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  • Articles  (234)
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  • IEEE Signal Processing Letters  (234)
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  • Electrical Engineering, Measurement and Control Technology  (234)
  • 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)
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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)
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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)
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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)
    Institute of Electrical and Electronics Engineers (IEEE)
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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)
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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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    Detection of Vowel Offset Point From Speech Signal (2013)
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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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    One-Bit Quantizer Design for Multisensor GLRT Fusion (2013)
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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)
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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)
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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)
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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)
    Institute of Electrical and Electronics Engineers (IEEE)
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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)
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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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    Co-Saliency Detection Based on Hierarchical Segmentation (2013)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2013-12-07
    Description: Co-saliency detection, an emerging and interesting issue in saliency detection, aims to discover the common salient objects in a set of images. This letter proposes a hierarchical segmentation based co-saliency model. On the basis of fine segmentation, regional histograms are used to measure regional similarities between region pairs in the image set, and regional contrasts within each image are exploited to evaluate the intra-saliency of each region. On the basis of coarse segmentation, an object prior for each region is measured based on the connectivity with image borders. Finally, the global similarity of each region is derived based on regional similarity measures, and then effectively integrated with intra-saliency map and object prior map to generate the co-saliency map for each image. Experimental results on two benchmark datasets demonstrate the better co-saliency detection performance of the proposed model compared to the state-of-the-art co-saliency models.
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    Noise-Robust Texture Description Using Local Contrast Patterns via Global Measures (2013)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2013-12-14
    Description: This letter presents a noise-robust descriptor by exploring a set of local contrast patterns (LCPs) via global measures for texture classification. To handle image noise, the directed and undirected difference masks are designed to calculate three types of local intensity contrasts: directed, undirected, and maximum difference responses. To describe pixel-wise features, these responses are separately quantized and encoded into specific patterns based on different global measures. These resulting patterns (i.e., LCPs) are jointly encoded to form our final texture representation. Experiments are conducted on the well-known Outex and CUReT databases in the presence of high levels of noise. Compared to many state-of-the-art methods, the proposed descriptor achieves superior texture classification performance while enjoying a compact feature representation.
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    Posterior Cramér–Rao Bound for Anchorless Tracking (2013)
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    Publication Date: 2013-10-16
    Description: We derive the posterior Cramér–Rao bound (PCRB) for tracking in anchorless dynamic networks consisting of static nodes at unknown locations and mobile nodes. We show that this is a type of PCRB under parametric constraints where the parameter space cannot be modeled by equation constraints. We propose a novel framework for modeling the constrained parameter manifold, as a factor manifold under the actions of a group. The approach we propose is generic, and can be used to derive lower error bounds for other parameter estimation problems where the parametric constraints cannot be optimally described by equation constraints.
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    Robust Multi-Bernoulli Sensor Selection for Multi-Target Tracking in Sensor Networks (2013)
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    Publication Date: 2013-10-12
    Description: This letter addresses the sensor selection problem for tracking multiple dynamic targets within a sensor network. Since the bandwidth and energy of the sensor network are constrained, it would not be feasible to directly use the entire information of sensor nodes for detection and tracking of the targets and hence the need for sensor selection. Our sensor selection solution is formulated using the multi-Bernoulli random finite set framework. The proposed method selects a minimum subset of sensors which are most likely to provide reliable measurements. The overall scheme is a robust method that works in challenging scenarios where no prior information are available on clutter intensity or sensor detection profile. Simulation results demonstrate successful sensor selection in a challenging case where five targets move in a close vicinity to each other. Comparative results show the superior performance of our method in terms of accuracy of estimating the number of targets and their states.
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    A Shrinkage Linear Minimum Mean Square Error Estimator (2013)
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    Publication Date: 2013-10-12
    Description: The conventional linear minimum mean square error (LMMSE) estimator is commonly implemented through the sample covariance matrix. This estimator can only be implemented if the sample size $N$ is higher than the observation dimension $M$ . Moreover, this estimator performs poorly when the sample size is not sufficiently large. To address this problem, we propose a new shrinkage LMMSE estimator. The proposed estimator performs efficiently over a wide range of observation dimensions and sample sizes. In contrast to existing methods, the proposed estimator can be applied if $Mgeq N$ . Even if $M〈N$ , the proposed estimator performs more efficiently than existing estimators.
