nils-herrmann/clean_scirepeval huggingface dataset:
Mean shift based video segment representation and applications to replay detection

Effective and efficient representation of the low-level features of groups of frames or shots is an important yet challenging task for video analysis and retrieval. Key frame-based representation is limited by the difficulties in shot boundary detection of gradual transitions, and the variety of methods of key frame extraction. In this paper, we employ the mean shift-based mode seeking function to develop a new approach for compact representation of the video segment. The proposed video representation is motivated by recognizing that, on the global level, humans perceive images only as a combination of the few most prominent colors. We exploit the spatiotemporal mode seeking in feature space to simulate the "subjectivity" of human decisions in video segment retrieval and identification. The effectiveness of the video representation and matching scheme is shown by initial experiments on replay detection in broadcast sports videos.
Formation Control of Autonomous Underwater Vehicles under Fixed Topology

Formation control is a basic problem in cooperation of multiple autonomous underwater vehicles (AUVs). To address this problem, we proposed a control method under fixed interconnection topology. In our algorithm, each AUV has two competitive objectives, one is following a predefined path, and another is to maintain a geometric configuration with its neighbors. Based on this, we defined the system error function and designed an adaptive sliding variable structure control law for each AUV because of the parameter uncertainty and environmental disturbances. Convergence of the proposed control law was proved in theory. Further, the path variable update law was discussed. Simulation results demonstrate the effectiveness of the proposed control method.
RH6-based multi-robots teleoperation experimental system

The CAN-based RH6 control system is developed by the Institute of Robotics and Information Automatic System, Nankai University. In this system, users can operate the robot through high-level instructions on host computers. Furthermore, based on this system, an experimental teleoperating robot system was also built. The experiment results demonstrate that through our alteration, the RH6 robot can meet the research requirements for the teleoperating robot system. This paper mainly focuses on the CAN-BUS based RH6 control system, the architecture and the implementation of the RH6-based teleoperation robot system, and the experiment. At the end of this article, we give our conclusion and forecast of the experimental system.
The design of a Ka-band two-stage monolithic low noise amplifier

A Ka-band two-stage monolithic low noise amplifier has been designed using a commercial 0.18-/spl mu/m pseudomorphic high electron-mobility transistor (pHEMT) process. The gate widths of FETs and source inductors are adjusted to achieve best tradeoff among gain, noise figure and return loss. The simulated results of the low noise amplifier chip show a gain of more than 11 dB, a noise figure of less than 2 dB, an input return loss of greater than 15 dB, and an output return loss of greater than 9.7 dB in the frequency range of 27 to 33 GHz. The chip size is 1.4/spl times/0.9 mm/sup 2/.
A Magnetic Coupling Dipole for UHF Near-Field RFID Reader

In this paper, a novel printed-folded dipole for ultra-high frequency (UHF) near-field (NF) radio-frequency identification (RFID) reader using magnetic coupling is proposed. The strong and uniform magnetic field is excited by two opposite-directed currents (ODCs). The folded dipole is printed on an FR-4 substrate with the scale 32 cm × 20 cm. The bandwidth of the antenna ranges from 826 to 853 MHz. When the transmit power of the proposed antenna is 17 dBm, the reading area can be managed to 35.3 × 24 cm2 on the surface for Impinj F43 tag, and the reading distance is around 5 mm. For the case of ceramics ISO1800-6-C tag, and at the same time, the reading area increases to 58 × 42 cm2, and the reading distance is around 77 mm at the center of the antenna. Both simulations and measurements show that the proposed antenna can provide a broad reading area with a very low transmit power of 17 dBm.
Research on the Millimeter Wave Power Equalizer

Power equalizer is often used to adjust the output power of traveling wave tubes (TWT) or high power solid state amplifiers. Traditionally, a power equalizer is composed of coaxial line with electromagnetic wave absorber or microstrip line with resistance in resonance circuit. However, those structures only adapt to lower millimeter wave. In this paper, a millimeter wave power equalizer is fulfilled by waveguide resonance cavity with electromagnetic wave absorber. The measured results and the simulated results show a very good agreement.
Simple to Complex Transfer Learning for Action Recognition.

