nils-herrmann/clean_scirepeval huggingface dataset:
Monitoring the meteorological drought in the middle reaches of Heihe River basin based on TRMM precipitation data

Tropical Rainfall Measurement Mission (TRMM) is an useful data for different application, whereas the data was less used in monitoring the meteorological drought in arid regions. In this study, we investigated the spatial-temporal process of a meteorological drought event (occurred at middle reach of Heihe River basin from May 2009 to September 2009) use TRMM 3B43 precipitation data product and observed precipitation anomaly. Some of the conclusions can be drawn from our study. Firstly, when the cumulative effects of pre-precipitation were taken into account, the meteorological drought monitoring results represented by the precipitation anomaly percentage index coincide with the actual situation. Secondly, the spring wheat in our study area experienced a constantly aggravating to gradually easing meteorological drought during May 2009 to September 2009. The drought approached severest at April 2009 and the core areas of drought were mainly distributed in the regions of Middle reach of Heihe River basin. At the end of April 2009, with the multiple effects of precipitation, the drought pressure in our study area greatly relieved. Thirdly, due to the better temporal and spatial continuity than the ground-based meteorological observation, TRMM precipitation data shown good application potential in drought monitoring at national or regional scale.
An experimental study of deep convolutional features for iris recognition

Iris is one of the popular biometrics that is widely used for identity authentication. Different features have been used to perform iris recognition in the past. Most of them are based on hand-crafted features designed by biometrics experts. Due to tremendous success of deep learning in computer vision problems, there has been a lot of interest in applying features learned by convolutional neural networks on general image recognition to other tasks such as segmentation, face recognition, and object detection. In this paper, we have investigated the application of deep features extracted from VGG-Net for iris recognition. The proposed scheme has been tested on two well-known iris databases, and has shown promising results with the best accuracy rate of 99.4%, which outperforms the previous best result.
Current reference with temperature compensation for low power applications

This paper proposes an all CMOS current reference with temperature compensation for low power applications. An offset voltage between the gate terminals of two MOS transistors is utilized to improve the temperature dependency caused by the carrier mobility. The circuit is designed and simulated in 0.18 μm standard CMOS technology. With a 52 nA output current, the temperature coefficient is 60 ppm/°C in a temperature range from -40 °C to 85 °C, the maximum process corner deviation is ±3%, the line regulation is 1780 ppm/V with a supply voltage ranging from 0.95 V to 2.5 V.
Cooperative Source and Channel Coding for Wireless Video Transmission

Past work on cooperative communications has indicated substantial improvement in channel reliability through cooperative transmission strategies. To exploit this benefit for video transmission, we propose to jointly allocate bits among video coding, channel coding and cooperation to optimize the decoded video quality. Recognizing that not all source bits are equal, we further propose to protect the more important bits through user cooperation. We simulate and compare four modes of video transmission that differ in their error protection strategy (equal vs. layered, with vs. without cooperation). Our simulation uses the H.263+ video codec and the RCPC channel code over quasi-static Rayleigh fading channels. We show that cooperation can provide significant improvement over no-cooperation when the average channel SNR is in the low to medium range, and that layered cooperation can extend this benefit to the entire range of channel quality.
The Differences between Self-Organized Group and Designated Group for Cooperative Learning

The literatures have indicated that cooperative learning facilitates enhancing learning performance, which focuses on (1) the interaction and the collaboration between grouping members, and (2) one member helping other members to be successful. The literatures have shown that the grouping organization is a critical issue for promoting learning achievement in cooperative learning. To evaluate the differences between the self-organized groups and the designated groups, this paper has conducted a serial of experiments and assessments to study the differences of learning performance, participation and satisfaction degree. In the self-organized groups the grouping members are decided by the grouping members, whereas the grouping members in the designated group are composited of the considering conditions. The experiment have demonstrated that the learning achievement of the designated group is high than the self-organized group. For the participation and the satisfaction degree of the curriculum, the self-organized group is high than the designated group.
Evaluation of Carrier-Based Modulation Techniques With Common-Mode Voltage Reduction for Neutral Point Clamped Converter

Common-mode voltage output is closely associated with switching states in three-phase three-wire neutral point clamped converter. Detailed analysis shows avoiding redundant states can effectively improve the common-mode voltage, so this paper proposes a pulse width modulation (PWM) technique with zero redundant state to reduce the common-mode voltage output, and evaluates their performance with the conventional PWM schemes. This paper also takes the compensation of neutral point potential deviation into consideration. Laboratory test results are presented to verify the effectiveness of the proposed scheme.
A Machine Vision Apparatus and Method for Can-End Inspection

