nils-herrmann/clean_scirepeval huggingface dataset:
An efficient, low-complexity, normalized LMS algorithm for echo cancellation

Modem teleconferencing systems contain multiple audio channels. Algorithms for acoustic echo cancellation form an integral part of these systems. They need to be computationally efficient and rapidly converging. Normalized least mean square (NLMS) algorithms, because of their simple architecture and robust performance, form the backbone of the echo cancellers used in the industry. They become computationally expensive if used for echo cancellation in multi-channel systems. The algorithm proposed in this paper addresses this problem by incorporating the principle of partial updating of the filter coefficients in the NLMS algorithm. The performance of the proposed algorithm is compared with other adaptive algorithms for acoustic echo cancellation. It is shown that the proposed algorithm has a reduced complexity, while providing a good overall performance.
Pyramidal motion estimation techniques exploiting intra-level motion correlation

The conventional pyramidal motion estimation technique, although capable of reducing the heavy computational load as required by the full search block matching algorithm, has the problem of propagating false motion vectors. In this work, pyramidal motion estimation techniques that exploit the intra-level motion correlation to overcome this serious drawback are presented. Instead of scaling the motion vectors from the corresponding positions at the adjacent lower pyramidal level as the prediction motion vectors for the current pyramidal level as is done in the conventional technique, the prediction motion vectors are generated through either median filtering or linear prediction of a set of neighboring motion vectors. Various pyramidal data structures are employed to test the proposed techniques. Simulation results show that the proposed techniques not only improve the prediction performance, but also result in a more consistent motion field. It is further shown that this improvement in the performance is achieved with negligible extra computational load.
A Robust Multibit Multiplicative Watermark Decoder Using a Vector-Based Hidden Markov Model in Wavelet Domain

The vector-based hidden Markov model (HMM) is a powerful statistical model for characterizing the distribution of the wavelet coefficients, since it is capable of capturing the subband marginal distribution as well as the inter-scale and cross-orientation dependencies of the wavelet coefficients. In this paper we propose a scheme for designing a blind multibit watermark decoder incorporating the vector-based HMM in wavelet domain. The decoder is designed based on the maximum likelihood criterion. A closed-form expression is derived for the bit error rate and validated experimentally with Monte Carlo simulations. The performance of the proposed watermark detector is evaluated using a set of standard test images and shown to outperform the decoders designed based on the Cauchy or generalized Gaussian distributions without or with attacks. It is also shown that the proposed decoder is more robust against various kinds of attacks compared with the state-of-the-art methods.
Basic nonuniform transmission lines

It is shown that for any given nonuniform transmission line (n.u.t.l.) ℒ, (which may be a multilayered RC network or a multiwire transmission line), with a per-unit-length series impedance Z(x) and shunt admittance Y(x), there always exists an electrically equivalent ‘inverse line’ ℒE, for which ZY is constant. Further, two more electrically equivalent lines may be found for which either Z or Y is constant. Although the study of any n.u.t.l. can be carried out directly, it is often advantageous to do so in terms of its equivalents. At present, the inverse line seems to be very attractive from a constructional point of view. It is also shown that for 2-wire lines, the different classes of n.u.t.l.s (including all their generalisations) known to have hyperbolic solutions are all equivalent to one of the ‘basic lines‘, namely the uniform line, the exponential line, the algebraic line z = z0(l + kx)±2, y = y0(l + kx)±2, the trigonometric line z = z0 cos ±2 (mx + n), y = y0 cos±2 (mx + n), or the hyperbolic line z = z0 cosh±2 (mx + n), y = y0 cosh±2 (mx + n). Thus, the study of networks containing n.u.t.l.s with hyperbolic solutions may be carried out in terms of these basic lines.
Rapid identification of epileptogenic sites in the intracranial EEG

