nils-herrmann/clean_scirepeval huggingface dataset:
An improved radix-16 FFT algorithm

An improved radix-16 decimation-in-frequency (DIF) FFT algorithm is proposed by introducing new indices for some of the output sub-sequences resulting from the conventional radix-16 DIF decomposition of the DFT. This improved radix-16 DIF FFT algorithm achieves savings of more than 46% in the number of twiddle factor evaluations or accesses to the lookup table and address generations compared to the conventional radix-16 DIF FFT algorithm. These savings are achieved without imposing any additional computational or structural complexity in the algorithm.
Lp norm design of stack filters

This paper addresses the problem of designing optimal stack filters by employing an Lp norm of the error between the desired signal and the estimated one. It is shown that the Lp norm can be expressed as a linear function of the decision errors at the binary levels of the filter. Thus, an Lp-optimal stack filter can be determined as the solution of a linear program. The conventional design of using the mean absolute error (MAE), therefore, becomes a special ease of the general Lp norm-based design developed here. Other special cases of the proposed approach, of particular interest in signal processing, are the problems of optimal mean square error (p=2) and minimax (p-->infinity) stack filtering. Since an Linfinity optimization is a combinatorial problem, with its complexity increasing faster than exponentially with the filter size, the proposed Lp norm approach to stack filter design offers an additional benefit of a sound mathematical framework to obtain a practical engineering approximation to the solution of the minimax optimization problem. The conventional MAE design of an important subclass of stack filters, the weighted order statistic filters, is also extended to the Lp norm-based design. By considering a typical application of restoring images corrupted with impulsive noise, several design examples are presented, to illustrate the performance of the Lp-optimal stack filters with different values of p. Simulation results show that the Lp-optimal stack filters with p=or>2 provide a better performance in terms of their capability in removing impulsive noise, compared to that achieved by using the conventional minimum MAE stack filters.
A hidden Markov model-based blind detector for multiplicative watermarking

Nowadays, transmission of data via Internet has made illegal data distribution a major problem in digital world. Watermarking is known as a possible solution to protect digital data. In this work, we propose a blind detector for multiplicative watermarking of images in the wavelet domain. To this end, the vector-based hidden Markov model (HMM) is employed as a prior model for the wavelet coefficients of the host image. This model is known to provide an accurate fit to the distribution of the wavelet coefficients by capturing both their heavy-tailed marginal statistics and their inter-subbands and cross-orientations dependencies. Analytical expressions for the proposed watermark detector such as the mean and variance of the log-likelihood ratio test are derived and used to evaluate its performance. The performance of the proposed detector is shown to outperform that of the other detectors by providing higher detection rate and better imperceptibility of the embedded watermark. It is also shown that the proposed vector-based HMM detector under various attacks such as compression, rotation, filtering and noise, is more robust than other existing detectors.
A Signal Perturbation Free Whitening-Rotation-Based Semiblind Approach for MIMO Channel Estimation

It was shown in our previous work that, in the noise-free case, the whitening-rotation (WR)-based MIMO channel estimation algorithm is subject to a signal perturbation error, justifying that the WR-based method is efficient only in the low signal-to-noise ratio (SNR) case. In this paper, a very efficient signal-perturbation-free WR-based approach is proposed for semiblind channel estimation of MIMO systems. A novel transmit scheme is developed based on the eigenvalue decomposition of the correlation matrix of the transmitted signal. The new scheme is to send a small volume of data bearing the information of the correlation matrix to the receiver for the cancellation of the signal perturbation error so as to improve the performance of the WR-based method in the case of high SNRs. Then, a perturbation analysis of the proposed WR-based semiblind method with the new transmit scheme is conducted, leading to a closed-form expression for the mean square error (MSE) of the channel estimate. Computer simulations show that the proposed approach significantly outperforms the original WR-based method as well as some other channel estimation methods for all SNR levels.
Vector computation of the discrete Fourier transform

Since the invention of fast algorithms for the computation of the discrete Fourier transform (DFT) by Cooley and Tukey (1965), the DFT has been widely used for the frequency-domain analysis and design of signals and systems in communications, digital signal processing, and in many other areas of science and engineering. While the Cooley-Tukey algorithms are simple, regular, and efficient, they have the drawback of requiring a significant amount of overhead operations such as bit-reversal, data-swapping, etc. In this paper, we develop a generalized version of a new family of DFT algorithms by decomposing a form of the DFT relation in which the input data and transform quantities are represented as vectors. These algorithms have the features that eliminate or reduce the drawbacks of the Cooley-Tukey algorithms while improving the simplicity and regularity of their implementations. The generalized version makes it easier to deduce a large family of algorithms with different features. The relative merits of the various algorithms with different vector lengths are discussed and the optimum vector length for DFT computation is pointed out.
Linear Prediction Based Semi-Blind Channel Estimation for MIMO-OFDM System

