nils-herrmann/clean_scirepeval huggingface dataset:
Novel Global and Local Features for Near-Duplicate Document Image Matching

A new near-duplicate document image matching approach is proposed. Globally, we model the spatial arrangements of objects in an image. Locally, the micro-patterns within each object are captured. To define a micro-pattern, the N-nary center-symmetric gray value differences in an image local neighborhood of a variable radius are exploited. A visual descriptor is proposed to characterize the appearance of the object based on micro-pattern distributions. By combining the global and local features, each document image is represented by a compact signature with a variable length. We employ Earth Mover's Distance for image dissimilarity computation, which stands out for its remarkable ability to tolerate the instability of object segmentation by allowing many-to-many correspondence among objects. Extensive experiments on two data sets demonstrate the effectiveness of the proposed approach.
Express waybill extraction on parcel images by logo matching

We propose an efficient method for automatically extracting express waybills from parcel images, which is challenging due to varied resolution of parcel images, arbitrary direction of waybills and different information filled by senders. To address these challenges, logo matching is employed to extract the waybills. We begin by extracting scale-invariant feature-transformation (SIFT) keypoints from both the reference logo image and a parcel image, and matching them subject to a consistent projective transformation (homography) by using random sample consensus (RANSAC). Once the homography matrix is computed, we extract the waybill of parcel image by mapping all pixels from a standard waybill image to the parcel image. Experimental results on test datasets demonstrate the effectiveness of the proposed method.
Finding Optimal Integral Sampling Lattices for a given Frequency Support in Multidimensions

The search for alias-free sampling lattices for a given frequency support, in particular those lattices achieving minimum sampling densities, is a fundamental issue in various applications of signal and image processing. In this paper, we propose an efficient computational procedure to find all alias-free integral sampling lattices for a given frequency support with minimum sampling density. Central to this algorithm is a novel condition linking the alias-free sampling with the Fourier transform of the indicator function defined on the frequency support. We study the computation of these Fourier transforms based on the divergence theorem, and propose a simple closed-form formula for a fairly general class of support regions consisting of arbitrary N-dimensional polytopes, with polygons in 2D and polyhedra in 3D as special cases. The proposed algorithm can be useful in a variety of applications involving the design of efficient acquisition schemes for multidimensional bandlimited signals.
Scene text detection using sequential nontext filtering

We present a scene text detection method based on sequential nontext filtering. Firstly, we start our work with multi-channel maximally stable extremal region (MSER) detection. Then nontext components are eliminated by a four-stage sequential nontext filtering strategy which consists of inner-channel MSER pruning, between-channel MSER pruning, unary feature-based nontext filtering, and binary feature-based nontext filtering. Finally, text components are grouped into words and false positives are eliminated. The proposed method achieves the state-of-the-art on the ICDAR2013 database when compared with some existing methods.
Efficient ozonizer using PDM and PWM controlled resonant inverter and its performance evaluations

This paper presents a high-frequency voltage source-fed load-resonant IGBT inverter with a series-compensated resonant inductor for an ozonizer, which incorporates the power control strategy on the basis of pulse density modulation (PDM). The inverter system configuration is illustrated through the circuit modeling of the silent discharge-based ozone generating tube as well as high-voltage transformer. The state-of-the art ozonizer using a voltage-fed type high-frequency load resonant inverter is demonstrated, and its operating principle is described for driving the ozone generating tube. It is noted that the pulse density modulated series resonant high-frequency inverter using IGBTs makes an ozone generating system simple, thus leading to an improvement of the ozone generation efficiency. Finally, it is proved that the series load-resonant inverter type ozonizer developed in this paper provides stable silent discharge characteristics over a wide output power range from 10% to 100%.
A graph theoretical regression model for brain connectivity learning of Alzheimer'S disease

Learning functional brain connectivity is essential to the understanding of neurodegenerative diseases. In this paper, we introduce a novel graph regression model (GRM) which regards the imaging data as signals defined on a graph and optimizes the fitness between the graph and the data, with a sparsity level regularization. The proposed framework features a nice interpretation in terms of low-pass signals on graphs, and is more generic compared with the previous statistical models. Results based on the simulated data illustrates that our approach can obtain a very close reconstruction of the true network. We then apply the GRM to learn the brain connectivity of Alzheimer's disease (AD). Evaluations performed upon PET imaging data of 30 AD patients demonstrate that the connectivity patterns discovered are easy to interpret and consistent with known pathology.
A Theory for Sampling Signals From a Union of Subspaces