The paper presents a novel computationally simple, easy-to-interpret compressed EEG display for multichannel intracranial EEG recordings. The compressed display is based on the level of sharp activity (relative sharpness index (RSI)) in the EEG, which profoundly increases during paroxysmal activities. RSI is graphically presented as a color-intensity plot that allows compressing several hours of EEG into a single display page. RSI display is a bird's-eye-view of the EEG that may reveal seizure evolution ('build-up'), seizure precursors, or sites associated with the seizures. We present examples from two patients to illustrate the method's ability to identify epileptogenic sites that may be difficult to observe in the conventional review process. RSI is compared with the color density spectral array (CDSA) and amplitude integrated EEG (aEEG) display. Examples demonstrate the RSI display to be simple, easy to interpret, computationally light and fast enough for online application.
An upper bound for the zeros of a polynomial

Introduces an upper bound for the zeros of a polynomial. The result is obtained by applying Cauchy's theorem to a polynomial derived from the original polynomial through an exponential transformation. The advantage of such a transformation is illustrated through several examples, where the results are compared to existing results. The authors also examine convergence of the bound after iterative transformations of a polynomial, and show the improvement over the existing bounds, when they are applied to the transformed polynomial.<<ETX>>
New active RC-networks for the generation and detection of single-sideband signals

A class of polyphase active coupled networks for quadrature signal generation is presented. These networks employ resistors, lowprecision capacitors, and commercial integrated operational amplifiers. The sequence discrimination approach implemented in the analysis of these networks is very promising in single-sideband (SSB) generation and detection. It has the advantage of achieving both frequency translation and filtering in a single step. Consequently, the iteration problems associated with active filters at high frequencies are solved. It is shown that by applying feedback between the input and output terminals of these networks, both the gain and phase sensitivities to the component variations are improved. The performance of the proposed networks is analyzed and compared to that of the well-established all-pass realization. The superiority of the proposed networks, due to their relative insensitivity to the component variations, in comparison with the all-pass technique is also verified experimentally.
A compressive sensing method for noise reduction of speech and audio signals

Recently, compressive sensing (CS) has been intensively studied in the fields of applied mathematics and signal processing. However, its application to speech processing has not been well discussed. In this paper, we propose a compressive sensing method for noise reduction of speech and audio signals. The noise reduction problem is formulated in the theoretical framework of CS, as an ℓ1-minimisation problem with a linear combination of a constrained term, by adopting a random partial Fourier transform operator. Furthermore, a gradient descend line search (GDLS) algorithm is adopted to efficiently solve the optimisation problem. Finally, we demonstrate that the proposed method is quite effective in reducing the noise of speech signals, especially for stationary and nonstationary white Gaussian noises.
A general framework for the design of stack filters using the L/sub p/ norm objective function

It has been thought for some time now that the design of stack filters using the L/sub p/ norm is mathematically intractable. This paper, for the first time, addresses the problem of designing optimal stack filters by employing an L/sub p/ norm of the error between the desired signal and the estimated one. It is shown that the L/sub p/ norm can be expressed as a linear function of the decision errors at the binary levels of the filter. Thus, an L/sub p/-optimal stack filter can be obtained as a solution of a linear program. The conventional design of using the mean absolute error (MAE), therefore, becomes a special case of the general, L/sub p/ norm based design, developed here. The conventional MAE design of an important subclass of stack filters, the weighted order statistic filters, is also extended to the L/sub p/ norm-based design. By considering a typical application of restoring images corrupted with impulsive noise, several design examples are presented, to illustrate that the L/sub p/-optimal stack filters with p/spl ges/2 can provide a far superior performance in terms of their capability of removing impulsive noise, compared to that achieved by using the conventional minimum MAE stack filters.
A least-square method for the design of 2-D FIR digital filters with arbitrary frequency responses