In this paper, a semi-blind channel estimation method is presented for MIMO-OFDM systems. The new method uses the linear prediction for obtaining a blind constraint on the MIMO-OFDM channel matrix as well as the least-squares approximation for the training signal. The proposed method can be regarded as an extension of an existing semi-blind MIMO channel estimation algorithm. Yet the extension is nontrivial, since the formulation of the MIMO-OFDM signal and the related blind constraint cannot easily be obtained from the MIMO counterpart. The proposed algorithm is simulated using Monte-Carlo method and compared with the LS method in terms of the mean square error (MSE) of the estimation. Simulation results show that the proposed method consistently outperforms the LS method when the same training signal is used.
An efficient FFT algorithm based on the radix-2/4 DIF approach for computing 3D DFT

We propose a 3D split vector-radix decimation-in-frequency (DIF) FFT algorithm for computing the 3D DFT, based on a mixture of radix-(2/spl times/2/spl times/2) and radix-(4/spl times/4/spl times/4) index maps. It is shown that the proposed algorithm reduces the computational complexity significantly in comparison to the existing 3D vector radix FFT algorithms as well as algorithms that are based on row-column decomposition. In addition, since the proposed algorithm is expressed in a simple matrix form using the Kronecker product, it facilitates easy software or hardware implementation of the algorithm.
Learning the Conformal Transformation Kernel for Image Recognition

In this paper, we present a multiclass data classifier, denoted by optimal conformal transformation kernel (OCTK), based on learning a specific kernel model, the CTK, and utilize it in two types of image recognition tasks, namely, face recognition and object categorization. We show that the learned CTK can lead to a desirable spatial geometry change in mapping data from the input space to the feature space, so that the local spatial geometry of the heterogeneous regions is magnified to favor a more refined distinguishing, while that of the homogeneous regions is compressed to neglect or suppress the intraclass variations. This nature of the learned CTK is of great benefit in image recognition, since in image recognition we always have to face a challenge that the images to be classified are with a large intraclass diversity and interclass similarity. Experiments on face recognition and object categorization show that the proposed OCTK classifier achieves the best or second best recognition result compared with that of the state-of-the-art classifiers, no matter what kind of feature or feature representation is used. In computational efficiency, the OCTK classifier can perform significantly faster than the linear support vector machine classifier (linear LIBSVM) can.
A new systolic array algorithm for a high throughput low cost VLSI implementation of DCT

This paper presents a new design approach for the VLSI implementation of a prime-length discrete cosine transform DCT based on a new hardware algorithm for DCT that can be implemented using a multi-port ROM-based systolic array. The proposed algorithm is based on the idea of reformulating prime-length DCT into several cycle convolutions having the same length and similar structures. Using the proposed approach we can efficiently exploit the inherent parallelism thus doubling the throughput without to double the hardware and I/O cost but only slightly increasing them. Moreover, the proposed VLSI implementation preserves all the other advantages of the VLSI algorithms based on circular correlations or cycle convolutions such as modular and regular structures with local interconnection topology..
New Systolic Algorithm and Array Architecture for Prime-Length Discrete Sine Transform

Using a simple input-regeneration approach and index-transformation techniques, a new formulation is presented in this paper for computing an N-point prime-length discrete sine transform (DST) through two pairs of [(N-1)/4]-point cyclic convolutions, where [(N-1)/4] is an odd number. The cyclic convolution-based algorithm is used further to obtain a simple regular and locally connected linear systolic array for concurrent pipelined implementation of the DST. It is shown that the proposed systolic structure involves significantly less area-time complexity compared with that of the existing structures
A new homomorphic Bayesian wavelet-based MMAE filter for despeckling SAR images

In this paper, we introduce a new homomorphic Bayesian wavelet-based minimum mean absolute error filter for despeckling synthetic aperture radar (SAR) images. The wavelet coefficients of the logarithmically transformed reflectance image and the speckle noise image are modeled using a Cauchy prior and an additive white Gaussian noise distribution, respectively. These models are then exploited to develop a Bayesian minimum mean absolute error estimator. A method is presented for estimating the dispersion parameter of the assumed Cauchy prior. Experiments are carried out on a typical noise-free image corrupted with synthetic speckle noise and a real SAR image, and the results show that the proposed method has a performance that is superior to that of the other existing methods in terms of the peak signal-to-noise ratio, ability to suppress the speckle in the homogeneous regions and the visual quality.
New technique for designing nearly orthogonal wavelet filter banks with linear phase

