nils-herrmann/clean_scirepeval huggingface dataset:
Preparation of nano-structured LiMn 2 O 4 thin films by electrostatic spray deposition

A novel fabrication technique has been used to prepare nano-structured LiMn2O4 thin films using electrostatic spray deposition (ESD) from suspensions containing LiMn2O4 fine particles. It is found that a mixture of acetylacetone and ethanol in a ratio of 1:1 by volume is a suitable suspension medium to disperse LiMn2O4 fine particles effectively and form colloidally stable suspension. The effects of the processing parameters, such as applied voltages, suspension flow rates and deposition times, on the surface morphology and microstructure of the thin films are examined with scanning electron microscopy (SEM). The results show that the deposited LiMn2O4 thin films are uniform, dense and well adhered to the substrates. Analysis of the crystal structure of the thin films using X-ray diffraction (XRD) indicates the deposited material as being spinel LiMn2O4, which is in fair agreement with the original fine particles.
An adaptive filtering interpolator using neural networks

Filtering interpolators presented by Lucke and Stocker (1993) have advantages in reducing interpolation error in image background clutter-suppression systems especially for data with low sampling rates. Before they are to be applied, a fixed parameter /spl alpha/ should be predetermined. The authors think if the parameter /spl alpha/ is well adjusted, it may also be useful to recover an image from a less densely sampled image. Experiments show that interpolation error relies greatly on the parameter /spl alpha/ and the best values of /spl alpha/ for certain images are much different. Therefore, how to determine the values of /spl alpha/ becomes the key problem for this application. In this paper, the authors develop a neural network based adaptive system to automatically adjust the value of /spl alpha/. A modified robust BP algorithm is used in the training procedure for the authors' special use. Simulation results show that /spl alpha/ can be generated automatically by the neural networks instead of being blindly predetermined to a fixed value. Compared to the interpolator with best fixed parameter /spl alpha/, interpolation results are also improved.
Game Theoretic Packet Scheduling to Combat Non-Cooperativeness in Wireless Mesh Networks

In many practical scenarios, wireless mesh clients are autonomous and thus, may exhibit non-cooperative behaviors due to self-interests. For instance, a client may report bogus channel information to a mesh router in order to gain resource allocation advantages. Such non-cooperative behaviors are practicable as the client device's software could be modified by the user. In this paper, we analyze the impact of these rationally selfish and non-cooperative behaviors on the performance of packet scheduling algorithms in wireless mesh networks. Using a mixed strategy game theoretic model, we found that the traditional rate maximizing packet scheduling algorithms can lead non-cooperative clients to an undesirable Nash equilibrium, in which the wireless channel is used inefficiently. Motivated by this observation, we propose a novel repeated game theoretic approach to optimize packet scheduling and achieve efficient equilibria.
Weighted Sum Energy Efficiency Maximization in Ad Hoc Networks

In this letter, we propose a distributed adaptive-pricing algorithm aimed at solving the weighted sum energy efficiency (EE) maximization problem in ad hoc networks. It is theoretically proven that the proposed distributed algorithm strictly converges to the Karush-Kuhn-Tucker (KKT) point of the problem. Significant performance enhancement is observed by numerical results with fast convergence. Moreover, it is shown that the proposed algorithm degrades gracefully when decreasing overhead of information exchange.
A fractional programming method for interval-valued intuitionistic fuzzy multi-attribute decision making

In decision making problems, the increasing complexity of the world and the fuzziness of human thinking make it difficult to express a decision maker's preference information over alternatives and attributes with crisp data. As such, the interval-valued intuitionistic fuzzy sets (IVIFS) have become convenient and suitable to deal with the vagueness. This article investigates the multi-attribute decision making (MADM) problem under the IVIFS environment, where both the attribute weights and the assessments of alternatives with respect to attributes are given by the decision maker in terms of interval-valued intuitionistic fuzzy numbers. Several fractional programming models are derived from TOPSIS to determine the fuzzy relative closeness intervals of alternatives, and the corresponding decision-making method has also been developed. Feasibility and effectiveness of the proposed method are illustrated with an investment decision problem.
Spectral Efficiency of Distributed MIMO Systems

