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Signal Processing, IEEE Transactions on

Issue 9 • Date Sept. 2006

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Displaying Results 1 - 25 of 46
  • Table of contents

    Page(s): c1 - c4
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  • IEEE Transactions on Signal Processing publication information

    Page(s): c2
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  • Bayesian blind separation of generalized hyperbolic processes in noisy and underdeterminate mixtures

    Page(s): 3257 - 3269
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    In this paper, we propose a Bayesian sampling solution to the noisy blind separation of generalized hyperbolic signals. Generalized hyperbolic models, introduced by Barndorff-Nielsen in 1977, represent a parametric family able to cover a wide range of real signal distributions. The alternative construction of these distributions as a normal mean variance (continuous) mixture leads to an efficient implementation of the Markov chain Monte Carlo method applied to source separation. The incomplete data structure of the generalized hyperbolic distribution is indeed compatible with the hidden variable nature of the source separation problem. Both overdeterminate and underdeterminate noisy mixtures are solved by the same algorithm without a prewhitening step. Our algorithm involves hyperparameters estimation as well. Therefore, it can be used, independently, to fitting the parameters of the generalized hyperbolic distribution to real data View full abstract»

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  • Quadratic optimization for simultaneous matrix diagonalization

    Page(s): 3270 - 3278
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (818 KB) |  | HTML iconHTML  

    Simultaneous diagonalization of a set of matrices is a technique that has numerous applications in statistical signal processing and multivariate statistics. Although objective functions in a least-squares sense can be easily formulated, their minimization is not trivial, because constraints and fourth-order terms are usually involved. Most known optimization algorithms are, therefore, subject to certain restrictions on the class of problems: orthogonal transformations, sets of symmetric, Hermitian or positive definite matrices, to name a few. In this paper, we present a new algorithm called QDIAG that splits the overall optimization problem into a sequence of simpler second order subproblems. There are no restrictions imposed on the transformation matrix, which may be nonorthogonal, indefinite, or even rectangular, and there are no restrictions regarding the symmetry and definiteness of the matrices to be diagonalized, except for one of them. We apply the new method to second-order blind source separation and show that the algorithm converges fast and reliably. It allows for an implementation with a complexity independent of the number of matrices and, therefore, is particularly suitable for problems dealing with large sets of matrices View full abstract»

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  • A new DOA estimation technique based on subarray beamforming

    Page(s): 3279 - 3290
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    A new direction-of-arrival (DOA) estimation technique using subarray beamforming is proposed. Two virtual subarrays are used to form a signal whose phase relative to the reference signal is a function of the DOA. The DOA is then estimated based on the computation of the phase shift between the reference signal and its phase-shifted version. Since the phase-shifted reference signal is obtained after interference rejection through beamforming, the effect of cochannel interference on the estimation is significantly reduced. The proposed technique is computationally simple, and the number of signal sources detectable is not bounded by the number of antenna elements used. Performance analysis and extensive simulations show that the proposed technique offers significantly improved estimation resolution, capacity, and accuracy relative to existing techniques View full abstract»

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  • Tracking an unknown time-varying number of speakers using TDOA measurements: a random finite set approach

    Page(s): 3291 - 3304
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    Speaker location estimation techniques based on time-difference-of-arrival measurements have attracted much attention recently. Many existing localization ideas assume that only one speaker is active at a time. In this paper, we focus on a more realistic assumption that the number of active speakers is unknown and time-varying. Such an assumption results in a more complex localization problem, and we employ the random finite set (RFS) theory to deal with that problem. The RFS concepts provide us with an effective, solid foundation where the multispeaker locations and the number of speakers are integrated to form a single set-valued variable. By applying a sequential Monte Carlo implementation, we develop a Bayesian RFS filter that simultaneously tracks the time-varying speaker locations and number of speakers. The tracking capability of the proposed filter is demonstrated in simulated reverberant environments View full abstract»

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  • Single-block differential transmit scheme for broadband wireless MIMO-OFDM systems

    Page(s): 3305 - 3314
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (671 KB) |  | HTML iconHTML  

