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  • IEEE Signal Processing Letters  (684)
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  • 1
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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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  • 2
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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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  • 3
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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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  • 4
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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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  • 5
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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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  • 6
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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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  • 7
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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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  • 8
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    Bayesian Early Mode Decision Technique for View Synthesis Prediction-Enhanced Multiview Video Coding (2013)
    Institute of Electrical and Electronics Engineers (IEEE)
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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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  • 9
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    On the Fisher Information Matrix for Multivariate Elliptically Contoured Distributions (2013)
    Institute of Electrical and Electronics Engineers (IEEE)
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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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  • 10
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    QoS Feasibility in MIMO Broadcast Channels With Widely Linear Transceivers (2013)
    Institute of Electrical and Electronics Engineers (IEEE)
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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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  • 11
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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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  • 12
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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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  • 13
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    A New Variable Tap-Length and Step-Size FxLMS Algorithm (2013)
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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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  • 14
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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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  • 15
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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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  • 16
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    Directional Chamfer Matching in 2.5 Dimensions (2013)
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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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  • 17
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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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  • 18
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    An Extension of STFT Uncertainty Propagation for GMM-Based Super-Gaussian a Priori Models (2013)
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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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  • 19
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    Optimized Nonlinear Discriminant Analysis (ONDA) for Supervised Pixel Classification (2013)
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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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  • 20
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    Adaptive Time Delay Estimation Using Filter Length Constraints for Source Localization in Reverberant Acoustic Environments (2013)
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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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  • 21
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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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  • 22
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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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  • 23
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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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  • 24
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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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  • 25
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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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  • 26
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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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  • 27
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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)
    Institute of Electrical and Electronics Engineers (IEEE)
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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)
    Institute of Electrical and Electronics Engineers (IEEE)
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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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    Revisiting Inter-Genre Similarity (2013)
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    Publication Date: 2013-09-14
    Description: We revisit the idea of “inter-genre similarity” (IGS) for machine learning in general, and music genre recognition in particular. We show analytically that the probability of error for IGS is higher than naive Bayes classification with zero-one loss (NB). We show empirically that IGS does not perform well, even for data that satisfies all its assumptions.
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    Iterative Directional Total Variation Refinement for Compressive Sensing Image Reconstruction (2013)
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    Publication Date: 2013-09-18
    Description: We propose a novel compressive sensing (CS) image reconstruction method based on iterative directional total variation (TV) refinement. As is generally known, classical TV-based CS reconstruction methods tend to produce over-smoothed image edges and texture details, since they favor piece-wise constant solutions. Hence, directional TV is introduced to describe the sparsity of the image gradient in order to overcome this drawback. However, it is difficult to estimate orientation field robustly and accurately from CS measurements. Inspired by vectorial ROF model, orientation field refinement model is presented and introduced into CS reconstruction. Extended experiments show that the proposed CS reconstruction method has a better improvement in the quality of the reconstructed image details over related TV-based CS reconstruction methods.
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    Rao and Wald Tests for Distributed Targets Detection With Unknown Signal Steering (2013)
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    Publication Date: 2013-09-18
    Description: In this letter, we consider the problem of detecting a distributed target with unknown signal steering in the Gaussian noise. We derive the Rao and Wald tests. It is found that the Rao test coincides with the so-called modified two-step generalized likelihood ratio test (M2S-GLRT), while the Wald test is equivalent to the plain two-step GLRT (2S-GLRT). We also give some intuitive interpretations about the Rao and Wald tests, as well as other existing detectors.
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    Improving the Bound on the RIP Constant in Generalized Orthogonal Matching Pursuit (2013)
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    Publication Date: 2013-09-18
    Description: The generalized Orthogonal Matching Pursuit (gOMP) is a recently proposed compressive sensing greedy recovery algorithm which generalizes the OMP algorithm by selecting $N(geq 1)$ atoms in each iteration. In this letter, we demonstrate that the gOMP can successfully reconstruct a $K$ -sparse signal from a compressed measurement ${bf y}={mmbPhi}{bf x}$ by a maximum of $K$ iterations if the sensing matrix ${mmbPhi}$ satisfies the Restricted Isometry Property (RIP) of order $NK$ , with the RIP constant $delta_{NK}$ satisfying $delta_{NK}〈{sqrt{N}over sqrt{K}+2sqrt{N}}$ . The proposed bound is an improvement over the existing bound on $delta_{NK}$ . We also show that by increasing the RIP order just by one (i.e., $NK+1$ from $NK$ ), it is possible to refine the bound further to $delta_{NK+1}〈{sqrt{N}oversqrt{K}+sqrt{N}}$ , which is consistent (for $N=1$ ) with the near optimal bound on $delta_{K+1}$ in OMP.
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    A Novel Nonlinear Regression Approach for Efficient and Accurate Image Matting (2013)
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    Publication Date: 2013-09-18
    Description: Current image matting approaches are often implemented based upon color samples under various local assumptions. In this letter, a novel image matting algorithm is investigated by treating the alpha matting as a regression problem. Specifically, we learn spatially-varying relations between pixel features and alpha values using support vector regression. Via the learning-based approach, limitations caused by local image assumptions can be greatly relieved. In addition, the computed confidence terms in learning phase can be conveniently integrated with other matting approaches for the matting accuracy improvement. Qualitative and quantitative evaluations are implemented with a public matting benchmark. And the results are compared with some recent matting algorithms to show its advantages in both efficiency and accuracy.
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    Optimal Bayesian Resampling for OFDM Signaling Over Multi-scale Multi-lag Channels (2013)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2013-09-25
    Description: Underwater acoustic (UWA) communication channels are ultra-wideband in nature and experience long delay spreads and significant Doppler effects. The typical UWA channel distortion can be described by a multi-scale, multi-lag (MSML) channel model. Many UWA communication systems employ resampling by a single-scale at the front-end to compensate for the scale effects of UWA channels. In this letter, the optimal resampling parameter for OFDM signaling over MSML channels is investigated from a Bayesian perspective. The resampling parameter is selected to minimize the inter-carrier interference (ICI) resulting from the MSML channel for OFDM signaling. The exact interference energy is computed, but is intractable for optimization, thus, an upper bound is employed for optimization. Numerical results verify the tightness of the bound and quantify the performance of the Bayesian approach. As expected, the proposed method outperforms previous deterministic methods for resampling in MSML channels, which in turn outperform the classical packet-length-based approach, and is more effective in ICI mitigation.
