nils-herrmann/clean_scirepeval huggingface dataset:
Objective quality assessment for image super-resolution: A natural scene statistics approach

There has been an increasing number of image super-resolution (SR) algorithms proposed recently to create images with higher spatial resolution from low-resolution (LR) images. Nevertheless, how to evaluate the performance of such SR and interpolation algorithms remains an open problem. Subjective assessment methods are useful and reliable, but are expensive, time-consuming, and difficult to be embedded into the design and optimization procedures of SR and interpolation algorithms. Here we make one of the first attempts to develop an objective quality assessment method of a given resolution-enhanced image using the available LR image as a reference. Our algorithm follows the philosophy behind the natural scene statistics (NSS) approach. Specifically, we build statistical models of frequency energy falloff and spatial continuity based on high quality natural images and use the departures from such models to quantify image quality degradations. Subjective experiments have been carried out that verify the effectiveness of the proposed approach.
A highly efficient method for blind image quality assessment

Blind image quality assessment (BIQA) has attracted a great deal of attention due to the increasing demand in industry and the promising recent progress in academia. To bridge the gap between academic research accomplishment and industrial needs, high efficiency BIQA approaches that allow for real-time computation are highly desirable. In this paper, we propose a novel BIQA method by selecting statistical features extracted from binary patterns of local image structures. This allows us to largely reduce the feature space to eventually one dimension. Somewhat surprisingly, such a single feature, faster-than-real-time approach named local pattern statistics index (LPSI) exhibits impressive generalization ability across different distortion types and achieves competitive quality prediction performance in comparison with state-of-the-art approaches on public databases such as LIVE II and TID2008.
Innovation paths and evolving mechanism of business model for manufacturing Enterprises based on value chain

The article has analyzed the business model of traditional manufacturing Enterprises based on the value chain, pointed out that manufacturers were homology in the resources and elements markets, their own operations, products and services market with the market competition intensifies. Then, the paper proposed that innovations of business model is the source of their competitive advantage, at the same time, analyzed four paths of innovations of business model for manufacturing enterprises: the resources and elements use patterns, enterprise value activity patterns, customer value service patterns and business revenue patterns. At last, the paper constructed evolving mechanism of business model innovation.
Microcellular CDMA mobile communications, in frequency overlay situations

The code-division multiaccess (CDMA) network is assumed to share a common spectrum with various narrowband microwave users. An interference suppression filter at each CDMA receiver is used to reject the narrowband interference. The problem of interference from adjacent cells is also considered. Average power control is assumed to combat the, near/far problem, and multipath diversity, in conjunction with simple interleaved channel coding, is considered for improving the performance of the CDMA system. Numerical results illustrate, at a given bit error rate (BER), the number of users supported by the CDMA system as the propagation loss exponent increases. When the ratio of the narrowband interference power to the desired signal power (J/S) is small with respect to the multiple access and adjacent-cell interference, the suppression filter is unnecessary, whereas when J/S is on the order of the interference, use of the suppression filter yields a significant improvement in performance.<<ETX>>
SSIM-inspired image denoising using sparse representations

Perceptual image quality assessment (IQA) and sparse signal representation have recently emerged as high-impact research topics in the field of image processing. Here we make one of the first attempts to incorporate the structural similarity (SSIM) index, a promising IQA measure, into the framework of optimal sparse signal representation and approximation. In particular, we introduce a novel image denoising scheme where a modified orthogonal matching pursuit algorithm is proposed for finding the best sparse coefficient vector in maximum-SSIM sense for a given set of linearly independent atoms. Furthermore, a gradient descent algorithm is developed to achieve SSIM-optimal compromise in combining the input and sparse dictionary reconstructed images. Our experimental results show that the proposed method achieves better SSIM performance and provide better visual quality than least square optimal denoising methods.
A generalized method for automatic segmentation of neighboring cells

Robust and automatic segmentation of the neighboring cells remains a challenging problem due to the diversity of the cell types, frequently occurring artifacts, weak borders between adjacent cells, the arbitrary shape and large number of cells. Currently, the widely used segmentation and quantification tools are still manual or semi-automatic, which is time-consuming and labor intensive. With a breakthrough in the accuracy of threshold selection method proposed by the author, it is feasible now for a completely automatic and generalized method to fulfill the segmentation task. Even if the automatic method might not be perfect for segmenting all the neighboring cells in all the acquired images, the manual work after automatic segmentation will be minor. The proposed generalized method for cell segmentation comprises three parts: 1) Preparatory segmentation; 2) Cell identification; 3) Border delineation. We tested the proposed method on two types of cell images: 1) stained mice skeletal muscle micrograph; 2) human anaplastic astrocytoma micrograph. Experimental results show that the proposed method is effective in segmenting the neighboring cells and is thus promising for the future streamlined cell segmentation tool.
A Hybrid Evolutionary Algorithm Based on ACO and PSO for Real Estate Early Warning System

