nils-herrmann/clean_scirepeval huggingface dataset:
A robust quantization-based image watermarking scheme in the wavelet-based contourlet domain

Watermarking has attracted much attention in last decade due to its application in copyright protection of digital multimedia. In this paper, a blind watermarking scheme in the wavelet-based contourlet domain using the singular value decomposition technique is proposed. To realize a blind watermarking scheme, quantization-based embedding and extraction procedures are employed. Experiments are conducted to evaluate the performance of the proposed watermarking scheme. Simulation results show that the proposed scheme outperforms some of the existing schemes in terms of imperceptibility of the embedded watermark and providing lower bit error rate values. It is also shown that the proposed watermarking scheme is more robust against various kinds of attacks such as compression, median filtering, scaling and Gaussian noise, than other existing methods.
An Efficient Unified Framework for Implementation of a Prime-Length DCT/IDCT With High Throughput

In this paper, a unified design framework for prime-length forward and inverse discrete cosine transforms with a high throughput is presented. The proposed design facilitates trade-off between the throughput and hardware cost or power consumption, and is well suited for low-power applications. The VLSI structure is highly regular and modular with a topology well suited for the VLSI implementation. The proposed approach is based on the derivation of new efficient systolic algorithms. The algorithms have the same core structure for both the transforms, and the core structure consists of two circular correlations, which unlike other similar computational structures, have the same length and form. Thus, they can be computed in parallel and mapped on the same linear systolic array with channels having a low I/O bandwidth requirement and their number being independent of the transform length N. Further, it is shown that the two transforms, can be efficiently implemented on the same VLSI chip, where only the pre- and post-processing stages are different. The proposed systolic algorithms retain the benefits provided by VLSI implementations based on circular or cyclic convolution structures, and at the same time has a simpler control structure, high speed and low complexity
A Frequency-Domain Correlation Matrix Estimation Algorithm for MIMO-OFDM Channel Estimation

The second-order statistics of a time-domain signal are very often used in blind and semi-blind channel estimation. Considering that the received signal in MIMO-OFDM systems might be corrupted in the time-domain due to some adverse factors such as frequency offset and large peak-to-average power ratio (PAPR), an IFFT processor is required in the receiver to achieve a good-quality time-domain signal. This additional IFFT incurs extra computational complexity and probably a long time delay as well 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 frequency-domain correlation matrix estimation method is then used to develop a new semi-blind MIMO-OFDM channel estimation approach. A number of computer simulation based experiments are conducted, confirming the effectiveness of the proposed method.
ASIC implementation architecture for pulse shaping FIR filters in 3G mobile communications

This paper presents two system-on-chip oriented implementation architectures for pulse shaping FIR filters used in 3G mobile communications. One is the look-up-table based distributed arithmetic architecture and the other one a modified multiply and accumulation (MAC) implementation. Both schemes employ the pre-upsampling procedure to minimize the consumption of hardware resources. The proposed architectures have been prototyped with FPGA implementation and can be integrated directly to ASIC products.
Inference of gene regulatory networks from time-series microarray data

The regulation of gene expression is a dynamic process, hence it is of vital interest to identify and characterize these dynamic processes. Since genes work in a cascade of networks, gene regulatory network (GRN) reconstruction is a crucial process for thorough understanding of the underlying biological interactions. We present here a technique based on partial correlations to infer the GRN. Our proposed technique takes into account the possible and variable time delays between various genes. The results have shown that our proposed algorithm has results consistent with the existing biological knowledge. Our algorithm is implemented in R.
A split-radix algorithm for 2-D DFT

In this paper, the split-radix approach for computing the one-dimensional (1-D) discrete Fourier transform (DFT) is extended for the vector-radix fast Fourier transform (FFT) to compute the two-dimensional (2-D) DFT of size 2(r/sub 1/)/spl times/2(r/sub 2/), using a radix-2/spl times/2 index map and a radix-8/spl times/8 map instead of a radix-2/spl times/2 index map and a radix-4/spl times/4 map as is done in the classical split vector radix FFT algorithms. Since a radix-8/spl times/8 index map and a method for combining the twiddle factors are used, the new algorithm provides significant savings compared to the 2-D FFT algorithms previously reported in terms of the arithmetic complexity, data transfer, index generation and twiddle factor evaluation or access to the lookup table. In addition, since the algorithm is expressed in a simple matrix form using the Kronecker product, it facilitates an easy implementation of the algorithm, and allows for an extension to the multidimensional case.
A new lower bound for fast block motion estimation algorithms

