruction error of this optimization is bounded (stable) if the sensing matrix satisfies an extended restricted isometry property. Numerical results show that the proposed method, which extends the range of $p$ and $q$ comparing with previous works, outperforms other noise-aware basis pursuit programs. For $p 〈 1$ , since the optimization is not convex, we use a variant of an iterative reweighted ${ell _2}$ algorithm for computing a local minimum.
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    Efficient Unwrap Representation of Faces for Video Editing (2015)
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    Publication Date: 2015-05-09
    Description: Unwrap mosaic is a method for decomposing a video into a 2D texture and a dense mapping that enable the reconstruction of the video from the texture. This representation is useful in some frameworks because we can edit videos by simply retouching 2D textures. However, the complexity of conventional approaches is too high to be adopted in time-critical applications (it takes up to several hours). In this letter, we focus on face-related applications such as face editing and replacement, and propose a face unwrap approach for these applications. To be precise, we adopt the view-based active appearance model (AAM) trackers and estimate dense mappings from the AAM results. The AAM also provides pose information which is also exploited in building the texture map. Experimental results show that our method is very efficient compared with the conventional unwrap mosaic approach. Moreover, based on the proposed system, we develop face-related applications.
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    An MRF-Based Depth Upsampling: Upsample the Depth Map With Its Own Property (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-05-09
    Description: In this letter, we propose a novel method for upsampling the noisy low resolution depth map with the guidance of the companion color image. The problem is modeled with an Markov Random Field (MRF)-based optimization framework. The novelty relies on the smoothness term that is modeled with an exponential function as the error norm. By using this novel error norm, our method can take the property of the depth map into account. Depth discontinuity cues are not only obtained from the color image but also the depth map itself. Our method has much better performance in preserving sharp depth discontinuities and suppressing the texture copy artifacts. Experimental results show that our method outperforms state-of-art solutions in both visual quality and accuracy.
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    A New Stochastic Optimization Algorithm to Decompose Large Nonnegative Tensors (2015)
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    Publication Date: 2015-05-09
    Description: In this letter, the problem of nonnegative tensor decompositions is addressed. Classically, this problem is carried out using iterative (either alternating or global) deterministic optimization algorithms. Here, a rather different stochastic approach is suggested. In addition, the ever-increasing volume of data requires the development of new and more efficient approaches to be able to process “Big data” tensors to extract relevant information. The stochastic algorithm outlined here comes within this framework. Both flexible and easy to implement, it is designed to solve the problem of the CP (Candecomp/Parafac) decomposition of huge nonnegative 3-way tensors while simultaneously enabling to handle possible missing data.
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    Convergence of a Fixed-Point Algorithm under Maximum Correntropy Criterion (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-05-09
    Description: The maximum correntropy criterion (MCC) has received increasing attention in signal processing and machine learning due to its robustness against outliers (or impulsive noises). Some gradient based adaptive filtering algorithms under MCC have been developed and available for practical use. The fixed-point algorithms under MCC are, however, seldom studied. In particular, too little attention has been paid to the convergence issue of the fixed-point MCC algorithms. In this letter, we will study this problem and give a sufficient condition to guarantee the convergence of a fixed-point MCC algorithm.
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    A Sparsity Basis Selection Method for Compressed Sensing (2015)
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    Publication Date: 2015-05-13
    Description: This letter presents a new sparsity basis selection compressed sensing method (SBSCS) for improving signal reconstruction from compressed sensing (CS) measurements. Based on the observation that different classes of transform cause different sparsity expressions and better sparsity expression leads to better signal recovery, the proposed SBSCS method searches the best class of transform and basis in a set of redundant tree-structured dictionaries by nesting sparsity maximization within the CS minimization. The SBSCS method adaptively selects the class of transform and basis with the best sparsity measure at each ${ell ^1}$ iteration and converges quickly to the final class of transform and basis. Numerical experiments show that the proposed SBSCS method improves the quality of signal recovery over the existing best basis compressed sensing method (BBCS) proposed by Peyré in 2010.
