nils-herrmann/clean_scirepeval huggingface dataset:
Energy Efficient Security Algorithm for Power Grid Wide Area Monitoring System

Modern power grid is the most complex human-made system, which is monitored by wide-area monitoring system (WAMS). Providing time-synchronized data of power system operating states, WAMS will play a crucial role in next generation smart grid protection and control. WAMS helps secure efficient energy transmission as well as reliable and optimal grid management. As the key enabler of a smart grid, numerous sensors such as PMU and current sensors transmit real-time dynamic data, which is usually protected by encryption algorithm from malicious attacks, over wide-area-network (WAN) to power system control centers so that monitoring and control of the whole system is possible. Security algorithms for power grid need to consider both performance and energy efficiency through code optimization techniques on encryption and decryption. In this paper, we take power nodes (sites) as platforms to experimentally study ways of energy consumptions in different security algorithms. First, we measure energy consumptions of various security algorithms on CrossBow and Ember sensor nodes. Second, we propose an array of novel code optimization methods to increase energy consumption efficiency of different security algorithms. Finally, based on careful analysis of measurement results, we propose a set of principles on using security algorithms in WAMS nodes, such as cryptography selections, parameter configuration, and the like. Such principles can be used widely in other computing systems with energy constraints.
A highly effective error concealment method for whole frame loss

When videos are transmitted over the Internet, packet missing is inevitable and an entire frame may get lost. However, most of the literature on error concealment problems can only deal with block loss. For the case of frame loss, they usually fail to achieve satisfactory results. In this paper, we propose a highly effective frame concealment method, which gives amazingly good recovery quality. We base on the theory of multiple hypotheses and devise an adaptive integration scheme to make full use of each hypothesis' strength. Different from the existing methods which mostly rely on motion vectors of previous frames, we fully exploit the correlation between consecutive frames. A novel idea for generating multiple estimates of the lost frame is adopted. Experimental results demonstrate that the proposed algorithm significantly outperforms the state-of-the-art error concealment methods for whole frame loss in both subjective and objective quality.
Color space compatible coding framework for YUV422 video coding

A new video coding framework for YUV422 video sources is proposed in this paper. It features color space compatibility to the more popular YUV420 syntax. Specifically, the chrominance components are separately into two parts and coded differently. The first part, together with the luminance component, conforms to the YUV420 layout and is coded the same way as a normal YUV420 video to produce a YUV420-compatible base bit stream. The second part, i.e., the remaining chrominance components, is coded to generate an enhancement chrominance bitstream for improving the chrominance quality. This is in sharp contrast to the YUV422 coding method of MPEG-2/4 standards where all the chrominance are coded together and in the same way. Consequently, the resulting YUV422 bitstream can be easily converted to a YUV420 bitstream by simple truncation instead of undergoing an expensive transcoding process. New coding modes are also introduced for more efficient coding of the enhancing chrominance components. Performance-wise, the new framework also outperforms existing methods thanks to the new coding modes introduced.
Control and description of gesture of virtual human

It's an advanced behavior for virtual humans to gesticulate. Control and description of this kind of behavior are among the most challenging problems in the field of computer graphics. In order to lower its complexity, a gesture description language is designed and developed in this paper, in which a fuzzy declarative description is used to specify the shape of hands to lower the difficulty of the description of gestures. That lays the foundation for synthesis and recognition of gesture in computer.
Continuous-time equivalents of Welch bound equality sequences

The theory developed on the sequence optimization for equal-power code-division multiple-access (CDMA) systems is generalized in this paper. Unlike previous works, where discrete-time or vector channel models are predominantly employed, the overloaded channels are modeled as strictly band-limited, continuous-time multiple-access channels corrupted by additive white Gaussian noise (AWGN). After being posed as a multiple-input/single-output (MISO) joint transmitter and receiver optimization problem with the total mean-squared error (MSE) as the objective functional to be minimized, the variational problem is solved by using a frequency-domain approach. It is shown that there exist continuous-time equivalents of Welch bound equality (CTE-WBE) sequences as the jointly optimum transmit waveforms and that the matched filters are the jointly optimum receivers. The user capacity of the band-limited channels is characterized by a necessary and sufficient condition for the admissibility of users into the system in terms of the channel load, the received signal-to-noise ratio, and the signal-to-interference-plus-noise ratio (SINR) requirement. It is also shown that CTE-WBE sequences achieve the lower bound on the continuous-time equivalent of total squared correlation (CTE-TSC).
Dual Frame Motion Compensation With Optimal Long-Term Reference Frame Selection and Bit Allocation