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    Efficient Saliency-Model-Guided Visual Co-Saliency Detection (2014)
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    Publication Date: 2014-11-08
    Description: This letter proposes a novel framework to detect common salient objects in a group of images automatically and efficiently. Different from most existing co-saliency models which directly redesign algorithms for multiple images, the saliency model for a single image is fully exploited under the proposed framework to guide the co-saliency detection. Given single image saliency maps, a two-stage guided detection pipeline led by queries is proposed to obtain the guided saliency maps of the image set through a ranking scheme. Then the guided saliency maps generated by different queries are fused in a way that takes advantages of both averaging and multiplication. The proposed model makes existing saliency models work well in co-saliency scenarios. Experimental results on two benchmark databases demonstrate that the proposed framework outperforms the state-of-the-art models in terms of both accuracy and efficiency.
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    Bivariate Empirical Mode Decomposition for Cognitive Radar Scene Analysis (2014)
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    Publication Date: 2014-11-08
    Description: A method based on the Bivariate Empirical Mode Decomposition (BEMD) is addressed to facilitate radar scene analysis for cognitive radar, building and expanding upon a previous contribution. The method exploits the response of BEMD to the fractional Gaussian character of coherent sea clutter returns. Second–order properties of the intrinsic mode functions are used to form a null hypothesis, which indicates the absence of target(s) if accepted. Extensive experiments on real–world radar data show that the proposed radar scene analysis procedure leads to significantly enhanced statistical separability for target+clutter and clutter–alone data. The results are judged from an information–theoretic perspective using the Kullback–Leibler distance as well as by visual inspection.
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    Simple Features for Separating CPFSK from QAM and PSK Modulations (2014)
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    Publication Date: 2014-11-08
    Description: In this letter we propose simple and robust features to distinguish continuous-phase frequency shift keying from quadrature amplitude modulation and phase shift keying modulations. The features are based on sample mean and sample variance of the imaginary part of the product of two consecutive complex signal values. Root raised cosine pulses are used to generate the linearly modulated signals. Support vector machines are employed to distinguish the signals. One benefit of using support vector machines is that it requires very few realizations for training. Moreover, no a priori information is required about carrier amplitude, carrier phase, carrier offset, symbol rate, pulse shape, initial symbol phase (timing offset) and channel impulse response. Effectiveness of the features and signal separation by support vector machines is tested by observing the joint effects of additive white Gaussian noise, carrier offset, lack of symbol and sampling synchronization, and either fast or slow fading. In the course of doing that, the proposed classifier is compared to the wavelet based classifier, equipped by support vector machines.
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    Information Theoretic Analysis of OFDM/OQAM with Utilized Intrinsic Interference (2014)
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    Publication Date: 2014-11-08
    Description: In this paper, the capacity of OFDM/OQAM with isotropic orthogonal transfer algorithm (IOTA) pulse shaping is evaluated through information theoretic analysis. In the conventional OFDM systems the insertion of a cyclic prefix (CP) decreases the system’s spectral efficiency. As an alternative to OFDM, filter bank based multicarrier systems adopt proper pulse shaping with good time and frequency localisation properties to avoid interference and maintain orthogonality in real field among sub-carriers without the use of CP. We evaluate the spectral efficiency of OFDM/OQAM systems with IOTA pulse shaping in comparison with conventional OFDM/QAM systems, and our analytical model is further extended in order to gain insights into the effect of utilizing the intrinsic interference on the performance of our system. Furthermore, the spectral efficiency of OFDM/OQAM systems is analyzed when the effect of inter-symbol and inter-carrier interference is considered.
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    Fast Iterative Hard Thresholding for Compressed Sensing (2014)
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    Publication Date: 2014-11-08
    Description: Algebraic Pursuit (ALPS) is an effective class of iterative hard thresholding algorithms for compressed sensing, with 1-ALPS(2) being the most computationally efficient variant of ALPS. We present a proof of convergence, using restricted isometry constants, for 1-ALPS(2) as well as the recently introduced Fast Iterative Hard Thresholding (FIHT). Large scale empirical testing shows FIHT is superior to 1-ALPS(2) in terms of both the sizes of the problems that are recoverable and overall computational time.
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    Asymptotic Analysis of Distributed Bayesian Detection with Byzantine Data (2014)
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    Publication Date: 2014-11-08
    Description: In this letter, we consider the problem of distributed Bayesian detection in the presence of Byzantine data. The problem of distributed detection is formulated as a binary hypothesis test at the fusion center (FC) based on 1-bit data sent by the sensors. Adopting Chernoff information as our performance metric, we study the detection performance of the system under Byzantine attack in the asymptotic regime. The expression for minimum attacking power required by the Byzantines to blind the FC is obtained. More specifically, we show that above a certain fraction of Byzantine attackers in the network, the detection scheme becomes completely incapable of utilizing the sensor data for detection. When the fraction of Byzantines is not sufficient to blind the FC, we also provide closed form expressions for the optimal attacking strategies for the Byzantines that most degrade the detection performance.
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    To Listen or Not: Distributed Detection with Asynchronous Transmissions (2014)
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    Publication Date: 2014-11-08
    Description: This letter examines a variation of the canonical distributed detection system: a sensor may overhear other sensors’ transmissions and thus may choose to refine its output in the hope of achieving a better detection performance. We show that while this is indeed possible for the fixed sample size test, asymptotically (in the number of samples) there is no performance gain, as measured by the Kullback-Leibler distance achievable at the fusion center, provided that the observations are conditionally independent. For conditionally dependent observations, however, we demonstrate that asymptotic detection performance may indeed be improved when overhearing is utilized.
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    Phase Estimation in Single Channel Speech Enhancement Using Phase Decomposition (2014)
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    Publication Date: 2014-11-08
    Description: Conventional speech enhancement methods typically utilize the noisy phase spectrum for signal reconstruction. This letter presents a novel method to estimate the clean speech phase spectrum, given the noisy speech observation in single-channel speech enhancement. The proposed method relies on the phase decomposition of the instantaneous noisy phase spectrum followed by temporal smoothing in order to reduce the large variance of noisy phase, and consequently reconstructs an enhanced instantaneous phase spectrum for signal reconstruction. The effectiveness of the proposed method is evaluated in two ways: phase enhancement-only and by quantifying the additional improvement on top of the conventional amplitude enhancement scheme where noisy phase is often used in signal reconstruction. The instrumental metrics predict a consistent improvement in perceived speech quality and speech intelligibility when the noisy phase is enhanced using the proposed phase estimation method.
