nils-herrmann/clean_scirepeval huggingface dataset:
Multichannel color image watermark detection utilizing vector-based hidden Markov model

Multimedia data piracy in the Internet is a growing problem, since it provides easy and fast data transmission. Watermarking is regarded as a solution to restrain unauthorized duplication or distribution data. Image watermarking research mostly focuses on grayscale images with an extension to color images. However, most of these techniques ignore dependencies between color channels. In view of this, in this work, a multichannel color image watermarking technique and its corresponding detector in the wavelet domain is proposed. The inter-channel dependencies between RGB channels and inter-scale dependencies of the wavelet coefficients of color image are taken into account by employing the vector-based hidden Markov model. We conduct experiment on a set of color images to assess the performance of the proposed watermark detector. The results show that the performance of the proposed detector is superior to that of the other detectors in terms of the imperceptibility of the embedded watermark and the detection rate. It is also shown that the proposed detector has better performance in presence or absence of different kinds of attacks in comparison to the other existing methods.
Analytical methods for the design of 2-D circularly symmetric digital filters using McClellan transformation

Analytical methods are developed for choosing the coefficients of the first-order McClellan transformation for the design of two-dimensional (2-D) circularly symmetric digital filters from one-dimensional digital filters. New, extremely simple formulas for finding the original and the scaling free coefficients are presented. They are derived by forcing the lower-order significant terms in the power series expansion of the linear error function to zero. Formulas are also derived for finding N-D McClellan's coefficients for the design of N-D approximately spherically symmetric digital filters. Many examples are provided to demonstrate the application of the formulas. The coefficients obtained approximate a circular contour with a higher degree of accuracy than the McClellan coefficients. The number of multiplications required to implement the designed filters is shown to be small. The formulas are very useful for real-time adaptive filter design and filtering by low-cost, stand-alone 2-D signal processors.<<ETX>>
A study on compression rate bounds in distributed video coding based on correlation noise models

In a distributed video coding problem, use of a correct model for the correlation noise plays a significant role in improving the decoding performance and consequently in providing higher coding efficiency. In this work, we first study the predictive and additive correlation noise models at the DCT coefficient band level for transform-domain distributed video coding. Then, bounds on compression rates for encoding the quantized DCT coefficient band are obtained for both the correlation noise models. We then investigate how the distribution of the DCT coefficient bands in each WZ frame affects the compression rate bound in each correlation noise model. It is shown that for the DCT coefficient bands being non-uniformly distributed, the compression rate bound in the additive correlation noise model lower than that in the predictive one. Moreover, it is shown that selecting a wrong correlation model leads to compression rate loss in the decoder. The simulation results are provided to validate the theoretical investigation.
Analysis of Genomics and Proteomics Using DSP Techniques

With the enormous amount of data that is available in the public domain, signal processing concepts are playing an important role in genomic and proteomic data processing. The binary sequence method is well known in the literature for genomic signal processing. As an alternative to the binary sequence method, this paper presents a new view to the genomic sequence processing with the electron-ion interaction potential (EIIP) values for nucleotides. The efficacy of the EIIP values-based method is demonstrated through implementation results for the prediction of the gene F56F11.4 with five exons and for cystic-fibrosis gene identification. The biological function of a protein is determined by the amino-acid sequence within the protein. Identification of the amino acids (hot spots) that contribute to the characteristic frequency characterizing a particular biological function is an important task in the protein sequence analysis. Hence, using EIIP values for amino acids, this paper presents a continuous-wavelet transform approach using the modified Morlet wavelet for the identification of active sites (hot spots) in a protein sequence. The efficacy of the approach is illustrated through implementation results on hemoglobin human alpha, human immunodeficiency virus, and oncogene protein sequences.
Constrained notch filtering of nonuniformly spaced samples for enhancement of an arbitrary signal corrupted by a strong FM interference

The proposed method is based on exploring the concept of constrained notch filtering (CNF) as applied to any given arbitrary signal with time varying parameters. First, it is shown that any signal with a constant envelope such as FM may be transformed to a discrete sinusoidal one by applying a nonuniform sampling strategy. Second, a signal buried under a strong FM interference is retrieved by applying CNF in the transformed time domain. The main assumption made is that there exists an auxiliary input which provides information about the instantaneous frequency of the interference.
Modelling and Analysis of Regional Service Behavior Properties of Mobile Internet Applications