A new technique based on nonlinear optimisation to design nearly orthogonal wavelet filter banks with linear phase is proposed. The main idea is to impose a certain number of zeros at z=-1 for a symmetric filter and make it satisfy the power complementary condition as accurately as possible. From this filter, a semi-orthogonal wavelet filter bank which is nearly orthogonal can be constructed. This semi-orthogonal filter bank can be approximately implemented using a filter bank consisting of only one prototype filter. The frequency selectivity can also be designed at the same time by using a weighted cost function.
Wavelet-based spatially adaptive method for despeckling SAR images

In this paper, we introduce a new spatially adaptive homomorphic Bayesian wavelet-based method for despeckling synthetic aperture radar (SAR) images. The wavelet coefficients of the logarithmically transformed reflectance image and the speckle noise image are modeled using a symmetric normal inverse Gaussian prior and an additive white Gaussian noise distribution, respectively. These models are then exploited to develop a Bayesian maximum a posteriori estimator. A method is proposed for estimating the parameters of the assumed prior. The noise-free variance of a wavelet coefficient is locally estimated, and used in a minimum mean square error estimator to obtain the corresponding noise-free coefficient. Experiments are carried out on two synthetically speckled images and a real SAR image. The results show that the proposed method has a performance that is superior to that of the other existing methods in terms of the peak signal-to-noise ratio, ability to suppress the speckle in the homogeneous regions
Synthesis using symmetric distributed RC-structures

This paper deals with the synthesis of transfer functions using symmetric structures, a symmetric structure being a three-terminal network composed of distributed and/or lumped RC elements, and having z11 = z22. Necessary and sufficient conditions are presented for the realization of a class of transfer functions by a three-terminal structure, which is a cascade of a number of networks of two types, one type being a ladder network whose different elements are cascades of symmetric structures, and the other type being a cascade of n symmetric structures. Realization procedures for this class of transfer functions are given. An extension is considered for the realization of a transfer function as a parallel interconnexion of such structures. Existing transfer function synthesis procedures using uniform lines are extended to include symmetric structures.
Recursive implementation of statistically-optimal null filters

Statistically-optimal null filters (SONFs), based on the combination of the maximum output SNR and the least-squares criteria, have been recently presented in literature. Orthogonality of the basis functions used in the series representation of the signal in question plays a key role in the implementation of such filters. In this paper, we first present the globally-optimal SONF, wherein the orthogonalization procedure is not necessary and then show how it can be implemented recursively. Simulation results are presented comparing the Gram-Schmidt (GS)-orthogonalized SONF, recursive SONF and the constrained notch filter (CNF). Ability of the SONFs to process short duration signals is emphasized.
A fast, low complexity image fusion algorithm based on multiscale transforms

In this paper, a novel image fusion algorithm based primarily on an improved multi-scale coefficient decomposition framework is proposed. The proposed framework uses a combination of non-subsampled contourlet and wavelet transforms for the initial multi-scale decompositions. The decomposed multiscale coefficients are then fused twice using various local activity measures. Experimental results show that the proposed approach performs better or in par with the existing state-of-the art image fusion algorithms in terms of quantitative and qualitative results. In addition, the proposed image fusion algorithm can produce high quality fused images even with a computationally inexpensive two-scale decomposition.
A new map estimator for wavelet domain image denoising using vector-based hidden Markov model

There are a number of image denoising methods in the wavelet domain using statistical models. It is known that the performance of such methods can be significantly improved by taking into account the statistical dependencies between the wavelet coefficients. It is shown that the vector-based hidden Markov model (VB-HMM) is capable of capturing both the subband marginal distribution and the inter-scale, intra-scale and cross orientation dependencies of the wavelet coefficients. In view of this, we propose a new maximum a posteriori estimator using the VB-HMM as a prior for the wavelet coefficients of images. This is realized by deriving an efficient closed-form expression for the shrinkage function. Experimental results are performed to evaluate the performance of the proposed denoising method. The results demonstrate that the proposed method outperforms some of the state-of-the-art techniques in terms of both the peak signal to noise ratio and perceptual quality.
Results on maximally flat fractional-delay systems

The two classes of maximally flat finite-impulse response (FIR) and all-pass infinite-impulse response (IIR) fractional-sample delay systems are thoroughly studied. New expressions for the transfer functions are derived and mathematical properties revealed. Our contributions to the FIR case include a closed-form formula for the Farrow structure, a three-term recurrence relation based on the interpolation algorithm of Neville, a concise operator-based formula using the forward shift operator, and a continued fraction representation. Three types of structures are developed based on these formulas. Our formula for the Farrow structure enhances the existing contributions by Valimaki, and by Vesma and Sarama/spl uml/ki on the subsystems of the structure. For the IIR case, it is rigorously proved, using the theory of Pade approximants, that the continued fraction formulation of Tassart and Depalle yields all-pass fractional delay systems. It is also proved that the maximally flat all-pass fractional-delay systems are closely related to the Lagrange interpolation. It is shown that these IIR systems can be characterized using Thiele's rational interpolation algorithm. A new formula for the transfer function is derived based on the Thiele continued fractions. Finally, a new class of maximally flat FIR fractional-sample delay systems that exhibit an almost all-pass magnitude response is proposed. The systems possess a maximally flat group-delay response at the end frequencies 0 and /spl pi/, and are characterized by a closed-form formula. Their main advantage over the classical FIR Lagrange interpolators is the improved magnitude response characteristics.
Spectral Efficiency Maximization for Massive Multiuser MIMO Downlink TDD Systems via Data Power Allocation with MRT Precoding

