nils-herrmann/clean_scirepeval huggingface dataset:
A Study of Multiplicative Watermark Detection in the Contourlet Domain Using Alpha-Stable Distributions

In the past decade, several schemes for digital image watermarking have been proposed to protect the copyright of an image document or to provide proof of ownership in some identifiable fashion. This paper proposes a novel multiplicative watermarking scheme in the contourlet domain. The effectiveness of a watermark detector depends highly on the modeling of the transform-domain coefficients. In view of this, we first investigate the modeling of the contourlet coefficients by the alpha-stable distributions. It is shown that the univariate alphastable distribution fits the empirical data more accurately than the formerly used distributions, such as the generalized Gaussian and Laplacian, do. We also show that the bivariate alpha-stable distribution can capture the across scale dependencies of the contourlet coefficients. Motivated by the modeling results, a blind watermark detector in the contourlet domain is designed by using the univariate and bivariate alpha-stable distributions. It is shown that the detectors based on both of these distributions provide higher detection rates than that based on the generalized Gaussian distribution does. However, a watermark detector designed based on the alpha-stable distribution with a value of its parameter α other than 1 or 2 is computationally expensive because of the lack of a closed-form expression for the distribution in this case. Therefore, a watermark detector is designed based on the bivariate Cauchy member of the alpha-stable family for which α = 1. The resulting design yields a significantly reduced-complexity detector and provides a performance that is much superior to that of the GG detector and very close to that of the detector corresponding to the best-fit alpha-stable distribution. The robustness of the proposed bivariate Cauchy detector against various kinds of attacks, such as noise, filtering, and compression, is studied and shown to be superior to that of the generalized Gaussian detector.
A multiresolution motion estimation technique with indexing

In the multiresolution motion estimation (MRME) techniques originally proposed by Zhang and Zafar, four MRME algorithms have been proposed. In one of these algorithms, the motion vectors in the low-pass subband are properly scaled and used as the final motion vectors for all the other subbands, and, in another, the properly scaled motion vectors in the first algorithm are used as predictions and further refined. The former algorithm requires a much lighter computational load and fewer coding bits for the motion vectors than the latter; on the other hand, the latter is able to provide a better MRME performance than the former. In this paper, we propose a new MRME technique that takes advantage of both of the above algorithms. In the proposed algorithm, the sum of absolute difference associated with each of the scaled motion vectors as in the first algorithm is calculated, and the result compared with the sum of the absolute values of the amplitudes of the wavelet coefficients within the motion block to be compensated. The outcome of the comparison decides if these scaled motion vectors are accepted as the final ones. For the coding of motion information, the motion vectors used for the prediction and their patterns of applicability to higher resolution levels, called the indices, are coded.
Contourlet domain image denoising using normal inverse gaussian distribution

A new contourlet-based method is introduced for reducing noise in images corrupted by additive white Gaussian noise. It is shown that a symmetric normal inverse Gaussian distribution is more suitable for modeling the contourlet coefficients than formerly-used generalized Gaussian distribution. To estimate the noise-free coefficients, a Bayesian maximum a posteriori estimator is developed utilizing the proposed distribution. In order to estimate the parameters of the distribution, a moment-based technique is used. The performance of the proposed method is studied using typical noise-free images corrupted with simulated noise and compared with that of the other state-of-the-art methods. It is shown that compared with other denoising techniques, the proposed method gives higher values of the peak signal-to-noise ratio and provides images of good visual quality.
A fast convergence algorithm for adaptive FIR filters

The authors present a fast convergence algorithm for adaptive FIR (finite impulse response) filters. The algorithm controls the step size on the basis of the moving average of the misadjustment level for a faster convergence. Several operations in addition to the LMS operations are used to estimate the misadjustment and average it to provide a stable step size. The algorithm is applied to adaptive noise cancellation to observe the performance. Computer simulation results for noise cancellation show that the convergence characteristics of the algorithm are improved by as much as 90% over those of the lease-mean-squares (LMS) algorithm and even those of the VS (variable step) algorithm for a multitone signal. For a real speech signal, improvement of the proposed algorithm is degraded, but still more than 50% of the LMS convergence time is saved in the simulated case.<<ETX>>
Blind Deconvolution of Hodgkin-Huxley neuronal model