Distributed multi-input multi-output (D-MIMO) system is a promising system to greatly improve the spectral efficiency and power efficiency of the cellular system. The performance analysis of the spectral efficiency of D-MIMO system is a fundamental problem for both theoretical study and technique evaluation. In this paper, the theoretical performances of spectral efficiency for D-MIMO system and traditional collocated MIMO (C-MIMO) system are studied and compared. First, a composite D-MIMO channel including path loss, shadow fading and multipath fading is given. Conditioned on the desired user position, by using the tight bounds and central limit theory, the analytical approximations of the mean and the cumulative distribution function (CDF) of the mutual information (MI) are derived for C-MIMO and D-MIMO channels at both high signal to noise ratio (SNR) and low SNR. Assuming that the users are randomly distributed in the cell, the CDFs of the spectral efficiency are also given for the C-MIMO and D-MIMO cellular system, and the closed-form expressions for the mean spectral efficiency, mean outage spectral efficiency are derived. Finally, the theoretical comparisons between C-MIMO and D-MIMO for large number of antennas are given, and simulation results are presented which validate the analytical results.
A second-order delay-locked loop of a spread spectrum receiver

The tracking performance of 2nd-order delay-locked loops (DLL) with high gain for PN code chip synchronization is theoretically analyzed and evaluated by means of a Fokker-Planck equation. The results show that the steady state probability density function of normalized delay error of 2nd-order DLL with high gain is similar to that of a lst-order DLL; for the same mean-squared tracking jitter, the former has 10 log (1 + 1/4/spl xi//sup 2/) (db) less signal-to-noise ratio than the latter. Also, some experimental results are presented. Though only DLL is analyzed, the analytical method is valid for other kinds of tracking loops.<<ETX>>
Downlink transmission of broadband OFCDM Systems-part I: hybrid detection

The broadband orthogonal frequency and code division multiplexing (OFCDM) system with two-dimensional spreading (time and frequency domain spreading) is becoming a very attractive technique for high-rate data transmission in future wireless communication systems. In this paper, a quasianalytical study is presented on the downlink performance of the OFCDM system with hybrid multi-code interference (MCI) cancellation and minimum mean square error (MMSE) detection. The weights of MMSE are derived and updated stage by stage of MCI cancellation. The effects of channel estimation errors and sub-carrier correlation are also studied. It is shown that the hybrid detection scheme performs much better than pure MMSE when good channel estimation is guaranteed. The power ratio between the pilot channel and all data channels should be set to 0.25, which is a near optimum value for the two-dimensional spreading system with time domain spreading factor (N/sub T/) of 4 and 8. On the other hand, in a slow fading channel, a large value of the channel estimation window size /spl gamma/N/sub T/, where /spl gamma/ is an odd integer, is expected. However, /spl gamma/=3 is large enough for the system with N/sub T/=8 while /spl gamma/=5 is more desirable for the system with N/sub T/=4. Although performance of the hybrid detection degrades in the presence of the sub-carrier correlation, the hybrid detection still works well even the correlation coefficient is as high as 0.7. Finally, given N/sub T/, although performance improves when the frequency domain spreading factor (N/sub F/) increases, the frequency diversity gain is almost saturated for a large value of N/sub F/ (i.e., N/sub F/ /spl ges/ 32).
Radio Resource Allocation in Multiuser Distributed Antenna Systems

The distributed antenna system (DAS) is a promising system to provide high data rate transmissions in wireless communications. So far, most researches in the performance analysis for the DAS have been focused on single-user cases. This paper aims to investigate and present an optimal resource allocation scheme in downlink multiuser DASs. Different from the resource allocation in a conventional cellular system, the resource allocation in the multiuser DAS must consider multiple channel conditions of the transmission links between multiple remote antenna unites (RAUs) and any user, which increases the complexity of the resource allocation. In order to provide insights in the multiuser DAS, the effect of the number of RAUs used to communicate with each user is investigated extensively. In addition to power allocation, subcarrier allocation and bit allocation are studied in the orthogonal frequency division multiple access (OFDMA) DAS. To reduce the complexity of the resource allocation in the downlink multiuser DAS, the chunk allocation technique is adopted by grouping a set of contiguous subcarriers into one chunk and allocating resources chunk by chunk to users and an equivalent channel fading factor per subcarrier for a user is proposed to represent the combined channel gain from multiple RAUs to the user. The numerical results show that by using the proposed resource allocation method, each user only needs to be connected to a few RAUs, depending on the location of the user.
The Application of Visual Saliency Models in Objective Image Quality Assessment: A Statistical Evaluation.