Recently some cities' investments on fix assets increase too fast that lead to a property bubble. In order to prevent the overheating of real estate investment, this paper presents a pre-warning system developed to monitor and provide pre-warning to the governmental decision makers in real estate market. In the overall structure plan, the warning classification system is the most important so that we make an innovation to it using the novel ACO-PSO-hybrid algorithm. The hybrid algorithm makes use of advantages of both ACO and PSO methods therefore it is of benefit in solving clustering problems. And the experiment results demonstrate that our algorithm is significantly better than K-means methods in terms of quality. It is adaptive, robust and efficient, achieving high autonomy, simplicity and efficiency. Therefore it can effectively provide early warning corresponding to reality so that the pre-warning system can provide useful information to regulate the property market.
BER-Based Chunk Allocation in Multiuser OFDM Wireless Systems

This paper analyzes throughput performance of the chunk-based subcarrier allocation by considering the average bit-error-rate (BER) constraint over a chunk in downlink multiuser orthogonal frequency division multiplexing (OFDM) transmission. The outage probabilities per subcarrier are compared between the average BER-constraint based chunk allocation and the average signal-to-noise-ratio (SNR) based chunk allocation. The effects of system parameters, including the coherence bandwidth, the number of subcarriers per chunk, and the number of users, are evaluated. The numerical results show that, when the chunk bandwidth is smaller than the coherence bandwidth, the average downlink throughput of the chunk-based subcarrier allocation is very close to that of the single-subcarrier-based allocation. The effective throughput of the average BER-constraint based chunk allocation is higher than that of the average SNR based chunk allocation, especially when the number of users is large.
Interference suppression for CDMA overlays of narrowband waveforms

An overview of the performance of CDMA networks which overlay narrowband signals in order to increase the spectral efficiency is presented. Such systems employ interference suppression filters in the CDMA receivers to minimize the interference caused by the narrowband waveforms. The results presented in this paper include the effects of multipath fading, as well as both intracell and intercell interference for cellular systems.<<ETX>>
A new layered space-time-frequency architecture with LDPC coding for OFDM MIMO multiplexing

This paper is focused on the study of layered space-time-frequency (LSTF) architectures with channel coding for orthogonal frequency division multiplexing (OFDM) multiple-input multiple-output (MIMO) multiplexing system in high speed wireless transmission under frequency-selective fading channel. In order to achieve the available spatial, temporal and frequency diversity, and at the same time make the system implementation feasible for ultra high data rate OFDM MIMO multiplexing, we propose a new LSTF architecture with each independently coded layer being threaded in the three-dimensional space-time-frequency transmission array. List sphere decoding is used for MIMO multiplexing detection, and the irregular low-density parity-check code (LDPC) is chosen as the channel code. Three construction rules are adopted to generate high coding rate irregular LDPC codes with low encoding complexity and little performance loss. Simulation results show that the proposed LSTF architecture can significantly outperform the LSTF assuming each independently coded layer being transmitted through a permanently assigned antenna, and get almost the same performance as the LSTF applying coding across the whole information stream. On the other hand, due to its structure of multiple parallel lower-speed codecs with shorter codeword length, the proposed LSTF architecture is more realizable for ultra high data rate transmission than the LSTF applying coding across the whole information stream
Adaptive frame prediction for foveation scalable video coding

Embedded rate scalable video coding allows for the extraction of coded visual information at continuously varying bit rates from a single compressed bitstream. This is a very attractive feature for many multimedia communication applications. Motion estimation (ME) /motion compensation (MC) techniques are widely employed in various video coding systems to reduce temporal information redundancy. One of the major challenging problems in ME/MC based rate scalable video coding is how to generate the prediction frame from the previous frame to match the current frame. This problem is more difficult in rate scalable coding than in fixed rate coding because the decoding data rate is unavailable to the encoder. We propose an adaptive frame prediction scheme for foveation scalable video coding (FSVC), which is a new video coding algorithm that combines a foveation-based human visual system (HVS) model with a wavelet-based rate scalable coding algorithm. The new frame prediction algorithm provides an adaptive mechanism to control the prediction errors while reduce error propagation.
Perceptual Quality Assessment for Multi-Exposure Image Fusion

