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This paper tackles the problem of decomposing binary data using matrix factorization. We consider the family of mean-parametrized Bernoulli models, a class of generative models that are well suited for modeling binary data and enables interpretability of the factors. We factorize the Bernoulli parameter and consider an additional Beta prior on one of the factors to further improve the model’s expressive power. While similar models have been proposed in the literature, they only exploit the Beta prior as a proxy to ensure a valid Bernoulli parameter in a Bayesian setting; in practice it reduces to a uniform or uninformative prior. Besides, estimation in these models has focused on costly Bayesian inference. In this paper, we propose a simple yet very efficient majorization-minimization algorithm for maximum a posteriori estimation. Our approach leverages the Beta prior whose parameters can be tuned to improve performance in matrix completion tasks. Experiments conducted on three public binary datasets show that our approach offers an excellent trade-off between prediction performance, computational complexity, and interpretability. [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]

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]

Linear prediction (LP) is an ubiquitous analysis method in speech processing. Various studies have focused on sparse LP algorithms by introducing sparsity constraints into the LP framework. Sparse LP has been shown to be effective in several issues related to speech modeling and coding. However, all existing approaches assume the speech signal to be minimum-phase. Because speech is known to be mixed-phase, the resulting residual signal contains a persistent maximum-phase component. The aim of this paper is to propose a novel technique which incorporates a modeling of the maximum-phase contribution of speech, and can be applied to any filter representation. The proposed method is shown to significantly increase the sparsity of the LP residual signal and to be effective in two illustrative applications: speech polarity detection and excitation modeling. [ABSTRACT FROM AUTHOR]

These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal. [ABSTRACT FROM AUTHOR]

In this paper, a novel boundary processing technique is proposed for bidimensional empirical mode decomposition (BEMD). BEMD is a new form of multiscale decomposition and has superior quality in extracting intrinsic components of textures. Although the boundary effect of BEMD is one of its main questions, there have been no concrete discussions or effective methods for it. A modified algorithm of nonparametric sampling for texture synthesis is used to extend images before applying BEMD. Experiments indicate that the technique proposed in this paper can form better decomposition compared with past algorithms not only for textures but also for natural images. [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]

Orthogonal chirp division multiplexing (OCDM) is a recently proposed multiplexing scheme and has drawn increasing attention in underwater communications. In an OCDM system, a series of orthogonal chirp signals carry the data information. However, the time variation feature of underwater acoustic (UWA) channels is rarely considered in the existing OCDM systems. In this paper, a particular transmission structure is proposed for the OCDM system to process the signal block-by-block at the receiver. Moreover, a low complexity channel estimation algorithm and the corresponding equalizers are derived. The proposed structure has a better ability to track time-varying channels. The simulation results show that the proposed method achieves promising performance under time-varying channels. [ABSTRACT FROM AUTHOR]

In modern communication systems, power amplifiers (PAs) are important components and inherently nonlinear. The nonlinearity of the PA causes bandwidth expansion of the communication signal, often referred to as spectral regrowth, at the PA output. Conventionally, spectral regrowth is treated as a distortion, and a range of compensation and filtering techniques have been considered to mitigate its effect. In this paper, we propose to exploit spectral regrowth to enhance channel identification accuracy. Our approach is motivated by the fact that the nonlinearly amplified communication signal carries more bandwidth and allows better probing of the channel. We introduce an iterative algorithm which jointly estimates the PA characteristics and the channel impulse response. The effectiveness of the proposed algorithm is illustrated by computer simulation. [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]

Image compression evidence has been utilized as an important forensic feature to justify image authenticity. However, some recent studies showed that the compression evidence of block transform-based image coding, e.g., JPEG and JPEG2000, can be effectively erased by adding designed dither noise in the transform domain. In this paper, we demonstrate that it is also feasible to hide the compression evidence of lossy predictive image coding, a class of compression paradigm widely employed in critical scenarios. To tackle the challenging issue of error propagation inherent to predictive coding, we design a prediction-direction preserving strategy, allowing us to add dither noise in the prediction error (PE) domain, while minimizing the incurred distortion. Extensive experimental results are provided to verify the effectiveness of the proposed anti-forensic algorithm for lossy predictive image coding. [ABSTRACT FROM PUBLISHER]

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]

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]

