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A rectangular-window sidelobe magnitude reduction method by widening the main lobe width was proposed by Saramäki. A technique for trading off main lobe width against sidelobe magnitude for any arbitrary window was reported by Lim et al.; a fast convergence algorithm for its implementation was proposed by the same authors in another article, where the derivatives of the window function are expressed in Chebyshev polynomials which have high arithmetic complexity. All the coefficients of a rectangular-window are equal; this special property is exploited, in this paper, for deriving the window function’s derivatives without the use of Chebyshev polynomials resulting in a great reduction in the arithmetic complexity. [ABSTRACT FROM AUTHOR]

During vehicle driving, all aspects of data monitorings are not accurate enough for the vehicle, and there may be packet loss of measurement data. In addition, the accuracy of vehicle data is more difficult to guarantee when the vehicle state is continuously changing, which may lead to some potential safety hazards during driving. Consequently, many algorithms, which only use the statistical characteristics of packet loss information, have been proposed to improve the accuracy. However, with the rapid development of technology, the time-stamp technique in sensor networks can obtain packet loss information at the current moment. In contrast, although the time-stamp technique can effectively improve the filter performance, it cannot analyze the convergence of the Riccati equation. Therefore, this paper proposes a modified EKF algorithm for balancing these two algorithms, and meanwhile, simulation experiments test and verify the effectiveness and feasibility of our proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper describesa computationally-efficient optimization algorithm for the blind source separation (BSS) of overdetermined mixtures. In the determined case, a matrix-inversion-free iterative source steering (ISS) algorithm has been proposed for estimating a square demixing matrix as a computationally-efficient alternative to the popular iterative projection (IP) algorithm. The IP algorithm is based on source-wise (i.e., row-wise) updates of the demixing matrix, and lends itself naturally to an extension to overdetermined independent vector analysis (IVA) called OverIVA. In contrast, the ISS algorithm changes the whole demixing matrix at every update, making its extension to the overdetermined case non-trivial. In this paper, we propose a modified ISS algorithm for OverIVA fully exploiting the computational savings of ISS. We also derive an overdetermined extension of independent low-rank matrix analysis (OverILRMA) with the modified ISS algorithm. Experimental results showed that the proposed ISS-based OverIVA and OverILRMA were comparable or superior to the conventional IP-based counterparts in speech separation performance while achieving lower computational cost. [ABSTRACT FROM AUTHOR]

The performance of wideband radar detectors can be further improved if the features of a target's high-resolution range profile (HRRP) are fully utilized. In this paper, a target adaptive double threshold detection method is proposed. First, the amplitude distribution parameters of the HRRP's range cells are extracted for data generation, and the detection probability is statistically calculated. The online gradient descent method is then used to adaptively adjust the second thresholds of the double threshold detector, thereby improving the detection performance with a given constant false alarm rate. The effectiveness of the proposed algorithm is verified using two sets of measured data. The proposed algorithm exhibits superior detection performance compared to traditional detection algorithms for range-spread targets. [ABSTRACT FROM AUTHOR]

Deep learning-based intelligent electrocardiogram (ECG) diagnosis algorithms heavily rely on large annotated datasets. Unfortunately, in the context of ECG diagnosis, privacy issues and the high cost of data annotations lead to a shortage of ECG datasets which severely limits the performance of the state-of-the-art ECG diagnosis algorithms. In this paper, we propose a novel instance-level contrastive learning scheme for ECG signals, namely CLECG, to mine effective information from unlabeled data. During the pre-training, CLECG encourages the representations of different augmented views of the same signal (positive samples) to be similar and increases the distance between representations of augmented views from the different signals (negative samples). The whole pre-training process does not require any form of labeling. Experimental results show that the proposed CLECG strategy outperforms other self-supervised methods and supervised transfer learning strategies. [ABSTRACT FROM AUTHOR]

Challenging due to the absence of color information and low resolution, infrared pedestrian tracking is still of great importance in video surveillance for its ability to distinguish and track persons in night vision. Recent researches show that the CF (Correlation Filters) based methods have a satisfying performance on the infrared pedestrian tracking task when combined with deep features. However, most of these algorithms only rely on the general convolutional features and online model updating to trace object variation, resulting in the lost track of pedestrians with large deformation and infrequent poses. In this paper, we propose a long-term inter-frame graph memory feature as a stronger descriptor for infrared pedestrians. We show that this cross-frame memory graph can enrich deep features with time-variant pedestrian appearance information, boosting the robustness and accuracy of the baseline CF tracker. Extensive experiments and analyses are carried out on the widely-used PTB-TIR dataset, demonstrating the effectiveness and universality of the proposed graph memory features for infrared pedestrians. [ABSTRACT FROM AUTHOR]