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    A Track-Before-Detect Algorithm With Thresholded Observations and Closely-Spaced Targets (2013)
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    Publication Date: 2013-10-12
    Description: In this letter, we consider the detection architecture in , where a track-before-detect processor elaborates the plot-lists provided on a scan-by-scan basis by the detector and plot-extractor of a radar system. We derive a novel track formation procedure in order to provide improved performance in the presence of multiple, closely-spaced targets. Numerical examples are provided to assess the detection capabilities and the accuracy in the estimation of the target position.
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    Hierarchized Block Wise Image Approximation by Greedy Pursuit Strategies (2013)
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    Publication Date: 2013-10-12
    Description: An approach for effective implementation of greedy selection methodologies, to approximate an image partitioned into blocks, is proposed. The method is specially designed for approximating partitions on a transformed image. It evolves by selecting, at each iteration step, i) the elements for approximating each of the blocks partitioning the image and ii) the hierarchized sequence in which the blocks are approximated to reach the required global condition on sparsity.
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    Temporal Envelope Fit of Transient Audio Signals (2013)
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    Publication Date: 2013-10-19
    Description: We address the problem of temporal envelope modeling for transient audio signals. We propose the Gamma distribution function (GDF) as a suitable candidate for modeling the envelope keeping in view some of its interesting properties such as asymmetry, causality, near-optimal time-bandwidth product, controllability of rise and decay, etc. The problem of finding the parameters of the GDF becomes a nonlinear regression problem. We overcome the hurdle by using a logarithmic envelope fit, which reduces the problem to one of linear regression. The logarithmic transformation also has the feature of dynamic range compression. Since temporal envelopes of audio signals are not uniformly distributed, in order to compute the amplitude, we investigate the importance of various loss functions for regression. Based on synthesized data experiments, wherein we have a ground truth, and real-world signals, we observe that the least-squares technique gives reasonably accurate amplitude estimates compared with other loss functions.
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    LED Backlight Adjustment for Backward-Compatible Stereoscopic Display (2013)
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    Publication Date: 2013-10-19
    Description: It was recently shown that a high-speed optoelectronic display, via a novel signal processing technique called temporal psychovisual modulation (TPVM), can exhibit stereoscopic images to viewers wearing 3-D glasses and clean 2-D images to those without glasses all at the same time. This research aims to improve the above backward-compatible stereoscopy method by adjusting the backlight of today’s common LED-lit liquid crystal display systems. Visual quality of the system can be enhanced by jointly optimizing the backlight intensity and the image signal at a negligible extra computational cost. For real-time applications of low-cost consumer electronics, this work also provides a low-complexity solution of backward-compatible stereoscopic display, in which highest 3-D quality is ensured with a small compromise of the 2-D quality.
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    Joint Action Segmentation and Classification by an Extended Hidden Markov Model (2013)
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    Publication Date: 2013-10-19
    Description: Hidden Markov models (HMMs) provide joint segmentation and classification of sequential data by efficient inference algorithms and have therefore been employed in fields as diverse as speech recognition, document processing, and genomics. However, conventional HMMs do not suit action segmentation in video due to the nature of the measurements which are often irregular in space and time, high dimensional and affected by outliers. For this reason, in this paper we present a joint action segmentation and classification approach based on an extended model: the hidden Markov model for multiple, irregular observations (HMM-MIO). Experiments performed over a concatenated version of the popular KTH action dataset and the challenging CMU multi-modal activity dataset (CMU-MMAC) report accuracies comparable to or higher than those of a bag-of-features approach, showing the usefulness of improved sequential models for joint action segmentation and classification tasks.
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    Cooperative Energy Harvesting Networks With Spatially Random Users (2013)
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    Publication Date: 2013-10-19
    Description: This letter considers a cooperative network with multiple source-destination pairs and one energy harvesting relay. The outage probability experienced by users in this network is characterized by taking the spatial randomness of user locations into consideration. In addition, the cooperation among users is modeled as a canonical coalitional game and the grand coalition is shown to be stable in the addressed scenario. Simulation results are provided to demonstrate the accuracy of the developed analytical results.