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    Sparse Phase Retrieval from Short-Time Fourier Measurements (2014)
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    Publication Date: 2014-11-08
    Description: We consider the classical 1D phase retrieval problem. In order to overcome the difficulties associated with phase retrieval from measurements of the Fourier magnitude, we treat recovery from the magnitude of the short-time Fourier transform (STFT). We first show that the redundancy offered by the STFT enables unique recovery for arbitrary nonvanishing inputs, under mild conditions. An efficient algorithm for recovery of a sparse input from the STFT magnitude is then suggested, based on an adaptation of the recently proposed GESPAR algorithm. We demonstrate through simulations that using the STFT leads to improved performance over recovery from the oversampled Fourier magnitude with the same number of measurements.
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    Comments on “Near-Field Source Localization via Symmetric Subarrays” (2014)
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    Publication Date: 2014-11-08
    Description: In the aforementioned letter, the authors indicate that with a $2M + 1$ sensor uniform linear array (ULA), up to $2M - 1$ sources can actually be localized by the proposed algorithm. In this comment, however, we prove that the algorithm will no longer be valid if the number of sources exceeds $M$ . A numerical simulation is performed to verify this conclusion.
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    Efficient Histogram PMHT Via Single Target Chip Processing (2014)
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    Publication Date: 2014-11-05
    Description: The Histogram Probabilistic Multi-Hypothesis Tracker is a multi-target track before detect method with low computational complexity. However, for high resolution sensors with millions of pixels, a naive implementation can be too slow. Single target chip processing is proposed as an approximate implementation scheme that dramatically reduces computation cost without significant performance degradation. Simulated examples are used to quantify the improvements achieved.
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    An Adaptive Motion Model for Person Tracking with Instantaneous Head-Pose Features (2014)
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    Publication Date: 2014-11-05
    Description: This letter presents novel behaviour-based tracking of people in low-resolution using instantaneous priors mediated by head-pose. We extend the Kalman Filter to adaptively combine motion information with an instantaneous prior belief about where the person will go based on where they are currently looking. We apply this new method to pedestrian surveillance, using automatically-derived head pose estimates, although the theory is not limited to head-pose priors. We perform a statistical analysis of pedestrian gazing behaviour and demonstrate tracking performance on a set of simulated and real pedestrian observations. We show that by using instantaneous ‘intentional’ priors our algorithm significantly outperforms a standard Kalman Filter on comprehensive test data.
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    Bayesian Compressive Sensing Using Normal Product Priors (2014)
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    Publication Date: 2014-11-05
    Description: In this letter, we introduce a new sparsity-promoting prior, namely, the “normal product” prior, and develop an efficient algorithm for sparse signal recovery under the Bayesian framework. The normal product distribution is the distribution of a product of two normally distributed variables with zero means and possibly different variances. Like other sparsity-encouraging distributions such as the Student’s $t$ -distribution, the normal product distribution has a sharp peak at the origin, which makes it a suitable prior to encourage sparse solutions. A two-stage normal product-based hierarchical model is proposed. We resort to the variational Bayesian (VB) method to perform the inference. Simulations are conducted to illustrate the effectiveness of our proposed algorithm as compared with other state-of-the-art compressed sensing algorithms.
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    Discriminative Clustering and Feature Selection for Brain MRI Segmentation (2014)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2014-11-05
    Description: Automatic segmentation of brain tissues from MRI is of great importance for clinical application and scientific research. Recent advancements in supervoxel-level analysis enable robust segmentation of brain tissues by exploring the inherent information among multiple features extracted on the supervoxels. Within this prevalent framework, the difficulties still remain in clustering uncertainties imposed by the heterogeneity of tissues and the redundancy of the MRI features. To cope with the aforementioned two challenges, we propose a robust discriminative segmentation method from the view of information theoretic learning. The prominent goal of the method is to simultaneously select the informative feature and to reduce the uncertainties of supervoxel assignment for discriminative brain tissue segmentation. Experiments on two brain MRI datasets verified the effectiveness and efficiency of the proposed approach.
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    Visual Tracking via Structure Constrained Grouping (2014)
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    Publication Date: 2014-12-06
    Description: This letter introduces a novel two-pass structural grouping algorithm and casts visual tracking as foreground superpixels grouping problem. In the first step, pairwise superpixel grouping is conducted in four orientations. Grouping prototypes containing the prior information of foreground and background are generated to determine whether any pair of neighboring superpixels should be grouped together. In the second step, superpixels selected by the first step are grouped into a single region which serves as the object region. The proposed grouping method has two benefits over the state-of-the-art ones. First, pairwise grouping is independently conducted in four orientations, which exploits the local structure of the foregound/backgroud and facilitates a more robust grouping process. Second, rather than considering the similarity of two neighboring superpixels, the grouping process is performed via accounting for the prior information of the object and the background, which is more suitable for visual tracking. Many experiments on challenging video clips demonstrate that our method achieves good performance than the state-of-the-art trackers in a wide range of tracking scenarios.
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    Local Topographic Shape Patterns for Texture Description (2014)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2014-12-06
    Description: This letter introduces a new image descriptor named Local Topographic Shape Pattern (LTSP) for texture description by relying on complete shape-based image representation. Firstly, a texture image is decomposed into a tree of shapes, i.e., topographic map, to obtain a multi-scale and contrast-invariant representation. Secondly, the shape of each node in the tree is handled with four shape patterns which are proposed as spatial codes of the shape. Finally, statistical histograms of shape patterns are used as texture descriptors. Contrast experiments of the proposed method show satisfactory performance in terms of both texture retrieval and classification tasks when applied to the Brodatz and UMD texture datasets.
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    On the Performance of Secure Full-Duplex Relaying under Composite Fading Channels (2014)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2014-12-06
    Description: We assume a full-duplex (FD) cooperative network subject to hostile attacks and undergoing composite fading channels. We focus on two scenarios: a) the transmitter has full CSI, for which we derive closed-form expressions for the average secrecy rate; and b) the transmitter only knows the CSI of the legitimate nodes, for which we obtain closed-form expressions for the secrecy outage probability. We show that secure FD relaying is feasible, even under strong self-interference and in the presence of sophisticated multiple antenna eavesdropper.
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    Higher-Order-Statistics-Based Fractal Dimension for Noisy Bowel Sound Detection (2014)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2014-12-06
    Description: Bowel sounds is an important physiological parameter of distinguishing the gastrointestinal motility dysfunction. Auscultation of bowel sounds provides a noninvasive way for clinical diagnosis, but it is also easily affected by environmental noise. In this study, a novel higher-order-statistics (HOS)-based fractal dimension algorithm was proposed for detecting noisy bowel sounds. By using the nature of preserving non-Gaussianity for higher order statistics technique, the proposed method can effectively detect bowel sounds under different noise conditions, and its performance is insensitive to the change of noise type and noise level.
