nils-herrmann/clean_scirepeval huggingface dataset:
Joint Precoding and RRH Selection for User-Centric Green MIMO C-RAN

This paper jointly optimizes the precoding matrices and the set of active remote radio heads (RRHs) to minimize the network power consumption for a user-centric cloud radio access network, where both the RRHs and users have multiple antennas and each user is served by its nearby RRHs. Both users’ rate requirements and per-RRH power constraints are considered. Due to these conflicting constraints, this optimization problem may be infeasible. In this paper, we propose to solve this problem in two stages. In Stage I, a low-complexity user selection algorithm is proposed to find the largest subset of feasible users. In Stage II, a low-complexity algorithm is proposed to solve the optimization problem with the users selected from Stage I. Specifically, the re-weighted  $l_{1}$ -norm minimization method is used to transform the original problem with non-smooth objective function into a series of weighted power minimization (WPM) problems, each of which can be solved by the weighted minimum mean square error (WMMSE) method. The solution obtained by the WMMSE method is proved to satisfy the Karush-Kuhn-Tucker conditions of the WPM problem. Moreover, a low-complexity algorithm based on Newton’s method and the gradient descent method is developed to update the precoder matrices in each iteration of the WMMSE method. Simulation results demonstrate the rapid convergence of the proposed algorithms and the benefits of equipping multiple antennas at the user side. Moreover, the proposed algorithm is shown to achieve near-optimal performance in terms of NPC.
Prediction of ozone concentration using back propagation neural network with a novel hybrid training algorithm

This paper proposed a new hybrid forecasting model for the prediction of ozone concentrations in semi-arid area. It is based on chaotic, particle swarm optimization algorithm (CPSO) and back propagation (BP) neural network, called CPSO-BP neural network. The results show that the proposed hybrid model is superior to both the BP neural network and the regression model being tested. The hybrid model achieves 18.7% in root mean square error reduction compared to BP model, and 8.1% reduction compared to stepwise regression model. It could be a promising model on forecasting ozone concentration in semi-arid area.
Depth perception of distorted stereoscopic images

How to measure the perceptual quality of depth information in stereoscopic natural images, especially images undergoing different types of symmetric and asymmetric distortions, is a fundamentally important issue that is not well understood. In this paper, we present two of our recent subjective studies on depth quality. The first one follows the absolute category rating (ACR) protocol that is widely used in general image quality assessment research. We find that traditional approaches such as ACR is problematic in this scenario because monocular cues and the spatial quality of images have strong impacts on the depth quality scores given by subjects, making it difficult to single out the actual contributions of stereoscopic cues in depth perception. To overcome this problem, we carry out the second subjective study where depth effect is synthesized at different depth levels before various types and levels of symmetric and asymmetric distortions are applied. Instead of following the traditional approach, we ask subjects to identify and label depth polarizations, and a Depth Perception Difficulty Index (DPDI) is developed based on the percentage of correct and incorrect subject judgements. We find this approach highly effective at quantifying depth perception induced by stereo cues and observe a number of interesting effects regarding image content dependency, distortion type dependency, and the impacts of symmetric versus asymmetric distortions. We believe that these are useful steps towards building comprehensive 3D quality-of-experience models for stereoscopic images.
Totally Distributed Energy-Efficient Transmission in MIMO Interference Channels

In this paper, we consider the problem of maximizing the energy efficiency (EE) for multiple-input-multiple-output (MIMO) interference channels (ICs), subject to the per-link power constraint. To avoid extensive information exchange among all links, the optimization problem is formulated as a noncooperative game, where each link maximizes its own EE. We show that this game always admits a Nash equilibrium (NE) and the sufficient condition for the uniqueness of the NE is derived for the case of large enough maximum transmit power constraint. To reach the NE of this game, we develop a totally distributed EE algorithm, in which each link updates its own transmit covariance matrix in a completely distributed and asynchronous way. Some players may update their solutions more frequently than others or even use the outdated interference information. The sufficient conditions that guarantee the global convergence of the proposed algorithm to the NE of the game have been given as well. We also study the impact of the circuit power consumption on the sum EE performance of the proposed algorithm in the case when the links are separated sufficiently far away. Moreover, the tradeoff between the sum EE and the sum spectral efficiency (SE) is investigated with the proposed algorithm under two special cases: 1) low transmit power constraint regime; and 2) high transmit power constraint regime. Finally, extensive simulations are conducted to evaluate the impact of various system parameters on the system performance.
Multicarrier CDMA overlay for ultra-wideband communications

