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  • 1
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    Detection of Glottal Activity Using Different Attributes of Source Information (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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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)
    Institute of Electrical and Electronics Engineers (IEEE)
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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)
    Institute of Electrical and Electronics Engineers (IEEE)
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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)
    Institute of Electrical and Electronics Engineers (IEEE)
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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)
    Institute of Electrical and Electronics Engineers (IEEE)
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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)
    Institute of Electrical and Electronics Engineers (IEEE)
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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)
    Institute of Electrical and Electronics Engineers (IEEE)
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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)
    Institute of Electrical and Electronics Engineers (IEEE)
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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)
    Institute of Electrical and Electronics Engineers (IEEE)
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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)
    Institute of Electrical and Electronics Engineers (IEEE)
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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)
    Institute of Electrical and Electronics Engineers (IEEE)
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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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  • 24
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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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  • 26
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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)
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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)
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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)
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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)
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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)
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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)
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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)
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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)
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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)
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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)
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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)
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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)
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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)
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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)
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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)
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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)
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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 Blind AM/PM Estimation Method for Power Amplifier Linearization (2013)
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    Publication Date: 2013-09-11
    Description: A higher order statistics (HOS)-based method is proposed in this letter to estimate the nonlinear amplitude-to-phase (AM/PM) conversion function of power amplifiers (PAs) in communications systems. By utilizing the symmetry property of the modulated signal, the proposed method estimates the AM/PM conversion function using only the PA output signal, from which the phase predistorter (PD) function can be derived. Simulation results show that the HOS-based method can accurately determine the phase PD function required to compensate for the AM/PM distortion in the PA, yielding a linearization performance close to that of non-blind methods in terms of adjacent channel power ratio and error vector magnitude.
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    Lossy Depth Image Compression using Greedy Rate-Distortion Slope Optimization (2013)
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    Publication Date: 2013-09-14
    Description: We introduce a method to create lossy versions of one image, either by successively merging the constant regions of the original image, or by iteratively splitting the regions from a created lossy image using horizontal or vertical line segments. Merging and split decisions are greedily taken, according to the best slope towards next point in the rate-distortion curve. For each created lossy image, the region contours and the optimal depth values can be entropy coded in three ways: with a new algorithm, or with two existing lossless coding algorithms. The obtained results compare favorably with the existing lossy methods.
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    Third-Order Kalman Filter: Tuning and Steady-State Performance (2013)
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    Publication Date: 2013-09-18
    Description: This letter deals with the Kalman filter (KF) based on a third-order integrated random walk model (RW3). The resulting filter, noted as RW3-KF, is well suited to track slow time-varying parameters with strong trend behaviour. We first prove that the RW3-KF in steady-state admits an equivalent structure to the third-order digital phase-locked loops (DPLL). The approximate asymptotic mean-squared-error (MSE) is obtained by solving the Riccati equations, which is given in a closed-form expression as a function of the RW3 model parameter: the state noise variance. Then, the closed-form expression of the optimum state noise variance is derived to minimize the asymptotic MSE. Simulation results are given for the particular case where the parameter to be estimated is a Rayleigh channel coefficient with Jakes' Doppler spectrum.
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    A New Modified SLM Scheme for Wireless OFDM Systems Without Side Information (2013)
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    Publication Date: 2013-09-21
    Description: To avoid the transmission of side information and decrease the computational complexity in the conventional selected mapping (SLM) scheme, a novel set of conversion matrices (CMs) is proposed for the modified SLM scheme, and the signal processing scheme to remove the weighted factors on each subcarrier is also derived for wireless orthogonal frequency-division multiplexing (OFDM) systems. Computer simulations show that, without side information, the new scheme can achieve similar peak-to-average power ratio (PAPR) reduction and bit error rate (BER) performance with even lower computational complexity as compared with the available modified SLM schemes with the CMs.
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    Kernel Based Multiple Cue Adaptive Appearance Model For Robust Real-time Visual Tracking (2013)
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    Publication Date: 2013-09-21
    Description: In this letter, we propose a robust and real-time visual tracking algorithm via a novel kernel based multiple cue adaptive appearance model (KBMCAAM). In particular, the appearance model is constructed with a naive Bayes classifier which is trained utilizing sparse multi-scale Haar-like features weighted by a spatial kernel function. Moreover, multiple image cues are integrated to improve the model's discriminative capacity. Experimental results demonstrate the superior performance of our proposed method to many state-of-art algorithms.