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    Optimum Wirelessly Powered Relaying (2015)
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    Publication Date: 2015-05-13
    Description: This letter maximizes the achievable throughput of a relay-assisted wirelessly powered communications system, where an energy constrained source, assisted by an energy constrained relay and both powered by a dedicated power beacon (PB), communicates with a destination. Considering the time splitting approach, the source and relay first harvest energy from the PB, which is equipped with multiple antennas, and then transmits the information to destination. Simple closed-form expressions are derived for the optimal PB energy beamforming vector and time split for energy harvesting and information transmission. Numerical results and simulations demonstrate the superior performance compared with some intuitive benchmark beamforming scheme. Also, it is found that placing the relay at the middle of the source-destination path is no longer optimal.
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    Iterative Receiver Design for ISI Channels Using Combined Belief- and Expectation-Propagation (2015)
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    Publication Date: 2015-05-13
    Description: In this letter, a message-passing algorithm that combines belief propagation and expectation propagation is applied to design an iterative receiver for intersymbol interference channels. We detail the derivation of the messages passed along the nodes of a vector-form factor graph representing the underlying probabilistic model. We also present a simple but efficient method to cope with the “negative variance” problem of expectation propagation. Simulation results show that the proposed algorithm outperforms, in terms of bit-error-rate and convergence rate, a LMMSE turbo-equalizer based on Gaussian message passing with the same order of computational complexity.
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    A Genetic Algorithm-Based Moving Object Detection for Real-time Traffic Surveillance (2015)
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    Publication Date: 2015-04-04
    Description: Recent developments in vision systems such as distributed smart cameras have encouraged researchers to develop advanced computer vision applications suitable to embedded platforms. In the embedded surveillance system, where memory and computing resources are limited, simple and efficient computer vision algorithms are required. In this letter, we present a moving object detection method for real-time traffic surveillance applications. The proposed method is a combination of a genetic dynamic saliency map (GDSM), which is an improved version of dynamic saliency map (DSM) and background subtraction. The experimental results show the effectiveness of the proposed method in detecting moving objects.
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    Transceiver Optimization for Unicast/Multicast MIMO Cognitive Overlay/Underlay Networks (2015)
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    Publication Date: 2015-03-27
    Description: In this work, we develop a majorization theory based linear precoding framework for optimal transceiver design in MIMO cognitive radio networks. Closed form expressions are derived for the optimal MIMO precoders using two new transceiver design paradigms, the zero-forcing transceiver (ZFT) and the interference optimized transceiver (IOT), for overlay and underlay MIMO cognitive radio networks respectively. Further, another novel contribution of this work is to derive the precoders for multicast MIMO cognitive radio scenarios based on novel multi-user mean-squared error (MSE) bounds. Simulation results demonstrate the performance of the proposed optimal MIMO transceivers.
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    Visual Saliency Detection With Free Energy Theory (2015)
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    Publication Date: 2015-03-27
    Description: Visual saliency can be thought of as the product of human brain activity. Most existing models were built upon local features or global features or both. Lately, a so-called free energy principle unifies several brain theories within one framework, and tells where easily surprise human viewers in a visual stimulus through a psychological measure. We believe that this “surprise” should be highly related to visual saliency, and thereby introduce a novel computational Free Energy inspired Saliency detection technique (FES). Our method computes the local entropy of the gap between an input image signal and its predicted counterpart that is reconstructed from the input one with a semi-parametric model. Experimental results prove that our algorithm predicts human fixation points accurately and is superior to classical/state-of-the-art competitors.
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    Reduced-Rank DOA Estimation Algorithms Based on Alternating Low-Rank Decomposition (2016)
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    Publication Date: 2016-03-25
    Description: In this work, we propose an alternating low-rank decomposition (ALRD) approach and novel subspace algorithms for direction-of-arrival (DOA) estimation. In the ALRD scheme, the decomposition matrix for rank reduction consists of a set of basis vectors. A low-rank auxiliary parameter vector is then employed to compute the output power spectrum. Alternating optimization strategies based on recursive least squares (RLS), denoted as ALRD-RLS and modified ALRD-RLS (MARLD-RLS), are devised to compute the basis vectors and the auxiliary parameter vector. Simulations for large sensor arrays with both uncorrelated and correlated sources are presented, showing that the proposed algorithms are superior to existing techniques.
