nils-herrmann/clean_scirepeval huggingface dataset:
Researches on the method of the hardware in loop simulation for vehicle stability control system based on scilab/scicos

V process development method is generally used in the development of modern automotive electronics systems. The development process of ESC contains several steps as modeling, off-line simulation, hardware-in-loop testing and road test of vehicle. We build the vehicle dynamic model under scilab--an open-source scientific software, and make simulation in scicos. Then we construct the ESC hardware-in-loop testing system through communication between external interface of computer and digital IO board. Finally, we test the software/hardware system of ESC.
Hardware oriented algorithm analysis and modification for high definition AVS video encoder VLSI implementation Digest of technical papers

In AVS video coding standard, some algorithms consume huge computation with relatively little coding performance contribution, and some algorithms create data dependencies that are harmful for efficient hardware pipeline. This paper focuses on hardware oriented algorithm analysis and modification. Motion estimation and mode decision algorithms are reviewed and modified to a hardware friendly configuration for high definition (HD) AVS video encoder VLSI implementation. The resulting performance penalties are simulated and analyzed.
Feedback Dynamic Algorithms for Preemptable Job Scheduling in Cloud Systems

An infrastructure-as-a-service cloud system provides computational capacities to remote users. Parallel processing in the cloud system can shorten the execution of jobs. Parallel processing requires a mechanism to scheduling the executions order as well as resource allocation. Furthermore, a preemptable scheduling mechanism can improve the utilization of resources in clouds. In this paper, we present a preemptable job scheduling mechanism in cloud system. We propose two feedback dynamic scheduling algorithms for this scheduling mechanism. We compare these two scheduling algorithms in simulations. The results show that the feedback procedure in our algorithms works well in the situation where resource contentions are fierce.
Instance-specific canonical correlation analysis for pose alignment

Canonical correlation analysis (CCA) based methods achieve great success for pose alignment. However, CCA has limitations as a linear and global algorithm. Although some variants have been proposed to overcome the limitations, neither of them achieves locality and nonlinearity at the same time. In this paper, we propose a novel algorithm called Instance-Specific Canonical Correlation Analysis (ISCCA), which approximates the nonlinear data by computing the instance specific projections along the smooth curve of the manifold. Based on the framework of least squares regression, CCA is extended to the instance-specific case which obtains a set of locally-linear smooth but globally-nonlinear transformations. The optimization problem is proved to be convex and could be solved efficiently by alternating optimization. And the globally optimal solutions could be achieved with theoretical guarantee. Experimental results for pose alignment demonstrate the effectiveness of our proposed method.
A background model based method for transcoding surveillance videos captured by stationary camera

Real-world video surveillance applications require storing videos without neglecting any part of scenarios for weeks or months. To reduce the storage cost, the high bit-rate videos from cameras should be transcoded into a more efficient compressed format with as little quality loss as possible. In this paper, we propose a background model based method to improve the transcoding efficiency for surveillance videos captured by stationary cameras, and objectively measure it. The background model is trained by pre-decoded I frames, and then used to transcode the source stream. Following this method, an H.264/AVC based transcoder employing the background model as long-term reference frame and a difference frame coding based transcoder are implemented and evaluated. Experimental results show that both trancoders save nearly half the used bits while maintaining quality compared with the full-decoding-full-encoding method, and the latter one has slightly better performance.
Enhanced intra prediction for inter pictures

This paper presents a novel intra prediction method for inter pictures (i.e. P pictures and B pictures), denominated enhanced intra prediction (EIP). The traditional intra prediction only uses the reconstructed pixels to the left and above to derive intra prediction blocks. While the proposed method combines the left-above and right-below pixels to strengthen the prediction efficiency of the intra blocks in inter pictures. For accessing the pixels below and to the right of intra coding units (CUs), the encoding and decoding structures are adjusted to guarantee that all the inter CUs are reconstructed before all the intra CUs. With more available reference samples around the intra CUs, EIP achieves better prediction results. The proposed method is implemented on top of the H.265/HEVC reference software (HM-16.12), and the experimental results show that approximate 0.5% BD-rate reduction is achieved under Random Access (RA) and Low Delay P (LP) configurations.
A new motion model for panoramic video coding

Virtual reality (VR) has been a hot topic in both research and industry, calling for more efficient compression of panoramic videos. Currently, panoramic video is played as if it is spherical, but such video is actually mapped to planar video, e.g. using equirectangular projection, before compression and transmission. The projection causes deformation and thus makes the traditional translational motion model not efficient. In this paper, we propose a new motion model based on spherical coordinates transform to compensate for the deformation in panoramic videos. Our model requires no additional motion vector but rather derives pixel-wise 2D motion vectors from a block-level 3D motion vector. Our experimental results show the significant bits saving achieved by the new model, which leads to as high as 8.0% BD-rate on the test sequences.
A structure-preserving image restoration method with high-level ensemble constraints