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    High Throughput Stochastic Log-MAP Turbo-Decoder Based on Low Bits Computation (2013)
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    Publication Date: 2013-09-21
    Description: In this letter, we propose a high throughput stochastic Low Bits Computation (LBC) turbo decoder. We represent the signal by a 3-bits width stochastic stream, which improves the accuracy of stochastic computation significantly. We have designed and synthesized our design based on CMOS 90 nm technology. The report shows that the proposed decoder can achieve 4.0 Gbps with 7.1 M gate count to decode a 2048-length ${rm R}=1/3$ turbo code, when the bit error rate (BER) is $10^{-5}$ @ ${rm E}_{rm b}/{rm N}_{0}=1.25 {rm dB}$ .
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    Deterministic Construction of Real-Valued Ternary Sensing Matrices Using Optical Orthogonal Codes (2013)
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    Publication Date: 2013-09-21
    Description: In this letter, a new class of real-valued matrices is presented for deterministic compressed sensing. A base matrix is constructed by cyclic shifts of binary sequences in an optical orthogonal code (OOC). Then, a Hadamard matrix is used for its extension, which ultimately produces a real-valued matrix that takes the entries of 0, ${-}1$ and ${+}1$ before normalization. The new sensing matrix forms a tight frame with small coherence, which theoretically guarantees the average recovery performance of sparse signals with uniformly distributed supports. Several example sensing matrices are presented by employing a special type of OOCs obtained from modular Golomb rulers.
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    Bayesian Early Mode Decision Technique for View Synthesis Prediction-Enhanced Multiview Video Coding (2013)
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    Publication Date: 2013-09-28
    Description: View synthesis prediction (VSP) is a coding mode that predicts video blocks from synthesised frames. It is particularly useful in a multi-camera setup with large inter-camera distances. Adding a VSP-based SKIP mode to a standard Multiview Video Coding (MVC) framework improves the rate-distortion (RD) performance but increases the time complexity of the encoder. This letter proposes an early mode decision technique for VSP SKIP-enhanced MVC. Our method uses the correlation between the RD costs of the VSP SKIP mode in neighbouring views and Bayesian decision theory to reduce the number of candidate coding modes for a given macroblock. Simulation results showed that our technique can save up to 36.20% of the encoding time without any significant loss in RD performance.
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    On the Fisher Information Matrix for Multivariate Elliptically Contoured Distributions (2013)
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    Publication Date: 2013-09-28
    Description: The Slepian-Bangs formula provides a very convenient way to compute the Fisher information matrix (FIM) for Gaussian distributed data. The aim of this letter is to extend it to a larger family of distributions, namely elliptically contoured (EC) distributions. More precisely, we derive a closed-form expression of the FIM in this case. This new expression involves the usual term of the Gaussian FIM plus some corrective factors that depend only on the expectations of some functions of the so-called modular variate. Hence, for most distributions in the EC family, derivation of the FIM from its Gaussian counterpart involves slight additional derivations. We show that the new formula reduces to the Slepian-Bangs formula in the Gaussian case and we provide an illustrative example with Student distributions on how it can be used.
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    QoS Feasibility in MIMO Broadcast Channels With Widely Linear Transceivers (2013)
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    Publication Date: 2013-09-28
    Description: The use of proper, i.e., circularly symmetric, complex Gaussian signals for all users is known to be optimal in broadcast channels with proper complex Gaussian noise from an information theoretic point of view, i.e., they are employed in the capacity-achieving strategy. However, such proper per-user transmit signals are not necessarily optimal for problems with quality-of-service (QoS) constraints if the transmit strategy is restricted to widely linear transceivers without time-sharing. This is shown by deriving the QoS feasibility region of the multiple-input multiple-output broadcast channel with improper Gaussian per-user transmit signals and widely linear transceivers.
