nils-herrmann/clean_scirepeval huggingface dataset:
Optimum pulse shaping application of binary transversal filters used in satellite communications

The implementation of binary transversal filters (b.t.f.) is gaining wide acceptance, due to their operational adaptability to variable bit rates, their achievable high out-of-band attenuation and linear phase performance and to the recent availability of medium-scale integrated components such as charged-coupled devices.The design and evaluation results of a b.t.f. meeting the single channel per carrier Intelsat satellite communications specifications are presented, which are based on Mueller's synthesis method. The performance evaluation problems of these type of filters are also described and, in particular, emphasis is given to the following: out-of-band to in-band power ratio inaccuracies when wideband measurement techniques are employed, and the evaluation of the r.m.s. jitter of b.t.f. based on the jitter probability density function.
A new split-radix FHT algorithm for length-q*2/sup m/DHTs

In this paper, a new split-radix fast Hartley transform (FHT) algorithm is proposed for computing the discrete Hartley transform (DHT) of an arbitrary length N=q*2/sup m/, where q is an odd integer. The basic idea behind the proposed FHT algorithm is that a mixture of radix-2 and radix-8 index maps is used in the decomposition of the DHT. This idea and the use of an efficient indexing process lead to a new decomposition different from that of the existing split-radix FHT algorithms, since the existing ones are all based on the use of a mixture of radix-2 and radix-4 index maps. The proposed algorithm reduces substantially the operations such as data transfer, address generation, and twiddle factor evaluation or access to the lookup table, which contribute significantly to the execution time of FHT algorithms. It is shown that the arithmetic complexity (multiplications+additions) of the proposed algorithm is, in almost all cases, the same as that of the existing split-radix FHT algorithm for length- q*2/sup m/ DHTs. Since the proposed algorithm is expressed in a simple matrix form, it facilitates an easy implementation of the algorithm, and allows for an extension to the multidimensional case.
Incremental Distance and Diameter Sequences of a Graph: New Measures of Network Performance

Two new measures of network performance, namely, the incremental distance sequence and the incremental diameter sequence, are introduced for application in network topology design. These sequences can be defined for both vertex deletions and edge deletions. A complete characterization of the vertex-deleted incremental distance sequence is presented. Proof of this characterization is constructive in nature. A condition for the feasibility of an edge-deleted incremental distance sequence and a procedure for realizing such a sequence are given. Interrelationships between the elements of incremental distance sequences and incremental diameter sequences are studied. Using these results, it is shown that a graph that has a specified diameter and a specified maximum increase in diameters for deletions of vertex sets of given cardinalities can be designed.
Adaptive Projection Selection for Computed Tomography

The number of projections is a critical factor in tomographic imaging. The larger the number, the better the quality of the reconstructed image; however, it increases the radiation dose delivered to the patient. Therefore, it is important to keep the number of projections as small as possible. Traditionally, the projections are taken by moving the x-ray source around the patient at uniform angular steps. Taking projections at nonuniform steps may result in better images as compared with that obtained using uniform projections. This paper describes two different approaches that adjust the step size to adaptively select the angle of projections. The first one is based on the spectral richness of the acquired projections and the second relies on the amount of new information added by successive projections. The superior performance of the two proposed methods over the uniform projection scheme is demonstrated through simulation results using both phantom and real images.
Fully-pipelined CORDIC implementation of subspace-based speech enhancement

This paper presents a fully-pipelined CORDIC architecture for the simultaneous diagonalization of the covariance matrices. As an example, the problem of speech enhancement in a subspace based approach is considered, where in the covariance matrices of speech and noise are diagonalized concurrently. In order to compare the system performance of the proposed algorithm, objective measurements of speech enhancement are shown in terms of the signal to noise ratio and mean bark spectral distortion at various noise levels. In addition, the resource utilization of the proposed architecture on a Xilinx FPGA is studied.
Joint DOD and DOA Estimation for MIMO Array With Velocity Receive Sensors

This letter investigates the problem of joint estimation of the direction of departure (DOD) and the direction of arrival (DOA) for multi-input multi-output (MIMO) array systems. A new bistatic MIMO array system, configured with multiple transmit sensors and multiple velocity receive sensors is introduced, and a new joint DOD and DOA estimation algorithm is proposed. The key idea behind the proposed algorithm is to use the DOA information embedded in the velocity sensors to start 1-D MUSIC searches for the DOD and DOA in succession. The proposed successive MUSIC algorithm is suitable for irregular array geometry, imposes less constraint on the receive sensor spacing, and requires no parameter pairing nor two-dimensional searching.
Optimization of the three-step search algorithm by exclusion of stationary macroblocks from the search process

