nils-herrmann/clean_scirepeval huggingface dataset:
A Fake Iris Detection Method Based on FFT and Quality Assessment

In recent years, iris recognition is becoming a very active topic in both research and practical applications. However, fake iris is a potential threat there are potential threats for iris-based systems. This paper presents a novel fake iris detection method based on the analysis of 2-D Fourier spectra together with iris image quality assessment. First, image quality assessment method is used to exclude the defocused, motion blurred fake iris. Then statistical properties of Fourier spectra for fake iris are used for clear fake iris detection. Experimental results show that the proposed method can detect photo iris and printed iris effectively.
Separation of interleaved Markov chains

We study the problem of separating interleaved sequences from discrete-time finite Markov chains. Previous work has considered the setting where the Markov chains participating in the interleaving have disjoint alphabets. In this work, we consider the more general setting where the component chains' alphabets can overlap. We formulate the problem as a hidden Markov model (HMM) and develop a deinterleaving algorithm by modifying classical HMM estimation techniques to take advantage of the special structure of our deinterleaving problem. Numerical results verify the effectiveness of the proposed method.
Research on a Global Optimization Method for QoS of Web Service Composite

With the development of Web service technology, applications constructed by Composite Web Service becoming possible, so the importance of optimization problems for QoS of Web Service composite are becoming increasingly evident. Genetic algorithm and ant colony algorithm are both belong to the global optimization methods of QoS. For ant colony algorithm easy to drop into the slow pace of convergence while genetic algorithm easy to fall into local optimal solution and the low efficiency problem, this paper combined with the two algorithms' advantages, give full play to the positive feedback characteristics of ant colony algorithm and genetic algorithm's rapid global search capability, improves the global optimization algorithm's ability. After testing and analysis, it shows that the presented algorithm resolve the optimization problem of QoS better.
An ultra-low-power variable-accuracy bit-serial FFT butterfly processing element for IoT sensors

A key challenge in the design of Internet-of-Things sensors, specifically those powered by energy harvesters, is to minimise the power consumption and realize energy-scalable operation to meet the requirements of the variable power source. This paper proposes an ultra-low power Decimation-in-Time (DIT) radix-2 butterfly arithmetic block for an FFT processor, designed using a bit-serial architecture and operating at a near-threshold voltage (0.6 V). The bit-precision of the design can be dynamically adjusted between 8 and 16 bits. The reduced precision processing can decrease operation clock cycles and energy consumption. The proposed design is demonstrated using IBM 130-nm CMOS logic process and results show that at a 16kHz throughput frequency, the active power and energy consumption are only 315 nW and 31 pJ per butterfly respectively for 16-bit accuracy. This represents a saving of 61 % and 84 % respectively compared with an equivalent state-of-the-art parallel design. Furthermore, approximately a 46.2 % additional energy saving can be achieved if the bit precision is reduced to 8 bits.
Tractor and semi-trailer transportation's effect on the reduction of CO2 emission in China

Tractor and semi-trailer (T&St) transportation is helpful to promote productivity of freight transportation and to reduce CO2 (carbon dioxide) emission according to different countries' experience. The supervisors of China have released certain policies to encourage the development of T&St transportation, while logistics enterprises and academia haven't adapted timely to practice. It is analyzed in this paper some basic characters of T&St transportation. The practice periods of T&St transportation in China have been reviewed and the most necessary issues of T&St transportation are brought forward. A descriptive model has been set up to analyze the relationship between economic profits and CO2 emission of T&St transportation. It is found out that T&St transportation could generate considerable economic benefits and effects on CO2 emission reduction.
Kaczmarz Method for Solving Quadratic Equations

Estimating low-rank positive-semidefinite (PSD) matrices from symmetric rank-one measurements is of great importance in many applications, such as high-dimensional data processing, quantum state tomography, and phase retrieval. When the rank is known a priori, this problem can be regarded as solving a system of quadratic equations of a low-dimensional subspace. The authors develop a fast iterative algorithm based on an adaptation of the Kaczmarz method, which is traditionally used for solving overdetermined linear systems. In particular, the authors characterize the dynamics of the algorithm when the measurement vectors are composed of standard Gaussian entries in the online setting. Numerical simulations demonstrate the compelling performance of the proposed algorithm.
Multidimensional Directional Filter Banks and Surfacelets

