nils-herrmann/clean_scirepeval huggingface dataset:
Analytical Synthesis of Current-Mode High-Order Single-Ended-Input OTA and Equal-Capacitor Elliptic Filter Structures With the Minimum Number of Components

None of the previously reported third-order and fourth-order operational transconductance amplifier and capacitor (OTA-C) elliptic filter structures use the minimum number of active and passive components. After an innovative algebraic decomposition for a complicated transfer function, i.e., a new analytical synthesis method, a current-mode odd-nth-order and a current-mode even-nth-order OTA-C elliptic filter structure having the following advantages are presented: 1) all the OTAs have single-ended inputs; 2) all the capacitors are grounded; 3) the minimum active and passive components are used. An equal-capacitance-type structure is designed taking into account the difficulty of precisely fabricating capacitances in integrated circuits. Although the above three advantages lead to the lowest total parasitics and to the most precise output response, the small deviations (such as the 3.5159% error in fp) of the four parameters of the new third-order OTA-C elliptic filter can be drastically reduced (for example, to 0.1553% error in fp) by only slightly tuning the four transconductances, but fixing the ratio of the given three capacitances, and without adding any other active and passive components. H-spice high-pass and low-pass simulations with 0.35-mum process are provided to demonstrate the theoretical results.
Modeling and radiation analysis of uniform circular array

The radiation characteristics of a uniform circular array with linear centrally-fed dipole elements is analyzed. This array is suitable for wireless base station which apply smart antenna technique. The radiation patterns and the gain of the array over a single element are derived based on the thin-wire model. Numerical simulations using the thin-wire model and the method of moment have been conducted and compared for a half-wavelength dipole array. It is shown that the thin-wire model is only applicable for analyzing the radiation pattern in the azimuthal direction and is inaccurate for analyzing the elevation pattern. For uniform supply with progressive phase shift, the sinusoidal current distribution is shown to be a good approximation only when the wire diameter is one-hundredth of a wavelength or less and all the elements are in phase.
Optimizing the kernel in the empirical feature space

In this paper, we present a method of kernel optimization by maximizing a measure of class separability in the empirical feature space, an Euclidean space in which the training data are embedded in such a way that the geometrical structure of the data in the feature space is preserved. Employing a data-dependent kernel, we derive an effective kernel optimization algorithm that maximizes the class separability of the data in the empirical feature space. It is shown that there exists a close relationship between the class separability measure introduced here and the alignment measure defined recently by Cristianini. Extensive simulations are carried out which show that the optimized kernel is more adaptive to the input data, and leads to a substantial, sometimes significant, improvement in the performance of various data classification algorithms.
Competitive splitting for codebook initialization

Codebook initialization usually has a significant effect on the performance of vector quantization algorithms. This letter presents a new scheme of codebook initialization in which the competitive learning and code vector splitting are incorporated together to produce a good initial codebook. Based mainly on the geometrical measurements of the learning tracks of the code vectors, the competitive splitting mechanism shows an ability to appropriately allocate code vectors according to the spatial distribution of the input data and, therefore, tends to give a better initial codebook. Comparisons with other initialization techniques demonstrate the effectiveness of the new scheme.
Contourlet domain image denoising based on the Bessel k-form distribution

Statistical image modeling has attracted great attention in the field of image denoising. In this work, a new image denoising method in the contourlet domain is introduced in which the contourlet coefficients of images are modeled by using the Bessel k-form prior. A noisy image is decomposed into a low frequency approximation sub-image and a series of high frequency detail sub-images at different scales and directions via the contourlet transform. To estimate the noise-free coefficients in detail subbands, a Bayesian estimator is developed utilizing the Bessel k-form distribution. In order to estimate the parameters of the distribution, a characteristic function-based technique is used. Simulation results on standard test images show improved performance both in visual quality and in terms of the peak signal-to-noise ratio and structural similarity index as compared to some of the existing denoising methods. The proposed method also achieves an excellent balance between noise suppression and details preservation.
A user oriented active network simulator

A digital computer simulator for the frequency response and tolerance network comprising RLCM elements, ideal operational amplifiers and controlled sources is presented. The simulator is based on `tableau approach'. Reordering of the sparse tableau matrix is done using Markowitz Criterion and the diagonal pivots are chosen for simplicity. The simulator also employs dynamic allocation for maximum utilization of memory and faster turn around time. Three network are simulated and their results are presented. A network in which the operational amplifiers are assumed to have single pole behaviour is also analyzed.
Simple and practical cyclostationary beamforming algorithms