A short train of equidistant samples rarely suffices to compute a reasonable approximation to a continuous-time signal. Hence, there arises a need for signal reconstruction methods that exploit multiple trains of samples. If the starting times of the trains are known, then stroboscopic techniques can be used for signal reconstruction. If these times are not given, i.e., in the case of asynchronous trains of samples, it is still possible to profit from multiple trains provided that the starting times are uniformly random when considered modulo the period of the signal. For such a scenario, it is known that an infinite set of sample trains allows for reconstruction of the signal up to a time shift provided that the sampling period is small enough and that the derivative of the signal satisfies some regularity conditions. In this paper, we generalize that discovery by showing that the regularity conditions concerning the derivative of the signal can be dropped. The reconstruction of the signal is still possible even if the derivative of the signal does not exist. We also show how to estimate the signal from a finite set of asynchronous trains of samples. Eventually, we prove that the proposed signal estimator is consistent. [ABSTRACT FROM AUTHOR]

After revealing that both late reverberation and noise are additive interference components in the residual domain, this paper proposes to suppress these additive interference components by using a constrained minimum mean square error linear prediction (LP) residual estimator, where the optimal filter can be obtained by the generalized singular value decomposition. We propose to estimate the LP residuals for both late reverberation and noise continuously, which is based on the non-VAD related noise power spectral density estimator and the incessant late reverberant spectral variance estimator. The non-intrusive objective measure and the PESQ show that the proposed algorithm is better than traditional LP residual-based algorithms and spectral subtraction-based algorithms. [ABSTRACT FROM AUTHOR]

It is well-known that the multiple signal classification (MUSIC) algorithm is computationally time-consuming because it requires a complex-valued full-dimension eigenvalue decomposition (EVD) and a complex-valued spectral searching. In this paper, we exploit the virtual signal model of forward/backward average of array covariance matrix (FBACM) to show that its real part (R-FBACM) is a real symmetric matrix. Based on that, we prove that by evaluating two half-dimension EVD after an orthogonally similar transformation performed on the estimated R-FBACM, we are able to reconstruct the original eigenspace whereas the maximum number of estimated sources is reduced as compared to the upper limit $\mathit{M}-\text{1}$ for original MUSIC. Numerical results show that the proposed method provides satisfactory estimation accuracy and improved resolution with reduced complexity. [ABSTRACT FROM AUTHOR]

In this paper we provide a theoretical and numerical comparison of convergence rates of forward-backward, Douglas-Rachford, and Peaceman-Rachford algorithms for minimizing the sum of a convex proper lower semicontinuous function and a strongly convex differentiable function with Lipschitz continuous gradient. Our results extend the comparison made in [1], when both functions are smooth, to the context where only one is assumed differentiable. Optimal step-sizes and rates of the three algorithms are compared theoretically and numerically in the context of texture segmentation problem, obtaining very sharp estimations and illustrating the high efficiency of Peaceman-Rachford splitting. [ABSTRACT FROM AUTHOR]

Independent component analysis is intended to recover the mutually independent components from their linear mixtures. This technique has been widely used in many fields, such as data analysis, signal processing, and machine learning. To alleviate the dependency on prior knowledge concerning unknown sources, many nonparametric methods have been proposed. In this paper, we present a novel boosting-based algorithm for independent component analysis. Our algorithm consists of maximizing likelihood estimation via boosting and seeking unmixing matrix by the fixed-point method. A variety of experiments validate its performance compared with many of the presently known algorithms. [ABSTRACT FROM AUTHOR]

Correntropy has been successfully used for signal processing in a large variety of applications. Usually, correntropy estimator uses a Gaussian kernel, which leads to a measure that takes into account all even order statistical moments of the underlying signal. In this paper we analyze correntropy implemented with the Epanechnikov kernel, which is given by a second order polynomial. Considering an equalization scenario, we compare such criterion with the one obtained through the use of the Gaussian kernel and also the Correlation Retrieval Criterion. We discuss similarities and differences, theoretically and through simulations, between the three criteria. [ABSTRACT FROM AUTHOR]