In this letter, the performance of multicarrier code-division multiple-access (CDMA) systems is studied in the presence of narrowband interference for future ultra-wideband (UWB) communications. A Nakagami fading channel is assumed, and notch filters along with diversity techniques are used in the multicarrier CDMA receiver. A complete performance analysis of error probability is given. It is shown that when the number of subcarriers jammed by narrowband interference is small, the multicarrier receiver without notch filters can work well, due to the gain of frequency diversity from nonjammed subcarriers. On the other hand, when the number of subcarriers jammed by the narrowband interference is large, using notch filters can improve the multicarrier system performance significantly.
A Novel Hybrid Evolutionary Algorithm Based on PSO and AFSA for Feedforward Neural Network Training

In recent years, the multilayer feedforward neural network (FNN) has been received considerable attention and have been extensively used in many fields. Levenberg-Marquardt back-propagation (LMBP) algorithm as an FNN training method has some limitations associated with overfitting, local optimum problems and slow convergence rate. In order to overcome the limitations, some people proposed particle swarm optimization (PSO) as an evolutionary algorithm to train the FNN. But PSO has disadvantages such as low precision, slow convergence in the later stage of the evolution, and parameter selection problems. In this paper, a novel hybrid evolutionary algorithm based on AFSA and PSO, also referred to as AFSA-PSO-parallel-hybrid evolutionary (APPHE) algorithm, has been used in FNN training. Compared to FNN trained by LMBP algorithm, FNN training by the novel hybrid evolutionary algorithm show satisfactory performance, converges quickly towards the optimal position, convergent accuracy, high stability and can avoid overfitting in some extent. FNN training by the novel method has been testified by using in Iris data classification and the results are much more accurate and stable than by Levenberg-Marquardt back-propagation algorithm.
A multi-stage MCI cancellation receiver for multicode CDMA systems

Multicode CDMA is a new transmission scheme for flexible and high-speed data communications. In a multipath environment, these intra-user multicode signals from different delay paths no longer maintain their orthogonality, thus causing significant interference, i.e. multicode interference (MCI), to each other. A multicode CDMA receiver with RAKE structure and multi-stage MCI cancellation is proposed. By pilot aided channel estimation, the MCI associated with the reference user is regenerated and subtracted from the received signal. The detailed receiver structure is presented and theoretical analysis is given to show that the system performance is significantly improved by the MCI cancellation. It is shown that the optimal cancellation factor in the k-th stage can be approximated by /spl lambda//sub opt//sup (k)//spl les/1-2P/sub e//sup (k-1)/, where P/sub e//sup (k-1)/ is the error probability of the (k-1)th stage.
SSIM-based non-local means image denoising

Perceptually inspired image processing has been an emerging field of study in recent years. Here we make one of the first efforts to incorporate the structural similarity (SSIM) index, a successful perceptual image quality assessment measure, into the framework of non-local means (NLM) image denoising, which is a state-of-the-art method that delivers superior desnoising performance. Specifically, a denoised image patch is obtained by weighted averaging of neighboring patches, where the similarity between patches as well as the weights assigned to the patches are determined based on an estimation of SSIM. A two-stage approach is proposed for robust SSIM estimation in the presence of noise. Moreover, motivated by the ideas behind SSIM, we adjust the contrast and mean of each patch before feeding it into the weighted averaging process. Our experimental results show that the proposed SSIM-based NLM algorithm achieves better SSIM and PSNR performance and provides better visual quality than least square based NLM method.
Facial Range Image Matching Using the ComplexWavelet Structural Similarity Metric

We propose a novel 3D face recognition algorithm based on facial range image matching using the complex wavelet structural similarity metric (CW-SSIM) metric. Compared with many existing 3D surface matching methods, CW-SSIM is computationally efficient and is robust to small geometrical distortions. Using a data set that contains 360 3D face models of 12 subjects, we tested the performance of the proposed method and compared it with existing 3D surface matching based face recognition algorithms. Verification and identification performance of each algorithm was evaluated by means of the receiver operating characteristic curve and the cumulative match characteristic curve. Among the algorithms tested, the proposed algorithm based on the CW-SSIM resulted in the best overall performance with an equal error rate of 9.13% and a rank 1 recognition rate of 98.6%, significantly better than all the other algorithms. Besides the introduction of a novel approach for 3D face recognition, this is also the first attempt to expand the application scope of complex wavelet domain similarity measure to range image matching in general
When ICN meets C-RAN for HetNets: an SDN approach