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    Two Efficient Algorithms for Approximately Orthogonal Nonnegative Matrix Factorization (2014)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2014-12-06
    Description: Nonnegative matrix factorization (NMF) with orthogonality constraints is quite important due to its close relation with the K-means clustering. While existing algorithms for orthogonal NMF impose strict orthogonality constraints, in this letter we propose a penalty method with the aim of performing approximately orthogonal NMF, together with two efficient algorithms respectively based on the Hierarchical Alternating Least Squares (HALS) and the Accelerated Proximate Gradient (APG) approaches. Experimental evidence was provided to show their high efficiency and flexibility by using synthetic and real-world data.
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    Bi-DCT: DCT-based Local Binary Descriptor for Dense Stereo Matching (2014)
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    Publication Date: 2014-12-06
    Description: In this letter, we present a novel DCT-based local binary descriptor for the dense matching of multiple view stereo (MVS). Recently, although much progress has been made in the field of MVS, a key component of which, i.e., dense matching, is still a challenging task because it has two difficult issues: robust matching over non-salient regions (e.g., lines and textureless regions) and fast matching of a large number of pixels. To deal with these issues effectively, in the proposed dense descriptor, 2D DCT-based local features are utilized to achieve high discriminative power even for the non-salient regions. A binary representation is adopted to increase the matching performance as well as accelerate the matching speed via the Hamming distance. In addition, the discriminability of binarized vectors is further improved by a space-frequency pooling scheme. Through extensive experiments on the benchmark datasets for MVS, we demonstrate the superiority of the proposed descriptor over the state-of-the-art descriptors in terms of accuracy and efficiency.
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    Joint Power Splitting and Antenna Selection in Energy Harvesting Relay Channels (2014)
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    Publication Date: 2014-12-06
    Description: The simultaneous wireless transfer of information and power with the help of a relay equipped with multiple antennas is considered in this letter, where a “harvest-and-forward” strategy is proposed. In particular, the relay harvests energy and obtains information from the source with the radio-frequent signals by jointly using the antenna selection (AS) and power splitting (PS) techniques, and then the processed information is amplified and forwarded to the destination relying on the harvested energy. This letter jointly optimizes AS and PS to maximize the achievable rate for the proposed strategy. Considering that the joint optimization is according to the non-convex problem, a two-stage procedure is proposed to determine the optimal ratio of received signal power split for energy harvesting, and the optimized antenna set engaged in information forwarding. Simulation results confirm the accuracy of the two-stage procedure, and demonstrate that the proposed “harvest-and-forward” strategy outperforms the conventional amplify-and-forward (AF) relaying and the direct transmission.
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    On the Behavior of EMD and MEMD in Presence of Symmetric $alpha $ -Stable Noise (2014)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2014-12-06
    Description: Empirical Mode Decomposition (EMD) and its extended versions such as Multivariate EMD (MEMD) are data-driven techniques that represent nonlinear and non-stationary data as a sum of a finite zero-mean AM-FM components referred to as Intrinsic Mode Functions (IMFs). The aim of this work is to analyze the behavior of EMD and MEMD in stochastic situations involving non-Gaussian noise, more precisely, we examine the case of Symmetric $alpha $ -Stable ( ${rm S}alpha{rm S} $ ) noise. We report numerical experiments supporting the claim that both EMD and MEMD act, essentially, as filter banks on each channel of the input signal in the case of ${rm S}alpha {rm S}$ noise. Reported results show that, unlike EMD, MEMD has the ability to align common frequency modes across multiple channels in same index IMFs. Further, simulations show that, contrary to EMD, for MEMD the stability property is well satisfied for the modes of lower indices and this result is exploited for the estimation of the stability index of the ${rm S}alpha{rm S}$ input signal.
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    Non-Local Means Image Denoising With a Soft Threshold (2014)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2014-12-06
    Description: Non-local means (NLM) are typically biased by the accumulation of small weights associated with dissimilar patches, especially at image edges. Hence, we propose to null the small weights with a soft threshold to reduce this accumulation. We call this method the NLM filter with a soft threshold (NLM-ST). Its Stein’s unbiased risk estimate (SURE) approaches the true mean square error; thus, we can linearly aggregate multiple NLM-STs of Monte-Carlo-generated parameters by minimizing SURE to surpass the performance limit of single NLM-ST, which is referred to as the Monte-Carlo-based linear aggregation (MCLA). Finally, we employ a simple moving average filter to smooth the MCLA image sequence to further improve the denoising performance and stability. Experiments indicate that the NLM-ST outperforms the classic patchwise NLM and three other well-known recent variants in terms of the peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and visual quality. Furthermore, its PSNR is higher than that of BM3D for certain images.
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    Fast Decomposition of Large Nonnegative Tensors (2014)
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    Publication Date: 2014-12-06
    Description: In signal processing, tensor decompositions have gained in popularity this last decade. In the meantime, the volume of data to be processed has drastically increased. This calls for novel methods to handle Big Data tensors. Since most of these huge data are issued from physical measurements, which are intrinsically real nonnegative, being able to compress nonnegative tensors has become mandatory. Following recent works on HOSVD compression for Big Data, we detail solutions to decompose a nonnegative tensor into decomposable terms in a compressed domain.
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    Detection of Vowel Offset Point From Speech Signal (2013)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2013-02-20
    Description: Vowel regions play important role in various speech tasks, such as speech segmentation, speaker-verification, prosody modification and emotion conversion. The instants at which the onset and offset of vowel take place in the speech signal are known as vowel onset point and vowel offset point, respectively. Vowel regions start with the vowel onset point and end with the vowel offset point. In this letter, we have proposed two methods for determining the vowel offset points from the speech signal. The first method explores the combination of evidences from excitation source, spectral peaks and modulation spectrum for determining the vowel offset point. In the second method, spectral energy within glottal closure region is used for determining the vowel offset point. The proposed vowel offset point detection methods are evaluated on TIMIT database under clean and noisy environments.
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    An Efficient Matrix Factorization Method for Tensor Completion (2013)
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    Publication Date: 2013-02-20
    Description: Most recent low-rank tensor completion algorithms are based on tensor nuclear norm minimization problems. The convex relaxation problem of tensor $n$ -rank minimization has to be solved iteratively and involves multiple singular value decompositions (SVDs) at each iteration, and thus such algorithms suffer from high computation cost. In this letter, we propose an efficient low-rank tensor completion approach. First, we introduce a matrix factorization idea into the tensor nuclear norm model, and then can achieve a much smaller scale matrix nuclear norm minimization problem. Moreover, we develop an efficient iterative scheme for solving the proposed model with orthogonality constraint. Our extensive evaluation results validate both the effectiveness and efficiency of the proposed approach.
