nils-herrmann/clean_scirepeval huggingface dataset:
Objective assessment of tone mapping algorithms

There has been a growing interest in recent years to develop tone mapping algorithms that can convert high dynamic range (HDR) to low dynamic range (LDR) images, so that they can be visualized on standard displays. With a number of tone mapping algorithms proposed, a natural question is which one gives the best performance. Although subjective assessment methods provide useful references, they are expensive and time-consuming, and are difficult to be embedded into the design stage of tone mapping algorithms for optimization and parameter tuning purposes. This paper focuses on objective assessment of tone mapping operators. Inspired by the success of the structural similarity index method for image quality assessment, we propose a new objective assessment algorithm that creates multi-scale similarity maps between HDR and LDR images. Our experiments show that the proposed method correlates well with subjective rankings of existing tone mapping operators. Furthermore, we demonstrate how the proposed algorithm can be employed in an existing tone mapping algorithm for optimal parameter tuning.
Study on electron mobility in nanoscale DG MOSFETs with symmetric, asymmetric and independent operation modes

Electron mobility in nanoscale double-gate (DG) MOSFETs with symmetric, asymmetric and independent operation modes is studied in this paper by using a comprehensive numerical model. A numerical program is first developed and then the dependence of electron mobility on device geometry and bias conditions are simulated. It is shown that that electron mobility changes monotonically with effective field in symmetric conditions while a peek in the curve of phonon-limited mobility versus the gate biases shows a new feature due to the impact of effective field on intra-valley and inter-valley scattering for independent gate operation mode.
dipIQ: Blind Image Quality Assessment by Learning-to-Rank Discriminable Image Pairs.

Objective assessment of image quality is fundamentally important in many image processing tasks. In this paper, we focus on learning blind image quality assessment (BIQA) models, which predict the quality of a digital image with no access to its original pristine-quality counterpart as reference. One of the biggest challenges in learning BIQA models is the conflict between the gigantic image space (which is in the dimension of the number of image pixels) and the extremely limited reliable ground truth data for training. Such data are typically collected via subjective testing, which is cumbersome, slow, and expensive. Here, we first show that a vast amount of reliable training data in the form of quality-discriminable image pairs (DIPs) can be obtained automatically at low cost by exploiting large-scale databases with diverse image content. We then learn an opinion-unaware BIQA (OU-BIQA, meaning that no subjective opinions are used for training) model using RankNet, a pairwise learning-to-rank (L2R) algorithm, from millions of DIPs, each associated with a perceptual uncertainty level, leading to a DIP inferred quality (dipIQ) index. Extensive experiments on four benchmark IQA databases demonstrate that dipIQ outperforms the state-of-the-art OU-BIQA models. The robustness of dipIQ is also significantly improved as confirmed by the group MAximum Differentiation competition method. Furthermore, we extend the proposed framework by learning models with ListNet (a listwise L2R algorithm) on quality-discriminable image lists (DIL). The resulting DIL inferred quality index achieves an additional performance gain.
Quality-of-experience of streaming video: Interactions between presentation quality and playback stalling

Network streaming video services have been growing explosively in the past decade, but how to measure and assure the video quality-of-experience (QoE) of end consumers is still an open problem. Poor presentation quality and playback stalling have been identified as the most dominant factors that degrade user QoE. Although both factors have been studied individually, little is known about the interactions between them. In this work, we first construct a streaming video database that contains compressed videos at different distortion levels and with different stalling patterns. We then carry out a subjective test to evaluate the QoE of the videos. The results reveal some interesting dependency between presentation quality and playback stalling. Specifically, playback stalling always causes QoE degradation, but the strength of such degradation depends on the presentation quality when the stalling event occurs.
Evaluation of the Himawari-8 Shortwave Downward Radiation (SWDR) Product and its Comparison With the CERES-SYN, MERRA-2, and ERA-Interim Datasets

The land surface shortwave downward radiation (SWDR), also known as surface incident solar radiation, is usually defined as the total solar spectral energy between 300 nm and 3000 nm that arrives at the earth's surface. The SWDR is an essential variable for numerous land models and thus need to be accurately mapped using various approaches. Although numerous efforts have been made to assess the applicability and accuracy of the SWDR datasets produced by satellite and numeric models, few studies have evaluated the performance of the SWDR products from Himawari-8. In this paper, the Himawari-8 SWDR products with both 1 km and 5 km spatial scales were evaluated using ground measurements. The validation results indicate that both of the Himawari-8 SWDR products are in close agreement with ground measurements, even though obvious biases are found. Also, an in-depth intercomparison between the Himawari-8 SWDR product at a 5 km resolution and three other SWDR radiation datasets (CERES-SYN, MERRA-2, and ERA-Interim) was conducted. Here, the Himawari-8 showed the highest levels of accuracy among the four SWDR datasets. Considering its higher temporal and spatial resolutions, the Himawari-8 SWDR products are more detailed and provide the unprecedented potential to improve land surface modeling and reveal the cloud-radiation interactions, even at a 10-min temporal scale.
SSIM-Motivated Rate-Distortion Optimization for Video Coding