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    On Fast Bilateral Filtering Using Fourier Kernels (2016)
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    Publication Date: 2016-03-25
    Description: It was demonstrated in earlier work that, by approximating its range kernel using shiftable functions, the nonlinear bilateral filter can be computed using a series of fast convolutions. Previous approaches based on shiftable approximation have, however, been restricted to Gaussian range kernels. In this work, we propose a novel approximation that can be applied to any range kernel, provided it has a pointwise-convergent Fourier series. More specifically, we propose to approximate the Gaussian range kernel of the bilateral filter using a Fourier basis, where the coefficients of the basis are obtained by solving a series of least-squares problems. The coefficients can be efficiently computed using a recursive form of the QR decomposition. By controlling the cardinality of the Fourier basis, we can obtain a good tradeoff between the run-time and the filtering accuracy. In particular, we are able to guarantee subpixel accuracy for the overall filtering, which is not provided by the most existing methods for fast bilateral filtering. We present simulation results to demonstrate the speed and accuracy of the proposed algorithm.
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    Audio Coding Using Overlap and Kernel Adaptation (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-04-01
    Description: Perceptual audio coding schemes typically apply the modified discrete cosine transform (MDCT) with different lengths and windows, and utilize signal-adaptive switching between these on a perframe basis for best subjective performance. In previous papers, the authors demonstrated that further quality gains can be achieved for some input signals using additional transform kernels such as the modified discrete sine transform (MDST) or greater inter-transform overlap by means of a modified extended lapped transform (MELT). This work discusses the algorithmic procedures and codec modifications necessary to combine all of the above features—transform length, window shape, transform kernel, and overlap ratio switching—into a flexible input-adaptive coding system. It is shown that, due to full time-domain aliasing cancelation, this system supports perfect signal reconstruction in the absence of quantization and, thanks to fast realizations of all transforms, increases the codec complexity only negligibly. The results of a 5.1 multichannel listening test are also reported.
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    Accurate Facial Landmark Extraction (2016)
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    Publication Date: 2016-04-05
    Description: Facial landmark extraction system is crucial in various applications, including face recognition, expression analysis, face tracking, and face animation. This letter aims to improve the performance of an existing landmark extraction method proposed by Ren et al. in terms of error rate. Specifically, the Gaussian blur filter is applied on the input image to reduce noise interference and the theta-based split rule is deployed to strengthen the performance of the random forests. Then, global linear regression is applied instead of treating each landmark independently. Experimental results demonstrate that the proposed modified facial landmark extraction algorithm outperforms the conventional methods for both the LFPW and Helen databases.
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    Sparse Code Multiple Access Decoding Based on a Monte Carlo Markov Chain Method (2016)
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    Publication Date: 2016-04-08
    Description: Nonorthogonal multiple access technology has been proposed for use in 5G communications systems. In particular, the sparse code multiple access (SCMA) scheme is believed to be one of the most promising techniques among the various nonorthogonal approaches that have been investigated. In this letter, we focus on reducing the complexity of SCMA decoding and we propose a Monte Carlo Markov Chain (MCMC) based SCMA decoder. Benefiting from the linearly increasing complexity of the MCMC method, the proposed SCMA decoder has only 10% of the computational load compared to previous state-of-the-art methods when the codebook size is 64. Consequently, the MCMC SCMA decoder has great potential for use in practical system implementations.
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    Solution of a Bivariate $ell_1$ Regularized Problem (2016)
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    Publication Date: 2016-04-12
    Description: We derive the mapping that takes an observation vector to the minimizer of a bivariate cost consisting of the sum of a quadratic data fidelity term and an $ell_1$ norm. The derived mapping is useful for accelerating convergence of iterative algorithms that aim to solve $ell_1$ regularized problems. We discuss how to use the mapping in practice and demonstrate the improvement in convergence rate with numerical experiments.