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    Set-Valued State Estimation and Attack Detection for Uncertain Descriptor Systems (2013)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2013-09-21
    Description: A set-valued descriptor state estimation algorithm is given for determining all those descriptor state values consistent with a given uncertain descriptor system model, sensor model and a general class of uncertainty model. An approach for detecting faults or attacks signals that are corrupting the uncertain descriptor system and/or the set of sensor readings is also provided.
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  • 78
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    Dynamic Diffusion Estimation in Exponential Family Models (2013)
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    Publication Date: 2013-09-25
    Description: This letter proposes a new dynamic diffusion estimation method for a collaborative inference of a common model parameter using a distributed network of cooperating nodes. Unlike the existing single problem-oriented diffusion methods, it is formulated abstractly for the exponential family of models. The resulting advantage—its easy and straightforward application to the family members—is demonstrated on three selected cases: i) the diffusion autoregression, ii) the diffusion Poisson modelling and iii) the diffusion estimation of a Bernoulli process with unknown proportions. The first case is shown to coincide with the diffusion recursive least squares.
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    A New Variable Tap-Length and Step-Size FxLMS Algorithm (2013)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2013-09-25
    Description: The filtered-X least mean-square (FxLMS) algorithm is widely used for active noise control (ANC). A long tap-length is usually required for some FxLMS applications, and consequentially the convergence rate becomes very slow. In this letter, a new variable tap-length and step-size FxLMS algorithm is proposed, especially suited to a long tap-length filter. Taking into account the lowpass filter effect in the secondary path of ANC, the new algorithm is developed for the control filter with an unsymmetric and two-sided exponential decay envelop over its impulse response. Simulation results show that the new algorithm provides faster convergence and cancellation performance compared to previously proposed variable-step-size FxLMS algorithms.
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  • 80
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    Wideband Gaussian Source Processing Using a Linear Nested Array (2013)
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    Publication Date: 2013-09-25
    Description: Based on a recently proposed linear nested array, we consider the problem of source number detection and direction of arrival estimation for wideband Gaussian sources. This array provides $O(N^{2})$ degrees of freedom with $O(N)$ sensors, enabling us to estimate $K$ sources with $N 〈 K$ sensors. To employ the nested array for the wideband case, we propose effective strategies to apply nested-array processing to each frequency component, and combine all the spectral information of various frequencies to conduct the detection and estimation. In particular, for source detection, we propose a novel approach employing the idea of ensemble, used in machine learning and statistics. Numerical simulations demonstrate the advantages of our strategies.
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    Complexity Outage Probability of MIMO Detection in Multiple-User Scenario (2013)
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    Publication Date: 2013-10-02
    Description: This letter presents the complexity study of multiple-input multiple-output (MIMO) detection in a multiple-user scenario. For variable-complexity MIMO detection in multiple-user systems, the complexity outage probability, defined as the probability of event that the complexity exceeds some complexity constraint, is proved to have a Chernoff-type upper bound that decays exponentially with an increasing number of users. The closed-form expression of such a Chernoff-type upper bound is also derived.
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  • 82
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    Directional Chamfer Matching in 2.5 Dimensions (2013)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2013-10-05
    Description: Directional chamfer matching (DCM) has shown good results in many areas such as object recognition and pose estimation. Currently DCM has been applied only for two-dimensional (2-D) matching. In this letter, we present a DCM scheme that utilizes depth in addition to 2-D input, which we refer to as 2.5D DCM. We show that in situations such as 3-D model-based pose estimation, depth information can be exploited to achieve robust performance. We apply the proposed method for human motion capture (HMC), using the Human Eva I dataset. We compare our approach with alternative methods used for HMC. Our results show that using depth information makes traditional DCM robust. Furthermore, the proposed method outperforms the alternatives used for HMC in state of the art systems.
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  • 83
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    Invexity of the Minimum Error Entropy Criterion (2013)
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    Publication Date: 2013-10-05
    Description: In this letter, optimization properties of Minimization of Error Entropy (MEE) and Minimization of Error Entropy with Fiducial points (MEEF) are presented. It is proved that by varying the kernel parameter of the MEE and/or MEEF objective function, the resulting problem, in general leads to an invex problem. Furthermore, for certain values of the kernel parameter it is shown that the problems may transform to convex or pseudo-convex problems.
