nils-herrmann/clean_scirepeval huggingface dataset:
Despeckling of medical ultrasound images using data and rate adaptive lossy compression

A novel technique for despeckling the medical ultrasound images using lossy compression is presented. The logarithm of the input image is first transformed to the multiscale wavelet domain. It is then shown that the subband coefficients of the log-transformed ultrasound image can be successfully modeled using the generalized Laplacian distribution. Based on this modeling, a simple adaptation of the zero-zone and reconstruction levels of the uniform threshold quantizer is proposed in order to achieve simultaneous despeckling and quantization. This adaptation is based on: 1) an estimate of the corrupting speckle noise level in the image; 2) the estimated statistics of the noise-free subband coefficients; and 3) the required compression rate. The Laplacian distribution is considered as a special case of the generalized Laplacian distribution and its efficacy is demonstrated for the problem under consideration. Context-based classification is also applied to the noisy coefficients to enhance the performance of the subband coder. Simulation results using a contrast detail phantom image and several real ultrasound images are presented. To validate the performance of the proposed scheme, comparison with two two-stage schemes, wherein the speckled image is first filtered and then compressed using the state-of-the-art JPEG2000 encoder, is presented. Experimental results show that the proposed scheme works better, both in terms of the signal to noise ratio and the visual quality.
Efficient Two-Dimensional Direction-of-Arrival Estimation for a Mixture of Circular and Noncircular Sources

In this paper, the two-dimensional (2-D) direction-of-arrival (DOA) estimation problem for a mixture of circular and noncircular sources is considered. In particular, we focus on a 2-D array structure consisting of two parallel uniform linear arrays and build a general array model with mixed circular and noncircular sources. The received array data and its conjugate counterparts are combined together to form a new data vector, based on which a series of 2-D DOA estimators is derived. Compared with existing methods, the proposed one has three main advantages. First, it can give a more accurate estimation in situations, where the number of sources is within the traditional limit of high-resolution methods. Second, it can still work effectively when the number of mixed signals is larger than that of the array elements. Finally, the paired 2-D DOAs of the proposed method can be obtained automatically without the complicated 2-D spectrum peak search and, therefore, has a much lower computational complexity.
Low-complexity 8×8 transform for image compression

An efficient 8 times 8 sparse orthogonal transform matrix is proposed for image compression by appropriately introducing some zeros in the 8 times 8 signed discrete cosine transform (SDCT) matrix. An algorithm for its fast computation is also developed. It is shown that the proposed transform provides a 25% reduction in the number of arithmetic operations with a performance in image compression that is much superior to that of the SDCT and comparable to that of the approximated discrete cosine transform.
Fast Block Motion Estimation With 8-Bit Partial Sums Using SIMD Architectures

In order to take advantage of the byte-type data parallelism in the existing single-instruction multiple-data (SIMD) technique, this paper introduces the concept of 8-bit partial sums, obtained by a 4-bit right-shift operation on the sum of the 16 luminance values in a column of a 16 x 16 block of a video frame. Since these partial sums are of only eight bits, eight of them can be processed concurrently in a single 64-bit SIMD register. A method of employing these partial sums in order to speed up a given block motion-estimation algorithm is then proposed. The notion of the 8-bit partial sums is extended to the four-level case. It is shown that there are 15 possible methods of utilizing these multilevel 8-bit partial sums to accelerate a block motion-estimation algorithm without any loss of accuracy of the algorithm. Each of these 15 methods is used in the full-search algorithm to determine the one that provides the lowest computational complexity. This method is adopted as the chosen scheme to accelerate various block motion-estimation algorithms. Extensive simulations are carried out on eight video sequences showing that substantial speed-up can be achieved when the chosen scheme is incorporated with the various motion-estimation algorithms. The simulation results also demonstrate that the implementation on SIMD architectures can further accelerate the execution of the proposed scheme by more than 93% percent.
A unified VLSI algorithm for a high performance systolic array implementation of type IV DCT/DST

An efficient design approach to derive a unified high performance systolic array architecture for prime length type IV DCT and DST is proposed. This approach is based on a unified VLSI algorithm that uses a parallel restructuring of type IV DCT and DST. It uses parallel pseudo-circular correlation structures as basic computational forms. Most of the unified algorithm can be implemented on the same hardware structure leading to a VLSI chip with a very high percentage of the chip area being used by both the transforms. The unified algorithm can be mapped onto a linear systolic array that have a small number of I/O channels and low I/O bandwidth, which can be efficiently implemented into a VLSI chip.
Design of two-dimensional digital filters using analog reference filters without second kind singularities