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    Decoding Method Based on Complex ICA for a Multicell Massive MIMO Uplink System (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-04-12
    Description: Pilot signal reuse in neighboring cells causes the pilot contamination, and performance will be degraded significantly in a multicell, massive multiple-input, multiple-output (MIMO) uplink system. In this letter, a new decoding method is proposed to alleviate the degradation. In the proposed method, the principal component analysis (PCA) is used to reduce the dimension of the received signals from both intercell and intracell. Then, the complex independent component analysis (ICA) is implemented to estimate the channels, and the mean minimum square error (MMSE) decoder is employed to decode the transmitted signals. At last, few pilot signals are used to overcome the ambiguity caused by the complex ICA. In our method, orthogonality or asymptotic orthogonality of the channels is not required. Moreover, the path-loss and shadowing factors can be unknown. Simulation results show that the performance of our decoder is better than the MMSE channel estimation-based MMSE decoder. Moreover, error floor does not appear even when signal-to-noise ratio (SNR) is high, while it occurs if the MMSE decoder is used.
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    Characteristic Functions of the Product of Two Gaussian Random Variables and the Product of a Gaussian and a Gamma Random Variable (2016)
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    Publication Date: 2016-04-12
    Description: We derive the characteristic function (CF) for two product distributions—first for the product of two Gaussian random variables (RVs), where one has zero mean and unity variance, and the other has arbitrary mean and variance. Next, we develop the characteristic function for the product of a gamma RV and a zero mean, unity variance Gaussian RV. The underlying rationale for this is to develop a model for a “quasi-Gaussian” RV—an RV that is nominally Gaussian, but with mean and variance parameters that are not constant, but instead, are RVs themselves. Due to the central limit theorem, many “real-world” processes are modeled as being Gaussian distributed. However, this implicitly assumes that the processes being modeled are perfectly stationary, which is often a poor assumption. The quasi-Gaussian model could be used as a more conservative description of many of these processes.
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    Non-Negative Temporal Decomposition Regularization With an Augmented Lagrangian (2016)
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    Publication Date: 2016-04-12
    Description: Non-negative matrix factorization (NMF) has recently been applied to temporal decomposition (TD) of speech spectral envelopes represented by line spectral frequencies. A couple of inherent TD constraints, which are otherwise handled as ad hoc exceptions, has also been incorporated using NMF, including line spectral frequency (LSF) ordering and monotonic event functions. Here, these constraints are analyzed and a third inherent constraint is incorporated into an NMF analysis. This is the complementarity in the sense that two overlapping event functions uniformly add up to one, which has been handled, at best, by a quadratic penalty term. We propose the use of an augmented Lagrangian including a term with the Lagrange multipliers (LMs). Additionally, a multiplicative update rule for the LMs is proposed, which fits nicely into the nature of NMF updates. Further, previous difficulties with nonsmooth spectral envelopes have been resolved by obtaining the spectral envelopes from TANDEM-STRAIGHT spectrograms. Good results are reached at the tight event rate of 12.3 ev/s, featuring mean log-spectral distortions ranging from 1.2 dB to about 1.5 dB depending on the regularizations.
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    Graph-based Dequantization of Block-Compressed Piecewise Smooth Images (2016)
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    Publication Date: 2016-01-08
    Description: Block-based image or video coding standards (e.g. JPEG) compress an image lossily by quantizing transform coefficients of non-overlapping pixel blocks. If the chosen quantization parameters (QP) are large, then hard decoding of a compressed image—using indexed quantization bin centers as reconstructed transform coefficients—can lead to unpleasant blocking artifacts. Leveraging on recent advances in graph signal processing (GSP), we propose a dequantization scheme specifically for piecewise smooth (PWS) images: images with sharp object boundaries and smooth interior surfaces. We first mathematically define a PWS image as a low-frequency signal with respect to an inter-pixel similarity graph with edges of weights 1 or 0. Using quantization bin boundaries as constraints, we then jointly optimize the desired graph-signal and the similarity graph in a unified framework. A generalization to consider generalized piecewise smooth (GPWS) images—where sharp object boundaries are replaced by transition regions—is also proposed. Experimental results show that our proposed scheme outperforms a state-of-the-art dequantization method by 1 dB on average in PSNR.