In this paper, the least-square design problem of 2-D FIR digital filters with arbitrary frequency responses is investigated. By exposing and exploiting some of the characteristics of the complex matrices associated with the approximation problem, a closed-form solution is developed. The solution is presented as an explicit expression for the impulse response corresponding to the desired frequency response specifications. Thus, the method avoids the usual time-consuming procedures of optimization or matrix inversion and makes an extremely fast calculation of the filter's coefficients possible. It is also shown that when this solution is used to design some specific classes of filters, further computational savings can be achieved. Design examples for both linear-phase and nonlinear-phase filters are considered.
Wavelet-based image denoising with the normal inverse Gaussian prior and linear MMSE estimator

A new spatially adaptive wavelet-based method is introduced for reducing noise in images corrupted by additive white Gaussian noise. It is shown that a symmetric normal inverse Gaussian distribution is highly suitable for modelling the wavelet coefficients. In order to estimate the parameters of the distribution, a maximum- likelihood-based technique is proposed, wherein the Gauss-Hermite quadrature approximation is exploited to perform the maximisation in a computationally efficient way. A Bayesian minimum mean-squared error (MMSE) estimator is developed utilising the proposed distribution. The variances corresponding to the noise- free coefficients are obtained from the Bayesian estimates using a local neighbourhood. A modified linear MMSE estimator that incorporates both intra-scale and inter-scale dependencies is proposed. The performance of the proposed method is studied using typical noise-free images corrupted with simulated noise and compared with that of the other state-of-the-art methods. It is shown that the proposed method gives higher values of the peak signal-to-noise ratio compared with most of the other denoising techniques and provides images of good visual quality. Also, the performance of the proposed method is quite close to that of the state-of-the-art Gaussian scale mixture (GSM) method, but with much less complexity.
Image encryption using the reciprocal-orthogonal parametric transform

Discrete transforms have been widely used in various signal processing applications. Specifically, transform-based data encryption techniques have become attractive for many recent communication systems. In this paper, we propose a fast and efficient image encryption method based on the reciprocal-orthogonal parametric (ROP) transform. By exploiting the properties of the ROP transform, we show that its independent parameters can successfully be used as an additional secret key for encryption. Experiments results carried clearly show the efficiency of the proposed encryption method.
A Semiblind Channel Estimation Approach for MIMO–OFDM Systems

In this paper, a very efficient semiblind approach that uses a training-based least square criterion along with a blind constraint is proposed for multiple-input-multiple-output-orthogonal frequency-division multiplexing (MIMO-OFDM) channel estimation. The blind constraint is derived from the linear prediction of the received MIMO-OFDM signal and is used in conjunction with a weighting factor in the semiblind cost function. An appealing scheme for the determination of the weighting factor is presented as a part of the proposed approach. A perturbation analysis of the proposed method is conducted to justify the superiority of the semiblind solution and to obtain a closed-form expression for the mean square error (MSE) of the blind constraint, further facilitating the calculation of the weighting factor. The proposed method is validated through computer simulation-based experimentations, showing a very high estimation accuracy of the semiblind solution in terms of the MSE of the channel estimate.
A neighborhood-blocks motion estimation technique using the pyramidal data structure

In this paper, a pyramidal motion estimation technique that makes use of the motion correlation within a pyramidal level is proposed. Instead of scaling the motion vectors from the adjacent lower pyramidal level as motion predictions as is done in the conventional technique, in the proposed technique, the motion vectors from the neighboring motion blocks are taken into consideration as possible candidates. Each of these candidate motion vectors is used for prediction and refined. The motion vector that has the least matching distortion is chosen as the final motion vector. Compared to the conventional pyramidal motion estimation technique, the proposed method effectively overcomes the problem of propagation of false motion vectors. Simulation studies show that a substantial performance improvement is achieved, both in terms of the prediction mean square error and the number of coding bits for the motion vectors.
Technique for designing biorthogonal wavelet filters with an application to image compression