In this paper, we present a new image restoration framework based on two high-level regularizations that can predict and preserve the better informative structures in the image. The sparse representation of a blurred image is first obtained to globally encode the salient structures by applying a group of coupled framelet filters. Then a physical meaning regularizer is derived to estimate the point spread function based on the frequency response characteristics of the image. Moreover, based on the operator of structure tensor, a novel nonlocal total variation as the regularizer is established to measure the image variation and non-local self-similarity. Finally, these two high-level regularizers are integrated into an objective function to constrain the ill-posedness. Compared with the state-of-the-art restoration methods, our algorithm can not only suppress strong noises effectively but also recover the sharp structures from the severe and complex blurred images.
DSP Implementation of Deblocking Filter for AVS

The in-loop deblocking filter contains highly adaptive processing on both sample level and block edge level, which inevitably appears in the loop kernel of the algorithm. Therefore it is a challenge for parallel processing on a digital signal processor (DSP) platform. In this paper, pipelined DSP solutions to the in-loop deblocking filter in AVS1-P2 are presented. First, the whole filter process is divided into six sub-processes, so that the global filter structure can be improved to achieve regular processing flow. Then software pipelines are designed for these sub-processes, with elaborately allocating functional units and carefully choosing enhanced assembly instructions based on the DSP platform. The simulated results show that this efficient implementation can easily support real-time filter processing for high resolution videos.
Bandwidth adaptive quality smoothing for unequal error protected scalable video streaming

We address the problem of inter-GOF bit allocation for FEC-MDC protected scalable video sequences. The objective is to minimize the variation in quality while streaming over packet-loss channels with time-varying bandwidth. We present an online heuristic algorithm to adaptively allocate bits for every GOF. Before transmitting a GOF, we first estimate the current available network bandwidth, and calculate the spare channel bit rate available to be used for current GOF. Then we propose a novel AIMD (additive increase/multiplicative decrease) quality control mechanism to regulate the changing behavior of target quality: (a) if the spare channel bit rate is greater than a certain value, then we increase the target quality gracefully in a linear mode; (b) else if it is less than another certain value, then we decrease the target quality aggressively. Finally, once the target quality is determined, we allocate bits for current GOF to meet the target quality requirement by iteratively increasing or decreasing the packet length used by FEC-MDC packetization. Since this procedure is time costly, we propose a fast approximate-approaching based dual-stage iteration technique to accelerate it. Experimental results show that our techniques can achieve near constant or graceful increasing quality in a segment by segment scheme, and the improved algorithm is very efficient and can be used in real-time online computation. Besides, we also analyze the impacts of some algorithm parameters to the quality smoothing results.
A low-noise switched-capacitor interface for a capacitive micro-accelerometer

This paper presents a low-noise single-ended open-loop switched-capacitor interface circuit in 0.35μm CMOS technology for a comb structure micro-accelerometer. Using correlated double sampling, the low-frequency noise of the charge sensitive amplifier and the gain stage is suppressed, whereas the noise of the sensor charging reference voltage and the sample-and-hold circuit which follows the gain stage contributes considerably to the total output noise. In order to optimize the noise performance, an on-chip reference voltage generator and a dual-sample-and-hold circuit are designed. A detailed noise analysis of the two blocks is also presented. The fabricated prototype interface circuit achieves a measured capacitive sensitivity of 734mV/pF with an input equivalent noise floor of 0.41aF/√Hz and a dynamic range of 119.93dB over a 200Hz bandwidth at 1MHz sampling frequency.
A Real-Time Full Architecture for AVS Motion Estimation

In the advanced audio video coding standard (AVS), the motion estimation employs many new techniques such as variable block size, multiple reference frames, motion vector prediction, symmetric searching, etc, for high coding efficiency. However, these techniques are so complex for hardware implementation due to their high data dependence and high computation requirement. In this paper, we firstly improved the AVS motion estimation algorithm from the viewpoint of hardware implementation and secondly propose the architecture for the improved algorithm. This architecture has two 2-D systolic arrays and fully supports the AVS variable block size match. It is able to process forward, backward and symmetric prediction concurrently, and totally produces 27 motion vectors for each macro-block. Experimental results shows that the architecture can achieve 29 fps under the condition of two reference pictures, 65times65 search range and 720times576 picture size.
Prediction Based Hierarchical Compensation for Delays in Wide-Area Control Systems