Advances in image quality assessment have shown the potential added value of including visual attention aspects in its objective assessment. Numerous models of visual saliency are implemented and integrated in different image quality metrics (IQMs), but the gain in reliability of the resulting IQMs varies to a large extent. The causes and the trends of this variation would be highly beneficial for further improvement of IQMs, but are not fully understood. In this paper, an exhaustive statistical evaluation is conducted to justify the added value of computational saliency in objective image quality assessment, using 20 state-of-the-art saliency models and 12 best-known IQMs. Quantitative results show that the difference in predicting human fixations between saliency models is sufficient to yield a significant difference in performance gain when adding these saliency models to IQMs. However, surprisingly, the extent to which an IQM can profit from adding a saliency model does not appear to have direct relevance to how well this saliency model can predict human fixations. Our statistical analysis provides useful guidance for applying saliency models in IQMs, in terms of the effect of saliency model dependence, IQM dependence, and image distortion dependence. The testbed and software are made publicly available to the research community.
Quality Prediction of Asymmetrically Distorted Stereoscopic 3D Images

Objective quality assessment of distorted stereoscopic images is a challenging problem, especially when the distortions in the left and right views are asymmetric. Existing studies suggest that simply averaging the quality of the left and right views well predicts the quality of symmetrically distorted stereoscopic images, but generates substantial prediction bias when applied to asymmetrically distorted stereoscopic images. In this paper, we first build a database that contains both single-view and symmetrically and asymmetrically distorted stereoscopic images. We then carry out a subjective test, where we find that the quality prediction bias of the asymmetrically distorted images could lean toward opposite directions (overestimate or underestimate), depending on the distortion types and levels. Our subjective test also suggests that eye dominance effect does not have strong impact on the visual quality decisions of stereoscopic images. Furthermore, we develop an information content and divisive normalization-based pooling scheme that improves upon structural similarity in estimating the quality of single-view images. Finally, we propose a binocular rivalry-inspired multi-scale model to predict the quality of stereoscopic images from that of the single-view images. Our results show that the proposed model, without explicitly identifying image distortion types, successfully eliminates the prediction bias, leading to significantly improved quality prediction of the stereoscopic images.
Adaptive lattice filters for CDMA overlay

This paper presents the behavior of reflection coefficients of a stochastic gradient lattice (SGL) filter applied to a code division multiple-access overlay system. Analytic expressions for coefficients for a two-stage filter are derived in a Rayleigh fading channel with the presence of narrow-band interference and additive white Gaussian noise. It is shown that the coefficients of the lattice filter exhibit separate tracking and convergent properties, and that compared to an LMS filter, the lattice filter provides fast rate of convergence, while having good capability of narrow-hand interference suppression.
Begin with the End in Mind : A Unified End-to-End Quality-of-Experience Monitoring , Optimization and Management Framework

There has been an increasing consensus in the video distribution industry that the design and operation of the full video delivery chain needs to be driven by the quality-of-experience (QoE) appropriate to the end-user. In practice, however, this is an extremely difficult task, largely due to the lack of effective mechanisms to instantaneously measure or predict every viewer's QoE. Moreover, existing quality assurance methods operate independently at different points along the delivery chain, reporting partial, inopportune, and incoherent measurements. This siloed structure leads to a fragmented understanding of the resulting video as it moves through each stage of the delivery network from the acquisition point and head-end down to the media data center, the network, and eventually the end-user's viewing devices. Here we propose a new framework that uses a unified end-to-end solution to produce consistent QoE scores at all points along the delivery chain under the same evaluation criterion. This is a framework that produces a clear picture instantaneously to operation engineers, managing
Accuracy comparison of micro-angiographic detector and image intensifier for an interventional localization task

In order to assess the effect of the use of a high-resolution micro-angiographic detector on the accuracy of performing interventional localization tasks, we set up a simple task to localize a guide wire tip relative to one edge of a thin marker wire. Trained observers were asked to evaluate the micro-angiographic images as well as the comparison standard x-ray image intensifier (XRII) images, both of which were taken with the guidewire in the same positions. The observers were able to get more accurate estimation results using images obtained with the micro-angiographic detector than the XRII; the average distance error and performance variability both were reduced by factors of 2-3.
Constraint-based sensor network nodes particle swarm search localization algorithm

A constraint-based sensor network nodes particle swarm search localization algorithm (CPL) is presented. First of all, a constraint domain of an unknown node must be determined; Then the positions which meet specific criteria is searched out by particle swarm optimization algorithm and the searching results within the constraint domain are recorded; Finally, the unknown node's localization can be obtained by calculating the average recording results. As is shown in the experiment results, CPL has strong robustness, and comparing with normal schemes such as least square method (LS), CPL's positioning accuracy can improve 50% when the ranging error is 35%.
Perceptual experience of time-varying video quality

