nils-herrmann/clean_scirepeval huggingface dataset:
On the Quality of Experience of SopCast

Peer-to-Peer (P2P) file sharing has become immensely popular in the Internet. Recently, there has been a growing interest in academic and commercial environments for live streaming using P2P technology. A number of new P2P digital television (P2PTV) applications have emerged. Such P2PTV applications are developed with proprietary technologies and the quality of experience (QoE) provided by them is not well known. Therefore, investigating their mechanisms, analyzing their performance, and measuring their quality are important for researchers, operators and end users. In this paper, we present results from a measurement study of a P2PTV application called SopCast, using both objective and subjective measurement technologies. The results obtained in our study reveal important design issues of SopCast and the QoE that the end users perceive.
Keyword searching in compressed document images

A compressed pattern matching method for searching keywords from the CCIT group 4-compressed document images, without explicit decompression, is presented. According to the CCIT Group 4 standards, each coded position indicates current pixel color is different from its previous pixel, except for the next coded positions of the pass mode. The changing elements from the compressed images are extracted and are then utilized to segment and bound the word objects and to measure the similarity of two word images. A two-stage matching strategy is constructed to measure the dissimilarity between the template image of the user's query word and the word extracted from document images. Experiments were conducted to verify the validity of the approach. The results show that the proposed approach was much faster than the traditional approach, because it avoids the pixel-level processing for analyzing the connected components and extracting word features.
Image Skew Detection for Formulas without Fraction Bars Using Connected Components Analysis

Skew detection acts as an important role in document image analysis systems. Many methods have been designed for text-dominant document images, but they usually fail in handling formula-dominant images. Hough transform is an effective method to estimate the skew angle of formulas with fraction bars, but cannot dealing with formulas without fraction bars. This paper presents a skew angle detection method for formula images that have not fraction bars, by analyzing connected components. All of connected components in a formula are clustered to two classes based on their widths and heights. The connected components in the class that have good alignment characteristics are selected to calculate the skew angle of the formula. Experimental results on various types of skewed formulas without fraction bars have showed the validity of the proposed method.
Reduced-Threshold Emission of Capillary Filled by Doped-Dye Cholesteric Liquid Crystals With Photo-Alignment Polyimide Films

A microcavity laser is reported using dye-doped cholesteric liquid crystals (CLCs) filled in a glass capillary with photo-alignment polyimide films. This laser runs based on the properties that a CLC has a refractive index much higher than that of glass, so whispering-gallery-mode (WGM) laser and distributed-feedback-mode lasers are included in the emission spectrum. We obtain a low threshold energy of <inline-formula><tex-math notation="LaTeX">$4.5\;\mu {\rm{J}} \cdot {\rm{puls}}{{\rm{e}}^{ - 1}} \cdot {\rm{m}}{{\rm{m}}^{ - 2}}$</tex-math></inline-formula>, establish the shift of 3.04 nm of the WGMs in the temperature range 43–48 °C, and investigate electric field tunability.
SRMF-CLEAN Imaging Algorithm for Space Debris

A single-range matching filtering (SRMF)-CLEAN imaging algorithm for space debris is proposed in this paper. The proposed algorithm can successfully incorporate the prior moving information of the space debris into the imaging process. Since the SRMF algorithm can improve the resolution and imaging speed effectively, and the disturbance of the point spread function can be cancelled by the modified CLEAN algorithm, the high resolution images of space debris can be obtained by SRMF-CLEAN method effectively. Studies and simulations confirm the validness of the proposed algorithm.
A parallel primal-dual interior-point method for DC optimal power flow

Optimal power flow (OPF) seeks the operation point of a power system that maximizes a given objective function and satisfies operational and physical constraints. Given real-time operating conditions and the large scale of the power system, it is demanding to develop algorithms that allow for OPF to be decomposed and efficiently solved on parallel computing systems. In our work, we develop a parallel algorithm for applying the primal-dual interior point method to solve OPF. The primal-dual interior point method has a much faster convergence rate than gradient-based algorithms but requires solving a series of large, sparse linear systems. We design efficient parallelized and iterative methods to solve such linear systems which utilize the sparsity structure of the system matrix.
Chinese Writer Identification Using Contour-Directional Feature and Character Pair Similarity Measurement

