By Topic

Signal Processing Letters, IEEE

Issue 1 • Date Jan. 2010

Filter Results

Displaying Results 1 - 25 of 38
  • [Front cover]

    Page(s): C1
    Save to Project icon | Request Permissions | PDF file iconPDF (193 KB)  
    Freely Available from IEEE
  • IEEE Signal Processing Letters publication information

    Page(s): C2
    Save to Project icon | Request Permissions | PDF file iconPDF (39 KB)  
    Freely Available from IEEE
  • Table of contents

    Page(s): 1 - 2
    Save to Project icon | Request Permissions | PDF file iconPDF (48 KB)  
    Freely Available from IEEE
  • Advertisement - Free Electronic Access to SP Publications

    Page(s): 3
    Save to Project icon | Request Permissions | PDF file iconPDF (25 KB)  
    Freely Available from IEEE
  • Semi-Supervised Nonnegative Matrix Factorization

    Page(s): 4 - 7
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (274 KB) |  | HTML iconHTML  

    Nonnegative matrix factorization (NMF) is a popular method for low-rank approximation of nonnegative matrix, providing a useful tool for representation learning that is valuable for clustering and classification. When a portion of data are labeled, the performance of clustering or classification is improved if the information on class labels is incorporated into NMF. To this end, we present semi-supervised NMF (SSNMF), where we jointly incorporate the data matrix and the (partial) class label matrix into NMF. We develop multiplicative updates for SSNMF to minimize a sum of weighted residuals, each of which involves the nonnegative 2-factor decomposition of the data matrix or the label matrix, sharing a common factor matrix. Experiments on document datasets and EEG datasets in BCI competition confirm that our method improves clustering as well as classification performance, compared to the standard NMF, stressing that semi-supervised NMF yields semi-supervised feature extraction. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • GMP-Based Channel Estimation for Single-Carrier Transmissions over Doubly Selective Channels

    Page(s): 8 - 11
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (168 KB) |  | HTML iconHTML  

    We present a graph-based channel estimation approach for SC-IFDE (single-carrier transmissions with iterative frequency domain equalization) without CP (cyclic prefix) over doubly selective channels using the recently developed Gaussian message passing (GMP) technique. A direct application of the GMP updating rules in the FFG (Forney-style factor graph) of the SC-IFDE system model incurs high complexity. Approximate updating rules are therefore developed to overcome this problem. The proposed GMP-based channel estimation approach has similar complexity as the low-complexity Kalman-filtering based frequency domain channel estimation approach in the literature, but significantly outperforms the latter due to its enhanced capability in capturing the time correlation information of doubly selective channels through bidirectional processing. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Adaptive Noise Variance Estimation in BayesShrink

    Page(s): 12 - 15
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1369 KB) |  | HTML iconHTML  

    A method of noise variance estimation in BayesShrink image denoising is presented. The proposed approach competes with the well known MAD-based method and outperforms this method in more than 99% of our experimental results. The approach, called Residual Autocorrelation Power (RAP), provides a more accurate noise variance estimate and results in a smaller MSE. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • A Power Allocation Method for DMT-Based DSL Systems Using Geometric Programming

    Page(s): 16 - 119
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (217 KB) |  | HTML iconHTML  

    In this letter, we consider a power allocation problem for digital subscriber line (DSL) systems. The goal of this problem is to minimize the total transmit power under some constraints on minimum data-rate and maximum transmit power for each modem where we take into account various sources of interference. We convert our problem into an auxiliary geometric programming (GP) which gives the optimum solution for transmit powers in a neighborhood of a given feasible point. Then, we use an iterative scheme for obtaining the solution to our original problem by exploiting this auxiliary GP problem. Numerical examples show that the proposed method outperforms the existing schemes. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Low-Resolution Face Recognition via Coupled Locality Preserving Mappings

    Page(s): 20 - 23
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (291 KB) |  | HTML iconHTML  

