nils-herrmann/clean_scirepeval huggingface dataset:
Fast and Accurate Human Detection Using a Cascade of Boosted MS-LBP Features

In this letter, a new scheme for generating local binary patterns (LBP) is presented. This Modified Symmetric LBP (MS-LBP) feature takes advantage of LBP and gradient features. It is then applied into a boosted cascade framework for human detection. By combining MS-LBP with Haar-like feature into the boosted framework, the performances of heterogeneous features based detectors are evaluated for the best trade-off between accuracy and speed. Two feature training schemes, namely Single AdaBoost Training Scheme (SATS) and Dual AdaBoost Training Scheme (DATS) are proposed and compared. On the top of AdaBoost, two multidimensional feature projection methods are described. A comprehensive experiment is presented. Apart from obtaining higher detection accuracy, the detection speed based on DATS is 17 times faster than HOG method.
Dense Correspondence Using Non-Local DAISY Forest

Dense correspondence computation is a critical computer vision task with many applications. The most existing dense correspondence methods consider all the neighbors connected to the center pixels and use local support region. However, such approach might only achieve a locally-optimal solution.In this paper, we propose a non-local dense correspondence computation method by calculating the match cost on a tree structure. It is non-local because all other nodes on the tree contribute to the match cost computing for the current node. The proposed method consists of three steps, namely: 1) DAISY descriptor computation, 2) edge-preserving segmentation and forest construction, 3) PatchMatch fast search. We test our algorithm on the Middlebury and Moseg datasets. The results show that the proposed method outperforms the state-of-the-art methods in dense correspondence computing and has a low computation complexity.
Performance improvement of BOTDR system using wavelength diversity technique

In this paper, a novel technique was proposed to improve the sensing performance by employing wavelength diversity in Brillouin optical time domain reflectometry (BOTDR). This technique enables to maximize the launch pump power to achieve a higher measurement accuracy, without activating the nonlinear effects, which limit the conventional BOTDR performance. Experimentally, we have demonstrated the proposed technique, that provides measurement accuracy improvement of 3.6 times at far end of the sensing fibre compared to the conventional BOTDR system.
Research on voltage regulation of a permanent magnet generator

In this paper, the relationship between the voltage regulation of a permanent magnet (PM) generator and the parameters of the electric machine is analyzed. According to the conditions for low voltage regulation with inductive load, the methods of reducing the voltage regulation of a PM generator by designing the proper parameters of electric machine are proposed. A low-voltage-regulation PM generator is designed. The parameters of the electric machine and the voltage regulation are achieved by using the finite element method (FEM), and the accuracy and validity of the design method are proved.
TRISHUL: A single-pass optimal two-level inclusive data cache hierarchy selection process for real-time MPSoCs

Hitherto discovered approaches analyze the execution time of a real-time application on all the possible cache hierarchy setups to find the application specific optimal two-level inclusive data cache hierarchy to reduce cost, space and energy consumption while satisfying the time deadline in real-time Multi-Processor Systems on Chip (MPSoC). These brute-force like approaches can take years to complete. Alternatively, application's memory access trace driven crude estimation methods can find a cache hierarchy quickly by compromising the accuracy of results. In this article, for the first time, we propose a fast and accurate application's trace driven approach to find the optimal real-time application specific two-level inclusive data cache hierarchy. Our proposed approach “TRISHUL” predicts the optimal cache hierarchy performance first and then utilizes that information to find the optimal cache hierarchy quickly. TRISHUL can suggest a cache hierarchy, which has up to 128 times smaller size, up to 7 times faster compared to the suggestion of the state-of-the-art crude trace driven two-level inclusive cache hierarchy selection approach for the application traces analyzed.
Real-time SAR processing of ice-sounding data integrated with mitigation of RFI signals

In this paper, we propose an approach to integrate the radio-frequency interference (RFI) mitigation technique into a real-time synthetic aperture radar (SAR) imaging algorithm applied to very high frequency (VHF) ice-sounding data that produces swath measurements of ice sheet surface topography, ice thickness, and radar reflectivity of both Internal Reflecting Horizons (IRHs) and bedrock of the ice sheet. According to the proposal, the RFI suppression method-an adaptive line enhancer (ALE) controlled by the normalized least mean square (NLMS) , is incorporated into the real-time imaging algorithm named modified Planar Subarray Processing (PSAP) algorithm based on inverse scaled Fourier transform (ISFT). The approach is tested successfully on real data recorded over Chinese Kunlun Station during the 29th Chinese Antarctic Research Expedition (CHINARE 29). The experimental result indicates that the narrowband RFI could be subtracted from the desired chirp signal return during the imaging processing, and the computational efficiency of the imaging processing is preserved.
Object Detection Based on Co-occurrence GMuLBP Features