As a critical component of metal can containers, can end and its manufacture quality are closely relevant to product safety in food and beverage industry. To satisfy the requirements of quality control, a machine vision apparatus for real-time can-end inspection is presented in this paper. With a brief description of the apparatus system design and imaging system, our emphasis is put on the postprocessing image analysis. To detect defects and deformations across the imaged can-end surface, an entropy-rate clustering algorithm combined with prior shape constraint is proposed to locate the can-end object and divide it into multiple measuring regions. Then, a superpixel grouping and selection scheme is adopted to find defective areas inside the flat central panel. For the other three annular measuring regions, a multiscale ridge detection algorithm is introduced to seek defects and deformations along their projection profiles. According to in-line experiments and test, our apparatus can find out a majority of the can-end defects with a detection accuracy as high as 99.48% for various circular can ends.
An ACO-Based Trust Inference Algorithm

The trust value between two nodes is important for their interactions in social network . In this paper we propose an ACO-based trust inference algorithm. First, we use the Ant Colony Optimization algorithm to find two trust trains with high trust value between any two nodes, since the most accurate information comes from the node with the highest trust value. We optimized the corresponding parameters of the ACO algorithm to make it adaptive to our problem. Indirect trust value between two nodes is calculated according to direct trust value and trust chains got by ACO. The statistical analysis indicates that our algorithm gains more accuracy of trust value computing compared with a classical trust algorithm.
Alzheimer's disease classification based on gait information

Alzheimer's disease (AD) is becoming one of the major diseases of the elderly. Traditionally, patients take questionnaires or do some balance tests for clinical evaluation. However, results with such evaluation are subjective. For more objective quantitative measurement, this paper uses an inertial-sensor-based device to measure the gait information while participants walking. In the experiment, the participants are asked to walk on a 40m strike line and take single-task and dual-task tests. In the dual-task test, the participants are asked to count down from 100. This paper presents a stride detection algorithm to automatically acquire gait information of each gait cycle from the acceleration and angular velocity signals. Features are calculated from those inertial signals. After feature generation, we do feature selection to select the significant feature. Then, a probabilistic neural networks (PNNs) is used to classify if the participants suffer from AD. In this paper, we provide an objective way to evaluate the situation of the participants. The experimental results successfully validate the effectiveness of the proposed device and the proposed algorithm with an overall classification accuracy rates are 63.33% and 70.00% in women and men group, respectively.
Nonlocal denosing method for natural image via 2DPCA and direction information

Recently, the Nonlocal means filter have has gained wide attention. Unfortunately, the computational complexity of this methods is quite a burden. Several Speedup methods have been suggested. In our former work[17], we propose a scheme to more efficiently preselect similar patches, based on the two-dimensional principal component analysis(2DPCA). Although the method can yield good results, the computational complexity remains high. Besides, both practice and theory proved that nonlocal mean filter is suitable for processing the texture image. For natural image, its performance still needs improvement. Hence we proposed improved Semi-nonlocal version of the 2DPCA NL-mean filter, which directly employs features extracted by the 2DPCA to compute the weights, meanwhile the direction information of image patch is also used to design the weights function. Experimental results show that our method can achieve better filtering results in a variety of images, such as weak gradient image, face image and texture image.
Research and Development on Small Pipeline Natural Gas Generator

A 5 kW gasoline generator was converted into gas generator that could run on pipeline natural gas. A fire-restrain device and pressure reducer are designed to ensure safety. The measures such as increasing the compression ratio, enlarging spark advance and using a high-energy ignition are taken to improve the performance of the engine running on gas. Experiment has been done to compare the performances of the generator running on gasoline or natural gas. The result shows that the emission and economy of the generator are better when it runs on natural gas than that on gasoline.
Notes on synthetic phased-array with 2-D sparse arrays

Recent advances in 3-dimensional (3-D) imaging with 2-D sparse arrays have generated renewed interest in array synthesis theory. The 3-D imaging with arbitrary planar line shapes is particularly interesting. In this correspondence, we clarify some points presented in recent publications and show that aperture synthesis can be viewed with a very simple, physical approach. Its limitations due to approximations also will be discussed.
A Dynamic Trust Conference Algorithm for Social Network

More and more users joined in social network. The precise social trust value is critical for application system such as recommendation system. To a user, the egocentric network is formed by the user, his friends and social relationships between him and other users. We proposed an algorithm for inferring dynamic trust based on trust chains and interactions. Indirect trust values are calculated depending on direct trust values and trust chains in the egocentric network. As the social network evolves, the dynamic trust values can be resulted from the interactions between a user and his friends and trust reference. By statistical analysis of trust value in social network, this algorithm improved the accuracy of trust transitivity and trust value computing compared with a classical trust algorithm.
Multipath video transport over ad hoc networks