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  • 84
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    An Extension of STFT Uncertainty Propagation for GMM-Based Super-Gaussian a Priori Models (2013)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2013-10-05
    Description: Feature compensation is a low computational cost technique to achieve robust automatic speech recognition (ASR). Short-time Fourier Transform Uncertainty Propagation (STFT-UP) provides feature compensation in domains used for ASR as, e.g., Mel-Frequency Cepstra Coefficient (MFCC), while using STFT domain distortion models. However, STFT-UP is limited to Gaussian priors when modeling speech distortion, whereas super-Gaussian priors are known to provide improved performance. In this letter, an extension of STFT-UP is presented that uses approximate super-Gaussian priors. This is achieved by extending the conventional complex Gaussian priors to complex Gaussian mixture priors. The approach can be applied to any of the STFT-UP existing solutions, thus providing super-Gaussian uncertainty propagation. The method is exemplified by a Minimum Mean Square Error (MMSE) MFCC estimator with an approximate generalized Gamma speech prior. This estimator clearly outperforms the Gaussian-based MMSE-MFCC feature compensation on the AURORA4 corpus.
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  • 85
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    Optimized Nonlinear Discriminant Analysis (ONDA) for Supervised Pixel Classification (2013)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2013-10-05
    Description: Filter bank-based methods for pixel classification are attractive due to the potential of fast implementation with convolution operations. The design of optimal filter sets, however, is a challenging task given the nonlinear aspects of the problem. This letter extends the well known linear discriminant analysis method into a novel framework for local texture feature discrimination tasks. It proposes a mixture of linear models as a nonlinear classifier, where a number of filters are optimized locally by minimizing the prediction error. Through these filters, the ‘best separable’ features are selected. Experiments performed on two standard texture databases show that our method produces results which are comparable to state-of-the-art techniques while at the same time maintaining low computational complexity.
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  • 86
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    Adaptive Time Delay Estimation Using Filter Length Constraints for Source Localization in Reverberant Acoustic Environments (2013)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2013-04-03
    Description: Adaptive time delay estimation based on blind system identification (BSI) focuses on the impulse responses between a source and a microphone to estimate the time difference of arrival (TDOA) in reverberant environments. In this letter, we consider the adaptive eigenvalue decomposition (AED) BSI method based on the normalized multichannel frequency-domain least mean square (NMCFLMS) algorithm. We show that the use of filter length constraints (FLC) based on the maximum TDOA between microphones improves the performance of the NMCFLMS filter for the localization of different sound types in highly reverberant environments. The experimental results demonstrate the improvement of the proposed method for reverberation times $({rm RT}_{60})$ of up to 2 s. Applications for this method include teleconferencing systems, musical interfaces, videogames, and monitoring systems.
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    Joint Beamforming and Antenna Subarray Formation for MIMO Cognitive Radios (2013)
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    Publication Date: 2013-04-03
    Description: The antenna subarray formation (ASF) is a promising technique for multiple-input multiple-output (MIMO) receiver. For MIMO cognitive radio systems, we propose a joint beamforming and ASF scheme in this letter which maximizes the cognitive achievable capacity subject to the peak transmit power constraint at the secondary transmitter, peak interference power constraint at the primary receiver, and the limited number of nonzero elements in the ASF matrix. To solve the joint optimization problem, we propose a relax-and-recover scheme. Simulation results have shown that the proposed scheme outperforms the conventional antenna selection scheme.
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  • 88
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    Analysis Based Blind Compressive Sensing (2013)
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    Publication Date: 2013-04-03
    Description: In this letter, we address the problem of blindly reconstructing compressively sensed signals by exploiting the co-sparse analysis model. In the analysis model it is assumed that a signal multiplied by an analysis operator results in a sparse vector. We propose an algorithm that learns the operator adaptively during the reconstruction process. The arising optimization problem is tackled via a geometric conjugate gradient approach. Different types of sampling noise are handled by simply exchanging the data fidelity term. Numerical experiments are performed for measurements corrupted with Gaussian as well as impulsive noise to show the effectiveness of our method.
