nils-herrmann/clean_scirepeval huggingface dataset:
AVS standard - Audio Video Coding Standard Workgroup of China

To meet the requirements of the digital audio and video industry and related technology development, the Audio Video Coding Standard Working Group was established with the approval of the Science and Technology Department of the China Ministry of Information Industry (MII). AVS Workgroup takes the responsibility for the formulation of technical standards related to equipment for multimedia compression, decompression, processing and presentation and products like digital audio and video systems. The working results will be submitted for review and approval by the Science and Technology Department of MII to publish such standards as industrial standards, or be submitted by the Science and Technology Department of MII directly to the State Authority of National Standardization to be ratified as National Standards of China. AVS Standard's main purpose is to compress digital audio and video data to the range from more than 1/10 to even below 1/100 of the original data. AVS standard includes four main technical standards, which are system, video, audio and digital copyright management, and some supporting standards such as consistency versification. During the seventh meeting in Beijing on 18-19th of December in 2003, the Working group has completed the Final Committee Draft of the first part (system) and the second part (video) as well as the verification software.
Randomized Parallel Scheduling Algorithm for Input Queued Crossbar Switches

A significant research effort has been devoted in recent years to the design of simple and efficient scheduling algorithms for input-queued packet switches. Among these algorithms, scheduling policies based on maximum weight matching (MWM) were proved to achieve 100% throughput under any admissible traffic load. However, MWM is impractical for its high computational complexity O(N3). In this paper, we propose a new approximate algorithm to MWM using local search technique. Notably we observe that: (a) Local search is well suitable for parallel computation, (b) Currently each line card of high performance router has at least one processor. Based on the two important observations, a randomized parallel scheduling algorithm is proposed. It is proved to be rate stable and simulation results show that the proposed algorithm outperforms other randomized approximations to MWM
An AVS HDTV video decoder architecture employing efficient HW/SW partitioning

In this paper, we propose an optimized real-time AVS (a Chinese next-generation audio/video coding standard) HDTV video decoder. The decoder has been implemented in a single SoC with HW/SW partitioning. AVS algorithms and complexity are first analyzed. Based on the analysis, a hardware implementation of the MB level 7-stage pipeline is selected. The software tasks are realized with a 32-bit RISC processor. We further propose the optimization of interface and RISC processor based on the proposed architecture. The AVS decoder (RISC processor and hardware accelerators) is described in high-level Verilog/VHDL hardware description language and implemented in a single-chip AVS HDTV real-time decoder. At 148.5 MHz working frequency, the decoder chip can support real-time decoding of NTSC, PAL or HDTV (720p@60 frames/s or 1080i@60 fields/s) bit-streams. Finally, the decoder has been fully tested on a prototyping board
A resolution-adaptive interpolation filter for video codec

The fraction-pel interpolation filter varies in the video coding standards such as H.264/AVC, AVS and HEVC. Since fractional-pel motion compensation plays an important role in the video encoder, the interpolation of fractional-pel pixels can be refined and designed better to enhance the coding efficiency. In this paper, we firstly propose the generation algorithm of interpolation filter coefficients, and four different tap filters, namely 4tap, 6 tap, 8 tap and 10tap, are tested. A resolution-adaptive interpolation filter for different resolution videos is then introduced based on this algorithm to achieve the maximum bitrate saving. In the proposed scheme, 4 tap filter is applied for the UHD (2560×1600 and above) videos, 6 tap filter and 10 tap filter are performed in the videos whose resolution ranging from 720P (1280×720) to 1080P (1920×1080) and the videos with the resolution below 720P, respectively. When 4 tap filter and 6 tap filter are used in high-definition video, the coding efficiency can increase and the computational complexity will reduce greatly, which is actually beneficial to make hardware optimization more effectively especially SIMD (Single Instruction Multiple Data) and VLSI design. Experiments show that the average BD-rate gains on luma Y, chroma U and V are 1.4%, 0.7% and 0.7% for LP-Main configuration, when conducted in HEVC reference software HM11.0. The coding efficiency gains are significant for some video sequences and can reach up to 6.1%.
Continuous wavelet-based active filter design for harmonic mitigation in hybrid electric vehicles

Harmonic mitigation is one of the most important issues in hybrid electric vehicles (HEVs). This paper proposes a continuous wavelet-based active filter to mitigate the harmonics in the electrical torque of the induction motor drive caused by the application of multiple power electronic converters in HEVs. The basic theory and chief considerations on designing the proposed filter is discussed in detail. For verifying the proposed method, a numerical simulation is carried out to demonstrate the advantages and effectiveness of the proposed method. Further, the proposed filter is integrated with an induction motor drive system aiming to mitigate the harmonics in the electrical torque. The simulation results show the effectiveness of the proposed filter.
Fast intra coding unit size decision for HEVC with GPU based keypoint detection

