nils-herrmann/clean_scirepeval huggingface dataset:
A traffic adaptive round-robin algorithm for combined input-crosspoint-queued switches

The appeal of a combined input-crosspoint-queued (CICQ) switch is its distributed scheduling property, which is more scalable than an unbuffered crossbar switch. Round-robin algorithms are interesting because of simple hardware implementation. Although the existing round-robin algorithms achieve 100% throughput asymptotically under uniform traffic, these algorithms have poor performance under nonuniform traffic. In order to improve the performance of a CICQ switch (one cell per crosspoint buffer) under nonuniform traffic, this paper proposes a traffic adaptive round-robin algorithm named TARR. Unlike the existing round-robin algorithms, TARR has distinctive round-robin pointer updating rules which are powerful to cope with nonuniform traffic patterns. On the other hand, TARR is a quantum based algorithm, and the quantum assignment discipline is load adaptive. Extensive simulations show that TARR has a satisfactory performance under both uniform and nonuniform traffic patterns.
Comprehensive modeling and analysis of Permanent Magnet Synchronous Generator-Wind Turbine system with enhanced Low Voltage Ride Through Capability

With a growing penetration of Permanent Magnet Synchronous Wind Turbine Generations (PMSG-WT) into the modern power system, a comprehensive modeling and analysis of PMSG-WT is required to investigate its dynamic stability and interaction between large wind farm and power grids. In this work, a complete detailed MW-class variable speed Wind Turbine System based on a Permanent Magnet Synchronous Generator and a full-scale IGBT Voltage Source Converter is developed for PSCAD/EMTDC simulation study. This control scheme comprises both the mostly applied Maximum Point Power Tracking Operation and Double PWM active/reactive power independent control strategy. Moreover, a DC-link over-voltage protection scheme is designed and implemented in this model. A two mass drive train model is integrated into this WT model to achieve a reasonably accurate simulation on the transient stability of PMSG-WT. The feasibility of the established PMSG-WT physical model plus the effectiveness of proposed control and protection scheme are evaluated through a series of simulation studies under both variable wind speed conditions and a three-phase grid disturbance. Simulation results demonstrate that the PMSG-WT model possesses desirable capabilities of operation at the maximum power point as well as enhanced Low Voltage Ride Through Function (LVRT).
An extensible drive system for AM-OLED panel

A drive system for active-matrix OLED panel is presented. The developed system comprises a digital interface which can receive DVI or MCU signals directly, a digital control part, a SRAM for storing display information, common drivers, and the 64-step gray scale segment drivers. Both common driver and segment driver is capable to be cascaded, so it can drive panels in different resolutions by using multiple chips. The drivers are using 0.35um CMOS technology with 15V high voltage devices.
Subjective and Objective Quality Assessment of Compressed Screen Content Images

Objectively accessing the quality of screen content images (SCIs) is a challenging problem, as SCIs may not always have identical properties as natural scenes. Here we conduct comprehensive studies on the subjective and objective quality assessment of the compressed SCIs. Firstly, we build a database that contains the distorted SCIs generated by the high efficiency video coding standard as well as its extension on screen content compression. Subsequently, subjective experiments are conducted to evaluate the perceived quality of these SCIs with compression artifacts. To automatically predict the subjective quality, a reduced-reference quality assessment model is further learnt by a set of wavelet domain features concerning the generalized spectral behavior, the fluctuations of the energy, and the information content with relatively large scale training samples. Our experimental results show that the learnt model is able to achieve better prediction on the SCI quality with a few extracted meaningful features.
A radiation detection readout circuit with current feedback baseline holder

This paper describes a radiation detection readout circuit for portable dosimeter which is aimed at low power, low noise and high counting rate. A current feedback baseline holder circuit is proposed to solve the baseline shift problem without any other performance deterioration. The core circuit has been implemented in 0.35μm CMOS technology. It achieves 46mV/fC conversion gain, 200kcps counting rate and consumes 260μA current from 3.3V supply. When no detector is connected to the chip, the equivalent input noise charge is 0.094fC rms.
Power Transformer Fault Diagnosis Using Support Vector Machine and Particle Swarm Optimization

Dissolved gas analysis is a simple and effective methods for incipient fault detection in power transformers. To enhance fault diagnosis capability of related interpretation approaches, a new fault diagnosis model is proposed in this paper. Feature selection techniques are used to select the most informative features. Particle Swarm Optimization algorithm is integrated with support vector machine to optimize key parameters. Fault diagnosis model is established and tested based on the best features and the optimal parameters. The result and comparison show that the accuracy of PSO-SVM method has higher accuracy than that of conventional methods.
Expanding Training Set for Chinese Sign Language Recognition

