nils-herrmann/clean_scirepeval huggingface dataset:
A no-reference perceptual blur metric using histogram of gradient profile sharpness

No-reference measurement of blurring artifacts in images is a challenging problem in image quality assessment field. One of the difficulties is that the inherently blurry regions in some natural images may disturb the evaluation of blurring artifacts. In this paper, we study the image gradients along local image structures and propose a new perceptual blur metric to deal with the above problem. The gradient profile sharpness of image edge is efficiently calculated along horizontal or vertical direction. Then the sharpness distribution histogram rectified by just noticeable distortion (JND) threshold is used to evaluate the blurring artifacts and assess the image quality. Experimental results show that the proposed method can achieve good image quality prediction performance.
Multi-objective stochastic economic dispatch

This paper presents an optimized stochastic formulation for the economic dispatch problem of power system including wind power based on both short-term and long-term behaviour of the wind speed and wind power in a specific area. And according to the formulation, a multi-objective optimization formula is given to be used in the economic dispatch problem. The multi-objective optimization problem applies Weibull distribution function and Normal distribution function as short-term and long-term characterizing functions of the wind speed, respectively. The operation cost functions of the wind turbines are also obtained from real empirical data which fulfills the required accuracy of the method. The proposed method is also the first of its kind which considers the aging of the wind farms in the economic dispatch problem. Several different simulation scenarios are considered and various sensitivity analyses are performed.
Nonlocal In-Loop Filter: The Way Toward Next-Generation Video Coding?

In-loop filtering has emerged as an essential coding tool since H.264/AVC, due to its delicate design, which reduces different kinds of compression artifacts. However, existing in-loop filters rely only on local image correlations, largely ignoring nonlocal similarities. In this article, the authors explore the design philosophy of in-loop filters and discuss their vision for the future of in-loop filter research by examining the potential of nonlocal similarities. Specifically, the group-based sparse representation, which jointly exploits an image's local and nonlocal self-similarities, lays a novel and meaningful groundwork for in-loop filter design. Hard- and soft-thresholding filtering operations are applied to derive the sparse parameters that are appropriate for compression artifact reduction. Experimental results show that this in-loop filter design can significantly improve the compression performance of the High Efficiency Video Coding (HEVC) standard, leading us in a new direction for improving compression efficiency.
Realistic 3D facial animations with subtle texture changes

Illumination variance in texture is an important aspect of facial animation. Traditional facial animation techniques consider only facial feature geometric changes, but subtle texture changes in illumination are ignored. In this paper, by applying subtle illumination changes we present a novel technique to get realistic 3D facial animations. Firstly, we give the concept of PERI (partial expression ratio image) and the approach to get PERI. Then we parameterize the PERI according to the FAP (facial animation parameter) in the MPEG-4 based facial animation framework and develop the multi-view PERI technique, which makes 3D facial models have expressive expression at different viewpoints. Experimental results show that our technique can make facial expressions more expressive and convincing.
Singular value decomposition based image matching

This paper presents a simple and effective method for matching two uncalibrated images. Corner points are firstly extracted as interest points in the two images. Each interest point is assigned one dominant orientation. The initial set of point matches is then obtained by singular value decomposition of a correspondence strength matrix. A new expression of this matrix is introduced to handle more complicated imaging conditions. Each element of this matrix is the similarity measure between two interest points. The new similarity measure is invariant to image rotation by taking into account the dominant orientation of the two interest points. The epipolar geometry constraint is finally imposed to reject the false matches. Experimental results on real images show this approach to be effective for general image matching.
Sorting local descriptors for lowbit rate mobile visual search

State-of-the-art mobile visual search systems put emphasis on developing compact visual descriptors [4][6], which enables low bit rate wireless transmission instead of delivering an entire query image. In this paper, we address the orderless nature of the transmission set of query descriptors . We propose to adapt the orders of local descriptors in transmission, which subsequently yields more consistent statistic distributions in each feature dimension towards more efficient residual coding based compression. Our scheme further enables lossy sorting by an adaptive quantization strategy within each feature dimension, which largely improves the compression rates of the residual coding in each dimension. We show that the performance degeneration of such lossy sorting is acceptable in our mobile landmark search applications. Our approach's effectiveness and efficiency is demonstrated via extensive experimental comparisons to state-of-the art works in both mobile visual descriptors [2][4] and compact image signatures [5].
Acceleration of HEVC transform and inverse transform on ARM NEON platform