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    Application of a Minimum-Disturbance Description to Constrained Adaptive Filters (2013)
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    Publication Date: 2013-10-19
    Description: The development of adaptive filters is usually based either on a stochastic approximation of the gradient vector and the Hessian matrix, or on a deterministic minimization of quadratic a posteriori output errors. This paper investigates the design of adaptation algorithms by means of a minimum-disturbance approach together with added constraints. More than just rewriting objective functions minimized by the algorithms, the approach provides insight and extra tools for optimizing with respect to other parameters, e.g., the convergence factor $mu$ . Designing new algorithms by adding extra costs or constraints to the objective function follows naturally, whereas the main characteristics of the algorithms remain clear. Understanding subtleties that set similar algorithms apart is also made easier. We apply the method to known algorithms, such as the LMS and RLS algorithms, and also to their variants. Rather than proposing a new algorithm, we hope this article will facilitate the development of different algorithms to meet the challenges posed by demanding applications. In addition, ensuing discussions may help understanding better the behavior of each algorithm in a particular scenario.
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    Time-Reversal Wideband Communications (2013)
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    Publication Date: 2013-10-19
    Description: With the advance of semiconductor technologies, the performance of analog-to-digital converter (ADC) has been improved a lot during the past decade in terms of both sampling rate and resolution. The cost has gone down dramatically that makes the wideband communication much affordable. Under such circumstances, a natural question to ask is: Is there a low complexity high energy-efficient solution to high throughput wideband communications? In this letter, we explore this question by studying time-reversal communications. We find that compared with that of OFDM system, the computational complexity of a time-reversal system at the transmitter side is lower since it requires similar additions but no multiplications. Furthermore, the computational complexity of a time-reversal system at the receiver side is negligible since only onetap detection is performed, which means that the overall computational complexity of time-reversal system is much lower than that of OFDM system. Moreover, we find that when the bandwidth is large enough, time-reversal system can achieve much higher achievable rate than OFDM system. Therefore, time-reversal technique is a desired solution to low complexity high throughput wideband communications when more bandwidth is available.
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    Exact Reconstruction Analysis of Log-Sum Minimization for Compressed Sensing (2013)
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    Publication Date: 2013-10-19
    Description: The fact that fewer measurements are needed by log-sum minimization for sparse signal recovery than the ${ell _1}$ -minimization has been observed by extensive experiments. Nevertheless, such a benefit brought by the use of the log-sum penalty function has not been rigorously proved. This paper provides a theoretical justification for adopting the log-sum as an alternative sparsity-encouraging function. We prove that minimizing the log-sum penalty function subject to $Az = y$ is able to yield the exact solution, provided that a certain condition is satisfied. Specifically, our analysis suggests that, for a properly chosen regularization parameter, exact reconstruction can be attained when the restricted isometry constant ${delta _{3K}}$ is smaller than one, which presents a less restrictive isometry condition than that required by the conventional ${ell _1}$ -type methods.
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    A Two-Stage MMSE Beamformer for Underdetermined Signal Separation (2013)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2013-10-19
    Description: Blind separation of underdetermined instantaneous mixtures is a popular solution to inverse problems encountered in audio or biomedical applications where the number of sources exceeds the number of sensors. There are two non-equivalent tasks: to identify the mixing matrix and to separate the original sources. In this paper, we focus on the latter task by proposing a novel beamformer that minimizes the theoretical mean square error distance between the separated and original signals. The beamformer has two stages: one for the estimation of signals and one for their refinement. Within the former stage, the signals are assumed to be random and locally stationary, while the latter stage is based on a semi-deterministic model. The experiments prove superior performance of the proposed method compared to conventional MMSE beamforming.
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    Enhancing Frequency Shifted Speech Signals in Single Side-Band Communication (2013)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2013-10-19
    Description: The spectral quality of speech signals communicated over high-frequency single side band (HF-SSB) radio channels is affected by acoustic artifacts like linear frequency transpositions. In this letter, we propose an approach to automatically estimate and correct for the frequency shift given the degraded signal at the SSB receiver. The proposed method utilizes the harmonic nature of the speech signal in the voiced regions. The fundamental harmonic frequency, obtained from an autoregressive model of the spectrum, is used to estimate the offset value for the current frame. The offset values from the adjacent frames are pooled together to provide the most likely estimate for the received signal. Various experiments are performed on frequency shifted degraded speech signals received from the HF-SSB channel. The enhanced speech signal is also applied for a language identification task (LID) where the models are trained on speech data without any frequency shift. In these experiments, the proposed algorithm provides significant improvements over other baseline offset estimation methods in terms of accuracy of offset estimation as well as the LID system performance (with relative improvements of about 10-25%).