The key issue of Chinese writer identification is the uncertainty of the text content in the query and reference handwriting images. We propose a method for Chinese writer identification using Contour-directional Feature (CDF) and Character Pair Similarity Measurement (CPSM). CDFs are extracted from the query and reference handwriting images and are used to calculate the text-independent similarity between the query and reference handwriting images. Meanwhile, characters appearing in both the query and reference handwriting images are also utilized to measure the similarity of character pairs. The text-independent similarity and the similarity of character pairs are fused to the final similarity between the query and reference handwriting images. The proposed method is evaluated on two public datasets. The best Top-1 identification accuracy on the HIT-MW and CASIA-2.1 dataset reaches 96.7% and 97.9% respectively, which outperforms other previous approaches.
Performance of Spectrum Sensing Based on Absolute Value Cumulation in Laplacian Noise

Spectrum sensing based on absolute value cumulating (AVC sensing) has attracted much attention due to its effectiveness in the presence of Laplaican noise and simpleness of implementation. In this paper, we investigate its detection performance and optimal detection threshold. Specifically, based on the derived mean and variance of the test statistic, an accurate expression of the detection probability is given. Using the accurate expression, an optimization problem is formulated to minimize the total error rate of AVC sensing by optimizing the detection threshold with a constraint on false alarm probability, and the optimal detection threshold is derived. Numerical results are provided to support our work.
Binarization of degraded document image using Gaussian Markov random field model

This paper presents a binarization approach to degraded document images, which is based on Gaussian Markov Random Field (GMRF) model. The energy function with the single-site and pair-site clique potential functions is formulated for the GMRF. The parameters of the potential functions are estimated by expectation-maximization (EM) algorithm, without necessity of training process. Experiments on different types of degraded document images with various noise, contrast variation or uneven illumination, have demonstrated the validity of the proposed method.
Randomized Kaczmarz algorithm for inconsistent linear systems: An exact MSE analysis

We provide a complete characterization of the randomized Kaczmarz algorithm (RKA) for inconsistent linear systems. The Kaczmarz algorithm, known in some fields as the algebraic reconstruction technique, is a classical method for solving large-scale overdetermined linear systems through a sequence of projection operators; the randomized Kaczmarz algorithm is a recent proposal by Strohmer and Vershynin to randomize the sequence of projections in order to guarantee exponential convergence (in mean square) to the solutions. A flurry of work followed this development, with renewed interest in the algorithm, its extensions, and various bounds on their performance. Earlier, we studied the special case of consistent linear systems and provided an exact formula for the mean squared error (MSE) in the value reconstructed by RKA, as well as a simple way to compute the exact decay rate of the error. In this work, we consider the case of inconsistent linear systems, which is a more relevant scenario for most applications. First, by using a “lifting trick”, we derive an exact formula for the MSE given a fixed noise vector added to the measurements. Then we show how to average over the noise when it is drawn from a distribution with known first and second-order statistics. Finally, we demonstrate the accuracy of our exact MSE formulas through numerical simulations, which also illustrate that previous upper bounds in the literature may be several orders of magnitude too high.
Fundamental limits of phasemax for phase retrieval: A replica analysis

We consider a recently proposed convex formulation, known as the PhaseMax method, for solving the phase retrieval problem. Using the replica method from statistical mechanics, we analyze the performance of PhaseMax in the high-dimensional limit. Our analysis predicts the exact asymptotic performance of PhaseMax. In particular, we show that a sharp phase transition phenomenon takes place, with a simple analytical formula characterizing the phase transition boundary. This result shows that the oversampling ratio required by existing performance bounds in the literature can be further reduced. Numerical results confirm the validity of our replica analysis, showing that the theoretical predictions are in excellent agreement with the actual performance of the algorithm, even for moderate signal dimensions.
Italic font recognition using stroke pattern analysis on wavelet decomposed word images

This work describes an italic font recognition method using stroke pattern analysis on wavelet decomposed word images. The word images are extracted from scanned text documents containing word objects in various fonts and styles. Earlier font recognition methods mainly focus on slanted texture or pattern analysis on single character or large text blocks, which are sensitive to noise and subject to font and style variations such as size, serifness, boldness, etc. Our method takes advantage of 2-D wavelet decomposition on each word image and performs statistical analysis on stroke patterns obtained from wavelet decomposed sub-images. Experiments are carried out with 22,384 frequently used word images in both normal and italic styles of four different fonts. On average, a recognition accuracy of 95.76% for normal style and 96.49% for italic style is achieved. Experiments conducted on word images extracted from scanned documents with scattered italic words also show an encouraging result.
Demosaicking by Alternating Projections: Theory and Fast One-Step Implementation