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  • 89
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    Multiband Radar Signal Coherent Fusion Processing With IAA and apFFT (2013)
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    Publication Date: 2013-04-03
    Description: This letter proposes a new method for multiband radar signal fusion by making use of all-phase fast Fourier transform (apFFT) algorithm and iterative adaptive approach (IAA). Central to the proposed method are, first, the mutual incoherence compensation between various subbands by apFFT algorithm, and second, the application of IAA to the mutually coherent subband measurements for signal fusion. Taking advantage of both algorithms, the proposed method effectively improves the range resolution with low sidelobes and performs robustly in the presence of noise. In particular, it requires no model information and therefore enables flexible implementation for practical application. The feasibility and effectiveness of the proposed algorithm are validated through both numerical simulations and raw data processing results.
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    Voice Activity Detection Via Noise Reducing Using Non-Negative Sparse Coding (2013)
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    Publication Date: 2013-04-03
    Description: This letter presents a voice activity detection (VAD) approach using non-negative sparse coding to improve the detection performance in low signal-to-noise ratio (SNR) conditions. The basic idea is to use features extracted from a noise-reduced representation of original audio signals. We decompose the magnitude spectrum of an audio signal on a speech dictionary learned from clean speech and a noise dictionary learned from noise samples. Only coefficients corresponding to the speech dictionary are considered and used as the noise-reduced representation of the signal for feature extraction. A conditional random field (CRF) is used to model the correlation between feature sequences and voice activity labels along audio signals. Then, we assign the voice activity labels for a given audio by decoding the CRF. Experimental results demonstrate that our VAD approach has a good performance in low SNR conditions.
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    Direction Finding Using Higher Order Statistics Without Redundancy (2013)
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    Publication Date: 2013-04-03
    Description: In the last decade, $2q$ -MUSIC, an extension of MUSIC algorithm to an arbitrary even order $2q$ , has been proposed to process direction-finding problems. Despite of its better performance compared with MUSIC, it suffers from the high computational complexity, thus limiting its practical application. This letter proposes a method called Non-Redundant- $2q$ -MUSIC using $2q$ th order statistics for uniform linear arrays and uniform rectangular arrays. The proposed method lowers the computational complexity effectively by removing the redundancy of the virtual array and the $2q$ th-order cumulant matrix. And root-MUSIC can be applied to avoid calculating the pseudo-spectrum. It is illustrated in both theoretical proof and computer simulations that the proposed method performs properly and effectively.
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    A Blind Time-Reversal Detector in the Presence of Channel Correlation (2013)
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    Publication Date: 2013-04-03
    Description: A blind target detector using the time reversal transmission is proposed in the presence of channel correlation. We calculate the exact moments of the test statistics involved. The derived moments are used to construct an accurate approximative Likelihood Ratio Test (LRT) based on multivariate Edgeworth expansion. Performance gain over an existing detector is observed in scenarios with channel correlation and relatively strong target signal.
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    CORDIC Based Fast Radix-2 DCT Algorithm (2013)
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    Publication Date: 2013-04-03
    Description: This letter proposes a novel coordinate rotation digital computer (CORDIC)-based fast radix-2 algorithm for computation of discrete cosine transformation (DCT). The proposed algorithm has some distinguish advantages, such as Cooley-Tukey fast Fourier transformation (FFT)-like regular data flow, uniform post-scaling factor, in-place computation and arithmetic-sequence rotation angles. Compared to existing DCT algorithms, this proposed algorithm has lower computational complexity. Furthermore, the proposed algorithm is highly scalable, modular, regular, and suitable for pipelined VLSI implementation. In addition, this letter also provides an easy way to implement the reconfigurable or unified architecture for DCTs and inverse DCTs.
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    Ergodic Interference Alignment With Delayed Feedback (2013)
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    Publication Date: 2013-04-03
    Description: We propose new ergodic interference alignment techniques for $K$ -user interference channels with delayed feedback. Two delayed feedback scenarios are considered—delayed channel information at transmitter (CIT) and delayed output feedback. It is proved that the proposed techniques achieve total $2K/(K+2)$ DoF which is higher than that by the retrospective interference alignment for the delayed feedback scenarios.