In sign language recognition, one of the problems is to collect enough training data. Almost all of the statistical methods used in sign language recognition suffer from this problem. Inspired by the crossover of genetic algorithms, this paper presents a method to expand Chinese sign language (CSL) database through re-sampling from existing sign samples. Two original samples of the same sign are regarded as parents. They can reproduce their children by crossover. To verify the validity of the proposed method, some experiments are carried out on a vocabulary of 2435 gestures in Chinese sign language. Each gesture has 4 samples. Three samples are used to be the original generation. These three original samples and their offspring are used to construct the training set, and the remaining sample is used for test. The experimental results show that the new samples generated by the proposed method are effective
A new approach to edge extraction by image decomposition and restoration

This paper suggests a new approach to edge extraction in terms of image decomposition and restoration, which adopts some specific edge extraction operator as a seed operator to extract edge in a group of sub-images produced by sub-sampling the original image and restoring the extracted edge-pixels to their corresponding positions in original image. It is a fast method to extract edge features at any specified scale, whose time complexity, excluding additional spot removal procedure, is equal to that of the seed edge operator conducted in the original image.
AppManager: a powerful service-based application management system for clusters

Cluster application management plays an important role in cluster systems. With more and more applications being settled on clusters, application management faces more and more challenging requirements. This paper introduces a service-based cluster application management system called AppManager. Different from traditional job management systems, AppManager has a service-based architecture. The components of a job management system are modularized and encapsulated into services. All these services are accessible within a cluster or outsides over Internet. Besides the basic functions of a traditional application management system, such as resource management, application schedule and control, AppManager implements many excellent features including supporting a wide variety of application types, platform flexibility, high system scalability, high availability assurance for application, scheduling algorithm extensibility and so on.
Rate-distortion optimized scan for point cloud color compression

We propose an adaptive scanning scheme to improve color attribute compression performance of static point cloud. To better exploit the intrinsic correlations of point cloud color data, multiple scanning modes are dedicated to projecting the color attributes into a series of texture blocks before compression. The best mode is subsequently selected in the sense of rate-distortion optimization, such that the compression flexibility and performance can be greatly improved. To achieve rate-distortion optimized scan, the Lagrange multiplier that balances the rate and distortion is off-line derived based on the statistics of color attribute coding. The proposed algorithm is implemented in reference software for point cloud compression (PCC-MP3DG), which is introduced by 3D Graphics (3DG) group of Motion Picture Experts Group (MPEG). Experimental results show that proposed method performs significantly better than the PCC-MP3DG reference software, and on average 6.51% and 9.36% bit-rate savings can be achieved for high and low bit rate codings, respectively.
High efficiency VLSI implementation of an edge-directed video up-scaler using high level synthesis

Image scaling is a fundamental algorithm used in a large range of digital image applications. In this paper, we propose an efficient VLSI architecture for a novel edge-directed linear interpolation algorithm. Our VLSI design is implemented using high level synthesis (HLS) tool, which generates RTL modules from C/C++ functions. HLS provides significantly improved design productivity compared to the traditional RTL-based design flow. So we explored a large design space including several fine-grained and coarse-grained optimizations in the pipeline architecture design. Our architecture is verified in a working system based on Xilinx Kintex-7 FPGA. Experiments show that our design can process UHD (3840*2160) videos at 30fps with moderate resource utilization.
A compact shot representation for video semantic indexing

This paper presents a compact shot representation for video semantic indexing (SIN). The proposed representation consists of visual cues from only two frames, i.e., key frame (KF) and difference frame (DF), which are both constructed with spatial pyramid. The KF describes static information while the generated DF captures non-static information. Each region of DF is derived from the same location in a selected frame, which has the most salient difference compared with the key frame in that region. We introduce a variation of DF to further enhance our model. Experimental results on TRECVID SIN demonstrate that our method obtains better accuracy than the state-of-the-art, while requiring less storage space and consuming time.
Beyond Knowledge Distillation: Collaborative Learning for Bidirectional Model Assistance

Knowledge distillation (KD) is a powerful technique that enables a well-trained large model to assist a small model. However, KD is constrained in a teacher-student manner. Thus, this method may not be appropriate in general situations, where the learning abilities of two models are uncertain or not significantly different. In this paper, we propose a collaborative learning (CL) method, which is a flexible strategy to achieve bidirectional model assistance for two models using a mutual knowledge base (MKB). The MKB is used to collect mutual information and provide assistance, and it is updated along with the learning process of the two models and separately deployed when converged. We show that CL can be applied to any two deep neural networks and is easily extended to multiple networks. Compared with the teacher-student framework, CL can achieve bidirectional assistance and does not impose specific requirements on the involved models, such as pretraining and different abilities. The experimental results demonstrate that CL can efficiently improve the learning ability and convergence speed of the two models, with superior performance to a series of relevant methods, such as ensemble learning and a series of KD-based methods. More importantly, we show that the state-of-the-art models, such as DenseNet, can be greatly improved using CL along with other popular models.
On-line dynamic state estimation of power systems