Recognizing complex human actions is very challenging, since training a robust learning model requires a large amount of labeled data, which is difficult to acquire. Considering that each complex action is composed of a sequence of simple actions which can be easily obtained from existing data sets, this paper presents a simple to complex action transfer learning model (SCA-TLM) for complex human action recognition. SCA-TLM improves the performance of complex action recognition by leveraging the abundant labeled simple actions. In particular, it optimizes the weight parameters, enabling the complex actions to be learned to be reconstructed by simple actions. The optimal reconstruct coefficients are acquired by minimizing the objective function, and the target weight parameters are then represented as a combination of source weight parameters. The main advantage of the proposed SCA-TLM compared with existing approaches is that we exploit simple actions to recognize complex actions instead of only using complex actions as training samples. To validate the proposed SCA-TLM, we conduct extensive experiments on two well-known complex action data sets: 1) Olympic Sports data set and 2) UCF50 data set. The results show the effectiveness of the proposed SCA-TLM for complex action recognition.
A Belief Network Based Retrieval Model with Two Term Layers

The retrieval performance of an information system usually increases when it uses the relationships among the terms in a given document collection. This paper presents an improved co-occurrences frequency method to mine relationship among document index terms, then gives an extended model by adding one term layer in belief network model, shows its topology, probability estimating and information retrieval process. Experiment shows that this model can realize the semantic retrieval to some extent
Estimation of valence of emotion using two frontal EEG channels

Emotion recognition using EEG signals has become a hot research topic in the last few years. This paper aims at providing a novel method for emotion recognition using less channels of frontal EEG signals. By employing the asymmetry theory of frontal brain, a new method fusing spatial and frequency features was presented, which only adopted two channels of frontal EEG signals at Fp1 and Fp2. In order to estimate the efficiency of the method, a GBDT classifier was evaluated and selected, and the method was implemented on the DEAP database. The maximum and mean classification accuracy were achieved as 76.34% and 75.18% respectively, which exhibited the best result comparing with other related studies. This method is extremely suitable for wearable EEG monitoring applications in human daily life.
Broadband high efficiency GaN power amplifier

This paper reports on the design methodology and realization of a hybrid feedback power amplifier using discrete GaN HEMT die with chip & wire technology. The power amplifier was measured to have over 12.8 dB gain with ±0.85dB flatness and higher than 30.7 dBm output power over 100 MHz to 1GHz. Power added efficiency (PAE) of 61.3% was acquired at mid-band frequency and more than 54.5% from 100MHz to 1GHz.
Notice of RetractionAn improved predictor-corrector procedure for the dynamics of geometrically nonlinear shell structure

For geometrically nonlinear structure, the expressions of tangent stiffness matrix and internal forces of shell element are derived by CR (Co-rotational) theory, and the simplified formulation is performed on static analysis of a hinged cylindrical segment and validated by the results of experiment and references. Then, a predictor-corrector procedure with an approximately energy conservation is developed. With the application on nonlinear symmetric motion of a cylinder under two pinching forces, the improved procedure presents a better stability and accuracy than nonlinear Newmark algorithm.
An Obstacle Avoiding Control Strategy for Autonomous Underwater Vehicle

An obstacle avoiding control strategy for autonomous underwater vehicle (AUV) was proposed. According to the task of AUV, a variable structure control law was proposed for path following firstly. When AUV track the desired path, taking a looking forward sonar as the measuring instrument, a fuzzy inference system (FIS) was designed to derive the obstacle-avoiding angle based on the position relationship between AUV and the obstacle. Then, take the obstacle-avoiding angle as heading error of the path following control law. The proposed FIS and the path following control law construct an integrated obstacle avoiding control strategy. It was easy to be implemented. Simulation results show the efficiency of the strategy
GoDec+: Fast and Robust Low-Rank Matrix Decomposition Based on Maximum Correntropy