With ever growing mobile Internet and the explosion of its applications, users are experiencing abundant services from different content providers via different network service providers in the heterogeneous network. The information-centric network (ICN) advocates getting rid of the current host-centric network protocol because information dissemination rather than end-to-end communication contributes to the majority of today's network traffic. Furthermore, it is better for network entities to converge as a whole in order to take advantage of open, scalable, and smart traffic transmission. The cloud radio access network is one of the emerging architecture evolutions on the wireless side for easier infrastructure deployment and network management. Therefore, when the information-centric network meets the C-RAN in the Het-Net, it is worthwhile and consequential to integrate ICN protocol with C-RAN architecture to achieve more efficient communication and information management. Moreover, SDN has been recognized as another promising architecture evolution to achieve the flexibility and reconfigurability of dense HetNets; its inherent advantage lies in global uniform control of the wired network. Thus, any of ICN, C-RAN and SDN is the complementary to the others. In this article, we contribute to a fresh proposal for and elaboration of the integration of the ICN, CRAN, and SDN for the HetNet to achieve a win-win situation. The vision of the proposed system is demonstrated, followed by the advantages and challenges. We further present a hybrid system in a large-scale wireless heterogeneous campus network.
Multi-attribute decision making models and methods under interval-valued intuitionistic fuzzy environment

Interval-valued intuitionistic fuzzy sets generalize the concept of intuitionistic fuzzy sets and are well suited to dealing with vagueness. This article investigates multi-attribute decision making problems in which all the information provided by the decision makers is expressed as interval-valued intuitionistic fuzzy decision matrices where each of the elements is characterized by interval-valued intuitionistic fuzzy number. Information about attribute weights is partially known and may be constructed by various forms. Several optimization models are presented to generate optimal weights for attributes, and the corresponding decision-making methods have also been developed. Feasibility and effectiveness of the proposed methods are illustrated with an example of investment decision problem.
End-to-End Blind Image Quality Assessment Using Deep Neural Networks

We propose a multi-task end-to-end optimized deep neural network (MEON) for blind image quality assessment (BIQA). MEON consists of two sub-networks—a distortion identification network and a quality prediction network—sharing the early layers. Unlike traditional methods used for training multi-task networks, our training process is performed in two steps. In the first step, we train a distortion type identification sub-network, for which large-scale training samples are readily available. In the second step, starting from the pre-trained early layers and the outputs of the first sub-network, we train a quality prediction sub-network using a variant of the stochastic gradient descent method. Different from most deep neural networks, we choose biologically inspired generalized divisive normalization (GDN) instead of rectified linear unit as the activation function. We empirically demonstrate that GDN is effective at reducing model parameters/layers while achieving similar quality prediction performance. With modest model complexity, the proposed MEON index achieves state-of-the-art performance on four publicly available benchmarks. Moreover, we demonstrate the strong competitiveness of MEON against state-of-the-art BIQA models using the group maximum differentiation competition methodology.
Cooperative Transmission Strategy over Users' Mobility for Downlink Distributed Antenna Systems

Previously, a scheme in  was proposed for the outdated channel state information (CSI) problem, for data transmission in time division duplex (TDD) systems. In user movement environment, the actual channel of data transmission at downlink time slot is different from the estimated channel due to channel variation. In this paper, the effect of different user mobility on TDD downlink multiuser distributed antenna system is investigated. An efficient autocorrelation based feedback interval technique is proposed and updates CSI at less the cost of the downlink time slots. In the proposed technique, the frequency of CSI feedback for different users is proportional to their speed. Cooperative clusters are formed to maximize sum rate where channel gain based antenna selection and user clustering based on signal-to- interference plus noise ratio (SINR) threshold is applied to reduce computational complexity. Numerical results show that sum rate superiority of the proposed scheme over the user mobility.
Fault-tree analysis on computer security system using intuitionistic fuzzy sets

Intuitionistic fuzzy set is a useful tool to express experts' uncertain knowledge and experience information. Most computer security systems have abnormal states and their failure behaviors are always characterized in uncertainty and inaccuracy. How to calculate fault interval of system components is an interesting and important issue in intuitionistic fuzzy fault-tree analysis (IFFTA). Intuitionistic fuzzy fault diagnosis models are proposed in this paper to analyze fuzzy system reliability and to find the most critical system component for the decision-making based on some basic definitions. The proposed method is applied for the failure analysis problem of computer security systems and comparative studies are conducted to show its flexibility and effectiveness with the other fault tree analysis models.
Generalized bitplane-by-bitplane shift method for JPEG2000 ROI coding