We propose a rate-distortion optimization (RDO) scheme based on the structural similarity (SSIM) index, which was found to be a better indicator of perceived image quality than mean-squared error, but has not been fully exploited in the context of image and video coding. At the frame level, an adaptive Lagrange multiplier selection method is proposed based on a novel reduced-reference statistical SSIM estimation algorithm and a rate model that combines the side information with the entropy of the transformed residuals. At the macroblock level, the Lagrange multiplier is further adjusted based on an information theoretical approach that takes into account both the motion information content and perceptual uncertainty of visual speed perception. Finally, the mode for H.264/AVC coding is selected by the SSIM index and the adjusted Lagrange multiplier. Extensive experiments show that the proposed scheme can achieve significantly better rate-SSIM performance and provide better visual quality than conventional RDO coding schemes.
Image information restoration based on long-range correlation

A new class of image information restoration algorithms, virtually different from traditional techniques, is proposed. In comparison with other approaches, our methods use not only the information in local areas, but also that in the remote regions of the image. The methods originate from the idea that there exists abundant long-range correlation within natural images and the human vision system, composed of our eyes and brains, can sufficiently utilize such types of information redundancy to implement the functions of image interpretation, representation, restoration, enhancement and error concealment. Our general approach can be summarized as five basic steps: fetching, searching, matching, competing and recovering. The experimental results on several practical applications show that our methods perform substantially better than many other state-of-the-art methods.
Asymmetrically Compressed Stereoscopic 3D Videos: Quality Assessment and Rate-Distortion Performance Evaluation

Objective quality assessment of stereoscopic 3D video is challenging but highly desirable, especially in the application of stereoscopic video compression and transmission, where useful quality models are missing, that can guide the critical decision making steps in the selection of mixed-resolution coding, asymmetric quantization, and pre- and post-processing schemes. Here we first carry out subjective quality assessment experiments on two databases that contain various asymmetrically compressed stereoscopic 3D videos obtained from mixed-resolution coding, asymmetric transform-domain quantization coding, their combinations, and the multiple choices of postprocessing techniques. We compare these asymmetric stereoscopic video coding schemes with symmetric coding methods and verify their potential coding gains. We observe a strong systematic bias when using direct averaging of 2D video quality of both views to predict 3D video quality. We then apply a binocular rivalry inspired model to account for the prediction bias, leading to a significantly improved full reference quality prediction model of stereoscopic videos. The model allows us to quantitatively predict the coding gain of different variations of asymmetric video compression, and provides new insight on the development of high efficiency 3D video coding schemes.
Performance comparison of optimum and MMSE receivers with imperfect channel estimation for VSF-OFCDM systems

Performances of optimum and minimum mean square error (MMSE) receivers for variable spreading factor orthogonal frequency and code division multiplexing (VSF-OFCDM) systems are compared in this paper. In VSF-OFCDM systems, the existence of multicode interference (MCI) in the frequency domain due to frequency-selective fading channels dramatically degrades the system performance. A quasi-analytic bit error rate performance is presented in the presence of imperfect channel estimations. Numerical results show that with linear computation complexity the MMSE receiver can improve system performance significantly by suppressing the MCI, although it cannot perform as well as the optimum receiver. Thus, with a small number of code channels, an optimum receiver can be employed to achieve better performance, whereas the MMSE receiver is suitable for a system with a large number of code channels due to simple complexity. In addition, the MMSE receiver is more robust than the optimum receiver to the different configurations of system parameters, e.g., spreading factors. Moreover, it is found that pilot channel power should be carefully assigned by making tradeoffs between the channel estimation quality and the received SNR for each code data channel.
Image Sharpness Assessment Based on Local Phase Coherence