In this paper, a fast intra Coding Unit (CU) size decision framework based on keypoint detection on Graphic Processing Unit (GPU) is proposed. In this framework, firstly the original frames are sent to GPU and then keypoint detection is conducted with numerous threads, which is able to avoid bringing in additional computational complexity even in realtime systems. Then, based on the keypoint distribution, whether to split the CU to the next coding depth is efficiently predicted. Experiments show that the proposed algorithm can achieve over 25% time saving under all intra (AI) configuration with ignorable performance loss.
Spatiotemporal Grid Flow for Video Retargeting

Video retargeting is a useful technique to adapt a video to a desired display resolution. It aims to preserve the information contained in the original video and the shapes of salient objects while maintaining the temporal coherence of contents in the video. Existing video retargeting schemes achieve temporal coherence via constraining each region/pixel to be deformed consistently with its corresponding region/pixel in neighboring frames. However, these methods often distort the shapes of salient objects, since they do not ensure the content consistency for regions/pixels constrained to be coherently deformed along time axis. In this paper, we propose a video retargeting scheme to simultaneously meet the two requirements. Our method first segments a video clip into spatiotemporal grids called grid flows, where the consistency of the content associated with a grid flow is maintained while retargeting the grid flow. After that, due to the coarse granularity of grid, there still may exist content inconsistency in some grid flows. We exploit the temporal redundancy in a grid flow to avoid that the grids with inconsistent content be incorrectly constrained to be coherently deformed. In particular, we use grid flows to select a set of key-frames which summarize a video clip, and resize subgrid-flows in these key-frames. We then resize the remaining nonkey-frames by simply interpolating their grid contents from the two nearest retargeted key-frames. With the key-frame-based scheme, we only need to solve a small-scale quadratic programming problem to resize subgrid-flows and perform grid interpolation, leading to low computation and memory costs. The experimental results demonstrate the superior performance of our scheme.
Improved error concealment algorithms based on H.264/AVC non-normative decoder

We propose several improved error concealment (EC) algorithms based on the H.264/AVC non-normative decoder. The major differences are that motion compensated EC is introduced for intra frames, whereas spatial EC is introduced for inter frames. As for the EC of intra frames, scene change detection, motion activity detection and MV retrieval are hierarchically performed to decide whether spatial or temporal information is to be used. As for the EC of inter frames, scene change is also detected to avoid merging the scenes from different video shots. Therefore, the main idea of the proposed algorithms is that both spatial and temporal correlations are utilized for the EC of intra and inter frames. Both subjective and objective simulations under Internet conditions show that the proposed algorithm greatly outperforms that in the H.264/AVC non-normative decoder.
Steganalysis based on wavelet texture analysis and neural network

A new steganalysis technique is proposed on the basis of statistical analysis on the texture of an image for the detection of wavelet domain information hiding techniques. Wavelet coefficients in each sub-band of wavelet transform are modeled as a Generalized Gaussian distribution (GGD) with two parameters. It appears that those parameters are a good measure of image features and can be used to discriminate stego-images from innocent images. Neural network is adopted to train these parameters to get the inherent characteristic of innocent and stego-images. These experimental results have proved that this method is applicable for the detection of information hiding techniques in wavelet, and the proposed algorithm is more effective to previously existing techniques.
Gas Phase Effective Diffusion Coefficient of Gasoline and Characteristic Components in Dry Fine Sand

Soil vapor extraction (SVE) is an economic and effective in situ remediation technology for contaminated soil. The effective diffusion coefficient of VOCs in porous medium is one of the most important parameters in SVE and directly affects the remediation efficiency and remediation phase classification. Column experiments were conducted with the gas phase diffusion of gasoline and characteristic components which included benzene series and N-alkanes (pentane to dodecane). The gas phase effective diffusion coefficients of the characteristic components were studied. The experiments performed indicate that the components detected are primarily hydrocarbons with fewer carbons in the initial stage of diffusion. The effective diffusion coefficient increases along the distance possibly resulting from the steam partial pressure of gas-phase gasoline. The diffusion of characteristic components is time-ordered and the detected components diffuse steadily with little interaction. The factors of the gas phase effective diffusion coefficient include molecular mass, matter structure, mineral component and water content. The characteristic components fall into four categories and the classification feature is similar to plate theory of gas chromatography.
A Riemannian Weighted Filter for Edge-sensitive Image Smoothing

This paper describes a new method for image smoothing. We view the image features as residing on a differential manifold, and we work with a representation based on the exponential map for this manifold (i.e. the map from the manifold to a plane that preserves geodesic distances). On the exponential map we characterise the features using a Riemannian weighted mean. We show how both gradient descent and Newton's method can be used to find the mean. Based on this weighted mean, we develop an edge-preserving filter that combines Gaussian and median filters of gray-scale images. We demonstrate our algorithm both on direction fields from shape-from-shading and tensor-valued images
Feature dimension reduction based on genetic algorithm for mandarin digit recognition