Traditional state estimators based on steady state system model cannot capture the dynamics of power system very well because of the slow updating rate of SCADA systems (several seconds). The emergence of wide-area measurement systems offers new opportunity for developing more effective methods to monitor power system dynamics online. But due to the nonlinearity of state transition and observation equation, linearization and Jacobian Matrix calculation are indispensible in the existing methods of power system state estimation. This makes WAMS' high performance compromised by burdensome calculation and inaccuracy of the state estimation. In order to overcome the drawbacks, this study tries to develop an effective state estimation method without the linearization and Jacobian Matrix calculation. Firstly, unscented transformation is introduced as an effective method to calculate the means and covariances of a random vector undergoing a nonlinear transformation. Secondly, by embedding the unscented transformation in the Kalman Filter process, a new method is developed for power system dynamic state estimation. Finally, some simulation results are presented showing accuracy and easier implementation of the proposed method.
An efficient VLSI architecture for rate disdortion optimization in AVS video encoder

A dedicated VLSI architecture is proposed to implement rate distortion optimization (RDO) for AVS video coding, with 4CIF format video supported at a system clock of 54 MHZ for low power applications. The seven-step block level pipeline architecture is employed for RDO with parallel structure to satisfy the timing constraint with all coding modes supported in AVS-P2. Fast transform domain SSD calculation algorithm is employed to reduce the computation redundancy in RDO. The run length pair detection and Golomb coding bits estimation modules are implemented using four-way parallel structure with 2D-VLC tables shared mutually. Other modules in the RDO pipeline are implemented with eight-way parallel structure. The architecture is implemented using VHDL language and successfully verified on Xilinx Virtex-2 FPGA.
Chip waveform design for DS/SSMA systems with aperiodic random spreading sequences

The dependence of the error performance and spectral efficiency of direct-sequence spread-spectrum multiple-access (DS/SSMA) systems with matched filter receivers on the chip waveform is examined. The actual shape of the chip waveform, as well as its energy, is found to influence the statistical properties of the multiple-access interference (MAI). An approach to design waveforms that may result in interchip interference (ICI) is proposed and a criterion for design based on the conditional Gaussian approximation of the MAI for systems with aperiodic random spreading sequences is derived. For a simplified system, a closed-form solution for optimal band-limited waveforms is obtained for excess bandwidth less than or equal to one by using a performance metric that includes the effect of ICI. Numerical results, based on an analytical method, as well as Monte Carlo simulations, are provided to evaluate the performance of the proposed waveforms in general systems with conventional matched filter receivers.
A low complexity and high performance interpolation filter for MPEG IVC

Fractional-pel motion compensation is widely adopted in the modern video coding standards such as H.264/AVC, AVS, and HEVC etc. The interpolation filter is a critical factor that influences the coding efficiency. In this paper, a generation algorithm of interpolation filter coefficients is utilized. Based on the coefficients generation algorithm, three different tap filters, namely 6 tap, 8 tap, 10tap, are tested. A combination of 6 tap and 8 tap interpolation filters is proposed and proved to be optimal scheme considering both performance and computational complexity. And it is beneficial to make software optimization more effective, especially when SIMD-like (Single Instruction Multiple Data) operation is used. Experiments show that the average BD-rate gains on luma Y, chroma U and V are 8.01%, 5.08% and 4.98% for CS1 (Constraint set 1), and 9.21%, 7.53% and 7.63% for CS2 (Constraint set 2) in MPEG IVC reference software ITM5.0, compared with the traditional IVC interpolation method. The coding efficiency gains are significant for some video sequences and can reach up to 28.7%. With the merits of high performance and low complexity, our proposed method is formally adopted by MPEG IVC.
Diversity combining of a fast-varying, correlated multipath fading channel for direct-sequence spread-spectrum systems

Diversity combining of fast-varying, correlated multiple-fading paths is investigated for direct-sequence spread-spectrum (DS-SS) systems. The multiple correlated paths are modeled as a vector autoregressive process, whose parameters are estimated using the expectation maximization (EM) technique. The proposed technique, requiring no derivatives of the log likelihood function, is computationally efficient and able to achieve near-optimum performance.
Context-based Arithmetic Coding Reexamined for DCT Video Compression