One interesting feature of the new JPEG2000 image coding standard is support of region of interest (ROI) coding using the maximum shift (Maxshift) method, which allows for arbitrarily shaped ROI image compression without shape coding or explicitly transmitting any shape information to the decoder. The major disadvantage of the Maxshift method is that it cannot adjust the scaling value which determines the degree of relative importance between the ROI and the background wavelet coefficients. The bitplane-by-bitplane shift (BbBShift) method was introduced to support both arbitrary ROI shape and arbitrary scaling without shape coding. We propose a generalized BbBShift (GBbBShift) method, which delivers much more flexibility than both Maxshift and BbBShift for "degree-of-interest" adjustment of the ROI with insignificant effect on coding efficiency and computational complexity. Experiments show that it can provide significantly better visual quality than Maxshift at low bit rates. GBbBShift is not compliant with the current JPEG2000 definitions. In order to use it, a new ROI coding mode would need to be added to the standard.
Mean squared error: Love it or leave it? A new look at Signal Fidelity Measures

In this article, we have reviewed the reasons why we (collectively) want to love or leave the venerable (but perhaps hoary) MSE. We have also reviewed emerging alternative signal fidelity measures and discussed their potential application to a wide variety of problems. The message we are trying to send here is not that one should abandon use of the MSE nor to blindly switch to any other particular signal fidelity measure. Rather, we hope to make the point that there are powerful, easy-to-use, and easy-to-understand alternatives that might be deployed depending on the application environment and needs. While we expect (and indeed, hope) that the MSE will continue to be widely used as a signal fidelity measure, it is our greater desire to see more advanced signal fidelity measures being used, especially in applications where perceptual criteria might be relevant. Ideally, the performance of a new signal processing algorithm might be compared to other algorithms using several fidelity criteria. Lastly, we hope that we have given further motivation to the community to consider recent advanced signal fidelity measures as design criteria for optimizing signal processing algorithms and systems. It is in this direction that we believe that the greatest benefit eventually lies.
Blind Image Quality Assessment Based on Rank-Order Regularized Regression

Blind image quality assessment (BIQA) aims to estimate the subjective quality of a query image without access to the reference image. Existing learning-based methods typically train a regression function by minimizing the average error between subjective opinion scores and model predictions. However, minimizing average error does not necessarily lead to correct quality rank-orders between the test images, which is a highly desirable property of image quality models. In this paper, we propose a novel rank-order regularized regression model to address this problem. The key idea is to introduce a pairwise rank-order constraint into the maximum margin regression framework, aiming to better preserve the correct perceptual preference. To the best of our knowledge, this is the first attempt to incorporate rank-order constraints into margin-based quality regression model. By combing with a new local spatial structure feature, we achieve highly consistent quality prediction with human perception. Experimental results show that the proposed method outperforms many state-of-the-art BIQA metrics on popular publicly available IQA databases (i.e., LIVE-II, TID2013, VCL@FER, LIVEMD, and ChallengeDB).
On the Impact of Selfish Behaviors in Wireless Packet Scheduling

In many practical scenarios, wireless devices are autonomous and thus, may exhibit non-cooperative behaviors due to self-interests. For instance, a wireless user may report bogus channel information to gain resource allocation advantages. Such non-cooperative behaviors are practicable as the device's software could be modified by the user. In this paper, we first analyze the impact of these rationally selfish behaviors on the performance of packet scheduling algorithms in time-slotted wireless networks. Using a mixed strategy game theoretic model, we show that the traditional Maximum Rate packet scheduling algorithm can lead non-cooperative users to undesirable Nash equilibriums, in which the wireless channels are used inefficiently. By using repeated game to enforce cooperation, we further propose a novel game theoretic approach that can lead to an efficient equilibrium.
The Research of Software Product Line Engineering Process and Its Integrated Development Environment Model

In order to realize the industrialization production of software, people have carried out research on and analysis the software product line architecture of the growing maturity, component technology and development methods for product line. In this paper, a novel software engineering process model is proposed based on the modern industrial production systems and automated production method: that is ¿N-life-cycle model¿. Based on this new model, not only integrated software engineering environment model and framework have been proposed, which are based on the product line development process model, but also study systematically on theirs implementation. "N-life-cycle model" and "integrated software engineering environment model based on the product line" which are set up in the article are brand-new open models possessing modern manufacturing production characteristic. The models can impel the research development quickly of product line engineering and product line software engineering environment towards the industrialisation and automatization of the software industry.
Design of stray loss test system for three-phase asynchronous motor

Asynchronous motor is widely used in every field and department of people's daily life and in industry and agriculture. Electricity consumption of asynchronous motor is the main asynchronous motor. Now the whole society is calling for energy-saving and environment-protection. It is important to improve AC motor's energy efficiency level for energy-saving, environment-protection and fund-saving. The manual computation of stray load loss in electric motor is very complex and has low precision so the stray load loss test system for three-phase asynchronous motor was designed. It discussed the application of linear regression analysis method in stray loss analysis in detail, and the computation was implemented by MATLAB, and a DLL was generated, last the VC program does data acquisition and calls the DLL to fulfill computation.