nils-herrmann/clean_scirepeval huggingface dataset:
Research on non-motor vehicle traffic space optimization at signal control intersections

Electric bicycle is very flexible, convenient and environmental, which is the ideal mode of transport for short distance travel. Electric bicycle has become an indispensable important component of the transportation system. The fact is that traffic organization and channelizing design of signal control intersections is not intensive, which can not adapt to the current traffic demand of non-motor vehicle, such as unclear traffic rules and poor visibility, thus the traffic safety of non-motor vehicle is not optimistic. Therefore, it is necessary to study on traffic intersection traffic organization method based on the demand of non-motor vehicle, which can provide certain theoretical basis for traffic administrative department.
Enhancing Tit-for-Tat Strategy to Cope with Free-Riding in Unreliable P2P Networks

P2P applications suffer from free-riding. In economics terminology, free-riding is the rational behavior of the participants. So it's feasible to use game theory to analyze this problem and design countermeasures. Tit-For-Tat is a simple and efficient equilibrium strategy in repeated game environments. In this paper, we construct a game model in P2P environments and deduce the constraint under which the strict tit-for-tat is an equilibrium strategy. We then improve and adapt the tit-for-tat strategy to the dynamic property in P2P networks, and deduce the constraint under which it is still an equilibrium strategy. Finally we study through simulations the performance improvement of the enhanced tit-for-tat and give suggestion of how to choose proper system parameters under different network conditions.
Episodic Memory-Based Robotic Planning Under Uncertainty

This paper presents a robotic behavior planning method under uncertainty based on biology-inspired episodic memory. Adaptive behavior planning, prediction and reasoning are achieved between tasks, environment, and threats. Through building a novel episode model and introducing the activation and stimulation mechanism of state neurons, the framework of an episodic memory-driving Markov decision process (EM-MDP) is proposed for incremental self-learning of robotic experience and cognitive behavior planning. Two main challenges in robot behavior control under uncertainty are addressed: high computational complexity and perceptual aliasing. The approach for robotic global planning and behaviors sequence prediction based on the EM-MDP is developed utilizing neuron synaptic potential. A local behavioral planning method based on risk function and feasible paths is employed to achieve path optimization and behavior reasoning under the condition of imperfect memory. Robot can evaluate the past events sequence, predict the current state, and plan the desired behaviors. The proposed method is evaluated in several real-life environments for a mobile robot system. The robot system is able to successfully produce solutions in general scenarios under uncertainty.
Performance characterization and acceleration of Optical Character Recognition on handheld platforms

Optical Character Recognition (OCR) converts images of handwritten or printed text captured by camera or scanner into editable text. OCR has seen limited adoption in mobile platforms due to the performance constraints of these systems. Intel® Atom™ processors have enabled general purpose applications to be executed on handheld devices. In this paper, we analyze a reference implementation of the OCR workload on a low power general purpose processor and identify the primary hotspot functions that incur a large fraction of the overall response time. We also present a detailed architectural characterization of the hotspot functions in terms of CPI, MPI, etc. We then implement and analyze several software/algorithmic optimizations such as i) Multi-threading, ii) image sampling for a hotspot function and iii) miscellaneous code optimization. Our results show that up to 2X performance improvement in execution time of the application and almost 9X improvement for a hotspot can be achieved by using various software optimizations. We designed and implemented a hardware accelerator for one of the hotspots to further reduce the execution time and power. Overall, we believe our analysis provides a detailed understanding of the processing overheads for OCR running on a new class of low power compute platforms.
Interactive visual content sharing and telestration: A novel network multimedia service

Traditional telestration is a technique for TV broadcast operators to draw freehand sketches over live video streams . In this paper, we present a novel, interactive visual content sharing and telestration (IVCST) system that support n-way telestration. IVCST uses a network based telestration method on shared visual content. The superimposition of telestration drawings on shared video is done at each participanting client who executes a sequence of telestration commands received from an IVCST telestration server, via a dedicated data channel. Multiple clients may telestrate on a shared drawing board concurrently on a common visual background, without altering original visual content. With no dependency to background visual content format, an IVCST application can be a software component that easily fits into a multi-platform, multi-screen environment. IVCST can be used in multiple fields such as telemedicine, virtual office collaboration, tele-education, entertaining, remote command/control, and social networking, to enhance the overall shared experience.
Collision-safe wavelength assignment scheme for lightpath establishment in WSONs