Color image demosaicking is a key process in the digital imaging pipeline. In this paper, we study a well-known and influential demosaicking algorithm based upon alternating projections (AP), proposed by Gunturk, Altunbasak and Mersereau in 2002. Since its publication, the AP algorithm has been widely cited and compared against in a series of more recent papers in the demosaicking literature. Despite good performances, a limitation of the AP algorithm is its high computational complexity. We provide three main contributions in this paper. First, we present a rigorous analysis of the convergence property of the AP demosaicking algorithm, showing that it is a contraction mapping, with a unique fixed point. Second, we show that this fixed point is in fact the solution to a constrained quadratic minimization problem, thus, establishing the optimality of the AP algorithm. Finally, using the tool of polyphase representation, we show how to obtain the results of the AP algorithm in a single step, implemented as linear filtering in the polyphase domain. Replacing the original iterative procedure by the proposed one-step solution leads to substantial computational savings, by about an order of magnitude in our experiments.
Phase retrieval using iterative projections: Dynamics in the large systems limit

We study phase retrieval by using a simple non-convex algorithm based on iterative projections. Our main contribution in this work is to establish an exact analysis of the dynamics of the algorithm in an online setting, with Gaussian measurement vectors. We show that the algorithm dynamics, measured by the squared distance between the current estimate and the true solution, can be fully characterized by a 2D Markov process, irrespective of the underlying signal dimension. Furthermore, in the large systems limit (i.e., as the signal dimension tends to infinity), the random sample paths of the 2D Markov process converge to the solutions of two deterministic and coupled ordinary differential equations (ODEs). Numerical simulations verify the accuracy of our analytical predictions, even for moderate system sizes. This suggests that the ODE approach presented in this work provides an effective tool for analyzing the performance and convergence of the algorithm.
Pulse density modulated soft switching high frequency parallel load resonant current-source inverter with a single auxiliary active resonant snubber

In this paper, a current-source type parallel inductor compensated load resonant high-frequency soft switching inverter using IGBTs for driving the newly-produced silent discharge type ozone generating tube and excimer lamp for UV generation which incorporates a single zero voltage switching assisted switched capacitor resonant snubber between two ports in DC busline side is presented, together with its pulse modulated unique output power regulation characteristics.
Spectral initialization for nonconvex estimation: High-dimensional limit and phase transitions

We study a simple spectral method that serves as a key ingredient in a growing line of work using efficient iterative algorithms for estimating signals in nonconvex settings. Unlike previous work, which focuses on the phase retrieval setting and provides only bounds on the performance, we consider arbitrary generalized linear sensing models and provide an exact characterization of the performance of the spectral method in the high-dimensional regime. Our analysis reveals a phase transition phenomenon that depends on the sampling ratio. When the ratio is below a critical threshold, the estimates given by the spectral method are no better than random guesses drawn uniformly from the hypersphere; above the threshold, however, the estimates become increasingly aligned with the underlying signal. Worked examples and numerical simulations are provided to illustrate and verify the analytical predictions.
Multidimensional Nonsubsampled Hourglass Filter Banks: Geometry of Passband Support and Filter Design

Recently, the classical two-dimensional directional filter banks have been extended to higher dimensions. In this paper, we study one of the key components in this new construction, namely the multidimensional nonsubsampled hourglass filter banks. Starting with a rigorous analysis on the geometry of multidimensional hourglass-shaped passband supports, we propose a novel design for these filter banks in arbitrary dimensions, featuring perfect reconstruction and finite impulse response (FIR) filters. We analyze necessary and sufficient conditions for the resulting filters to achieve good frequency responses, and provide an optimal solution that satisfies these conditions using simplest filters. The proposed filter design technique is verified by a design example in 3-D.
Timing Analyzing for Systems with Task Execution Dependencies

This paper presents a novel approach to timing analysis of complex real-time systems containing data-driven tasks with intricate execution dependencies. Using a system model inspired by industrial control systems, we show how the execution time of tasks can be represented as a mathematical expression instead of a single numeric value. Next, based on this more detailed modeling, we introduce a concrete process of formally obtaining the exact value of both Worst-Case Execution-Time (WCET) and Worst-Case Response-Time (WCRT) of tasks by using upper-part binary search and TIMES (a timed model checker). Finally, in order to show the potential of the proposed approach, we apply it to a model created from a real robotic control system for which the traditional way of obtaining a WCET estimate (through static WCET analysis) on tasks for usage in basic RTA is not appropriate. Our results indicate a significant reduction of pessimism when compared to basic RTA using WCET estimates on tasks given by a basic assumption.
Three-phase voltage-fed inverter with new conceptual optimum PWM strategy for high-power applications