In 1992, Bamberger and Smith proposed the directional filter bank (DFB) for an efficient directional decomposition of 2-D signals. Due to the nonseparable nature of the system, extending the DFB to higher dimensions while still retaining its attractive features is a challenging and previously unsolved problem. We propose a new family of filter banks, named NDFB, that can achieve the directional decomposition of arbitrary N-dimensional (Nges2) signals with a simple and efficient tree-structured construction. In 3-D, the ideal passbands of the proposed NDFB are rectangular-based pyramids radiating out from the origin at different orientations and tiling the entire frequency space. The proposed NDFB achieves perfect reconstruction via an iterated filter bank with a redundancy factor of N in N-D. The angular resolution of the proposed NDFB can be iteratively refined by invoking more levels of decomposition through a simple expansion rule. By combining the NDFB with a new multiscale pyramid, we propose the surfacelet transform, which can be used to efficiently capture and represent surface-like singularities in multidimensional data
A 66Gb/s 46mW 3-tap decision-feedback equalizer in 65nm CMOS

Given the continuously climbing data rates of high-speed I/O's, equalizer circuits-and particularly decision-feedback equalizer (DFE) designs-are being pushed to operate at ever-higher speeds. At 20 to 40Gb/s data-rates, loop-unrolled DFEs are widely adopted to relieve the feedback timing constraints of the initial tap(s) . However, loop-unrolling introduces additional delay into the critical paths of later (non-unrolled) DFE taps due to the selection MUXes, and with its exponential growth in complexity, does not scale well as the number of unrolled taps increases. Perhaps due to this challenge, no multi-tap DFE solutions with single pJ/bit efficiencies have yet been demonstrated at data rates >40Gb/s.
Text-independent writer identification using SIFT descriptor and contour-directional feature

This paper presents a method for text-independent writer identification using SIFT descriptor and contour-directional feature (CDF). The proposed method contains two stages. In the first stage, a codebook of local texture patterns is constructed by clustering a set of SIFT descriptors extracted from images. Using this codebook, the occurrence histograms are calculated to determine the similarities between different images. For each image, we obtain a candidate list of reference images. The next stage is to refine the candidate list using the contour-directional feature and SIFT descriptor. The proposed method is evaluated with two datasets: the ICFHR2012-Latin dataset and the ICDAR2013 dataset. Experimental results show that the proposed method outperforms the state-of-the-art algorithms and archives the best performance.
Peak-to-average power ratio reduction in OFDM based on transformation of partial transmit sequences

A novel scheme (transformation of partial transmit sequences, T-PTS) is proposed for the partial transmit sequence (PTS) method to reduce peak-to-average power ratio of OFDM signals. In T-PTS certain transformations are applied on partial transmit sequences to generate more candidates for increasing performance. Simulations show that T-PTS outperforms conventional PTS for the same number of subblocks; and both schemes have similar performance for the same candidate number, but T-PTS is less complex.
The fuzzy logic clustering neural network approach for middle and long term load forecasting

Middle and long term load forecasting of power system is affected by various uncertain factors. Using clustering method numerous relative factors can be synthesized for the forecasting model so that the accuracy of the load forecasting would be improved significantly. A clustering neural network consisting of logic operators is quoted in this paper, which can be used in mid-long term load forecasting Applying logic operators and in the fuzzy theory, the algorithm speed of the clustering network will be increased. Although competitive learning algorithm is used here for the network, it solves the dead unit problem and gives more room to select the initial values of the clustering center in the clustering analysis of the history data. The proposed model considers the influences of both history and future uncertain factors. Compared with the traditional methods, the results show that the new algorithm improves the accuracy of load forecasting considerably.
Adaptive EEG signal classification using stochastic approximation methods

Classification of time-varying electrophysiological signals is an important problem in the development of brain-computer interfaces (BCIs). Designing adaptive classifiers is a potential way to address this task. In this paper, Bayesian classifiers with Gaussian mixture models (GMMs) are adopted as the decision rule to classify electroencephalogram (EEG) signals. The stochastic approximation method (SAM) is used as the specific gradient descent method for updating the parameters of mean values and covariance matrices in the distribution of GMMs, where the parameters are simultaneously updated in a batch mode. Experimental results using data from a BCI show that the stochastic approximation method is effective for EEG classification tasks.
A hybrid genetic algorithm for designing feedforward neural networks

In this paper, a hybrid algorithm is proposed for designing feedforward neural networks. A genetic algorithm is proposed to tune the connections and parameters between the input layer and the hidden layer, and orthogonal transformation is applied to tune the connections and parameters between the hidden layer and the output layer. The crossover operator and mutation operator are based on the singular value decomposition of the outputs of the hidden nodes. Using the proposed algorithm, both the structure and parameters of a neural network can be optimized efficiently. Simulations are presented to demonstrate the effectiveness of the proposed approach.
Ultrasonic motors using shear-type piezoelectric ceramics

