nils-herrmann/clean_scirepeval huggingface dataset:
A parametric model for saccadic eye movement

This paper proposes a parametric model for saccadic waveforms. The model has a small number of parameters, yet it effectively simulates a variety of physiologic saccade properties. In particular, the model reproduces the established relationship between peak saccadic angular velocity and saccadic amplitude (i.e., the saccadic main sequence). The proposed saccadic waveform model can be used in the evaluation and validation of methods for quantitative saccade analysis. For example, we use the proposed saccade model to evaluate four well-known saccade detection algorithms. The comparison indicates the most reliable algorithm is one by Nystrom et al. We further use the proposed saccade model to evaluate the standard technique used for the estimation of peak saccadic angular velocity. The evaluation shows the occurrence of systematic errors. We thus suggest that saccadic angular velocity values determined by the standard technique (low-pass differentiation) should be interpreted and used with caution.
Chirp-like transmit waveforms with multiple frequency-notches

This paper describes the construction of chirp-like constant-modulus transmit waveforms designed so as to possess multiple notches in their frequency spectra at user-specified frequencies. We propose an iterative projection algorithm with low computational complexity. In wide-band radar systems, such frequency-notched transmit waveforms are needed so as to avoid transmitting into frequency bands that are used by other systems such as for communication, navigation, etc.
Overcomplete Discrete Wavelet Transforms With Rational Dilation Factors

This paper develops an overcomplete discrete wavelet transform (DWT) based on rational dilation factors for discrete-time signals. The proposed overcomplete rational DWT is implemented using self-inverting FIR filter banks, is approximately shift-invariant, and can provide a dense sampling of the time-frequency plane. A straightforward algorithm is described for the construction of minimal-length perfect reconstruction filters with a specified number of vanishing moments; whereas, in the nonredundant rational case, no such algorithm is available. The algorithm is based on matrix spectral factorization. The analysis/synthesis functions (discrete-time wavelets) can be very smooth and can be designed to closely approximate the derivatives of the Gaussian function.
The slantlet transform

The discrete wavelet transform (DWT) is usually carried out by filter bank iteration, however, for a fixed number of zero moments, this does not yield a discrete-time basis that is optimal with respect to time-localization. This paper discusses the implementation and properties of an orthogonal DWT, with two zero moments and with improved time-localization. The basis is not based on filter bank iteration, instead different filters are used for each scale. For coarse scales, the support of the discrete-time basis functions approaches 2/3 that of the corresponding functions obtained by filter bank iteration. This slantlet basis is piecewise linear and retains the octave-band characteristic. Closed form expressions for the filters are given and improvement in a denoising example is shown. This basis, being piecewise linear, is reminiscent of the slant transform, to which it is compared.
Separation of chirp scattered returns from a mixture of sinusoidal tones in noise

We consider the problem of source separation of a signal whose elements consist of chirp scattered returns and sinusoidal tones overlapping in time and frequency. Due to the application, both signals are assumed to be highly non-stationary with their parameters rapidly changing. Such a mixture may occur when a device with a single antenna is spectrally constrained while simultaneously communicating information and sensing the environment. Another application is to mitigate interference between two devices which must share the spectrum. We consider a formulation where the chirp scattered returns are modeled as a convolution and the sinusoidal tones are modeled as sparse in the overcomplete inverse discrete Fourier domain. The objective function which is a combination of basis pursuit denoising and deconvolution is formulated with a l1-norm regularizer for each signal. A fast transform based iterative algorithm to obtain the solution is presented and the major steps for deriving the solution are outlined. Simulation results are provided where the non-linear separation algorithm yields a significant improvement in signal reconstruction relative to the standard matched-filter.
Smooth wavelet frames with application to denoising

This paper considers the design and application of wavelet tight frames based on iterated oversampled filter banks. The greater design freedom available makes possible the construction of wavelets with a high degree of smoothness, in comparison with orthonormal wavelet bases. Grobner bases are used to obtain the solutions to the nonlinear design equations. Following the dual-tree DWT of Kingsbury (see Proceedings of the Eighth IEEE DSP Workshop, Utah, 1998, and Proc.  1999), one goal is to keep the redundancy-factor bounded by 2, instead of allowing it to grow as it does for the undecimated DWT (which is exactly shift-invariant). For the tight frame presented here, optimal-tree based denoising algorithms can be directly applied.
Application of a sparse time-frequency technique for targets with oscillatory fluctuations