The dimensions are higher after combining Mel frequence cepstral coefficient with linear prediction cepstrum coefficient. In this paper, genetic algorithm is proposed to reduce the dimensions of the feature data to improve recognition performance of the system. First, extract Mel frequence cepstral coefficient and linear prediction cepstrum coefficient of the speech signal; then, reduce the dimensions of the feature data based on genetic algorithm; finally, the low dimensional data are sent into the support vector machine. Simulation results demonstrate that the recognition rate increases by 12.2% using genetic algorithm compared with principle component analysis, recognition rate almost has no change compared with the initial characteristics and the recognition speed gets improved effectively.
Mode-dependent pixel-wise motion refinement for HEVC

High efficiency video coding (HEVC) standard is within the block-based hybrid coding framework, which essentially adopts prediction unit (PU) as the basic motion compensation unit. However, in the case of tiny motion, the actual motion vectors (MVs) for each sample may differ from the PU's MV, thus resulting in more residual energy. In this paper, a novel pixel-wise motion refinement method (PMR) is presented by extending the traditional bidirectional optical flow (BIO) to PUs with two unidirectional reference blocks. To get the robust MV for each sample, a median filtering process is introduced to MV shifting values of the neighboring pixels. Furthermore, a PU level mode-dependent pixel-wise motion refinement (MPMR) scheme is also presented to improve the coding performance. Simulation results demonstrate that the proposed method achieves 0.83% bitrate reduction on average for HEVC test sequences under HM12.0 low delay B (LDB) configuration, and achieves up to 4.92% bitrate reduction for sequences with complex motion, e.g. affine motion.
Novel Spatio-Temporal Structural Information Based Video Quality Metric

Video quality assessment (VQA) is very important for many video processing applications, e.g., compression, archiving, restoration, and enhancement. An ideal video quality metric should achieve consistency between video distortion prediction and psychological perception of human visual system. Different from the quality assessment of single images, motion information and temporal distortion should be carefully considered for VQA. Most of previous VQA algorithms deal with the motion information through two ways: either incorporating motion characteristics into a temporal weighting scheme to account for their affects on the spatial distortion, or modeling the temporal distortion and spatial distortion independently. Optical flows need to be estimated in the two ways. In this paper, we propose a different methodology to deal with the motion information. Instead of explicitly calculating the optical flow and independently modeling the temporal distortion, both the spatial edge features and temporal motion characteristics are accounted for by some structural features in the localized spacetime regions. We propose to represent the structural information by two descriptors extracted from the 3-D structure tensors, which are the largest eigenvalue as well as its corresponding eigenvector. Experimental results on LIVE database and VQEG FR-TV Phase-I database show that the proposed VQA metric is competitive with state-of-the-art VQA metrics, while keeping relatively low computing complexity.
Skin-color detection based on adaptive thresholds

In this paper a new skin detection method based on adaptive thresholds is proposed. As compared with the fixed threshold histogram widely method used, ours can find optimal thresholds to the different complex backgrounds. Four clues are summarized from the skin probability distribution histogram (SPDH) to help search candidates of optimum thresholds, and an ANN classifier is trained to select the final optimum threshold. A novel image relation operation is also proposed to eliminate the confusing backgrounds. The method is fast thus appropriate for real-time applications since no iterative operation is involved. Experimental results show that the proposed method can achieve better performance than the fixed threshold histogram method.
A 14-bit two-segment column-level ADC for remote sensing imaging

This paper proposes a 14-bit two-segment column-level ADC which is applied for remote sensing imaging system. The first segment accomplishes 6-bit coarse conversion by charge redistribution structure while the second segment uses a ramp to obtain the 8-bit fine conversion. Both segments adopt single-slope algorithm and the joint of two segments is well designed to avoid code missing or overlap, so the monotonicity is excellent. Segmentation and pre-judgment of MSB decrease the clock frequency of counter, which reduces the dynamic power dissipation and relieves the requirement of comparator's speed. A 64×64 demo circuit is designed and fabricated in 0.35μm process. Simulation results reveal the average power of single column-level ADC is 160μW and SNR is 81.68dB.
Region-Aware Reflection Removal With Unified Content and Gradient Priors