Current video coding standards of H.263, MPEG-2, and MPEG-4 employ the block motion estimation technique to decorrelate video sequences. Since the full-search algorithm for block motion estimation has a great deal of computational complexity, it is not suitable for real-time implementations. Some fast block motion estimation algorithms, such as the selective elimination algorithm, the multilevel successive elimination algorithm, and the vector-based algorithm, have been proposed in the literature. These algorithms reduce the computational complexity of the full search algorithm by utilizing a lower bound for the block-matching criterion of the mean absolute difference (MAD). However, the partial sums for the computation of the lower bound in these algorithms cannot take advantage of the byte-type data parallelism in the existing single instruction multiple data (SIMD) technique. In this paper, a new lower bound for the MAD is established, and this is achieved with the following three objectives in mind: (i) the partial sums can be used to calculate a lower bound to discard the computation of the MAD, (ii) the partial sums are of only 8 bits and saved in a contiguous memory space, and (in) eight partial sums can be processed concurrently in a single 64-bit SIMD register. The new lower bound can be employed in conjunction with an existing block motion estimation algorithm to accelerate the execution of the algorithm without any loss of accuracy. Simulation results demonstrate that the above scheme can accelerate the execution of the vector-based fast algorithm, selective elimination algorithm, and full search algorithm by about 10, 20, and 40 percents, respectively.
Improved Coarse Timing Estimation in OFDM Systems Using High-Order Statistics

In this paper, we investigate new methods for preamble-aided coarse timing estimation in orthogonal frequency division multiplexing systems. Two novel timing metrics using high-order statistics-based correlation and differential normalization functions are first proposed. The performance of the new timing metrics is then evaluated using different criteria, including class separability, robustness to the carrier frequency offset, and computational complexity. It is shown that the new timing metrics can considerably increase the class separability due to their more distinct values at correct and wrong timing instants, and thus give a significantly better detection performance as compared with the existing timing metrics. Furthermore, a new method for coarse estimation of the start of the frame is proposed, which remarkably reduces the probability of intersymbol interference (ISI). The improved performances of the new schemes in multipath fading channels are shown by the probabilities of false alarm, missed detection, and ISI obtained through computer simulations.
Enhancement of low-quality fingerprint images by a three-stage filtering scheme

Fingerprint image quality heavily influences the recognition rate for fingerprint identification/verification systems. A low-quality fingerprint image may consists of broken ridges, scars, smears, falsely conglutinated ridges, poor ridge and valley contrast, etc. In this paper, we propose a novel and effective three-stage scheme to enhance low-quality fingerprint images. The first-stage consists of an oriented linear anisotropic diffusion filter with a local ridge orientation estimation that differs from traditional estimation method. The second and the third stages consist of an oriented local ridge compensation filter and a novel angular filter, respectively. Simulation results show that the proposed method can improve the quality of the fingerprint images with a lower computational cost.
An analytical approach for obtaining a closed-form solution to the least-square design problem of 2-D zero-phase FIR filters

A closed-form least-square solution to the design problem of general two-dimensional (2-D) real zero-phase FIR filters is obtained. An in-depth study of the functions and matrices arising from the problem definition reveals some very useful structural properties. It is shown that these properties lead to an optimal analytical solution for the filter coefficients, making it unnecessary to use design procedures involving optimization techniques or matrix inversion operations. The derived closed-form expressions for filter coefficients allow their evaluation directly from the filter's frequency response specifications, resulting in a greatly reduced computational complexity. It is confirmed through design examples that the proposed technique enjoys a very short design time and it rises very slowly as the filter order is increased.
BIBO stability in the presence of nonessential singularities of the second kind in 2-D digital filters

In this paper, the open problem regarding the BIBO stability of two-dimensional linear shift invariant filters, in the presence of nonessential singularities of the second kind, is considered. Necessary and sufficient conditions for boundedness, and l_2 and l_1 stabilities of a function G(z_1, z_2)= P(z_1, z_2)/[Q(z_1, z_2)]^n , where P/Q has simple nonessential singularities of the second kind on T^2 , are obtained. These conditions are expressed in a very simple way in terms of the multiplicity of the zeros of certain resultants of two-variable polynomials. Many illustrative examples are also given.
Joint DOA Estimation and Source Signal Tracking With Kalman Filtering and Regularized QRD RLS Algorithm

In this brief, we present a nontraditional approach for estimating and tracking signal direction-of-arrival (DOA) using an array of sensors. The proposed method consists of two stages: in the first stage, the sources modeled by autoregressive (AR) processes are estimated by the celebrated Kalman filter, and in the second stage, the efficient QR-decomposition-based recursive least square (QRD-RLS) technique is employed to estimate the DOAs and AR coefficients in each observed time interval. The AR-modeled sources can provide useful temporal information to handle cases such as the number of sources being larger than the number of antennas. In addition, the symmetric array enables one to transfer a complex-valued nonlinear problem to a real-valued linear one, which can reduce the computational complexity. Simulation results demonstrate the superior performance of the algorithm for estimating and tracking DOA under different scenarios.
Modified silence suppression algorithms and their performance tests

