nils-herrmann/clean_scirepeval huggingface dataset:
Low Complexity Adaptive View Synthesis Optimization in HEVC Based 3D Video Coding

In this correspondence, we explore a low-complexity adaptive view synthesis optimization (VSO) scheme in the upcoming high-efficiency video coding (HEVC)-based 3-D video coding standard. We first devise a novel zero-synthesized view difference (ZSVD) model which jointly accounts for the distortion of the synthesized view induced by the compound impact of depth-disparity mapping, texture adaptation, and occlusion in the view synthesis process. This model can efficiently estimate the maximum allowable depth distortion in synthesizing a virtual view without introducing any geometry distortion. Then, an adaptive ZSVD-aware VSO scheme is proposed by incorporating the ZSVD model into the rate-distortion optimization process, which is developed by pruning the conventional view synthesis algorithm. Extensive experimental results confirm that the proposed model is capable of accurately predicting the zero distortion of the synthesized view and exhibit that the proposed ZSVD-aware VSO scheme can remarkably reduce the coding computational complexity with negligible performance loss.
Cloud Based Image Contrast Enhancement

We propose a cloud based image contrast enhancement framework, in which the context-sensitive and context-free contrast is improved via solving a multi-criteria optimization problem. Specifically, the context-sensitive contrast enhancement is based on the unsharp masking of the input and edge-preserving filtered images, while the context-free contrast enhancement is achieved by the sigmoid transfer mapping. The parameters in the optimization process are determined with the reference to the image that has a similar content and better enhancement quality in the cloud. The image complexity from the free energy based brain theory and the "surface" quality statistics is collaboratively optimized to infer the parameters. Experimental results demonstrate that the proposed technique can efficiently create visually-pleasing enhanced images with the guidance image from cloud.
Hybrid Laplace Distribution-Based Low Complexity Rate-Distortion Optimized Quantization

Rate distortion optimized quantization (RDOQ) is an efficient encoder optimization method that plays an important role in improving the rate-distortion (RD) performance of the high-efficiency video coding (HEVC) codecs. However, the superior performance of RDOQ is achieved at the expense of high computational complexity cost in two stages RD minimization, including the determination of optimal quantized level among available candidates for each transformed coefficient and the determination of best quantized coefficients for transform units with the minimum total cost, to softly optimize the quantized coefficients. To reduce the computational cost of the RDOQ algorithm in HEVC, we propose a low-complexity RDOQ scheme by modeling the statistics of the transform coefficients with hybrid Laplace distribution. In this manner, specifically designed block level rate and distortion models are established based on the coefficient distribution. Therefore, the optimal quantization levels can be directly determined by optimizing the RD performance of the whole block, while the complicated RD cost calculations can be eventually avoided. Extensive experimental results show that with about 0.3%−0.4% RD performance degradation, the proposed low-complexity RDOQ algorithm is able to reduce around 70% quantization time with up to 17% total encoding time reduction compared with the original RDOQ implementation in HEVC on average.
Robust video watermarking based on DC coefficients of selected blocks

This paper presents a novel blind video watermarking method based on the stability of the direct current (DC) coefficient values in blocks transformed by discrete cosine transform (DCT). The frame is randomly selected to embed watermark, and then its luminance is blocked by 8-by-8 and transformed by DCT. The watermark is embedded in the high frequency coefficient of some consecutive DC coefficients which have been transformed by DCT. During the detection process, the correlation between the watermark and the high frequency coefficients of the DC coefficients transformed by DCT is computed to judge whether the frame has been embedded watermark. The experimental results indicate that the correlation curve peaks emerge at the watermarked I frames and P frames which suffered MPEG-4 and H.264 compression. The proposed watermarking method has strong robustness against some attacks such as frame deleting, frame inserting, frame shifting, frame statistical average and collusion attack.
A low noise, fast set-up low-dropout regulator in 65nm technology

Low noise, fast set-up low-dropout (LDO) regulators are critical for noise-sensitive analog blocks, such as ADCs, PLLs, and RF SoC, etc. This paper presents a two-stage LDO regulator with low output noise, fast set-up, and high power supply rejection ratio (PSRR), based on internal noise filter with a novel fast set-up module, which solves dc shift problems. The proposed LDO is fabricated in a standard 65nm CMOS process. Measurement showed that output noise rms voltage is about 50nV/rtHz ranging from 10Hz to 100 KHz, set-up time is less than 20us, and PSRR is 65B at 1 KHz and 54dB at 1MHz.
Distributed video coding using wavelet