The step-size in well-known variable step-size least mean square (VSSLMS) is updated as μn+1 = αμn + γen2 with , γ > 0, and μn+1 is set to μmin or μmax when it falls below or above these lower and upper bounds, respectively. It provides fast convergence at early stages of adaptation while ensuring small steady-state misalignment. This paper considers the steady-state performance of the VSSLMS in non-Gaussian noise environments. The contribution of the paper to the VSSLMS is threefold; (1) when γ ≪ 1 - α, the VSSLMS has low steady-state misalignment. (2) when α ≪ 1, the VSSLMS achieves different steady-state misalignments for different noise distributions. (3) In theory, there are different optimal values α for different noise distributions, i.e., 0.17 (Gaussian distribution), 0.21 (Student distribution), 0.38 (Laplace distribution), 0 (Binary and Uniform distributions). Analytical results are compared with simulations and are shown to agree well. [ABSTRACT FROM AUTHOR]

The selective fixed-filter active noise control (SFANC) method selecting the best pre-trained control filters for various types of noise can achieve a fast response time. However, it may lead to large steady-state errors due to inaccurate filter selection and the lack of adaptability. In comparison, the filtered-X normalized least-mean-square (FxNLMS) algorithm can obtain lower steady-state errors through adaptive optimization. Nonetheless, its slow convergence has a detrimental effect on dynamic noise attenuation. Therefore, this paper proposes a hybrid SFANC-FxNLMS approach to overcome the adaptive algorithm’s slow convergence and provide a better noise reduction level than the SFANC method. A lightweight one-dimensional convolutional neural network (1D CNN) is designed to automatically select the most suitable pre-trained control filter for each frame of the primary noise. Meanwhile, the FxNLMS algorithm continues to update the coefficients of the chosen pre-trained control filter at the sampling rate. Owing to the effective combination of the two algorithms, experimental results show that the hybrid SFANC-FxNLMS algorithm can achieve a rapid response time, a low noise reduction error, and a high degree of robustness. [ABSTRACT FROM AUTHOR]

This paper studies the performance of OMP in comparison to Thresholding in the case in which only a perturbed version of the generating dictionary is known. Both theory and numerical simulations show that simple Thresholding is a viable alternative to OMP in applications where only a perturbed version of the generating dictionary is given, such as dictionary learning. [ABSTRACT FROM AUTHOR]

In this letter, the problem of non-coherent direction of arrival (DOA) estimation is investigated exploiting the sign of the measurements in order to resolve the inherent ambiguity of the problem. Although the phase values are inaccurate, the sign of real and imaginary parts of the measurements will most likely remain correct under limited phase errors. Furthermore, a new approach for solving the problem is proposed employing a modified version of the Majorization-Minimization (MM) technique, without any prior information about the number of incident signals. Some theoretical analyses of our proposed algorithm are also provided in the paper. Finally, the simulation results are presented, demonstrating the efficiency of the proposed algorithm in comparison to some state-of-the-art methods in the literature. [ABSTRACT FROM AUTHOR]

Person re-identification is to match the same person between non-overlapping cameras. This paper focuses on unsupervised video-based person re-identification. The mainstream approach is to obtain pseudo-labels by clustering samples for training the classification model. In this scheme, a potential threat is that noisy pseudo-labels may damage the optimization of the model. To mitigate this danger, we propose using a Successive Consensus Clustering framework for optimizing the pseudo-labels and the model iteratively. First, we leverage consensus clustering with respect to multiple frames of a video, which can generate high-quality pseudo-labels for pedestrians. Secondly, we develop contrastive learning based on the cluster successive memory mechanism, which can establish the correlation between different epochs of clustering so that makes the training of the model stable. Experiments on three large-scale data sets show that our method outperforms the previous state-of-the-art method, surpassing 10.6% for rank-1 and 18.6% for mAP on Mars, and 9.6% for rank-1 and 13.3% for mAP on DukeMTMC-VideoReID. [ABSTRACT FROM AUTHOR]

This paper addresses the target localization problem using time-of-arrival (TOA)-based technique under the non-line-of-sight (NLOS) environment. To alleviate the adverse effect of the NLOS error on localization, a total least square framework integrated with a regularization term (RTLS) is utilized, and with which the localization problem can get rid of the ill-posed issue. However, it is challenging to figure out the exact solution for the considered localization problem. In this case, we convert the RTLS problem into a semidefinite program (SDP), and then obtain the solution of the original problem by solving a generalized trust region subproblem (GTRS). The proposed method has a relatively good robustness in localization even under the circumstance that the prior knowledge of the NLOS links or its distribution does not know. The outperformance of the proposed method is demonstrated in the simulations compared with other state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