    In frequency-selective multiple-input multiple-output (MIMO) channel, differential space-time-frequency (DSTF) modulations are known as practical alternatives that are capable of exploiting the available spatial and frequency diversities without the requirement of multichannel estimation at the receiver. However, the encoding nature of the DSTF schemes that expand several OFDM symbol periods makes the DSTF schemes susceptible to fast-changing channel conditions. In this paper, we propose a differential scheme for MIMO-OFDM systems that is able to differentially encode signal within two OFDM symbol periods, and the proposed scheme transmits the differentially encoded signal within one OFDM block. The scheme not only reduces encoding and decoding delay but also relaxes the restriction on channel assumption. The successful differential decoding of the proposed scheme depends on the assumption that the fading channels keep constant over two OFDM symbol periods rather than multiple of them as required in the existing DSTF schemes. We also provide pairwise error probability analysis and quantify the performance criteria in terms of diversity and coding advantages. The design criteria reveal that the existing diagonal cyclic codes can be applied to achieve full diversity. Performance simulations under various channel conditions show that our proposed scheme yields superior performance to previously proposed differential schemes View full abstract»

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  • Multiuser detection for cooperative networks and performance analysis

    Page(s): 3315 - 3329
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (838 KB) |  | HTML iconHTML  

    We investigate strategies for user cooperation in the uplink of a synchronous direct-sequence code-division multiple-access (DS/CDMA) network employing nonorthogonal spreading codes and analyze their performance. We consider two repetition-based relay schemes: decode-and-forward (DAF) and amplify-and-forward (AAF). Focusing on the use of linear multiuser detectors, we first present cooperation strategies, i.e., signal processing at both the relay nodes and the base station (BS), under the assumption of perfectly known channel conditions of all links; then, we consider the more practical scenario where relays and BS have only partial information about the system parameters, which requires blind multiuser detection methods. We provide performance analysis of the proposed detection strategies in terms of the (asymptotic) signal-to-(interference plus noise) ratio and the bit error rate, and we show that AAF achieves a full second-order diversity when a minimum mean-square-error detector is employed at both the relay side and the BS. A simple, yet effective, partner selection algorithm is also presented. Finally, a thorough performance assessment is undertaken to study the impact of the multiple-access interference on the proposed cooperative strategies under different scenarios and system assumptions View full abstract»

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  • On joint detection and decoding of linear block codes on Gaussian vector channels

    Page(s): 3330 - 3342
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    Optimal receivers recovering signals transmitted across noisy communication channels employ a maximum-likelihood (ML) criterion to minimize the probability of error. The problem of finding the most likely transmitted symbol is often equivalent to finding the closest lattice point to a given point and is known to be NP-hard. In systems that employ error-correcting coding for data protection, the symbol space forms a sparse lattice, where the sparsity structure is determined by the code. In such systems, ML data recovery may be geometrically interpreted as a search for the closest point in the sparse lattice. In this paper, motivated by the idea of the "sphere decoding" algorithm of Fincke and Pohst, we propose an algorithm that finds the closest point in the sparse lattice to the given vector. This given vector is not arbitrary, but rather is an unknown sparse lattice point that has been perturbed by an additive noise vector whose statistical properties are known. The complexity of the proposed algorithm is thus a random variable. We study its expected value, averaged over the noise and over the lattice. For binary linear block codes, we find the expected complexity in closed form. Simulation results indicate significant performance gains over systems employing separate detection and decoding, yet are obtained at a complexity that is practically feasible over a wide range of system parameters View full abstract»

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  • A frequency-domain method for generation of discrete-time analytic signals

    Page(s): 3343 - 3352
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (262 KB) |  | HTML iconHTML  

    We consider a common frequency-domain procedure hilbert for generating discrete-time analytic signals and show how it fails for a specific class of signals. A new frequency-domain technique ehilbert is formulated that solves the defect. Moreover, the new technique is applicable to all discrete-time real signals of even length. It is implemented by the introduction of one additional zero of the continuous spectrum of the analytic signal hilbert at a negative frequency. Both frequency-domain methods generate equal length discrete-time analytic signals. The new analytic signal preserves the original signal (real part) and also the zeros of the discrete spectrum hilbert in the negative frequencies. The greater attenuation at the negative frequencies affects the degree of aliasing of the analytic signal. It is measured by applying the analytic signal to an orthogonal wavelet transform and determining the improved transform shiftability View full abstract»

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  • Time-varying techniques for multisensor signal detection

    Page(s): 3353 - 3362
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (924 KB) |  | HTML iconHTML  

    In source detection and localization, the presence of a common but unknown signal can be detected from several noisy sensor measurements using the generalized coherence (GC) estimate. We propose to improve the performance of the GC estimate for multisensor detection using noise-suppressed signal estimates obtained from time-varying techniques. If one of the sensors has a significantly higher signal-to-noise ratio (SNR) than the other sensors, then it could be preprocessed prior to the GC estimate to improve detection performance for the remaining, lower SNR sensors. We perform this processing by estimating time-varying signals of interest with nonlinear phase functions using two methods: a) a modified matching pursuit decomposition (MMPD) algorithm whose dictionary is similar, in time-frequency structure, to the signal and b) an instantaneous frequency (IF) estimation method using highly localized time-frequency representations. The MMPD can yield signal estimates with lower mean square errors than the IF estimation technique but at the expense of higher computational cost and memory requirements. Using simulations, we compare the performance of the GC estimate with the significantly improved performance of the GC estimate that employs the signal estimate from the high SNR sensor. For the two-sensor detection, the estimated signal is also used with a generalized likelihood ratio test statistic to further improve performance View full abstract»