HEVC (High Efficiency Video Coding) has recently been published as the next generation video coding standard. Compared with previous standards, the coding efficiency is greatly improved at the cost of much higher codec complexity. On the other hand, ARM with SIMD (Single Instruction Multiple Data) instructions is widely deployed on mobile platform, which makes it feasible to accelerate multimedia applications by software. However, the accelerations are not straight forward, and the modules have to be carefully redesigned to utilize SIMD instructions efficiently. Transform and inverse transform modules are frequently called in HEVC codec. In this paper, we propose an acceleration method of the transform and inverse transform by utilizing the data level parallelism on NEON platform. Experiment results show that the speed of these modules has been increased considerably.
Design and Implementation for Communicating between Computer Serial Port and Mobile Phone

In order to simulate the procedure of communicating between computer serial port and mobile phone, this paper describe the way how mobile phone sending short message and the way how procedure controlling computer serial port and mobile phone to communicate. It mainly introduces the PDU coding and AT instruction of short message. The procedure depending on a Serial Port controller called SerialPort to send short message in Terminal way, and it completes the core function of communicating between Computer Serial Port and mobile phone, so it is easy for expansion and secondary development.
Evolving defense mechanism for future network security

The past few years have witnessed revolutionary advances in network technology. Along with new techniques such as SDN come lots of new network security challenges. Conventional network security mechanisms are incompetent to overcome these challenges, since they are built on a static network configuration that facilitates attackers in finding the weaknesses of a network. In this article, we conceive a novel conceptual network security mechanism, the evolving defense mechanism (EDM), to resolve current and future security problems. EDM is based on a bio-inspired idea of network configuration variations. According to the security requirements of the system, the user, and the network security state, EDM selects an efficient network configuration variation strategy to prevent corresponding security threats. Combined with SDN implementation, EDM resolves security problems from a new angle and is capable of evolving with new network security technology. We sketch a way to implement EDM and present its reference framework, which serves as an ecosystem and coexisting environment for various kinds of network configuration variations. The proposed mechanism avoids the deficiency of conventional mechanisms and has potential to cope with emerging security threats.
Learning to Distribute Vocabulary Indexing for Scalable Visual Search

In recent years, there is an ever-increasing research focus on Bag-of-Words based near duplicate visual search paradigm with inverted indexing. One fundamental yet unexploited challenge is how to maintain the large indexing structures within a single server subject to its memory constraint, which is extremely hard to scale up to millions or even billions of images. In this paper, we propose to parallelize the near duplicate visual search architecture to index millions of images over multiple servers, including the distribution of both visual vocabulary and the corresponding indexing structure. We optimize the distribution of vocabulary indexing from a machine learning perspective, which provides a “memory light” search paradigm that leverages the computational power across multiple servers to reduce the search latency. Especially, our solution addresses two essential issues: “What to distribute” and “How to distribute”. “What to distribute” is addressed by a “lossy” vocabulary Boosting, which discards both frequent and indiscriminating words prior to distribution. “How to distribute” is addressed by learning an optimal distribution function, which maximizes the uniformity of assigning the words of a given query to multiple servers. We validate the distributed vocabulary indexing scheme in a real world location search system over 10 million landmark images. Comparing to the state-of-the-art alternatives of single-server search [5], , [16] and distributed search [23], our scheme has yielded a significant gain of about 200% speedup at comparable precision by distributing only 5% words. We also report excellent robustness even when partial servers crash.
An implemented VLSI architecture of inverse quantizer for AVS HDTV video decoder

AVS is Chinese new audio and video coding standard. A pipeline-based architecture of inverse quantizer for AVS video standard is proposed in this paper. Due to using one-pass processing for run length decoding, inverse scan and inverse quantization, the architecture can save many buffers, which are used to store intermediate results during multi-pass processing. Furthermore, the processing speed is up to one coefficient per clock cycle. This architecture has been described in Verilog HDL, simulated with VCS digital simulator, and synthesized using 0.18mum CMOS cells library by Synopsys design compiler. The circuit totally costs about 13.7k logic gates when running at 200MHz. Simulation results show that the architecture can support real-time inverse quantization for HDTV (1280times720, 60fps) video. This architecture has been implemented in a single chip HDTV decoder for AVS video and audio
2D Cascaded AdaBoost for Eye Localization

In this paper, 2D cascaded AdaBoost, a novel classifier designing framework, is presented and applied to eye localization. By the term "2D", we mean that in our method there are two cascade classifiers in two directions: The first one is a cascade designed by bootstrapping the positive samples, and the second one, as the component classifiers of the first one, is cascaded by bootstrapping the negative samples. The advantages of the 2D structure include: (1) it greatly facilitates the classifier designing on huge-scale training set; (2) it can easily deal with the significant variations within the positive (or negative) samples; (3) both the training and testing procedures are more efficient. The proposed structure is applied to eye localization and evaluated on four public face databases, extensive experimental results verified the effectiveness, efficiency, and robustness of the proposed method
Finding Multiple Object Instances with Occlusion

