nils-herrmann/clean_scirepeval huggingface dataset:
A comprehensive study of three moduli sets for residue arithmetic

One important problem in residue arithmetic is the choice of modulo sets to represent the binary numbers in a certain range. In recent years, several general three-modulo sets have been introduced, and each of them is claimed to have some advantages. In this paper, we carry out a comprehensive study for all these modulo sets from the point of view of the hardware complexity and the speed of their residue-to-binary converters. Based on a performance evaluation of the VLSI implementation in terms of area and delay, we conclude that to represent 8-bit, 16-bit, 32-bit and 64-bit binary numbers, the set of moduli {2/sup n/-1, 2/sup n/, 2/sup n/+1} has the fastest residue-to-binary converter requiring the smallest area. The converter for this moduli set is designed based on the new Chinese remainder theorem of Y. Wang (1998).
Joint 2-D DOA Estimation via Sparse L-shaped Array

In this paper, we address the problem of estimating the two-dimensional (2-D) directions of arrival (DOA) of multiple signals, by means of a sparse L-shaped array. The array consists of one uniform linear array (ULA) and one sparse linear array (SLA). The shift-invariance property of the ULA is used to estimate the elevation angles with low computational burden. The signal subspace is constructed by the cross-covariance matrix (CCM) of the received data without implementing eigendecomposition. The source waveforms are then obtained by the estimated elevation angles, which together with each sensor of the SLA, considered as a linear regression model, is used to estimate the azimuth angle by the modified total least squares (MTLS) technique. Our new algorithm yields correct parameter pairs without requiring the computationally expensive pairing operation, and therefore, has at least two advantages over the previous L-shaped array based algorithms: less computational load and better performance due to the use of SLA and CCM. Expressions for the asymptotic mean-squared error (MSE) of the 2-D DOA estimates are derived. Simulation results show that our method provides accurate and consistent 2-D DOA estimation results that could not be obtained by the existing methods with comparable computational complexity.
A novel transform for image compression

In this paper, we propose an orthogonal multiplication-free transform of order that is an integral power of two by an appropriate extension of the well-known fourthorder integer discrete cosine transform. Moreover, we develop an efficient algorithm for its fast computation. It is shown that the computational and structural complexities of the algorithm are similar to that of the Hadamard transform. By applying the proposed transform to image compression, we show that it outperforms the existing transforms having complexities similar to that of the proposed one.
Object-based image watermarking technique using wavelets

We present a wavelet-based image watermarking algorithm that is able to embed the watermark information into objects within an image. In the proposed algorithm, the zerotrees in the wavelet coefficient domain corresponding to the selected object(s) are identified. Then the watermark is embedded into the bandpass wavelet coefficients with large amplitudes within these zerotrees by using an appropriate quantization process. The bitmap of the zerotrees corresponding to the selected objects, the wavelet used for the transform, and the parameters for the transform and quantization are chosen as the keys for the proposed algorithm. The effectiveness of the proposed watermarking technique and its robustness against some commonly encountered image processing operations are demonstrated through simulation studies.
The applications of the new Chinese Remainder Theorems for three moduli sets

In this paper, we apply the New Chinese Remainder Theorem I (New CRT-I) to design the residue-to-binary converters for all the recently introduced three moduli sets M/sub 1/={22/sup n/+1,2/sup n/+1,2/sup n/-1}, M/sub 2/={2/sup n/,2/sup n/-1,2/sup n-1/-1}. M/sub 3/={2/sup n/,2/sup n/+1,2/sup n/-1}, and M/sub 4/=(2n, 2n+1, 2n-1). For all these moduli sets, the converters based on the New CRT-I are consistently faster and require less hardware, compared to the previous residue-to-binary converters which are designed based on the traditional Chinese Remainder Theorem.
General results concerning the BIBO stability of inverse 2-D digital filters

