nils-herrmann/clean_scirepeval huggingface dataset:
Automatic generation of prime length FFT programs

Describes a set of programs for circular convolution and prime length fast Fourier transforms (FFTs) that are relatively short, possess great structure, share many computational procedures, and cover a large variety of lengths. The programs make clear the structure of the algorithms and clearly enumerate independent computational branches that can be performed in parallel. Moreover, each of these independent operations is made up of a sequence of suboperations that can be implemented as vector/parallel operations. This is in contrast with previously existing programs for prime length FFTs: They consist of straight line code, no code is shared between them, and they cannot be easily adapted for vector/parallel implementations. The authors have also developed a program that automatically generates these programs for prime length FTTs. This code-generating program requires information only about a set of modules for computing cyclotomic convolutions.
Maximally flat low-pass FIR filters with reduced delay

This paper describes a new class of nonsymmetric maximally flat low-pass finite impulse response (FIR) filters. By subjecting the magnitude and group delay responses (individually) to differing numbers of flatness constraints, the new filters are obtained. It is found that these filters achieve a smaller delay than symmetric filters while maintaining relatively constant group delay around /spl omega/=0, with no degradation of the frequency response magnitude. The design of these filters is initially investigated using Grubner bases. An analytic design technique, applicable to a subset of the forgoing filters, is provided that does not depend on Grubner basis computations.
Angle-Doppler processing using sparse regularization

The detection of moving objects on the ground by airborne radar is one application of space-time adaptive processing (STAP). The goal is to estimate the position and velocity of objects. This paper considers the problem as a linear inverse problem and uses ℓ1-norm regularization to promote sparsity in the solution. It is proposed that the angle-Doppler plane be explicitly segmented into the clutter ridge component and a non-clutter-ridge component. We propose that the second component be modeled as sparse — as the moving objects are assumed to be well isolated in the angle-Doppler plane.
Interpolating multiwavelet bases and the sampling theorem

This paper considers the classical sampling theorem in multiresolution spaces with scaling functions as interpolants. As discussed by Xia and Zhang (1993), for an orthogonal scaling function to support such a sampling theorem, the scaling function must be cardinal (interpolating). They also showed that the only orthogonal scaling function that is both cardinal and of compact support is the Haar function, which is not continuous. This paper addresses the same question, but in the multiwavelet context, where the situation is different. This paper presents the construction of compactly supported orthogonal multiscaling functions that are continuously differentiable and cardinal. The scaling functions thereby support a Shannon-like sampling theorem. Such wavelet bases are appealing because the initialization of the discrete wavelet transform (prefiltering) is the identity operator.
Enhanced Low-Rank Matrix Approximation

This letter proposes to estimate low-rank matrices by formulating a convex optimization problem with nonconvex regularization. We employ parameterized nonconvex penalty functions to estimate the nonzero singular values more accurately than the nuclear norm. A closed-form solution for the global optimum of the proposed objective function (sum of data fidelity and the nonconvex regularizer) is also derived. The solution reduces to singular value thresholding method as a special case. The proposed method is demonstrated for image denoising.
Wavelet Based Image Denoising with A Mixture of Gaussian Distributions with Local Parameters

The performance of various estimators, such as maximum a posteriori (MAP) is strongly dependent on correctness of the proposed model for noise-free data distribution. Therefore, the selection of a proper model for distribution of wavelet coefficients is very important in the wavelet based image denoising. This paper presents a new image denoising algorithm based on the modeling of wavelet coefficients in each subband with a mixture of Gaussian probability density functions (pdfs) that parameters of mixture model are local. The mixture model is able to capture the heavy-tailed nature of wavelet coefficients and the local parameters can model the empirically observed correlation between the coefficient amplitudes. Therefore, by using this relatively new-statistical model, we are able to better model statistical property of wavelet coefficients. Within this framework, we describe a novel method for image denoising based on designing a MAP estimator, which relies on the mixture distributions with high local correlation. The simulation results show that our proposed technique achieves better performance than several published methods both visually and in terms of peak signal-to-noise ratio (PSNR)
Multiwavelet bases with extra approximation properties