In this paper we provide a framework of detection and localization of multiple similar shapes or object instances from an image based on shape matching. There are three challenges about the problem. The first is the basic shape matching problem about how to find the correspondence and transformation between two shapes; second how to match shapes under occlusion; and last how to recognize and locate all the matched shapes in the image. We solve these problems by using both graph partition and shape matching in a global optimization framework. A Hough-like collaborative voting is adopted, which provides a good initialization, data-driven information, and plays an important role in solving the partial matching problem due to occlusion. Experiments demonstrate the efficiency of our method.
Lossless reconfiguration protocol for multi-domain data plane in software-defined networks

Software-Defined Networking (SDN) opens the configuring interfaces for the data plane to improve its flexibility[1]. In the data plane, the switches are partitioned into multiple domains. Each domain is dynamically configured by its controller. The domains are physically connected via the border switches. In the control plane, each controller supports multiple services. Each service runs the same control program at multiple controllers in a distributed manner.
A hybrid text segmentation approach

In this paper, we present a hybrid text segmentation approach for embedded text in images, aiming to combining the advantages of the difference-based methods and the similarity-based methods together. First a new stroke edge filter is applied to obtain stroke edge map. Then a twothreshold method based on the improved Niblack thresholding technique is utilized to identify stroke edges. Those pixels between the edge pairs above the high threshold are collected to estimate the representative of stroke color, so that stroke pixels are further extracted by computing the color similarity. Finally some heuristic rules are devised to integrate stroke edge and stroke region information to obtain better segmentation results. The experimental results show that our approach can effectively segment text from background.
A platform-based architecture of loop filter for AVS

AVS is Chinese new audio and video coding standard, in which a loop filter has been applied to remove blocking artifacts. A platform-based architecture for the loop filter of AVS standard is proposed in this paper. The advantages of column-separated SRAM organization and two configurable matrix transposers have been adopted to accelerate loop filtering in this architecture. The architecture has been described in Verilog HDL, simulated with VCS and synthesized using 0.18 /spl mu/m CMOS cells library by Synopsys Design Compiler. The circuit totally costs about 38 k logic gates when the working frequency is set to 150 MHz. Simulation results show that the architecture can support real-time loop filter of HDTV (1280/spl times/720, 60 fps) AVS video. This architecture is valuable for the hardware design of AVS codec.
Symbolically aided model development for an induction machine in virtual test bed

A new phase-domain induction machine model for use in power system dynamic simulation is developed with the aid of a symbolic tool. The symbolic tool can automatically construct a time-domain power system component model in the resistive companion form (RCF) that is widely used in time-domain simulators. The automatic differentiation technique (ADT) is utilized within the context of a symbolic modeling language, and the tool has been implemented for the virtual test bed (VTB) simulation environment. The new induction machine model was used to study start-up transients of an induction motor. The new model was verified by comparing the simulation results to those obtained from a standard d-q model. This paper also demonstrates that a symbolically assisted technique can provide an efficient and rapid path for developing complex nonlinear device models for power system simulations.
Feature-matching based motion prediction for high efficiency video coding in cloud

Visual features of images and video frames have become pervasive and maturely developed in extensive research fields such as computer vision and visual search. For realtime retrieval applications, the compact visual features should be transmitted and stored at server side in cloud. These local feature descriptors are characterized by the invariance properties for the variances caused by camera motion, illumination changing, occlusion and different viewpoints. Inspired by these properties, the typical scale-invariant feature transform (SIFT) descriptor is leveraged to improve the video coding efficiency in this work. In particular the predicted motion using SIFT matching is used for merge mode and motion vector prediction (MVP) in the high efficiency video coding (HEVC) standard. A hierarchical motion derivation framework aiming at achieving robust and effective MVP is further proposed. Experimental results have shown that the proposed method can efficiently improve the coding performance according to the accurate feature-matching.
An efficient SNR scalability coding framework hybrid open-close loop FGS coding