In this paper, an application of the tunable Q-Factor wavelet transform (TQWT) to a maritime object classification problem is demonstrated. The TQWT, which depends on the two main parameters of the Q-factor and asymptotic redundancy, matches the oscillatory behaviour of the signal of interest when tuned. The approach, which differs from the Fourier and Wavelet transforms, decomposes a signal into a “high-Q-factor” and “low-Q-factor” component, and can be used to distinguish two radar range profiles of different oscillatory nature. The results of the paper show that the TQWT can provide sparse representation for some signals and that morphological component analysis (MCA) can be used to differentiate two radar signals based on their TQWT parameters.
Resonance-based decomposition for the manipulation of acoustic cues in speech: An assessment of perceived quality

The ultimate objective of this study is to employ a resonance-based decomposition method for the manipulation of acoustic cues in speech. Resonance-based decomposition (Selesnick, 2010) is a newly proposed nonlinear signal analysis method based not on frequency or scale but on resonance; the method is able to decompose a complex non-stationary signal into a ‘high-resonance’ component and a ‘low-resonance’ component using a combination of low- and high- Q-factors. In this study, we conducted a subjective listening experiment on five normal hearing listeners to assess the perceived quality of decomposed components, with the intention of deriving the perceptually relevant combinations of low- and high- Q-factors. Our results show that normal hearing listeners generally rank high-resonance components of speech stimuli higher than low-resonance components. This may be due to a greater salience of perceptually significant formant cues in high-resonance stimuli.
A Higher Density Discrete Wavelet Transform

This paper describes a new set of dyadic wavelet frames with two generators. The construction is simple, yet the wavelets cover the time-frequency plane in an arrangement that provides a higher sampling in both time and frequency. Specifically, the spectrum of the first wavelet is concentrated halfway between the spectrum of the second wavelet and the spectrum of its dilated version. In addition, the second wavelet is translated by half integers rather than whole integers in the frame construction. This arrangement leads to an expansive wavelet transform that is approximately shift invariant and has intermediate scales. The wavelet frames presented in this paper are compactly supported and have vanishing moments
Orthonormal FBs With Rational Sampling Factors and Oversampled DFT-Modulated FBs: A Connection and Filter Design

Methods widely used to design filters for uniformly sampled filter banks (FBs) are not applicable for FBs with rational sampling factors and oversampled discrete Fourier transform (DFT)-modulated FBs. In this paper, we show that the filter design problem (with regularity factors/vanishing moments) for these two types of FBs is the same. Following this, we propose two finite-impulse-response (FIR) filter design methods for these FBs. The first method describes a parameterization of FBs with a single regularity factor/vanishing moment. The second method, which can be used to design FBs with an arbitrary number of regularity factors/vanishing moments, uses results from frame theory. We also describe how to modify this method so as to obtain linear phase filters. Finally, we discuss and provide a motivation for iterated DFT-modulated FBs.
Doppler-streak attenuation via oscillatory-plus-transient decomposition of IQ data

In the formation of range-Doppler images, transient interference can result in Doppler-streak artifacts. These Doppler-streaks hinder the utility of the range-Doppler image and may obscure moving targets of interest. This paper describes a method for pre-processing the IQ data to substantially attenuate Doppler streaks in the subsequently formed range-Doppler images. The method consists of decomposing the IQ data for each range bin into the sum of an oscillatory component and a transient component.
Video coding using 3D dual-tree discrete wavelet transforms

The paper explores the use of a recently introduced 3D dual-tree discrete wavelet transform (DDWT) for video coding. The 3D DDWT is an attractive video representation because it isolates motion along different directions in separate subbands. However, it is an overcomplete transform with 8:1 or 4:1 redundancy. Based on the effectiveness of the iterative projection-based noise shaping scheme proposed by Kingsbury in reducing the number of coefficients, and our prior investigation about the correlation between subbands at the same spatial/temporal location, both in the significance map and in actual coefficient values, a new video coding scheme using 3D DDWT is proposed. The proposed video codec does not require motion compensation and provides better performance than the 3D SPIHT codec, both objectively and subjectively, despite the fact that the raw number of coefficients resulting from the 3D DDWT is much more than that of the conventional 3D DWT. The proposed coder allows full scalability in spatial, temporal and quality dimensions.
A Simple Construction for the M-Band Dual-Tree Complexwavelet Transform

The dual-tree complex wavelet transform (DT-CWT) which utilizes two 2-band discrete wavelet transforms (DWT) was recently extended to M-band by Chaux et al. In this paper, we provide a simple construction method for an M-band DT-CWT, with M = rd where r, d isin Z. In particular, we show how to extend a given r-band DT-CWT to an rd -band one. For convenience, the case where r = 2, d = 2 is considered. However, the scheme can be extended to general {r, d} pairs straightforwardly. The extension to 2-D which achieves a directional analysis is also provided
Extending Winograd's small convolution algorithm to longer lengths