In view of the facts that traditional traffic models are less effective in describing the new mobile internet applications and that the mobile user service behavior among regions with different levels of economic development are always different, a new modeling methodology of user service behavior specifically for mobile internet applications is proposed in this paper. Based on the crowdsourcing measurement data of the service behavior collected through massive end users in live mobile networks of China, the properties of three typical mobile internet services in three typical geographical regions are analyzed and compared with each other, by utilizing the proposed modeling methodology. And the relationships between some key properties of service behavior are analyzed with the maximal information coefficient method and the Pearson correlation coefficient method. The key parameters, differences, and internal relations among these services in typical geological regions of China are then identified. It provides a solid foundation for wireless product design, network planning, and optimization of mobile Internet services-oriented networks specifically in China, and is also a valuable reference for other countries of the similar situation.
Fast AWGN reduction in videos using change detection

In this paper, two low-complexity filters, which are based on a novel filter structure consisting of both the spatial and temporal estimation processes and use a change detection technique to measure the interframe motion, are proposed to reduce additive white Gaussian noise (AWGN) in videos. Both the filters use the edge-adaptive Wiener filter for spatial estimation, while for temporal estimation two schemes are proposed, one based on the scalar Kalman filter and the other on the running average filter. These temporal estimation techniques are carded out on the spatial estimate to get the spatiotemporal estimate. The final estimate is obtained by a suitable adaptive combination of the spatial and the spatiotemporal estimates. The use of a change detection technique to measure the interframe motion results in a computational complexity, which is considerably lower than that of the existing filters using motion estimation and compensation technique. The qualitative and quantitative performance of the proposed filters are studied and compared with that of some of the existing ones. It is found that the proposed filters have much less processing time and also have better noise reduction ability compared to the others.
A bound for the zeros of polynomials

A new kind of circular bound on the zeros of polynomials is derived by determining Cauchy's bound on zeros of its transformed pair first. The transformation is based on a nonlinear transformation of the variable, which conceptually should give a better upper bound. The advantage of such a transformation is illustrated through several examples that show the improvement over the existing bounds. Convergence of the bound after iterative transformations of the original polynomial is also examined.
Multiplicationless Burt and Adelson's pyramids for motion estimation

It is shown that, by choosing appropriate values for the parameter a of the generating kernel in constructing P.J. Burt and E.H. Adelson's pyramid for motion estimation (see  1983), one can eliminate the operation of the floating point multiplications needed in such a construction and reduce the computational load to the same order as in the case of the mean pyramid. When a is chosen to be 3/8, it is demonstrated, through simulation studies, that the corresponding Burt and Adelson's pyramid does not degrade the performance of motion estimation, as compared to that using kernels giving the best performance, and this pyramid also provides perceptually better motion-compensated images than those provided by the mean pyramid.
Exact fractional-order differentiators for polynomial signals

A discrete-time fractional-order differentiator is modeled as a finite-impulse response (FIR) system. The system yields fractional-order derivatives of Riemann-Liouville type for a uniformly sampled polynomial signal. The computation of the output signal is based on the additive combination of the weighted outputs of N cascaded first-order digital differentiators. For differentiators of fractional order with a terminal value equal to zero, the weights are time-varying. The weights are obtained in a closed form involving the Stirling numbers of the first kind. The system tends to a time-invariant integer-order differentiator when the order of the derivative tends to an integer value. It yields exact fractional- or integer-order derivatives of a sampled polynomial signal of a certain order.
Quadtree structured region-wise motion compensation for video compression

The conventional variable-size block motion compensation technique, even though superior to the fixed-size block motion-compensation technique, cannot fully utilize the motion information of a frame for its partitioning. This paper presents a quadtree structured region-wise motion-compensation technique that utilizes more effectively the motion content of a frame in terms of the shape, size, and location of the partitioned regions. The proposed technique is based on a new coding scheme of the quadtree structure, where a two-bit code is used. The partitioning of a given frame is carried out through the processes of merging and combining. The merging process partitions the frame into a number of regions by carrying out all possible 4-to-1, 3-to-1, and 2-to-1 merges along the quadtree hierarchy according to some specified criteria, while the combining process combines some of the partitioned regions that have the same motion vector into a single region. The motion vectors of the resulting partitioned regions are coded by a new coding scheme that uses the temporal correlation of the motion fields. Test results of applying the proposed method on a number of MPEG video sequences are included. These results indicate that the proposed method can provide a significantly improved rate-distortion performance.
Motion compensated ASMKF for restoration of color video sequences corrupted by impulsive noise