In dual frame motion compensation (DFMC), one short-term reference frame and one long-term reference frame (LTR) are utilized for motion compensation. The performance of DFMC is heavily influenced by the jump updating parameter and bit allocation for the reference frames. In this paper, first the rate-distortion performance analysis of motion compensated prediction in DFMC is presented. Based on this analysis, an adaptive jump updating DFMC (JU-DFMC) with optimal LTR selection and bit allocation is proposed. Subsequently, an error resilient JU-DFMC is further presented based on the error propagation analysis of the proposed adaptive JU-DFMC. The experimental results show that the proposed adaptive JU-DFMC achieves better performance over the existing JU-DFMC schemes and the normal DFMC scheme, in which the temporally most recently decoded two frames are used as the references. The performance of the adaptive JU-DFMC is significantly improved for video transmission over noisy channels when the specified error resilience functionality is introduced.
Image deblurring using robust sparsity priors

In this paper, we propose a robust method to remove motion blur from a single photograph. We find that an inaccurate kernel and an unreliable final latent image reconstruction method are two main factors leading to low-quality restored images. To improve image quality, we do the following technical contributions. For robust blur kernel estimation, first, an edge mask and a smooth constraint are used to provide reliable intermediate latent images for salient structure extraction; second, we adopt an effective salient structure selection method to remove detrimental edges for kernel estimation; third, we use a gradient sparsity prior to remove kernel noise and ensure the continuity of blur kernels. For final latent image reconstruction, we combine the merits of both the TV-l2 model and the hyper-Laplacian model to preserve tiny details and eliminate noise. Experimental results on synthetically blurred images and real photographs demonstrate that the proposed algorithm performs better than state-of-the-art approaches.
Fast multi reference frame motion estimation for high efficiency video coding

In this paper, a fast multi reference frame motion estimation algorithm is proposed to reduce the computational complexity for high efficiency video coding (HEVC). Firstly, according to the reference frame distribution, we define the motion complexity for a frame. Based on the motion complexity, an initial reference frame set (RFS) is constructed to reduce the number of the reference frame for motion estimation. Then for each prediction unit (PU), the average distortion per pixel together with the motion vector difference (MVD) in the first reference frame is employed to shrink the RFS in order to early terminate the motion estimation process. In addition, to enhance the robustness of the proposed scheme, an expansion method for RFS is proposed to guarantee the video quality. Experimental results demonstrate that the proposed scheme can significantly save the encoding time. For Low Delay testing configuration, over 30% of time saving can be achieved on average with ignorable performance loss.
Local Gabor binary pattern histogram sequence (LGBPHS): a novel non-statistical model for face representation and recognition

For years, researchers in face recognition area have been representing and recognizing faces based on subspace discriminant analysis or statistical learning. Nevertheless, these approaches are always suffering from the generalizability problem. This paper proposes a novel non-statistics based face representation approach, local Gabor binary pattern histogram sequence (LGBPHS), in which training procedure is unnecessary to construct the face model, so that the generalizability problem is naturally avoided. In this approach, a face image is modeled as a "histogram sequence" by concatenating the histograms of all the local regions of all the local Gabor magnitude binary pattern maps. For recognition, histogram intersection is used to measure the similarity of different LGBPHSs and the nearest neighborhood is exploited for final classification. Additionally, we have further proposed to assign different weights for each histogram piece when measuring two LGBPHSes. Our experimental results on AR and FERET face database show the validity of the proposed approach especially for partially occluded face images, and more impressively, we have achieved the best result on FERET face database.
Shape-based adult images detection

This paper reports an investigation on adult images detection based on the shape features of skin regions. In order to accurately detect skin regions, we propose a skin detection method using multi-Bayes classifiers in the paper. Based on skin color detection results, shape features are extracted and fed into a boosted classifier to decide whether or not the skin regions represent a nude. We evaluate adult image detection performance using different boosted classifiers and different shape descriptors. Experimental results show that classification using boosted C4.5 classifier and combination of different shape descriptors outperforms other classification schemes.
Single sequential minimal optimization: an improved SVMs training algorithm

We introduce homogeneous coordinates to represent support vector machines (SVMs) and develop a corresponding training algorithm: single sequential minimal optimization (SSMO). By this simple trick (homogeneous coordinates representation), linear constrains will not appear in quadratic programming (QP) optimization problem. So unlike the most popular used SVM training algorithm sequential minimal optimization (SMO) which solves the QP subproblem containing minimal two Lagrange multipliers, SSMO can analytically update only one Lagrange multiplier at every step. Because of avoiding double loops in heuristically choosing the two Lagrange multipliers in SMO, both CPU time and iterations can be decreased greatly. Experiments on MNIST database, under mild KKT conditions accuracy requirement, shows SSMO can be more than 2 times faster than SMO.
Local invariant descriptor for image matching