    Practical face recognition systems are sometimes confronted with low-resolution face images. Traditional two-step methods solve this problem through employing super-resolution (SR). However, these methods usually have limited performance because the target of SR is not absolutely consistent with that of face recognition. Moreover, time-consuming sophisticated SR algorithms are not suitable for real-time applications. To avoid these limitations, we propose a novel approach for LR face recognition without any SR preprocessing. Our method based on coupled mappings (CMs), projects the face images with different resolutions into a unified feature space which favors the task of classification. These CMs are learned through optimizing the objective function to minimize the difference between the correspondences (i.e., low-resolution image and its high-resolution counterpart). Inspired by locality preserving methods for dimensionality reduction, we introduce a penalty weighting matrix into our objective function. Our method significantly improves the recognition performance. Finally, we conduct experiments on publicly available databases to verify the efficacy of our algorithm. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Some Properties of an Empirical Mode Type Signal Decomposition Algorithm

    Page(s): 24 - 27
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (100 KB) |  | HTML iconHTML  

    The empirical mode decomposition (EMD) has seen widespread use for analysis of nonlinear and nonstationary time-series. Despite some practical success, it lacks a firm theoretical foundation. This work addresses two important theoretical properties. The original EMD algorithm is slightly modified, in a way that facilitates this analysis. For periodic, band-limited, signals the convergence and time scale separation of the algorithm are proved. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Convergence of the Multidimensional Minimum Variance Spectral Estimator for Continuous and Mixed Spectra

    Page(s): 28 - 31
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (121 KB) |  | HTML iconHTML  

    A proof of the pointwise convergence of the multidimensional minimum variance spectral estimator as the region of data support becomes infinite is given. It is shown that an octant is sufficient to ensure that the minimum variance spectral estimator will converge to the true power spectral density. The proof is valid for 1-D, multidimensional, continuous, and mixed spectra. Another useful result is that a normalized minimum variance spectral estimator can be defined to indicate sinusoidal power for processes with a mixed spectrum. Finally, upper and lower bounds on the continuous portion of the spectral estimate are given. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • The Equivalency of Minimum Error Entropy Criterion and Minimum Dispersion Criterion for Symmetric Stable Signal Processing

    Page(s): 32 - 35
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (236 KB) |  | HTML iconHTML  

    The minimum error entropy (MEE) criterion in information theoretic learning is an efficient way to deal with non-Gaussian signal processing. And the minimum dispersion (MD) criterion has been widely applied in stable signal processing. In this letter, we show that there exists an equivalence between the MD criterion and the MEE criterion where symmetric \alpha -stable (S\alpha S) random variables are considered as the errors of the adaptive signal processing. As an application, we propose an algorithm with the MEE criterion for the time delay estimation (TDE) problem which was solved by the MD criterion. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Structural Similarity Measure to Assess Improvement by Noise in Nonlinear Image Transmission

    Page(s): 36 - 39
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2387 KB) |  | HTML iconHTML  

    We show that the structural similarity index is able to register stochastic resonance or improvement by noise in nonlinear image transmission, and sometimes when not registered by traditional measures of image similarity, and that in this task this index remains in good match with the visual appreciation of image quality. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Linear Systems, Sparse Solutions, and Sudoku

    Page(s): 40 - 42
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (130 KB) |  | HTML iconHTML  

    In this paper, we show that Sudoku puzzles can be formulated and solved as a sparse linear system of equations. We begin by showing that the Sudoku ruleset can be expressed as an underdetermined linear system: Ax = b, where A is of size m times n and n > m. We then prove that the Sudoku solution is the sparsest solution of Ax = b, which can be obtained by lo norm minimization, i.e. min ||x:||0 s.t. Ax = b. Instead of this minimization SB problem, inspired by the sparse representation literature, we solve the much simpler linear programming problem of minimizing the l1 norm of x, i.e. min ||x||1 s.t. Ax = b, and show numerically that this approach solves representative Sudoku puzzles. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Analysis of the Security of Perceptual Image Hashing Based on Non-Negative Matrix Factorization

    Page(s): 43 - 46
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (133 KB) |  | HTML iconHTML  

