nils-herrmann/clean_scirepeval huggingface dataset:
A Robust Multiplicative Watermark Detector for Color Images in Sparse Domain

In recent years, digital watermarking has facilitated the protection of copyright information through embedding hidden information into the digital content. In this brief, for the first time, a blind multichannel multiplicative color image watermarking scheme in the sparse domain is proposed. In order to take into account the cross correlation between the coefficients of the color bands in the sparse domain, a statistical model based on the multivariate Cauchy distribution is used. The statistical model is then used to derive an efficient closed-form decision rule for the watermark detector. Experimental results and theoretical analysis are presented to validate the proposed watermark detector. The performance of the proposed detector is compared with that of the other detectors. The results demonstrate the improved detection rate and high robustness against the commonly used attacks such as JPEG compression, salt and pepper noise, median filtering, and Gaussian noise.
Design Of A Minimum Order Dynamic Pole Placement Controller For A Boiler

First a quadratic performance index with a prescribed degree of s tability is used to select the desired closed loop poles of a boiler plant of 150 MW power station. Using only the available output v ariables for feedback, Fortran computer programs are written to design a dynamic compensator of minimum order p to achieve placement of the desired closed loop poles. plant and the dynamic output feedback controller are simulated on a CDC Cyber 170-835 digital computer. The performance of the b oiler. plant controlled by the dynamic compensator is compared to performances under observer-based and state feedback controllers.
Scheduling of DSP data flow graphs onto multiprocessors for maximum throughput

A novel technique to obtain a rate- and processor-optimal schedule for a fully-static data flow graph (DFG) onto a multiprocessor system is presented. In this technique, Floyd-Warshall's shortest path algorithm is used to evaluate the relative firing times of the nodes of the given DFG. Despite its implementation simplicity, the proposed technique has a lower time complexity than all the previously proposed techniques. The technique is tested on various benchmark problems to demonstrate its optimal performance.
Routing algorithms for multi-chip modules

Routing algorithms for multi-chip modules are presented. Two routing strategies, a channel routing and a grid-based routing, are discussed. The channel routing enables the designer to examine an effective routing during the placement phase. The grid-based routing calculates the net ordering with a new cost function and includes an effective rip-up and reroute procedure. The routing results of three different multichip modules are presented. Experimental results show that there is no direct correlation between the routing results of the channel algorithm and the grid-based one. It is concluded that channel routing is preferable only if the placement structure enables the generation of regular channels. In all other cases the grid-based algorithm is more effective using the channel routing just as a fast placement estimation.<<ETX>>
A class of 2-D separable denominator filters designed via the McClellan transform

This paper presents a technique for separating the denominator of a particular class of 2-D zero-phase second-order filters, designed via the McClellan transform. It is found that when the transform is restricted only to the first-order, the condition for separability of the denominator of the 2-D zero-phase filter is that the 1-D prototype be critically damped. Examples are given for filters with elliptical and circular isopotential contours. Results show that the simplicity of the design, and the robustness and modularity of the implementation associated with separable denominators and with the cascading of low-order sections, far outweigh the restriction imposed by a critically damped response.
Contourlet domain image modeling by using the alpha-stable family of distributions

It is known that the contourlet coefficients of images have non-Gaussian property and heavy tails. In view of this, an appropriate distribution to model the statistics of the contourlet coefficients would be the one having large peaks, and tails heavier than that of a Gaussian PDF, i.e., a heavy-tailed PDF. This paper proposes a new image modeling in the contourlet domain, where the magnitudes of the coefficients are modeled by a symmetric alpha-stable distribution which is best suited for modeling transform coefficients with a high non-Gaussian property and heavy tails. It is shown that the alpha-stable family of distributions provides a more accurate model to the contourlet subband coefficients than the formerly used distributions, namely, the generalized Gaussian and Laplacian distributions, both in terms of the subjective measure of the Kolmogorov-Smirnov distance and the objective measure of comparing the log-scale histograms.
Closed-loop self-synchronized signal controlled constrained notch filter with application for rejection of arbitrary frequency variation speed sinusoidal interference

The closed-loop self-synchronized constrained notch filter (CLSS-CNF) is proposed, and its ability for retrieval of narrowband signals corrupted by a strong FM interference is studied. Self-synchronization is achieved through the estimation of the instantaneous phase of the FM interference to control time-varying coefficients of SC-CNF by using a closed-loop structure. The CLSS-CNF can overcome the tracking capability threshold of a conventional constrained adaptive notch filter (CANF), and reject a sinusoidal interference with arbitrary speed of frequency variation. Based on this approach, two such filters are discussed, and their system performances compared. It is shown that the proposed filters are superior to both the conventional CANF as well as the recently proposed SC-CNF with open loop structure. Use of closed loop and the specially developed projection scheme for time-varying coefficients allow one to achieve a signal to the interference ratio (SIR/sub O/) of up to 20 dB compared to the input SIR/sub i/ of less than -20 dB.<<ETX>>
An efficient algorithm for the conjugate symmetric sequency-ordered complex Hadamard transform

