nils-herrmann/clean_scirepeval huggingface dataset:
Computationally fast techniques to reduce AWGN and speckle in videos

Fast schemes to reduce additive white Gaussian noise (AWGN) and speckle in videos are presented. The proposed schemes use a change detection technique to measure the interframe motion and carry out estimations in both the spatial and temporal directions of the video. In the case of AWGN reduction, the well-known edge adaptive Wiener filter is used to perform the spatial estimation. Two different filters to carry out temporal estimation are presented based on novel weighted scalar Kalman and weighted running average filters, respectively. These temporal estimators are applied on the spatial estimate to obtain the spatiotemporal estimate. A new method is then used to appropriately combine the spatial and spatiotemporal estimates in order to obtain the final estimate of the uncorrupted signal. To achieve speckle reduction, we use an unbiased homomorphic system that comprises an edge adaptive filter for spatial estimation and the weighted running average filter for temporal estimation. The effectiveness of the various proposed algorithms is demonstrated and compared with that of some of the existing schemes through extensive simulations. It is found that the use of a change detection technique, instead of the popularly used complex motion estimation and compensation technique, to measure the interframe motion results in a considerable reduction of processing time. The proposed schemes perform equally well or better than the existing schemes in reducing the noise in videos.
General results concerning the BIBO stability of inverse 2-D digital filters

The problem concerning the bounded-input-bounded-output (BIBO) stability of two-dimensional digital filters in the presence of nonessential singularities of the second kind on T/sup 2/ is discussed. Necessary and sufficient conditions are obtained for the BIBO stability, as well as the boundedness of a large class of mutually inverse two-dimensional digital filters having such singularities on T/sup 2/. Some illustrative examples are included.<<ETX>>
Joint Carrier Frequency Offset and Doubly Selective Channel Estimation for MIMO-OFDMA Uplink With Kalman and Particle Filtering

This paper presents two novel approaches for joint carrier frequency offset (CFO) and doubly selective channel estimation in the uplink of multiple-input multiple-output orthogonal frequency division multiple access (MIMO-OFDMA) systems. Considering high-mobility situations, where channels change within one OFDMA symbol interval, and the time varying nature of CFOs, basis expansion modeling (BEM) is employed to represent the time variations of the channel. Two new approaches are then proposed based on Schmidt–Kalman filtering (SKF). The first approach utilizes Schmidt-extended Kalman filtering for each user to estimate CFO and BEM coefficients. The second approach uses Gaussian particle filtering along with SKF to estimate CFO and BEM coefficients of each user. The Bayesian Cramér–Rao bound is derived, and the performances of the new schemes are evaluated using the mean square errors. It is demonstrated that the new approaches can significantly improve the mean-square error performance in comparison with the existing methods.
Realization of general 2-D linear-phase FIR filters using singular-value decomposition

In this paper, the singular-value decomposition (SVD) technique is investigated for the realization of 2-D linear-phase FIR filters with arbitrary magnitude responses. A parallel realization structure consisting of a number of 1-D FIR filters is obtained by applying the SVD to the impulse response of a 2-D filter. By using the symmetry property of the 2-D impulse response and by developing an appropriate unitary transform, a singular-value decomposition yielding 1-D linear-phase constituent filters is obtained. It is shown that the number of parallel sections can be reduced significantly by neglecting the smaller singular values and allowing only a very small error in the magnitude response of the realized 2-D filter.
Weighted Hybrid Fusion for Multimodal Biometric Recognition System

In this paper, first, a new fusion technique, referred to as hybrid fusion (HBF) technique, based on feature-level fusion and the best unimodal system for multimodal biometric system recognition, is proposed. Secondly, a new weighting technique, referred to as mean-extrema based confidence weighting (MEBCW) technique, based on the scores obtained from feature-level fusion and the best unimodal system, is proposed. Finally, a weighted hybrid fusion, referred to as weighted hybrid fusion (WHBF) technique, is developed by incorporating MEBCW in HBF, in order to improve the overall recognition rate of a multimodal biometric system. The performance of the proposed method, in terms of equal error rate and genuine acceptance rates @5.3% and @7.2% false acceptance rates, is evaluated on a multi-biometric system. The experimental results show that the performance of a multi-biometric systems using the proposed fusions is superior to that of the uni-biometric systems or to that of the system using existing level of fusions.
High resolution formant extraction from linear-prediction phase spectra