    In this letter, we analyze the security of a perceptual image hashing technique based on non-negative matrix factorization which was recently proposed and reported in the literature. We theoretically demonstrate that, although the technique uses different secret keys in subsequent stages, the first key plays an essential role to secure the hashing system. We next act as an attacker and propose a technique to estimate the secret key. Extensive experiments support our theoretical analysis and validate the proposed key estimation technique. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • A Contrast Enhancement-Based Filter for Removal of Random Valued Impulse Noise

    Page(s): 47 - 50
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1344 KB) |  | HTML iconHTML  

    This letter presents a new filtering scheme based on contrast enhancement within the filtering window for removing the random valued impulse noise. The application of a nonlinear function for increasing the difference between a noise-free and noisy pixels results in efficient detection of noisy pixels. As the performance of a filtering system, in general, depends on the number of iterations used, an effective stopping criterion based on noisy image characteristics to determine the number of iterations is also proposed. Extensive simulation results exhibit that the proposed method significantly outperforms many other well-known techniques. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Multiple Description Distributed Video Coding Using Redundant Slices and Lossy Syndromes

    Page(s): 51 - 54
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (322 KB) |  | HTML iconHTML  

    During the last years, video coding designers have proposed robust coding approaches that combine Multiple Description Coding (MDC) schemes with Distributed Video Coding (DVC) principles. In this way, it is possible to obtain a better error resilience since the distortion drifting through the sequence is significantly mitigated by the DVC coding unit. The paper presents a Multiple Description Distributed Video Coder (MDDVC) that codes the input video signal generating a set of "lossy" syndromes for each pixel block and creates different descriptions multiplexing primary and redundant video packets. Experimental results show that at high loss probabilities the proposed solution improves the results of the original MDC approach. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • An Adaptive Constant Modulus Blind Equalization Algorithm and Its Stochastic Stability Analysis

    Page(s): 55 - 58
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (227 KB) |  | HTML iconHTML  

    A constant modulus algorithm is presented for blind equalization of complex-valued communication channels. The proposed algorithm is obtained by solving a novel deterministic optimization criterion which comprises the minimization of a priori as well as a posteriori dispersion error, leading to an update equation having a particular zero-memory continuous Bussgang-type nonlinearity. We also derive a stochastic bound for the range of step-sizes for a generic Bussgang-type constant modulus algorithm. The theoretical result is validated through computer simulations. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Split Table Extension: A Low Complexity LVQ Extension Scheme in Low Bitrate Audio Coding

    Page(s): 59 - 62
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (224 KB) |  | HTML iconHTML  

    Embedded algebraic vector quantization (EAVQ) is a fast and efficient lattice vector quantization (LVQ) scheme used in low-bitrate audio coding. However, a defect of EAVQ is the overload distortion which causes unpleasant noises in audio coding. To solve this problem, specific base codebook extension schemes should be carefully considered. In this letter, we present a novel EAVQ codebook extension scheme-split table extension (STE), which splits a vector into two smaller vectors: one in the base codebook and the other in the split table. The base codebook and the split table are designed according to the appearance probability of quantized vectors in audio segments. Experiments on encoding multiple audio and speech sequences show that, compared with the existed Voronoi extension scheme, STE greatly reduces computation complexity and storage requirement while achieving similar coding quality. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Sequence Sets With Optimal Integrated Periodic Correlation Level

    Page(s): 63 - 66
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (189 KB) |  | HTML iconHTML  

    Sequence sets with low periodic correlations are used in many areas, such as asynchronous code-division multiple access (CDMA) systems, medical imaging, radar and sonar. Lower bounds on the integrated sidelobe level (ISL) and the peak sidelobe level (PSL) of periodic sequence sets, under a power constraint, have been previously derived in the literature. In this letter, we obtain the ISL and PSL lower bounds using a different framework. The main contribution of the letter consists in using this framework to derive closed-form expressions for all power constrained periodic sequence sets that meet the ISL lower bound. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Acoustic Data Transmission Based on Modulated Complex Lapped Transform

    Page(s): 67 - 70
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (187 KB) |  | HTML iconHTML  