This paper highlights the differences between traditional wavelet and multiwavelet bases with equal approximation order. Because multiwavelet bases normally lack important properties that traditional wavelet bases (of equal approximation order) possess, the associated discrete multiwavelet transform is less useful for signal processing unless it is preceded by a preprocessing step (prefiltering). This paper examines the properties and design of orthogonal multiwavelet bases, with approximation order >1 that possess those properties that are normally absent. For these "balanced" bases (so named by Lebrun and Vetterli (see Proc.  Munich, Germany, vol.3, p.2473-76, 1997 and ibid., vol.46, p.1119-24, 1998)), prefiltering can be avoided. By reorganizing the multiwavelet filter bank, the development in this paper draws from results regarding the approximation order of M-band wavelet bases. The main result thereby obtained is a characterization of balanced multiwavelet bases in terms of the divisibility of certain transfer functions by powers of (z/sup -2r/-1)/(z/sup -1/-1). For traditional wavelets (r=1), this specializes to the usual factor (z+1)/sup K/.
Image Denoising Based on A Mixture of Bivariate Laplacian Models in Complex Wavelet Domain

Recently, it has been shown that algorithms exploiting dependencies between coefficients for modeling probability density function (pdf) of wavelet coefficients, could achieve better results for image denoising in wavelet domain compared with the ones based on the independence assumption. In this context, we design a bivariate maximum a posteriori (MAP) estimator which relies on a mixture of bivariate Laplacian models. This model not only is bivariate but also is mixture and therefore, using this new statistical model, we are able to better capture heavy-tailed natures of the data as well as the interscale dependencies of wavelet coefficients. The simulation results show that our proposed technique achieves better performance than several published methods both visually and in terms of peak signal-to-noise ratio (PSNR)
Video denoising using oriented complex wavelet transforms

Video processing techniques using true 3D transforms are largely unexploited, partly because of the drawbacks of traditional separable 3D transforms. In this paper, we use a new type of non-separable 3D wavelet transform for video denoising and overcome the motion-mixture problem by using oriented complex wavelets. This wavelet transform is a 3D version of Kingsbury's 1D and 2D dual-tree wavelet transforms. We also investigate video denoising techniques using a combination of both 2D and 3D oriented wavelet transforms. The results are compared with those obtained by separable wavelet transforms.
Exchange algorithms that complement the Parks-McClellan algorithm for linear-phase FIR filter design

The authors describe an exchange algorithm for the frequency domain design of linear-phase FIR equiripple filters where the Chebyshev error in each band is specified. The algorithm is a hybrid of the algorithm of Hofstetter, Oppenheim and Siegel and the Parks-McClellan algorithm. The authors also describe a modification of the Parks-McClellan algorithm where either the passband or the stopband ripple size is specified and the other is minimized.
Design of 2-band orthogonal near-symmetric CQF

The FIR 2-band wavelets have found wide applications in practice. One of their disadvantages, however, is that they cannot be made both symmetric and orthogonal. There have been some works on filters which are orthogonal and nearly symmetric. Grobner methods are used to design orthogonal conjugate quadrature filters (CQF) with a sub-set of exactly symmetric coefficients of various lengths, as opposed to nearly symmetric coefficients.
Total variation denoising with overlapping group sparsity

This paper describes an extension to total variation denoising wherein it is assumed the first-order difference function of the unknown signal is not only sparse, but also that large values of the first-order difference function do not generally occur in isolation. This approach is designed to alleviate the staircase artifact often arising in total variation based solutions. A convex cost function is given and an iterative algorithm is derived using majorization-minimization. The algorithm is both fast converging and computationally efficient due to the use of fast solvers for banded systems.
Efficient and Robust Image Restoration Using Multiple-Feature L2-Relaxed Sparse Analysis Priors