Consideration is given to the open problem concerning the BIBO (bounded-input, bonded-output) stability of inverse 2-D digital filters in the presence of nonessential singularities of the second kind on T/sup 2/, the distinguished boundary of the unit bidisk. Necessary and sufficient conditions are obtained for the BIBO stability as well as the boundedness of a large class of mutually inverse 2-D digital filters having such singularities on T/sup 2/. Some illustrative examples are included.
An iterative procedure for uniform resampling of a 2-D signal from its nonequally-spaced samples

An efficient technique is presented for an approximate reconstruction of a finite-energy bandlimited two-dimensional (2-D) signal from its unequally spaced samples which are located in the vicinity of uniformly distributed (with a rate at or above the Nyquist frequency) synchronous points. The signal is given by the set of its unequally spaced samples, and it suffices to find their uniformly distributed counterparts and then use the 2-D Shannon sampling theorem for signal reconstruction. A set of uniformly spaced samples is evaluated by solving a system of linear equations given by the 2-D Shannon theorem. To reduce the computational complexity caused by the large size of the system matrix and avoid the ill-conditioning that can occur if the nonuniform samples deviate too far from the synchronous points, the proposed method uses the partitioning of a given data block into very short overlapping subintervals of size 3*3. An innovative iterative procedure is applied to reduce the error caused by applying an extremely short 3*3 observation range. Repeated application of this procedure is used to update the estimated values of the uniformly spaced samples. A simulation example shows that the proposed windowing technique combined with the iterative procedure is simple, efficient, and rapidly convergent.<<ETX>>
General FFT pruning algorithm

The efficiency of the fast Fourier transform may be increased by removing operations on input values which are zero, and on output values which are not required; this procedure is known as FFT pruning algorithm. Up to now some algorithms have been proposed considering decimation-in-time (DIT) or decimation-in-frequency (DIF) procedures, and considering that for a N = 2/sup M/ input points of the FFT only quantities equals to 2/sup k/ (to an integer k), of nonzero input or desired output points are required. In this paper we propose a new FFT pruning algorithm where the number of nonzero inputs or desired outputs can be arbitrary. The idea of the proposed algorithm works well with DIT as well as DIEF procedures, and the implementation is similar to the FFT algorithms that use in-place computation, with a small alteration.
On the Performance of Non-Gaussian Distributions in Modelling the Wavelet Coefficients of Medical Ultrasound Images

A major problem concerning ultrasound images is their inherent corruption with speckle noise. Homomorphic wavelet-based methods using parametric models are widely used for despeckling ultrasound images. However, the efficiency of these methods greatly depends on the accuracy of the prior distribution used for modelling of the non-Gaussian statistics of the wavelet coefficients of the log-transformed reflectivity. An extensive study is carried out on the performance of the generalized Gaussian, symmetric alpha-stable and symmetric normal inverse Gaussian distributions in modelling the wavelet coefficients. It is shown that the symmetric normal inverse Gaussian distribution is a more suitable prior than the other distributions.
Semi-blind channel estimation of MIMO-OFDM systems with pulse shaping

Most of the existing MIMO-OFDM channel estimation methods do not take into account the effect of the pulse- shaping Alter in the transmitter nor of the matched Alter in the receiver, thus leading to an estimation solution for the composite channel including the pulse-shaping and matched Alters, instead of for the pure wireless channel. This solution is neither directly applicable to practical communication systems nor sufficiently accurate due to the extra length of the composite channel induced by the two Alters. In this paper, a semi-blind channel estimation method is proposed for pulse-shaped MIMO-OFDM systems. By utilizing the knowledge of pulse-shaping and matched Alters, a time domain semi-blind estimation method is developed for the pure multi-path channel. In order to reduce the computational burden of the time-domain algorithm, a frequency-domain alternative is derived. A number of computer simulation-based experimentations are conducted, and these simulations confirm the effectiveness of the proposed semi-blind method.
Sensitivity invariants for nonlinear networks