GoDec is an efficient low-rank matrix decomposition algorithm. However, optimal performance depends on sparse errors and Gaussian noise. This paper aims to address the problem that a matrix is composed of a low-rank component and unknown corruptions. We introduce a robust local similarity measure called correntropy to describe the corruptions and, in doing so, obtain a more robust and faster low-rank decomposition algorithm: GoDec+. Based on half-quadratic optimization and greedy bilateral paradigm, we deliver a solution to the maximum correntropy criterion (MCC)-based low-rank decomposition problem. Experimental results show that GoDec+ is efficient and robust to different corruptions including Gaussian noise, Laplacian noise, salt & pepper noise, and occlusion on both synthetic and real vision data. We further apply GoDec+ to more general applications including classification and subspace clustering. For classification, we construct an ensemble subspace from the low-rank GoDec+ matrix and introduce an MCC-based classifier. For subspace clustering, we utilize GoDec+ values low-rank matrix for MCC-based self-expression and combine it with spectral clustering. Face recognition, motion segmentation, and face clustering experiments show that the proposed methods are effective and robust. In particular, we achieve the state-of-the-art performance on the Hopkins 155 data set and the first 10 subjects of extended Yale B for subspace clustering.
Electrically tunable array antenna with beam steering from backfire to endfire based on liquid crystal miniaturized phase shifter

In this paper, an electrical array antenna based on liquid crystal (LC) miniaturized phase shifter is designed and simulated, with the capability of beam steering from backfire to endfire. Special chamfered design of meander line is proposed to obtain compact phase shifter based on LC. The antenna integrates phase shifters, power dividers and patch antennas on a substrate to achieve reduced occupied area. The simulated results reveals the beam steering range is from −21° to + 15° at 12.5 GHz.
Electrical characterization of RF TSV for 3D multi-core and heterogeneous ICs

In this paper, radio frequency (RF) through-silicon via (TSV) designs and models are proposed to achieve high-frequency vertical connectivity for three dimensional (3D) multi-core and heterogeneous ICs. Specifically, coaxial dielectric and novel air-gap-based TSVs are designed and simulated to reduce signal degradation during RF operations. The simulation results demonstrate that these RF TSVs can provide decay-tolerance frequencies two orders of magnitude higher than simple Cu-plug TSVs. The data rate and energy per bit of the RF TSVs are summarized, providing an important guideline for future 3D high-frequency TSV designs.
GaSb Inversion-Mode PMOSFETs With Atomic-Layer-Deposited  $\hbox{Al}_{2}\hbox{O}_{3}$ as Gate Dielectric

GaSb inversion-mode PMOSFETs with atomic-layer-deposited (ALD) Al<sub>2</sub>O<sub>3</sub> as gate dielectric are demonstrated. A 0.75- μm-gate-length device has a maximum drain current of 70 mA/mm, a transconductance of 26 mS/mm, and a hole inversion mobility of 200 cm<sup>2</sup>/V ·s. The off-state performance is improved by reducing the ALD growth temperature from 300°C to 200°C. The measured interface trap distribution shows a low interface trap density of 2 × 10<sup>12</sup>/cm<sup>2</sup>·eV near the valence band edge. However, it increases to 1 - 4 ×10<sup>13</sup>/cm<sup>2</sup> ·eV near the conduction band edge, leading to a drain current on-off ratio of 265 and a subthreshold swing of ~ 600 mV/decade. GaSb, similar to Ge, is a promising channel material for PMOSFETs due to its high bulk hole mobility, high density of states at the valence band edge, and, most importantly, its unique interface trap distribution and trap neutral level alignment.
Design and development of starfish-like robot: Soft bionic platform with multi-motion using SMA actuators