Inspired by the asymmetric principal component analysis (APCA) neural model and based on the signal cyclostationarity, the authors propose a simple gradient-descent beamforming (GDB) algorithm. The GDB algorithm is an adaptive blind beamforming algorithm and can be used to extract signals with cyclostationarity under a complex signal environment. Although the GDB algorithm suffers from slow convergence, it has a low computational complexity. Two more algorithms, called the boosted GDB and the beta GDB algorithms have been defined based on the GDB algorithm. All the algorithms have been simulated and compared. The boosted GDB and beta GDB algorithms are shown capable of providing fast convergence and satisfactory signal-to-interference-and-noise ratio (SINR) performance, and can be used for implementation in real-time systems.
Estimation of an Initial Phase over Extremely Short Record-Length of a Sinewave Signal

It is known that the traditionally used statistically optimal estimator for the initial phase of a sinewave signal possesses a certain systematic bias whose value depends on an unknown initial phase. Due to the inaccuracies involved in estimating an initial phase, such an estimator fails in practice when the record length of a sinusoidal signal is less than one period. This paper presents a new algorithm for unbiased estimation of an initial phase of a sinewave burst in the presence of white noise. Such an algorithm can be thought of as a certain modification of a traditional one that is statistically optimal in the case of infinite record length of a signal under analysis. The proposed algorithm has the same structure and acts as the traditionally used one, but the systematic bias is canceled. The main difference between these two algorithms is that the orthogonal sineweighting functions in the traditional algorithm are substituted by the nonorthogonal functions whose parameters can be established a priori of the measurements. A comparative study of two algorithms is discussed. The proposed method is shown to give a more accurate estimate of an initial phase, rather than the traditional direct form. The analytic results are supplemented by a simulation study.
A Class of Cosine-Modulated Filter Banks with Multiple Prototype Filters

In this paper, several new cosine-modulated filter banks (CMFBs) are developed by using multiple prototype filters in conjunction with a proper modulation scheme. First, the conventional CMFB is formulated as a cascade of a modulation matrix and a polyphase matrix with a bidiagonal structure. Under this framework, it is then revealed that the proposed new CMFBs are a special class of paraunitary filter banks (PUFBs), whose polyphase matrices are of different sparse patterns. It is shown that when M prototype filters are used, a full polyphase matrix representing a general PUFB can be obtained, thus providing a bridge connecting CMFBs and PUFBs. It is also shown that with more free parameters involved in the CMFBs, one can achieve a tradeoff between the performance of the filter bank and its design/implementation complexity
Video noise reduction in the wavelet domain using temporal decorrelation and adaptive thresholding

This paper proposes a novel wavelet domain based spatio-temporal filter for video denoising that exploits the temporal as well as spatial correlations which exist in the subband representation of the video sequence. The temporal redundancy or correlation among the corresponding wavelet coefficients in neighboring frames is minimized by the use of discrete cosine transform. These decorrelated noisy wavelet coefficients are then denoised spatially via a low-complexity wavelet shrinkage method, which utilizes the correlation that exists between subsequent resolution levels. The proposed scheme shows promising results and outperforms state-of-the-art spatio-temporal filters in time as well as wavelet domains, both in terms of PSNR and visual quality
A fast three-step search algorithm by the utilization of multilevel vector partial sums

Due to the high computational requirement of the full-search algorithm for block motion estimation, fast block motion estimation algorithms are needed for real-time implementations of the video coding standards. Recently, a three-step search algorithm for block motion estimation has been proposed in the literature. In this paper, a fast three-step search algorithm is proposed to further reduce the computational complexity of the three-step search algorithm with no loss of accuracy. By using a multilevel vector partial sums and lower bounds in the proposed algorithm, a large number of possible candidate motion vectors are discarded while still retaining the optimal motion vector of the three-step search algorithm. It is shown that not all the levels of partial sums and lower bounds are needed. A method to select these vector partial sums and lower bounds are also presented. Simulations of the proposed algorithm are carried out for various benchmark video sequences and the results demonstrate that the new algorithm can reduce the computational complexity of the three-step search algorithm by 20 to 60 percent with no loss of accuracy.
Cross coupled signal controlled constrained null filter and separation of superimposed frequency modulated sinusoids