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    Joint Decompression and Decoding for Cloud Radio Access Networks (2013)
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    Publication Date: 2013-04-03
    Description: In this work, joint decompression and decoding is studied for the uplink of multi-antenna cloud radio access networks. In this system, a set of multi-antenna mobile stations (MSs) wish to communicate with a “cloud” decoder through a set of multi-antenna base stations (BSs), which are connected to the cloud decoder through digital backhaul links of limited capacity. The BSs compress the received signal and send it to the cloud decoder, which performs joint decoding of the signals from all MSs. While the conventional solution prescribes that the cloud decoder performs first decompression and then decoding, recent work has shown that potentially larger rates can be achieved with joint decompression and decoding (JDD) at the cloud decoder. The sum-rate maximization problem with JDD, under the assumption of Gaussian test channels, is shown here to be an instance of a class of non-convex problems known as Difference of Convex (DC) problems. Based on this observation, an iterative algorithm based on the Majorization Minimization (MM) approach is proposed that guarantees convergence to a stationary point of the sum-rate maximization problem. Numerical results demonstrate the advantage of the proposed algorithm compared to the conventional approach based on separate decompression and decoding.
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    An Ultra-Low Power QRS Complex Detection Algorithm Based on Down-Sampling Wavelet Transform (2013)
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    Publication Date: 2013-04-03
    Description: Low-power design has become a key technology for battery-power biomedical devices in Wireless Body Area Network. In order to meet the requirement of low-power dissipation for electrocardiogram related applications, a down-sampling QRS complex detection algorithm is proposed. Based on Wavelet Transform (WT), this letter characterizes the energy distribution of QRS complex corresponding to the frequency band of WT. Then this letter details for the first time the process of down-sampled filter design, and presents the time and frequency response of the filter. The algorithm is evaluated in fixed point on MIT-BIH and QT database. Compared with other existing results, our work reduces the power dissipation by 23%, 61%, and 72% for 1 $,times $ , 2 $,times $ , and 3 $,times $ down-sampling rate, respectively, while maintaining almost constant detection performance.
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    A Direct Algorithm for 1-D Total Variation Denoising (2013)
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    Publication Date: 2013-09-14
    Description: A very fast noniterative algorithm is proposed for denoising or smoothing one-dimensional discrete signals, by solving the total variation regularized least-squares problem or the related fused lasso problem. A C code implementation is available on the web page of the author.
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    Background Subtraction With Video Coding (2013)
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    Publication Date: 2013-09-14
    Description: The classic Gaussian mixture model is based on the statistical information of every pixel; it is not robust to light changes. Before analysing every pixel in videos, it must be decoded to raw videos. In this letter, the method combining video coding and the Gaussian mixture model together is proposed. We use intra mode and motion vectors to find the foreground macroblock, then add one overhead flag in the compressed video to indicate it. In the decoder, we just decode possible foreground areas and detect moving objects in these areas. In our experiments, we test this method on two datasets, both of them with unique, dynamic, illumination conditions. Results show that the proposed method is effective to detect moving objects and easily assemble to current automated video surveillance systems.
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    On the Gradient Descent Localization of Radioactive Sources (2013)
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    Publication Date: 2013-09-14
    Description: We consider the robust localization of radioactive sources by using their gamma-ray count at the smallest number of sensors needed to theoretically localize. We formulate a class of non-convex cost functions and consider their gradient descent optimization. We show that in $N$ -dimensions, if there are exactly $N+1$ sensors and the source lies in their open convex hull, then this convex hull is devoid of false stationary points. Thus we augment gradient descent with random projections into the convex hull, when an estimate leaves it. We argue that convergence in probability to the correct source location, will occur. Simulations demonstrate the efficacy of this algorithm.
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    A New Algorithm for Fitting a Gaussian Function Riding on the Polynomial Background (2013)
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    Publication Date: 2013-09-14
    Description: In this letter, an efficient algorithm is presented for fitting a Gaussian signal riding on a polynomial background. It is shown that the nonlinear least-squares fitting can be transformed into a standard linear least-squares fitting. The proposed method has the advantage of not requiring the initial estimates of the parameters, and it significantly reduces the computational cost. Various applications of this method have been successfully applied to real world problems; including the problem of estimating the parameters of characteristic waveforms on the modeling of electrocardiogram (ECG) signals and the problem of robust ECG RS-amplitude estimation.
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