Visual saliency is a probabilistic estimate of how likely a spatial area in an image or video frame is to attract human visual attention relative to other areas. When existing bottom-up saliency models aggregate low-level features to construct a plausible saliency map, only 2-D motion cues are used as motion features, even though videos typically capture dynamic 3-D scenes. In this paper, we introduce 3-D motion into bottom-up saliency modeling for texture-plus-depth videos. We first propose an efficient 3-D motion estimation algorithm, which computes a 3-D motion vector (3DMV) for each sub-block in the frame. Using the computed 3DMVs, we then derive several saliency channels (called 3DMV channels), which are incorporated into a bottom-up saliency model to obtain enhanced saliency maps. Experiments tracking human gaze show that incorporating our 3DMV channels into bottom-up saliency model significantly improves the accuracy of derived saliency maps. [ABSTRACT FROM PUBLISHER]

Dementia is one of the most common neurological disorders among the elderly. Identifying those who are of high risk suffering dementia is important to the administration of early treatment in order to slow down the progression of dementia symptoms. However, to achieve accurate classification, significant amount of subject feature information are involved. Hence identification of demented subjects can be transformed into a pattern recognition problem with high-dimensional nonlinear datasets. In this paper, we introduce trace ratio linear discriminant analysis (TR-LDA) for dementia diagnosis. An improved ITR algorithm (iITR) is developed to solve the TR-LDA problem. This novel method can be integrated with advanced missing value imputation method and utilized for the analysis of the nonlinear datasets in many real-world medical diagnosis problems. Finally, extensive simulations are conducted to show the effectiveness of the proposed method. The results demonstrate that our method can achieve higher accuracies for identifying the demented patients than other state-of-art algorithms. [ABSTRACT FROM PUBLISHER]

This paper proposes a new variable step-size least-mean-square (VSLMS) algorithm with an approach in which a gradient-based weighted average is used to improve the weakness of previous VSLMS for application to unknown channel estimation or system identification in Iow-SNR or colored input environments. The proposed scheme leads to a faster convergence rate and a lower misadjustment error. [ABSTRACT FROM AUTHOR]

This paper presents an algorithm for the estimation of the 2-D AR parameters. This new method is based on the functional Schur coefficients recently introduced by the authors. The algorithm is efficient in terms of computation and preserves in the 2-D case the advantages of the 1-D Schur algorithm vs 1-D Levinson algorithm. This approach is illustrated on several numerical examples. [ABSTRACT FROM AUTHOR]

This paper describes computationally efficient implementations for the ITU-T G.729 speech codec. Focus is given to the adaptive codebook search, more specifically in the open-loop stage, which first estimates the pitch period of the speech frame being coded. Different strategies are discussed to achieve an excellent compromise between computational complexity and signal quality. The result is an accelerated procedure for the G.729 and G.729A versions, reducing encoding time in about 12% and 9%, respectively, while sustaining original signal quality, as verified by objective and subjective measurements. [ABSTRACT FROM AUTHOR]

Bogomjakov et al. proposed a universal algorithm for permutation steganography. In this paper, we introduce a more effective algorithm. A theoretical analysis indicates that our algorithm achieves 99 % of optimal when using 128 elements, while Bogomjakov et al.'s method attains only 98.34% of optimal even when employing as many as 1,048,576 elements. We conducted an experiment using a 3-D polygon dataset for comparison. The analysis and practical experiments demonstrate that our algorithm attains a capacity closer to the optimal but has the same runtime complexity as Bogomjakov et al.'s algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we study the restoration of blurred images corrupted by impulse noise or mixed impulse plus Gaussian noises. In the proposed method, we use the nodified total variation minimization scheme to regularize the deblurred image and fill in suitable values for noisy image pixels where these are detected by median-type filters. An alternating minimization algorithm is employed to solve the proposed total variation minimization problem. Our experimental results show the proposed algorithm is very efficient and the quality of restored images I y the proposed method is competitive with those restored by the existing variational image restoration methods. [ABSTRACT FROM AUTHOR]

Automatic modulation recognition is a topic of interest in many fields including signal surveillance, multi-user detection and radio frequency spectrum monitoring. In this paper, we present an algorithm for recognition of different types continuous phase modulation signals that uses a combination of features extracted through cyclic spectral analysis and an ICA-SVM hybrid recognition system. Simulation results demonstrate the ability of the algorithm to correctly identify modulation types over a wide range of SNR scenarios. The effects of pulse shaping and partial response waveforms are also investigated. [ABSTRACT FROM AUTHOR]