In packet voice communication systems, silence suppression algorithms are employed to achieve bandwidth efficiency by suppressing the inactive part of the speech. In this paper, we consider a Nortel fixed-point silence suppression algorithm to investigate means of reducing the computational complexity with a little or no loss in the performance. In view of the compact structure and superior performance of the existing Nortel algorithm, certain modifications are introduced without altering the structure of the original design. Based on these modifications, two algorithms are proposed. Results from objective and subjective quality tests show that the performance of each of the two modified algorithms is comparable to that of the Nortel algorithm. As for the average computational complexity, simulation tests based on TMS320C5402 DSK show that reduction of about 40% and 22% can be achieved for the two modified algorithms.
Fast VLSI architecture for rank order based filtering using a bit-serial window partitioning technique

Based on a recently proposed algorithm for stack filtering, the bit-serial window partitioning (BSWP) algorithm, a new architecture suitable for the VLSI implementation of very fast rank order based filters for signal and image processing, is developed. The proposed architecture provides important improvements in terms of the running time (i.e., of the order of 30%-40%) compared to the conventional bit-serial binary tree search configuration for stack filtering, at the expense of only slightly increased chip area. The improved computational efficiency is obtained by evaluating the Boolean function at thresholds corresponding to the sample-values within the filter-window, and by taking advantage of the ordering information associated with the threshold sequences.
The Theory of Compressive Sensing Matching Pursuit Considering Time-domain Noise with Application to Speech Enhancement

Compressive sampling matching pursuit (CoSaMP) is an efficient compressive sensing algorithm holding rigorous estimation error bounds and low computational complexity, when it deals with an additive noise signal model in the observation domain. However, in some applications, e.g., speech enhancement (SE), noise is added to a signal in the time domain, where the conventional CoSaMP cannot be directly applied. In this paper, we establish the theory of CoSaMP to address the time-domain noise, referred to as Tdn-CoSaMP, which extends the canonical theory of CoSaMP. In particular, we prove the existence of a new upper bound of Tdn-CoSaMP, which is found to be larger than that of the conventional CoSaMP by appending two additional terms: a multiplier 1+√{N/s}, where N is the dimension of the signal, and an ℓ1 norm of the noise [1/(√s)]||e||1 scaled by the sparse level s of the signal. We also apply Tdn-CoSaMP to the SE task based on the sequential denoising of overlapped frames in the discrete cosine transform (DCT) domain. The proposed system, CoSaMP-based speech enhancement (CoSaMPSE), has been evaluated in terms of both objective and subjective criteria on various types of noise. Positive results have been achieved for denoising stationary and nonstationary white Gaussian noise (WGN) and are comparable to other SE methods. Moreover, due to its low computational complexity, CoSaMPSE is possible to be combined with optimally modified log-spectrum amplitude estimation (OMLSA) and able to achieve complementary denoising effects in various noisy conditions.
An Alternate Approach for Developing Higher Radix FFT Algorithms

In this paper, the authors propose an alternate method to derive higher radix FFT algorithms by using a recursive approach and by appropriately combining the twiddle factors without increasing the structural complexity. Specifically, efficient radix-8 and radix-16 FFT algorithms are designed and their arithmetic complexities shown to be slightly less than those of the corresponding existing Cooley-Tukey FFT
An efficient multimodal biometric face recognition using speech signal

Multimodal biometric face recognition using speech signal has engrossed much attention and has been applied in various domains primarily for surveillance, security & access control and law enforcement. In recent years much advancement have been made in face recognition techniques to cater to the challenges such as pose, expression, illumination, aging and disguise. However, due to advances in technology, there are new emerging challenges for which the performance of face recognition systems degrades and plastic/cosmetic surgery is one of them. In this paper we comment on the effect of plastic surgery face image in multimodal biometric face recognition using speech signal. Speaker identity is correlated with the physiological and behavioral characteristics of the speaker. These characteristics exist both in the spectral envelope (vocal tract characteristics) and in the suprasegmental features (voice source characteristics and dynamic features spanning several segments). Selecting the most effective fusion techniques depends on operational issues such as accuracy requirements, availability of training data, and the validity of simplifying assumptions.
A systolic array architecture for the discrete sine transform