A method of designing two-dimensional recursive digital filters is to employ wave digital filter techniques on two-variable analog networks possessing the desired frequency response characteristics. In this paper a study is initiated on the structures of singly terminated and doubly terminated 2-variable lossless ladder networks which are often the starting points in the design of wave digital filters. It is shown that transfer functions of singly terminated ladder networks possess second kind singularities. A class of doubly terminated cascade ladder networks whose transfer functions are free of second kind singularities is identified and their properties studied. The necessary and sufficient condition that a given rational function has to satisfy so that it can be realized as the voltage transfer function of a network belonging to the above class is obtained. A procedure to design such networks from a given transfer function is also described.
Robust Transmission of Watermarked Medical Images over Wireless Channels

In this paper, a robust approach for transmission of medical images with concealed patient information as watermark is evinced. In this approach, spatial domain digital watermarking technique is adapted wherein the patient information is embedded as watermark into the lower order bits of the medical image pixels. The watermark, which comprises of text data, is encrypted to prevent unauthorized access of data. To enhance the robustness of the embedded information, the encrypted watermark is coded by concatenation of Reed Solomon (RS) codes and low density parity check codes (LDPC). However, even under no noise conditions, the extracted watermark accuracy depends on the region of the medical image into which it is embedded. Hence extracted watermark accuracy is evaluated for three different regions of the image with no noise. The bursty wireless channel is simulated by adding impulse noise to the embedded image. Furthermore, turbo channel coding is proposed to correct the transmission errors over impulsive noisy wireless channels. The efficacy of the proposed approach is illustrated with the implementation results on a watermarked medical image.
Nonlinear signal processing with self-organizing neural networks

The application of self-organizing neural networks in processing nonlinear dynamic signals directly is investigated. The processing of a signal uses a model-based approach. The signal generating system is modeled by decomposing it into simpler subsystems and each subsystems is associated with a neuron on a single-layer network. Each subsystem is implemented using a temporally local linear combiner. The network is trained with a self-organizing procedure and the parameters of the linear combiners are updated by using the Widrow-Hoff adaptive rule. A competitive rule which takes into consideration the temporal dependence among the signal samples is presented. Simulation results are presented to illustrate the method.<<ETX>>
A new involutory parametric transform and its application to image encryption

In this paper, a novel involutory parametric transform is proposed by exploiting the reciprocal-orthogonal parametric transform. In addition, a recursive algorithm is proposed for its simple construction and fast computation. The transform has a very large number of independent parameters that are useful for many applications. Specifically, we show by implementing the double random phase encoding technique that the independent parameters of the proposed transform can successfully be used as an additional secret key for image encryption.
Univibrator analysis for a tunnel diode with a transmission line

A univibrator circuit using a tunnel diode and a transmission line is analysed. A graphical method is presented to calculate the width and the magnitude of the univibrator pulse. Conditions for the univibrator action to take place are derived. The effects of variation of the tunnel diode bias and the characteristic impedance of the line on the pulse width are also studied. The modifications to be made in the graphical analysis for the case of a line with small losses are discussed in detail.
Color image denoising using multivariate cauchy PDF in the contourlet domain

A new color image denoising method in the contourlet domain is proposed for reducing noise in images corrupted by Gaussian noise. This method takes into account the statistical dependencies among the contourlet coefficients of the RGB color channels. To this end, the multivariate Cauchy distribution is employed to capture these inter-channel dependencies. This model is then exploited in a Bayesian maximum a posteriori estimator to restore the clean coefficients by deriving an efficient closed-form shrinkage function. Experimental results are performed on a set of color images to evaluate the performance of the proposed denoising method. The results demonstrate that the proposed method outperforms some of the existing methods in terms of both subjective and objective criteria.
Spectral efficiency maximization of single cell massive multiuser MIMO systems via optimal power control with ZF receiver

This paper investigates the spectral efficiency of multiuser multiple-input multiple-output systems with a large number of antennas at the base station that serves single-antenna users in one cell. It is assumed that the base station estimates the channel with the help of uplink training and then employs the zero-forcing technique to detect the data signals transmitted by the various users. An optimal power control scheme over pilot and data power based on large-scale fading is proposed to maximize the sum spectral efficiency for a given total energy budget in a coherence interval. Simulation results show that the spectral efficiency of the proposed method is superior to that of other existing methods. It Is also shown that, In order to maximize the sum spectral efficiency, more power should be allocated to the data signal power at high signal-to-nolse ratios and less power at low signal-to-noise ratios.
A modified Hebbian learning rule for total least-squares estimation with complex-valued arguments