The shear piezoelectric constant value for d<inf>15</inf> has a higher value compared with constant d<inf>31</inf> or d<inf>33</inf>. Also, the value of the shear electromechanical coupling coefficient k<inf>15</inf> is bigger than that of k<inf>31</inf> or k<inf>33</inf>. A design for an ultrasonic motor based on the shear coefficients has the potential for superior performance, relative to conventional d<inf>31</inf> and d<inf>33</inf> based ones. Thus, this paper presents ring type, tube type, disk type and rod type piezoelectric ceramic elements, which use the d<inf>15</inf> shear piezoelectric constant effect. The application methods and driving mechanism of the typical ultrasonic motors which use the shear piezoelectric ceramic elements are analyzed. The applications of shear piezoelectric ceramic elements accords with the requirements of the development trends of ultrasonic motors, such as large strain and miniaturization.
Towards feature-oriented requirements validation for automotive systems

In the modern automotive industry, feature models have been widely used as a domain-specific requirements model, which can capture commonality and variability of a software product line through a set of features. Product variants can thus be configured by selecting different sets of features from the feature model. For feature-oriented requirements validation, the variability of feature sets often makes the hidden flaws such as behavioral inconsistencies of features, hardly to avoid. In this paper, we present an approach to feature-oriented requirements validation for automotive systems w.r.t. both functional behaviors and non-functional properties. Our approach first starts with the behavioral specification of features and the associated requirements by following a restricted use case modeling approach, and then formalizes such specifications by using a formal yet literate language for analysis. We demonstrate the applicability of our approach through an industrial application of a Vehicle Locking-Unlocking system.
Optimal Spectrum Sensing Interval in MISO Cognitive Small Cell Networks

This paper considers a cognitive small cell network, where one cognitive base station (CBS) transmits information to the cognitive user and energy to the energy harvesting receivers (EHRs). The Markov channel model is exploited to characterize the state change of the macrocell base station. The spectrum sensing time, the spectrum sensing interval, and the beamforming matrixes of the CBS are jointly optimized to achieve three goals: the maximization of the CBS throughput, the minimization of the energy cost of the CBS, and the minimization of the interferences to the macrocell users (MUEs). These objectives are optimized subject to the interference constraints of the MUEs, the secrecy rate constraint, the transmit power constraint of the CBS, and the energy harvesting constraints of the EHRs. The formulated problems are challenging non-convex and difficult to solve. A 1-D line search method and semidefinite relaxation-based algorithm is proposed to solve these problems. It is proved that the optimal solution can be obtained under some conditions. If the conditions are not satisfied, Gaussian randomization procedure is used to obtain the suboptimal solutions. Simulation results verify our theoretical findings and demonstrate the effectiveness of the proposed resource allocation scheme.
Sparse spectral factorization: Unicity and reconstruction algorithms

Spectral factorization is a classical tool in signal processing and communications. It also plays a critical role in X-ray crystallography, in the context of phase retrieval. In this work, we study the problem of sparse spectral factorization, aiming to recover a one-dimensional sparse signal from its autocorrelation. We present a sufficient condition for the recovery to be unique, and propose an iterative algorithm that can obtain the original signal (up to a sign change, time-shift and time-reversal). Numerical simulations verify the effectiveness of the proposed algorithm.
A Visual Attention Based Approach to Text Extraction

A visual attention based approach is proposed to extract texts from complicated background in camera-based images. First, it applies the simplified visual attention model to highlight the region of interest (ROI) in an input image and to yield a map, named the VA map, consisting of the ROIs. Second, an edge map of image containing the edge information of four directions is obtained by Sobel operators. Character areas are detected by connected component analysis and merged into candidate text regions. Finally, the VA map is employed to confirm the candidate text regions. The experimental results demonstrate that the proposed method can effectively extract text information and locate text regions contained in camera-based images. It is robust not only for font, size, color, language, space, alignment and complexity of background, but also for perspective distortion and skewed texts embedded in images.
A Noise Robust Content-Based Music Retrieval System for Mobile Devices

This paper proposes a noise robust content-based music retrieval system for mobile devices. It takes the user's humming/singing audio input and queries the desired songs from music database. Since the system is deliberately designed for mobile devices, noise disturbance are inevitable in practical application. In order to improve the noise robustness of the retrieval system, we propose a new humming/singing audio feature extraction algorithm. A frame-to-note matching engine is employed to compute the similarity distance. The experimental results show that the proposed algorithm is efficient and robust under various noisy environments and SNR levels. For 91.46% queries, the correct songs can be retrieved among the top-10 matches in clean condition. About 85% average success rate of top-10 returns can be obtained in most noisy conditions. Even in low SNR conditions, the proposed algorithm can still achieve acceptable performance.
Optimal hypothesis testing with combinatorial structure: Detecting random walks on graphs