A new algorithm is proposed to extract formant information from linear-prediction (LP) spectra. The problems of resolving two closely spaced formants and accurately estimating the bandwidth of a formant are particularly addressed. The proposed method detects resonant poles from the peaks of the second derivative of the log-magnitude spectrum or from the second derivative of the group delay of the LP-coefficient polynomial. It then goes on to estimate frequencies and bandwidths of formants by observing the phase slope of the z-transform around the poles. Consequently, no minimum frequency separation is required in order to distinguish between two formants. The accuracy and utility of the proposed method, in reliably resolving closely spaced formants, is illustrated with several examples. Results on the performance of the algorithm on synthetic and natural speech are also included.
Median filtering-based pyramidal motion vector estimation

A median filtering-based hierarchical motion vector estimation scheme making use of a pyramidal data structure is proposed. Compared to the conventional hierarchical motion vector estimation schemes, the proposed scheme overcomes the problem of propagation of false motion vectors across resolutions. Simulation studies show that the proposed scheme not only improves the prediction accuracy with respect to the prediction mean square error, but also results in a smoother motion field, which can be encoded with less number of coding bits. It is shown that an improvement in the rate distortion performance is achieved with little increase in the computational complexity. It is also shown that Burt and Adelson's (1983) pyramidal data structure provides the best performance among a number of the generating kernels considered in our study.
Model-based seizure detection method using statistically optimal null filters

In this paper, a model-based seizure detection method using statistically optimal null filters (SONFs) is presented. A template seizure from a patient is first selected and the basis functions required by the SONF are derived from this template seizure using wavelet transform. Subsequent EEG (electroencephalogram) recording is processed by the SONF and the output represents the noise-free estimate of the seizure. The energy ratio between the output and the input of the SONF is calculated and used as the test statistic for the seizure detection. Experiments using the SEEG (stereoelectroencephalogram, or intracerebral EEG) recordings of two patients show that this is an effective and promising method, with the possibility of reduced false detections.
A new blind wavelet domain watermark detector using hidden Markov model

The wavelet coefficients of images show heavy-tailed marginal statistics as well as strong inter- and intra-subbands and across orientations dependencies. The vector-based hidden Markov model (HMM) has been shown to be an effective statistical model for wavelet coefficients, which is capable of capturing both the subband marginal distribution and the inter-scale and intra-scale dependencies of the wavelet coefficients. In this paper, we propose a locally-optimum watermark detector using the HMM model for image wavelet coefficients. The performance of the proposed detector is studied through simulation and is shown to be superior to that of other detectors in terms of the imperceptibility of the embedded watermark and detection rate.
A simple neural learning algorithm for total least-squares adaptive filtering

A Hebbian-type learning algorithm for the total least-squares parameter estimation is presented. An asymptotic analysis is carried out to show that the algorithm allows the weight vector of a linear neuron unit to converge to the eigenvector associated with the smallest eigenvalue of the correlation matrix of the input signal. When the algorithm is applied to solve parameter estimation problems, the converged weights directly yield the total least-squares solution. It is shown that the implementations of the proposed algorithm have the simplicity of those of the LMS algorithm, but its noise rejection capability is much superior to those of the least-squares-based algorithms. The applicability and performance of the algorithm are demonstrated through computer simulations of adaptive FIR and IIR parameter estimation problems.<<ETX>>
An 8 Kb/s low-delay CELP speech coder

This paper presents an efficient low-delay CELP speech coder based on a structure given by Chen et el. (1992). The proposed coder can operate at a rate of 8 Kb/s and has an arithmetic complexity that is 20% lower than that of the CELP of Chen et al. with an acceptable increase in the delay. The proposed coder has been tested to provide a good-quality speech.
A robust technique for multiuser detection in non Gaussian channels

In many physical channels where multiuser detection techniques are to be applied, the ambient channel noise is known through experimental measurements to be decidedly non-Gaussian, due largely to impulsive phenomenon. This is due to impulsive nature of man-made electromagnetic interference and a great deal of natural noise. This paper presents a robust multiuser detector for combating multiple access interference and impulsive noise in CDMA communication systems. This detector is essentially a robust version of the linear decorrelating multiuser detector. The robustification is based on the Hampel's M-estimator found in robust statistics. The approach is corroborated with simulation results. Simulation results show that the proposed approach yields significant performance gain over the linear decorrelating multiuser detector with little attendant increase in the computational complexity. Further, it offers better gain over the Huber's M-estimator.
A high secure approach for medical images transmission over wireless channels

In this paper, a high secure approach for transmission of medical images over wireless channels is presented. In this approach, algorithms based on Chaos and Brahmagupta-Bhãskara (BB) equation are proposed for encryption and decryption of the medical images. and no lossy encoding is used for coding the encrypted medical images. Furthermore, turbo channel coding is proposed to correct the transmission errors over impulsive noisy wireless channels. The efficacy of the proposed approach is illustrated with the implementation results on a medical image.
A new bivariate MAP estimator for DT-CWT-based video denoising

