nils-herrmann/clean_scirepeval huggingface dataset:
A new multiplicative noise removal model combining cartoon-texture decomposition method

This paper proposes a new model to remove the multiplicative noise. We incorporate the cartoon-texture decomposition into a multiplicative denoising model, and design a new convex optimization model. The superiority of the proposed model lies in adding the H-1 norm of texture component, which can help to recover more image information. We solve the model numerically by Douglas-Rachford splitting algorithm and the Fourier domain filtering. Experimental results show that the designed algorithms can solve the model effectively, the recovery images of the new model have better visual quality, and also have higher signal to noise ratios and peak signal to noise ratios.
Hybrid Probabilistic Sparse Coding With Spatial Neighbor Tensor for Hyperspectral Imagery Classification

Under the umbrella of tensor algebra, this paper proposes a new sparse-coding-based classifier (SCC) for hyperspectral imagery classification (HIC). By utilizing the tensor forms of hyperspectral pixels, we advance a tensor sparse-coding model which preserves as many original spatial constraints of a pixel and its spatial neighbors as possible. Furthermore, to alleviate the classification uncertainty resulted from widely existing mixed pixels, this paper constructs a regularization term for maximizing the likelihood of sparse-coding tensor defined on the posterior class probability. By combining the tensor sparse coding with maximizing likelihood estimation, a hybrid probabilistic SCC with spatial neighbor tensor (HPSCC-SNT) is proposed, which makes the pixels be well represented by the training pixels belonging to the same class. The performance of HPSCC-SNT is evaluated on three real hyperspectral imagery data sets, and the results show that it can achieve accurate and robust HIC results, and outperforms the state-of-the-art methods.
Patch Group Based Nonlocal Self-Similarity Prior Learning for Image Denoising

Patch based image modeling has achieved a great success in low level vision such as image denoising. In particular, the use of image nonlocal self-similarity (NSS) prior, which refers to the fact that a local patch often has many nonlocal similar patches to it across the image, has significantly enhanced the denoising performance. However, in most existing methods only the NSS of input degraded image is exploited, while how to utilize the NSS of clean natural images is still an open problem. In this paper, we propose a patch group (PG) based NSS prior learning scheme to learn explicit NSS models from natural images for high performance denoising. PGs are extracted from training images by putting nonlocal similar patches into groups, and a PG based Gaussian Mixture Model (PG-GMM) learning algorithm is developed to learn the NSS prior. We demonstrate that, owe to the learned PG-GMM, a simple weighted sparse coding model, which has a closed-form solution, can be used to perform image denoising effectively, resulting in high PSNR measure, fast speed, and particularly the best visual quality among all competing methods.
Improvement of BM3D Algorithm Based on Wavelet and Directed Diffusion

Block-matching and 3D filtering (BM3D) has showngreat power for image denoising. By decomposing the BM3Drestored image into three orthogonal subbands, we find thatthe subband of the high-frequency component, of which thewavelet coefficients are less than a given threshold, has madesome true textures of the original image lost and, the PSNR ofthe restored image is pulled down. Therefore, in this paper, wepropose a modified BM3D algorithm to solve these problems. We diffuse the subband of the high-frequency component, ofwhich the wavelet coefficients are less than the threshold of theBM3D restored image to the corresponding subband of thenoisy image to get a new estimate by using an improveddirected diffusion equation model. The Laplace operator isreplaced by an anisotropic diffusion operator and two differentcoefficients are added in the two diffusion terms. By replacingthe corresponding subband of the BM3D restored image withthe new estimate, we obtain the improved denoising result. Experimental results demonstrate that the proposed methodachieves better performance than original BM3D algorithm interms of both removing noise and persevering the edges anddetails of the image.
Storage algorithm for wavelet Galerkin method

The representation of an integral operator in wavelet bases is a large numerical sparse matrix. Thus, a suitable approach is needed that converts the matrix from full storage mode into sparse storage mode, which not only saves the storage, but also saves the execution time. In this paper, the storage of this kind of matrix is discussed in the symmetric case. First, we present a standard storage approach. Then, a modified one is proposed, which is based on a special decomposition of the symmetric matrix. When iterative solvers are used to solve the corresponding linear system, the main cost is the multiplication of the symmetric sparse matrix and a given vector. The modified sparse storage mode is optimized for this purpose and reduces nearly half of the number of multiplications.
A square-root adaptive V-BLAST algorithm for fast time-varying MIMO channels