This paper presents a new context modeling technique for arithmetic coding of DCT coefficients in video compression. A key feature of the new technique is the inclusion of all previously coded coefficient magnitudes in a DCT block in context modeling. This enables adaptive arithmetic coding to exploit the redundancy of the high-order Markov process in the DCT domain with a few conditioning states. In addition, a context weighting technique is used to further improve the coding efficiency. The complexity of the new arithmetic coding scheme is slightly lower than that of context-based adaptive binary arithmetic coding (CABAC) of H.264. Moreover, the scheme is made compatible to the AVS baseline profile. It achieves on average 13% improvement in compression ratio over context-based two dimension variable length coding (C2DVLC) designed for the DCT domain, and a similar coding efficiency as the CABAC technique in H.264.
Pose normalization for robust face recognition based on statistical affine transformation

A framework for pose-invariant face recognition using the pose alignment method is described in this paper. The main idea is to normalize the face view in depth to frontal view as the input of face recognition framework. Concretely, an inputted face image is first normalized using the irises information, and then the pose subspace algorithm is employed to perform the pose estimation. To model the pose-invariance, the face region is divided into three rectangles with different mapping parameters in this pose alignment algorithm. So the affine transformation parameters associated with the different poses can be used to align the input pose image to frontal view. To evaluate this algorithm objectively, the views after the pose alignment are incorporated into the frontal face recognition system. Experimental results show that it has the better performance and it increases the recognition rate statistically by 17.75% under the pose that rotated within 30 degree.
A Fast Inter Frame Prediction Algorithm for Multi-View Video Coding

The multi-view video coding improves the coding efficiency by utilizing motion-compensated prediction (MCP) and disparity-compensated prediction (DCP). However, the complexity of the inter frame prediction is very high, especially when the rate-distortion optimization is used. This paper presents a fast inter frame prediction algorithm to reduce the complexity. Firstly the prediction type is decided according to reference frames. Then some unuseful search regions in view direction are removed. Finally a fast inter mode decision strategy is proposed based on the relationship between MCP and DCP. Experimental results verify that the proposed algorithm can greatly increase the speed of prediction with negligible loss of coding efficiency.
A Method of seismic capacity evaluation for ribs of CFST arch bridges

The seismic capability of concrete filled steel tube (CFST) arch bridge is determined by the arch ribs. In this paper, the seismic capacity of arch ribs is evaluated by using the method based on the strength and ductility. First, the internal forces are calculated respectively by using finite element method under gravity action and small earthquake. Then, the ultimate bearing capacities, the ratio of ductility and reduction coefficient are obtained according to the failure modes. Finally, the acceleration of ground movement and seismic capability of arch ribs are obtained. Taking a CFST arch bridge as an example, the results show that the method is efficiency for evaluating the seismic capability of CFST arch ribs under earthquake and has important the reference value.
An edge-to-edge congestion control scheme for assured forwarding

The differentiated services (DiffServ) architecture is proposed to enable service differentiation for aggregated flows in an IP network. One of the critical requirements to guarantee QoS (quality of service) across a DiffServ network is the use of an effective congestion control scheme. We present an edge-to-edge congestion control scheme (E2E-CCS) to provide (proportional) fairness among competing aggregates and improvement in packet loss ratio. The simulation results show that the proposed scheme is capable of protecting aggregated TCP flows from congestion caused by unresponsive UDP flows, sharing excessive bandwidth among aggregates according to their committed information rate, and reducing the packet loss ratio.
An efficient hardware implementation for motion estimation of AVC standard

In the advanced video coding standard (AVC), motion estimation adopts many new features such as variable block size searching, multiple reference frames, motion vector prediction, etc, for enhancing the coding performance. However, the high data dependence and high computation requirement of these new features makes the hardware implementation very complex, especially for real-time applications. Therefore base on the reference software JM9.0, this paper firstly improved the motion estimation algorithm from hardware-oriented viewpoint, and secondly proposed the systolic architecture of improved algorithm. It adopts 2-D systolic arrays, fully supports the AVC 's variable block size matching, and can produce 41 motion vectors for one macroblock. Experimental results show that the improved algorithm can avoid the data dependences while with the same coding performance as JM9.0, and the proposed architecture can achieve the real-time requirement for 720/spl times/576 picture size at 30 fps with the search range of 65/spl times/65.
A Verification Method for Viewpoint Invariant Sign Language Recognition

Viewpoint variance is one of the inevitable problems in vision based sign language recognition. However, most researchers avoid this problem by assuming a special view, especially the front view. In the paper, we propose a verification method for viewpoint invariant sign language recognition. In general, there are two major variances between two video sequences of the same sign: performance variance and viewpoint variance. For small performance variance, DTW can help us eliminate it. When there is only viewpoint variance between two sequences, we can consider the two sequences as obtained synchronously by a stereo vision system. Thus, for the current input, we can judge whether the known template is the matched one by verifying whether the two sequences can be considered as obtained by a stereo vision system. Our experiments demonstrate the efficiency of the proposed method. Furthermore, such verification method can be easily extended to other recognition tasks