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    Spatially Coherent Fuzzy Clustering for Accurate and Noise-Robust Image Segmentation (2013)
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    Publication Date: 2013-02-20
    Description: In this letter, we present a new FCM-based method for spatially coherent and noise-robust image segmentation. Our contribution is twofold: 1) the spatial information of local image features is integrated into both the similarity measure and the membership function to compensate for the effect of noise; and 2) an anisotropic neighborhood, based on phase congruency features, is introduced to allow more accurate segmentation without image smoothing. The segmentation results, for both synthetic and real images, demonstrate that our method efficiently preserves the homogeneity of the regions and is more robust to noise than related FCM-based methods.
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    Mixed-Order MUSIC Algorithm for Localization of Far-Field and Near-Field Sources (2013)
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    Publication Date: 2013-02-20
    Description: This letter presents a new mixed-order MUSIC algorithm for far-field and near-field sources localization using a sparse symmetric array. By exploiting the special array geometry, the proposed algorithm constructs a cumulant matrix to estimate the directions of arrival (DOAs) of both far-field and near-field sources using the conventional MUSIC method. With the estimated DOAs and the covariance matrix of the sparse array, the far-field and near-field sources are identified and the range parameters of near-field sources are also obtained by defining the range spectrum. Compared with the traditional algorithms, the proposed algorithm has moderate computation complexity, and provides higher resolution, and also improves the parameters estimation accuracy. Simulation results are provided to demonstrate the performance improvement of the proposed method.
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    An Adaptive-ADMM Algorithm With Support and Signal Value Detection for Compressed Sensing (2013)
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    Publication Date: 2013-02-20
    Description: This letter presents a novel adaptive alternating direction method of multipliers with support/signal value detection for compressed sensing. The support/signal value detection in our algorithm can achieve an efficient reconstruction by leveraging more information that goes beyond simple sparsity. Especially for time-correlated signals in large-scale problems, our proposal performs better than conventional methods, since more accurate signal information could be estimated from prior knowledge during initialization. Simulation results show that our method can improve the average PSNR by 1.02–2.05 dB for undersampled video sequences.
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    A Fast Method for Reconstruction of Total-Variation MR Images With a Periodic Boundary Condition (2013)
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    Publication Date: 2013-02-20
    Description: We use a small positive parameter to change the total-variation function for unconstrained MR image reconstruction to a strictly convex perturbed function. Bregman iteration is applied to solve the modified total-variation MR image (TVMRI) reconstruction problem. A lagged diffusivity fixed-point algorithm is applied to solve the minimization problem in the Bregman iteration. We use the periodic boundary condition and a Fourier transform to accelerate TVMRI reconstruction. Real MR images are used to test the approach in numerical experiments. The experimental results demonstrate that the proposed method is very efficient for TVMRI reconstruction.
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    Analysis of Non-Local Euclidean Medians and Its Improvement (2013)
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    Publication Date: 2013-02-20
    Description: Non-Local Euclidean Medians (NLEM) has recently been proposed and shows more effective than Non-Local Means (NLM) in removing heavy noise. In this letter, we find the inconsistency between the two dissimilarity measures in NLEM can affect its robustness, thus develop an improved version (INLEM) to compensate such an inconsistency. Further, we provide a concise convergence proof for the iterative algorithm used in both NLEM and INLEM. Finally, our experiments on synthetic and natural images show that INLEM achieves encouraging results.
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    Edge Strength Similarity for Image Quality Assessment (2013)
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    Publication Date: 2013-02-20
    Description: The objective image quality assessment aims to model the perceptual fidelity of semantic information between two images. In this letter, we assume that the semantic information of images is fully represented by edge-strength of each pixel and propose an edge-strength-similarity-based image quality metric (ESSIM). Through investigating the characteristics of the edge in images, we define the edge-strength to take both anisotropic regularity and irregularity of the edge into account. The proposed ESSIM is considerably simple, however, it can achieve slightly better performance than the state-of-the-art image quality metrics as evaluated on six subject-rated image databases.
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    Performance Optimization of Digital Spectrum Analyzer With Gaussian Input Signal (2012)
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    Publication Date: 2012-12-13
    Description: Analog to digital converters (ADC) and cascade integrator-comb (CIC) filters are the basic modules in a digital intermediate frequency (IF) spectrum analyzer. The optimal output signal-to-noise ratio (SNR) of the digital IF spectrum analyzer with the Gaussian input signal is considered in this letter. The idea is to strike a trade-off between the saturation error and granular error when quantizing the Gaussian input signal. This letter firstly derives a relationship among the maximum allowed input signal amplitude, input signal power, ADC quantization bits and optimal quantization SNR. Besides, an optimal clipping strategy for the CIC decimation filter with variable decimation rates is proposed. Both numerical and simulation results are presented to demonstrate that the proposed clipping method is able to achieve significant SNR gain compared with the traditional rounding or truncation method.
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    Cooperative Secure Beamforming for AF Relay Networks With Multiple Eavesdroppers (2012)
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    Publication Date: 2012-12-13
    Description: This letter studies cooperative secure beamforming for amplify-and-forward (AF) relay networks in the presence of multiple eavesdroppers. Under both total and individual relay power constraints, we propose two schemes, namely secrecy rate maximization (SRM) beamforming and null-space beamforming. In the first scheme, our design problem is based on SRM. Using a suboptimal, but convex, technique—semidefinite relaxation (SDR), we show that this problem can be handled by performing a one-dimensional search which involves solving a sequence of semidefinite programs (SDPs). To reduce the complexity, in the second scheme, we instead maximize the information rate at the destination while completely eliminating the information leakage to all eavesdroppers. We prove that this problem can be exactly solved by SDR with one SDP only. Simulation results demonstrate the performance gains of the two proposed designs.
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    Joint Cooperative Beamforming and Jamming to Secure AF Relay Systems With Individual Power Constraint and No Eavesdropper's CSI (2012)
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    Publication Date: 2012-12-13
    Description: Cooperative beamforming and jamming are two efficient schemes to improve the physical-layer security of a wireless relay system in the presence of passive eavesdroppers. However, in most works these two techniques are adopted separately. In this letter, we propose a joint cooperative beamforming and jamming scheme to enhance the security of a cooperative relay network, where a part of intermediate nodes adopt distributed beamforming while others jam the eavesdropper, simultaneously. Since the instantaneous channel state information (CSI) of the eavesdropper may not be known, we propose a cooperative artificial noise transmission based secrecy strategy, subjected to the individual power constraint of each node. The beamformer weights and power allocation can be obtained by solving a second-order convex cone programming (SOCP) together with a linear programming problem. Simulations show the joint scheme greatly improves the security.