A typical video frame contains many macroblocks that experience little motion with respect to the reference frame. These macroblocks are referred to as stationary macroblocks. This paper aims at accelerating the three-step search algorithm for block motion estimation (BME) by excluding these stationary macroblocks from the search process. A method to detect these stationary macroblocks by comparing the mean absolute difference between the current macroblock and the reference macroblock with a given threshold is first proposed. In order to make the algorithm more efficient, a mechanism to adapt the threshold level to the changing statistics of the video sequence is then developed. Simulation results demonstrate that by using the proposed algorithm, a speedup of 20 to 40 percent is achieved for typical video test sequences. The accuracy of the proposed algorithm remains about the same as that of the three-step search algorithm.
A new wavelet-based method for despeckling SAR images

In this paper, a wavelet-based despeckling method is proposed for suppressing speckle noise in synthetic aperture radar (SAR) images. A new threshold is proposed to classify the wavelet coefficients into significant and insignificant ones. Local statistic in the wavelet domain is used to further classify the significant coefficients into the edge and non-edge coefficients. The edge coefficients remain unaltered, whereas the non-edge and the insignificant ones are reduced in magnitude. Experiments are carried out on a noise-free image corrupted with simulated speckle noise, and a real SAR image. The results show that the proposed method provides a performance better than that of other methods in terms of the peak signal-to-noise ratio and ability to suppress speckle in the homogeneous areas. In addition, it introduces a bias that is much smaller than that of the other methods as well as preserves edges quite well.
Unbiased homomorphic system and its application in reducing multiplicative noise

The problem of reducing the multiplicative noise corrupting a signal is discussed. A generalisation of the existing sampled function weighted order (SFWO) filter is proposed by relaxing the symmetry condition on the probability density function (PDF) of the noise. This generalised SFWO filter is then used within a homomorphic system to reduce the multiplicative noise. It is shown that the output from such a system is biased, and hence, a suitable bias compensation technique is suggested. An unbiased homomorphic system, whose design is based on the PDF of the corrupting multiplicative noise, is proposed to reduce the noise. Images generated by coherent imaging systems are always corrupted by speckle, a kind of multiplicative noise having a lognormal distribution. A filter called the mean median filter, to reduce additive white Gaussian noise, is first proposed and then used within the unbiased homomorphic system to reduce the speckle in images. The effectiveness of the various proposed algorithms is demonstrated and compared with that of some of the existing schemes through extensive simulations.
Normalization and Weighting Techniques Based on Genuine-Impostor Score Fusion in Multi-Biometric Systems

The performance of a multi-biometric system can be improved using an efficient normalization technique under the simple sum-rule-based score-level fusion. It can also be further improved using normalization techniques along with a weighting method under the weighted sum-rule-based score-level fusion. In this paper, at first, we present two anchored score normalization techniques based on the genuine and impostor scores. Specifically, the proposed normalization techniques utilize the information of the overlap region between the genuine and impostor scores and their neighbors. Second, we propose a weighting technique that is based on the confidence of the matching scores by considering the mean-to-maximum of genuine scores and mean-to-minimum of impostor scores. A multi-biometric system having three biometric traits, fingerprint, palmprint, and earprint, is utilized to evaluate the performance of the proposed techniques. The performance of the multi-biometric system is evaluated in terms of the equal error rate and genuine acceptance rate @0.5% false acceptance rate. The receiver operating characteristics are also plotted in terms of the genuine acceptance rate as a function of the false acceptance rate.
Stability of second order time-varying constrained notch filters

The stability of second order time-varying constrained notch filters (TVCNF) which are capable of rejecting a sine signal with time-varying frequency is analysed. Two cases are considered: TVCNF1, where one of the two coefficients of the filter is allowed to vary with time while the value of the second coefficient is kept constant; TVCNF2, where both the coefficients can vary with time. It is shown that TVCNF2 is always asymptotically stable while the TVCNF1, possesses unstable regions. Thus for tracking a sine signal with timevarying frequency, TVCNF2 is preferable to TVCNF1.
Hopfield network-based image retrieval using re-ranking and voting

Content-based image retrieval is a technology that is used to identify similar images based on their visual content. Relevant images are found by employing methods that rank images and show the top-ranked images. One important query pertaining to image retrieval methods is regarding as to how to rank the results. This paper proposes a new method based on an unsupervised Hopfield neural network that models human visual memory. In addition, a re-ranking algorithm using post-retrieval analysis is also proposed to refine results by rejecting those top-ranked images that are visually dissimilar. The re-ranking process is based on a combination of spatial information. Results obtained so far indicate that our method is more efficient and promising than other neural network based methods.
Compressive sensing-based speech enhancement in non-sparse noisy environments