This paper proposes a distributed video coding scheme based on the zero tree entropy (ZTE) coding. Wyner-Ziv theory on source coding with side information is taken as the basic coding principle, which makes independent encoding and joint decoding possible. In this scheme, wavelet transform is used to exploit the spatial correlation of a Wyner-Ziv frame. The quantized wavelet coefficients are reorganized in terms of the zero tree structure so as to identify the significant and insignificant coefficients. The significance map is intra-codec and transmitted. In particular, the significant coefficients are independently encoded with turbo coder, and only the parity bits are transmitted. At the decoder, a predictive frame generated through motion-compensated prediction is used as the side information, with which the Wyner-Ziv frame can be conditionally decoded. Experimental results show that, compared to the traditional intra-frame coding and pixel-domain Wnyer-Ziv video coding, the proposed scheme can achieve a better coding performance, especially at low bit rates
Robust video fingerprinting based on visual attention regions

This paper presents a robust video fingerprinting based on visual attention regions. Video fingerprints, which are a set of short feature vectors, are unique to video clips and used for video identification. The performance of video fingerprinting is usually measured in terms of robustness and accuracy of identification. In our proposed approach, we extract video fingerprints using visual attention regions which remain the same for the perceptually same scenes with different types of distortions and different for different scenes. The experimental results show that the proposed video fingerprinting is effective for constructing video fingerprints that are robust against various content-preserving distortions and accurate in identifying different video clips.
Stability of Burgers-Korteweg-de Vries Equation

For Burgers-Korteweg-de Vries(KdVB) equation defined on a finite spatial interval with unknown viscosity, a nonlinear boundary control law and an adaptation law are proposed. Based on Lyapunov direct method, the global asymptotic stability of the closed-loop system is proved. The stochastic case of Burgers-Korteweg-de Vries equation is also considered. The exponential stability of the delayed KdVB equation is also proved if the delay parameter is sufficiently small.
Component hashing of variable-length binary aggregated descriptors for fast image search

Compact locally aggregated binary features have shown great advantages in image search. As the exhaustive linear search in Hamming space still entails too much computational complexity for large datasets, recent works proposed to directly use binary codes as hash indices, yielding a dramatic increase in speedup. However, these methods cannot be directly applied to variable-length binary features. In this paper, we propose a Component Hashing (CoHash) algorithm to handle the variable-length binary aggregated descriptors indexing for fast image search. The main idea is to decompose the distance measure between variable-length descriptors into aligned component-to-component matching problems independently, and build multiple hash tables for the visual word components. Given a query, its candidate neighbors are found by using the query binary sub-vectors as indices into their corresponding hash tables. In particular, a bit selection based on conditional mutual information maximization is proposed to reduce the dimensionality of visual word components, which provides a light storage of indices and balances the retrieval accuracy and search cost. Extensive experiments on benchmark datasets show that our approach is 20~25 times faster than linear search, without any noticeable retrieval performance loss.
Sign Language Recognition from Homography

It is difficult to recognize sign language in different viewpoint. The HMM method is hindered by the difficulty of extracting view invariant features. The general template matching methods have a strong constraint such as accurate alignment between the template sign and the test sign. In the paper, we introduce a novel approach for viewpoint invariant sign language recognition. The proposed approach requires no view invariant features, low training and no alignment. Its basic idea is to consider a sign as a series of tiny hand motions and utilize the HOMOGRAPHY of tiny hand motions. Using the word of "homography", we mean that there are the same tiny hand motions as well as their appearance order in different performances of the same sign. The experimental results demonstrate the efficiency of the proposed method
Building Invariances Into Sparse Subspace Clustering

Subspace clustering is the process of assigning subspace memberships to a set of unlabeled data points assumed to have been drawn from the union of an unknown number of low-dimensional subspaces, possibly interlaced with outliers or other data corruptions. By exploiting the fact that each inlier point has a sparse representation with respect to a dictionary formed by all the other points, an <inline-formula><tex-math notation="LaTeX">$\ell _1$ </tex-math></inline-formula> regularized sparse subspace clustering method has recently shown state-of-the-art robustness and practical extensibility in a variety of applications. But there remain important lingering weaknesses. In particular, the <inline-formula><tex-math notation="LaTeX">$\ell _1$</tex-math></inline-formula>-norm solution is highly sensitive, often in a detrimental direction, to the very types of data structures that motivate interest in subspace clustering to begin with, sometimes leading to poor segmentation accuracy. However, as an alternative source of sparsity, we argue that a certain data dependent, nonconvex penalty function can compensate for dictionary structure in a way that is especially germane to subspace clustering problems. For example, we demonstrate that this proposal displays a form of invariance to feature-space transformations and affine translations that commonly disrupt existing methods, and moreover, in important settings we reveal that its performance quality is lower bounded by the <inline-formula><tex-math notation="LaTeX">$\ell _1$</tex-math></inline-formula> solution. Finally, we provide empirical comparisons on popular benchmarks that corroborate our theoretical findings and demonstrate superior performance when compared to recent state-of-the-art models.
Fast Rate Distortion Optimized Quantization Method Based on Early Detection of Zero Block for HEVC