In this letter, we adapt the geodesic distance-based recursive filter to the sparse data interpolation problem. The proposed technique is general and can be easily applied to any kind of sparse data. We demonstrate its superiority over other interpolation techniques in three experiments for qualitative and quantitative evaluation. In addition, we compare our method with the popular interpolation algorithm presented in the paper on EpicFlow optical flow, which is intuitively motivated by a similar geodesic distance principle. The comparison shows that our algorithm is more accurate and considerably faster than the EpicFlow interpolation technique. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a novel and robust approach to quantized matrix completion. First, we propose a rank minimization problem with constraints induced by quantization bounds. Next, we form an unconstrained optimization problem by regularizing the rank function with Huber loss. Huber loss is leveraged to control the violation from quantization bounds due to two properties: first, it is differentiable; and second, it is less sensitive to outliers than the quadratic loss. A smooth rank approximation is utilized to endorse lower rank on the genuine data matrix. Thus, an unconstrained optimization problem with differentiable objective function is obtained allowing us to advantage from gradient descent technique. Novel and firm theoretical analysis of the problem model and convergence of our algorithm to the global solution are provided. Another contribution of this letter is that our method does not require projections or initial rank estimation, unlike the state-of-the-art. In the Numerical Experiments section, the noticeable outperformance of our proposed method in learning accuracy and computational complexity compared to those of the state-of-the-art literature methods is illustrated as the main contribution. [ABSTRACT FROM AUTHOR]

In this paper, we describe a new algorithm to build a few sparse principal components from a given data matrix. Our approach does not explicitly create the covariance matrix of the data and can be viewed as an extension of the Kogbetliantz algorithm to build an approximate singular value decomposition for a few principal components. We show the performance of the proposed algorithm to recover sparse principal components on various datasets from the literature and perform dimensionality reduction for classification applications. [ABSTRACT FROM AUTHOR]

Whenever an audio device introduces unwanted nonlinear distortions into the manipulated signal, finding a tractable system to approximately model and estimate such degradations can be instrumental to recover the undistorted audio. This paper approaches such blind estimation task (for which classical identification tools are unsuitable) by bringing into the Bayesian framework a Hammerstein system model: the cascade of a static memoryless nonlinearity with a memory-inducing linear filter, which has been shown to be effective in describing many real systems. By assuming the underlying clean audio signal is autoregressive in short sections, the proposed method identifies the distorting system by simulating, in a Markov-Chain Monte Carlo context, the posterior distribution of the model parameters conditioned on the distorted signal. To deal with the resulting non-standard posterior distribution, a combination of the Metropolis-Hastings (MH) algorithm and the Gibbs Sampling is adopted. MH proposals are based on the Laplace approximation of the posterior distribution thanks to its almost Gaussian shape around modes. A heuristic that forces a broad region of the parameter space to be visited on an occasional basis prevents the Markov Chain from getting stuck around local maxima. A series of experiments with artificially distorted music recordings attests the effectiveness of the proposed algorithm. [ABSTRACT FROM PUBLISHER]

It is often desirable to find the underlying trends in time series data. This is a well known signal processing problem that has many applications in areas such as financial data analysis, climatology, biological and medical sciences. Mean filtering finds a piece-wise constant trend in the data while trend filtering finds a piece-wise linear trend. When the signal is noisy, the main difficulty is finding the changing points in the data that mark the transition points when the mean or the trend changes. Previously proposed methods based on \ell _1 filtering suffer from the occurrence of false changing points in the estimate. This is known as the stair-case effect. The main contribution in this paper is incorporating a technique to remove these false changing points to a fast mean filtering algorithm, referred to as the taut-string method, resulting in an efficient procedure with accurate change point detection and thus the removal of the stair-case effect. [ABSTRACT FROM PUBLISHER]