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    Power Allocation for Statistical QoS Provisioning in Opportunistic Multi-Relay DF Cognitive Networks (2012)
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    Publication Date: 2012-12-13
    Description: In this letter, we propose a power allocation scheme for statistical quality-of-service (QoS) provisioning in multi-relay decode-and-forward (DF) cognitive networks (CN). By considering the direct link between the source and destination, the CN first chooses the transmission mode (direct transmission or relay transmission) based on the channel state information. Then, according to the determined transmission mode, efficient power allocation will be performed under the given QoS requirement, the average transmit and interference power constraints as well as the peak interference constraint. Our proposed power allocation scheme indicates that, in order to achieve the maximum throughput, at most two relays can be involved for the transmission. Simulation results show that our proposed scheme outperforms the max-min criterion and equal power allocation policy.
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    Bit Error-Rate Minimizing Detector for Amplify-and-Forward Relaying Systems Using Generalized Gaussian Kernel (2012)
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    Publication Date: 2012-12-13
    Description: In this letter, a new detector is proposed for amplify-and-forward (AF) relaying system when communicating with the assistance of $L$ relays. The major goal of this detector is to improve the bit error rate (BER) performance of the receiver. The probability density function is estimated with the help of kernel density technique. A generalized Gaussian kernel is proposed. This new kernel provides more flexibility and encompasses Gaussian and uniform kernels as special cases. The optimal window width of the kernel is calculated. Simulations results show that a gain of more than 1 dB can be achieved in terms of BER performance as compared to the minimum mean square error (MMSE) receiver when communicating over Rayleigh fading channels.
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    Receiver-Driven Adaptive Enhancement Layer Switching Algorithm for Scalable Video Transmission Over Link-adaptive Networks (2012)
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    Publication Date: 2012-12-13
    Description: A receiver-driven adaptive layer switching algorithm is proposed for adapting the video bitrate to match the achievable network throughput. It relies on a QoS-constrained equivalent bandwidth estimator employed at the receiver, which is used for triggering the adjustment of video layers at the video source. Simulations are conducted to illustrate its efficiency by showing that it is capable of accommodating different channel qualities without their prior knowledge.
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    Joint Clock Synchronization and Ranging: Asymmetrical Time-Stamping and Passive Listening (2012)
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    Publication Date: 2012-12-13
    Description: A fully asynchronous network with one sensor and $M$ anchors (nodes with known locations) is considered in this letter. We propose a novel asymmetrical time-stamping and passive listening (ATPL) protocol for joint clock synchronization and ranging. The ATPL protocol exploits broadcast to not only reduce the number of active transmissions between the nodes, but also to obtain more information. This is used in a simple estimator based on least-squares (LS) to jointly estimate all the unknown clock-skews, clock-offsets, and pairwise distances of the sensor to each anchor. The Cramér–Rao lower bound (CRLB) is derived for the considered problem. The proposed estimator is shown to be asymptotically efficient, meets the CRLB, and also performs better than the available clock synchronization algorithms.
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    Joint Voltage and Phase Unbalance Detector for Three Phase Power Systems (2012)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2012-12-13
    Description: This letter develops a fast detection algorithm for voltage and phase unbalance in three phase power systems. It is suitable for real time applications since the required observation length is one cycle. It is shown to successfully detect small unbalance conditions at low SNRs. Its detection performance is shown to outperform traditional detectors that rely on changes in only a subset of positive, negative and zero sequence voltages. Unbalance detection is formulated as a hypothesis test under a framework of detection theory and solved by applying a generalized likelihood ratio test (GLRT). We first obtain an approximate maximum likelihood estimate (MLE) of the system frequency and then use it to substitute the true unknown frequency in the GLRT. A closed form expression is provided to detect unbalance conditions. Theoretical derivations are supported by simulations.
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    Face Hallucination via Similarity Constraints (2012)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2012-12-13
    Description: In this letter, we present a new face hallucination method based on similarity constraints to produce a high-resolution (HR) face image from an input low-resolution (LR) face image. This method is modeled as a local linear filtering process by incorporating four constraint functions at patch level. The first two constraints focus on checking if the training images are similar to the input face image. The third is defined in the HR face image, which is to impose the smoothness constraint between neighboring hallucinated patches. The final constraint computes the spatial distance to reduce the effect of patches that are far from the hallucinating patch. Experimental evaluation on a number of face images demonstrates the good performance of the proposed method on the face hallucination task.
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    Shifted-Delta MLP Features for Spoken Language Recognition (2012)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2012-12-13
    Description: This letter presents our study of applying phoneme posterior features for spoken language recognition (SLR). In our work, phoneme posterior features are estimated from a multilayer perceptron (MLP) based phoneme recognizer, and are further processed through transformations including taking logarithm, PCA transformation, and appending shifted delta coefficients. The resulting shifted-delta MLP (SDMLP) features show similar distribution as conventional shifted-delta cepstral (SDC) features, and are more robust compared to the SDC features. Experiments on the NIST LRE2005 dataset show that the SDMLP features fit well with the state-of-the-art GMM-based SLR systems, and SDMLP features outperform SDC features significantly.
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    On Perceptually Consistent Image Binarization (2012)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2012-12-13
    Description: Compared with gray images, bi-level images have only two values for each pixel and are more convenient for transmission and storing. Hence, they are very useful in many practical applications such as in image printing and display. Of various image binarization methods, error diffusion is a popular one that can produce perceptually consistent halftone images. However, most of the existing error diffusion techniques have not given rigid theoretical foundation or explicit theoretical derivation, and most of their diffusion domains and diffusion weights are constant, which make them inconvenient to be used and inefficient in some practical applications. In this paper, a new error diffusion scheme for image binarization is derived and established based on the analysis of features of the human visual system (HVS) and the heat transfer theory, where the local image information and the local pixels' value distribution are kept stable or little changed during the binarization process. As a result, the overall visual quality of binarized images can be kept perceptually similar to the original ones. Experiments are conducted and convincing results are acquired.