In real-world visual communications, it is a common experience that end-users receive video with significantly time-varying quality due to the variations in video content/complexity, codec configuration, and network conditions. How human visual quality-of-experience (QoE) changes with such time-varying video quality is not yet well-understood. To investigate this issue, we conduct subjective experiments designed to examine the quality predictability between individual video segment of relatively constant quality and combined video consisting of multiple segments that have significantly different quality. Our data analysis suggests that simple models that pool segment-level quality, such as linear averaging and weighted-averaging, nonlinear min- and median-filtering, and distortion-weighted averaging, are limited in predicting the overall human quality assessment of the combined video. We thus propose a quality adaptation model that is asymmetrically tuned to increasing and decreasing quality. The proposed asymmetric adaptation (AA) model leads to improved performance of both subjective and objective quality assessment approaches when using segment-level quality scores to predict multi-segment time-varying video quality. The video database together with the subjective data will be made available to the public.
Bespectacled face recognition algorithm based on random non-uniform local Gabor binary pattern

For problem of bespectacled face recognition, a non-uniform local Gabor binary pattern or method is proposed. The method utilizes local Gabor binary pattern to represent face image, so as to extract multi-scale and multi-directional feature information of the image and to be able to be insensitive to light and noise robustness in identifying. Also, non-uniform zoning policy is presented, and the policy can also discriminate contents information while ensures adequate access to image-space information, to weaken role of glasses, etc. to the disadvantage of discrimination of interference information. RNLGBP method makes full use of the characteristics of random subspace method and NLGBP method, complementing each other's shortcomings while maximizing their strengths, in order to maximize recognition performance, and experimental results show that the bespectacled face recognition rate of this method is improved significantly compared to traditional methods.
Estimating daytime surface air temperature using multi-source remote sensing and climate reanalysis data at glacierized basins: A case study at Langtang valley, Nepal

Estimate surface air temperature (Ta) accurately in fine scale is very necessary for hydrological simulation, especial in glacierized basins. The purpose of this paper is to present a framework to mapping the Ta using multi-source remote sensing data and reanalysis dataset. The main content includes two parts: (a) filling the gaps in remotely sensed land surface temperature (LST) using spatial-temporal Kriging method and (b) developing a semi-empirical method to relate Ta and LST that is applicable in glacierized basins. The framework is further tested in the Langtang valley, Nepal which is a glacierized basin in the central Hindu-Kush-Himalaya (HKH) region. The validation results show that the estimated Ta has generally good spatial and temporal variations. The RMSE of Ta at Langtang Kyangjin station is 9.1K and 7.7K at 10:30 and 13:30, respectly.
A database for perceptual evaluation of image aesthetics

Objective image aesthetics assessment (IAA) is attracting an increasing amount of attention in recent years. One of the most critical issues that hampers IAA research is the lack of publicly available and reliable image databases that can be used to train and test IAA features and models, especially those databases that offer continuous-valued subjective opinion scores. In this work, we construct a Waterloo IAA database containing more than 1,000 images, and carry out a lab-controlled subjective user study. There are several unique and desirable features of the new database as compared to existing ones — It helps us better understand the level of diversity of subject opinions; it provides continuous-valued IAA scores approximately evenly distributed from poor to excellent aesthetics levels; it also allows us to test the effectiveness of various aesthetics features on predicting continuous aesthetics scores. Using the new database as a benchmark, we test more than 1,000 IAA features. The results indicate that existing features are still weak at aesthetics estimation, and the effectiveness of aesthetics features are content dependent. Therefore, understanding and assessing image aesthetics remain a major challenge for future research. The database will be made publicly available.
Cognitive Radio for Home Networking

Cognitive Radios have emerged as one the most promising methods to increase wireless system efficiency through dynamic spectrum access combined with other cross-layer optimization methods. Most of the research prototypes and demonstrations have so far focused on either general platforms or scenarios that are predominantly taken from military or emergency communications domain. In this demonstration we show the prototype environment that is build around realistic home networking scenarios. The demonstration has two purposes. First, it demonstrates how a set of different implemented and integrated components can achieve local area optimization both in frequency allocation and other domains. Second, it shows the viability and attractiveness of cognitive radio methods for future commercial home networking devices. The demonstration showcases dynamic spectrum allocation and policy based behavioral changes in a home environment, where several multimedia stream and data communication connections are competing against each other.
Supply Chain Safety Stock Quantity's Fractal Forecast and Study

Safety stock, a very important composing of the stock management, which is significant for reducing the cost and increasing interests for an enterprise, is the base of the setting of enterprise's stocks, so it is crucial for stock management to presume a rational and exact SS. In this paper, introducing fractal theory, a new method based on the fractal collage theorem and the iterative process of fractal interpolation functions was proposed to forecast the safety stock. Based on the fractal collage theorem, it uses the iterated function system whose attractor is close to the historical data to establish the fractional function, and set the forecasting model proposed according to the iterative process of fractional function. Then by an example the rationality and reliability of this method is verified, and the result show that it is helpful indeed to set the reasonable safety stock for enterprise.