A vehicle detection algorithm based on Bispectral entropy is proposed in this paper. Based on Quadratic-Phase Coupling(QPC) analysis, Bispectral entropy, as the complexity measure of bispectra, is calculated from the vehicle acoustic signal database which is acquired in several real world experiments. Furthermore, an effective bispectral entropy-based algorithm is developed for vehicle detection. Experiments show that the proposed algorithm can achieve longer distance alert than other two detectors. [ABSTRACT FROM AUTHOR]

This paper proposes an improved algorithm of the error-resilient entropy coding (EREC). The idea is to record and transmit the states of variable-length data blocks (VLBs) and fixed-length data slots (FLSs) during EREC encoding process. The state information (SI) of VLBs and FLSs is used at the receiver to resynchronize VLBs during EREC decoding process. It is proved that the cost of SI is fewer than 3 bits per VLB. To combat errors in SI bits, we propose to code SI bits into EREC structure. Experimental results show that our proposed method improves recovery quality of VLC bitstream significantly. [ABSTRACT FROM AUTHOR]

We consider sparse representations of signals with at most L nonzero coefficients using a frame F of size M in CN. For any F, we establish a universal numerical lower bound on the average distortion of the representation as a function of the sparsity ∈ = L/N of the representation and redundancy (r - 1) = M/N -- 1 of F. In low dimensions (e.g., N = 6, 8, 10), this bound is much stronger than the analytical and asymptotic bounds given in another of our papers. In contrast, it is much less straightforward to compute. We then compare the performance of randomly generated frames to this numerical lower bound and to the analytical and asymptotic bounds given in the aforementioned paper. In low dimensions, it is shown that randomly generated frames perform about 2 dB away from the theoretical lower bound, when the optimal sparse representation algorithm is used. In higher dimensions, we evaluate the performance of randomly generated frames using the greedy orthogonal matching pursuit (OMP) algorithm. The results indicate that for small values of ∈, OMP performs close to the lower bound and suggest that the loss of the suboptimal search using orthogonal matching pursuit algorithm grows as a function of ∈. In all cases, the performance of randomly generated frames hardens about their average as N grows, even when using the OMP algorithm. [ABSTRACT FROM AUTHOR]

This paper presents a transformation-based rapid adaptation technique for robust speech recognition using a linear spectral transformation (LST) and a maximum mutual information (MMI) criterion. Previously, a maximum likelihood linear spectral transformation (ML-LST) algorithm was proposed for fast adaptation in unknown environments. Since the MMI estimation method does not require evenly distributed training data and increases the a posteriori probability of the word sequences of the training data, we combine the linear spectral transformation method and the MMI estimation technique in order to achieve extremely rapid adaptation using only one word of adaptation data. The proposed algorithm, called MMI-LST, was implemented using the extended Baum-Welch algorithm and phonetic lattices, and evaluated on the TIMIT and FFMTIMIT corpora. It provides a relative reducion in the speech recognition error rate of 11.1% using only 0.25 s of adaptation data. [ABSTRACT FROM AUTHOR]

This paper presents a new subspace-based technique for automatic detection of the number of exponentially damped sinusoids. It consists in studying the shift-invariance of the dominant subspace of the Hankel data matrix. No threshold setting and no penalization terms are necessary. This model-based method, easy to implement, can be plugged into most subspace-based harmonic retrieval algorithms. [ABSTRACT FROM AUTHOR]

Under Gaussian assumptions, the sample covariance matrix (SCM) is encountered in many covariance based processing algorithms. In case of impulsive noise, this estimate is no more appropriate. This is the reason why when the noise is modeled by spherically invariant random vectors (SIRV), a natural extension of the SCM is extensively used in the literature: the well-known normalized sample covariance matrix (NSCM), which estimates the covariance of SIRV. Indeed, this estimate gets rid of a fluctuating noise power and is widely used in radar applications. The aim of this paper is to derive closed-form expressions of the first- and second-order moments of the NSCM. [ABSTRACT FROM AUTHOR]

In this paper, a new method for the simultaneous learning of a function and its derivative is presented. The method, setting out the problem inside of the Support Vector Machine (SVM) framework, relies on the kernel-based Support Vector expansion. The resultant optimization problem is solved by a computationally efficient Iterative Re-Weighted Least Squares (IRWLS) algorithm. [ABSTRACT FROM AUTHOR]

In this letter, we first recall the relation between discrete B-splines and the control points of B-splines. We then use some convergence properties of the control points of B-splines to derive a new algorithm that approximates the wavelet decomposition at rational scales and rational location. We conclude the paper by some numerical experiments illustrating the behavior of our algorithm and by a discussion on an efficient implementation of the proposed method. [ABSTRACT FROM AUTHOR]