For a general class of nonlinear networks, explicit expressions for sensitivities of a response due to nonlinear elements are derived. These expressions are used to establish invariance relationships for the sums of sensitivities over different sets of parameters of two classes of nonlinear networks. It is indicated how these relationships can be used to establish the invariant nature of the sum of higher order sensitivities and a lower bound for the quadratic sensitivity index.
An hybrid filter for restoration of color images in the mixed noise environment

In this paper, an hybrid filter is presented for restoration of color images in a mixed noise environment, where both impulsive and correlation noise may be present. The proposed hybrid filter is composed of two stages, the first stage to remove the impulsive noise and the second to remove the correlated noise. The median filters and their variants are the most popular filter types used for impulsive noise suppression. However, median filters and their variants tend to remove fine image details and destroy fine texture in the mixed noise environment or when the signal to noise ratio is low. In order to overcome the limitations of the median filters, an adaptive simplified-model Kalman filter (ASMKF) is propsed for the suppression of impulsive noise in the first stage of the hybrid filter. In the second stage of the proposed hybrid filter, to remove correlated noise, discrete Wavelet Transform (DWT) filter is applied on the impulsive-noise-free image obtained from the first stage. The efficacy of the proposed hybrid filter is illustrated through implementation results obtained on the restoration of a color image.
Approximation of feature pyramids in the DCT domain and its application to pedestrian detection

Feature extraction from each scale of an image pyramid to construct a feature pyramid is considered as a computational bottleneck for many object detectors. In this paper, we present a novel technique for the approximation of feature pyramids in the 2D discrete cosine transform (2DDCT) domain. The proposed method is based on a feature resampling technique in the 2DDCT domain, and exploits the effect of resampling an image on the feature responses. Experimental results show that the proposed scheme provides feature approximation accuracy higher than that of the spatial domain counterpart using gradient magnitude or gradient histograms. Further, when the proposed method is employed for pedestrian detection, it provides a logaverage miss-rate lower than that provided by the state-of-the-art techniques on INRIA, ETH, and TUD datasets and performs favorably on Caltech dataset, while performing in real-time.
A new variable size block motion compensation

The variable size block motion compensation (VSBMC) technique is known to be more effective than the fixed-size block motion compensation technique (FSBMC) for video coding. However, the existing VSBMC techniques do not fully utilize the motion information of a frame for its partitioning. This paper presents a new adaptive partitioning scheme that utilizes more effectively the motion content of a frame in terms of the shape and size of the blocks. A new tree structure is proposed that allows not only the conventional four-to-one merge, but also three-to-one and two-to-one merges. This new VSBMC method results in a significantly improved rate-distortion performance. Test results of applying the proposed method on some MPEG video sequences are included.
Studies on N-dimensional filter transfer functions without second kind singularities

In this paper the concept of very strict Hurwitz polynomials which find applications in the design of stable two-dimensional digital filters, is extended to three and higher dimensions and the properties of such polynomials are discussed. A testing procedure to check whether a given multi-variable polynomial is a very strict Hurwitz polynomial or not is developed. Application of this concept in the design of n-dimensional digital filters without non-essential singularities of the second kind is then considered. It is shown that the concept of minimum reactive and susceptive, strict positive real functions in several variables is quite useful in the generation and testing of these filters. Such a positive real function is defined and its properties outlined. The application of transformation method to generate 3-dimensional filters from one - and two - dimensional filters is discussed.
Perceptual-Shaping Comparison of DWT-Based Pixel-Wise Masking Model with DCT-Based Watson Model

It is very important to perceptually shape the watermark signal before embedding it into a host image according to the characteristics of the HVS (human vision system) since watermark invisibility is a necessary requirement for a successful watermarking application. Two popular HVS models have been proposed to deal with this problem: DCT-based Watson model and DWT-based PWM (pixel-wise masking) model, which correspond to the DCT-based and DWT-based watermarking techniques, respectively. Even though there is a common belief that the PWM model is better than the Watson model, there have been no studies that compare these two approaches. This paper is devoted to such a comparison. Our results show that the believed superiority of the PWM model relative to the Watson model is not correct and the Watson model indeed outperforms the PWM model. We argue that more accurate masking strategies in the wavelet domain are needed for DWT-based watermarking applications.
Recursive-in-order least squares parameter estimation for 2D Gaussian Markov random field model