This paper presents a novel high-efficient hybrid open-close loop based fine granularity scalable (HOCFGS) coding framework supporting different decoding complexity applications. The open-loop motion compensation for inter-pictures is introduced to efficiently exploit the temporal correlation among adjacent pictures for both base layer and FGS enhancement layer within a wide bit-rate range. An efficient rate-distortion optimized macro-block mode decision rule is used to reduce drifting error and achieve comparable coding performance at the lowest bit-rate point (base layer) with non-scalable coding. An approach like MPEG-4 FGS coding with close-loop motion compensation only at base layer is used for some inter-pictures to stop the drifting error propagation. Furthermore, to achieve better coding performance for these inter-pictures, block based progressive fine granularity scalable (BLPFGS) is introduced, in which leaky prediction is used to generate high-quality reference for FGS enhancement layer. In BLPFGS coding, efficient bit-plane coding and de-blocking techniques are investigated to improve the coding performance for FGS enhancement layer, especially at low bit-rate points. The coding performance for the proposed method is verified by integrating it into MPEG SVC reference software
Complexity scalable audio coding algorithm based on wavelet packet decomposition

In this paper, a complexity scalable audio coding algorithm based on wavelet packet decomposition is proposed. The incomplete wavelet packet decomposition with the property of complexity scalability to the signals is described in this paper. The zerotree coding of the coefficients is implemented which uses the characteristics of human ears and the self-similarity among different subbands wavelet coefficients. This algorithm cannot only be used to achieve transparent quality reconstruction audio at low bit rate, but also enable to support complexity scalable coding and multirate scalable coding. Audio encoding and decoding can be realized in real-time utilizing this algorithm on computers with different levels of computational power. The experimental results indicate that this algorithm is of practical value to the real-time multimedia applications.
Drift-free switching of compressed video bitstreams at predictive frames

Two schemes are proposed to efficiently compress video contents into bitstreams that support drift-free switching at predictive frames. They are inspired by the original SP coding scheme presented in the early H.26L. First, we propose a Flex SP coding scheme in which the prediction signal of the SP frame is directly subtracted from the input without quantization and de-quantization. The decoded video quality of the Flex SP scheme is significantly improved when additional inverse discrete cosine transform (DCT) and post-filter are provided. Then, the Hybrid SP scheme is presented to further improve the quality of the display image, as well as the reconstructed reference, by defining two coding modes for each DCT coefficient. Moreover, a rate-distortion algorithm is proposed to determine the coding mode for each coefficient. The bitstreams generated by the two proposed schemes can be decoded successfully by a decoder that complies with MPEG-4 AVC/H.264. In addition, we also investigate how to choose the quantization parameters for switching. An empirical method is proposed to achieve a good tradeoff between high coding efficiency of SP frames and small size of switching bits.
Assessment of system frequency support effect of PMSG-WTG using torque-limit-based inertial control

To release the “hidden inertia” of variable-speed wind turbines for temporary frequency support, a method of torque-limit based inertial control is proposed in this paper. This method aims to improve the frequency support capability considering the maximum torque restriction of a permanent magnet synchronous generator. The advantages of the proposed method are improved frequency nadir (FN) in the event of an under-frequency disturbance; and avoidance of over-deceleration and a second frequency dip during the inertial response. The system frequency response is different, with different slope values in the power-speed plane when the inertial response is performed. The proposed method is evaluated in a modified three-machine, nine-bus system. The simulation results show that there is a trade-off between the recovery time and FN, such that a gradual slope tends to improve the FN and restrict the rate of change of frequency aggressively while causing an extension of the recovery time. These results provide insight into how to properly design such kinds of inertial control strategies for practical applications.
A Hardware-Efficient Multi-Resolution Block Matching Algorithm and its VLSI Architecture for High Definition MPEG-Like Video Encoders

High throughput, heavy bandwidth requirement, huge on-chip memory consumption, and complex data flow control are major challenges in high definition integer motion estimation hardware implementation. This paper proposes an efficient very large scale integration architecture for integer multi-resolution motion estimation based on optimized algorithm. There are three major contributions in this paper. First, this paper proposes a hardware friendly multi-resolution motion estimation algorithm well-suited for high definition video encoder. Second, parallel processing element (PE) array structure is proposed to implement three-level hierarchical motion estimation, only 256PEs are enough for one reference frame real-time high definition motion estimation by efficient PE reuse. Third, efficient on-chip reference pixel buffer sharing mechanism between integer and fractional motion estimation is proposed with almost 50% SRAM saving and memory bandwidth reduction. The proposed multi-resolution motion estimation algorithm reached a good balance between complexity and performance with rate distortion optimized variable block size motion estimation support. Also, we have achieved moderate logic circuit and on-chip SRAM consumption. The proposed architecture is well-suited for all MPEG-like video coding standards such as H.264, audio video coding standard, and VC-1.