Establishing reliable correspondences from a putative correspondence set is a challenging task. Most of state-of-the-art methods utilize the local context and global context to address the task. However, the local and global context often contains large number of outliers, which have a negative impact on capturing scene geometry. In this paper, we propose a Multi-scale Attention Network (called MANet), which introduces the attention mechanism for feature matching, to improve the ability of capturing scene geometry. Specifically, we first fuse the features of low and high levels by an multi-scale strategy network to enhance the representative ability of features. Then, we propose an attentive PointCN block and an attentive pooling layer, to discriminatively capture global context and local context information, respectively. We demonstrate through extensive experiments on both indoor and outdoor datasets that MANet provides a significant improvement in the performance of the two-view geometry and correspondences accuracy compared to the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Frames with a large minimum angle between any two distinct frame vectors are desirable in many present day applications. For a unit norm frame, the absolute value of the cosine of the minimum frame angle is also known as coherence. Two frames are equivalent if one can be obtained from the other via left action of an invertible linear operator. Frame angles can change under the action of a linear operator. Most of the existing works solve different optimization problems to find an optimal linear operator that maximizes the minimal frame angle (in other words, minimizes the coherence). In the present work, nevertheless, we consider the question: Is it always possible to find an equivalent frame with smaller coherence for a given frame?. In this paper, we derive properties of the initial unit norm frame that can ensure an equivalent frame with strictly larger minimal frame angle compared to the initial one. It turns out that the nullspace property of a certain matrix obtained from the initial frame can guarantee such an equivalent frame. We also present the numerical results that support our theoretical claims. [ABSTRACT FROM AUTHOR]

In this letter, we present a novel method of solving the Generalized Waterfilling problem (GWFP) using equivalence with a waterfilling problem (WFP) which has a closed form solution. Consequentially, a class of WFPs with only one-step called ‘1-step’ WFP is used to derive a low complexity algorithm which has the smallest worst case complexity reported. The proposed algorithm is verified by simulations. [ABSTRACT FROM PUBLISHER]

Algorithms for signal denoising that combine wavelet-domain sparsity and total variation (TV) regularization are relatively free of artifacts, such as pseudo-Gibbs oscillations, normally introduced by pure wavelet thresholding. This paper formulates wavelet-TV (WATV) denoising as a unified problem. To strongly induce wavelet sparsity, the proposed approach uses non-convex penalty functions. At the same time, in order to draw on the advantages of convex optimization (unique minimum, reliable algorithms, simplified regularization parameter selection), the non-convex penalties are chosen so as to ensure the convexity of the total objective function. A computationally efficient, fast converging algorithm is derived. [ABSTRACT FROM AUTHOR]

Performance guarantees for the algorithms deployed to solve underdetermined linear systems with sparse solutions are based on the assumption that the involved system matrix has the form of an incoherent unit norm tight frame. Learned dictionaries, which are popular in sparse representations, often do not meet the necessary conditions for signal recovery. In compressed sensing (CS), recovery rates have been improved substantially with optimized projections; however, these techniques do not produce binary matrices, which are more suitable for hardware implementation. In this paper, we consider an underdetermined linear system with sparse solutions and propose a preconditioning technique that yields a system matrix having the properties of an incoherent unit norm tight frame. While existing work in preconditioning concerns greedy algorithms, the proposed technique is based on recent theoretical results for standard numerical solvers such as BP and OMP. Our simulations show that the proposed preconditioning improves the recovery rates both in sparse representations and CS; the results for CS are comparable to optimized projections. [ABSTRACT FROM AUTHOR]

Pitch detection is a fundamental problem in speech processing as F0 is used in a large number of applications. Recent papers have proposed deep learning for robust pitch tracking. In this letter, we consider voicing detection as a classification problem and F0 contour estimation as a regression problem. For both tasks, acoustic features from multiple domains and traditional machine learning methods are used. The discrimination power of existing and proposed features is assessed through mutual information. Multiple supervised and unsupervised approaches are compared. A significant relative reduction of voicing errors over the best baseline is obtained—20% with the best clustering method (K-means) and 45% with a multi-layer perceptron. For F0 contour estimation, the benefits of regression techniques are limited though. We investigate whether those objective gains translate in a parametric synthesis task. Clear perceptual preferences are observed for the proposed approach over two widely used baselines (robust algorithm for pitch tracking (RAPT) and distributed inline-filter operation (DIO)). [ABSTRACT FROM AUTHOR]

Energy-efficient resource allocation in a single-user, amplify-and-forward (AF), relay-assisted, multiple-input-multiple-output (MIMO) system is considered in this paper. Previous results in this area assume that perfect CSI is available for at least one of the source-relay and relay-destination channels. Instead, the case in which statistical CSI is available for both the source-relay and relay-destination channel is tackled in this letter. Using fractional programming theory and the alternating maximization algorithm, low-complexity source and relay precoding is performed, subject to quality-of-service (QoS) and power constraints. [ABSTRACT FROM AUTHOR]

Practically, in the underdetermined model Y = AX, where X is a K-group sparse matrix (i.e., it has no more than K nonzero rows), both Y and A could be totally perturbed. In this paper, based on restricted isometry property, for the greedy block coordinate descent algorithm, a sufficient condition of exact recovery is presented under the total perturbations, to guarantee that the support of the sparse matrix X is recovered exactly. It is pointed out that there exists some case satisfying our condition, but not the mutual coherence condition. We also discuss the upper bound of our sufficient condition. [ABSTRACT FROM AUTHOR]