We propose a novel formulation for relaxed analysis-based sparsity in multiple dictionaries as a general type of prior for images, and apply it for Bayesian estimation in image restoration problems. Our formulation of a ℓ2 -relaxed ℓ0 pseudo-norm prior allows for an especially simple maximum a posteriori estimation iterative marginal optimization algorithm, whose convergence we prove. We achieve a significant speedup over the direct (static) solution by using dynamically evolving parameters through the estimation loop. As an added heuristic twist, we fix in advance the number of iterations, and then empirically optimize the involved parameters according to two performance benchmarks. The resulting constrained dynamic method is not just fast and effective, it is also highly robust and flexible. First, it is able to provide an outstanding tradeoff between computational load and performance, in visual and objective, mean square error and structural similarity terms, for a large variety of degradation tests, using the same set of parameter values for all tests. Second, the performance benchmark can be easily adapted to specific types of degradation, image classes, and even performance criteria. Third, it allows for using simultaneously several dictionaries with complementary features. This unique combination makes ours a highly practical deconvolution method.
K-complex detection using sparse optimization

This work describes a method using sparse optimization for the detection of K-complex in sleep EEG. K-complex is an important feature in sleep stage identification, which is helpful to sleep disorder diagnostics process. In this work, a discrete-time sleep EEG signal Y ϵ R<sup>N</sup> is modeled as: Y=X<sub>1</sub>+X<sub>2</sub>+W, (1) where x<sub>1</sub> is the baseline trend, X<sub>2</sub> is composed of K-complexes, and w presents noise. More specifically, x<sub>1</sub> is piecewise smooth comprising a lowpass signal component f, and a sparse order-K<sub>1</sub> derivative component g<sub>1</sub> i.e., x<sub>1</sub> = f + g<sub>1</sub>. Further, x<sub>2</sub> is assumed as a transient waveform with a negative wave followed by a positive wave, and modeled as a `wavelet' (e.g. Fig. 1(b)). Moreover, x<sub>2</sub> is modeled as the output of a high-pass filter, i.e., x<sub>2</sub> = H<sub>2</sub>g<sub>2</sub>. In addition, we assume the order-K<sub>1</sub> derivative of g<sub>1</sub> is sparse, and we likewise assume the order-K<sub>2</sub> derivative of g<sub>2</sub> is sparse. In another word, g<sub>1</sub> and g<sub>2</sub> are sparse-derivative signals, where u<sub>1</sub> = D<sub>1</sub>g<sub>1</sub>, and u<sub>2</sub> = D<sub>2</sub>g<sub>2</sub> are both sparse. Adopting the zero-phase filter design techniques discussed in Ref. , and the idea of morphological component analysis (MCA) , we formulate the optimization problem: {u<sub>1</sub>*, u<sub>2</sub>*} = arg min <sub>u1, u2</sub> 1/2|| H<sub>1</sub>y - A<sub>1</sub><sup>-1</sup>B<sub>1</sub>u<sub>1</sub> - A<sub>2</sub><sup>-1</sup>B<sub>2</sub>u<sub>2</sub>||<sup>2</sup><sub>2</sub> + λ<sub>1</sub>Σ<sub>n</sub> ρ<sub>1</sub>([u<sub>1</sub>]<sub>n</sub>)+ λ<sub>2</sub> Σ<sub>n</sub>ρ<sub>2</sub>([u<sub>2</sub>]n). (2) where P<sub>1</sub> and P<sub>2</sub> denote penalty functions. The high-pass filters are expressed as H<sub>1</sub> = A<sub>1</sub><sup>-1</sup>B, H<sub>2</sub>= A<sub>2</sub><sup>-1</sup>B, with B = B<sub>1</sub>D<sub>1</sub> = B<sub>2</sub>D<sub>2</sub>. Using the solution from (2) we recover x<sub>1</sub> and x<sub>2</sub> by: x<sub>1</sub> = Y - H<sub>1y</sub> + A<sub>1</sub><sup>-1</sup>B<sub>1</sub>u<sub>1</sub>*, = x<sub>2</sub>=A<sub>2</sub><sup>-1</sup>B<sub>2</sub>u<sub>2</sub>*.  both decomposes the data y into x<sub>1</sub> and X<sub>2</sub>, and performs denoising. It can be solved iteratively by majorization-minimization (MM) . Further, the proposed algorithm is computationally efficient as it makes use of banded matrices. We use an asymmetric penalty function to capture the morphology of K-complex, and implement a simple detector by thresholding the local energy of X<sub>2</sub>. We test the proposed method by the public dataset collected in . It achieves a better accuracy (F-measurement) than the result reported in . An example is illustrated in Fig. 2.
Multivariate Quasi-Laplacian Mixture Models Forwavelet-Based Image Denoising