One of the fundamental assumptions in traditional sampling theorems is that the signals to be sampled come from a single vector space (e.g., bandlimited functions). However, in many cases of practical interest the sampled signals actually live in a union of subspaces. Examples include piecewise polynomials, sparse representations, nonuniform splines, signals with unknown spectral support, overlapping echoes with unknown delay and amplitude, and so on. For these signals, traditional sampling schemes based on the single subspace assumption can be either inapplicable or highly inefficient. In this paper, we study a general sampling framework where sampled signals come from a known union of subspaces and the sampling operator is linear. Geometrically, the sampling operator can be viewed as projecting sampled signals into a lower dimensional space, while still preserving all the information. We derive necessary and sufficient conditions for invertible and stable sampling operators in this framework and show that these conditions are applicable in many cases. Furthermore, we find the minimum sampling requirements for several classes of signals, which indicates the power of the framework. The results in this paper can serve as a guideline for designing new algorithms for various applications in signal processing and inverse problems.
Investigation of Passivation Layer of CdZnTe Detector

The process of surface passivation of CdZnTe(CZT) plays dominant role in detector performance, which can decrease the noise of the detector and improve the spectral energy resolution. The thickness of the passivation film has been measured by infrared spectroscopic ellipsometry in this paper. The relationship between thickness of the passivation films and the passivation time was obtained. The results show that the thickness of three samples passivated for 5 min, 10 min, 15 min are 15.669 nm, 17.288 nm and 24.434 nm, respectively
An improved two-layer SOM classifier for handwritten numeral recognition

In the past several years, wepsilave been developing a high performance two-layer SOM classifier for handwritten Bangla numeral recognition. We have reported previously the structure and the learning algorithm of the two-layer SOM. In this paper, we fuse the outputs of the second layer SOMs to improve the classification ability, based on confidence coefficient of the SOMs in the second layer. Experimental results on the numeral images obtained from real Bangladesh envelopes have proved the validity of the proposed method.
Research on the Staff Relationship Risks in the Merger and Acquisition Process of State-Owned Enterprises

With the degree development of China's market economy deep, enterprises' merger and acquisition is used widely as a main way of the reform of state-owned enterprises. However, the merger and acquisition of state-owned enterprises not only creates profit¿Cbut also brings about risks. On the basis of collecting risk-related literatures, with "Tonghua Iron and Steel Incident" occurrence, this paper analyzes characterization and causes of staff relation risks in the merger and acquisition process of state-owned enterprises. According to risks appearance, risk prevention measures are provided and the early-warned system of staff relation risks is built in the merger and acquisition process of state-owned enterprises, so as to make for achieving a win-win development of enterprises and employees.
Simulation-Based Timing Analysis of Complex Real-Time Systems

This paper presents an efficient best-effort approach for simulation-based timing analysis of complex real-time systems. The method can handle in principle any software design that can be simulated, and is based on controlling simulation input using a simple yet novel hill-climbing algorithm. Unlike previous approaches, the new algorithm directly manipulates simulation parameters such as execution times, arrival jitter and input. An evaluation is presented using six different simulation models, and two other simulation methods as reference: Monte Carlo simulation and MABERA. The new method proposed in this paper was 4-11 % more accurate while at the same time 42 times faster, on average, than the reference methods.
Multiscale guided deblurring: Chromatic aberration correction in color and near-infrared imaging

Chromatic aberration, caused by photographic lens imperfections, results in the image of only one spectral channel being sharp, while the other channels are blurred depending on their wavelengths difference with the sharp channel. We study chromatic aberration for a system that jointly records color and near-infrared (NIR) images on a single sensor. Chromatic aberration in such a system leads to a blurred NIR image when the color image is in-focus and sharp. We propose an algorithm that deblurs the NIR image using the gradients of the sharp color image, as both scene representations are generally similar. However, the details of these images often exhibit significant differences due to varying scene reflection and absorption in the corresponding bands. To account for this, we compute the correlation between color and NIR gradients, and use the gradients of the color image in reconstructing NIR only where the gradients are highly correlated. We propose a multiscale scheme that gradually deblurs NIR and accurately computes similarities between color and NIR gradients. Experimental results show that our algorithm recovers details of NIR without producing visible artifacts.
Design Techniques for a 60-Gb/s 288-mW NRZ Transceiver With Adaptive Equalization and Baud-Rate Clock and Data Recovery in 65-nm CMOS Technology

Design techniques for a complete 60-Gb/s non-return-to-zero transceiver with adaptive equalization as well as baud-rate clock and data recovery (CDR) are demonstrated. A complete equalization front end with per-path adaptation and per-sampler offset calibration enables 60-Gb/s operation over realistic channels. Current integration in the front end for energy-efficient equalization is combined with integration phase dithering to realize a robust baud-rate CDR. Correlation of the adaptive error sampler output with the phase dithering sequence indicates the direction of phase offset, and the resulting baud-rate CDR saves power and complexity compared to an oversampling CDR by not requiring additional clock phases/deserializers. The proposed 65-nm CMOS transceiver operates at 60 Gb/s with an eye opening of 30% UI and consumes 288 mW while equalizing 21 dB of loss at 30 GHz over a 0.7-m Twinax cable.
A framework for adaptive parameter estimation with finite memory