Matrices are often required to be well-conditioned in a wide variety of areas including signal processing. Problems to find the nearest positive definite matrix or the nearest correlation matrix that simultaneously satisfy the condition number constraint and sign constraints are presented in this paper. Both problems can be regarded as those to find a projection to the intersection of the closed convex cone corresponding to the condition number constraint and the convex polyhedron corresponding to the other constraints. Thus, we can apply a successive projection method, which is a classical algorithm for finding the projection to the intersection of multiple convex sets, to these problems. The numerical results demonstrated that the algorithm effectively solved the problems. [ABSTRACT FROM AUTHOR]

In this paper, the problem of blind equalization of high-order quadrature amplitude modulation (QAM) signals is tackled by using a batch equalizer based on support vector regression (SVR). A new set of error functions weighted by neighborhood symbol decisions and augmented by generalized power factors p and q, are proposed to be used as the penalty terms in SVR, and the optimal values of p and q are determined. In addition, we propose a method to remove the high online computational complexity incurred by the inclusion of neighborhood terms in the new error function. Simulation results show that with about the same complexity, the optimized SVR-NA-SBD-(p,q) attain much lower residual inter-symbol-interference and higher probability of convergence than the best known SVR-MMA, and it needs only about 1400 symbols to achieve a BER of 10−4 for 256QAM in a multipath channel. In contrast, the conventional SVR-MMA needs more than 4000 symbols to achieve such BER. [ABSTRACT FROM AUTHOR]

Low power requirements, real-time constraints and big data applications present several challenges to digital signal processing (DSP) that span multiple domains including filtering and frequency analysis. Design of digital filters that can efficiently process signals with low computational complexity is desirable and requires innovative approaches. In this paper, we present an efficient two-step design methodology to design low complexity finite-impulse response (FIR) filters. In the first step, the filter design is formulated as an optimization problem to find the minimum number of coefficients, based on K-means clustering with a Euclidean distance metric. In the second step, a mapping approach is proposed to compensate for the mean square error (MSE) that results from the clustering. We compare the proposed method with three different methods from the literature to demonstrate its effectiveness in terms of computational complexity without compromising the filtering performance. [ABSTRACT FROM AUTHOR]

This paper proposes an adaptive multiscale decomposition algorithm for graph signals. We develop two types of graph signal cost functions: $\alpha$-sparsity functional and graph signal entropies, to capture the energy compaction of the signal components. The adaptive decomposition can then be constructed by applying a minimum cost constraint during the full subband decomposition. The proposed adaptive decomposition is shown to outperform graph wavelet decomposition in compressing nonpiecewise constant graph signals. [ABSTRACT FROM PUBLISHER]

This paper presents a window-based three-dimens-ional (3-D) aggregation technique, which can approximate the surfaces of all kinds of objects, in stereo matching. The 3-D aggregation, which means to aggregate in 3-D surfaces, is implemented by decomposing the adaptive support window into horizontal segments; we allow the disparity to change smoothly in or between segments. Compared to traditional local stereo methods, the 3-D aggregation greatly improves the accuracy of results in slanted surfaces and occlusion areas while keeping excellent performance near depth discontinuities. We also propose an acceleration method that surprisingly improves the accuracy at the same time. The evaluation experiments confirm our achievements. [ABSTRACT FROM PUBLISHER]

In order to detect space shift keying (SSK) signals, we propose to use the Monte Carlo (MC) expectation maximization (EM) algorithm in this paper. First, we formulate the EM algorithm for the maximum likelihood detection of SSK signals. Then, we derive a Gibbs sampler to approximate the E-step. Through simulation results, we can see that the performance of the derived MCEM algorithm is better than that of the orthogonal matching pursuit algorithm, which was adopted to detect SSK signals with low complexity by Yu et al. [ABSTRACT FROM PUBLISHER]

The goal in the whisper activity detection (WAD) is to find the whispered speech segments in a given noisy recording of whispered speech. Since whispering lacks the periodic glottal excitation, it resembles an unvoiced speech. This noise-like nature of the whispered speech makes WAD a more challenging task compared to a typical voice activity detection (VAD) problem. In this paper, we propose a feature based on the long term variation of the logarithm of the short-time sub-band signal energy for WAD. We also propose an automatic sub-band selection algorithm to maximally discriminate noisy whisper from noise. Experiments with eight noise types in four different signal-to-noise ratio (SNR) conditions show that, for most of the noises, the performance of the proposed WAD scheme is significantly better than that of the existing VAD schemes and whisper detection schemes when used for WAD. [ABSTRACT FROM PUBLISHER]