Among the methods that have been proposed for a multiple-input multiple-output (MIMO) receiver, the V-BLAST algorithm provides a good compromise between transmission rate, achievable diversity, and decoding complexity. In this letter, we derive a new adaptive V-BLAST-type equalization scheme for fast time-varying, flat-fading MIMO channels. The proposed equalizer stems from the Cholesky factorization of the MIMO system's output data autocorrelation matrix, and the equalizer filters are updated in time using numerically robust unitary Givens rotations. The new square-root algorithm exhibits identical performance to a recently proposed V-BLAST adaptive algorithm, offering at the same time noticeable reduction in computational complexity. Moreover, as expected due to its square-root form and verified by simulations, the algorithm exhibits particularly favorable numerical behavior.
Turbo Equalization of Non-Linear Satellite Channels Using Soft Interference Cancellation

Satellite communication channels can be well described as non-linear functions with memory. The Volterra series representation provides accurate modeling of satellite channel dynamics, and thus, it constitutes a widely used approach to mathematically describe them. In this work, iterative correction of the non-linear distortion introduced by such channels is considered, by employing a soft interference canceller operating in a turbo equalization framework.
Adaptive covariance-type internally symmetric algorithm for LS FIR filtering with linear phase

The least-squares design of linear phase filters is approached from a novel perspective to take advantage of the symmetry which is intrinsic to the problem. The structure of the proposed filter is based on an adaptive covariance-type algorithm that results in a substantial reduction in computational complexity compared to other existing schemes. The algorithm requires 12.5 multiplications and 18.5 additions per time recursion with p being the order of the filter. A fast O(p) initialization scheme is also presented.<<ETX>>
Computer Aided Detection of Prostate Cancer using Fused Information from Dynamic Contrast Enhanced and Morphological Magnetic Resonance Images

This paper presents a computer-aided diagnosis scheme for the detection of prostate cancer. The pattern recognition scheme proposed, utilizes fused dynamic and morphological features extracted from magnetic resonance images (MRIs). The performance of the proposed scheme has been evaluated through extensive training and testing on several patient cases, where the staging of their condition has been previously evaluated by both ultrasoundguided biopsy and radiological assessment. The classification scheme is based on Probabilistic Neural Networks (PNNs), whose parameters are estimated using the Expectation-Maximization (EM) algorithm during a training phase. Fusion of the image characteristics is performed by properly aligning the respective T1-weighted dynamic and T2-weighted morphological images, allowing accurate feature selection from both images. The proposed classification scheme as well as the effect of fusion on the extracted features is tested, with respect to the correct classification rate (CCR) of each case.
Supervised energy disaggregation using dictionary — based modelling of appliance states

In this paper, a supervised energy disaggregation method is proposed. The appliances that are monitored, are modelled by multi-state finite state machines. Each state of an appliance is described by exactly one vector of power consumptions from a carefully designed set of such vectors (called atoms), that comprise a dictionary. The latter is constructed during a training phase, where it is assumed that individual power consumption signals are available. A clustering algorithm is applied on overlapping patches extracted from the training signals to select a fixed number of representatives, i.e., the atoms of the dictionary. Moreover, in the training phase, an appropriate state transition probabilities matrix is constructed. During the operation phase, where the actual disaggregation task is performed, a trellis, with a reduced number of transitions, is used for the acquisition of the disaggregated power consumption signals per appliance. Numerical results, using the REDD dataset, are provided, in order to demonstrate the effectiveness of the proposed method.
Distributed Incremental-Based LMS for Node-Specific Adaptive Parameter Estimation

We introduce an adaptive distributed technique that is suitable for parameter estimation in a network where nodes have different but overlapping interests. At each node, the parameters to be estimated can be of local interest, global interest to the whole network and common interest to a subset of nodes. To estimate each set of local, common and global parameters, a least mean squares (LMS) algorithm is implemented under an incremental mode of cooperation. Coupled with the estimation of the different sets of parameters, the implementation of each LMS algorithm is only undertaken by the nodes of the network interested in a specific set of local, common or global parameters. Besides obtaining the conditions under which the proposed strategy converges in the mean to the solution of a centralized unit that processes all the observations, a spatial-temporal energy conservation relation is provided to evaluate the steady-state performance at each node across the network. Finally, the theoretical results are validated through generic computer simulations as well as simulation results in the context of cooperative spectrum sensing in cognitive radio networks.
A scheme for X-ray medical image denoising using sparse representations

This paper addresses the problem of noise removal in X-ray medical images. A novel scheme for image denoising is proposed, by leveraging recent advances in sparse and redundant representations. The noisy X-ray image is decomposed, with respect to an overcomplete dictionary which is either fixed or trained on the noisy image, and it is reconstructed using greedy techniques. The new scheme has been tested with both artificial and real X-ray images and it turns out that it may offer superior denoising results as compared to other existing methods.
New efficient LS and SVD based techniques for high-resolution frequency estimation