We consider the problem of estimating an unknown parameter from a finite collection of different statistical experiments. The measurements are taken sequentially. Based on the observations made so far, we adaptively select the next experiment that provides the most information about the parameter. Summarizing past information with finite memory, we present a general framework for efficient adaptive estimation, with the sensing schemes fully characterized by finite-state parametric Markov chains. We establish an analytic formula linking the asymptotic performance of adaptive estimation schemes to the steady-state distributions of the associated Markov chains. Consequently, finding optimal adaptive strategies can be reformulated as the problem of designing a (continuous) family of Markov chains with prescribed steady-state distributions. We also propose a quantitative design criterion for optimal sensing policies based on minimax ratio regret.
Cost-sensitive MQDF classifier for handwritten Chinese address recognition

To overcome the class imbalance problem in Chinese address recognition, we propose a cost-sensitive learning method for MQDF classifier. In the learning process, a cost vector is introduced to the discriminative learning process of MQDF, and minimization of misclassification cost is used as the convergence criteria. A cost-sensitive MQDF classifier (CMQDF) is then obtained, and it is integrated into a handwritten Chinese address recognition (HCAR) system to validate its effectiveness. The experimental results show that CMQDF is an effective cost-sensitive classifier for the class imbalance problem in HCAR system. Moreover, it enhances the reliability of the HCAR system.
Video Processing using the 3-Dimensional Surfacelet Transform

Motion estimation is a common ingredient in many state-of- the-art video processing algorithms, serving as an effective way to capture the spatial-temporal correlation in video signals. However, the robustness of motion estimation often suffers from problems such as ambiguities of motion trajectory (i.e. the aperture problem) and illumination variances. In this paper, we explore a new framework for video processing based on the recently proposed surfacelet transform. Instead of containing an explicit motion estimation step, the surfacelet transform provides a motion-selective subband decomposition for video signals. We demonstrate the potential of this new technique in a video denoising application.
Design and Analysis of Energy-Efficient Reconfigurable Pre-Emphasis Voltage-Mode Transmitters

This paper analyzes the signaling and digital power overhead of pre-emphasis voltage-mode transmitters. Utilizing a shunt branch in parallel with the differential channel to implement pre-emphasis is shown to have the best overall energy-efficiency. Leveraging this technique, an efficient pre-emphasis voltage mode transmitter architecture with output amplitude control, pre-emphasis coefficient control, and online impedance calibration is proposed and demonstrated. A 65 nm LP CMOS implementation of this architecture dissipates ~ 10 mW from a 1.2 V supply when transmitting 10 Gb/s 200 mV differential amplitude data with 2-tap pre-emphasis, achieving 1 pJ/bit energy efficiency.
New conceptual electromagnetic induction fluid heating system using resonant PWM inverter with auto-tuning PID controller

A new concept of a novel prototype electromagnetic induction fluid heating appliance using series capacitor-compensated load resonant high-frequency inverter with a phase-shifted PWM and power factor correction schemes. Its operating characteristics in steady-state are illustrated and evaluated using the computer simulation and experimental results. From a practical point of view, a promising cost effective application of this electromagnetic induction fluid heating appliance is proposed, which is more suitable for industrial, chemical and consumer pipeline systems.
Assessment of trace-differences in timing analysis for Complex Real-Time Embedded Systems

In this paper, we look at identifying temporal differences between different versions of Complex Real-Time Embedded Systems (CRTES) by using timing traces representing response times and execution times of tasks. In particular, we are interested in being able to reason about whether a particular change to CRTES will impact on their temporal performance, which is difficult to answer due to the complicated timing behavior such CRTES have. To be specific, we first propose a sampling mechanism to eliminate dependencies existing in tasks' response time and execution time data in the traces taken from CRTES, which makes any statistical inference in probability theory and statistics realistic. Next, we use a mature statistical method, i.e., the non-parametric two-sample Kolmogorov-Smirnov test, to assess the possible temporal differences between different versions of CRTES by using timing traces. Moreover, we introduce a method of reducing the number of samples used in the analysis, while keeping the accuracy of analysis results. This is not trivial, as collecting a large amount of samples in terms of executing real systems is often costly. Our evaluation using simulation models describing an industrial robotic control system with complicated tasks' timing behavior, indicates that the proposed method can successfully identify temporal differences between different versions of CRTES, if there is any. Furthermore, our proposed method outperforms the other statistical methods, e.g., bootstrap and permutation tests, that are often widely used in contexts, in terms of bearing on the accuracy of results when other methods have failed.
A modified ESPRIT algorithm for signal DOA estimation