This paper considers the problem of compressively sampling wide sense stationary random vectors with a low rank Toeplitz correlation matrix. A new structured deterministic sampling method known as the “Generalized Nested Sampling” (GNS) is used to fully exploit the inherent redundancy of low rank Toeplitz matrices. For a Toeplitz matrix of size N \times N with rank r, this sampling scheme uses only O(\sqrt r) measurements and allows exact recovery from noiseless measurements. This compression factor is independent of N and is shown to be larger than that achieved by existing random sampling based techniques for compressing Toeplitz matrices. The recovery procedure exploits the connection between Toeplitz matrices and linear prediction. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a novel graph-based salient object detection algorithm which exploits higher order potential to capture the cross-scale grouping cues instead of using multi-scale graph model or naive multi-scale fusion (i.e. individually compute a saliency result for each scale and then combine them). And, we investigate the importance of graph affinities in graph labeling. We take both local (spatial distribution) and nonlocal (feature distribution) priors into account and learn the pairwise similarity values in a semi-supervised manner, thereby obtaining a faithful graph affinity model. With the guidance of foreground and background seeds, salient object detection is formulated as a labeling inference problem. Extensive experiments on two large benchmark datasets demonstrate the proposed method performs well when against the state-of-the-art methods in terms of accuracy. [ABSTRACT FROM AUTHOR]

The operator-based signal separation approach uses an adaptive operator to separate a signal into a set of additive subcomponents. In this paper, we show that differential operators and their initial and boundary values can be exploited to derive corresponding integral operators. Although the differential operators and the integral operators have the same null space, the latter are more robust to noisy signals. Moreover, after expanding the kernels of Frequency Modulated (FM) signals via eigen-decomposition, the operator-based approach with the integral operator can be regarded as the matched filter approach that uses eigen-functions as the matched filters. We then incorporate the integral operator into the Null Space Pursuit (NSP) algorithm to estimate the kernel and extract the subcomponent of a signal. To demonstrate the robustness and efficacy of the proposed algorithm, we compare it with several state-of-the-art approaches in separating multiple-component synthesized signals and real-life signals. [ABSTRACT FROM AUTHOR]

The ℓ1/ℓ2 ratio regularization function has shown good performance for retrieving sparse signals in a number of recent works, in the context of blind deconvolution. Indeed, it benefits from a scale invariance property much desirable in the blind context. However, the ℓ1/ℓ2 function raises some difficulties when solving the nonconvex and nonsmooth minimization problems resulting from the use of such a penalty term in current restoration methods. In this paper, we propose a new penalty based on a smooth approximation to the ℓ1/ℓ2 function. In addition, we develop a proximal-based algorithm to solve variational problems involving this function and we derive theoretical convergence results. We demonstrate the effectiveness of our method through a comparison with a recent alternating optimization strategy dealing with the exact ℓ1/ℓ2 term, on an application to seismic data blind deconvolution. [ABSTRACT FROM AUTHOR]

For the dermoscopy image, uneven illumination will influence segmentation accuracy and lead to wrong aided diagnosis result. In this paper, a no reference uneven illumination assessment metric is proposed for dermoscopy images. Firstly, the distorted image is decomposed to illumination and reflectance components through variational framework for Retinex (VFR). Then, the illumination component is extracted by basis function fitting. Lastly, average gradient of the illumination component (AGIC) is calculated as the uneven illumination metric. A series of experiments show that, the proposed illumination extraction method is insensitive to the image content, and the proposed metric delivers an accurate illumination assessment result. [ABSTRACT FROM AUTHOR]

Fractal image compression (FIC) is one of the most widely approved image compression approaches for its high compression ratio and quality of retrieved images. However, FIC suffers from high computational cost in searching local self-similarities in natural image. Although many papers aiming at speeding up FIC have been published, they use pre-processing tools or approximation methods. Reducing the intrinsic computational complexity of FIC is still an open problem. Since quantum mechanics based Grover's quantum search algorithm (QSA) is able to achieve square-root speedup over classical algorithms in unsorted database searching, we propose an interdisciplinary approach by using Grover's QSA to reduce the intrinsic computational complexity of FIC in this letter. In particular, both domain blocks and range blocks are represented as quantum states, then Grover's QSA is employed to search the most similar domain block for each range block under the criterion of maximizing quantum fidelity between these two kinds of quantum states. Without sacrificing compression ratio, experimental results show that the execution time of the proposed method is 100 times shorter than that of the baseline FIC. Moreover, retrieved images from our proposal are also less distorted than those from other state-of-the-art FIC approaches. [ABSTRACT FROM AUTHOR]