Image matching is a fundamental task of many computer vision problems. In this paper we present a novel approach for matching two images in the presence of image rotation, scale, and illumination changes. The proposed approach is based on local invariant features. A two-step process detects local invariant regions. Characteristic circles associated with these regions illustrate the position and radius of the regions. Then, the regions are represented by a new image descriptor. To test the new descriptor, we evaluate it in image matching and retrieval experiments. The experimental results show that using our descriptors results in effective and faster matching.
Study on ultrafast disordered crystal lasers

We reviewed the study on the mode-locked disordered crystal lasers in our group. Diode-pumped passively mode-locked Nd:CNGG, Nd:CLNGG and Nd:CLNGG-CNGG hybrid lasers were demonstrated based on semiconductor saturable absorber mirror (SESAM) technique. The Nd:CNGG laser generated synchronous two-color picosecond pulses with a repetition rate of 88 MHz and an average output power of 95 mW. The 0.63-THz optical beat frequency has been generated in the laser with optical beat pulse duration of 660 fs. The Nd:CLNGG laser generated 900-fs mode locked pulses with a repetition rate of ∼88 MHz at 1061 nm wavelength. By employing Nd:CLNGG and Nd:CNGG disordered crystals together as gain media, the laser emitted stable mode-locked pulses with a pulse width of 534 fs, a repetition rate of 88 MHz, and an average output power of 60 mW.
Expand training set for face detection by GA re-sampling

Data collection for both training and testing a classifier is a tedious but essential step towards face detection and recognition. All of the statistical methods suffer from this problem. This paper presents a genetic algorithm (GA)-based method to swell face database through re-sampling from existing faces. The basic idea is that a face is composed of a limited components set, and the GA can simulate the procedure of heredity. This simulation can also cover the variations of faces in different lighting conditions, poses, accessories, and quality conditions. All the collected face samples are aligned and randomly divided into three sub-sets: training, validating, and testing set. The training set is then used to train a sparse network of winnow (SNoW). In addition, it is also used as the initial population of the GA. After each generation, we use the initial generation and the solutions with high fitness values to re-train the SNoW, and the newly-trained SNoW is used to evaluate the individuals of next generation and also tested on validation set and test set. To verify the generalization capability of the proposed method, we also use the expanded database to train an AdaBoost-based face detector and test it on the MIT+CMU frontal face test set. The experimental results show that the data collection can be speeded up efficiently by the proposed methods.
A low power high speed readout circuit for 320×320 IRFPA

A low power high speed Readout Integrated Circuit(ROIC) design for 320 × 320 IRFPA is proposed in this paper. The ROIC operates as follows: after integration phase, voltages on column bus of odd rows and even rows are read out alternately. And the results are sampled and stored alternately on two sample capacitors added at the output point of column CSA. When sample capacitor for odd row samples and holds data, sample capacitor for even row works as feedback capacitor of output buffer so that voltage stored on sample capacitor can be read out directly. In this design, each column has one low power charge amplifier, and output buffer's power is optimized. Besides, capacitance of sample capacitor is much larger than that of CSA's feedback capacitor, so the KTC noise is lower and the charge injection is suppressed while the output range is not impaired. This design is also applicable to window readout. The readout speed can reach 8MHz with power consumption lower than 50mW. A 320 × 320 ROIC with pixel size of 30 × 30 µm2 has been designed and fabricated with a 0.35 µm DPTM CMOS process under 5v supply voltage.
High definition IEEE AVS decoder on ARM NEON platform

Nowadays, mobile devices are capable of displaying video up to HD resolution. In this paper, we propose two acceleration strategies for Audio Video coding Standard (AVS) software decoder on multi-core ARM NEON platform. Firstly, data level parallelism is utilized to effectively use the SIMD capability of NEON and key modules are redesigned to make them SIMD friendly. Secondly, a macroblock level wavefront parallelism is designed based on the decoding dependencies among macroblocks to utilize the processing capability of multiple cores. Experiment results show that AVS (IEEE 1857) HD video stream can be decoded in real-time by applying the proposed two acceleration strategies.
Facial feature tracking combining model-based and model-free method

In this paper, we propose a novel facial feature tracker, which integrates model-free and model-based method to reliably track dense facial features with complex non-rigid motions in low quality video sequence. The tracker consists of the mouth motion model part and the enhanced KLT tracker part. There are three key elements in our algorithms: dense facial feature tracking, noise removal with global rank constraints and characteristic non-rigid motion description. Experiments show good results on tracking dense facial features under various expressions, even some facial features have degenerate features.
Image quality assessment based on local orientation distributions