In this paper, an efficient algorithm for fast computation of the conjugate symmetric sequency-ordered complex Hadamard transform (CS-SCHT) of any length that is a power of two is proposed using the Kronecker product. Since the CS-SCHT matrix is factored into a product of sparse matrices, the resulting structure for the algorithm is very attractive for implementation and similar to that of the well-known Walsh-Hadamard transform, except for some multiplications by −1 or (−√−1). It is shown that the proposed N-point complex-valued CS-SCHT algorithm requires Nlog2(N) complex additions/subtractions and (N/2−1) multiplications by (−√−1).
Fast generation of universal maximally flat FIR filters

The relation for impulse response coefficients of Baher's universal maximally flat filters is a threefold summation formula. The summand involves four binomial coefficients whose computation requires an extensive number of additions and multiplications. There are three main parameters that characterize the filters, the order N, the number of zeros K, and the group delay parameter d. With fixed values for these parameters, the total number of additions, multiplications, and divisions required to compute the impulse response coefficients h/sub k/ are of the orders O(N/sup 4/), O(N/sup 5/) and O(N/sup 4/), respectively. We present a recurrence for the computation of h/sub k/ for all possible values of K under fixed values for N and d. The recurrence provides speedups of the orders O(N/sup 2/), O(N/sup 3/) and O(N/sup 2/) for the number of additions, multiplications and divisions, respectively.
A fast neural beamformer for antenna arrays

A fast neuro-beamformer is presented in this paper. The new algorithm is based on the radial-basis function network. To meet real-time requirement, we customize the basis function for fast computation, and apply a recursive least square learning rule to speed up the network training. By comparing the effects of center location and distribution, we can achieve a minimum network for recalling. The network recalling does not require the knowledge of direction-of-arrival, and thus the method is a blind method.
An analytical method for the frequency-domain least square design of centro-symmetric 2-D FIR filters

The authors develop an analytical technique for the design of centrosymmetric two-dimensional FIR (finite impulse response) filters by formulating a frequency-domain least-square approximation problem. A study of some of the properties of the matrices involved in this design problem leads to an optimal analytical solution for the filter co-efficients. This solution permits fast calculation of the coefficients directly from the given frequency-response specifications without requiring either optimization procedures or matrix inversion operations. It is shown through design examples that the design time and computational complexity using the proposed technique are significantly lower than those for the usual optimization techniques.<<ETX>>
Comparison of wavelets for multiresolution motion estimation

The performance of various wavelets, including those known to be well suited for the coding of still images, has been evaluated for the multiresolution motion estimation of video sequences. The multiresolution motion estimation scheme proposed by Zhang and Zafar, which has been widely cited in the literature, is used as the simulation scheme in this study. In our study, the prediction mean square error in the wavelet transform coefficient domain is used as the measure for prediction performance. In order to show the overall rate distortion performance, the number of bits needed to encode the motion vectors is also calculated. Simulation results show that the 7/9 biorthogonal wavelet, one of the best wavelets for the coding of still images, is the best wavelet for the task of multiresolution motion estimation among the wavelets evaluated in this study.
Semi-Blind Most Significant Tap Detection for Sparse Channel Estimation of OFDM Systems

In this paper, a very efficient semi-blind approach for the detection of most significant taps (MSTs) in sparse orthogonal frequency-division multiplexing (OFDM) channel estimation is developed. The least square (LS) estimation problem of sparse OFDM channels is first formulated, showing that the key to sparse channel estimation lies in the detection of the MSTs. An in-depth study of the second-order statistics of the signal received through a noise-free sparse OFDM channel reveals the sparsity and other properties of the correlation functions of the received signal. These properties lead to a direct relationship between the positions of the MSTs of the sparse channel and the most significant lags of the correlation functions, which is then used in conjunction with a pilot-assisted LS estimation to detect the MSTs in a semi-blind fashion. It os also shown that the new MST detection algorithm can be extended for the estimation of multiple-input–multiple-output (MIMO)–OFDM channels. A number of computer-simulation-based experiments for various sparse channels are carried out to confirm the effectiveness of the proposed semi-blind approach.
Side information generation using optical flow and block matching in Wyner-Ziv video coding