This paper presents an efficient zero block early detection method to accelerate Rate Distortion Optimized Quantization (RDOQ) for High Efficiency Video Coding (HEVC). RDOQ brings significant improvement of coding performance, but it results in high computational complexity to determine the best quantization levels with rate and distortion optimization operations. It is worthwhile to detect all-zero-quantized blocks before RDOQ since a lot of transform units are zero blocks after RDOQ process. In this paper, we analyze the transform coefficients characteristics in zero blocks firstly. And then, the relationship among the zero block detection thresholds, transform unit sizes and quantization parameters is studied. An adaptive zero block detection model is designed to speed up RDOQ process. Experimental results show that the proposed method achieves 44.10% and 41.68% quantization time saving with only average 0.16% and 0.01% BD-Rate loss under random access and low delay configuration, respectively.
Double input dc/dc converter topology for hybrid electrical vehicles

The objective of this paper is to propose a new topology of a double - input dc/dc converter for implementation in fuel cell vehicles in order to simplify the power system and reduce the cost of power electronics. The proposed topology consists of two parallel connected buck-boost fused dc-dc converters with fuel cell and battery as voltage sources driving a traction induction motor interfaced through a voltage source inverter. The operational principle of the proposed double - input converter and control strategy are explained. The proposed topology is modeled and simulated in Matlab/Simulink. The characteristics of the fuel cell and the battery are introduced.
A low-power self-calibration digital-output CMOS temperature sensor with ±0.1°C inaccuracy from −40°C to 85°C

An accurate, low-power, self-calibration digital-output CMOS temperature sensor for thermal monitoring is proposed. Our structure takes advantage of the fact that the same type of resistor has the same temperature coefficient to make the sensor calibrate itself. The final output of temperature sensor is 11-bit digital signal. The accuracy and the frequency of the temperature sensor is appropriate to transmit the information of temperature to the temperature compensation module of micro-accelerometer. The BJT-based temperature sensor was designed in 0.18μm CMOS process with 1.5V supply voltage. It has low power consumption of about 1.35μW. Simulation results show that temperature sensor has a nonlinear error bounded of ±0.1 °C over the target operating temperature range (−40 °C, 85 °C).
Application of artificial intelligence (AI) methods for designing and analysis of reconfigurable cellular manufacturing system (RCMS)

This work focuses on the design and control of a novel hybrid manufacturing system: Reconfigurable Cellular Manufacturing System (RCMS) by using Artificial Intelligence (AI) approach. It is hybrid as it combines the advantages of Cellular Manufacturing System (CMS) and Reconfigurable Manufacturing System (RMS). In addition to inheriting desirable properties from CMS and RMS, RCMS provides additional benefits including flexibility and the ability to respond to changing products, product mix and market conditions during its useful life, avoiding premature obsolescence of the manufacturing system. The emphasis of this research is the formation of Reconfigurable Manufacturing Cell (RMC) which is the dynamic and logical clustering of some manufacturing resources, driven by specific customer orders, aiming at optimally fulfilling customers' orders along with other RMCs in the RCMS.
Motion vector derivation of deformable block

Motion estimation (ME) plays an important role in most video encoding systems since it could significantly affect coding performance. However, both the next generation video coding standard High Efficiency Video Coding (HEVC) and the current video coding standard H.264/MPEG-4 AVC employ block matching motion estimation (BMME) which is based on translation motion model. This makes it difficult to represent the complex motion accurately such as rotation, zoom, and etc. In this paper, we propose an adjacent-block-based prediction model to improve the prediction performance of a deformable block. Based on this model, the motion information of each 4×4 block, i.e. the minimum partition (MP) in a coding unit (CU) of HEVC, is derived from the motion information of the nearest neighbors to the four corners of current prediction unit (PU). We integrate our method into HEVC as an additional choice of its merge mode. Simulation results show that our proposed method has better performance compared to HM4.0, the BD bit rate saving is up to 15.4%, while the encoding and decoding complexities are almost the same.
An inter-image redundancy measure for image set compression

Image set coding improves the compression efficiency by reducing both intra- and inter-image redundancy. The key of success is to select representative image(s) to predict set of similar images. This paper proposes an inter-image redundancy measure for representative image selection in image set compression. In the proposed method, the inter-image redundancy is measured jointly by the extent of similar content (EOS) and the correlation of similar content (COS) shared in two images. We take the covered area of matched SIFT points to measure the EOS, and take the distance of the matched SIFT descriptors to measure the COS. The image with largest redundancy for the set is selected as the representative one to predict other images. Experimental results show that the proposed method can select better representative image, and achieve bitrate saving up to 9.2% and 20.8% compared with state-of-the-art image set compression method and HEVC inter coding method.
Research on the Numerical Simulation of the 110kV Transformer Fire Extinguishing by Foam Spraying