Suppose we have a time series of observations for each node in a network, and we wish to detect the presence of a particle undergoing a random walk on the network. If there is no such particle, then we observe only zero-mean Gaussian noise. If present, however, the location of the particle has an elevated mean. How well can we detect the particle at low signal-to-noise ratios? This is a special case of the problem of detecting hidden Markov processes (HMPs). The performance metric we analyze is the error exponent of the optimal detector, which measures the exponential rate of decay in the miss probability if the false alarm probability is held fixed as the observation time increases. This problem exhibits deep connections to a problem in statistical physics: computing the free energy density of a spin glass. We develop a generalized version of the random energy model (REM) spin glass, whose free energy density provides a lower bound for our error exponent, and compute the bound using large deviations techniques. The bound closely matches empirical results in numerical experiments, and suggests a phase transition phenomenon: below a threshold SNR, the error exponent is nearly constant and near zero, indicating poor performance; above the threshold, there is rapid improvement in performance as the SNR increases. The location of the phase transition depends on the entropy rate of the Markov process.
Blind estimation and low-rate sampling of sparse mimo systems with common support

We present a blind estimation algorithm for multi-input and multi-output (MIMO) systems with sparse common support. Key to the proposed algorithm is a matrix generalization of the classical annihilating filter technique, which allows us to estimate the nonlinear parameters of the channels through an efficient and noniterative procedure. An attractive property of the proposed algorithm is that it only needs the sensor measurements at a narrow frequency band. By exploiting this feature, we can derive efficient sub-Nyquist sampling schemes which significantly reduce the number of samples that need to be retained at each sensor. Numerical simulations verify the accuracy of the proposed estimation algorithm and its robustness in the presence of noise.
Combining color feature for scale adaptive correlation filter tracker

In order to improve the robustness of correlation filtering(CF) tracker, and solve the problems that the traditional CF methods cannot overcome the change of the object scale and the color information was not used. A scale adaptive correlation filtering tracker based on color feature fusion is proposed. Firstly, we transform the target search area of the image from RGB color space to Lab color space, and then extract the three channel Lab color features of the search area. Secondly, we combine the color feature and HOG feature into multi-channel feature. The multi-channel correlation filter was employed to locate the center of the target. Finally, the target scale model is established by Lab color feature. Acquiring different scales image block from the target position at the current frame. We get the optimal target scale by comparing with the scale model. Experiments were performed on 35 public benchmark challenging color sequences and compared with other 5 tracking methods. The results show that our proposed approach outperforms state-of-the-art tracking methods, and has a real-time tracking speed of 66 frames/s.
New kind of humidity sensitive ceramic with linear property

Using the solid phase reaction method, La/sub 1-x/Sr/sub x/FeO/sub 3/ (0<or=x<or=1) ceramic was synthesized in an oxygen structure. Its resistance vs. relative humidity (RH) characteristics as well as the dielectric and complex impedance spectral characteristics were investigated. These results indicate that the resistance of LaFeO/sub 3/ decreases with RH, while the resistance of La/sub 1-x/Sr/sub x/FeO/sub 3/ (0.1<or=x<or=0.4) increases with increasing RH, and its sensitivity increases with the increasing x value. The equivalent circuit model of La/sub 1-x/Sr/sub x/FeO/sub 3/ has been established by complex impedance analysis, from which the conclusion can be drawn that the linear positive humidity sensitivity of the material is caused by the change in grain boundary resistance.<<ETX>>
Human Body Detection Using Multi-Scale Shape Contexts

In this paper, we propose a prototype-based human detection approach using shape information. Multi-scale shape contexts descriptor is utilized to model the shapes in the procedure of human body detection. As a partial shape presentation, it is capable of modeling shapes and measuring their similarity at different scale. The multi-scale shape contexts help human detection own robustness to the variations result from noise, illumination, movement, and clutter in image. The approach consists of two steps: An edge detector is firstly performed to acquire the edges; the multi-scale shape contexts are then applied to find human body in the edges based on the similarities between the edges and a predefined human body prototype. Experimental results demonstrate the advantage of the proposed approach.
A trace-based statistical worst-case execution time analysis of component-based real-time embedded systems

This paper describes the tool support for a framework for performing statistical WCET analysis of realtime embedded systems by using bootstrapping sampling and Extreme Value Theory (EVT). To be specific, bootstrapping sampling is used to generate timing traces, which not only fulfill the requirements given by statistics and probability theory, but also are robust to use in the context of estimating the WCET of programs. Next, our proposed statistical inference uses EVT to analyze such timing traces, and computes a WCET estimate of the target program, pertaining to a given predictable probability. The evaluation results show that our proposed method could have the potential of being able to provide a tighter upper bound on the WCET estimate of the programs under analysis, when compared to the estimates given by the referenced WCET analysis methods.