New least squares and singular value decomposition based methods for the estimation of the frequencies of complex sinusoids in white noise are presented. The methods are based on a new symmetric prediction problem that has some very useful properties leading to algorithms that have considerably reduced complexity. The new symmetric predictor is superior in performance as compared to the well known symmetric Smoother and has a performance comparable to other well known methods. Finally a new LS based method, which combines the new prediction technique with the FBLP method is proposed. This method performs slightly better than the FBLP offering at the same time a significant computational saving. As a by-product in the derivation of the new methods is the establishment of some useful properties concerning the eigenstructure of Hermitian and Persymmetric matrices.
Rate optimal power policies in underlay cognitive radios with limited channel feedback

In this paper, two new power policies (PPs), namely the Rate Maximization Policy (RMP) and the Rate-Bound Maximization Policy (RBMP), for Cognitive Radio (CR) systems are developed based on an expected rate maximization criterion. Both policies are designed such as to satisfy a constraint related to the average interference caused by the CR network to primary communication. The key novelty here is that the proposed PPs take into account the existence of limited (hybrid) channel feedback, where the direct secondary channel is known instantaneously while the interference caused by the primary transmission to the secondary receiver is only known statistically. The optimal policy (RMP) is characterized, and a low complexity algorithm is presented that allows for its efficient and accurate implementation. The two policies are compared and RMP is shown to lead to substantial energy consumption savings at equal rate performance.
Training design in single relay AF cooperative systems with correlated channels

In this paper, training design is studied for a single relay amplify-and-forward cooperative network. The taps of the frequency selective channels are assumed to be correlated and OFDM modulation is used for transmission. Based on the least squares (LS) criterion, conditions for pilot tone positioning and a number of power allocation schemes are described. Analytical closed-form power allocation expressions are provided for both the source and the relay. The theoretical results are fully corroborated by simulations.
Autonomous driving in 5G: Mitigating interference in OFDM-based vehicular communications

Automotive industry will be greatly benefited by the advent of 5G Networking and the huge boost in performance and coverage it will support. Road safety and traffic efficiency services will be significantly upgraded through seamlessly interconnected devices, while latency decrease will most likely allow autonomous driving to become a commodity, available to everyone. This technology will have a huge societal and economical impact, since it will render severe traffic accidents, long commute times and increased energy consumption obsolete. Current vehicular communication systems are usually equipped with orthogonal frequency division multiplexing (OFDM) transceivers that operate in suboptimal modes for the upcoming 5G standards. The problem originates in the existing intercarrier interference (ICI) on the receiver end, often partially tackled by integrated successive interference cancellation (OSIC) architectures. However, for decreasing complexity, system designers attempt to mitigate ICI by considering only a small number or sub-carriers, leading to error floor introduction and unacceptable bit-error-rates (BER). This paper presents an OSIC-based solution for cancelling interference in OFDM systems operating over specific conditions such as doubly selective channels. The experimental results indicated that the proposed equalizer outperforms all existing non-banded ICI cancellation methods, by achieving lower BER despite operating in a resource-savvy manner.
Finite word length analysis of the EBA-DFE

In this paper a finite word length analysis of the EBA-DFE is presented. It is shown that moderate word lengths are required, facilitating the hardware implementation of EBA-DFE. Both truncation and rounding schemes are studied for product quantization. Furthermore, a simplified approximate rounding scheme is proposed. It is shown that the proposed scheme simplifies the complexity of rounding a product, without affecting the behavior of the algorithm.
User selection schemes for multiuser MISO downlink with eavesdropping

In this paper, the problem of secrecy sum-rate maximization is addressed for a multiuser multiple-input multiple-output wiretap channel. We consider the case of a single eavesdropper, when the information signals of the legitimate users are spatially multiplexed by using either zero forcing beamforming (ZFB) or Tomlinson-Harashima precoding (THP). By exploiting channel correlation of legitimate users with the eavesdropper, two low-complexity user selection schemes are investigated. In both schemes, the key idea is to extract multiuser diversity in order to protect information signals from eavesdropping rather than creating jamming interference. In the first scheme, user selection is performed in the null space of the eavesdropper's channel and an iterative power allocation method is proposed. In the second scheme, the eavesdropper is considered to be a selected authorized user but its allocated power is forced to be zero. Numerical results are provided to illustrate the secrecy sum-rate performance of the proposed schemes.
Decentralized Adaptive Eigenvalue-Based Spectrum Sensing for Multiantenna Cognitive Radio Systems