This paper presents a new approach to select the low frequency switching optimum PWM patterns in a voltage-source-fed PWM inverter for large capacity utility interactive power conditioner which is to be specified under a condition of minimized THD of output-side line voltage in order to suppress higher harmonic voltage and current components. A feasible test is implemented for utility-power source by using a trially-produced three-phase optimized PWM inverter with low-pass filter using IGBTs which is designed for high power application. It is verified that low frequency switching optimized PWM patterns are effective in minimizing higher harmonic voltage components in terms of a compact low-pass filter. Furthermore, this experimental work provides high efficient and high-power quality DC/AC converter characteristics. It is also proved that this optimum PWM pattern under a low frequency switching scheme has excellent voltage and current waveform performances as compared to voltage-fed inverters operating at various types of PWM patterns.
3D packaging based 4-channel LTCC receiver module

A 3D packaging based 4-channel X-band receiver module is implemented using low temperature co-fired ceramic (LTCC) technology. A dual-mode X-band bandpass filter (BPF), an lumped-element branch-line coupler and an lumped BPF is proposed for the receiver module, respectively. The overall size of the module is only 54 mm × 60 mm × 1 mm. The measured gain parameter, noise figure (NF), image rejection and isolation between adjacent channels is better than 51 dB, 2.5 dB, 37 dB and 35 dB, respectively.
Uncertainty principles for signals defined on graphs: Bounds and characterizations

The classical uncertainty principle provides a fundamental tradeoff in the localization of a signal in the time and frequency domains. In this paper we describe a similar tradeoff for signals defined on graphs. We describe the notions of “spread” in the graph and spectral domains, using the eigenvectors of the graph Laplacian as a surrogate Fourier basis. We then describe how to find signals that, among all signals with the same spectral spread, have the smallest graph spread about a given vertex. For every possible spectral spread, the desired signal is the solution to an eigenvalue problem. Since localization in graph and spectral domains is a desirable property of the elements of wavelet frames on graphs, we compare the performance of some existing wavelet transforms to the obtained bound.
Variable-Length Signature for Near-Duplicate Image Matching

We propose a variable-length signature for near-duplicate image matching in this paper. An image is represented by a signature, the length of which varies with respect to the number of patches in the image. A new visual descriptor, viz., probabilistic center-symmetric local binary pattern, is proposed to characterize the appearance of each image patch. Beyond each individual patch, the spatial relationships among the patches are captured. In order to compute the similarity between two images, we utilize the earth mover's distance which is good at handling variable-length signatures. The proposed image signature is evaluated in two different applications, i.e., near-duplicate document image retrieval and near-duplicate natural image detection. The promising experimental results demonstrate the validity and effectiveness of the proposed variable-length signature.
Building a Compact MQDF Classifier by Sparse Coding and Vector Quantization Technique

The modified quadratic discriminant function (MQDF) is a very popular handwritten Chinese character classifier thanks to its high performance with low computational complexity. However, it suffers from high memory requirement for the storage of its parameters. This paper proposes a compact MQDF classifier developed by integrating sparse coding and vector quantization (VQ) technique. To be specific, we use sparse coding to represent the parameters of MQDF in sparsity first, and then employ the VQ technique to further compress the sparse coding. The proposed method is evaluated by comparing the performance with three models, i.e., the original MQDF classifier, the compact MQDF classifier using the VQ technique, and the compact MQDF classifier using sparse coding. The effectiveness of our proposed approach has been confirmed and demonstrated by comparative experiments on ICDAR2013 competition dataset.
Decomposition of Space-Variant Blur in Image Deconvolution

Standard convolution as a model of radiometric degradation is in majority of cases inaccurate as the blur varies in space and we are thus required to work with a computationally demanding space-variant model. Space-variant degradation can be approximately decomposed to a set of standard convolutions. We explain in detail the properties of the space-variant degradation operator and show two possible decomposition models and two approximation approaches. Our target application is space-variant image deconvolution, on which we illustrate theoretical differences between these models. We propose a computationally efficient restoration algorithm that belongs to a category of alternating direction methods of multipliers, which consists of four update steps with closed-form solutions. Depending on the used decomposition, two variations of the algorithm exist with distinct properties. We test the effectiveness of the decomposition models under different levels of approximation on synthetic and real examples, and conclude the letter by drawing several practical observations.
Word Extraction Using Area Voronoi Diagram

A method of word extraction based on the area Voronoi diagram is presented in this paper. Firstly, connected components are generated from the input image. Secondly, noise removal is performed including a special symbol detection technique to find some types of special symbols lying between words. Thirdly, base on the area Voronoi diagram, we select appropriate Voronoi edges which separate two neighboring connected components. Finally, words are extracted by merging the connected components based on the Voronoi edge between them. The result generated by this method is satisfactory with the ability to correctly group words of different size, font and arrangement. Experiments show that the proposed method achieves a high accuracy.