This paper studies the problem of maximizing the spectral efficiency in a massive multi-user MIMO downlink (DL) system where the base station employs a large number of antennas and serves single-antenna users in one cell, assuming time- division duplex transmission. In order to maximize the spectral efficiency in a DL transmission, a new method for power allocation among users is proposed based on the water-filling approach. To this end, a lower bound on the achievable rate is derived for each user in the presence of maximum- ratio transmission precoding and employed in defining the spectral efficiency. Since maximizing the spectral efficiency is an NP-hard problem, an effective algorithm is also proposed to find Karush-Kuhn-Tucker (local maximum) points. The performance of the proposed power allocation method is verified by conducting simulations and shown to be superior to other existing methods in terms of higher spectral efficiency.
Digital all-pass filter design through discrete Hilbert transform

A simple method for the design of a digital all-pass filter, satisfying the given group delay specification, is presented. The design is based on the discrete Hilbert transform relation, relating the log-magnitude and phase of the Fourier transform of the minimum phase signal. The transfer function of an all-pass filter is completely determined by the coefficients of the denominator polynomial. For the filter to be stable, the denominator polynomial must be minimum phase. From the given group delay specification, the phase corresponding to the pole part of the desired filter is first determined. The magnitude spectrum corresponding to the pole part of the desired filter is obtained from the above phase through the discrete Hilbert transform relation. The method needs just four fast Fourier transform operations. There is no restriction on the order of the filter, and the number of filter coefficients can be selected after the final design, depending on the accuracy desired. The procedure is illustrated through design examples.<<ETX>>
A genetic algorithm approach to the problem of factorization of general multidimensional polynomials

In this paper, a solution to the problem of the multidimensional (m-D) polynomial factorization is attempted by using genetic algorithms (GAs). The proposed method is based on an appropriate minimization of the norm of the difference between the original polynomial and its desirable factorized form. Using GAs, we can obtain better results than with other methods of minimization (numerical techniques, neural networks, etc.). The present methodology, which can also be used for every type of m-D factorization, is illustrated by means of a numerical example.
Determination of quantization error in two-dimensional digital filters

The evaluation of the quantization error in two-dimensional (2-D) digital filters involves the following computation J = Σ<inf>m=0</inf><sup>∞</sup>Σ<inf>n=0</inf><sup>∞</sup>y<sup>2</sup>(m, n). In this paper general method for the evaluation of J based on Laurent expansion of the integrand is presented. Illustrative example for such a computation is given.  We denote with V<sup>-</sup><sup>2</sup>= {(z<inf>1</inf>, z<inf>2</inf>): |z<inf>1</inf>| ≤ 1, |z<inf>2</inf>| ≤ 1} the closed unit bidisc, with V<sup>2</sup>= {(z<inf>1</inf>, z<inf>2</inf>): |z<inf>1</inf>| < 1, |z<inf>2</inf>| < 1} the open unit bidisc, and with T<inf>2</inf>= {(z<inf>1</inf>, z<inf>2</inf>): |z<inf>1</inf>| = 1, |z<inf>2</inf>| = 1} the distinguished boundary of the unit bidisc.
Neural LS estimator with a non-quadratic energy function

Least-squares (LS) estimation with a standard feedback neural network (SFBNN) which is based on an electrical model is investigated. In the energy function of a SFBNN, a non-quadratic term is included which is often neglected while solving an optimization problem. It is shown that the non-quadratic term affects the solution of a continuous optimization problem. Properties of the non-quadratic term and the relation between the estimation error and several parameters of the SFBNN are discussed. A technique, called extended space iterative search (ESIS), is introduced to reduce the estimation error. Simulation results are presented to confirm the analysis result and the effectiveness of the proposed technique.<<ETX>>
A dynamic spectrum access scheme for cognitive radio networks

In this paper, the dynamic spectrum access problem for cognitive radio (CR) networks is formulated as maximizing the sum channel capacity while satisfying the power budgets of individual secondary user radios as well as the SINR constraints on both the secondary and primary users. By applying the Karush-Kuhn-Tucker theorem, we derive a water-filling soluton. An iterative water-filling algorithm is proposed for implementing joint channel and power allocation in a dynamically changing set of available channels. The proposed algorithm has a complexity that increases linearly with both the number of channels and the number of users.