Real-time multimedia transport has stringent bandwidth, delay, and loss requirements. It is a great challenge to support such applications in wireless ad hoc networks, which are characterized by frequent link failures and congestion. Using multiple paths in parallel for a real-time multimedia session (called multipath transport) provides a new degree of freedom in designing robust multimedia transport systems. In this article, we describe a framework for multipath video transport over wireless ad hoc networks, and examine its essential components, including multistream video coding, multipath routing, and transport mechanisms. We illustrate by three representative examples how to extend existing video coding schemes in order to fully explore the potential of multipath transport. We also examine important mechanisms in different layers for supporting multipath video transport over ad hoc networks. Our experiments show that multipath transport is a promising technique for efficient video communications over ad hoc networks.
Human detection in fish-eye images using HOG-based detectors over rotated windows

Fish-eye cameras are efficient means to provide an omni-view video recording over a large area using a single camera. Although effective algorithms for human detection in images captured by conventional cameras have been developed, human detection in fish-eye images remains an open challenge. Recognizing that humans typically appear on radial lines emitted from the center in fish-eye images, we propose to apply the popular human detection algorithm based on the Histogram of Oriented Gradient (HOG) features after rotating each search window on a radial line to the vertical reference line. We extract positive and negative examples by such rotations to train the SVM classifier using HOG features. To detect humans in a given image, we rotate the image successively and detect windows containing humans along the reference line after each rotation using the trained classifier. We use multiple window sizes to detect people with different appearance sizes. We further develop an algorithm to discover multiple overlapping windows covering the same person and identify the window that encloses the person the best. The proposed method has yielded highly accurate human detection in low-resolution, low-contrast images containing multiple people with varying poses and sizes.
Robust face verification via Bayesian sparse representation

This research proposes a novel Bayesian sparse representation (BSR) method along with extracting facial parameters of SIFT to create sparse dictionaries, which are invariant to rotation, scale, and shift. By using K-means and information theory, a new dictionary called extended dictionary is developed. Compared with conventional orthogonal matching pursuit (OMP) algorithm, the proposed system that utilized Bayesian method to model the optimization problem of sparse representation can reduce the uncertainty of observed signals and expand the modeling ability of dictionaries by using variance. The experimental results show that the proposed extended dictionary can enhance the sparsity. Furthermore, it can improve accuracy rates of face identification and residues of reconstruction.
Study on Residual Current Protective Strategy Based on Network

Residual current protective devices play an important role in electrical safety engineering. When dangerous residual current occurs, automatic disconnection of power supply can prevent dangerous residual currents which may cause burns, fires and electrocution. Discrimination protection is to ensure that only the faulty part of a circuit is de-energized by tripping of the protective device. Common residual current protective strategy includes vertical discrimination and horizontal discrimination. Meanwhile, smart elements have been added to electrical devices, such as intelligent control, communication, etc. Communications among the residual current devices become practical. This paper presents a novel residual current protective strategy based on network. Coordination mechanism is introduced into the strategy as well.
Adaptive Dynamic State Prediction Control Algorithms for Quantitative Weighing System

In the most industrial processes, weighing machines are designed to automatically fill one or more types of the containers with a predetermined weight of particulates raw material using a controller. The performance of the machine is closely related to the accuracy of measurement and speed of dynamic response, which are contradictory. For a gravimetric filling machine, a novel adaptive dynamic state prediction control (ADSPC) algorithm running in the controller is proposed in this paper to regulate conflicts. This algorithm will consider the weighing process as a finite state. State migration is depended on the resulting weight and current state. The resulting weight is determined by the weight of measurement and current state. Compared with normal algorithm and simple prediction algorithm separately under the same condition, the result indicated that the algorithm is effective and feasible.
Design and analysis of a PZT-based micromachined acoustic sensor with increased sensitivity.