In the authors previous work, a compressive sensing (CS)-based method has been proposed to address speech enhancement (SE) in adverse environments (CS-SPEN) based on an assumption of sparse noise. However, this assumption may not be satisfied in practical noisy environments. In this study, the authors study this issue by relaxing this assumption to consider a general non-sparse noise case, such that the proposed method naturally extends the previous one. In particular, they solve the theoretic difficulty of CS-SPEN on the treatment of non-sparse noise by using a relaxed upper bound for the constraint governing data consistency and a relaxed estimation error bound. Their main result is mathematically proved. In addition, the effectiveness of the proposed method is demonstrated by computational simulations, showing certain improvements to the previous method for both stationary and non-stationary white Gaussian noises across various segmental signal-noise-ratios (SNRs). In these cases, the proposed method is shown to have comparable results to the state-of-the-art SE alogrithms and some advantages over them at low SNRs. CS-SPEN without the sparse noise assumption works evenly with CS-SPEN with the sparse noise assumption for car internal and F16 cockpit noises.
Improved radix-4 and radix-8 FFT algorithms

In this paper, improved algorithms for radix-4 and radix-8 FFT are presented. This is achieved by re-indexing a subset of the output samples resulting from the conventional decompositions in the radix-4 and radix-8 FFT algorithms. These modified radix-4 and radix-8 algorithms provide savings of more than 33% and 42% respectively in the number of twiddle factor evaluations or accesses to the lookup table compared to the corresponding conventional FFT algorithms without imposing any additional complexity.
A New Method for Denoising of Images in the Dual Tree Complex Wavelet Domain

In this paper, a new dual tree complex wavelet transform-based Bayesian method is proposed for denoising of images corrupted by additive white Gaussian noise. The symmetric normal inverse Gaussian distribution is used to model the real and imaginary parts of the complex wavelet coefficients of the noise-free images. The coefficients that correspond to the noise are assumed to approximate a Gaussian distribution. These models are then exploited to develop a Bayesian minimum mean squared error estimator. A method is presented for estimating the parameters of the assumed normal inverse Gaussian prior. Experiments are carried out on typical noise-free images corrupted with simulated white Gaussian noise. The results show that the proposed method performs better than some of the existing methods in terms of the peak signal-to-noise ratio and visual quality
Palmprint recognition based on histograms of sparse codes

In this paper, we propose a palmprint recognition scheme using histograms of sparse codes (HSC) as a new feature for palmprint image. In the feature extraction stage, the HSC feature is obtained by computing sparse codes for a given dictionary from a palmprint image, which results in a feature image. In the feature encoding stage, a hash table is designed from the feature image using the binary hashing technique. Finally, the hash table is matched with the templates of hash tables for the purpose of identifying an individual. Extensive experiments are performed on three publicly-available palmprint databases. Experimental results show that the performance of the palmprint recognition system using the proposed scheme is superior to that of other schemes in terms of equal error rate (EER), genuine acceptance rate (GAR) at 11% false acceptance rate (FAR), and processing time.
An expanded MFBLP method with reduced computational complexity for estimating sinusoid frequencies

The modified forward backward linear prediction method (MFBLP) method for estimating the frequencies of multiple sinusoids performs well on short data records, but a large predictor order L is required to achieve the minimal estimation variance. A new expanded MFBLP method is presented, which attains the same (or slightly better) performance for a substantially smaller L, and a hence smaller computational complexity. For example, in the case of 2 closely spaced sinusoids and 64 samples, L/sub opt/ decreases from 48 to 9, and computational complexity is reduced to less than 25%.
Wavelet filters in multi-resolution motion estimation

The performance of various wavelets, including those known to be well suited for the coding of still images, are evaluated for the multi-resolution motion estimation of video sequences. The multi-resolution motion estimation scheme proposed by Zhang and Zafar (1992), which has been widely cited in the literature, is used as the simulation scheme in this study. The prediction mean square error (PMSE) in the wavelet transform coefficient domain is used in our study 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 multi-resolution motion estimation among the wavelets evaluated in this study.
A two-stage multi-path searcher for uplink WCDMA base station

Multi-path searcher (MPS) is used to determine and track the finger positions of a RAKE receiver. It takes advantage of the multipath diversity and assigns fingers to the correct multi-path component. A drawback of the existing multipath searchers for uplink WCDMA base-station RAKE receiver is that their computational complexity is high or the probability of detection of a valid multipath is low. In order to solve this problem, in this paper, a two-stage MPS, based on a downlink reduced search range by using an offset interleaving search approach and a single correlator, is proposed. The algorithm is shown to have a low computational complexity and to provide a high detection performance in comparison to the conventional MPS.
Locally Adaptive Wavelet-Based Image Denoising using the Gram-Charlier Prior Function