Sharpness is an important determinant in visual assessment of image quality. The human visual system is able to effortlessly detect blur and evaluate sharpness of visual images, but the underlying mechanism is not fully understood. Existing blur/sharpness evaluation algorithms are mostly based on edge width, local gradient, or energy reduction of global/local high frequency content. Here we understand the subject from a different perspective, where sharpness is identified as strong local phase coherence (LPC) near distinctive image features evaluated in the complex wavelet transform domain. Previous LPC computation is restricted to be applied to complex coefficients spread in three consecutive dyadic scales in the scale-space. Here we propose a flexible framework that allows for LPC computation in arbitrary fractional scales. We then develop a new sharpness assessment algorithm without referencing the original image. We use four subject-rated publicly available image databases to test the proposed algorithm, which demonstrates competitive performance when compared with state-of-the-art algorithms.
Reduced-reference SSIM estimation

The structural similarity (SSIM) index has been shown to be a good perceptual image quality predictor. In many real-world applications such as network visual communications, however, SSIM is not applicable because its computation requires full access to the original image. Here we propose a reduced-reference approach that estimates SSIM with only partial information about the original image. Specifically, we extract statistical features from a multi-scale, multi-orientation divisive normalization transform and develop a distortion measure by following the philosophy analogous to that in the construction of SSIM. We found an interesting linear relationship between our reduced-reference SSIM estimate and full-reference SSIM when the image distortion type is fixed. A regression-by-discretization method is then applied to normalize our measure between image distortion types. We use the LIVE database to test the proposed distortion measure, which shows strong correlations with both SSIM and subjective evaluations. We also demonstrate how our reduced-reference features may be employed to partially repair a distorted image.
Chaotic Time Series Forecasting Base on Fuzzy Adaptive PSO for Feedforward Neural Network Training

Short-term electricity demand forecasting for the next hour to several days out is one of the most important tools by which an electric utility plans and dispatches the loading of generating units in order to meet system demand. But there exists chaos in electricity systems to a great extent. Complicated electricity systems are nonlinear systems and the forecasting is very complex in nature and quite hard to solve by conventional algorithm. The inaccuracy or large error in the forecast simply means that load matching is not optimized and consequently the generation and transmission systems are not being operated in an efficient manner. In this paper, feedforward neural network trained by fuzzy adaptive PSO algorithm is proposed for chaotic load time series global prediction. The results which are compared with feedforward neural network trained by Levenberg-Marquardt back-propagation (LMBP) algorithm show much more satisfactory performance, converges quickly towards the optimal position, convergent accuracy and can avoid overfitting in some extent.
Achievable Rate Evaluation of In-Building Distributed Antenna Systems

Over the last few years high data rate wireless transmission has gained considerable attention in hot spot areas, including high buildings. It has been demonstrated that distributed antenna systems (DASs) improve the performance of the indoor environment by covering dead spots. In this paper, the achievable rate is investigated for indoor high data rate wireless transmission in high buildings when DASs are employed. In the system, radio transmission/reception within one or several neighbouring floors is controlled by one central unit (CU) exploiting the entire system bandwidth. In order to efficiently use the spectrum, the same frequency channels are reused among floors controlled by different CUs. The radio signals in high buildings can propagate vertically and reach the neighbouring floors. Therefore, the performance of the system is limited by cochannel interference. Direct propagation inside the building and reflection from nearby buildings are considered in the channel model. The achievable rate of the system is evaluated through analysis and simulation. The impact of representative system parameters on the achievable rate, including the number of remote antenna units (RAUs), the frequency reuse factor, the floor penetration loss, the path loss exponent, and the Nakagami fading parameter, is discussed.
Automatic Alignment of High-Resolution NMR Spectra Using a Bayesian Estimation Approach

Nuclear magnetic resonance (NMR) spectral analysis has recently become one of the major means for the detection and recognition of metabolic changes of disease state, physiological alteration, and natural biological variation. For the pattern recognition tasks in which two or more NMR spectra need to be compared, it is critical to properly align the spectra for the subsequent pattern recognition analysis. Previous spectral alignment methods do not consider any baseline intensity variation between the spectra and disregard the effect of noise. Here we formulate the spectra alignment problem in a Bayesian statistical framework, which allows us to simultaneously and efficiently estimate the spectral shift and the baseline intensity variation in the existence of independent additive noise. Experimental results with real high-resolution NMR spectral data from human plasma demonstrate the effectiveness and robustness of the proposed approach
Downlink Transmission of Distributed Antenna Systems in High Building Environments