In this paper we introduce a class of multivariate quasi-Laplacian models as a generalization of the single-variable Laplacian distribution to multi-dimensions. A mixture model is used as the wavelet coefficient prior for the wavelet-based Bayesian image denoising algorithm. As a multivariate probability model, it is able to capture the intra-scale or inter-scale dependencies among wavelet coefficients. Two special cases are studied for orthogonal transform based image denoising. Efficient parameter estimation methods and denoising rules are derived for the two cases. Denoising results are compared with existing techniques in both PSNR values and visual qualities.
Wavelet Transform With Tunable Q-Factor

This paper describes a discrete-time wavelet transform for which the Q-factor is easily specified. Hence, the transform can be tuned according to the oscillatory behavior of the signal to which it is applied. The transform is based on a real-valued scaling factor (dilation-factor) and is implemented using a perfect reconstruction over-sampled filter bank with real-valued sampling factors. Two forms of the transform are presented. The first form is defined for discrete-time signals defined on all of Z. The second form is defined for discrete-time signals of finite-length and can be implemented efficiently with FFTs. The transform is parameterized by its Q-factor and its oversampling rate (redundancy), with modest oversampling rates (e.g., three to four times overcomplete) being sufficient for the analysis/synthesis functions to be well localized.
Enhanced Sparsity by Non-Separable Regularization

This paper develops a convex approach for sparse one-dimensional deconvolution that improves upon L1-norm regularization, the standard convex approach. We propose a sparsity-inducing non-separable non-convex bivariate penalty function for this purpose. It is designed to enable the convex formulation of ill-conditioned linear inverse problems with quadratic data fidelity terms. The new penalty overcomes limitations of separable regularization. We show how the penalty parameters should be set to ensure that the objective function is convex, and provide an explicit condition to verify the optimality of a prospective solution. We present an algorithm (an instance of forward-backward splitting) for sparse deconvolution using the new penalty.
Balanced multiwavelet bases based on symmetric FIR filters

This paper describes a basic difference between multiwavelets and scalar wavelets that explains, without using zero moment properties, why certain complications arise in the implementation of discrete multiwavelet transforms. Assuming we wish to avoid the use of prefilters in implementing the discrete multiwavelet transform, it is suggested that the behavior of the iterated filter bank associated with a multiwavelet basis of multiplicity r is more fully revealed by an expanded set of r/sup 2/ scaling functions /spl phi//sub i,j/. This paper also introduces new K-balanced orthogonal multiwavelet bases based on symmetric FIR filters. The nonlinear design equations arising in this work are solved using the Grobner basis. The minimal-length K-balanced multiwavelet bases based on even-length symmetric FIR filters are better behaved than those based on odd-length symmetric FIR filters, as illustrated by special relations they satisfy and by examples constructed.
New exchange rules for IIR filter design

This paper introduces a new set of exchange rules for a Remez-like algorithm for the Chebyshev design of IIR digital filters. It is explained that the essential difficulty, in applying the Remez algorithm to rational functions, is that on some iterations, there may be no solution to the interpolation problem for which the denominator is strictly non-zero in the interval of approximation. Then the usual procedure for updating the interpolation points cannot be applied. The new rules for updating the interpolation points address precisely this problem for the two-pole case. It is shown with examples that, when the Remez-like algorithm of Hofstetter et al. (1971) is applied to rational functions, there is a way to update the interpolation points so that the algorithm converges rapidly, even when poles arise in the interval of approximation.
Frequency-Domain Design of Overcomplete Rational-Dilation Wavelet Transforms