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  • A diagonal growth curve model and some signal-processing applications

    Page(s): 3363 - 3371
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (445 KB) |  | HTML iconHTML  

    We consider a variation of the growth-curve (GC) model, referred to as the diagonal growth-curve (DGC) model, where the steering vectors and waveforms are both known and the complex amplitude matrix is constrained to be diagonal. A closed-form approximate maximum likelihood (AML) estimator for this model is derived based on the maximum likelihood principle. We analyze the statistical properties of this method theoretically and show that the AML estimate is unbiased and asymptotically statistically efficient for a large snapshot number. Via several numerical examples in array signal processing and spectral analysis, we also show that the proposed AML estimator can achieve better estimation accuracy and exhibit greater robustness than the best existing methods View full abstract»

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  • A novel approach to detection of intrusions in computer networks via adaptive sequential and batch-sequential change-point detection methods

    Page(s): 3372 - 3382
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (742 KB) |  | HTML iconHTML  

    Large-scale computer network attacks in their final stages can readily be identified by observing very abrupt changes in the network traffic. In the early stage of an attack, however, these changes are hard to detect and difficult to distinguish from usual traffic fluctuations. Rapid response, a minimal false-alarm rate, and the capability to detect a wide spectrum of attacks are the crucial features of intrusion detection systems. In this paper, we develop efficient adaptive sequential and batch-sequential methods for an early detection of attacks that lead to changes in network traffic, such as denial-of-service attacks, worm-based attacks, port-scanning, and man-in-the-middle attacks. These methods employ a statistical analysis of data from multiple layers of the network protocol to detect very subtle traffic changes. The algorithms are based on change-point detection theory and utilize a thresholding of test statistics to achieve a fixed rate of false alarms while allowing us to detect changes in statistical models as soon as possible. There are three attractive features of the proposed approach. First, the developed algorithms are self-learning, which enables them to adapt to various network loads and usage patterns. Secondly, they allow for the detection of attacks with a small average delay for a given false-alarm rate. Thirdly, they are computationally simple and thus can be implemented online. Theoretical frameworks for detection procedures are presented. We also give the results of the experimental study with the use of a network simulator testbed as well as real-life testing for TCP SYN flooding attacks View full abstract»

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  • Performance analysis of semiblind channel estimation in long-code DS-CDMA systems

    Page(s): 3383 - 3399
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    The performance of second-order statistics (SOS)-based semiblind channel estimation in long-code direct-sequence code-division multiple-access systems is analyzed. The covariance matrix of second-order statistics estimates is obtained in the large system limit and is used to analyze the large-sample performance of two SOS-based semiblind channel estimation algorithms. A notion of blind estimation efficiency is also defined and is examined via simulation results View full abstract»

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  • Diversity and channel estimation using time-varying signals and time-frequency techniques

    Page(s): 3400 - 3413
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    We propose the use of time-varying (TV) signaling in modulation schemes to provide multiuser detection and multipath diversity in TV wireless channels. Specifically, we design an orthogonal linear chirp modulation scheme that is based on assigning different users with optimally designed parameters in order to reduce multiple-access interference. We also derive conditions on the parameters of the modulation signals to achieve multipath diversity. Furthermore, we propose the use of TV pilot signals with nonlinear instantaneous frequency and matched time-frequency (TF) techniques to estimate fast-fading channels with unknown state information. The proposed algorithm simplifies to the estimation of the parameters of multiple linear chirps, which we perform using the modified matching pursuit decomposition. We compare our estimation method with the use of pilot signals with linear instantaneous frequency, which we implement using the reassigned spectrogram. The proposed modulation scheme is applied to a frequency-hopped code-division multiple-access system for which we demonstrate improved performance when compared with frequency-shift-keying (FSK) modulation due to the designed multipath diversity and low multiple-access interference. Our simulations also demonstrate the increased estimation performance when pilot signals with nonlinear structures are used instead of linear structured ones to estimate TV channel parameters View full abstract»

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  • Statistical analysis for multiplicatively modulated nonlinear autoregressive model and its applications to electrophysiological signal analysis in humans