Eigenvalue-based spectrum sensing (EBSS) techniques may operate in a totally blind manner while they offer remarkably improved performance for specific types of signals compared with energy-based methods. In the literature so far, only batch and centralized cooperative EBSS techniques have been considered, which, however, suffer from limitations that render them impractical in several cases. Thus, the aim of this paper is to develop practical cooperative adaptive versions of typical EBSS techniques that could be applied in a completely decentralized manner. To this end, at first, novel adaptive EBSS techniques are developed for the maximum eigenvalue detector, the maximum-minimum eigenvalue detector, and the generalized likelihood ratio test scheme, respectively, for a single-user (no cooperation) case. Then, a novel distributed subspace tracking method is proposed, which enables the cooperating nodes to track the joint subspace of their received signals. Based on this method, cooperative decentralized versions of the adaptive EBSS techniques are subsequently developed that overcome the limitations of the existing batch centralized approaches. The performance of the proposed methods is verified via indicative simulations.
Semi-blind channel estimation schemes based on a cooperative form of the cross relation criterion

In this paper, two channel estimation schemes are derived for specific cooperative scenarios. Both schemes are based on the cross-relation criterion that has been extensively studied in the (semi-) blind literature. As shown in the paper, in a cooperative system, the channel estimator can be constructed in a natural way either by fractionally-spaced or symbol-spaced samples. We investigate the performance of these two schemes using semi-analytic arguments accompanied by corresponding experimental results.
Spatial Domain Simultaneous Information and Power Transfer for MIMO Channels

In this paper, we theoretically investigate a new technique for simultaneous information and power transfer (SWIPT) in multiple-input multiple-output (MIMO) point-to-point with radio frequency energy harvesting capabilities. The proposed technique exploits the spatial decomposition of the MIMO channel and uses the eigenchannels either to convey information or to transfer energy. In order to generalize our study, we consider channel estimation error in the decomposition process and the interference between the eigenchannels. An optimization problem that minimizes the total transmitted power subject to maximum power per eigenchannel, information and energy constraints is formulated as a mixed-integer nonlinear program and solved to optimality using mixed-integer second-order cone programming. A near-optimal mixed-integer linear programming solution is also developed with robust computational performance. A polynomial complexity algorithm is further proposed for the optimal solution of the problem when no maximum power per eigenchannel constraints are imposed. In addition, a low polynomial complexity algorithm is developed for the power allocation problem with a given eigenchannel assignment, as well as a low-complexity heuristic for solving the eigenchannel assignment problem.
Adaptive Conjugate Gradient DFEs for Wideband MIMO Systems Using Galerkin Projections

Three new adaptive equalization algorithms for wireless systems operating over frequency selective MIMO channels are proposed. The problem of the MIMO DFE design is formulated as a set of linear equations with multiple right-hand sides (RHS) evolving in time. By applying an adaptive modified conjugate gradient algorithm, originally proposed for a single linear system, to the problem at hand, we arrive at an equalizer of performance identical to RLS, numerically robust, but of higher computational cost. To reduce its complexity, two updating strategies of the equalizer filters are derived based on Galerkin projection in time and space respectively. The two alternative schemes exhibit a complexity lower than RLS while offering slightly inferior convergence properties.
Robust and fast converging decision feedback equalizer based on a new adaptive semi-blind channel estimation algorithm

An adaptive equalization scheme based on channel estimation is proposed. The structure of the whole scheme is novel as well as some of its most important constituent parts. The equalization part comprises an adaptive decision feedback equalizer (DFE) whose performance is significantly improved due to the information provided by the channel estimation part. To estimate the channel impulse response (CIR) a new adaptive semi-blind scheme based on the sub-channel response matching (SRM) criterion and implemented in the frequency domain has been developed. The equalization part exhibits a much faster convergence rate as compared to the conventional DFE, hence a much shorter training sequence is required. Moreover a significant reduction of the error propagation phenomenon can be achieved. The above advantages are offered with only a slight increase in computational load compared to classical LMS-based DFE.
An adaptive normalized lattice algorithm for LS FIR multichannel filters with linear phase

A novel adaptive normalized lattice algorithm for the least-squares (LS) design of multichannel FIR (finite-impulse response) filters with symmetric impulse response is presented. A byproduct is an algorithm for the LS estimation of the unit step backward predictor, based on the simultaneous minimization of the forward and backward total squared error powers, which has successfully been used in MEM spectral analysis. An order-recursive scheme that computes the filters from the normalized lattice variables is also presented.<<ETX>>
Bandwidth efficient transmission through sparse channels using a parametric channel-estimation-based DFE

A novel equalisation scheme based on parametric channel estimation is presented. The proposed scheme is appropriate for channels with a long and sparse impulse response, such as those encountered in high-definition digital video-transmission systems. The main feature of the channel estimation is that instead of seeking the whole channel impulse response only the unknown time delays and attenuation factors of the physical channel multipath components are efficiently estimated. The equalisation consists of an adaptive decision-feedback equaliser (DFE), whose initialisation is properly designed for sparse channels. The information needed in the initialisation procedure is provided by the channel estimation part. The DFE converges much faster compared to a conventional DFE, and the whole scheme utilises a very short training sequence and hence a significant saving in bandwidth is achieved.