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    One-Bit Quantizer Design for Multisensor GLRT Fusion (2013)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2013-02-09
    Description: In this letter, we consider a decentralized detection problem in which a number of sensor nodes collaborate to detect the presence of an unknown deterministic signal. Due to stringent power/bandwidth constraints, each sensor quantizes its local observation into one bit of information. The binary data are then sent to the fusion center (FC), where a generalized likelihood ratio test (GLRT) detector is employed to make a global decision. In this context, we study one-bit quantizer design and analyze the asymptotic performance of the one-bit GLRT detector for cases where the quantized data are sent to the FC via perfect or imperfect channels. Simulation results are carried out to corroborate our theoretical analysis and to illustrate the performance of the proposed scheme.
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    Variational Bayesian View of Weighted Trace Norm Regularization for Matrix Factorization (2013)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2013-02-09
    Description: Matrix factorization with trace norm regularization is a popular approach to matrix completion and collaborative filtering. When entries of the matrix are sampled non-uniformly (which is the case for collaborative prediction), a properly weighted correction to the trace norm regularization is known to improve the performance dramatically. While the weighted trace norm regularization has been rigorously studied, its generative counterpart is not known yet. In this paper we show that the weighted trace norm regularization emerges from variational Bayesian matrix factorization where variational distributions over factor matrices are restricted to be isotropic Gaussians with the common variance. We show that variational variance corresponds to the regularization parameter. Thus, the regularization parameter can be automatically learned by variational inference rather than cross-validation. Experiments on MovieLens and Netflix datasets confirm the variational Bayesian perspective of the weighted trace norm regularization, demonstrating that variational parameter learned by variational inference agrees with the value of the regularization parameter found by cross-validation.
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    Compressed Sensing via Dual Frame Based -Analysis With Weibull Matrices (2013)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2013-02-09
    Description: This letter considers the problem of recovering signals via dual frame based $ell_{1}$ -analysis model under the assumption that signals are compressible in a general frame. Our main result shows that Weibull random matrices (not only subgaussian matrices) with optimal number of measurements could guarantee accurate recovery of signals with high probability. We derive that result by generalizing a recent Lemma due to Foucart and with the help of the parameterized representation of any dual frame. Our result should be significant for existing and upcoming $ell_{1}$ -analysis models for signal recovery.
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    An Information-Geometric Characterization of Chernoff Information (2013)
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    Publication Date: 2013-02-09
    Description: The Chernoff information was originally introduced for bounding the probability of error of the Bayesian decision rule in binary hypothesis testing. Nowadays, it is often used as a notion of symmetric distance in statistical signal processing or as a way to define a middle distribution in information fusion. Computing the Chernoff information requires to solve an optimization problem that is numerically approximated in practice. We consider the Chernoff distance for distributions belonging to the same exponential family including the Gaussian and multinomial families. By considering the geometry of the underlying statistical manifold, we define exactly the solution of the optimization problem as the unique intersection of a geodesic with a dual hyperplane. Furthermore, we prove analytically that the Chernoff distance amounts to calculate an equivalent but simpler Bregman divergence defined on the distribution parameters. It follows a closed-form formula for the singly-parametric distributions, or an efficient geodesic bisection search for multiparametric distributions. Finally, based on this information-geometric characterization, we propose three novel information-theoretic symmetric distances and middle distributions, from which two of them admit always closed-form expressions.
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    Cooperative Estimation of Path Loss in Interference Channels Without Primary-User CSI Feedback (2013)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2013-02-09
    Description: Knowledge of the channel path-loss between a primary user and a secondary user in underlay cognitive radio systems could be vital for deciding how a secondary user accesses a specific frequency band. The conventional way to acquire this knowledge relies on some collaboration with primary user systems. This letter proposes a novel cooperative technique for estimating the path-loss parameter in cognitive radio interference channels without the need for channel measurements from any primary user. The results reveal that, relative to existing techniques, the proposed offers improved performance.
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    Selection Combining for Differential Amplify-and-Forward Relaying Over Rayleigh-Fading Channels (2013)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2013-02-09
    Description: This letter proposes and analyses selection combining (SC) at the destination for differential amplify-and-forward (D-AF) relaying over slow Rayleigh-fading channels. The selection combiner chooses the link with the maximum magnitude of the decision variable to be used for non-coherent detection of the transmitted symbols. Therefore, in contrast to the maximum ratio combining (MRC), no channel information is needed at the destination. The exact average bit-error-rate (BER) of the proposed SC is derived and verified with simulation results. It is also shown that the performance of the SC method is very close to that of the MRC method, albeit with lower complexity.
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    Spectral Domain Speech Enhancement Using HMM State-Dependent Super-Gaussian Priors (2013)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2013-02-09
    Description: The derivation of MMSE estimators for the DFT coefficients of speech signals, given an observed noisy signal and super-Gaussian prior distributions, has received a lot of interest recently. In this letter, we look at the distribution of the periodogram coefficients of different phonemes, and show that they have a gamma distribution with shape parameters less than one. This verifies that the DFT coefficients for not only the whole speech signal but also for individual phonemes have super-Gaussian distributions. We develop a spectral domain speech enhancement algorithm, and derive hidden Markov model (HMM) based MMSE estimators for speech periodogram coefficients under this gamma assumption in both a high uniform resolution and a reduced-resolution Mel domain. The simulations show that the performance is improved using a gamma distribution compared to the exponential case. Moreover, we show that, even though beneficial in some aspects, the Mel-domain processing does not lead to better results than the algorithms in the high-resolution domain.
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    Making a “Completely Blind” Image Quality Analyzer (2013)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2013-02-02
    Description: An important aim of research on the blind image quality assessment (IQA) problem is to devise perceptual models that can predict the quality of distorted images with as little prior knowledge of the images or their distortions as possible. Current state-of-the-art “general purpose” no reference (NR) IQA algorithms require knowledge about anticipated distortions in the form of training examples and corresponding human opinion scores. However we have recently derived a blind IQA model that only makes use of measurable deviations from statistical regularities observed in natural images, without training on human-rated distorted images, and, indeed without any exposure to distorted images. Thus, it is “completely blind.” The new IQA model, which we call the Natural Image Quality Evaluator (NIQE) is based on the construction of a “quality aware” collection of statistical features based on a simple and successful space domain natural scene statistic (NSS) model. These features are derived from a corpus of natural, undistorted images. Experimental results show that the new index delivers performance comparable to top performing NR IQA models that require training on large databases of human opinions of distorted images. A software release is available at http://live.ece.utexas.edu/research/quality/niqe_release.zip .