    Page(s): 3414 - 3425
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (855 KB) |  | HTML iconHTML  

    Modulating the dynamics of a nonlinear autoregressive model with a radial basis function (RBF) of exogenous variables is known to reduce the prediction error. Here, RBF is a function that decays to zero exponentially if the deviation between the exogenous variables and a center location becomes large. This paper introduces a class of RBF-based multiplicatively modulated nonlinear autoregressive (mmNAR) models. First, we establish the local asymptotic normality (LAN) for vector conditional heteroscedastic autoregressive nonlinear (CHARN) models, which include the mmNAR and many other well-known time-series models as special cases. Asymptotic optimality for estimation and testing is described in terms of LAN properties. The mmNAR model indicates goodness-of-fit for surface electromyograms (EMG) using electrocorticograms (ECoG) as the exogenous variables. Concretely, it is found that the negative potential of the motor cortex forces change in the frequency of EMG, which is reasonable from a physiological point of view. The proposed mmNAR model fitting is both useful and efficient as a signal-processing technique for extracting information on the action potential, which is associated with the postsynaptic potential View full abstract»

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  • Acoustic feedback cancellation for long acoustic paths using a nonstationary source model

    Page(s): 3426 - 3434
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    While several proactive acoustic feedback (Larsen-effect) cancellation schemes have been presented for speech applications with short acoustic feedback paths as encountered in hearing aids, these schemes fail with the long impulse responses inherent to, for instance, public address systems. We derive a new prediction error method (PEM)-based scheme (referred to as PEM-AFROW) which identifies both the acoustic feedback path and the nonstationary speech source model. A cascade of a short- and a long-term predictor removes the coloring and periodicity in voiced speech segments, which account for the unwanted correlation between the loudspeaker signal and the speech source signal. The predictors calculate row operations which are applied to prewhiten the speech source signal, resulting in a least squares system that is solved recursively by means of normalized least mean square or recursive least squares algorithms. Simulations show that this approach is indeed superior to earlier approaches whenever long acoustic channels are dealt with View full abstract»

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  • A linear model for TF distribution of signals

    Page(s): 3435 - 3447
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (901 KB) |  | HTML iconHTML  

    We describe a new linear time-frequency model in which the instantaneous value of each signal component is mapped to the curve functionally representing its instantaneous frequency. This transform is linear, uniquely defined by the signal decomposition, and satisfies linear marginal-like distribution properties. We further demonstrate the transform generated surface may be estimated from the short time Fourier transform by a concentration process based on the phase of the short-time Fourier transform (STFT), differentiated with respect to time. Interference may be identified on the concentrated STFT surface, and the signal with the interference removed may be estimated by applying the linear-time-marginal to the concentrated STFT surface from which the interference components have been removed View full abstract»

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  • On the LMS algorithm with constant and variable leakage factor in a nonlinear environment

    Page(s): 3448 - 3458
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    This paper studies the application of the linear least-mean-square (LMS) algorithm with leakage to a system having a memoryless saturation-type nonlinearity. This approach represents an interesting alternative to the nonlinear LMS (NLLMS) algorithm. The major drawback to implement the latter is that the model parameters of the system nonlinearity must be known. In contrast, the linear LMS algorithm with leakage does not require such knowledge. It permits to approximate the performance of the NLLMS by properly selecting a constant leakage factor value. To cope with an eventual change of the environment, a strategy for a variable leakage is also proposed. Several numerical simulations are presented with the aim of ratifying the feasibility of the proposed approach View full abstract»

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  • A unified approach to multistage frequency-response masking filter design using the WLS technique

    Page(s): 3459 - 3467
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (339 KB) |  | HTML iconHTML  

    This paper presents a unified approach to the optimal design of sharp linear-phase finite-impulse-response (FIR) digital filters synthesized using the multistage frequency-response masking (FRM) technique. In this approach, the design of a k-stage FRM filter is achieved in a recursive manner. The minimax design problem arising at each step of the synthesis process is converted into a corresponding weighted least-squares (WLS) problem. The WLS problem is highly nonlinear with respect to the coefficients of the filter. Consequently, it is decomposed into several linear least-squares (LS) problems, each of which can be solved analytically. It is then solved iteratively by using an alternating variable approach. Numerical design examples are included to demonstrate the effectiveness of the method View full abstract»

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  • On the use of a priori information for sparse signal approximations