Removing the undesired reflections in images taken through the glass is of broad application to various image processing and computer vision tasks. Existing single image-based solutions heavily rely on scene priors such as separable sparse gradients caused by different levels of blur, and they are fragile when such priors are not observed. In this paper, we notice that strong reflections usually dominant a limited region in the whole image, and propose a region-aware reflection removal approach by automatically detecting and heterogeneously processing regions with and without reflections. We integrate content and gradient priors to jointly achieve missing contents restoration, as well as background and reflection separation, in a unified optimization framework. Extensive validation using 50 sets of real data shows that the proposed method outperforms state-of-the-art on both quantitative metrics and visual qualities.
Fault-tolerant control methodology based free-weighting matrices of multi-variable NCSs

The issue of fault-tolerant control for uncertain continuous-time multi-variable networked control systems with actuators failure is researched, where its network transmission is connected with network-induced delay and packet dropout. The complete mathematical model is derived. And the sufficient condition for asymptotical stability is analyzed and the maximum allowable delay conditions for systems are derived by using Lyapunov stability theory combined with free-weighting matrices techniques. The efficacy and feasibility of the proposed methods is shown by presenting simulation results from multi-variable networked control example with uncertainty.
A Wavelet-Based Data Compression Technique for Smart Grid

This paper proposes a wavelet-based data compression approach for the smart grid (SG). In particular, wavelet transform (WT)-based multiresolution analysis (MRA), as well as its properties, are studied for its data compression and denoising capabilities for power system signals in SG. Selection of the Order 2 Daubechies wavelet and scale 5 as the best wavelet function and the optimal decomposition scale, respectively, for disturbance signals is demonstrated according to the criterion of the maximum wavelet energy of wavelet coefficients (WCs). To justify the proposed method, phasor data are simulated under disturbance circumstances in the IEEE New England 39-bus system. The results indicate that WT-based MRA can not only compress disturbance signals but also depress the sinusoidal and white noise contained in the signals.
Analysis of the intrinsic characteristics of the partial discharge induced by typical defects in GIS

Partial discharge (PD) monitoring plays an important role in the maintenance of the high voltage (HV) apparatus, such as transformer and gas-insulated substation (GIS). In recent years, the ultra high frequency (UHF) method for PD detection arouses great interest of many researchers because of its high sensitivity and anti-interference capability. The coupling of UHF signal and process of the signal constitute the UHF detection method. There are three key components for the coupling of UHF signal in GIS, including the frequency spectrum characteristics of the PD source, the propagation characteristics in GIS, the coupling style and the capability of the UHF coupler. Many researchers are interested in the recognition algorithm and the analysis procedure of the PD data. But the intrinsic attributes of the partial discharges should attract more attentions because they are the foundation of the UHF detection method. This paper focuses on the intrinsic characteristics of the typical defects in GIS, such as the amplitude of the time-domain signal and frequency spectrum characteristics. Four types of typical partial discharge sources are designed and fabricated, including floating metal, protrusions, particle on the spacer, and void in solid insulation. Several different structure sizes of each defect are applied to ensure the consistency of the results and discover the factors that affect the frequency spectrum characteristics. The results of the study present the essential characteristics of the partial discharge. It can be applied to the design of the coupler and amplifier circuit. In addition, the sensitivity and anti-interference capability of the UHF method are promoted effectively, and it is valuable for calibrating the intensity of PD in GIS.
Mining audio/visual database for speech driven face animation

The authors present a data mining framework in audio-visual interaction, and apply it to speech driven lip motion facial animation system. First, an unsupervised cluster algorithm is proposed to build a set of clusters in which each has similar configurations. Then, a statistical visual model is constructed by specifying all the possible cluster trajectories. The audio is analyzed with regard to learned clusters of facial gesture. For every cluster, two neural networks are trained to build mapping from audio features to cluster label and velocity respectively. Given a new vocal track, the statistical visual model and neural networks are combined together to analyze control audio, resulting in a most likely facial state sequence. The proposed method not only automatically incorporates vocal and facial dynamics such as co-articulation, but also is characterized by easy training, and being more robust, extensible and interpretable. Two approaches for an evaluation test are also proposed. The performance of our system shows that the proposed learning algorithm is suitable, which greatly improves the realism of face animation during speech.
Fast rate-distortion optimized transform for Intra coding

In our previous work, the rate-distortion optimized transform (RDOT) is introduced for Intra coding, which is featured by the usage of multiple offline-trained transform matrix candidates. The proposed RDOT achieves remarkable coding gain for KTA Intra coding, while maintaining almost the same computational complexity at the decoder. However, at the encoder, the computational complexity is increased drastically by the expensive ratedistortion (R-D) optimized selection of transform matrix. To resolve this problem, in this paper, we propose a fast RDOT scheme using macroblock- and block-level R-D cost thresholding. With the proposed method, unnecessary mode trials and R-D evaluations of transform matrices can be efficiently skipped from the mode decision process. Extensive experimental results show that, with negligible coding performance degradation, about 88.9% of the total encoding time is saved by the proposed method.