Image co-occurrence has shown great powers on object classification because it captures the characteristic of individual features and spatial relationship between them simultaneously. For example, Co-occurrence Histogram of Oriented Gradients (CoHOG) has achieved great success on human detection task. However, the gradient orientation in CoHOG is sensitive to noise. In addition, CoHOG does not take gradient magnitude into account which is a key component to reinforce the feature detection. In this paper, we propose a new LBP feature detector based image co-occurrence. Building on uniform Local Binary Patterns, the new feature detector detects Co-occurrence Orientation through Gradient Magnitude calculation. It is known as CoGMuLBP. An extension version of the GoGMuLBP is also presented. The experimental results on the UIUC car data set show that the proposed features outperform state-of-the-art methods.
Analysis on the Spillover Effect of Foreign Direct Investment: Case of Suzhou

Based on time series data of Suzhou from 1991 to 2008, this paper carries out empirical research on the spillover effect of foreign direct investment by using ADF test, Granger causality test and regression method. The result shows that foreign direct investment has spillover effect to GDP which is the Granger cause of GDP in Suzhou. Meanwhile, the inverse relationship does not hold. We also find in the short run, foreign direct investment can increase GDP significantly and positively, but in the long run, it has negative effect on GDP of Suzhou.
Research of an Improved BP Decoding Algorithm

That the computation load is big is a shortcoming of traditional BP decoding algorithm. According to this shortcoming we give an improved BP decoding algorithm. The decoding algorithm in the iterative decoding process, only need to update the possibility of a wrong bit of information without having to update the information whose reliability is very high, raises the decoding efficiency and reduces decoding time delay. The simulation result shows: the calculation load of improved BP decoding algorithms descends distinctively with the increasing iteration numbers; Along with the times increasing of LDPC code iterations upper limit, coding performance can be obviously improved.
Robust Multifactor Speech Feature Extraction Based on Gabor Analysis

The performance of speech recognition systems relies on the consistency and adaptation of the speech feature in complex conditions during the training and testing stages. Traditional systems usually perform poorly under adverse noisy conditions and are not applicable to most real world problems. In this paper, we investigate the speech feature extraction problem in a noisy environment and propose a novel approach based on Gabor filtering and tensor factorization. Recent physiological and psychoacoustic experimental results suggest that the localized spectro-temporal features are essential for auditory perception. To explore this property, we represent the speech signal by using a general higher order tensor and employ two-dimensional Gabor functions with different scales and directions to analyze the localized patches of the power spectrogram. Then the Nonnegative Tensor PCA with sparse constraints is proposed to learn the projection matrices from multiple interrelated feature subspaces. The objective of the sparse constraints is to preserve the statistical characteristic of clean speech data by finding projection matrices of speech subspaces and reduce the noise components which have distributions different from those of clean speech. A multifactor analysis method is proposed to extract robust sparse features by processing the data samples in tensor structure. The simulation results indicate that our proposed method is able to improve the speech recognition performance, especially in noisy environments, compared with the traditional speech feature extraction methods.
Lossy-to-lossless ROI coding of chromosome images using modified SPIHT and EBCOT

This paper proposes a lossy-to-lossless region of interest (ROI) compression scheme based on set partitioning in hierarchical trees (SPIHT) and embedded block coding with optimized truncation (EBCOT) (EBCOT is the base of the JPEG-2000 standard). However, SPIHT does not support ROI coding and JPEG-2000 does not allow lossy-to-lossless ROI compression. For our application, we modify the original SPIHT and EBCOT algorithms for lossy-to-lossless ROI coding of both the foreground and the background of chromosome images. Experiments show that our implementations offer better compression performance with exact ROI support than other image coders.
Extended Depth-of-Field Microscope Imaging: MPP Image Fusion VS. WAVEFRONT CODING

Biomedical specimens thicker than the microscope depth-of-field (DOF) produces images of limited quality where a part of the specimen is in focus and others out of focus. Even the best in-focus image always contains objects that are blurred. These blurred contents make the quantitative and accurate analysis of the specimen difficult. The problem can be remedied by extending the DOF of the imaging system. One way to achieve this is to apply digital image fusion, a software technique that combines multiple images into a carefully synthesized image. Another way, albeit more expensive, is to incorporate modification in the optical system. In this paper we describe an extended DOF method that is based on multiscale pointwise product (MPP) image fusion, and compare it with the state-of-the-art wavefront coding technique which involves optical modification. We show competitive performance of our method and contrast its pros and cons with those of the wavefront coding.
Design and Implementation of a Delay-Based PUF for FPGA IP Protection