In this context, we establish the fire extinguishing calculation model based on the combustion dynamics, fire physics and fire chemistry to simulate the process of the foam spraying fire extinguishing. Derive the fire extinguishing coefficient from small fire extinguishing test, verify the accuracy and reliability of the model by exhaustive fire extinguishing test. Guide to optimize the design of the actual fire extinguishing system. Reduce the test cost and pollution of the environment caused by the fire extinguishing test.
An advanced method for detection of partial discharge in oil-paper insulated transformer bushings and current transformers

Partial discharge (PD) detection is an important method to detect the defects in high-voltage bushings. Existing PD detection methods for bushings still have some problems related to the sensitivity and the anti-interference capacity. In this paper, an advanced method for the detection of PD in capacitor bushings is proposed to overcome the disadvantages of the conventional PD detection methods. The PD signal is captured by an inductor attached to the tap of the bushing, and all pulses that appear in a certain period are recorded by the oscilloscope. This method is considered to be more sensitive compared to conventional methods, and with the available information such as the frequency spectrum and phase distribution, the interference signal can be eliminated. Experiments on PD due to typical defects in capacitor bushings are carried out to validate the proposed method, and the results show that this method is effective in detecting some typical defects in bushing.
Distributed Video Coding with Spatial Correlation Exploited Only at the Decoder

A new pixel-domain distributed video coding (DVC) scheme is proposed in this paper, in which both the temporal and the spatial correlations are exploited only at the decoder. A video is treated as a collection of data correlated in temporal and spatial directions. Besides splitting a video into frames at different time instants, a frame is further split by spatially sub-sampling. Each yielded part is then encoded individually. At the decoder, the side information signals are from both adjacent frames and the spatially decoupled signals. To utilize these multiple side information signals, a new probability model is proposed, in which the transitional probability is calculated from the conditional probabilities on the multiple side information signals. The coding efficiency is enhanced by further removing the spatial redundancy, while the encoding complexity remains the same as the previous pixel-domain DVC techniques that only consider the temporal correlation
Study on Comprehensive Evaluation Model of Cyber Marketing Performance Based on the Multi-Level Fuzzy Theory

Recently, with the network technology widely applied in enterprises, Cyber marketing must be the main stream in the process of enterprise marketing. Therefore, performance evaluation of cyber marketing would be one of the important questions faced in the process of enterprise managing. From the website effect, cyber marketing efficiency and cyber marketing performance etc three inspects, this paper designs the following indexes, including website design, website function, spread, flow rate, sales efficiency, retail efficiency, advertisement effect, competition effect, service effect, the public effect and finance achievement etc 11 second-class evaluation indexes and 44 third-class indexes. So we set up 4 layers system of evaluation index and make use of multi-level fuzzy synthetic evaluation method, combining AHP and Delpel to evaluate performance of cyber marketing.
Improved spatial aided low delay Wyner-Ziv video coding by wavelet shrinkage

To tackle the problem of lacking auxiliary information while generating the side information, we have proposed a novel spatial aided low delay Wyner-Ziv video coding scheme in which the wavelet transformation is applied to generate the auxiliary information. However, there exists an inefficiency that lowers the coding performance. The inefficiency is that many wavelet coefficients having very small absolute value in the high-pass subbands and these small coefficients decrease the correlation between the source and the side information. The diminished correlation makes the bit error rate increased sharply and declines the performance of the channel decoding. This inefficiency is a widespread phenomenon among the wavelet based Wyner-Ziv video coding schemes. Therefore, we propose a scheme to remove these noises like coefficients in high-pass subbands by using a wavelet shrinkage method. In our method, a soft-thresholding operator which is based on the statistical properties of the high-pass coefficients is proposed. The result reveals that the correlation between the smoothed WZ frame and the side information is improved by using the wavelet shrinkage method. Accordingly, the R-D performance is also improved.
Maximizing intra-individual correlations for illumination-insensitive face recognition

Illumination variation has been one of the most intractable problems in face recognition and many approaches have been proposed to handle illumination problem in the last decades of years. The key problem is how to get stable similarity measurements between two face images of the same individual but captured under dramatically different lighting conditions. We propose a framework to optimize the illumination normalization for a pair of gallery and probe face images by maximizing a correlation (MAC) between them. The illumination normalization in the proposed framework tends to maximize the intra-individual correlations instead of both the inter- and intra-individual correlations. Experiments on Extended YaleB and CMU-PIE face databases show the effectiveness of our proposed approach in face recognition across varying lighting conditions.