Recently, new efficient algorithms, based on Lepski's approach [7], have been proposed for spatially adaptive varying scale de-noising. Special statistical rules are exploited in order to select the estimate with the best point-wise varying scale h from a set of test-estimates yh(x), h ∈ H. In this paper, a novel multiresolution (MR) nonparametric regression technique is developed. The adaptive algorithm consists of two steps. The first step transforms the data into noisy spectrum coefficients (MR analysis). In the second step, these noisy spectrum is filtered by the thresholding procedure and exploited for estimation (MR synthesis). This nonlinear estimate is built using the test-estimates yh(x) of all scales. Simulation confirms the advanced performance of the new de-noising algorithms based on the MR nonparametric regression. [ABSTRACT FROM AUTHOR]

Block-based motion estimation is widely used in video compression for exploitation of video temporal redundancy. Although effective, the process is arguably the most computationally intensive part of a typical video encoder. To speed up the process, a large number of fast block-matching algorithms (BMAs) have been proposed for motion estimation by limiting the number of search locations or simplifying the measure of match between two blocks under comparison. In this paper we propose a new BMA that measures the match by using the block sum of pixel values, a quantity that can be easily computed from an integral frame. Experimental results show that the proposed BMA can reduce notably the computational load and achieve compression performance very close to that of existing BMAs using conventional block-matching measures. [ABSTRACT FROM AUTHOR]

Test of orthogonality of projected subspaces (TOPS) estimates directions of arrival of wideband sources by exploiting orthogonality between signal and noise subspaces in spectral domain. TOPS performs well at mid signal-to-noise ratio (SNR) range, but fares poorly at high SNR and noise-free cases. The TOPS pseudospectrum often exhibits spurious peaks at all SNR levels. This paper attempts to explain the cause of poor performance, and proposes suitable modifications to extend the effectiveness of TOPS from low SNR to noise-free case. The proposed modified-TOPS (mTOPS) achieves reduction in spurious peaks by incorporating signal-subspace projection instead of null-space projection used in TOPS. It also uses trace as performance metric, instead of loss of rank used in TOPS. The effectiveness of mTOPS has been studied via simulations. [ABSTRACT FROM PUBLISHER]

Dual-axis swallowing accelerometry has been proposed as a method for quantifying swallowing function. Acceleration signals in anterior–posterior and superior–inferior anatomical directions are processed and automatically segmented. However, the latter is often too liberal, admitting pre- and post-swallowing activity while also giving rise to non-swallow segments. These segmentation shortcomings adversely affect feature extraction and ultimately classification of swallowing function. In this paper, we propose a kernel density estimation-based algorithm to adaptively trim the swallow segments, and energy and noise-floor algorithms to reduce the number of false positive swallow segments. The balance between false positive reduction and loss of true positives can be adjusted according to algorithmic thresholds. Dramatic reductions ($-$ 85.4%) in false positives can be achieved with a moderate loss of true positives ( $-$15.1%). [ABSTRACT FROM PUBLISHER]

Synchrotron-based soft X-ray ptychography has enabled the reconstruction of both the phase and attenuation projections of samples relevant to the physical and biological sciences. The phase projection images typically have higher fidelity and hence are used for tomographic reconstruction. In practice, three-dimensional tomographic reconstruction can be challenging because the measurements may have outliers, a fluctuating background and may be restricted to a limited angular range of sample rotations. Thus, conventional reconstruction algorithms such as filtered back projection can result in reconstructions with strong artifacts. In this paper, we present a robust model-based iterative reconstruction algorithm for X-ray ptychography-based phase tomography. Our method casts the reconstruction as a regularized inverse problem, involving a novel data fitting term that accounts for noise, the fluctuating background as well as outliers, combined with an image model term that enforces regularity on the volume to be reconstructed. We use a majorization–minimization strategy to find a minimum of the formulated cost function. Reconstructions on a simulated as well as a real dataset show that it is possible to acquire high-quality phase reconstructions compared to the typically used filtered-back projection algorithm as well as conventional regularized inversion approaches. [ABSTRACT FROM PUBLISHER]

A considerable number of papers in the literature provide solutions for fixed-gain filters, such as the α-β filter, that minimize the mean squared error under various dynamic models. This note demonstrates that those specialized models are unnecessary as the tools to explicitly find optimal fixed gains for arbitrary observable systems are already in the literature. The results produced by this method agree with published results for specialized algorithms. The solution of this letter also obviates the need for special "aeolotropic" filter design. Code implementing the algorithm and simulations is available in Matlab on IEEEXplore. [ABSTRACT FROM AUTHOR]