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    On Computing Amplitude, Phase, and Frequency Modulations Using a Vector Interpretation of the Analytic Signal (2013)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2013-10-19
    Description: The amplitude-modulation (AM) and phase-modulation (PM) of an amplitude-modulated frequency-modulated (AM-FM) signal are defined as the modulus and phase angle, respectively, of the analytic signal (AS). The FM is defined as the derivative of the PM. However, this standard definition results in a PM with jump discontinuities in cases when the AM index exceeds unity, resulting in an FM that contains impulses. We propose a new approach to define smooth AM, PM, and FM for the AS, where the PM is computed as the solution to an optimization problem based on a vector interpretation of the AS. Our approach is directly linked to the fractional Hilbert transform (FrHT) and leads to an eigenvalue problem. The resulting PM and AM are shown to be smooth, and in particular, the AM turns out to be bipolar. We show an equivalence of the eigenvalue formulation to the square of the AS, and arrive at a simple method to compute the smooth PM. Some examples on synthesized and real signals are provided to validate the theoretical calculations.
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    Dictionary Learning for Sparse Representation: A Novel Approach (2013)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2013-10-19
    Description: A dictionary learning problem is a matrix factorization in which the goal is to factorize a training data matrix, ${bf Y}$ , as the product of a dictionary, ${bf D}$ , and a sparse coefficient matrix, ${bf X}$ , as follows, ${bf Y} simeq {bf DX}$ . Current dictionary learning algorithms minimize the representation error subject to a constraint on ${bf D}$ (usually having unit column-norms) and sparseness of ${bf X}$ . The resulting problem is not convex with respect to the pair $({bf D},{bf X})$ . In this letter, we derive a first order series expansion formula for the factorization, ${bf DX}$ . The resulting objective function is jointly convex with respect to ${bf D}$ and ${bf X}$ . We simply solve the resulting problem using alternating minimization and apply some of the previously suggested algorithms onto our new problem. Simulation results on recovery of a known dictionary and dictionary learning for natural image patches show that our new problem considerably improves performance with a little additional computational load.
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    An Efficient HMM-Based Feature Enhancement Method With Filter Estimation for Reverberant Speech Recognition (2013)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2013-10-19
    Description: This letter presents an efficient feature enhancement method for reverberant speech recognition that derives a minimum mean square error estimate of clean logarithmic mel-frequency power spectral coefficients (LMPSCs) based on a hidden-Markov-model(HMM) prior. Although an observation model of the reverberant LMPSCs can be simply formulated by coarse modeling of the room impulse response (RIR) , the presented method estimates not only the clean LMPSCs but also the RIR to reflect detailed reverberation. The experimental results indicate that the described method can further reduce relative word error rate (WER) by 18.09% on average compared to a method based on RIR coarse modeling.
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    Recurrent Leaky Bucket (2013)
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    Publication Date: 2013-10-26
    Description: In this letter, we propose an extension of the conventional leaky bucket (LB) algorithm which we call recurrent leaky bucket (RLB). It is suited for traffic characterized by a recurring cycle, such as variable bit-rate video. RLB extends LB by including a new parameter to describe the recurring cycle: the recurring interval. We show the correctness of our design where we illustrate its impulse response and traffic envelope. The improved efficiency of RLB is also numerically demonstrated.
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    Optimization of Bilateral Filter Parameters via Chi-Square Unbiased Risk Estimate (2013)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2013-12-14
    Description: Bilateral filter is an edge-preserving diffusion filter and is widely used for image denoising. The denoising quality is sensitive to the choice of the bilateral filter parameters. Here, we propose to use the Chi-Square Unbiased Risk Estimate (CURE) for optimizing bilateral filter parameters in squared magnitude MR images, which follow a non-central chi-square distribution with two degrees of freedom. CURE is an unbiased estimate of the Mean Squared Error (MSE) and it does not require any knowledge of the noise-free data. We derive explicit analytical expressions for CURE in the setting of bilateral filters and present experimental results that confirm the optimality of CURE.
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    Learning Boundary and Appearance for Video Object Cutout (2013)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2013-12-14
    Description: This letter presents an approach to object cutout for arbitrary videos. Our approach is in a “learning and propagating” way. First, the object(s) of interest is (are) cut out in the first frame (or the key frame) in the video clip using some interactive image segmentation tool. Then some statistical features of the object regions, the non-object regions, and the boundaries are learnt from the result of image segmentation. Finally, the result is propagated to the other frames automatically. We formulate the “learning and propagating” step in Markov random fields (MRFs). In this model, we well design the patch term and the boundary term to significantly improve the performance of the algorithm. Experimental results indicate that the algorithm performs excellent.