A technique for designing new symmetric biorthogonal wavelets from a given symmetric regular filter is presented. The main idea is to find a symmetric complementary filter of a given regular filter such that it has the least mean square (LMS) amplitude deviation from the ideal halfband lowpass filter. New biorthogonal wavelet filter pairs can be obtained via factoring the product of the complementary polynomial and the given binomial. By applying these new symmetric biorthogonal wavelet filters to the compression of some complicated images an improved result in reducing artefacts may be achieved.
Performance analysis of 2-D DOA estimation via L-shaped array

Signal direction-of-arrival (DOA) estimation using an L-shaped array of sensors configured by two uniform linear arrays (ULA) has been an active research topic in array signal processing. A simple but efficient DOA estimation method has recently been proposed by exploiting the L-shape array geometry and the cross-correlation information of sensor data. In this paper, the asymptotic variance of the estimated azimuth and elevation angles is discussed. Unlike the traditional asymptotic analysis of ESPRIT algorithm where the eigen-decomposition of auto-correlation is used, herein we will focus on the first order perturbation for the cross-correlation matrix where the noise is eliminated. The theoretical analysis is verified through numerical results.
Improved image restoration using wavelet-based denoising and fourier-based deconvolution

Deconvolution of images is an ill-posed problem, which is very often tackled by using the diagonalization property of the circulant matrix in the discrete Fourier transform (DFT) domain. On the other hand, the discrete wavelet transform (DWT) has shown significant success for image denoising because of its space-frequency localization. In this paper, we propose an iterative image restoration algorithm, wherein the DFT-based adaptive regularized constraint total least-squares deconvolution is performed followed by our previously proposed DWT-based maximum a posteriori estimator. The convergence of the proposed method is assured. Experimental results on standard images show that the proposed method provides a restoration performance, which is better than that of several existing methods in terms of signal-to-noise ratio and visual quality.
Real-Valued ESPRIT for two-dimensional DOA estimation of noncircular signals for acoustic vector sensor array

In this paper, we propose a real-valued ESPRIT algorithm for two-dimensional direction of arrival (2D DOA) estimation of noncircular signals using arbitrarily spaced acoustic vector sensor array. By utilizing the noncircularity of the signals, the proposed algorithm provides a better estimation performance while having only slightly larger computational complexity as compared to the traditional complex ESPRIT algorithm. Furthermore, the proposed algorithm gives automatically paired azimuth and elevation angle estimates without requiring extra pair matching. Simulation results are presented to demonstrate the estimation performance of the proposed algorithm as compared to the traditional ESPRIT algorithm as well as Cramer-Rao bound (CRB).
Moduli selection in RNS for efficient VLSI implementation

In this paper, we carry out a study on an important issue concerning the use of residue numbers in the design of digital systems, namely, the moduli selection. Based on a new formulation of the Chinese remainder theorem and the efficient residue-to-binary (R/B) converters designed therefrom, we propose a guideline for the selection of the low-cost moduli sets for different dynamic ranges. These sets consist of the low-cost moduli of the form 2/sup n/, ( 2/sup n/-1) or (2/sup n/ + 1), which can offer simplified modulo adders and multipliers. It is shown that for medium dynamic ranges (less than 22 bits), the three-moduli set {2/sup n/,2/sup n/ + 1,2/sup n/ - 1} is the most efficient one in terms of the design of a complete RNS system. On the other hand, for large dynamic ranges (equal to or larger than 22 bits), the general-moduli set in the form (2/sup n/, 2/sup n/ + 1,2/sup n/ - 1,2/sup n/ /spl plusmn/1,...,2/sup n/, /spl plusmn/1), with a length greater than three, is the most efficient one. These results provide the possibility of a wide range of applications of the residue number system in the design of DSP, telecommunication and cryptography systems.
An iterative blind cyclostationary beamforming algorithm

The cross-correlation neural network proposed in Diamantaras and Kung (1994) is an efficient iterative method for singular value decomposition. In this paper, we propose an iterative blind cyclostationary beamforming algorithm, which is inspired by the cross-correlation neural model. It can be used to extract signals with cyclostationarity. The new algorithm is a gradient decent-based method. It is fast, simple, and easy to implement. Simulation shows that it can provide good performance as long as the learning rate is suitably selected.
TMS artifact removal from neurophysiolical recordings using a novel iterative adaptive filtering