Neuron transforms information via a complex interaction between its previous states, its intrinsic properties, and the synaptic input it receives from other neurons. Inferring synaptic input of a neuron only from its membrane potential (output) that contains both sub-threshold and action potentials can effectively elucidate the information processing mechanism of a neuron. The term coined blind deconvolution of Hodgkin-Huxley (HH) neuronal model is defined, for the first time in this paper, to address the problem of reconstructing the hidden dynamics and synaptic input of a single neuron modeled by the HH model as well as estimating its intrinsic parameters only from single trace of noisy membrane potential. The blind deconvolution is accomplished via a recursive algorithm whose iterations contain running an extended Kalman filtering followed by the expectation maximization (EM) algorithm. The accuracy and robustness of the proposed algorithm have been demonstrated by our simulations. The capability of the proposed algorithm makes it particularly useful to understand the neural coding mechanism of a neuron.
Stability analysis of 2-D digital filters having second kind singularities-analog approach

The authors show how stability analysis of a 2-D digital function can be carried out in the analog domain itself by testing the two-variable analog function from which the 2-D digital function is derived by the double bilinear transformation. This is particularly useful since one of the common methods of generating a 2-D digital function is by the application of double bilinear transformation on an appropriate two-variable analog function satisfying given frequency-response characteristics. By observing the conditions of the theorem, it may be stated that a necessary condition for stability or boundedness of a 2-D digital function is that the denominator of the corresponding 2-V analog function be scattering Hurwitz, as defined by A. Fetteweis (1984). Finally, these results have been used to study the existence of the double square integral in the analog domain.<<ETX>>
A VLSI architecture for the implementation of real-time order statistic filters

The authors give an algorithm for the real-time computation of the running order statistic (ROS). Order statistic filters (OSFs) or ranked order filters (ROFs) are a class of nonlinear digital filters which have found applications in image processing and in smoothing of speech data. Real-time implementation of an OSF requires the computation of the order statistic (ranked order) of the samples in a window (usually containing an odd number of samples) which gets periodically updated with the arrival of a new sample(s). A modular and highly parallel architecture characterized by programmable window size and required rank order is proposed for the implementation of the algorithm in VLSI form.<<ETX>>
A fully digital timing recovery scheme using two samples per symbol

In this paper, a new symbol timing recovery scheme is presented for digital receiver. The proposed algorithm belongs to the category of feedforward recovery and consists of two stages-phase estimation and interpolation. The phase estimation is to detect the clock phase of the sampled signal, which indicates the deviation of the reference sample from the point of maximum eye-opening. The interpolation which is implemented with an FIR filter is to reconstruct the transmitted symbols. Although many estimation algorithms have been developed in the literature, these methods usually need an input sampling rate of at least four samples per symbol. The proposed scheme requires only two samples/symbol, thus allowing the use of a very low sampling rate at the receiver. This feature is very important in the implementation of a digital receiver for high-rate transmission, since the hardware cost of the receiver depends heavily on the required processing speed.
Stability of mutually inverse rational 2-D digital transfer functions

This paper considers the open problem concerning the BIBO stability of mutually inverse 2-D digital filters in the presence of nonessential singularities of the second kind on T/sup 2/. Necessary and sufficient conditions are obtained for the BIBO stability of mutually inverse pairs of 2-D digital filters having such singularities on T/sup 2/. Several illustrative examples are considered.<<ETX>>
Sequency-ordered discrete Hartley transform

A novel sequency-ordered discrete Hartley transform (SO-DHT) of an arbitrary length is proposed by introducing a systematic ordering of the rows of the DHT matrix, so that the resulting transform matrix has the sign changes of its entries similar to that of the discrete cosine transform (DCT) matrix. The experiments carried out show that the shape of the spectrum of the new SO-DHT is very similar to that of the DCT, especially from the point of view of energy compaction.
GPS navigation with increased immunity to modeling errors

A robust extended Kalman filter (EKF) is presented for GPS navigation with outlying errors in the GPS measurements due to failed satellites or unmodeled errors. In this approach, the innovation step of the conventional EKF is modified using the robust statistics concept to obtain a more accurate state estimate in the presence of outlying errors. Performance of the proposed robust EKF in comparison with the conventional EKF, and adaptive EKF is demonstrated through simulation results.
Joint carrier and frame synchronization for MPSK demodulation

In this paper, an efficient feedforward implementation scheme for joint carrier and frame synchronization in MPSK systems is presented. By inserting a unique word in the beginning of each frame, a fast carrier synchronization is achieved without requiring extra computations for the frequency estimation. The phase synchronization is also achieved by exploiting the unique word for initial phase acquisition followed by phase tracking. The proposed scheme is simulated for 8PSK demodulation showing its effectiveness and low implementation complexity.
Statistical detector for wavelet-based image watermarking using modified GH PDF