For short data sequences, Winograd's convolution algorithms attaining the minimum number of multiplications also attain a low number of additions, making them very efficient. However, for longer lengths they require a larger number of additions. Winograd's approach is usually extended to longer lengths by using a nesting approach such as the Agarwal-Cooley (1977) or Split-Nesting algorithms. Although these nesting algorithms are organizationally quite simple, they do not make the greatest use of the factorability of the data sequence length. The algorithm we propose adheres to Winograd's original approach more closely than do the nesting algorithms. By evaluating polynomials over simple matrices we retain, in algorithms for longer lengths, the basic structure and strategy of Winograd's approach, thereby designing computationally refined algorithms. This tactic is arithmetically profitable because Winograd's approach is based on a theory of minimum multiplicative complexity.<<ETX>>
Nonconvex Sparse Regularization and Convex Optimization for Bearing Fault Diagnosis

Vibration monitoring is one of the most effective ways for bearing fault diagnosis, and a challenge is how to accurately estimate bearing fault signals from noisy vibration signals. In this paper, a nonconvex sparse regularization method for bearing fault diagnosis is proposed based on the generalized minimax-concave (GMC) penalty, which maintains the convexity of the sparsity-regularized least squares cost function, and thus the global minimum can be solved by convex optimization algorithms. Furthermore, we introduce a k-sparsity strategy for the adaptive selection of the regularization parameter. The main advantage over conventional filtering methods is that GMC can better preserve the bearing fault signal while reducing the interference of noise and other components; thus, it can significantly improve the estimation accuracy of the bearing fault signal. A simulation study and two run-to-failure experiments verify the effectiveness of GMC in the diagnosis of localized faults in rolling bearings, and the comparison studies show that GMC provides more accurate estimation results than L1-norm regularization and spectral kurtosis.
Sparse Signal Estimation by Maximally Sparse Convex Optimization

This paper addresses the problem of sparsity penalized least squares for applications in sparse signal processing, e.g., sparse deconvolution. This paper aims to induce sparsity more strongly than L1 norm regularization, while avoiding non-convex optimization. For this purpose, this paper describes the design and use of non-convex penalty functions (regularizers) constrained so as to ensure the convexity of the total cost function F to be minimized. The method is based on parametric penalty functions, the parameters of which are constrained to ensure convexity of F. It is shown that optimal parameters can be obtained by semidefinite programming (SDP). This maximally sparse convex (MSC) approach yields maximally non-convex sparsity-inducing penalty functions constrained such that the total cost function F is convex. It is demonstrated that iterative MSC (IMSC) can yield solutions substantially more sparse than the standard convex sparsity-inducing approach, i.e., L1 norm minimization.
Hilbert transform pairs of wavelet bases

This paper considers the design of pairs of wavelet bases where the wavelets form a Hilbert transform pair. The derivation is based on the limit functions defined by the infinite product formula. It is found that the scaling filters should be offset from one another by a half sample. This gives an alternative derivation and explanation for the result by Kingsbury (1999), that the dual-tree DWT is (nearly) shift-invariant when the scaling filters satisfy the same offset.
Subband adaptive image denoising via bivariate shrinkage

It is well known that the wavelet coefficients of natural images have significant statistical dependencies. To model the non-Gaussian nature of these statistics, a new bivariate PDF is proposed in this paper and applied to the image denoising problem. For this purpose, the corresponding new bivariate shrinkage function is derived using the MAP estimator. Using this function, a subband dependent data-driven system is described and applied to both orthogonal and dual-tree complex wavelet coefficients. Also, some comparisons to the other effective data-driven techniques are given.
Cardinal multiwavelets and the sampling theorem

This paper considers the classical Shannon 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. They also showed that the only orthogonal scaling function that is both cardinal and of compact support is the Haar function, which has only 1 vanishing moment and 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 orthogonal multiscaling functions that are simultaneously cardinal, of compact support, and have more than one vanishing moment. 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-the projection of a function onto the scaling space is given by its samples.
Perceived quality of resonance based decomposed speech components under diotic and dichotic listening

This study investigates the feasibility of using binaural dichotic presentation of speech components decomposed using a recently proposed resonance-based decomposition method to release listeners from intra-speech masking and yield better perceived sound quality. Resonance-based decomposition is a nonlinear signal analysis method based not on frequency or scale but on resonance. We decomposed different categories of speech stimuli (vowels, consonants, and sentences) into low- and high-resonance component using various combination of low- and high-Q-factors {Q1, Q2}. 10 normal hearing listeners were asked to rate the perceived quality of each individual decomposed component presented diotically, and in pair presented dichotically. We found that the perceived quality rating of these resonance components when presented in pair was higher than the mean of perceived quality ratings of these resonance components when presented individually. Our result suggests that listeners were able to fuse binaural dichotic presentation of high- and low-resonance components and perceived better sound quality.
Iterative reweighted least-squares design of FIR filters