The problem of separation of superimposed frequency modulated (FM) sinusoids is addressed. A cross-coupled signal controlled constrained null filter (CC-SCCNF) is proposed to separate the individual signal from the sum of a number of FM signals whose power spectra may overlap. An advantage of the proposed CC-SCCNF is that one does not have to make any assumption on the mixture of the superimposed FM signals, other than that the number of FM signals is a priori known. A necessary condition is described in terms of the trajectories of the SCCNF coefficients. These trajectories are monitored by measuring the instantaneous Euclidean distance between any two SCCNF trajectories. A proof of the CC-SCCNF convergence to a unique solution for the separation of signals is provided. Several computer simulation results are also presented.<<ETX>>
Compression of long-term EEG using power spectral density

We propose to use the features based on power spectral density as a descriptor of the EEG in the compression of the long-term intensive care unit EEG to obtain the temporal evolution of the recurrent patterns. Sleep EEG is used as a baseline since the sleep stages can be mapped to recurrent patterns in the background EEG. Our results indicate that the spectral features provide a better classification of the sleep EEG and assist in a better formation of homogenous clusters compared to the results obtained with the previously used features. The average overall agreement compared against manual scoring of seven sleep EEG records is 68.5%. It is an improvement compared to 62.7% obtained with the previously used features. Although our results for computer classification use only the EEG information from one frontal and one occipital channel, they are similar to the manual classification of sleep EEG, which is based on additional information.
A new approach to suppression of finite-length pulse interference using modified linear predictor

In this paper the problem of pulse interference suppression using prediction technique is considered. It is shown that the conventional predictor cannot be used to suppress a finite-length pulse interference even when the data are ideal, for example, in the absence of a background random process. A new method is presented for the design of an adaptive tapped delay line filter used for finite-length pulse suppression. The method is based on a modified form of linear prediction procedure using a set of artificial state variables given by the product of input samples and their delayed versions. The advantages of this modified linear predictor are demonstrated by two examples, using computer simulation.
Orthogonal space time code based partial rank affine projection adaptive filtering algorithm

A space time code based partial rank affine projection (PRAP) algorithm is proposed. The proposed algorithm uses an input signal where the input signal matrix Xk becomes an orthogonal matrix. For this input signal, matrix (XkTXk) becomes a diagonal matrix whose inverse can be easily computed. Thus, the proposed algorithm saves a significant amount of computations. Due to this feature the proposed PRAP algorithm is shown to offer a faster convergence speed and a smaller computational burden per iteration than the NLMS algorithm does.
A new anchored normalization technique for score-level fusion in multimodal biometrie systems

Dissimilarities in equal error rates (EERs) of multiple matchers heavily influence the performance of multi-biometric systems. A normalization technique aims at improving the recognition rate of such a system. In view of this, in this paper, an anchored normalization technique, referred to as improved anchored min-max (IAMM) technique for a multimodal biometric system, is developed. In the proposed technique, the anchor value is computed from the raw matching score sets corresponding to each of the modalities used in the system. This anchor value does not require a priori knowledge of the equal error rates and genuine/impostor score distributions of the individual matchers used in the system. It takes into account the average and variations of the score values that occur more than once in each score set. The performance of IAMM, in terms of EER and genuine acceptance rates @10% and @20% false acceptance rates, is evaluated on a multi-biometric system. The experimental results show that the performance of a multi-biometric system using the proposed normalization technique is superior to that of the uni-biometric systems or to that of the system using the existing normalization techniques.
A Signal Perturbation Free Transmit Scheme for MIMO Channel Estimation

In this paper, a novel signal perturbation free transmit scheme is proposed for MIMO channel estimation. A perturbation analysis of the WR-based method is first conducted, showing that the method is subject to a signal perturbation error and therefore, its performance is very poor under the moderate to high signal-to-noise ratios (SNRs). A new transmit structure is then proposed to cancel the signal perturbation error at the receiver in order to improve the performance of the WR-based method in the high SNR case. Computer simulations show that the WR-based method with the proposed signal perturbation free transmit scheme significantly outperforms the original WR-based method as well as the training-based LS method in terms of the MSE of the channel estimate.
A new parametric transform

In this paper, a novel method for the parameterization of the Hadamard matrices is proposed. The resulting parametric Hadamard matrix of order N has N/2-1 independent parameters. It is a normalized matrix and its inverse can easily be obtained by taking the reciprocal of each of the N/2-1 independent parameters in the forward matrix and transposing the resulting matrix. The corresponding transform reduces to a new parametric integer transform when the independent parameters in the proposed matrix operator are all from the set {plusmn2m, plusmn 2n j, 2l (plusmn1plusmn j)}, where m, n and l are chosen positive or negative integers. Other interesting special cases of the proposed transform can also be obtained. In addition, the proposed parametric transform can be computed using an algorithm having a very simple structure. This transform can be used in many transform-based applications. For example, it can be employed in watermarking and encryption where its independent parameters can be used as an additional secret key.
LMS-optimal notch filters with improved transient performance