A constrained anti-Hebbian algorithm that is used for processing complex signals is presented. It is shown that the algorithm adaptively extracts the eigenvector associated with the smallest eigenvalue of the correlation matrix of the input signal. The operation of the algorithm is simple, similar to that of the LMS (least mean square) algorithm, and it can be applied to an adaptive prediction-error filter directly, giving an estimate of the parameters that is optimal in the total least-squares sense. Simulation results on estimating the frequencies of sinusoids corrupted by white noise are presented.<<ETX>>
Data and rate adaptive quantization for joint image denoising and compression

The techniques proposed for joint denoising and compression of images corrupted with additive white Gaussian noise are mostly based on Rissanen's minimum description length principle and tend to operate at a particular point (or a set of points) on the rate-distortion curve. These offer some compression along with denoising, but not a practical encoding solution. This paper suggests a simple adaptation of the zero-zone and the reconstruction levels of the uniform threshold quantizer based on the noise level in the image and the required compression rate. Context-based classification is also described for the noisy coefficients, and this raises the performance of the subband coder significantly.
Spacial Extrapolation-Based Blind DOA Estimation Approach for Closely Spaced Sources

This paper presents a new blind direction of arrival (DOA) estimation approach for closely-spaced sources. The new method first estimates the autoregressive (AR) coefficients via an initial DOA estimation and then uses the AR coefficients for the linear extrapolation of the correlation matrix to implement a fine DOA estimation. Both initial and fine DOA estimations are performed using the estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm. Unlike a conventional AR coefficient estimation method which estimates the AR coefficients on the snapshot basis, our AR coefficients are estimated in the correlation domain once for a block of snapshots, thus significantly reducing the computational complexity of the antenna array. Moreover, the proposed spatial extrapolation-based DOA estimation approach is analyzed using perturbation theory. Both the theoretical analysis and computer simulations show that the proposed method outperforms the conventional techniques in terms of the mean square error (MSE) of the DOA estimation when the angle of separation of the signal sources is very small.
A novel dual-stage classifier for automatic detection of epileptic seizures

In long-term monitoring of electroencephalogram (EEG) for epilepsy, it is crucial for the seizure detection systems to have high sensitivity and low false detections to reduce uninteresting and redundant data that may be stored for review by the medical experts. However, a large number of features and the complex decision boundaries for classification of seizures eventually lead to a trade-off between sensitivity and false detection rate (FDR). Thus, no single classifier can fulfill the requirements of high sensitivity with a low FDR and at the same time be a computationally efficient system suitable for real-time application. We present a novel, simple, computationally efficient seizure detection system to enhance the sensitivity with a low FDR by proposing a dual-stage classifier. This overall system consists of a pre-processing unit, a feature extraction unit and a novel dual-stage classifier. The first stage of the proposed classifier detects all true seizures, but also many false patterns, whereas the second stage of the proposed classifier minimizes false detections by rejecting patterns that may be artifacts. The performance of the novel seizure detection system has been evaluated on 300 hours of single-channel depth electroencephalogram (SEEG) recordings obtained from fifteen patients. An overall improvement has been observed in terms of sensitivity, specificity and FDR.
On stability properties of three- and higher dimensional linear shift-invariant digital filters

This paper presents a detailed discussion on the stability properties of 3- and higher dimensional linear digital filters, and the differences between 2- and higher dimensional cases. In particular, it is shown that straightforward extension of Goodman's 2-D bounded-input-bounded-output (BIBO) stability theorem  to 3- and higher dimensions would be too restrictive. Necessary conditions for stability are presented along with results, showing that these conditions are the same for any case where the number of dimensions is higher than two. Two examples of 3-D digital filters illustrating these results are given. Sufficient conditions for the stability of n-D (n > 2) filters are also presented.
An enhanced scheme for second-order-statistics estimation in MIMO-OFDM systems

The second-order statistics (SOS) of the received signal are very often used in blind and semi-blind channel estimation. In this paper, an analysis of signal perturbation in SOS of the received signal is first conducted, revealling that, even in the noise-free case, some SOS-based blind and semi-blind algorithms are subject to a signal perturbation error. Based on the analysis, a very efficient transmit scheme that can completely cancel the signal perturbation error at the receiver in the noise-free case is proposed. Computer simulations show that by employing the proposed signal perturbation cancellation approach, the mean square error (MSE) of the SOS estimate can be sufficiently suppressed in the noisy case.
Bayesian algorithm for video noise reduction in the wavelet domain