This paper describes a number of fast digital-reversal algorithms based on progressive index mapping. The proposed digital-reversal approach can obtain higher efficiency by decreasing the number of index mapping operations, unrolling the innermost loop, and vectorizing data operations. Comparing with other digital-reversal algorithms, we find that the new approach have higher efficiency than the known fastest algorithms. [ABSTRACT FROM PUBLISHER]

The Non-Negative Least-Mean-Square (NNLMS) algorithm and its variants have been proposed for online estimation under non-negativity constraints. The transient behavior of the NNLMS, Normalized NNLMS, Exponential NNLMS and Sign-Sign NNLMS algorithms have been studied in the literature. In this letter, we derive closed-form expressions for the steady-state excess mean-square error (EMSE) for the four algorithms. Simulation results illustrate the accuracy of the theoretical results. This work complements the understanding of the behavior of these algorithms. [ABSTRACT FROM AUTHOR]

Commuting coprime integer matrices arise in signal processing in a number of contexts. This paper develops two ways, one nonlinear and the other linear, to generate new commuting coprime pairs from known pairs. The first method is based on computing powers of the initial pair of matrices, and the second method is based on appropriate types of linear combinations of the initial pair of matrices. This enriches the already known families of coprime matrices reported in recent literature. Several properties of the newly generated coprime pairs are also addressed. [ABSTRACT FROM PUBLISHER]

A modified decision based unsymmetrical trimmed median filter algorithm for the restoration of gray scale, and color images that are highly corrupted by salt and pepper noise is proposed in this paper. The proposed algorithm replaces the noisy pixel by trimmed median value when other pixel values, 0's and 255's are present in the selected window and when all the pixel values are 0's and 255's then the noise pixel is replaced by mean value of all the elements present in the selected window. This proposed algorithm shows better results than the Standard Median Filter (MF), Decision Based Algorithm (DBA), Modified Decision Based Algorithm (MDBA), and Progressive Switched Median Filter (PSMF). The proposed algorithm is tested against different grayscale and color images and it gives better Peak Signal-to-Noise Ratio (PSNR) and Image Enhancement Factor (IEF). [ABSTRACT FROM PUBLISHER]

We propose in this letter two new subspace learning methods, called nearest feature space analysis (NFSA) and discriminant nearest feature space analysis (DNFSA), for pattern classification. While many subspace learning algorithms have been proposed in recent years, most of them apply the conventional nearest neighbor (NN) metric to derive the subspace and may not effectively characterize the geometrical information of the samples, especially when the number of training samples per class is limited. In this paper, we propose using the nearest feature space (NFS) metric to seek a NFSA subspace to improve the discriminating power of the subspace for classification. To further enhance the discriminative power of NFSA, we also propose a new DNFSA method to minimize the within-class feature space (FS) distances and maximize the between-class FS distances simultaneously in the derived subspace. Experimental results on face and facial expression recognition are presented to demonstrate the efficacy of the proposed methods. [ABSTRACT FROM PUBLISHER]

A maximum-amplitude-based coarse-to-fine algorithm is proposed with two novel ideas highlighted: adopting the maximum value of the fractional Fourier amplitude spectrum to measure the compactness of a signal, and using a coarse-to-fine strategy to speed up the searching process. The simulation results on synthetic and real signals show the validity of the proposed method. [ABSTRACT FROM AUTHOR]

This paper presents a method of scheduling stepsizes for the normalized least-mean-squares (SS-NLMS) algorithm. Geometrically interpreting the mean square deviation (MSD) learning curve leads to establishing an objective curve and to constructing a lookup table of stepsizes in order for the MSD to follow the curve. The SS-NLMS shows not only good performance but also robustness with respect to different signal-to-noise ratio (SNR) in measurement noise and different correlation in input signals with a very small number of online computations. Moreover, the scalability of the tabled stepsize with respect to the number of taps is described. For the efficient memory usage in practice, a modified version replaces the tabled stepsizes by down-sampled stepsizes with no performance degradation. [ABSTRACT FROM AUTHOR]

Knowing the statistical properties of a complex-valued signal is important in many signal processing applications by providing the necessary information for choosing the appropriate algorithm. In this paper, we provide generalized likelihood ratio tests (GLRT), based on the complex generalized Gaussian distribution (CGGD), for detecting two important signal properties: 1) the circularity of a complex random variable, not constrained to the Gaussian case and 2) whether a complex random variable is complex Gaussian. These tests can be combined to statistically determine if a complex random variable is the often assumed, circular Gaussian. Simulations are used to quantify the performance of the detectors followed by application to communication signals and actual radar data. [ABSTRACT FROM AUTHOR]