    Acoustic data transmission is a technique to embed the data in a sound wave imperceptibly and to detect it at the receiver. This letter proposes a novel acoustic data transmission system designed based on the modulated complex lapped transform (MCLT). In the proposed system, data is embedded in an audio file by modifying the phases of the original MCLT coefficients. The data can be transmitted by playing the embedded audio and extracting it from the received audio. By embedding the data in the MCLT domain, the perceived quality of the resulting audio could be kept almost similar as the original audio. The system can transmit data at several hundreds of bits per second (bps), which is sufficient to deliver some useful short messages. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • A Fast Algorithm for Recognizing Translated, Rotated, Reflected, and Scaled Objects From Only Their Projections

    Page(s): 71 - 74
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (290 KB) |  | HTML iconHTML  

    The subject of 2-D and higher dimensional object recognition finds widespread applications in areas such as image registration and pattern recognition. Radon transform is one technique used for efficient object matching (e.g., and ). However, so far as we know, no results have been obtained that solves the recognition problem completely in the projection domain due to coupling of transform parameters. We develop a novel method for such parameter decoupling and an improved phase correlation method for accurate practical shift estimation, resulting in a fast matching algorithm based on projection data only. Simulation results show that the proposed algorithm is much faster than similar state-of-the-art approaches such as that in with comparable estimation accuracy. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • A Comparison Between ULP and MDC With Many Descriptions for Image Transmission

    Page(s): 75 - 78
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (267 KB) |  | HTML iconHTML  

    In this letter, we present a performance comparison between multiple description coding (MDC) and unequal loss protection (ULP) for progressive image transmission over lossy packet networks. Two optimization criteria are considered, i.e., a multi-quality criterion, when N distinct quality levels are guaranteed at the decoder side, and the optimization of the expected quality at the receiver. We resort to both a semi-analytical approach and simulation results. To enable numerical comparisons, we address a specific MDC algorithm suitable for progressive imaging, and a state-of-the-art ULP algorithm based on Reed Solomon codes. The results, although cannot be generalized to any MDC and ULP methods, are useful to put into evidence some general features that can drive the selection of the most proper technique for the application at hand. In fact, they allow to put into evidence the main advantages and drawbacks of either technique. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Blind Extraction of Smooth Signals Based on a Second-Order Frequency Identification Algorithm

    Page(s): 79 - 82
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (242 KB) |  | HTML iconHTML  

    We propose a novel blind source separation method tailored for retrieving baseband signals having different bandwidths. Such a configuration is characterized by the existence of inactive bands in the frequency domain. By exploiting the eigenstructure of the mixtures covariance matrix calculated in these inactive bands, we develop a simple yet efficient extraction procedure that works in an ordered fashion, in which the sources are extracted according to their degree of smoothness. Numerical results attest the viability of the proposal. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Cramer–Rao Bound for MIMO Radar Target Localization With Phase Errors

    Page(s): 83 - 86
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (203 KB) |  | HTML iconHTML  

    Recent research indicates the potential of MIMO radar with dispersed antennas to achieve high target localization accuracy via coherent processing. Coherent processing requires phase synchronization. Usually, perfect phase synchronization is difficult to realize. Assuming frequency synchronization, possibly through reception of a beacon, and white noise, possibly due to estimating the covariance matrix and whitening the observations, we consider the impact of static phase errors at the transmitters and receivers for cases with sufficiently high SNR such that the Cramer-Rao bound (CRB) provides accurate performance estimates. We model the phase errors as random variables and discuss the impact of these errors on target localization performance. In a few example cases the CRB is computed and compared with those in the ideal coherent and noncoherent processing cases. For these examples, using numerical results, we will show that at high enough signal-to-noise ratio (SNR), phase errors degrade performance only by a relatively small amount. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.

Aims & Scope

The IEEE Signal Processing Letters is a monthly, archival publication designed to provide rapid dissemination of original, cutting-edge ideas and timely, significant contributions in signal, image, speech, language and audio processing.

Full Aims & Scope

Meet Our Editors

Editor-in-Chief
Peter Willett
University of Connecticut
Storrs, CT 06269
peter.willett@uconn.edu