    Page(s): 3468 - 3482
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    Recent results have underlined the importance of incoherence in redundant dictionaries for a good behavior of decomposition algorithms like matching and basis pursuit. However, appropriate dictionaries for a given application may not be able to meet the incoherence condition. In such a case, decomposition algorithms may completely fail in the retrieval of the sparsest approximation. This paper studies the effect of introducing a priori knowledge when recovering sparse approximations over redundant dictionaries. Theoretical results show how the use of reliable a priori information (which in this paper appears under the form of weights) can improve the performances of standard approaches such as greedy algorithms and relaxation methods. Our results reduce to the classical case when no prior information is available. Examples validate and illustrate our theoretical statements. EDICS: 2-NLSP View full abstract»

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  • Stability and stability margin for a two-dimensional system

    Page(s): 3483 - 3488
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (301 KB)  

    In this paper, a necessary and sufficient condition is derived for the stability of the Fornasini-Marchesini (FM) first model proposed for two-dimensional (2-D) dynamic system descriptions. A connection has been established between the stability of this model and the structured singular value (SSV) of a constant matrix, which is now widely known in control theories. Based on this connection, a novel sufficient condition is obtained for the stability of the FM first model that is more computationally convenient in filter design. Numerical simulations show that this sufficient condition is usually less conservative than that of . Moreover, the stability margin of the FM first model is also investigated. It is shown that a 2-D system remains stable under parametric variations if and only if the SSV of a constant matrix is smaller than one View full abstract»

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  • Public-key cryptography using paraunitary matrices

    Page(s): 3489 - 3504
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    In this paper, we propose an algebraic approach for designing multivariate cryptosystems. Our method is based on formulating a general system of multivariate polynomial equations by paraunitary matrices. These matrices are a special family of invertible polynomial matrices that can be completely parameterized and efficiently generated by primitive building blocks. Using the general formulation that involves paraunitary matrices, we design a one-way function that operates over the fields of characteristic two. In order to include a trapdoor, we make some approximations to the paraunitary matrix. The result is a trapdoor one-way function that is efficient to evaluate but hard to invert unless secret information about the trapdoor is known. Using this function, we propose a paraunitary asymmetric cryptosystem (PAC). We present an instance of the PAC and show how it can be efficiently implemented. This instance operates on the finite field GF(256). The message block consists of 16 to 32 symbols from GF(256), i.e., the block size n is an integer between 16 and 32. The ciphertext block takes its elements from the same field and has at least ten extra symbols. We show that the encryption and decryption can be efficiently performed with complexities O(n3) and O(n2), respectively, where n is the size of the message block. Comparing complexities of the PAC to those in the hidden-field equation (HFE) family, we show that the PAC is faster in public-key generation and decryption. We study the computational security of the PAC. In addition, we show that the attacks developed for the HFE are not applicable on the PAC View full abstract»

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  • Operational rate-distortion modeling for wavelet video coders

    Page(s): 3505 - 3517
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    Based on our statistical investigation of a typical three-dimensional (3-D) wavelet codec, we present a unified mathematical model to describe its operational rate-distortion (RD) behavior. The quantization distortion of the reconstructed video frames is assessed by tracking the quantization noise along the 3-D wavelet decomposition trees. The coding bit-rate is estimated for a class of embedded video coders. Experimental results show that the model captures sequence characteristics accurately and reveals the relationship between wavelet decomposition levels and the overall RD performance. After being trained with offline RD data, the model enables accurate prediction of real RD performance of video codecs and therefore can enable optimal RD adaptation of the encoding parameters according to various network conditions View full abstract»

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  • Effective channel order estimation based on combined identification/equalization

    Page(s): 3518 - 3526
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (568 KB)  

    Channel order estimation is a critical step in most blind single-input multiple-output (SIMO) channel identification/equalization algorithms. Several methods for estimating either the true channel order or its most significant part (the so-called effective channel order) have been recently proposed, but a solution able to work in practical scenarios (low or moderate signal-to-noise ratios (SNRs) and channels with small leading and/or trailing coefficients) has not been found yet. In this paper, a new criterion for effective channel order detection of SIMO channels is presented. The method is based on the fact that the cost function typically used in blind identification algorithms decreases monotonically with the estimated channel order, whereas for blind equalization algorithms, the cost function increases monotonically. In this paper, it is shown that a straightforward combination of both cost functions attains its minimum at the correct channel order even for moderate SNRs. The proposed method is able to work with small data sets, colored signals, and channels with small head and tail taps, which is a common problem in communication applications. The improvement of the proposed criterion over a number of existing algorithms is demonstrated through simulations View full abstract»

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Aims & Scope

IEEE Transactions on Signal Processing covers novel theory, algorithms, performance analyses and applications of techniques for the processing, understanding, learning, retrieval, mining, and extraction of information from signals

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Meet Our Editors

Editor-in-Chief
Sergios Theodoridis
University of Athens