A new detector using modified Gauss-Hermite (GH) probability density function (PDF) is proposed for the wavelet-domain image watermarking scheme. It is shown that the proposed PDF matches the empirical one of image wavelet coefficients better than other conventional PDFs such as the generalized Gaussian and Bessel K-form. This is because of the fact that the modified GH PDF utilizes an arbitrary number of higher order moments of the wavelet coefficients instead of considering only the first few for the parameter estimation process. The proposed PDF is then used for designing the statistical detector for a wavelet-based image watermarking algorithm. Experimental results on a standard image database show that the proposed detector provides a higher detection probability and lower false alarm than that provided by the others.
A New Class of Dual-Mode Microwave Filters for Space Application

A new class of direct-coupled cavity bandpass filters having the same number of equi-ripple attenuation-zeros as the order of the filter and an attenuation-pole on either side of passband is described. Filters are configured in dual-mode structure operating in the dominant or higher order modes. Synthesis is accomplished using optimization techniques. Experimental results for a sixth order filter at 12 GHz agree closely with the dicted response. The simplicity of structure and the sharp cutoff characteristics make this class of filters most promising for the multiplexing networks of communications satellites.
An unsupervised learning based method for content-based image retrieval using hopfield neural network

Presently, corporations and individuals have large image databases due to the explosion of multimedia and storage devices available. Furthermore, the accessibility to high speed internet has escalated the level of multimedia exchanged by users across cyberspace every second. Accordingly, it has increased the demand for searching among large databases of images. Conventionally, text-based image retrieval is used. The major problems in text-based image retrieval are related to annotation that is often impossible due to human perception of images being subjective, and also due to the size of the information that needs indexing. To overcome such limitations, content-based image retrieval systems have been proposed. However, there is a key hindrance, namely, the need to match the human visual system to overcome the semantic gap between human perception and low-level features. In this paper, we propose a new unsupervised method based on Hopfield neural networks that seeks to model human visual memory to increase the efficacy of retrieval and reduce the semantic gap. A comparative study with other neural-network based methods, such as the feed forward backpropagation and Boltzmann deep learning, shows the effectiveness of our method.
An efficient systolic array algorithm for the VLSI implementation of a prime-length DHT

In this paper, we present a new systolic algorithm for an efficient design approach using a bi-port memory-based VLSI implementation of the DHT. Using auxiliary input and output sequences and appropriate permutations it is shown that it is possible to compute a prime-length DHT using two cyclic convolutions having the same form and length that can be computed in parallel leading to improve throughput with a reduced hardware and I/O costs. The two computation structures can be mapped on the same linear systolic array using an appropriate hardware-sharing technique. Adopting a bi-port memory-based systolic architecture we can further reduce the hardware complexity and I/O costs while preserving all the advantages of a cyclic convolution based systolic array implementation such as regularity, modularity and a good topology of the interconnection structure.
MOD-CHAR: an implementation of Char's spanning tree enumeration algorithm and its complexity analysis

Two complexity analyses of MOD-CHAR are presented. It is shown that MOD-CHAR leads to better complexity results for J.P. Char's algorithm than what could be obtained using the straightforward implementation implied in Char's original presentation (see  1968). The class of graphs for which MOD-CHAR and, hence, Char's algorithm, has linear time complexity per spanning tree generated is identified. This class is more general than the corresponding one identified in R. Jayakumar et al. (see ibid., vol.31, no.10, p.853-60, 1984). Using a result on random graphs, it is proved that for almost all graphs MOD-CHAR has linear worst-case time complexity per spanning tree generated. It is also shown that for any complete graph MOD-CHAR requires, on the average, at most seven computational steps to generate a spanning tree. This result and computational experiences provide evidence to believe that for dense graphs of any order the time complexity of MOD-CHAR is O(t), where t is the number of spanning trees generated. On the other hand, there is enough evidence to conclude that for sparse graphs, Char's original implementation is superior to MOD-CHAR.
Planar embedding: linear-time algorithms for vertex placement and edge orderings

The problem of obtaining a planar embedding of a biconnected planar graph is discussed. The approach is based on the planarity testing algorithm of A. Lemple, et al. (1966) and its implementation using PQ-trees. In the planar embedding the vertices of the planar graph are placed in the plane at different horizontal and vertical levels so that no two distinct vertices appear in the same horizontal or vertical level, and higher-numbered vertices appear at higher vertical levels. The left-to-right order of the vertices in such a planar embedding is called the vertex order. For each vertex i, the anticlockwise order in which edges enter i from lower numbered neighbors is denoted by tau (i). Linear-time algorithms to determine a tau (i) for each i, and the vertex order, are developed. The vertex order captures the structural information about the relative placement of vertices in a planar embedding provided by the PQ-tree reduction algorithm. A systematic procedure to obtain an intersection-free drawing of the edges is described. A linear-time algorithm to construct a very compact horvert representation of a planar graph is also presented. This algorithm does not require the construction of the dual of the original graph.
Least squares inverse polynomials and a proof of practical BIBO stability of n-D digital filters