The closed-loop delay is one of the most important factors affecting the control performance of a wide-area control system (WACS), due to the network transmission of feedback and control signals. A prediction based hierarchical delay compensation (PHDC) method is proposed in this letter at the data processing level to deal with delays. For the control strategy on the top-level, all feedback and control information are real-time and transparent without consideration of delay. The PHDC method compensates the random and distributed delay effectively and reduces the impact of delays on the control performance.
An efficient foreground-based surveillance video coding scheme in low bit-rate compression

Many works have been done in the area of surveillance video compression, while problems still exist. The block-based schemes have blocking artifacts in the edge of foreground, while the object-based coding schemes have excessive bit consumption for coding the object shape. A novel foreground-based (FG-based) coding scheme is presented in this paper to solve these two problems and can gain better video quality at low bit-rate. The improvement comes from: 1) obtaining a foreground frame (FG-frame) by segmentation, in which proper constant value 128 is adopted to represent the luminance and chrominance value of background pixel and thus the residue error is reduced; 2) FG-based motion estimation (ME) and motion compensation (MC), which are more accurate for the foreground prediction and reduce the residue error of edge block in the foreground; 3) a new coding mode (BG-mode) is designed to better code the background when it is falsely segmented as foreground in FG-frames; 4) FG-based rate distortion optimized (RDO) mode decision (MD) is proposed to emphasize the foreground by calculating the distortion in the foreground domain; 5) avoiding shape coding by recovering the shape mask from the reconstructed foreground (REC-FG) frame and the constant background value 128. Our scheme is implemented with AVS encoder platform and the experiment results show the efficiency of the proposed scheme.
Compact Deep Invariant Descriptors for Video Retrieval

With emerging demand for large-scale video analysis, the Motion Picture Experts Group (MPEG) initiated the Compact Descriptor for Video Analysis (CDVA) standardization in 2014. In this work, we develop novel deep-learning features and incorporate them into the well-established CDVA evaluation framework to study its effectiveness in video analysis. In particular, we propose a Nested Invariance Pooling (NIP) method to obtain compact and robust Convolutional Neural Network (CNNs) descriptors. The CNNs descriptors are generated by applying three different pooling operations to the feature maps of CNNs in a nested way towards rotation and scale invariant feature representation. In particular, the rational, advantages and performance on the combination of CNNs and handcrafted descriptors are provided to better investigate the complementary effects of deep learnt and handcrafted features. Extensive experimental results show that the proposed CNNs descriptors outperform both state-of-the-art CNNs descriptors and canonical handcrafted descriptors adopted in CDVA Experimental Model (CXM) with significant mAP gains of 11.3% and 4.7%, respectively. Moreover, the combination of NIP derived deep invariant descriptors and handcrafted descriptors not only fulfills the lowest bitrate budget of CDVA, but also significantly advances the performance of CDVA core techniques.
High-Efficiency Image Coding via Near-Optimal Filtering

Wiener filtering, which has been widely used in the field of image restoration, is statistically optimal in the sense of mean square error. The adaptive loop filter in video coding inherits the design of Wiener filters, and has been proved to achieve significant improvement on compression performance by reducing coding artifacts and providing high-quality references for subsequent frames. To further improve the compression performance via filtering technique, we explore the factors that may hinder the potential performance of Wiener-based filters, and propose a near-optimal filter learning scheme for high-efficiency image coding. Based on the analyses, we observe that the foremost factor affecting the performance of Wiener-based filters is the divergence of statistical characteristics of training samples, instead of the filter taps or shapes. In view of this, we propose an iterative training method to derive the near-optimal Wiener filter parameters by simultaneously labeling sample categories at the pixel level. These parameters are compressed and transmitted to the decoder side to improve the quality of decoded images by reducing the coding artifacts. Experimental results show that the proposed scheme achieves significant bitrate savings compared with high-efficiency video coding in high-bitrate intra coding scenario.
Animating 3D facial models with MPEG-4 FaceDefTables

In this paper, the principle of facial animation compatible with the MPEG-4 standard is introduced. Then the algorithm of calculating the displacements of face mesh vertices within the influence of facial animation parameters (FAPs) is discussed and improved. At last, a method of constructing FaceDefTables, which is one field of facial definition parameters (FDPs), is presented Using this technique, realistic facial animation can be generated.
Recovering 3D facial shape via coupled 2D/3D space learning