Fingerprint image enhancement is one of the most crucial steps in an automated fingerprint identification system. In this paper, an effective algorithm for fingerprint image quality improvement is proposed. The algorithm consists of two stages. The first stage is decomposing the input fingerprint image into four subbands by applying two-dimensional discrete wavelet transform. At the second stage, the compensated image is produced by adaptively obtaining the compensation coefficient for each subband based on the referred Gaussian template. The experimental results indicated that the compensated image quality was higher than that of the original image. The proposed algorithm can improve the clarity and continuity of ridge structures in a fingerprint image. Therefore, it can achieve higher fingerprint classification rates than related methods can. [ABSTRACT FROM AUTHOR]

Motivated by application of Gaussian sum filters (GSF) and multiple model adaptive estimation (MMAE) approaches in scenarios where assumption of proper (circular) Gaussian signals is not valid, the letter proposes a novel complex-valued Gaussian sum filter (C/GSF) for non-linear filtering of non-Gaussian/non-circular measurement noise. Although the literature on recursive state estimation using GSF is rich, its complex-valued counterpart which incorporates the full second-order statistics of the system and can cope with non-Gaussian/non-circular measurements, has not yet been investigated in the literature. The paper addresses this gap. The C/GSF is a computationally attractive adaptive filter where the number of non-circular Gaussian components is controlled utilizing a modified Bayesian learning technique which is used to collapse the resulting non-Gaussian sum mixture into an equivalent complex-valued Gaussian term. Simulation results indicate that the C/GSF provides significant performance improvement over its traditional counterparts. [ABSTRACT FROM AUTHOR]

Several generic summarization algorithms were developed in the past and successfully applied in fields such as text and speech summarization. In this paper, we review and apply these algorithms to music. To evaluate their performance, we adopt an extrinsic approach: we compare a Fado genre classifier's performance using truncated contiguous clips against the summaries extracted with those algorithms on two different datasets. We show that Maximal Marginal Relevance (MMR), LexRank, and Latent Semantic Analysis (LSA) all improve classification performance in both datasets used for testing. [ABSTRACT FROM AUTHOR]

This paper presents an efficient color matching approach to address the photometric inconsistency problem that commonly exists in panoramic images. Color correction, as the first step, is to adjust the color and luminance of source images so that the differences between adjacent images can be minimized. Color blending is used after the color correction to further smooth the color transition between adjacent images. With the first image being selected as a basis image, the proposed approach can start the color matching and stitching process once the second image is captured. The proposed approach is simple and is very suitable for panoramic imaging on mobile devices. Experimental results demonstrate that the color transitions between neighboring images are smooth without visible seams and the color tone of the final image is kept as close as the basis image. [ABSTRACT FROM AUTHOR]

We design a decentralized stochastic optimization algorithm for machine-to-machine (M2M) networks, in which machines in the networks can attend the optimization process in a randomly-unexpected manner. This property of random attendance aims at capturing unexpected events that prevent the machines to successfully communicate with each other. The property, beside simulating the unforeseen nature of the networks, additionally helps reduce the communication cost of machines while still maintaining a reasonable optimization outcome. Our contribution gives practical insights for deploying optimization algorithms over machines in complex environments, including the Internet of Things. [ABSTRACT FROM AUTHOR]

Numerous audio signal processing and analysis techniques using non-negative matrix factorization (NMF) have been developed in the past decade, particularly for the task of source separation. NMF-based algorithms iteratively optimize a cost function. However, the correlation between cost functions and application-dependent performance metrics is less known. Furthermore, to the best of our knowledge, no formal heuristic to compute a stopping criterion tailored to a given application exists in the literature. In this paper, we examine this problem for the case of supervised and semi-supervised NMF-based source separation and show that iterating these algorithms to convergence is not optimal for this application. We propose several heuristic stopping criteria that we empirically found to be well correlated with source separation performance. Moreover, our results suggest that simply integrating the learning of an appropriate stopping criterion in a sweep for model size selection could lead to substantial performance improvements with minimal additional effort. [ABSTRACT FROM AUTHOR]