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    Quality Assessment for Reassembled Image Files (2016)
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    Publication Date: 2016-01-08
    Description: Reassembling fragmented image files plays a crucial role in seizing digital evidence from scattered digital image files. However, little attention has been paid to the quality assessment for reassembled image files. We propose a method to assign a reassembled image file with a score indicating its quality, defined as the amount of information that the reassembled image affords for its original image. The score ranges from 0 to 1. A higher score indicates better quality. The basic idea of the proposed method is to disintegrate the quality assessment of a reassembled image file into the calculations of the contributions of the fragments contained in the reassembled image file. For each fragment, if it is not from the original image file, it affords the least contribution. Otherwise its contribution depends on the extent to which the fragment and its neighbor fragments are concatenated correctly. We validate the performance of the proposed method with an extensive subjective study involving 588 reassembled image files. We also show that the existing quality assessments for images cannot be directly used to assess the quality of reassembled image files.
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    Supervised Monaural Speech Enhancement Using Complementary Joint Sparse Representations (2016)
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    Publication Date: 2016-01-08
    Description: Sparse representation is one of the most well-known methods that are applied to monaural speech enhancement. In order to make full use of the relationships among speech, noise, and mixture in sparse representation for speech enhancement, this letter proposes a novel sparsity model that consists of a couple of joint sparse representations (JSRs). One JSR uses the mapping relationship between mixture and speech while the other uses that between mixture and noise. Both relationships are used to constrain the joint dictionary learning, which effectively solves the source confusion problem of traditional methods. Moreover, the latter JSR can be complementary to the former JSR, depending on the level of structure of the noise. Thus, we propose a Gini index based weighting parameter to take their complementary advantages. The experimental results show that the proposed method outperforms state-of-the-art methods using various objective measures.
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    Unscented von Mises–Fisher Filtering (2016)
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    Publication Date: 2016-03-08
    Description: We introduce the unscented von Mises–Fisher filter (UvMFF), a nonlinear filtering algorithm for dynamic state estimation on the $n$ -dimensional unit hypersphere. Estimation problems on the unit hypersphere occur in computer vision, e.g., when using omnidirectional cameras, as well as in signal processing. As approaches in literature are limited to very simple system and measurement models, we propose a deterministic sampling scheme on the unit hypersphere, which allows us to handle nonlinear system and measurement models. The proposed approach can be seen as a hyperspherical variant of the unscented Kalman filter (UKF). The advantages of the novel method are shown by means of simulations.
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    A Robust Gaussian Approximate Fixed-Interval Smoother for Nonlinear Systems With Heavy-Tailed Process and Measurement Noises (2016)
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    Publication Date: 2016-03-08
    Description: In this letter, a robust Gaussian approximate (GA) fixed-interval smoother for nonlinear systems with heavy-tailed process and measurement noises is proposed. The process and measurement noises are modeled as stationary Student’s t distributions, and the state trajectory and noise parameters are inferred approximately based on the variational Bayesian (VB) approach. Simulation results show the efficiency and superiority of the proposed smoother as compared with existing smoothers.
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    Region Based Exemplar References for Image Segmentation Evaluation (2016)
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    Publication Date: 2016-03-08
    Description: Quantitative evaluation of image segmentation quality is usually based on comparing a segmentation with multiple reference segmentations. Instead of holistically comparing with each reference, we propose a region based evaluation framework, where an exemplar reference is adaptively constructed and applied to a generally defined evaluation measure. As examples, we implement three well-known evaluation measures and present an efficient scheme to compute each measure. Extensive experiments on the benchmark databases show that the proposed evaluation framework can improve the evaluation precision of existing measures.