A new bivariate maximum a posteriori estimator is proposed for the magnitude components of the dual-tree complex wavelet transform (DT-CWT) coefficients in order to reduce additive white Gaussian noise in a video. The estimator considers the fact that the magnitude components of the DT-CWT coefficients of the Gaussian distributed noise fit the generalized Gamma distribution very well . For spatial filtering, the joint distribution function of the magnitude components of the DT-CWT coefficients of the two neighboring frames of a video is considered to be locally-adaptive bivariate Gaussian having a non-negative mean. The correlation coefficient of this distribution function acts as an indirect measure of the motion of the DT-CWT coefficients between two neighboring frames. A recursive time averaging of the spatially filtered magnitude components is adopted for further noise reduction. Experimental results on test video sequences show that the proposed estimator provides an average peak signal-to-noise ratio that is higher than that provided by the others.
Bayesian Wavelet-Based Image Denoising Using the Gauss–Hermite Expansion

The probability density functions (PDFs) of the wavelet coefficients play a key role in many wavelet-based image processing algorithms, such as denoising. The conventional PDFs usually have a limited number of parameters that are calculated from the first few moments only. Consequently, such PDFs cannot be made to fit very well with the empirical PDF of the wavelet coefficients of an image. As a result, the shrinkage function utilizing any of these density functions provides a substandard denoising performance. In order for the probabilistic model of the image wavelet coefficients to be able to incorporate an appropriate number of parameters that are dependent on the higher order moments, a PDF using a series expansion in terms of the Hermite polynomials that are orthogonal with respect to the standard Gaussian weight function, is introduced. A modification in the series function is introduced so that only a finite number of terms can be used to model the image wavelet coefficients, ensuring at the same time the resulting PDF to be non-negative. It is shown that the proposed PDF matches the empirical one better than some of the standard ones, such as the generalized Gaussian or Bessel K-form PDF. A Bayesian image denoising technique is then proposed, wherein the new PDF is exploited to statistically model the subband as well as the local neighboring image wavelet coefficients. Experimental results on several test images demonstrate that the proposed denoising method, both in the subband-adaptive and locally adaptive conditions, provides a performance better than that of most of the methods that use PDFs with limited number of parameters.
Image denoising utilizing the scale-dependency in the contourlet domain

A new contourlet-based method is introduced for reducing noise in images corrupted by additive white Gaussian noise. This method takes into account the statistical dependencies among the contourlet coefficients of different scales. In view of this, a non-Gaussian multivariate distribution is proposed to capture the across-scale dependencies of the contourlet coefficients. This model is then exploited in a Bayesian maximum a posteriori estimator to restore the clean coefficients by deriving an efficient closed-form shrinkage function. Experimental results are performed to evaluate the performance of the proposed denoising method using typical noise-free images contaminated by simulated noise. The results show that the proposed method outperforms some of the state-of-the-art methods in terms of both the subjective and objective criteria.
Compressive sensing-based DOA estimation using the Dantzig selector

In this paper, a new compressive sensing (CS)-based direction of arrival (DOA) estimation technique using the Dantzig selector is proposed. The proposed scheme can identify more source signals than the number of sensors used, without requiring an a priori knowledge of the number of source signals to be estimated and without any constraint or assumption about the nature of the signal sources using a fewer number of snapshots. The performance of the proposed scheme is compared to that of the Zhang penalty-based algorithm and the MVDR A-LASSO DOA estimation technique. The computational complexity of the proposed algorithm is substantially lower than that of the Zhang penalty-based method or MVDR A-LASSO.
RRNS quasi-chaotic coding and its FPGA implementation

In this paper, a new architecture of the redundant residue number system (RRNS) based quasi-chaotic coding is proposed for the secure telecommunication systems and networks. In the proposed architecture, a number of modulo operations required by the existing designs are replaced by binary coding operations to simplify the design. Also, a moduli selection method and a residue-to-binary converter with error-correction capability are proposed to further improve the efficiency of the design specifically for FPGA implementation. The proposed architecture is implemented and tested using Matlab and Xilinx FPGA hardware. The results show that compared to the existing design, the proposed design requires only 80% of the hardware resources while maintaining the same speed. The power consumption is also reduced by 25%.
On sparsity issues in compressive sensing based speech enhancement