Presents two recursive-in-order least squares algorithms for parameter estimation of 2D Gaussian Markov random field (GMRF) models. Algorithm I implements the recursive computation by introducing auxiliary variables without changing the structure of the model, while algorithm II realizes the recursive computation by replacing the noncausal symmetric GMRF model by an equivalent causal nonsymmetric model. The concept of recursive path, which is used to increase the speed of computation of the model parameters and accomplish the choice of the optimal model support, is proposed. The computational complexity of both algorithms is O(M/sup 2/m) multiplications per order, where m is the total number of parameters and M/sup 2/ is the size of a sample image.<<ETX>>
RNS application for digital image processing

In this paper, we carry out a study on the RNS (residue number system) application in digital image processing and propose a RNS image coding scheme that offers high-speed and low-power VLSI implementation for secure image processing. The proposed scheme is more efficient than the RNS image coding scheme of Ammar et al. (2001) in that the proposed method encrypts the entire image and does not require any additional component other than a standard RNS system. Further, the proposed scheme is based on the modified CRT and its associated residue-to-binary conversion and moduli selection methods and is more efficient than the scheme by Ammar et al. (2001) in terms of VLSI implementation. The design of an encoder and decoder pair for the greyscale image is carried out using MATLAB tool and some VLSI tools. The preliminary results of the Matlab simulation demonstrate the security ability of the proposed image coding scheme.
Multiplicative Watermark Decoder in Contourlet Domain Using the Normal Inverse Gaussian Distribution

In recent years, many works on digital image watermarking have been proposed all aiming at protection of the copyright of an image document or authentication of data. This paper proposes a novel watermark decoder in the contourlet domain . It is known that the contourlet coefficients of an image are highly non-Gaussian and a proper distribution to model the statistics of the contourlet coefficients is a heavy-tailed PDF. It has been shown in the literature that the normal inverse Gaussian (NIG) distribution can suitably fit the empirical distribution. In view of this, statistical methods for watermark extraction are proposed by exploiting the NIG as a prior for the contourlet coefficients of images. The proposed watermark extraction approach is developed using the maximum likelihood method based on the NIG distribution. Closed-form expressions are obtained for extracting the watermark bits in both clean and noisy environments. Experiments are performed to verify the robustness of the proposed decoder. The results show that the proposed decoder is superior to other decoders in terms of providing a lower bit error rate. It is also shown that the proposed decoder is highly robust against various kinds of attacks such as noise, rotation, cropping, filtering, and compression.
Statistical modeling of multimodal neuroimaging data in non-subsampled shearlet domain using the student's t location-scale distribution

Statistical modeling of high dimensional, correlated, and complex imaging data obtained from various longitudinal neuroimaging studies has become an inevitable part of automatic disease diagnosing tasks. In this paper, we propose a parametric brain image modeling based on the statistical properties of non-subsampled shearlet transform (NSST) coefficients. The NSST detail coefficients of multimodal neuroimaging data exhibit highly non-Gaussian property, i.e., the probability density function (PDF) of the NSST coefficients are sharply peaked around zero with heavy tails. As a consequence, the marginal statistics of the detail subband coefficients are modeled by student's t location-scale PDF, which has heavier tails (more prone to outliers) than the Gaussian distribution for smaller values of the shape parameter. The Jensen-Shannon divergence (JSD) goodness-of-fit shows that the detail NSST subbands of neuroimaging data in the longitudinal Alzheimer's disease neuroimaging initiative database are well approximated by student's t location-scale distribution compared to that by the traditional generalized Gaussian distribution.
A closed-form semi-blind solution to MIMO-OFDM channel estimation