Various nonlinear filters have been proposed in the literature for restoration of video sequences contaminated by impulsive noise. Among these, median filters and their variants are the most popular filter types used for impulsive noise suppression. However, median filters and their variants tend to remove fine image details and destroy fine texture, especially when the signal to noise ratio is low. In order to circumvent the limitations of the median filters, an adaptive simplified-model Kalman filter (ASMKF) is proposed for restoration of video sequences corrupted by impulsive noise. For motion estimation, a block matching technique is used. The efficacy of the proposed filter is illustrated through implementation results on the restoration of a color video sequence.
Perturbation analysis of subspace-based semi-blind MIMO channel estimation approaches

In this paper, a perturbation analysis of two subspace-based semi-blind MIMO channel estimation approaches is conducted. Our analysis shows that, in the noise-free case, the whitening-rotation (WR)-based algorithm is subject to a signal perturbation error, while the nulling-based algorithm is a signal perturbation free scheme with an ideal nulling constraint imposed on the channel matrix. This explains why the WR-based method is efficient only in the low SNR case, and concludes that the nulling-based approach is better for moderate to high SNRs. A novel closed-form mean square error (MSE) expression is also derived for the nulling-based blind estimation method, in which an appealing scheme for the determination of the weighting factor is presented. The nulling-based method with the proposed weighting scheme is validated via computer simulations, showing a very high estimation accuracy of our semi-blind solution in terms of the MSE of the channel estimate.
Multiresolution image coding using IIR digital filters

In this paper, we investigate IIR digital filters for multiresolution image coding. Some relationships between the frequency response of a filter and the quality of the processed images are discussed for decimation and interpolation. A special class of half-band IIR filters (including 1-D filters and 2-D diamond filters) is then proposed. Some simulation results are given for image decomposition/reconstruction and high compression image coding.<<ETX>>
An improved voice activity detection using higher order statistics

In this paper, by using the properties of the higher order statistics (HOS) of speech and noise signals, we develop an improved voice activity detection (VAD) scheme. The proposed scheme employs the logarithm of the kurtosis of the LPC residual of a speech signal and is shown to be more effective and efficient in detecting active speech in medium to low signal-to-noise ratio (SNR) conditions without being unduly affected by the variations in the signal energy. To overcome the inability of the HOS in detecting unvoiced speech, another metric (the low band to full band energy ratio) is introduced. Depending on the estimated mean SNR, the proposed scheme works adaptively in two modes: a simple mode using only the SNR, and an enhanced mode using the HOS, the low band to full band energy ratio and the SNR. This scheme is capable of avoiding unnecessary computations, while maintaining the same performance as that working only in the enhanced mode. Simulations results are presented to demonstrate the effectiveness of the proposed voice activity detection scheme.
Model-Based Seizure Detection for Intracranial EEG Recordings

This paper presents a novel model-based patient-specific method for automatic detection of seizures in the intracranial EEG recordings. The proposed method overcomes the complexities in the practical implementation of the patient-specific approach of seizure detection. The method builds a seizure model (set of basis functions) for a priori known seizure (the template seizure pattern), and uses the statistically optimal null filters as a building block for the detection of similar seizures. The process of modeling the template seizure is fully automatic. Overall, the detection method involves the segmentation of the template seizure pattern, rejection of the redundant and noisy segments, extraction of features from the segments to generate a set of models, selection of the best seizure model, and training of the classifier. The trained classifier is used to detect similar seizures in the remaining data. The resulting seizure detection method was evaluated on a total of 304 h of single-channel depth EEG recordings from 14 patients. The system performance is further compared to the Qu-Gotman patient-specific system using the same data. A significant improvement in the proposed system, in terms of specificity, is observed over the compared method.
Complex EKF neural network for adaptive equalization

Neural networks with real valued inputs have been proposed in the literature for adaptive equalization and have been used to improve performance of communication channel equalizers. However, neural networks with complex valued inputs and fast convergence are lacking for adaptive equalization. Therefore, in this paper, complex extended Kalman filter (CEKF)-based neural network with complex valued inputs for adaptive equalization of a communication channel is suggested. Performance comparison of the CEKF and complex backpropagation (CBP) neural networks is made through simulation results.
Fast computation of the discrete Fourier transform of real data