Over the last few years high data rate wireless transmission has gained considerable attention in hot spot areas, including high buildings. It has been demonstrated that distributed antenna systems (DASs) improve the performance of the indoor environment by covering dead spots. In this paper, the DAS is investigated in high building to provide high data rates for indoor wireless mobile communications by exploiting spatial diversity and reducing radio transmission distance. In the proposed indoor DAS, several neighbouring floors compose a floor-bank and are controlled by one central unit (CU). The spectrum is not reused in a floor-bank, but can be reused among different floor-banks, which causes co-channel interference. In order to analyse the performance of the indoor DAS in high building with the presence of co-channel interference, an accurate propagation model of the interference from other floor is a key requirement. Direct propagation inside the building and reflection from nearby buildings have been considered in the channel model. Based on the theoretical analysis, the impact of several system parameters on the performance is presented in terms of BER. Numerical results indicate that the position of the user in the floor has significant effect on the system performance and the attenuation introduced by the floor separation in high buildings should be taken into consideration during the planning of the number of floor in each floor-bank.
The effect of dynamic range on an open-loop power controlled CDMA system

Direct sequence code division multiple access (DS-CDMA) techniques have received a great deal of attention for current and future communication systems. One of the major considerations in implementing a DS-CDMA system is the necessity for accurate power control to ensure adequate quality-of-service and capacity. This paper investigates the measurement error of an open-loop power control algorithm under transmit power limitation. The power control error is analysed and numerically computed.
The Number of Chinese International Airlines Forecasting Based on Grey Model Using Fuzzy Adaptive PSO Algorithm

Nowadays, owing to the accelerated growth in the development of information technology, the aviation industry plays a prominent role in Chinese economy. Therefore, the accurate prediction for the number of international airlines helps in making major decisions in technological reform. In order to analyze the number of international airlines in China efficiently, in this paper, fuzzy adaptive PSO algorithm is proposed to choose the parameters of a GM(1,1) Grey forecasting model automatically in the prediction of the number of international airlines. It possess the merits of both simplicity of application and high forecasting precision. This paper was based on historical data of the number of Chinese international airlines from 1985 to 2003, and forecasted and analyzed the international airlines in China by the novel GM(1,1) Grey forecasting model. The results provide scientific basis for the making major decisions in technological reform in China.
PN code tracking for exponentially weighted chip despreading sequence

This paper presents the code tracking performance on the use of weighted chip despreading PN sequence in a coherent delay locked tracking scheme for direct-sequence spread-spectrum (DS/SS) systems. In the delay locked loop (DLL), the conventional rectangular chip waveform for early and late despreading PN sequences is replaced by a weighted exponential chip waveform. The chip weight (/spl gamma/) is tuned to optimise the DLL performance. The early-late spacing is considered as one chip time. This scheme is analysed for both ideal and band limited received signals in the presence of additive white Gaussian noise.
Chunk-Based Resource Allocation in OFDMA Systems—Part II: Joint Chunk, Power and Bit Allocation

By grouping a number of adjacent subcarriers into a chunk, resource allocation can be carried out chunk by chunk in orthogonal frequency division multiple access (OFDMA) systems. Chunk-based resource allocation is an effective approach to reduce the complexity of resource allocation in OFDMA systems. In this paper, a chunk-based resource allocation scheme, i.e. joint chunk, power and bit allocation, is proposed and analyzed by maximizing the throughput under a total transmit power constraint. A scaling factor is introduced to achieve optimal allocation. Considering the digital nature of bits per symbol per subcarrier (bits/symbol/subcarrier), a digitization process is proposed to digitize the theoretically allocated bits/symbol/subcarrier to integer. System parameters, such as the power constraint, number of users, coherence bandwidth, number of subcarriers and number of chunks, are introduced and their impacts on the average throughput are studied. The performance of the dynamic power allocation scheme is compared with the fixed power allocation scheme. The numerical results show that the theoretical throughput of the fixed power allocation scheme is quite close to that of the dynamic power allocation scheme. However, when the digital nature of bits/symbol/subcarrier is considered, the average throughput of the dynamic power allocation outperforms the fixed power allocation scheme.
Crystal Growth and Atom Diffusion in (Cu)ZnTe/CdTe via Molecular Dynamics

Molecular dynamics (MD) simulations and experimental evaporation were applied to study the growth of evaporated (Cu)ZnTe on mono- and polycrystalline CdTe. The simulated structures show polytypism and polycrystallinity, including texturing and grain boundaries, diffusion, and other phenomena in excellent qualitative agreement with experimental atomic probe tomography, transmission electron microscope, and secondary ion mass spectrometry. Results show formation of Cu clusters in nonstoichiometric growths even at early stages of deposition. Results also show significantly faster diffusion along defected regions (uncorrelated CdTe grain boundaries) as compared with more highly crystalline areas (high-symmetry grain boundaries and pristine regions). Activation energies and pre-exponential factors of Cu, Zn, and Te diffusion were determined using simulation. The MD model captures crystal growth phenomena with a high degree of fidelity.