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    Consistent Wiener Filtering for Audio Source Separation (2013)
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    Publication Date: 2013-02-02
    Description: Wiener filtering is one of the most ubiquitous tools in signal processing, in particular for signal denoising and source separation. In the context of audio, it is typically applied in the time-frequency domain by means of the short-time Fourier transform (STFT). Such processing does generally not take into account the relationship between STFT coefficients in different time-frequency bins due to the redundancy of the STFT, which we refer to as consistency. We propose to enforce this relationship in the design of the Wiener filter, either as a hard constraint or as a soft penalty. We derive two conjugate gradient algorithms for the computation of the filter coefficients and show improved audio source separation performance compared to the classical Wiener filter both in oracle and in blind conditions.
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    On Optimal Linear Filtering of Speech for Near-End Listening Enhancement (2013)
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    Publication Date: 2013-02-02
    Description: In this letter the focus is on linear filtering of speech before degradation due to additive background noise. The goal is to design the filter such that the speech intelligibility index (SII) is maximized when the speech is played back in a known noisy environment. Moreover, a power constraint is taken into account to prevent uncomfortable playback levels and deal with loudspeaker constraints. Previous methods use linear approximations of the SII in order to find a closed-form solution. However, as we show, these linear approximations introduce errors in low SNR regions and are therefore suboptimal. In this work we propose a nonlinear approximation of the SII which is accurate for all SNRs. Experiments show large intelligibility improvements with the proposed method over the unprocessed noisy speech and better performance than one state-of-the art method.
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    JPEG Steganalysis With High-Dimensional Features and Bayesian Ensemble Classifier (2013)
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    Publication Date: 2013-02-02
    Description: This work proposes a JPEG steganalytic scheme based on high-dimensional features and Bayesian ensemble classifier. The proposed scheme employs 15700 dimension features calculated from the co-occurrence matrices of DCT coefficients and coefficient differences, which indicate the intra-block and inter-block dependencies of image content. Furthermore, a number of sub-classifiers trained on the features are integrated as an ensemble classifier with a Bayesian mechanism, which is used to give optimal decisions for suspicious images. Experimental results show that both the high-dimensional features and the Bayesian mechanism contribute to the extended scheme, and the performance of the extended scheme is better than those of previous schemes.
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    Supervised Low-Rank Matrix Recovery for Traffic Sign Recognition in Image Sequences (2013)
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    Publication Date: 2013-02-02
    Description: Correlations in image sequences can be potentially useful for recovering feature representation and subsequently prompting classification performance, which are often neglected by traditional classification approaches. In this letter, we present a supervised low-rank matrix recovery model to leverage these correlations for classification tasks by introducing a supervised penalty term to the classic low-rank matrix recovery model. This allows us to not only exploit these correlations to recover the underlying feature representation from corrupted observation, but also preserve discriminative information for classification. Our model is evaluated on both real-world data and synthetic data, and experimental results show that our model obtains highly competitive performance with state-of-the-art algorithms and is especially robust to different levels of corruptions.
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    Blind Signal Estimation in Widely-Linear Signal Models With Fourth-Order Circularity: Algorithms and Application to Receiver I/Q Calibration (2013)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2013-02-02
    Description: In-phase/quadrature (I/Q) imbalance degrades heavily the image rejection performance of direct-conversion radios. I/Q imbalance also changes the statistics of the received signal, and in particular makes a circular signal non-circular. This fact has been utilized in compensating receiver I/Q imbalances, utilizing second-order statistics. In this article, we investigate whether moment circularity of order higher than two can be exploited in receiver I/Q imbalance compensation. It is established that the fourth-order moment $ {rm E}[{x^{3}}{x^{ast} }] $ is a suitable statistic for measuring the circularity of common communications signals based on complex-valued alphabets such as $M$ -QAM and $M$ -PSK with $M 〉 2$ . Two blind algorithms, based on Newton's method, are then proposed for receiver I/Q imbalance compensation. They are shown by simulations to converge faster or, alternatively, to give lower steady-state variance, than the reference methods that are based on second-order statistics.
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    Full-Image Guided Filtering for Fast Stereo Matching (2013)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2013-02-02
    Description: A novel full-image guided filtering method is proposed. Different with many existing neighborhood filters, all input elements are employed during the proposed filtering approach. In addition, a novel scheme called weight propagation is proposed to compute support weights. It fulfills the requirements of edge preserving and low complexity. It is applied to the cost-volume filtering in the local stereo matching framework. The algorithm utilizing the proposed filtering method is currently one of the best local algorithms on the Middlebury stereo testbed in terms of both speed and accuracy.
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    Improved Generalized Eigenvalue Proximal Support Vector Machine (2013)
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    Publication Date: 2013-02-02
    Description: In this letter, we propose an improved version of generalized eigenvalue proximal support vector machine (GEPSVM), called IGEPSVM for short. The main improvements are 1) the generalized eigenvalue decomposition is replaced by the standard eigenvalue decomposition, resulting in simpler optimization problems without the possible singularity. 2) An extra meaningful parameter is introduced, resulting in the stronger classification generalization ability. Experimental results on both the artificial datasets and several benchmark datasets show that our IGEPSVM is superior to GEPSVM in both computation time and classification accuracy.
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    Analysis of the Characteristic of the Kalman Gain for 1-D Chaotic Maps in Cubature Kalman Filter (2013)
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    Publication Date: 2013-02-02
    Description: The characteristic of Kalman gain in a cubature Kalman filter for filtering 1-D chaotic signals is investigated. It is shown theoretically that the Kalman gain converges to zero for the case of periodic nonlinear systems, and it either approaches the Cramér–Rao lower bound or oscillates aperiodically for the case of chaotic nonlinear systems. Results from analysis of the Kalman gain are verified by simulations of some representative nonlinear systems.
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    Bivariate Empirical Mode Decomposition for Unbalanced Real-World Signals (2013)
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    Publication Date: 2013-02-02
    Description: The bivariate empirical mode decomposition (BEMD) algorithm employs uniform sampling on a circle to perform projections in multiple directions, in order to calculate the local mean of a bivariate signal. However, this approach is adequate only for equal powers in both the data channels within a bivariate signal, and results in suboptimal performance for data channels exhibiting power imbalance, a typical case in practice. To that end, we exploit second-order bivariate statistical properties to introduce a nonuniform sampling scheme for data adaptive selection of the projection directions. In this way, the resulting nonuniformly sampled BEMD (NS-BEMD) algorithm provides a more accurate time-frequency representation of bivariate data than standard BEMD, for the same number of projections. The advantages of the proposed approach are demonstrated in case studies on BEMD for correlated data channels, selection of optimal noise power in noise-assisted BEMD, and for speed estimation using Doppler radar.
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