The ever-growing applications of lead zirconate titanate (PZT) thin films to sensing devices have given birth to a variety of microsensors. This paper presents the design and theoretical analysis of a PZT-based micro acoustic sensor that uses interdigital electrodes (IDE) and in-plane polarization (IPP) instead of commonly used parallel plate-electrodes (PPE) and through-thickness polarization (TTP). The sensitivity of IDE-based sensors is increased due to the small capacitance of the interdigital capacitor and the large and adjustable electrode spacing. In addition, the sensitivity takes advantage of a large piezoelectric coefficient d33 rather than d31, which is used in PPE-based sensors, resulting in a further improvement in the sensitivity. Laminated beam theory is used to analyze the laminated piezoelectric sensors, and the capacitance of the IDE is deduced by using conformal mapping and partial capacitance techniques. Analytical formulations for predicting the sensitivity of both PPE- and IDE-based microsensors are presented, and factors that influence sensitivity are discussed in detail. Results show that the IDE and IPP can improve the sensitivity significantly.
LayerP2P: Using Layered Video Chunks in P2P Live Streaming

Although there are several successful commercial deployments of live P2P streaming systems, the current designs; lack incentives for users to contribute bandwidth resources; lack adaptation to aggregate bandwidth availability; and exhibit poor video quality when bandwidth availability falls below bandwidth supply. In this paper, we propose, prototype, deploy, and validate LayerP2P, a P2P live streaming system that addresses all three of these problems. LayerP2P combines layered video, mesh P2P distribution, and a tit-for-tat-like algorithm, in a manner such that a peer contributing more upload bandwidth receives more layers and consequently better video quality. We implement LayerP2P (including seeds, clients, trackers, and layered codecs), deploy the prototype in PlanetLab, and perform extensive experiments. We also examine a wide range of scenarios using trace-driven simulations. The results show that LayerP2P has high efficiency, provides differentiated service, adapts to bandwidth deficient scenarios, and provides protection against free-riders.
Rewiring using IRredundancy Removal and Addition

Redundancy Addition and Removal (RAR) is a restructuring technique used in the synthesis and optimization of logic designs. It can remove an existing target wire and add an alternative wire in the circuit such that the functionality of the circuit is intact. However, not every irredundant target wire can be successfully removed due to some limitations. Thus, this paper proposes a new restructuring technique, IRredundancy Removal and Addition (IRRA), which successfully removes any desired target wire by constructing a rectification network which exactly corrects the error caused by removing the target wire.
Image-based nutrition composition analysis with a local orientation descriptor

A local orientation descriptor (LOD) for nutrition analysis by quantity estimation is proposed. By observing nutrition properties, a texture-based LOD is designed to extract discriminant information, frequency and length among food items. Prior to classification, food detection is a challenging problem due to significant variety of backgrounds and containers. Thus, three food region detectors are designed in this study. A detector that employs a modified salient object detection algorithm using prior background knowledge provides promising segmentation results for non-uniform backgrounds. Integrating gradient information to construct graph weights yields more precise segmentation results. In addition, nutrition quantity is estimated using coins as reference objects. Three types of features, normalized colour, density, and symmetry properties are extracted for coin classification. Experimental results show that the proposed LOD outperforms existing object recognition features.
Oxide-Nitride Storage Dielectric Formation in a Single-Furnace Process for Trench DRAM

A simplified and integrated technique has been proposed to form an oxide/nitride storage dielectric in a single-furnace process by low-pressure oxidation and nitride film deposition with an extra N2 O treatment for the trench dynamic random access memory (DRAM). Compared to the conventional nitride/oxide dielectric, this newly developed dielectric enjoys cell-capacitance-enhancement factor as high as 12.5% without degrading the leakage current and electron-trapping property. From the reliability test, the qualification for the DRAM application is also proven by the dielectric lifetime longer than 10-years. Most importantly, this technique can reduce the production cycle time without an additional equipment investment, which is essential in the cost-competitive DRAM arena
Analysis and evaluation of MAI in short-PRN communication system

How to transmit several links of high information capacity in limited bandwidth is a key factor of high-speed communication system for the high-speed data separates the PRN code into short ones, which lead the multiple-access interference (MAI) serious prejudice the stability and effectiveness of system. Accurate evaluation methods should be used in system designing while traditional evaluation method has a large error on BER for the particularity of MAI in short-PRN system. The paper put forward a analysis and evaluation method based on signal parameters included code length, code phase, carrier phase, cross-correlation distribution estimated parameter and so on. A test short-PRN communication system was established and the results prove the effectiveness of the method.
Camera calibration based on vertical objects

In intelligent surveillance, how to calibrate camera in absence of calibrated objects or scene's structure information or camera's motion information, is a significant topic. This paper proposes a novel approach based on vertical objects using 4∼5 positions labeled by human in video sequences with knowing height and obtains vertical vanishing point and plane vanishing line. Considering the importance of the vertical vanishing point, the plane vanishing line and the sensitivity of those two to noise, an optimal algorithm to vanishing point and line is proposed; Then according to the properties of vanishing point and vanishing line being function of camera parameters, all the intrinsic and extrinsic parameters are solved; Finally, this paper shows the optimal number and position of human in camera field. Both computer simulation and real data validate robustness and effectiveness of the algorithm.