Multi-exposure image fusion (MEF) is considered an effective quality enhancement technique widely adopted in consumer electronics, but little work has been dedicated to the perceptual quality assessment of multi-exposure fused images. In this paper, we first build an MEF database and carry out a subjective user study to evaluate the quality of images generated by different MEF algorithms. There are several useful findings. First, considerable agreement has been observed among human subjects on the quality of MEF images. Second, no single state-of-the-art MEF algorithm produces the best quality for all test images. Third, the existing objective quality models for general image fusion are very limited in predicting perceived quality of MEF images. Motivated by the lack of appropriate objective models, we propose a novel objective image quality assessment (IQA) algorithm for MEF images based on the principle of the structural similarity approach and a novel measure of patch structural consistency. Our experimental results on the subjective database show that the proposed model well correlates with subjective judgments and significantly outperforms the existing IQA models for general image fusion. Finally, we demonstrate the potential application of the proposed model by automatically tuning the parameters of MEF
MODIS Image Compression with Optimal Waveband Prediction Tree

Proposed a practical compression method using optimal waveband prediction tree, integer wavelet and SPIHT algorithm for MODIS multispectral images effectively. First, an optimal waveband prediction tree is designed for de-correlating the spectral redundancy. Then, spatial redundancy in the image is reduced by using IWT and SPIHT algorithm. Finally the arithmetic algorithm is used to improve the compression performance. We demonstrate the compression performance with different compression schemes in details. The experimental results show that the method is efficient and practical for MODIS images.
Social network enhanced collective recommendation

Aimed to address the data sparsity and cold start problem, this paper tries to improve the recommendation accuracy via exploiting the hidden social information from vast amounts of data, and proposes a general social network enhanced collective recommendation model using matrix factorization (SNERM), which measures the social relationship through user's historical behavior and the interplay between users, and conducts recommendation in each inferred social community respectively. The experimental results on large real world datasets demonstrate that our proposed method outperforms other state-of-the-art approaches, especially in recommendation accuracy and solving the cold start problem.
Characterization of Si3N4/SiC Microstructure Derived from UV curable polymer ceramic precursor pyrolyzing

The microstructure of Si<inf>3</inf>N<inf>4</inf>/SiC derived from the pyrolysis of UV curable polymer ceramic precursor was analyzed and characterized by TGA-DTA, XRD, FE-SEM, Electron Spectrometer, and TEM. It was found that two distinct weight loss rate peaks at 358°C and 450°C in TGA curve were identical with two DTA endothermic peaks. The total weight loss was 45wt%. The degree of crystallinity reached 91.25% after annealing at 1600°C for 2h with the composition of SiO<inf>2</inf>: Si<inf>3</inf>N<inf>4</inf>: SiC = 3:26:71 in pyrolysate. Not only did the degree of crystallinity increase, but also the change from α-Si<inf>3</inf>N<inf>4</inf> to β -Si<inf>3</inf>N<inf>4</inf> took place with the increase of holding time. Irregular whisker of Si<inf>3</inf>N<inf>4</inf> was explored by SEM after pyrolyzing at 1400 °C for 24 h.
Quality-aware images

We propose the concept of quality-aware image , in which certain extracted features of the original (high-quality) image are embedded into the image data as invisible hidden messages. When a distorted version of such an image is received, users can decode the hidden messages and use them to provide an objective measure of the quality of the distorted image. To demonstrate the idea, we build a practical quality-aware image encoding, decoding and quality analysis system, which employs: 1) a novel reduced-reference image quality assessment algorithm based on a statistical model of natural images and 2) a previously developed quantization watermarking-based data hiding technique in the wavelet transform domain.
The Design and Implement of Intelligent Trip Device for LV Circuit Breaker

Compared with normal trip devices, much more functions were realized in intelligent trip device we designed, such as electric parameters measure, circuit protect, breaker status online-detect, communicate with mainframe of switch automatic system and so on. In this paper, electric signal collection, amplification, isolation were investigated, harmonic analysis was carried out, spectrum leakage was solved in fast Fourier transform by using window function and interpolation in frequency domain, corresponding calculation of non-sine wave current, voltage, power factor were presented, and trip algorithm was designed elaborately. As a result, a set of intelligent trip device can realize different trip characteristics flexibly and be widely applied in many situations by setting different parameters. Disturbance from strong electromagnetism was considered during the process of hardware design. So it has higher reliability and wider applications compared with normal ones. The results show that all requirements in test have been implemented wholly.
Electrical spin injection into InAs nanowires by local measurement

Electrical spin injection into semiconductors is fundamental to realize spintronic devices using the electron spin degree of freedom instead of or in addition to the charge. Datta-Das spin eld-eect transistor is a classical scheme which comprises ferromagnetic spin injector and detector while injected spins in the channel are manipulated by electric field. InAs nanowire is a suitable candidate as semiconductor channel in spin-FET because of its high electron mobility and especially strong spin-orbital interaction which makes it easier to control the precession of injected spins. In this work, we report the local measurement of spin transport characteristics in a InAs lateral spin valve, with Co/MgAl2O4 as the tunnel barrier contact.
Deep Blur Mapping: Exploiting High-Level Semantics by Deep Neural Networks