Fast algorithms for the computation of the discrete Fourier transform (DFT) of real signals are important since the signals in practical situations are mostly real. The more efficient algorithms for real data are those that are derived from the algorithms for complex data. So far, all such algorithms use a real array to store the data. However, as the data values are real and their transform values are mostly complex, two possible data structures can be used for these algorithms: real or complex. DFT algorithms for real data that use a complex array for storing both the real data and their transform values are derived from the Cooley-Tukey radix-2 algorithm for complex data. This approach reduces the number of bit-reversal and array-index updating operations, eliminates independent data-swapping operations, and yields a computational structure that is almost as regular as that of the algorithms for complex data. Detailed derivations of the proposed algorithms for the computation of both the DFT of real data and the inverse DFT of the transform of real data, as well as their computational complexities, are presented. A C-language program of one of the proposed algorithms is given, illustrating the use of all the features of the new approach in software implementation. Comparison results are included to show that the proposed algorithms are faster and simpler than the real-valued split-radix and other algorithms.
A New Statistical Detector for DWT-Based Additive Image Watermarking Using the Gauss–Hermite Expansion

Traditional statistical detectors of the discrete wavelet transform (DWT)-based image watermarking use probability density functions (PDFs) that show inadequate matching with the empirical PDF of image coefficients in view o f the fact that they use a fixed number of parameters. Hence, the decision values obtained from the estimated thresholds of these detectors provide substandard detection performance. In this paper, a new detector is proposed for the DWT-based additive image watermarking, wherein a PDF based on the Gauss-Hermite expansion is used, in view of the fact that this PDF provides a better statistical match to the empirical PDF by utilizing an appropriate number of parameters estimated from higher-order moments of the image coefficients. The decision threshold and the receiver operating characteristics are derived for the proposed detector. Experimental results on test images demonstrate that the proposed watermark detector performs better than other standard detectors such as the Gaussian and generalized Gaussian (GG), in terms of the probabilities of detection and false alarm as well as the efficacy. It is also shown that detection performance of the proposed detector is more robust than the competitive GG detector in the case of compression, additive white Gaussian noise, filtering, or geometric attack.
Masked nearly-orthogonal wavelet-based image compression and its application to medical imaging

A perceptual non-expansive image compression scheme using the nearly-orthogonal wavelets is presented in this paper. The proposed approach differs from the conventional design scheme by incorporating the human visual system characteristics directly in each sub-band of the wavelet decomposed image. The enhancement in the visual quality of the reconstructed image is achieved by using the proposed contrast sensitivity function masking at each decomposition level during wavelet transformation phase in the compression system. The explored biorthogonal nearly coiflet wavelet is used to achieve an improvement in the compression performance for low and medium bit rate applications. Extensive simulations are carried out using the proposed approach and the results compared with those of some of the existing techniques.
A new architecture of RRNS error-correcting QC encoder/decoder and its FPGA implementation

In this paper, a new architecture of a redundant residue number system (RRNS) quasi-chaotic (QC) encoder/decoder with an error-correcting ability is proposed for secure communication and is tailored to FPGA implementation. In the proposed architecture, binary subencoders and subdecoders are used so that a number of modulo operations required in the existing design of D. R. Frey ( 2000) are replaced by a simple truncation. Furthermore, a size-reduced design of the R/B (residue-to-binary) converter with error-correcting function, which is the crucial part of the system, is proposed. As a case study, the proposed architecture is implemented in FPGA for the case of the 16-bit input and some simulation results obtained.
An efficient architecture for signal estimation using minimum mean absolute error rank-order filters with application to intrafield deinterlacing of video signals

An efficient architecture for the design and implementation of minimum mean absolute error (MMAE) rank-order filters (ROFs) is developed. The proposed architecture uses a realization of ROFs whereby an operation of rank-order filtering is performed by employing a Boolean rank-order operator. As a result, with the proposed architecture, the MMAE design of ROFs is accomplished without carrying out the operation of sorting the samples appearing at the input-window of the filter, which is required in the conventional techniques for the MMAE design of ROFs. An application of the proposed architecture to spatial interpolation is also investigated, by developing a procedure for intrafield deinterlacing of video signals.
An efficient multidimensional decimation-in-frequency FHT algorithm based on the radix-2/4 approach

An efficient multidimensional (MD) split vector-radix (SVR) decimation-in-frequency (DIF) fast Hartley transform (FHT) algorithm is proposed by introducing a two-step decomposition strategy coupled with an efficient index mapping and the Kronecker product. Compared to existing MD FHT algorithms, the proposed one significantly reduces the complexity. In addition, the butterfly of the proposed algorithm is characterized by simple closed-form expressions allowing easy software or hardware implementation of the algorithm.