Develops a new iterative reweighted least squares algorithm for the design of optimal L/sub p/ approximation FIR filters. The algorithm combines a variable p technique with a Newton's method to give excellent robust initial convergence and quadratic final convergence. Details of the convergence properties when applied to the L/sub p/ optimization problem are given. The primary purpose of L/sub p/ approximation for filter design is to allow design with different error criteria in pass and stopband and to design constrained L/sub 2/ approximation filters. The new method can also be applied to the complex Chebyshev approximation problem and to the design of 2D FIR filters.
A new sparsity-enabled signal separation method based on signal resonance

This paper proposes the separation of signal components based on resonance. The method relies on several recent developments in sparse signal processing: morphological component analysis (MCA), the rational-dilation wavelet transform (RADWT), and fast algorithms for ℓ1-norm regularized linear inverse problems (for example, SALSA). The RADWT allows one to extract signal components according to resonance characteristics because the RADWT allows the Q-factor (frequency resolution) of the wavelet transform to be varied. The sought decomposition can not be accomplished by frequency-based filtering. An example illustrates the method.
Morphological component analysis based compressed sensing technique on dynamic MRI reconstruction

Compressive sensing (CS) MRI have been developed to speed up data acquisition without significantly degrading image quality. This paper proposes a novel compressed sensing reconstruction method exploiting temporally complementary morphological characteristics. This method relies on some well-developed signal processing techniques: morphological component analysis (MCA) and sparse derivatives. It also relies on well-developed MRI reconstruction techniques: incoherent undersampling schemes and parallel imaging. Other MRI schemes were simulated to make comparsion with our MCA-based CS method. CS and parallel imaging has been merged together to highly increase acceleration rate. This work simulates this framework also. Performance of applying different temporal regularizations individually and hybrid signal models based on MCA with and without auxilary spatial regularization are all analyzed in this paper. Nonlinear conjugate gradient algorithm is applied to gain all signal components simultaneously.
Generalized digital Butterworth filter design

This paper presents a formula-based method for the design of IIR filters having more zeros than (nontrivial) poles. The filters are designed so that their square magnitude frequency responses are maximally-flat at /spl omega/=0 and at /spl omega/=/spl pi/ and are thereby generalizations of classical digital Butterworth filters. A main result of the paper is that, for a specified half-magnitude frequency and a specified number of zeros, there is only one valid way in which to split the zeros between z=-1 and the passband. IIR filters having more zeros than poles are of interest, because often, to obtain a good trade-off between performance and the expense of implementation, just a few poles are best.
Seizure detection methods using a cascade architecture for real-time implantable devices

Implantable high-accuracy, and low-power seizure detection is a challenge. In this paper, we propose a cascade architecture to combine different seizure detection algorithms to optimize power and accuracy of the overall seizure detection system. The proposed architecture consists of a cascade of two seizure detection stages. In the first-stage detector, a lightweight (low-power) algorithm is used to detect seizure candidates with the understanding that there will be a high number of false positives. In the second-stage detector-and only for the seizure candidates detected in the first detector-a high-accuracy algorithm is used to eliminate the false positives. We show that the proposed cascade architecture can reduce power consumption of seizure detection by 80% with high accuracy, offering a suitable option for real-time implantable seizure detectors.
Constrained least square design of FIR filters without specified transition bands

We consider the design of digital filters and discuss the inclusion of explicitly specified transition bands in the frequency domain design of FIR filters. We put forth the notion that explicitly specified transition bands have been introduced in the filter design literature as an indirect and sometimes inadequate approach for dealing with discontinuities in the desired frequency response. We also present a rapidly converging, robust, simple algorithm for the design of optimal peak constrained least square lowpass FIR filters that does not require the use of transition bands. This versatile algorithm will design linear and minimum phase FIR filters and gives the best L/sub 2/ filter and a continuum of Chebyshev filters as special cases.
Sparsity-based methods for interrupted radar data reconstruction

Missing radar data may be reconstructed by using the structure present in surrounding data to make intelligent estimates of values at missing locations. We formulate the interrupted radar data scenario as an l1-regularized least squares problem, and take advantage of the radar data's demonstrated sparsity in the discrete Fourier domain. Applying the split-variable augmented Lagrangian technique results in an iterative algorithm consisting of two alternating minimizations. The fast algorithm avoids explicit linear inverse solutions, and demonstrates good phase history reconstruction and improved imaging irrespective of the structure of the data loss. Experimental results are presented for synthetic aperture radar (SAR) image formation; however, the approach may also be used with other types of radar data.