Soft iterative detection and decoding techniques have been shown to be able to achieve near-capacity performance multiple-antenna systems. To obtain the optimal soft information by marginalizing over the entire observation space is intractable: one must resort to suboptimal methods to implement such receivers. Although list-type detectors such as those founded upon the sphere decoding algorithm provide outstanding error performance, issues such as the optimal initial sphere radius, optimal radius update strategy, and their highly variable computational complexity are still unresolved. In this paper, a new detection scheme is proposed addressing the above issues. Our simulation results show that by sacrificing less than 2 dB at error rate of 10-5, we are able to gain up to 38.6% complexity reduction as well as a detector structure that is more suitable for practical system implementation. [ABSTRACT FROM AUTHOR]

In this paper we define a scale-space for cortical mean curvature maps on the sphere, that offers a hierarchical representation of the brain cortical structures, useful in multiscale registration and analysis algorithms. A spherical feature map was obtained through inflation of the cortical surface of one hemisphere, extracted from structural MR images. Using the Beltrami framework, we embedded this spherical mesh in a higher dimensional space and the feature assigned to a mesh vertex became an additional component of its coordinates. This enhanced mesh then evolved under Beltrami flow. Imposing an appropriate aspect ratio for the feature components, we thus minimized an interpolation between the L2 and TV-norm of the map. The collection of all maps produced by this PDE formed a scale-space. Our results suggest that this scale-space provides a generalization of the brain map suitable for use e.g., within a multiscale registration framework. [ABSTRACT FROM AUTHOR]

Due to the variety of shapes as well as the diversity of applications there does not exist any single method for computing the shape orientation of all shapes. In this paper, we introduce a new shape orientation measure, shape orientation pattern, to find the crisp and fuzzy shape orientation as well as shape orientability from any shape. We also illustrate that the new shape orientation measure can be used as a RST invariant shape descriptor. [ABSTRACT FROM AUTHOR]

Lagrangian multiplier (LM) based mode decision is one of the most important technologies in standard H.264/AVC encoder. Based on LM theory, this paper presents a joint three-layer (JTL) model for H.264/AVC rate control. At macroblock (MB) level, we dynamically revise LM for each of MBs by its estimated complexities, which is able to select a better coding mode than the current scheme with the constant LM adopted in H.264/AVC. At frame level, a more flexible and effective quantization parameter (QP) adjustment scheme is designed for I-frame to avoid buffer overflow or undertow. In addition, we also present a new target bits allocation scheme in group of picture (GOP) level. Experimental results show that our JTL model can not only significantly improve the video quality with the average PSNR gain up to 0.97 dB, but also provide a more stable buffer occupancy with respect to other existing rate control methods. [ABSTRACT FROM AUTHOR]

In this paper, we present efficient hardware implementation of multiplication free one-bit transform (MF1BT) based and constraint one-bit transform (C-1BT) based motion estimation (ME) algorithms, in order to provide low bit-depth representation based full search block ME hardware for real-time video encoding. We used a source pixel based linear array (SPBLA) hardware architecture for low bit depth ME for the first time in the literature. The proposed SPBLA based implementation results in a genuine data flow scheme which significantly reduces the number of data reads from the current block memory, which in turn reduces the power consumption by at least 50% compared to conventional 1BT based ME hardware architecture presented in the literature. Because of the binary nature of low bit-depth ME algorithms, their hardware architectures are more efficient than existing 8 bits/pixel representation based ME architectures. [ABSTRACT FROM AUTHOR]

In this paper, we devise a new convex optimization strategy to both reduce the peak-to-average power ratio (PAPR) and the PAPR variance of orthogonal frequency-division multiplexing (OFDM) symbols. There are three distinctions of our proposed method as compared to existing methods: 1) PAPR, error vector magnitude (EVM), and free subcarrier power overhead are constrained simultaneously; 2) the EVM constraint is root-mean-square EVM (RMS-EVM); and 3) an auxiliary parameter, t, is used to generate quasi-constant PAPR through our proposed binary search algorithm (here, quasi-constant refers to the condition that most OFDM symbols' PAPR values are within a small, preset neighborhood). Simulation results are provided to demonstrate the effectiveness of our approach in comparison to others we consider. [ABSTRACT FROM AUTHOR]