Physical Unclonable Function (PUF) makes use of the uncontrollable process variations during the production of IC to generate a unique signature for each IC. It has a wide application in security such as FPGA Intellectual Property (IP) protection, key generation and digital rights management. Ring Oscillator (RO) based PUF and Arbiter-based PUF are the most popular PUFs, but they are not specially designed for FPGA. RO-based PUF incurs high resource overhead while obtaining less challenge-response pairs, and requires ``hard macros'' to implement on FPGA. The arbiter-based PUF brings low resource overhead, but its structure is hard to be mapped on FPGA. Anderson'PUF can address these weaknesses of current Arbiter-based and RO-based PUFs. However, it cannot be directly implemented on the new generation FPGAs, and therefore it has the scalability issue. In order to address these problems, this paper presents a delay-based PUF using the intrinsic structure of FPGA (look-up table and multiplexer). The proposed delay-based PUF is completely realized on 28nm FPGAs. The experimental results show its high uniqueness and reliability. Moreover, we test the proposed PUF in the high temperature, and the results show its availability. Finally, the prospect of the proposed PUF in the FPGA IP protection is discussed.
A New Model for Continuous Network Access Control of Trusted Network Connection

In current trusted network connection (TNC) model, access decisions are generally made at the time of request but typically do not recognize ongoing controls for relative continuous access or for immediate revocation. We propose a new model named trusted network connection based on usage control (U-TNC), which combine TNC solutions with usage control (UCON) to achieve pre-authorization and ongoing-authorization network access control. Moreover, attribute mutability that deal with updates on related endpoint or resource/network attributes as a consequence of access have been presented. This property enable the system disconnect the endpoint whose attributes disagree with security policies in time, and is very important to invoke ongoing authorization. At the end, the paper shows how to use the model in E-commerce and gives a logic specification of the example.
Delay-dependent output feedback H∞ guaranteed cost control for uncertain discrete-time switched systems with multiple time-varying delays

This paper deals with the problem of H∞ guaranteed cost control for uncertain discrete-time switched systems with multiple time-varying delays. The objective is to design a stable H∞ guaranteed cost controller, which guarantees that the closed-loop system is asymptotically stable with H∞ performance and the cost function value is not more than a specified upper bound for all admissible uncertainties. By resorting to switched Lyapunov function, some delay-dependent sufficient conditions are presented in terms of linear matrix inequalities. A numerical example is provided to demonstrate the effectiveness of the proposed algorithms.
Efficient People Counting with Limited Manual Interferences

People counting is a topic with various practical applications. Over the last decade, two general approaches have been proposed to tackle this problem: (a) counting based on individual human detection; (b)counting by measuring regression relation between the crowd density and number of people. Because the regression based method can avoid explicit people detection which faces several well-known challenges, it has been considered as a robust method particularly on a complicated environments. An efficient regression based method is proposed in this paper, which can be well adopted into any existing video surveillance system. It adopts color based segmentation to extract foreground regions in images. Regression is established based on the foreground density and the number of people. This method is fast and can deal with lighting condition changes. Experiments on public datasets and one captured dataset have shown the effectiveness and robustness of the method.
The application of BP neural networks in the Study of influence factor of CBM production capacity

The exploitation of coal seam gas is influenced by various geological factors. In order to study the weights of these factors, this paper establishes a BP neural network prediction model by taking No.3 coal seam in the representative area of southern Qinshui basin as an example. It finds out that the potential coal-seam gas capacity of southern area of Qinshui basin is influenced by some primary factors as follows: the depth of the coal seam, the thickness of the coal seam, the content of the gas, the degree of permeability, the reservoir pressure, the lithology of root floor, geological structures, ash content and so on. Among these factors, the degree of permeability, the content of gas, and the geological structures weight higher than the other factors, and the depth of coal seam is the least influence factor. The research findings are consistent with the actual mining emissions of CBM, which explains that BP neural networks prediction model has a strong nonlinear approximation ability, which can accurately evaluate the nonlinear relation between CBM exploration potential and main control factors.
Research on Campus Information Service Platform Based on Locating System of Campus Wireless LAN

Based on the analysis of the traditional passive information service and the locating technology of campus wireless LAN, a new idea of message pushing based on user location is discussed, and the architecture of information service platform based on campus location is studied, and the function is designed in order to strengthen campus management. The paper presents an experimental location-based information service platform which can active push personalized service messages to users based on different location. The platform also can be used for location management and location analysis, and the early warning would be triggered by the abnormal results. Finally, in order to maximize the effectiveness of the platform, this paper discusses the basic requirements of the platform in the actual operation, and the next step of the experimental platform research objectives.
Study on spatial interpolation method and its application