Statistical estimation techniques for the wavelet-based image denoising use suitable probability density functions (PDFs) as prior functions for the image coefficients. Due to the intrascale dependency of the local neighboring image wavelet coefficients, the prior functions are assumed to be stationary. In this paper, it is shown that the stationary Gram-Charlier (GC) PDF models the image coefficients better than the traditional ones, such as the stationary Gaussian and stationary generalized Gaussian PDFs. A Bayesian wavelet-based maximum a posteriori estimator is then developed by using the proposed GC prior function. Experimental results on standard images show that the proposed estimator provides a denoising performance, which is better than that of several existing denoising methods in terms of signal-to-noise ratio and visual quality.
Fast and accurate 2-D DOA estimation via sparse L-shaped array

In this paper, we address the problem of estimating the two-dimensional (2-D) directions of arrival (DOA) of multiple signals, by means of a sparse L-shaped array. The array consists of one uniform linear array (ULA) and one sparse linear array (SLA). The shift-invariance property of the ULA is used to estimate the elevation angles with low computational burden. The source waveforms are then obtained by the estimated elevational angles, which together with each sensor of the SLA, considered as a linear regression model, will be used to estimate the azimuth angle by the modified total least squares (MTLS) technique. The new algorithm yields correct parameter pairs without requiring the computationally expensive pairing operation, and therefore, it has at least two advantages over the previous L-shaped array based algorithms: less computational load and better performance due to using the SLA. Simulation results show that our method provides accurate and consistent 2-D DOA estimation results which could not be achieved by other methods with comparable computational complexity.
A note on "A high-speed residue-to-binary converter for three-moduli (2/sup k/ 2/sup k/ - 1, 2/sup k-1/ - 1) RNS and a scheme for its VLSI implementation"

In this paper, a high-speed residue-to-binary converter for the moduli set (2/sup k/, 2/sup k/-1, 2/sup k-1/-1) is proposed. Compared to the previous converter based on this moduli set, the proposed one is 40% faster. Also, the time-complexity product is improved by 20%. Following the top-down very large scale integration design flow, the proposed converter is implemented in 0.5 micron CMOS technology. Based on this moduli set, layouts of the 8-, 16-, 32-, and 64-bit residue-to-binary converters, which can be used in further residue number system designs, are generated and simulation results obtained.
Realization of 2-D linear-phase FIR filters by using the singular-value decomposition

The singular-value decomposition (SVD) technique is investigated for the realization of a general two-dimensional (2-D) linear-phase FIR filter with an arbitrary magnitude response. A parallel realization structure consisting of a number of one-dimensional (1-D) FIR subfilters is obtained by applying the SVD to the impulse response of a 2-D filter. It is shown that by using the symmetry property of the 2-D impulse response and by developing an appropriate unitary transformation, an SVD yielding linear-phase constituent 1-D filters can always be obtained so that the efficient structures of the 1-D linear-phase filters can be exploited for 2-D realization. It is shown that when the 2-D filter to be realized has some specified symmetry in its magnitude response, the proposed SVD realization would yield a magnitude characteristic with the same symmetry. An analysis is carried out to obtain tight upper bounds for the errors in the impulse response as well as in the frequency response of the realized filter. It is shown that the number of parallel sections can be reduced significantly without introducing large errors, even in the case of 2-D filters with nonsymmetric magnitude response.
STFT-Based Segmentation in Model-Based Seizure Detection

To aid the review of long-term electroencephalograph (EEG), it is necessary to develop automatic seizure detection methods. In the literature, numerous seizure detection methods based on parameterization of the EEG have been presented. Recently a new patient-specific model-based method using Statistically Optimal Null Filters (SONF) has been proposed for seizure detection , This method uses stationary segments of a template seizure to generate the necessary seizure model (basis functions) that is used for all subsequent seizure detections. In this approach, the necessary stationary segments within the template are manually identified based on the constancy of the dominant rhythm. The manual selection of stationary segments is cumbersome in practice. In this paper, we present short-time-Fourier-transform (STFT) based automatic segmentation of template seizure resulting in practically usable model-based seizure detection. To assess the performance of the proposed algorithm, a comparison with the visual (manual) method of epoch selection on simulated as well as on the template seizures of five different patients is done. The overall performance improvements are evident in terms of enhanced seizure detection sensitivity and reduced number of false positives.
Scaling-free McClellan transform for 2-D digital filters

The original McClellan transformation used in the design of 2-dimensional digital filters having quadrantally symmetrical magnitude response, involves the substitution cos ω = F(cos ω1, cos ω2). For real frequencies, a scaling procedure was proposed by Mersereau et al. (1976) to guarantee that |F|≤1 using the two values Fmax and Fmin. In the letter, we first present extremely simple formulas for the scaling factors, then suggest a modified transform which not only is scaling-free but also contains one less coefficient than the original transform.