Semi-blind channel estimation as a combination of the training-based or pilot-assisted method and a pure blind approach is considered to be a feasible solution for practical wireless systems due to its better estimation accuracy as well as spectral efficiency. However, in the existing semi-blind channel estimation techniques, the weighting factor employed to trade off the training-based and the blind criteria has not been appropriately determined. In this paper, a closed-form solution is developed for semi-blind channel estimation of MIMO-OFDM systems. An appealing scheme for the computation of the weighting factor is proposed, leading to an analytical expression for the weighting factor in terms the MSE (mean square error) of the training-based criterion and that of the blind part. A number of computer simulation-based experiments are conducted, confirming the effectiveness of the derived semi-blind solution.
Ramanujan sums and discrete Fourier transforms

A special class of even-symmetric periodic signals is introduced. The most distinctive feature of these signals is that their real-valued Fourier coefficients can be calculated by forming a weighted average of the signal values using integer-valued coefficients. The signals arise from number-theoretic concepts concerning a class of functions called even arithmetical functions. The integer-valued weighting coefficients, being sums of complex roots of unity, are the Ramanujan sums and may be computed recursively or through closed-form arithmetical relations. The recursive method of computation is based on the cyclotomic polynomials and is described in detail. If the signal values are integers, the computation of the discrete Fourier transform (DFT) coefficients of this class of signals can be performed in an exact quantization-error-free manner by performing arithmetical operations on integers. The theoretical development is supplemented by concrete examples.
L/sub /spl infin//-norm based partial-update adaptive filtering algorithm for echo cancellation

We provide a framework for developing a low-complexity adaptive filtering algorithm by incorporating the concept of partial-updating into the technique of finding the gradient vector in the hyperplane based on the L/sub /spl infin//-norm criterion. The resulting algorithm is referred to as the partial-update normalized sign LMS (PU-NSLMS) algorithm. A specific case of the PU-NSLMS algorithm, called the M-Max PU-NSLMS algorithm, based on the concept of having a minimum Euclidean length of the coefficient-update vector, is considered. It is shown that this algorithm is computationally less complex compared to the partial-update normalized least-mean squares (PU-NLMS) algorithm. Results concerning the mean-square analysis of the M-Max PU-NSLMS algorithm are given. The performance of this algorithm is compared with that of the PU-NLMS algorithm in the case of network echo cancellation. It is shown that the convergence rate of the proposed algorithm is comparable to that of the PU-NLMS algorithm, but with a reduced complexity, making it a good choice for applications requiring a long filter tap, especially for real-time implementations.
A CMOS current divider

In this article, a simple circuit is proposed to implement the division of one current by another. This circuit is composed of two MOS transistors, a voltage comparator, and two capacitors. The divider operates well in a weak current range. Using a 1.5 /spl mu/m single-poly CMOS technology, the proposed circuit occupies a silicon area of approximately 30 /spl mu/m/spl times/40 /spl mu/m.
Near-Field Localization of Partially Polarized Sources with a Cross-Dipole Array

The problem of near-field partially polarized electromagnetic source localization using an array of cross-dipoles, each of which consists of one x-axis dipole and one y-axis dipole, is addressed. The maximum likelihood (ML) algorithm for estimating the angle and range parameters is developed. This algorithm requires no search over the polarization parameters. We also show that the ML algorithm can be implemented by processing the x-axis dipole data and the y-axis dipole data separately. In addition, a subspace-based algorithm, which is based on the ideas of MUSIC and generalized ESPRIT, is presented. This algorithm decouples the two-dimensional (2D) search into two successive one-dimensional (1D) searches, where the angle and range parameters are estimated in succession. The deterministic Cramer-Rao bound (CRB), for the problem under consideration, is also derived. The performance of the subspace-based algorithm is evaluated and compared with that of the ML algorithm and the CRB.