Spatial interpolation is important and fundamental to graphing, analysing and understanding of spatial data. In this paper, we introduce some spatial interpolation methods which are suited to 3D geological modeling. We have designed the framework of interpolation algorithm, and encapsulate API to GeoSIS system which provides an interface for easily accessing various interpolations. By calling API, the interpolated values can be calculated out based on the given samples, and then triangle irregular network surfaces can be constructed and visualized in 3D. The results of application show that the methods designed can to produce smooth surfaces which pass through all or most of the given samples and minimize error.
Wideband noise cancelling LNA with tunable active inductor for DRM/DAB receiver

This article propose a novel tunable active inductor for high gain-bandwidth LNA. Using this active inductor, a noise-cancelling LNA with gain-peak technique is designed for DRM/DAB receiver. Consequently, a broader -3dB bandwidth is substantially achieved from the LNA. By adding a regulated capacitance and a feedback resistance into a conventional active inductor, the quality-factor and the inductance can be increased. The tuning range is widened and the performances of the active inductor are insensitive for the PVT variations at the same time. The proposed circuit is verified with a 0.18-μm CMOS process, the post-simulation results demonstrate that the LNA has broader 3-dB bandwidth.
Detection of Gaussian signals on a sensor array

In array signal processing, the most common model describing the received signals and noise is Gaussian. It is well known that if the model is proper, the estimator which gives the best performance is the one constructed by fully using the statistic information of the model. The authors develop a detector for the number of signals. Such an estimator is constructed with utilising the log-likelihood function and an information theoretical criterion. The consistency of the detector is proved and the simulations with a comparison to the MDL detector are also presented.<<ETX>>
Automatic Gait Recognition Using Weighted Binary Pattern on Video

Human identification by recognizing the spontaneous gait recorded in real-world setting is a tough and not yet fully resolved problem in biometrics research. Several issues have contributed to the difficulties of this task. They include various poses, different clothes, moderate to large changes of normal walking manner due to carrying diverse goods when walking, and the uncertainty of the environments where the people are walking. In order to achieve a better gait recognition, this paper proposes a new method based on Weighted Binary Pattern (WBP). WBP first constructs binary pattern from a sequence of aligned silhouettes. Then, adaptive weighting technique is applied to discriminate significances of the bits in gait signatures. Being compared with most of existing methods in the literatures, this method can better deal with gait frequency, local spatial-temporal human pose features, and global body shape statistics. The proposed method is validated on several well known benchmark databases. The extensive and encouraging experimental results show that the proposed algorithm achieves high accuracy, but with low complexity and computational time.
Study on Leaching Mechanism of Micro/Nano Particles from Cunninghamia lanceolata Wood in NaOH Liquor

In order to overcome the above-mentioned negative effects, the extractives of the fresh Cunninghamia lanceolata shavings, which were obtained by using both the sorbitic-extracting approaches, were analyzed by ZETA and SIZER to find out the leaching rule of nano particles in Chinese-fir wood. The results are as follows: (1) The extraction mass arrived at the maximum value when NaOH concentration is about 18.5. The micro/nano particles in Cunninghamia lanceolata shavings would be discharged sufficiently in the NaOH solution with the increaing of extracting time.(2) The diameter the micro/nano particles of wood extractives and hemicellulose is chiefly between 24.4-1720 nm according to volume fraction when NaOH concentration is <10%. And the diameter the micro/nano particles of wood extractives and hemicellulose is chiefly between <342 nm according to volume fraction when NaOH concentration is <10%. Micro/nano particles in the diameter of 24.4-91.3 nm, 342-1720 nm, 0-24.4 nm is the most at the extracting time of 3 h, 16 h, 24 h, respectively. (3)The diameter the micro/nano particles of wood extractives and hemicellulose is chiefly between 24.4-1720 nm according to number fraction.Micro/nano particles in the diameter of 91.3-342 nm, 91.3-342 nm, 91.3-1720 nm is the most at the extracting time of 3 h, 16 h, 24 h, respectively.
High Sensitivity Ammonia Gas Sensor Based on a Silica-Gel-Coated Microfiber Coupler

In this paper, a high-sensitivity ammonia gas sensor is proposed based on a silica-gel-coated microfiber coupler (MFC). The MFC structure is formed by the two tapered fibers with 3 μm waist diameter each, which were fabricated by using a customized microheater brushing technique. Silica gel coating was prepared by a sol-gel technique and applied on the surface of the MFC as a thin layer. The spectral characteristics of the proposed sensor were studied under various ammonia gas concentrations. The experimental results show that the coating thickness strongly affected the sensitivity of the MFC-based sensor to ammonia gas concentration. For the sensor with a 90 nm silica gel coating thickness, the highest measurement sensitivity is 2.23 nm/ppm for ammonia gas concentration, and the resolution is as good as 5 ppb, while the measured response and recovery times are ∼50 and 35 s, respectively. Finally, it is demonstrated that the proposed sensor offers good repeatability and selectivity to ammonia gas.