The paper proposes a Bayesian algorithm for the reduction of additive video noise in the wavelet domain. Spatial and temporal redundancies that exist in a video sequence in the time domain also persist in the wavelet domain. This allows video motion to be captured in the wavelet domain. Based on this fact, a new statistical model is proposed for video sequences. We not only model the subband coefficients in individual frames, but also the wavelet coefficient difference occurring between two consecutive frames using the generalized Laplacian distribution. Following this model, a Bayesian processor is developed that estimates the noise-free wavelet coefficients in the current frame, conditioned on the noisy coefficients in the current frame and the filtered coefficients in the past frame. Rigorous experimental results show that the proposed scheme outperforms several state-of-the-art spatio-temporal filters in time and wavelet domains in terms of quantitative performance as well as visual quality.
Wavelet-based segmentation techniques in the detection of microarousals in the sleep EEG

The presence of undesirable frequency bursts called microarousals (MA), within any stage of sleep, causes a medical condition known as excessive daytime sleepiness (EDS). Traditionally, using the electroencephalogram (EEG) and the electromyogram (EMG), a sleep technologist detects the MAs. To reduce the time, cost and errors associated with manual scoring, a three-stage computerized automatic detection procedure is proposed. The first stage involves spectral decomposition using the discrete wavelet transform (DWT). The second stage uses three different segmentation techniques: the autocorrelation function (ACF), the nonlinear energy operator (NLEO) and the generalized likelihood ratio (GLR) methods to segment the detail function into stationary segments. The third stage scores the MAs, by comparing the power and spectral content of each stationary segment with the rules established by Rechtschaffen and Kales. The procedure is applied to two cases: to one EEG channel, in one case, and to a combination of the EEG and EMG channels, in the other. The results show that the presence of the DWT significantly improves the correct MA detection while the combined channel provides the most impressive detection results.
Joint Maximum Likelihood Timing, Frequency Offset, and Doubly Selective Channel Estimation for OFDM Systems

This paper presents a novel preamble-aided method for joint estimation of timing, carrier frequency offset, and channel for orthogonal frequency division multiplexing systems that operate in high-mobility situations. Basis expansion modeling (BEM) that captures the time variations of the channel is used to reduce the number of unknown channel parameters. The BEM coefficients along with timing and frequency offsets are estimated by using a maximum likelihood approach. An efficient algorithm is then proposed for reducing the computational complexity of the joint estimation. The complexity of the new method is assessed in terms of the number of multiplications. The mean square estimation error of the proposed method is evaluated in comparison with previous methods and the hybrid Cramer–Rao lower bound, indicating a remarkable performance improvement by the new method.
O(n2) algorithms for graph planarization

The authors present two O(n/sup 2/) planarization algorithms, PLANARIZE and MAXIMAL-PLANARIZE. These algorithms are based on A. Lempel, S. Even, and I. Cederbaum's (1967) planarity testing algorithm and its implementation using PQ-trees. Algorithm PLANARIZE is for the construction of a spanning planar subgraph of an n-vertex nonplanar graph. The algorithm proceeds by embedding one vertex at a time and, at each step, adds the maximum number of edges possible without creating nonplanarity of the resultant graph. Given a biconnected spanning planar subgraph G/sub p/ of a nonplanar graph G, the MAXIMAL-PLANARIZE algorithm constructs a maximal planar subgraph of G which contains G/sub p/. This latter algorithm can also be used to planarize maximally a biconnected planar graph.
Improved neural network for checking the stability of multidimensional systems

In this paper, the author's previous work is extended and a new neural network is utilized to solve the stability problem of multidimensional systems. In the original authors work the problem is transformed into an optimization problem. Using the DeCarlo-Strintzis Theorem one has to check if |B(Z<inf>1</inf>,…, 1, Z<inf>m</inf>)| ≠ 0 for |Z<inf>1</inf> = … = |Z<inf>m</inf>| = 1 or equivalently if the min |B(Z<inf>1</inf>, …, 1, Z<inf>m</inf>)| is 0 or not, where B(Z<inf>1</inf>, Z<inf>2</inf>, …, Z<inf>m</inf>) is the denominator of the discrete transfer funcion. Then, the problem is reduced to a minimization problem and a neural network is proposed for solving it. To improve the chance of convergence towards the global minimum, an extension of this neural network based on random noise terms is proposed in this contribution. The numerical examples illustrate the validity and the efficiency of the new neural network.
Joint Space-Time Parameter Estimation for Multicarrier CDMA Systems

This paper addresses the problem of joint space-time parameter estimation for multicarrier code-division multiple-access (MC-CDMA) systems in the presence of multipath propagation. We propose a novel preprocessing technique termed as subcarrier smoothing (SCS) to decorrelate the coherency of the multipath signals. The basic idea behind the SCS is to restore the rank of the signal correlation matrix by averaging the space-time covariance matrices associated with each subcarrier. Unlike the other existing preprocessing techniques, such as spatial smoothing, the SCS preprocessing is not limited to any specific array geometry and does not decrease the array aperture.