The use of a pole-contraction factor (/spl alpha/) with a value close to unity in the Constrained Notch Filters (CNFs) causes excessively long transient response. For practically useful values of /spl alpha/, the transient duration is reduced, on the other hand however, the error in transparency as measured by the MSE is increased. In this paper, we increase the order of CNFs by a strategic pole/zero placement to address this tradeoff. Two approaches of finding these pole/zero locations are shown. One of the methods is optimal in the LMS sense, while the other, though suboptimal, yields a closed-form solution. The resulting filters show a significant improvement in the transient duration.
Compressed sensing for DOA estimation with fewer receivers than sensors

This paper addresses the problem of the direction-ofarrival (DOA) estimation using fewer receivers than sensors. Inspired by the Compressed Sensing (CS) theory developed in recent years, we present a new preprocessing scheme for a large array using a small size receiver. Unlike the traditional ℓ2 -norm-based algorithms by judicious selection of the preprocessing matrix, the proposed scheme uses a random weight generator as a measurement of the compressed sensing to form the output data for each time interval. The formulated CS problem for DOA estimation is then solved based on the convex programming via ℓ1 -norm approximation such as Dantzig Selector. We consider two different scenarios in the CS domain, i.e., the angle domain and the angle-frequency domain. It is shown that the number of receivers can be reduced significantly for a given number of sensors by using the proposed CS-based DOA estimation approach.
Instantaneous frequency based nonlinear adaptive filter for interference suppression in spread spectrum systems

This paper presents an approach based on jammer instantaneous frequency estimation for suppression of frequency modulated (FM) jammers in spread spectrum systems. The FM jammers are instantaneous narrowband and have clear time-frequency signatures that are distinct from spread spectrum code. In the proposed approach, an augmented state-space representation of the received signal (spread spectrum signal+noise+interference signal) is developed using a three-coefficient FIR model for the FM interference. Based on the state-space representation developed, a Kalman-type nonlinear adaptive filter namely, augmented state approximate conditional mean filter (ASACMF) is formulated to estimate the unknown jammer instantaneous frequency and the FM interference from the received signal. The efficacy of the proposed filter is corroborated with simulation examples for FM interference suppression in spread spectrum systems. The performance improvement achieved with the proposed filter is quantified in comparison with the three coefficient FIR excision filter. Simulation results show that the proposed filter is effective in suppressing the FM interference in spread spectrum systems with good receiver output SNRs.
Image restoration using a hybrid approach based on DWT and SMKF

Various approaches based on Kalman filtering exist in the literature for image restoration. The reduced order model Kalman filter (ROMKF) has comparable performance with less computational complexity. For further reduction in computational complexity, we suggest a simplified model Kalman filter (SMKF) for image restoration. Furthermore, a hybrid approach based on discrete wavelet transform (DWT) and SMKF is proposed for image restoration with better SNRs especially when the observed image signal-to-noise ratio is low. In the first step, the approach uses the DWT with few resolution levels and a moderate threshold value for denoising the image. The denoised image will provide a better data to SMKF in the second step. The proposed approach is implemented on a visual image to evaluate its performance in comparison with the SMKF and DWT approaches.
Multiple description image coding using pixel interleaving and wavelet transform

Multiple description coding encodes a source image into two or more mutually refinable bitstreams to overcome channel impairments. When one or more channels fail, received bitstream(s) are decoded to provide a lower but acceptable quality of the reconstructed image. This paper proposes a multiple description image coding scheme using pixel interleaving by taking advantage of the intrinsic correlation of the adjacent pixels. We improve substantially the performance of the reconstructed image from multiple descriptions by compromising to some extent the performance of the reconstructed from a single description. This is achieved by incorporating the wavelet transform into the pixel interleaving-based encoder so as to provide the ability to trade off the quantizations of the frequency coefficients and the spatial pixels. Simulations are presented to demonstrate the effectiveness of the proposed schemes.
A new approach for weighted least-square design of FIR filters

In this paper, a new approach for weighted least square (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 update factor of the weighting function, the inverse of a matrix is expanded as a convergent power series. By exploiting the properties of some of the matrices involved in the design, 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 has to evaluate only the first two or three terms of the series after a few initial iterations, implying that the conventional operation for matrix inversion is simplified significantly.