The dyadic wavelet transform is an effective tool for processing piecewise smooth signals; however, its poor frequency resolution (its low Q-factor) limits its effectiveness for processing oscillatory signals like speech, EEG, and vibration measurements, etc. This paper develops a more flexible family of wavelet transforms for which the frequency resolution can be varied. The new wavelet transform can attain higher Q-factors (desirable for processing oscillatory signals) or the same low Q-factor of the dyadic wavelet transform. The new wavelet transform is modestly overcomplete and based on rational dilations. Like the dyadic wavelet transform, it is an easily invertible 'constant-Q' discrete transform implemented using iterated filter banks and can likewise be associated with a wavelet frame for L2(R). The wavelet can be made to resemble a Gabor function and can hence have good concentration in the time-frequency plane. The construction of the new wavelet transform depends on the judicious use of both the transform's redundancy and the flexibility allowed by frequency-domain filter design.
Supervised object segmentation and tracking for MPEG-4 VOP generation

This paper presents an object-based segmentation and tracking scheme for video sequences. The probability density function (PDF) of each image to be segmented is modeled as a mixture of independent object PDF. In the first image of the sequence, the parameters of the mixture are initially estimated based on user interaction. These parameters are then iteratively updated using the expectation maximization (EM) algorithm. A classification procedure applied to the results of the EM algorithm allows an object-based segmentation of the image to be constructed. In subsequent images of the sequence, the segmented objects are automatically tracked using motion estimation/compensation and a similar EM-based segmentation framework. Results indicate that accurate object segmentations and robust tracking can be obtained using this flexible approach.
Quantifying radiographic knee osteoarthritis severity using deep convolutional neural networks

This paper proposes a new approach to automatically quantify the severity of knee osteoarthritis (OA) from radiographs using deep convolutional neural networks (CNN). Clinically, knee OA severity is assessed using Kellgren & Lawrence (KL) grades, a five point scale. Previous work on automatically predicting KL grades from radiograph images were based on training shallow classifiers using a variety of hand engineered features. We demonstrate that classification accuracy can be significantly improved using deep convolutional neural network models pre-trained on ImageNet and fine-tuned on knee OA images. Furthermore, we argue that it is more appropriate to assess the accuracy of automatic knee OA severity predictions using a continuous distance-based evaluation metric like mean squared error than it is to use classification accuracy. This leads to the formulation of the prediction of KL grades as a regression problem and further improves accuracy. Results on a dataset of X-ray images and KL grades from the Osteoarthritis Initiative (OAI) show a sizable improvement over the current state-of-the-art.
Dynamic voxel carving in tennis based on player localisation using a low cost camera network

In this paper, we address the problem of reconstructing 3D volumetric models, illustrating human sporting performance for use in coaching scenarios. We advocate the use of low cost camera networks for acquiring such data, allowing the approach to be feasibly adopted by both amateur and elite level sports athletes. A dynamic voxel carving approach is described, coupled with over-head player tracking and autonomous background subtraction, to automatically produce a 3D reconstruction technique that intelligently uses memory resources. We demonstrate the efficacy of our approach in the context of tennis as a challenging application scenario.
Fischlar: an on-line system for indexing and browsing broadcast television content

This paper describes a demonstration system which automatically indexes broadcast television content for subsequent non-linear browsing. User-specified television programmes are captured in MPEG-1 format and analysed using a number of video indexing tools such as shot boundary detection, keyframe extraction, shot clustering and news story segmentation. A number of different interfaces have been developed which allow a user to browse the visual index created by these analysis tools. These interfaces are designed to facilitate users locating video content of particular interest. Once such content is located, the MPEG-1 bitstream can be streamed to the user in real-time. This paper describes both the high-level functionality of the system and the low-level indexing tools employed, as well as giving an overview of the different browsing mechanisms employed.