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    Blind Deconvolution With Nonlocal Similarity and $l_0$ Sparsity for Noisy Image (2016)
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    Publication Date: 2016-03-08
    Description: The blind image deconvolution techniques with sparsity prior in gradient domain are sensitive to noise, even a small amount of noise. To address this problem, in this letter, we propose a novel blind deconvolution model that combines low-rank property, nonlocal similarity, and $l_0$ sparsity prior. Low-rank property makes the proposed deblurring model robust to image noise. The joint utilization of nonlocal similarity and $l_0$ sparsity prior has improved the accuracy of blur kernel estimation and restores the fine image details. A numerical method is also given to solve the proposed problem. Experimental results on synthetic and real data show that our algorithm performs better against with the state-of-the-art methods for both noise and noise-free images.
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    Joint Robust Transmit/Receive Adaptive Beamforming for MIMO Radar Using Probability-Constrained Optimization (2015)
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    Publication Date: 2015-12-11
    Description: In this letter, a joint robust transmit/receive adaptive beamforming for multiple-input multiple-output (MIMO) radar based on probability-constrained optimization approach is developed in the case of Gaussian and arbitrary distributed mismatches present in both the transmit and receive signal steering vectors. A tight lower bound of the probability constraint is also derived by using duality theory. The formulated probability-constrained robust beamforming problem is nonconvex and NP-hard. However, we reformulate its cost function into a bi-quadratic function while the probability constraint splits into transmit and receive parts. Then, a block coordinate descent method based on second-order cone programming is developed to address the biconvex problem. Simulation results show an improved robustness of the proposed beamforming method as compared to the worst-case and other existing state-of-the-art joint transmit/receive robust adaptive beamforming methods for MIMO radar.
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    Adaptive Overhearing in Two-Way Multi-Antenna Relay Channels (2015)
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    Publication Date: 2015-12-11
    Description: An adaptive overhearing protocol is proposed for the two-way multi-antenna-relay network composed of a base station (BS), relay, and two user equipments (UEs), where one UE is in the uplink (UL) transmission mode (UE-Tx) while the other is in the downlink (DL) reception mode (UE-Rx). Specifically, UE-Rx not only receives the DL signal transmitted by BS but also overhears the signal transmitted by UE-Tx, and exploits the overheard signal to improve the detection performance. The transmit adaptive weights of UE-Tx over the two times slots and the precoding matrix at the relay in the second time slot are jointly optimized via the proposed iterative algorithm in the sense of maximizing the minimum signal-to-interference-plus-noise-ratio. Numerical results show that the proposed joint design provides significant sum-rate gain over the existing overhearing scheme.
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    Probabilistic Kernels for Improved Text-to-Speech Alignment in Long Audio Tracks (2015)
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    Publication Date: 2015-12-11
    Description: The synchronization of text transcripts with audio tracks is typically solved by forced alignment at the phonetic level. However, when dealing with either very long audio tracks or acoustically inaccurate text transcripts, more complex methods are needed, usually based on heavy and costly ASR systems. In a previous work, we showed that a simple and lightweight method could be effectively applied, based on a free phonetic decoding of the speech signal and the alignment of the free and reference phonetic sequences, allowing the transfer of timestamps from the former to the latter. This method has yielded competitive results on the Hub4-97 dataset and is currently applied to synchronize the videos and minutes of the Basque Parliament plenary sessions. In this paper, probabilistic kernels (similarity functions) are applied, based on the hypothesis that a confusion matrix computed from a large corpus of speech conveys key information about the behavior of the phonetic decoder, and that the probabilistic interpretation of this information may help design informative kernels leading to improved alignments. The probabilistic kernels proposed in this work outperform our baseline kernels and other alternatives, including a reference ASR-based approach and a knowledge-based kernel, in experiments on the Hub4-97 dataset.
    Print ISSN: 1070-9908
    Electronic ISSN: 1558-2361
    Topics: Electrical Engineering, Measurement and Control Technology
    Published by Institute of Electrical and Electronics Engineers (IEEE) on behalf of The IEEE Signal Processing Society.
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