The problem of stabilizing unstable n-dimensional practical digital filters is discussed, where only one of the independent variables of the n-D signal is temporal and the other variables are spatial. The double-least-squares inverse method is used to stabilize such practical filters. It is proved that the least-squares inverse of the denominator polynomial of a practical n-D digital filter is always stable in the practical and less restrictive sense.
A new fractal zerotree coding for wavelet image

Based on the mechanisms underlying the performance of fractal and DWT, one method using fractal-based selfquantization coding approach to code different subband coefficients of DWT is presented. Within this method finer coefficients are fractal encoded according to the successive coarser ones. Self-similarities inherent between parent and their children at the same spatial location of the adjacent scales of similar orientation are exploited to predict variation of information across wavelet scales. On the other hand, with respect to the HVS model, we assign different error thresholds to different scales and different shape of range blocks to different orientations of the same scale, by which the perceptually lossless high compression ratio can be achieved and the matching processing can be quickened dramatically.
Design and implementation of a switched-current memory cell for low-power and weak-current operations

This paper describes the design and implementation of a new switched-current (SI) memory cell for current-mode signal processing. The SI memory cell operates in a pico-to-nanoampere range. To obtain an acceptable accuracy, a procedure to reduce the negative effects of the nonideal characteristics of MOS transistor in SI circuits is proposed and implemented. A prototype circuit including the new SI memory cell associated with optical sensors has been fabricated with a 0.35-/spl mu/m n-well technology. The test results show that, in a range of 0.5 pA to 15 nA, the error rate of current memorization/reproduction in the proposed SI memory is below 1% and the power dissipation is in a range of nanowatts or below.
Cascade implementation of CANFs in spectral analysis of short data auditory evoked brainstem responses

Auditory evoked brainstem response (ABR) can be modeled as two closely spaced sinusoids immersed in noise. Conventional methods on the FFT are often not useful in the spectral estimation of short-duration ABRs. The authors propose the use of constrained adaptive notch filters (CANFs) implemented as a cascade of second-order sections, and discuss two special configurations based on CANF combined with a constrained notch filter (CNF) with fixed parameters. The problem associated with the convergence of the direct-form implementation of higher-order CANFs to a local minimum is also addressed. The methods proposed are shown to provide reliable spectral estimation and enhancement of the real data ABRs.<<ETX>>
PLL synchronized time-varying constrained notch filter for retrieving a weak multiple sine signal jammed by FM interference

A method of retrieval of a multitone corrupted by a strong wideband FM interference is presented. The input mixture is transformed into a new time domain, resulting in a concentration of the FM interference around a chosen frequency; this inference is then rejected by a constrained notch filter with a fixed notch point. The time warping is implemented through nonequally spaced sampling. The method provides results superior to those of conventional adaptive processing, which fails to track the parameters of an FM interference with significantly large frequency deviations.
An adaptive parallel interference canceler using adaptive blind arrays

The performance of code-division multiple access (CDMA) receivers can be significantly enhanced by employing multiuser detection techniques and antenna arrays. A compromise between the system performance and the computational complexity is obtained by employing parallel interference cancellation due to its simple structure. We propose a technique in which an adaptive parallel interference canceler (APIC) and adaptive blind arrays are combined. The blind algorithm is formulated using a constrained optimization criterion, and the adaptation is carried out using LMS algorithm combined with the gradient-projection algorithm to incorporate the quadratic constraint. Simulation results are presented to show the excellent performance of the proposed structure in comparison with that of using either multiuser detection or smart antennas in a severe near-far situation.
Branching competitive learning Network:A novel self-creating model

This paper presents a new self-creating model of a neural network in which a branching mechanism is incorporated with competitive learning. Unlike other self-creating models, the proposed scheme, called branching competitive learning (BCL), adopts a special node-splitting criterion, which is based mainly on the geometrical measurements of the movement of the synaptic vectors in the weight space. Compared with other self-creating and nonself-creating competitive learning models, the BCL network is more efficient to capture the spatial distribution of the input data and, therefore, tends to give better clustering or quantization results. We demonstrate the ability of the BCL model to appropriately estimate the cluster number in a data distribution, show its adaptability to nonstationary data inputs and, moreover, present a scheme leading to a multiresolution data clustering. Extensive experiments on vector quantization of image compression are given to illustrate the effectiveness of the BCL algorithm.