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    Achieving Perfect Secrecy by pdf-Bandlimited Jamming (2013)
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    Publication Date: 2013-11-30
    Description: Nowadays, the most investigated concepts of physical layer security encompass asymptotic criteria of secrecy. Taking a different perspective, in this work we come back to Shannon’s original formulation of perfect secrecy, amounting to impose exactly zero mutual information between the source message and the data gathered by the eavesdropper. By jamming the intruder with a special class of noise—that we call pdf-bandlimited—and adopting a novel “out of the (pdf) band” information encoding technique, it is shown that a perfectly secure communication can be in fact sustained over the main channel, leaving no chances to the eavesdropper.
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    Matrix-Based Ramanujan-Sums Transforms (2013)
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    Publication Date: 2013-07-27
    Description: In this letter, we study the Ramanujan Sums (RS) transform by means of matrix multiplication. The RS are orthogonal in nature and therefore offer excellent energy conservation capability. The 1-D and 2-D forward RS transforms are easy to calculate, but their inverse transforms are not defined in the literature for non-even function $ ({rm mod}~ {rm M}) $ . We solved this problem by using matrix multiplication in this letter.
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    A Reduced-Complexity Partial-Interference-Cancellation Group Decoder for STBCs (2013)
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    Publication Date: 2013-07-27
    Description: In this letter, we propose a reduced-complexity implementation of partial interference cancellation group decoder with successive interference cancellation (PIC-GD-SIC) by employing the theory of displacement structures. The proposed algorithm exploits the block-Toeplitz structure of the effective matrix and chooses an ordering of the groups such that the zero-forcing matrices associated with the various groups are obtained through Schur recursions without any approximations. We show using an example that the proposed implementation offers a significantly reduced computational complexity compared to the direct approach without any loss in performance.
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    Interval-Passing Algorithm for Chemical Mixture Estimation (2013)
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    Publication Date: 2013-07-03
    Description: In this letter, we propose a compressive sensing scheme for the mixture estimation problem in spectroscopy. We show that by applying an appropriate measurement matrix on the chemical mixture spectrum, we obtain an overall measurement matrix which is sparse. This enables the use of a low-complexity iterative reconstruction algorithm, called the interval-passing algorithm, to estimate the concentration of each chemical present in the mixture. Simulation results for the proportion of correct reconstructions show that chemical mixtures with a large number of chemicals present can be recovered.
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    Extracting Colored Noise Statistics in Time Series via Negentropy (2013)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2013-07-03
    Description: In the analysis of some specific time series (e.g., Global Positioning System coordinate time series, chaotic time series, human brain imaging), the noise is generally modeled as a sum of a power-law noise and white noise. Some existing softwares estimate the amplitude of the noise components using convex optimization (e.g., Levenberg-Marquadt) applied to a log-likelihood cost function. This work studies a novel cost function based on an approximation of the negentropy. Restricting the study to simulated time series with flicker noise plus white noise, we demonstrate that this cost function is convex. Then, we show thanks to numerical approximations that it is possible to obtain an accurate estimate of the amplitude of the colored noise for various lengths of the time series as long as the ratio between the colored noise amplitude and the white noise is smaller than 0.6. The results demonstrate that with our proposed cost function we can improve the accuracy by around 5% when compared with the log-likelihood ones with simulated time series shorter than 1400 samples.
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    New Insights Into Optimal Acoustic Feedback Cancellation (2013)
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    Publication Date: 2013-07-13
    Description: In this letter, we present new insights into the bias problem for acoustic feedback cancellation when a probe signal approach is used. The optimum solution of the feedback canceler is not the feedback path but the product of the feedback path and the sensitivity function and hence, the solution is biased. The novelty of this paper also consists of the derivation of the conditions for unbiased feedback cancellation when a probe signal is used as input to the canceler. An adequate delay in the forward path is necessary to reduce, or remove the bias term. The theoretical analysis is verified with simulation results.
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    Estimating Visual Saliency Through Single Image Optimization (2013)
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    Publication Date: 2013-07-03
    Description: This letter presents a novel approach for visual saliency estimation through single image optimization. Instead of directly mapping visual features to saliency values with a unified model, we treat regional saliency values as the optimization objective on each single image. By using a quadratic programming framework, our approach can adaptively optimize the regional saliency values on each specific image to simultaneously meet multiple saliency hypotheses on visual rarity, center-bias and mutual correlation. Experimental results show that our approach can outperform 14 state-of-the-art approaches on a public image benchmark.