In this work, a new algorithm to generate high quality side information in Wyner-Ziv video coding is proposed. A block-matching algorithm is incorporated into the forward and backward optical flow and warping algorithms to find the forward and backward motion fields that are used for frame interpolation. Also, a symmetric optical flow algorithm for the purpose of frame interpolation is obtained by parameter modification in the energy functional of an optical flow algorithm. The average of the interpolated frames estimated using the forward/backward motion fields and symmetric flow is used to provide a high quality side information frame for decoding of the corresponding Wyner-Ziv frame in the Wyner-Ziv video coding problem. The proposed algorithm significantly improves the quality of the side information frames compared with those provided by the advanced block matching frame interpolation in the typical Wyner-Ziv video codecs. Simulation results showing significant improvements in side information quality and rate-distortion performance in Wyner-Ziv video coding are provided.
Multistage implementation of parameter-invariant null filter and its application to discrimination of closely spaced sinusoids

The authors present a two-stage parameter-invariant null filter based on the second-order null filters in cascade connection with low-pass sections. It is shown that such a filter is equivalent to a third-order parameter-invariant null filter by its ability to suppress two sinusoids of unknown frequencies. This filter was used to design the discriminator of two closely spaced sinusoids, and the results obtained demonstrate the potential of the proposed structure.<<ETX>>
Statistically optimal null filters: Kalman equivalence

A new approach in the enhancement/suppression of narrowband signals, based on the combination of the maximum output SNR and the least-squares optimizing criteria, has been presented previously. The resulting statistically-optimal null filters (SONFs) have also been presented as recursive filters. Because of their time-varying nature, optimality under the MSE criterion and the similarity to the RLS method, it is anticipated that the SONFs may be related to the well known Kalman filter. In this paper, we show the SONFs to be an alternate implementation of the Kalman filter. For the first order case we show this analytically. Due to mathematical intractability, for the higher order case we set up the mathematical formulations and use simulations for verification.
Erratum: Design of an active distributed RC lowpass filter using exponential lines

A new active distributed RC structure, utilising exponential lines, is proposed for realising lowpass transfer functions. Explicit design equations are given for realising a pair of complex poles using this structure. This is based on a dominantpole approximation of the structure. It is shown that the pole Q sensitivity decreases with increasing taper and that it may be made considerably lower than that of Kerwin's structure which uses uniform lines.
Weighted least-square design of FIR filters using a fast iterative matrix inversion algorithm

It has been shown by some researchers that in a problem of weighted least-square (WLS) design of finite-impulse response (FIR) filters, bulk of the design computation is concerned with the evaluation of the inverse of a matrix in order to solve a system of equations. In this paper, a new algorithm for the WLS design of FIR filters is presented, in which an iterative procedure is developed for the inversion of the matrix involved in the design. By imposing a mild constraint on the updation factor of the weighting function, the inverse of a matrix is expanded as a convergent power series. By investigating the properties of some of the matrices from the design formulation, a modified version of the series that converges rapidly is then proposed to evaluate the inverse in each iteration. It is shown that due to the fast convergence of the power series, one needs to evaluate only the first two or three terms of the series except during the initial stages of the iterations, implying that the conventional operation for matrix inversion is simplified significantly.
Design of centrally symmetric 2-D FIR fan filters using the McClellan transform

The authors present a novel design method for 2-D zero-phase finite impulse response (FIR) fan filters with centrally symmetric frequency responses, via the generalized McClellan transform. The formulas are derived from the prescribed frequency response specifications of the fan filter and are optimal in the mean square sense. The efficiency of the method is shown through design examples.<<ETX>>
A neural network least-square estimator

Problems in which the arguments of objective functions are real numbers are considered. Based on the concept of the Hopfield network, a neural network that solves the least-square estimation problem is derived. With this network, the objective function can converge to any inner point of a hypercube, giving a real-valued solution with very great speed. Because of the convex nature of the chosen energy function, the problem of convergence to a local minimum does not arise. Also introduced is a space iterative search technique for finding the optimum solution that can exist at any point within the space. Finally, simulation results are given for solving problems of linear systems and parameter estimations
A window-sequence coding transformation suitable for computationally-efficient bit-serial implementation of stack filters

A window-sequence coding (WSC) technique suitable for improving the computational efficiency of a bit-serial implementation of 2-D stack filters is proposed. The WSC technique takes advantage of the observation that in most images, many pixels appearing in the filter-window at a certain time-instant assume non-distinct values. An algorithm that uses the bit-serial binary-tree search (BTS) architecture for stack filtering and employs the WSC technique is developed. The proposed algorithm is designated as a modified binary-tree search (MBTS) algorithm. It is shown that the computational efficiency of MBTS algorithm for 2-D stack filtering is significantly better than the efficiency of a BTS algorithm employing a recently proposed technique called the input compression. The improvement stems from the fact that in the input compression method, the samples appearing in a filter-window of size M are always mapped to the set of integers (0, 1, ..., M-1), in spite of the fact that usually in a 2-D window, several groups of pixels assume non-distinct values.
A multi-scale non-linear vessel enhancement technique