This paper presents a starfish-like robot with bionic soft body, multi-motion inspired by live starfishes. The structural design principle is based on the biological investigation. The robot prototype is fabricated with help of 3D printing model. By the locomotion principle and control strategy, the soft starfish-like robot is able to fulfill in crawling on flat ground, climbing over viscous soil terrain, free motions in random directions, navigating through a target object, steering as well as grasping an imaginary prey. The results indicate that the starfish-like robot driven by shape memory alloy (SMA) actuators possesses a great robustness against strong impact and patially demonstrate its capacity of mobility and environmental adaption.
TTEthernet for launch vehicle communication network

In the paper, we research on the application of TTEthernet in the launch vehicle control system. Firstly, the communication network requirements of next generation launch vehicle is proposed in this paper. Then, we introduce the TTEthernet in the view of the mechanism of network communication, clock synchronization and fault-tolerant performance. And further, we analyze the application advantages of TTEthernet. Lastly, we design a dual redundant communication network topology for launch vehicle based on TTEthernet, studying the dataflow of the communication network during the process of vehicle launching. The results can not only show the TTEthernet exploration in launch vehicle, but also provide a guideline for TTEthernet research in the field of space application.
A Improved Algorithm for Forward Computation of Dynamic Program Slice

Dynamic program slice with respect to a variable of interest at some execution position contains only those statements that actually had an influence on this variable. It has been widely used in many software activities, such as debugging, testing and software maintenance.In this paper, we present a dynamic program slicing algorithm based on forward analysis. In this method, dynamic slices are computed during program execution without major recording of the execution trace. Moreover, it improved the precision of the dynamic program slice, the space complexity is bounded as opposed to the backward methods of slice, it is easy to realize. The experiment has proved that the method is effective.
Applying hybrid genetic algorithm to constrained trajectory optimization

Trajectory optimization is a typical optimal control problem. Aiming at the slow convergence characteristics and the poor local searching ability of a basic genetic algorithm, this paper proposed a new hybrid global-local optimization algorithm by coming genetic algorithm and complex algorithm to improve the convergence rate of genetic algorithm. The hybrid way adopted serial hybrid pattern in this paper. It can mean that the optimal solution of genetic algorithm is submitted as an initial parameter set to complex algorithm for refinement. In order to validate algorithm, hybrid genetic algorithm applied to lunar soft landing trajectory optimization problem. Simulation results demonstrate that the methodology and algorithms take on fast convergence rate and high optimization precision.
Study on Extended Kalman Filtering for Attitude Estimation of Micro Flight Vehicle

In this paper an extended Kalman filter for real-time estimation of micro flight vehicle body attitude by using MEMS gyros, accelerometers, and magnetometers is presented. The algorithm uses quaternions to represent rotation, which can avoid the singularity problem caused by using Euler angles. Attitude quaternion errors and drift bias of gyros were selected to construct a state vector. The state equation is established on the base of attitude quaternion error differential equation. The measurements of Kalman filter were obtained through iteration using acceleration and magnetic field vector. The algorithm can significantly reduce calculated amount and can converge quickly to rotation attitude.
A novel deep-learning based framework for multi-subject emotion recognition

Electroencephalogram (EEG) signal based emotion recognition, as a challenging pattern recognition task, has attracted more and more attention in recent years and widely used in medical, Affective Computing and other fields. Traditional approaches often lack of the high-level features and the generalization ability is poor, which are difficult to apply to the practical application. In this paper, we proposed a novel model for multi-subject emotion classification. The basic idea is to extract the high-level features through the deep learning model and transform traditional subject-independent recognition tasks into multi-subject recognition tasks. Experiments are carried out on the DEAP dataset, and our results demonstrate the effectiveness of the proposed method.
Image Haze Removal Using Depth-Based Cluster and Self-Adaptive Parameters

Removing haze from a single image is a very challenging problem. In this paper, we propose a new method for dehazing from a single image utilizing cluster segmentation. By using K-means to cluster the depth map and segment into several parts, we obtain several regions with different ranges of depth. Then we present to estimate the flexible attenuation coefficient based on intensity for each regions in order to improve the transmission accuracy. Finally, we modify the transmission by setting several boundaries for different regions to avoid oversaturation. Experimental results on various haze images demonstrate that the proposed algorithm is capable of recovering clear and natural haze-free images.
Constructing visibility graph and planning optimal path for inspection of 2D workspace