Image quality assessment (IQA) is very important for many image and video processing applications, e.g. compression, archiving, restoration and enhancement. An ideal image quality metric should achieve consistency between image distortion prediction and psychological perception of human visual system (HVS). Inspired by that HVS is quite sensitive to image local orientation features, in this paper, we propose a new structural information based image quality metric, which evaluates image distortion by computing the distance of Histograms of Oriented Gradients (HOG) descriptors. Experimental results on LIVE database show that the proposed IQA metric is competitive with state-of-the-art IQA metrics, while keeping relatively low computing complexity.
Multilevel Modified Finite Radon Transform Network for Image Upsampling

A local line-like feature is the most important discriminate information in the image upsampling scenario. In recent example-based upsampling methods, grayscale and gradient features are often adopted to describe the local patches, but these simple features cannot accurately characterize complex patches. In this paper, we present a feature representation of local edges by means of a multilevel filtering network, namely, multilevel modified finite Radon transform network (MMFRTN). In the proposed MMFRTN, the MFRT is utilized in the filtering layer to extract the local line-like feature; the nonlinear layer is set to be a simple local binary process; for the feature-pooling layer, we concatenate the mapped patches as the feature of local patch. Then, we propose a new example-based upsampling method by means of the MMFRTN feature. Experimental results demonstrate the effectiveness of the proposed method over some state-of-the-art methods.
A novel PLL-based phase control technique in the digitalized closed-loop gyroscope interface circuit

This paper presents a novel phase control technique based on the charge-pump PLL in an analog-digital-combined closed-loop interface circuit. The phase control resolution is 0.004° with the control range of 85~95° under worst case. The interface circuit achieves the resolution of 2.38aF with a maximum equivalent sensitivity of 98.9mV/aF in simulation. The chip is being fabricated in a 0.18um CMOS process and is 3×1.8mm2 in size.
Reusable Architecture and Complexity-Controllable Algorithm for the Integer/Fractional Motion Estimation of H.264

Motion estimation is the most computational intensive part of H.264 video coding. The motion estimation of H.264 includes integer motion estimation and fractional motion estimation. In this paper, a reusable architecture for both of them is proposed. This architecture can support fractional motion estimation and fast integer motion estimation algorithms including diamond search and cross search. The complexity of the motion estimation algorithm implemented on the proposed architecture can be flexibly controlled to meet variable system requirement. Cycle allocation is used in the algorithm design so that the cycle budget can be fully utilized. Experimental results show that this architecture-algorithm co-design can support HD (1280times720) video real-time encoding at 30 frames per second with 238K logic gates and 117 Mhz operation frequency.
A covariance-based method for dynamic background subtraction

Background subtraction in dynamic scenes is an important and challenging task. In this paper, we present a novel and effective method for dynamic background subtraction based on covariance matrix descriptor. The algorithm integrates two distinct levels: pixel level and region level. At the pixel level, spatial properties that are obtained from pixel coordinate values, and appearance properties, i.e., intensity, texture, gradient, etc, are used as features of each pixel. In the region level, the correlation of features extracted at the pixel level is represented by a covariance matrix that is calculated over a rectangle region around the pixel. Each pixel is modeled as a group of weighted adaptive covariance matrices. Experimental results on a diverse set of dynamic scenes show that the proposed method dramatically out-performs traditional methods for dynamic background subtraction.
Adaptive simulated annealing particle swarm optimization for catalyst protected region parameter identification

Aiming at the parameter identification problem of catalyst protected region in the process of propylene oxidation, a novel parameter identification method has been proposed for catalyst protected region using an adaptive simulated annealing particle swarm optimization (ASAPSO) algorithm. Synchronous change learning factors and linear decrease progressively inertia weights are embedded in the simulated annealing particle swarm optimization algorithm. The information exchange capacity is enhanced by the synchronous change learning factors. The overall search ability and local improved ability are balanced by the linear decrease progressively inertia weights. The proposed algorithm has some advantages in the aspect of good stability, strong information exchange capacity and fast convergence. Meanwhile, the shortcoming of local minimum valve is solved by the proposed algorithm. Simulation results show that the algorithm is feasible and accurate. The catalyst protected region of propylene oxidation from 6.35% to 11.25% is determined. Finally, the proposed ASAPSO algorithm is efficient.
An improved maximum entropy language model

An improved maximum entropy language model (IMELM) is presented based on three respects of language modeling (LM) improvement: the solution of long dependences, the integration of language knowledge into LM, and the general framework that combines all kinds of language knowledge. The proposed model combines trigram with base phrase structure knowledge in this paper. Trigram is used to capture the local relation between words, while base phrase structure knowledge is considered to represent the long-distance relations between syntactical structures. The knowledge of syntax, semantics and word is integrated in the maximum entropy framework. The experimental results show that the proposed model has a 24% improvement in perplexity over the conventional trigram model.