The human visual system excels at detecting the local blur of visual images, but the underlying mechanism is not well understood. Traditional views of blur such as the reduction in energy at high frequencies and loss of phase coherence at localized features have fundamental limitations. For example, they cannot well discriminate flat regions from blurred ones. Here, we propose that the high-level semantic information is critical in successfully identifying the local blur. Therefore, we resort to deep neural networks that are proficient at learning high-level features and propose the first end-to-end local blur mapping algorithm based on a fully convolutional network. By analyzing various architectures with different depths and design philosophies, we empirically show that the high-level features of deeper layers play a more important role than the low-level features of shallower layers in resolving challenging ambiguities for this task. We test the proposed method on a standard blur detection benchmark and demonstrate that it significantly advances the state-of-the-art (ODS F-score of 0.853). Furthermore, we explore the use of the generated blur maps in three applications, including the blur region segmentation, blur degree estimation, and blur magnification.
An approach to aggregating interval weights for hierarchical multiple criteria decision making

Priority weights derived from uncertain preference relations are often characterized by interval weights. A key stage in multiple criteria decision making (MCDM) with a hierarchical structure is to aggregate local interval weights into global interval weights. In this paper, a pair of linear programming models is devised to maximize the lower and upper bounds of the aggregated interval value where criteria weights are independently treated as decision variables for each alternative. By integrating a number of optimization models, a linear program is developed to obtain a unified criteria weight vector. The criteria weights are subsequently incorporated to aggregate local interval weighs into global interval weights for final alternative ranking. A numerical example and analysis is presented to show the feasibility and effectiveness of the proposed models.
A Goal-Programming-Based Heuristic Approach to Deriving Fuzzy Weights in Analytic Form from Triangular Fuzzy Preference Relations

Triangular fuzzy preference relations (TFPRs) have long been viewed as an effective framework to express subjective preferences with ambiguity in fuzzy multicriteria decision-making systems. This paper focuses on solving two important and challenging issues: First, how to judge the quality of nondiagonal preferences in a TFPR; and second, how to find an analytic solution of optimal fuzzy weights for consistent TFPRs and an analytic solution of heuristic fuzzy weights for inconsistent TFPRs. This paper analyzes two existing normalization models of triangular fuzzy weights and illustrates their deficiency. Notions of normalized triangular fuzzy multiplicative weights (TFMWs) and basic TFMWs are then introduced and used to characterize a group of triangular fuzzy weight vectors with equivalency. This paper proposes transformation models among triangular fuzzy weights, normalized TFMWs and basic TFMWs, and employs basic TFMWs to define consistent TFPRs. Some important properties are presented for the fuzziness of a consistent TFPR and its corresponding basic TFMWs. These properties are subsequently used to develop a two-step approach consisting of three-goal programming (GP) models to find basic TFMWs of consistent TFPRs. By using the Lagrangian multiplier method, this paper finds analytic solutions of the three GP models and obtains optimized basic TFMWs denoted by three computation formulas for consistent TFPRs. By changing the constraints of the three GP models and using heuristics, three concise and visualized formulas are devised, respectively, to obtain the lower and upper support bounds and the modal values of heuristic-based TFMWs for any TFPR. A numerical example comprising of a consistent TFPR and two inconsistent TFPRs is supplied and a comparative study is conducted to show that the two challenging issues are reasonably solved, and a hierarchical fuzzy multicriteria decision-making example is offered to illustrate the applicability of the proposed fuzzy priority model.
Droplet measurement in laser enhanced GMAW

In the laser enhanced gas metal arc welding (GMAW) process developed recently, droplets of melted metal can be detached from the wire under relatively low currents with the assistance of an auxiliary force provided by a laser. The stability and the arc and the quality of the resultant welds are improved. To compete with the gas tungsten arc welding (GTAW) of the much lower productivity in joining precision, the size of the droplet can be pre-defined and be controlled to meet the requirements from different applications. For this purpose, image processing algorithms are developed to measure the size of a growing droplet during the laser enhanced GMAW process. The relatively low contrast, strong illumination and reflection caused by the laser, and strong radiation from the arc make an automatic processing of the image challenging. Images are analyzed to understand its characteristics and design the image processing and recognition algorithms accordingly. In particular, a model based method is used to filter out non-droplet edge points and a second order equation in the polar coordinate system is introduced to model the droplet. Experimental results verified the effectiveness of the developed algorithms.