In this paper, we address the solution of solving the following problem: given a known workspace and a robot with limited sensors compute a short path for the vehicle so that every space in the workspace could be detected by the limited sensor of mobile robot on at least one point of the path. Visibility constraints including range of distance and angle of incidence are taken into consideration to model the real sensors with the view of 360 degrees. Instead of setting a number of guard location points for robot to travel, we here propose a concept called the visibility hallway which is constructed from the valid range of sensor. Our problem could be solved by two steps: we first construct the visibility graph according to the visibility hallway and then the path planner compute the short path from visibility graph with some specifications. Experiment results demonstrate that the proposed method could compute the path efficiently and the workspace could be inspected everywhere.
Implicit adaptive inverse dynamics control of robot manipulators

An implicit adaptive inverse dynamics control scheme for robot manipulators is presented. Instead of estimating manipulator parameters, the feedback gain is directly updated according to the bounds of uncertainties of the robotic system. Two theorems are proven, and the resulting algorithm can tolerate the variation of physical parameters to some extent. The controller parameters have a certain relation to the desired position and velocity accuracy, such that the choice for them is easy. Simulation results verify the effectiveness of the approach.<<ETX>>
Adaptive Content Condensation Based on Grid Optimization for Thumbnail Image Generation

An ideal thumbnail generator should effectively condense unimportant regions and keep the important content undeformed, completed, and at a proper scale, i.e., accuracy, completeness, and sufficiency. Each retargeting method has its own advantage for resizing arbitrary images. However, they often ignore the completeness and sufficiency for information presentation in thumbnails. In this paper, we formulate thumbnail generation as an image content condensation problem and propose a unified grid optimization framework to fuse multiple operators. From the view of accuracy, completeness, and sufficiency for information presentation, we exploit complementary relationships among three condensation operators and fuse them into a unified grid-based convex programming problem, which could be solved simultaneously and efficiently through numerical optimization. Besides warping energy to preserve the geometric structure of important objects, we put forward two grid-based energy terms to keep the completeness of important objects and retain them at a proper size. Finally, an adaptive procedure is proposed to dynamically adjust the contribution of loss functions for achieving optimal content condensation. Both qualitative and quantitative comparison results demonstrate that the proposed method achieves an excellent tradeoff among accuracy, completeness, and sufficiency of information preservation. The experimental results show that our approach is obviously superior to the state-of-the-art techniques.
Robust self-triggered min-max model predictive control for linear discrete-time systems

In this paper, we propose a self-triggered robust model predictive control (MPC) algorithm for constrained linear discrete-time systems subject to additive disturbances. First, worst case scenarios are considered in the MPC problem formulation to achieve robust constraint satisfaction. Second, a self-triggered control scheduler is proposed to minimize the frequency of performing optimization and the closed-loop system is shown to be recursive feasible and input-to-state practical stable in its region of attraction. Finally, a numerical example demonstrates the effectiveness of the proposed control strategy.
The analysis on the accuracy of DEM retrieval by the ground lidar point cloud data extraction methods in mountain forest areas

LiDAR data contains the elevation and brightness information of land surface, vegetation cover and construction. Ground filtering and interpolation method are the key for extracting the DEM accuracy based on the point clouds. This paper takes Zhangye City, Gansu Province in western mountainous areas as the study area, based on the point clouds of 0.7 points/m2, uses 5 m * 5 m grid screening method and the lowest Thiessen polygon point screening method to extract the ground point. Ordinary kriging interpolation method was used to retrieve Digital Elevation Model (DEM). Referring to the elevations of 1466 sample points, we analysed the accuracy for extracting DEM by the two selected methods of extracting ground point. The results showed that the DEM extracting accuracy by the near lowest point screening method is better than the grid screening method.