This paper presents a method for recovering 3D facial shape from single image via learning the relationship between the 2D intensity images and the 3D facial shapes. With a coupled training set, the intensity images and their corresponding facial shapes make up two vector spaces respectively. But only the correlated components in both spaces are useful for inference, so there must be embedded hidden subspaces in each space which preserve the inter-space correlation information. Thus by learning the projection onto hidden subspaces based on maximum correlation criteria and optimizing the linear transform between the hidden spaces, 3D facial shape is inferred from the intensity image. The effectiveness of the method is demonstrated on both synthesized and real world data.
Model Inversion of BBPV Based on DWD of MISR RS Image

This letter puts forward a novel algorithm for the inversion of biophysical and biochemical parameter of vegetation leaves (BBPV) by means of the multiscale two-dimension discrete wavelet decomposition (DWD) of remote sensing (RS) image. With the aid of a multiangle imaging spectroradiometer (MISR) RS image, the multiscale BBPV inversion is implemented with the algorithm on each scale of wavelet decomposition based on DWD of MISR RS image. The retrieved results of the rough-scale wavelet decomposition are regarded as a priori knowledge of the fine-scale inversion, and then, the retrieved results of the rough-scale wavelet decomposition are also regarded as the initial values of the fine-scale inversion. In this way, the wavelet decomposition of the MISR RS image is gradually refined, and the retrieved result is likewise gradually refined until optimal BBPV is achieved in the end. Furthermore, this letter also probes the algorithm principle of the model inversion of BBPV, by employing the radiative transfer model of vegetation leaves (RTMV). Taking the model inversion of leaf area index as an example, a satisfactory retrieval result of higher precision is finally obtained. The experiment demonstrates that the proposed algorithm is very effectual and reliable in the model inversion of BBPV. The proposed algorithm opens up a novel algorithmic pathway for the model inversion of BBPV by means of DWD of MISR RS image.
Optimal feature extraction for partial discharge recognition

Pattern recognition of partial discharge (PD) is significant for condition judgment of power equipment. To select the optimal features out of the original PD features, this paper introduces a novel feature extraction algorithm, which is based directly upon the decision boundary between the classes, and determines the feature transformation according to the separability as opposed to the more common approach of fitting, such as principal component transformation. Experiment calculations are conducted on PD data obtained through measurements on industrial models of generator stator winding bars. Statistical and moment features, drawn respectively from each PD sample, are used in the experiments. Results show that when used together, the two sets of features are more powerful in discrimination. It is also shown that most discriminant information is concentrated in a subset of features, which can be determined and extracted successfully using the proposed algorithm. With the few selected features, approximate recognition accuracy could be achieved while substantial increases in computation complexity are refrained.
An Accurate and Fast Current-Biased Voltage-Programmed AMOLED Pixel Circuit With OLED Biased in AC Mode

A current-biased voltage-programmed (CBVP) pixel circuit for active-matrix organic light-emitting diode (AMOLED) displays is proposed. The pixel circuit can not only ensure an accurate and fast compensation for the threshold voltage variation and degeneration of the driving TFT and the OLED, but also provide the OLED with a negative bias during the programming period. The negative bias prevents the OLED from a possible light emitting during the programming period and potentially suppresses the degradation of the OLED.
Generating Video Sequence from Photo Image for Mobile Screens by Content Analysis

To bridge the gap between the high resolution digital images and limited display capability of mobile devices, this paper proposes a method to automatically transform static images to dynamic video clips to fit the requirement of mobile devices. Face detection and attention region detection techniques are applied to extract important regions that users may be interested in. Then a procedure of video resolution adaptation is performed for different screen size and aspect ratio. An algorithm to imitate camera motions is applied to visit the detected important regions to generate the motion effects. Finally the video clip is encoded with AVS-M standard as an implementation to be displayed on different mobile platforms. The solution provided in this paper can enable users viewing photographs in a dynamic mode using mobile devices that could not only glance over the whole picture of the image automatically, but also enjoy the important details of the photo. Furthermore, vivid dynamic effect enhances the viewing experience of users.
A new frame interpolation method with pixel-level motion vector field

In this paper, a new frame interpolation method with pixel-level motion vector field (MVF) is proposed. Given that existing methods cannot handle occlusions and blocking artifacts well, there are three contributions in our method: (i) applying the pixel-level motion vectors (MVs) estimated by optical flow algorithm to eliminate blocking artifacts (ii) motion post-processing to keep spatial consistency (iii) robust warping method to address collisions and holes caused by occlusions. The method could remove blocking artifacts and alleviate the artifacts caused by occlusions. Experimental results show that the proposed method outperforms existing methods both in terms of objective and subjective performances, especially for sequences with complex motions.