Signal sparsity is the fundamental requirement of compressive sensing (CS) techniques. In our previous work, a CS-based speech enhancement algorithm has been proposed. However, several issues concerning speech sparsity have not yet been thoroughly studied. In this paper, we focus on studying the following issues: (1) the sparsity of clean speech and audio signals; (2) the sparsity of various noise signals; (3) analysis of the capacity of two sparse transforms i.e., wavelet and discrete cosine transform (DCT), to explore speech sparsity. In this respect, several measures are proposed to analytically compare the wavelet transform with DCT. We found that (1) signal compressibility is an important factor for the CS-based method. (2) DCT explores the best compressibility for noisy signals and achieves the best enhancement performance; (2) The CS-based speech enhancement methods are more efficient in reducing the noise with worse compressibility.
New Parametric Discrete Fourier and Hartley Transforms, and Algorithms for Fast Computation

In this paper, we propose a new reciprocal-orthogonal parametric discrete Fourier transform (DFT) by appropriately replacing some specific twiddle factors in the kernel of the classical DFT by independent parameters that can be chosen arbitrarily from the complex plane. A new class of parametric unitary transforms can be obtained from the proposed transform by choosing all its independent parameters from the unit circle. One of the special cases of this class is then exploited for developing a new one-parameter involutory discrete Hartley transform (DHT). The proposed parametric DFT and DHT can be computed using the existing fast algorithms of the DFT and DHT, respectively, with computational complexities similar to those of the latter. Indeed, for some special cases, the proposed transforms require less number of operations. In view of the fact that the transforms of small sizes are used in some image and video compression techniques and employed as building blocks for larger size transform algorithms, we develop new algorithms for the proposed small-size transforms. The proposed parametric DFT and DHT, in view of the introduction of the independent parameters, offer more flexibility in achieving better performance compared to the classical DFT and DHT. As examples of possible applications of the proposed transforms, image compression, Wiener filtering, and spectral analysis are considered.
A modified PCA algorithm for face recognition

In principal component analysis (PCA) algorithm for face recognition, the eigenvectors associated with the large eigenvalues are empirically regarded as representing the changes in the illumination; hence, when we extract the feature vector, the influence of the large eigenvectors should be reduced. In this paper, we propose a modified principal component analysis (MPCA) algorithm for face recognition, and this method is based on the idea of reducing the influence of the eigenvectors associated with the large eigenvalues by normalizing the feature vector element by its corresponding standard deviation. The Yale face database and Yale face database B are used to verify our method and compare it with the commonly used algorithms, namely, PCA and linear discriminant analysis (LDA). The simulation results show that the proposed method results in a better performance than the conventional PCA and LDA approaches, and the computation at cost remains the same as that of the PCA, and much less than that of the LDA.
Perturbation analysis of whitening-rotation-based semi-blind MIMO channel estimation

The whitening-rotation (WR)-based semi-blind methods have been shown to achieve much better channel estimation performance than the conventional training-based methods for MIMO systems. In this paper, the performance analysis is conducted for an ideal WR-based method, in which the knowledge of the ideal whitening matrix is known a priori. This analysis is a valuable study since the ideal WR-based method provides the upper bound of the channel estimation performance for WR-based methods. First, the expression of the error of the rotation matrix is derived by using a perturbation analysis. This result is then utilized to derive a closed-form expression of mean square error (MSE) of the ideal WR-based method. The simulation studies have shown that the theoretical MSE values are consistent with the simulation results, confirming the high accuracy of the derivation of the MSE expression.
Flexible models-a compromise between simplicity and accuracy in active devices modeling [MOST model

Describes a new way to reach a compromise between simplicity and accuracy in active device modeling for computer-aided circuit design. In contrast to the conventional approach founded on the use of preliminary optimized `fixed-structure' models, a method is introduced based on consistent model optimization during the circuit analysis procedure. For the application of the method it is necessary to develop a family of recursive models with different accuracy for each kind of active device and to establish an algorithm of model simplification in accordance with a prescribed accuracy. A family of models is presented as well as one of the possible optimization procedures for MOS transistor circuits. The same method may be used for the analysis of circuits with other active devices. The method helps to gain significant CPU time economy without loss of accuracy in comparison with the conventional approach. This procedure may be easily inserted into any of the existing computer-aided design programs.
Frequency-domain estimation of time-domain correlation matrix for MIMO-OFDM systems

Since the received MIMO-OFDM signal is usually corrupted in the time-domain due to some adverse factors such as frequency offset and large peak-to-average power ratio (PAPR) etc., a high-quality signal may only be obtained in the frequency domain. However, the second-order statistics of the time-domain signal are very often used in a blind or semi-blind channel estimation, which means that an IFFT processor is required in the receiver to achieve the time-domain signal. This additional IFFT incurs a high computational complexity and a long time delay in real-time communication systems. In this paper, we propose a new algorithm for the computation of the time-domain correlation matrix directly from the received frequency-domain signal. The proposed algorithm is proven to be equivalent to the original algorithm of estimating the time-domain correlation matrix, while the former can avoid an IFFT process when high-quality signal is only available in the frequency domain.