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    One-Bit Decentralized Detection With a Rao Test for Multisensor Fusion (2013)
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    Publication Date: 2013-07-10
    Description: In this letter, we propose the Rao test as a simpler alternative to the generalized likelihood ratio test (GLRT) for multisensor fusion. We consider sensors observing an unknown deterministic parameter with symmetric and unimodal noise. A decision fusion center (DFC) receives quantized sensor observations through error-prone binary symmetric channels and makes a global decision. We analyze the optimal quantizer thresholds and we study the performance of the Rao test in comparison to the GLRT. Also, a theoretical comparison is made and asymptotic performance is derived in a scenario with homogeneous sensors. All the results are confirmed through simulations.
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    An Improved Auto-Calibration Algorithm Based on Sparse Bayesian Learning Framework (2013)
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    Publication Date: 2013-07-17
    Description: This letter considers the multiplicative perturbation problem in compressive sensing, which has become an increasingly important issue on obtaining robust performance for practical applications. The problem is formulated in a probabilistic model and an auto-calibration sparse Bayesian learning algorithm is proposed. In this algorithm, signal and perturbation are iteratively estimated to achieve sparsity by leveraging a variational Bayesian expectation maximization technique. Results from numerical experiments have demonstrated that the proposed algorithm has achieved improvements on the accuracy of signal reconstruction.
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    A Study on Visible to Infrared Action Recognition (2013)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2013-07-20
    Description: Human action recognition is important in image and video processing with many applications. With the development of sensor technology, different cameras can be used for action acquisition, e.g., infrared cameras. Is it possible to adapt the visible light action recognizers to a new modality or domain? In this paper, we study the feasibility to adapt the action recognizer learned from visible light spectrum to infrared. A preliminary result is obtained on a large database based on an adaptive learning method, demonstrating the potential to perform cross-spectral action recognition.
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    Using Polynomial Kernel Support Vector Machines for Speaker Verification (2013)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2013-07-20
    Description: In this letter, we propose a discriminative modeling approach for the speaker verification problem that uses polynomial kernel support vector machines (PK-SVMs). The proposed approach is rooted in an equivalence relationship between the state-of-the-art probabilistic linear discriminant analysis (PLDA) and second degree polynomial kernel methods. We present two techniques for overcoming the memory and computational challenges that PK-SVMs pose. The first of these, a kernel evaluation simplification trick, eliminates the need to explicitly compute dot products for a huge number of training samples. The second technique makes use of the massively parallel processing power of modern graphical processing units. We performed experiments on the Phase I speaker verification track of the DARPA sponsored Robust Automatic Transcription of Speech (RATS) program. We found that, in the multi-session enrollment experiments, second degree PK-SVMs outperformed PLDA across all tasks in terms of the official evaluation metric, and third and fourth degree PK-SVMs provided a performance improvement over the second degree PK-SVMs. Furthermore, for the “30s-30s” task, a linear score combination between the PLDA and PK-SVM based systems provided 27% improvement relative to the PLDA baseline in terms of the official evaluation metric.
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    Joint Design of the Receive Filter and Transmit Sequence for Active Sensing (2013)
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    Publication Date: 2013-03-19
    Description: Due to its long-standing importance, the problem of designing the receive filter and transmit sequence for clutter/interference rejection in active sensing has been studied widely in the last decades. In this letter, we propose a cyclic optimization of the transmit sequence and the receive filter. The proposed approach can handle arbitrary peak-to-average-power ratio (PAR) constraints on the transmit sequence, and can be used for large dimension designs (with ${sim}10^3$ variables) even on an ordinary PC.
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    Efficient Multidimensional Harmonic Retrieval: A Hierarchical Signal Separation Framework (2013)
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    Publication Date: 2013-03-19
    Description: This paper presents a low-complexity one-dimensional (1-D) Unitary Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT)-based algorithm for multidimensional harmonic retrieval (MHR) problems based on an HIerarchical Signal Separation (HISS) technique, which interleaves the parameter estimation and filtering processes. The filtering process not only progressively partitions the signals with close parameters into separate groups, but also reduces the power of the additive noise, both of which entail higher parameter estimation accuracy. The pairing of the estimated parameters is also automatically achieved. Simulations show that the new algorithm provides satisfactory performance compared with previous works but with drastically reduced computations.
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