We propose a new algorithm to estimate the 2-D direction of arrival (DOA) of narrowband sources lying in the far field of the array. The algorithm requires no reference signal, which consists of two stage processes. The algorithm of the first stage using the method of ESPRIT acquires data. At the second stage, the method using the space-time DOA matrix method to processing the data. The method transfers the observed data from space domain to 2-D space-time domain by exploiting the cross-correlation of the array outputs. Simulation results show a good improvement in processor performance.
A vision-based lighting conditions classification method for video surveillance

To enhance the robustness of the vehicle detection system, an effective algorithm to identify the lighting conditions (daylight, night, lowlight (dawn, dusk)) based on histogram analysis is presented in this paper. The algorithm consists of two procedures: extracting and updating background image, and generating a lighting conditions classifier based on background image analysis. The algorithm is based on naive Bayes algorithm, and it is classified by the Bhattacharyya distance of the histogram of each color component(R-component, G-component, and B-component) in RGB space. The experiment has shown that the lighting conditions classification method proposed improves the performance in lighting classification.
Combination of ResNet and Center Loss Based Metric Learning for Handwritten Chinese Character Recognition

Nowdays, deep networks have played a dominant role in the filed of handwritten Chinese character recognition(HCCR), and their recognition performance have reported to surpass human beings significantly. Recent research shows that the performance of deep networks has been further improved with the assistance of metric learning, since more inter-class and intra-class information can be captured. Center loss is a powerful metric learning strategy, and has no necessity of selecting sample units compared with other metric learning strategies. In this paper, we explore the effectiveness of the center loss based metric learning in boosting deep networks for the task of HCCR. By combining with the residual network (ResNet), an accuracy of 97.03% has been achieved on the ICDAR-2013 dataset, which is much higher than the deep networks without metric learning.
Word searching in CCITT group 4 compressed document images

In this paper, we present a compressed pattern matching method for searching user queried words in the CCITT Group 4 compressed document images, without decompressing. The feature pixels composed of black changing elements and white changing elements are extracted directly from the CCITT Group 4 compressed document images. The connected components are labeled based on a line-by-line strategy according to the relative positions between the changing elements of the current coding line and the changing elements of the reference line. Word boxes are bounded by merging the connected components. A two-stage matching strategy is constructed to measure the dissimilarity between the template image of the user's query word and the words extracted from document images. Experimental results confirmed the validity of the proposed approach.
Design and experiment of a high gain axial-mode helical antenna

A high gain axial-mode helical antenna with parasitic element is proposed. A metal wafer is used as parasitic element, loaded in the front of general axial-mode helical antenna to achieve the aim of improving antenna gain. The diameter of the wafer and the distance between the wafer and the end of the helical wire, which can influence the antenna characteristics, are both simulated and optimized by CST Microwave Studio®, a 3-dimentional full wave electromagnetic simulation software. Prototype of the proposed antenna is manufactured according to the optimal simulated results and measured in the microwave anechoic chamber. Compared with general antennas, both the simulated and measured results indicate that the helical antenna with parasitic element can boost 0.8dB in gain under the circumstance of increasing 8.3% of the antenna axial length. In addition, this antenna can achieve the band 2.1–2.7GHz in its operating bandwidth, and the maximum axial ratio is up to 1.3dB in pass-band, which is not subject to the impact of parasitic element. The proposed antenna can applied in WLAN systems.
Understanding symmetric smoothing filters via Gaussian mixtures

We study a class of smoothing filters for image denoising. Expressed as matrices, these smoothing filters must be row normalized so that each row sums to unity. Surprisingly, if one applies a column normalization to the matrix before the row normalization, the denoising quality can often be significantly improved. This column-row normalization corresponds to one iteration of a symmetrization process called the Sinkhorn-Knopp balancing algorithm. However, a complete understanding of the performance gain phenomenon is lacking. In this paper, we analyze the performance gain from a Gaussian mixture model (GMM) perspective. We show that the symmetrization is equivalent to an expectation-maximization (EM) algorithm for learning the GMM. Moreover, we make modifications to the symmetrization procedure and present a new denoising algorithm. Experimental results show that the new algorithm achieves comparable denoising results to some state-of-the-art methods.
A 60Gb/s 173mW receiver frontend in 65nm CMOS technology

This paper presents a 65nm CMOS 60Gb/s receiver frontend incorporating CTLE, FFE, DFE, output slicers and clock generation as well as distribution circuits. Current-integration along with cascode gain control is used to maintain equalizer linearity under varying gain without sacrificing power consumption. Interleaved deserializing slicers achieve the high gain required for adaptive error sampling. The receiver operates error free over >1e12 bits at 60Gb/s, occupies 0.16mm2, and consumes 173mW.