We present an enhancement method based on nonlinear diffusion filter and statistical intensity approaches for smoothing and extracting 3-D vascular system from Magnetic Resonance Angiography (MRA) data. Our method distinguishes and enhances the vessels from the other embedded tissues. The Expectation Maximization (EM) technique is employed with non-linear diffusion in order to find the optimal contrast for enhancing vessels; therefore, smoothing while dimming the embedded tissues around the vessels and brightening the vessels. The non-linear diffusion filter smooths the homogeneous regions while preserving edges. The EM technique finds the optimal statistical parameters based on the probability distribution of the classes to discriminate the tissues in the image. Our enhancement technique has been applied to 4 3-D MRA-TOF datasets consisting of around 300 images and has been compared to the regularized Perona and Malik filter. Our experimental results show that the proposed method enhances the image, keeping only the vessels while eliminating the signal from other tissues. In comparison, the conventional non-linear diffusion filter keeps unwanted tissues in addition to the vessels.
A generalized MRI-based CAD system for functional assessment of renal transplant

In recent years, magnetic resonance imaging (MRI) has been explored for non-invasive assessment of renal transplant function. This paper proposes a computer-aided diagnostic (CAD) system for the assessment of renal transplant status, which integrates both clinical and MRI-derived biomarkers. The latter are derived from either 3D (2D + time) dynamic contrast-enhanced MRI or 4D (3D + b-value) diffusion-weighted (DW) MRI. In order to extract the MRI-based biomarkers, our framework performs multiple image processing steps, including MRI data alignment to handle the motion effects, kidney segmentation using a geometric deformable model, local motion correction, and estimation of image-based biomarkers. These biomarkers are fused with clinical biomarkers (creatinine clearance and serum plasma creatinine) for the classification of transplant status using a machine learning classifier. Our CAD system has been tested on a cohort of 100 subjects (50 DCE-MRI and 50 DW-MRI) using a “leave-one-subject-out” approach and distinguished rejection from non-rejection transplants with an overall accuracy of 98% for both DCE-MRI and DW-MRI data sets. These preliminary results demonstrate the promise of the proposed CAD system as a reliable non-invasive diagnostic tool for renal transplant assessment.
A new deformable model-based segmentation approach for accurate extraction of the kidney from abdominal CT images

Kidney segmentation is an essential step in developing any non-invasive Computer-Assisted Diagnostic (CAD) system for the early detection of acute renal rejection. This paper describes a 3-D approach for kidney segmentation from abdominal Computed Tomography (CT) images using a level set-based deformable model. Its evolution is controlled by a specially designed stochastic speed function that accounts for a shape prior and features of image intensity and spatial interactions. The shape prior is learned from the co-aligned 3-D kidney data. The current visual appearances are described with marginal gray level distributions obtained by separating their mixture over the kidney data. The spatial interactions between the kidney voxels are modeled by a 3-D 2nd-order translation and rotation variant Markov-Gibbs Random Field (MGRF) of “object-background” labels with analytically estimated potentials. The proposed approach has been evaluated on the CT data sets of 29 patients, yielding an average volumetric overlap error of 3.71%. The presented results indicate that combing CT images' characteristics into level set evolution leads to more accurate segmentation results.
Shape Analysis of the Human Brain: A Brief Survey

The survey outlines and compares popular computational techniques for quantitative description of shapes of major structural parts of the human brain, including medial axis and skeletal analysis, geodesic distances, Procrustes analysis, deformable models, spherical harmonics, and deformation morphometry, as well as other less widely used techniques. Their advantages, drawbacks, and emerging trends, as well as results of applications, in particular, for computer-aided diagnostics, are discussed.
A framework for the detection of acute renal rejection with dynamic contrast enhanced magnetic resonance imaging

Acute rejection is the most common reason of graft failure after kidney transplantation, and early detection is crucial to survive the transplanted kidney function. In this paper we introduce a new approach for the automatic classification of normal and acute rejection transplants from dynamic contrast enhanced magnetic resonance imaging (DCE-MRI). The proposed algorithm consists of three main steps; the first step isolates the kidney from the surrounding anatomical structures by evolving a deformable model based on two density functions; the first function describes the distribution of the gray level inside and outside the kidney region and the second function describes the prior shape of the kidney. In the second step, nonrigid-registration algorithms are employed to account for the motion of the kidney due to patient breathing, and finally, the perfusion curves that show the transportation of the contrast agent into the tissue are obtained from the cortex and used in the classification of normal and acute rejection transplants. Applications of the proposed approach yield promising results that would, in the near future, replace the use of current technologies such as nuclear imaging and ultrasonography, which are not specific enough to determine the type of kidney dysfunction