An efficient approach to design very large scale integration (VLSI) architectures and a scheme for the implementation of the discrete sine transform (DST), based on an appropriate decomposition method that uses circular correlations, is presented. The proposed design uses an efficient restructuring of the computation of the DST into two circular correlations, having similar structures and only one half of the length of the original transform; these can be concurrently computed and mapped onto the same systolic array. Significant improvement in the computational speed can be obtained at a reduced input-output (I/O) cost and low hardware complexity, retaining all the other benefits of the VLSI implementations of the discrete transforms, which use circular correlation or cyclic convolution structures. These features are demonstrated by comparing the proposed design with some of the previously reported schemes.
An adaptive Viterbi algorithm based on strongly connected trellis decoding

An adaptive Viterbi algorithm using strongly connected trellis decoding of binary convolutional codes is presented. It is known that the strongly connected trellis decoding method can be used to improve the efficiency of hardware utilization and the throughput of the decoding in a systolic array-based Viterbi decoder. However, this method makes the amount of ACS (addition, comparison, and selection) computations in the decoding process much larger than in the conventional trellis decoding. It is shown that the proposed adaptive Viterbi algorithm can reduce the large amount of ACS computations without a degradation in the performance. Further, this algorithm, unlike the adaptive Viterbi algorithm based on low connectivity trellis, does require a sorting operation to determine the most likely survivor paths among all the possible survivor paths. The simulation results show that the proposed adaptive Viterbi algorithm can reduce up to 70% of the average number of ACS computations per strongly connected stage over that using the conventional Viterbi algorithm, while keeping the same error performance as that of the latter.
Wavelet-based multiresolution motion estimation through median filtering

In this paper, a non-causal median filtering method is proposed to predict the motion vectors across the wavelet subbands of a video frame for multiresolution motion estimation. This median filtering method effectively overcomes the problem of propagation of false motion vectors that exists in the conventional multiresolution motion estimation schemes. A significant feature of the proposed technique is that it imposes no demand for additional bandwidth. Simulation studies show that this median filtering-based multiresolution motion; estimation technique effectively improves the motion prediction performance. It is further shown that this performance improvement is achieved with little increase in the computational complexity.
A closed-form solution to the least-square design problem of 2-D linear-phase FIR filters

In this paper, the least-square design problem of a general two-dimensional (2-D) linear-phase FIR filter with an arbitrary magnitude response is studied. By minimizing the frequency-domain error function and exploiting some of the properties of the functions and matrices associated with the design problem, a novel closed-form solution is developed. The solution is expressed in terms of the desired magnitude specifications and is eventually presented as an explicit expression for the impulse response of the filter to be designed, making a very fast evaluation of the filter's coefficients possible. It is also shown that when this solution is used to design filters that have centrosymmetric and quadrantally symmetric magnitude responses, some further computational savings can be achieved. Several design examples illustrating the effectiveness of the proposed solution are considered.
Synthesis of discrete time-varying null filters for frequency-varying signals using the time-warping technique

A synthesis of two second-order discrete time-varying filters (DTVF), capable of suppressing a sine signal with arbitrary frequency variation, is presented. These structures are obtained by using the time-warping concept. A noise analysis is carried out for these two DTVFs and their performance is compared. It is shown that for the particular application considered, the rule of time-dependence of the DTVF (the synchronization) may be effectively obtained by using a phase-locked loop (PLL) system. As an example, the DTVF-PLL system is used for synchronous phase estimation of a sine wave corrupted by closely spaced FM interference. Simulation results are provided to substantiate the analysis and demonstrate the efficiency of the proposed time-varying system. The main feature of the TLM method is its formulation and programming simplicity. The method also generates a large amount of information; not only is the impulse response of a structure obtained, yielding in turn its response to any excitation, but the characteristics of the dominant and higher order modes are also accessible in the frequency domain through the Fourier transform.
A low-complexity parametric transform for image compression

In this paper, a one-parameter eight-point orthogonal transform suitable for image compression is proposed. An algorithm for its fast computation is developed and an efficient structure for a simple implementation valid for all possible values of its independent parameter is proposed. It is shown that an appropriate selection of the values of the parameter results in a number of new multiplication-free transforms having a good compromise between the computational complexity and performance. Applying the proposed transform to image compression, we show that it outperforms the existing transforms having complexities similar to that of the proposed one.
Video Denoising Based on Inter-frame Statistical Modeling of Wavelet Coefficients

The paper proposes a joint probability density function to model the video wavelet coefficients of any two neighboring frames and then applies this statistical model for denoising. The parameter of the density function that measures the correlation between the wavelet coefficients of the two frames is used as an index for the motion. The joint density function is employed for spatial filtering of the noisy wavelet coefficients by developing a bivariate maximum a posteriori estimator. A recursive time averaging of the spatially filtered wavelet coefficients is adopted for further noise reduction. Simulation results on test video sequences show an improved performance both in terms of the peak signal-to-noise ratio and the perceptual quality compared to that of the other denoising algorithms