A novel iterative adaptive filtering approach is proposed to remove the Transcranial Magnetic Stimulation (TMS) induced artifact from multi-channel recordings of neural responses to sensory stimuli. For each specific channel, the average of all trials is considered as the input to the adaptive filter whose coefficients are calculated by minimizing the mean square error between the voltage trace of that trial and the filter output. The residues of all trials serve as an initial estimate of the neural response. Once this estimate is calculated, the input of the adaptive filter is modified by subtracting the mean residue. It is shown that the modified input provides a better estimate of the mean TMS artifact, which serves the input of the adaptive filer, in the next iteration. Therefore, new filter coefficients are estimated in the next iteration, for each single trial, and the procedure continues till no considerable changes in the residues occur. We report a quantitative verification of the accuracy of our method by generating a controlled simulation. Furthermore, applying the algorithm to experimental data confirms the accuracy of our approach and its usefulness for extracting neurophysiological responses occurring in temporal proximity to TMS pulses.
Further results on 4-fold rotational symmetry in 2-D functions

The presence of various types of symmetries in the frequency responses of a two-dimensional filter function reflects as constraints on its coefficients. The exploitation of these constraints leads to considerable reduction in the design and implementation complexities of two-dimensional filters. In this paper, some new constraints resulting from 4-fold rotational symmetry in frequency responses of two-dimensional filters are derived. The class of McClellan transformations that could be used in the generation of 2-D FIR filters possessing 4-fold rotational symmetry in their frequency responses is also obtained.
An Efficient Implementation of the Delay Compensation for Sub-Band Filtered-x Least-Mean-Square Algorithm

The sub-band filtered-x least-mean-square algorithm used in the active control of noise and vibration suffers from reduced stability bounds due to the error-path delay. The error-path delay results from both the secondary path and the analysis filter bank. To restore the stability bounds, a method has been suggested, whereby the delay is compensated. However, this results in an increased computational complexity. In this brief, we derive an efficient implementation of the error-path delay compensation that results in substantial computational saving
Low-complexity, hierarchically-adapted wavelet thresholding for image denoising

We present a low-complexity wavelet thresholding scheme that utilizes the correlation that exists between coefficients belonging to subsequent resolution levels in a multi-scale representation of images. The proposed threshold adapts to the data and noise statistics in each subband. Spatial adaptation for each subband coefficient is achieved by making use of its corresponding parent coefficient. Experimental results show that the proposed hierarchically-adapted threshold outperforms several popular thresholding schemes for image denoising, both in terms of PSNR and visual quality.
An FPGA implementation of an on-line radix-4 CORDIC 2-D IDCT core

In this paper, we present a new architecture for a two-dimensional (2D) inverse discrete cosine transform (IDCT) core based on a modified radix-4 on-line CORDIC algorithm and distributed arithmetic (DA). The architecture is designed to take advantage of the "carry-free" addition property of redundant number representation and the "multiplierless" property of DA. The core operates on blocks of 8/spl times/8 pixels, with 12-bit and 9-bit precision for inputs and outputs, respectively. The proposed design is implemented on Xilinx Virtex XC2V 1000 FPGA. The test results show that the core for IDCT can operate at 100 MHz, while meeting the accuracy requirements of the CCITT H.26x standard.
Sufficiency conditions for the stability of a class of 3-D functions with nonessential singularities of the second kind

In this correspondence, we state sufficiency conditions for boundedness, and 1_{2^-} and 1_{1^-} stabilities of a 3-D function G(z_1, z_2, z_3) = P(z_1, z_2, z_3)/Q(z_1, z_2, z_3) , where Q is a polynomial which is linear in all its three variables, and P/Q has some second kind nonessential singularities in \overline{U}^3 - U^3 . Some illustrative examples are also given.