In this paper, a new wavelength assignment scheme called CSSIR (collision-safe source-initiated reservation) has been proposed for distributed lightpath establishment in GMPLS/PCE based Wavelength-switched Optical Networks (WSONs), which has DIR-like forward blocking probability with SIR-like backward blocking probability. In CSSIR, the PCE selects one wavelength reserved with multi-backup to protect the collision. Simulations are conducted to compare it with SIR&DIR. The results show that CSSIR performs better in total blocking than the others under various traffic loads.
Input-parallel output-parallel (IPOP) three-level (TL) DC/DC converters with minimized capacitor ripple currents

This paper proposes input-parallel output-parallel (IPOP) three-level (TL) DC/DC converters with the interleaving control strategy, which is composed with two four-switch half-bridge TL (HBTL) DC/DC converters featuring with simple and compact circuit structures. Due to the IPOP structure, the component current stresses in the proposed converters are reduced. More significantly, the combination of the proposed IPOP TL circuit structure and the interleaving control strategy can largely reduce the ripple currents on the two input capacitors not only by doubling the frequencies of the ripple currents on two input capacitors but also by counteracting part of these ripple currents according to the operation principle of the proposed converters. Therefore, the proposed IPOP TL DC/DC converters with the interleaving control strategy can improve the performances of the converters in increasing the lifetimes of the input capacitors and minimizing the sizes of the input capacitors. Finally, the simulation and experimental results are presented to verify the effectiveness and feasibility of the proposed converters combined with the interleaving control strategy.
Convolutional Neural Network-Based Video Super-Resolution for Action Recognition

For video action recognition, convolutional neural networks (CNNs) especially two-stream CNNs have achieved remarkable progress in the recent years. However, most of the CNNs for action recognition are trained with high-resolution videos and not scale invariant, making it problematic to apply the trained CNNs directly on low-resolution videos. One possible solution to the problem is performing super-resolution (SR) prior to action recognition. In this paper, we investigate the effects of CNN-based video SR on the action recognition accuracy. We adopt a well trained two-stream CNN for action recognition, and analyze the spatial and temporal streams separately. For the spatial stream, we observe that video SR may improve the PSNR but may incur drop in recognition accuracy, this phenomenon is further analyzed in this paper. For the temporal stream, we observe that frame-by-frame SR may produce temporal inconsistency between consecutive video frames, which also incurs drop in recognition accuracy. We then propose a temporal consistency-oriented method for video SR, which indeed improves the recognition accuracy. Finally, we perform proper fusion of the two streams, and achieve a recognition accuracy of 88.95% on the UCF101 dataset when the input video is down-sampled by a factor of 4, compared to 93.49% accuracy on the original-resolution videos.
Robust controller design for switched fuzzy systems

This paper studies the problems of designing controller for switched systems. Using switching technique and Lyapunov function method, the continuous state feedback controller is built to ensure that for all allowable uncertainties the relevant closed-loop system is asymptotically stable. Moreover, switching strategy that achieving global asymptotic stability of the uncertain switched fuzzy system is given. In this model, each subsystem of switched system is an uncertain fuzzy system, and a common parallel distributed compensation controller and a switching controller are presented. The main condition is given in form of convex combinations which are more solvable. This method transforms of a certain switched system has strong robustness for the variation of system parameters. A simulation shows the feasibility and the effectiveness of the method.
A delay-dividing approach to stability criteria for neutral system with time-varying delay

This paper studies delay-dependent robust stability criteria problem for neutral system with time-varying delay. Based on a new class of Lyapunov functional approach and linear matrix inequality technology, a new delay dependent stability criterion for neutral system is derived by employing a delay-dividing approach. Finally, numerical examples are given to demonstrate the derived conditions are effectiveness and much less conservative than those given in the literature.
Low-distortion, low-loss varactor-based adaptive matching networks, implemented in a silicon-on-glass technology

A low-loss, low-distortion continuously tunable matching network, is demonstrated at 2 GHz in a silicon-on-glass varactor IC technology. The tuner uses an optimized varactor configuration to minimize distortion, and exhibits less than 0.5 dB loss and IM3<-50 dBc at 27 dBm output power, and tunes with a VSWR>250:1 to 1:1.
Spatio-temporal representation for multi-dimensional occlusion relation

Occlusion relation is the topological relation between the images of two bodies from a viewpoint. Qualitative representation of occlusion relation has been investigated in qualitative spatial reasoning. This research is important for computer vision and robot navigation. The previous models such as LOS and ROC-20 are all based on RCC (the famous topological theory). But those models couldn't support abstract objects such as point and line which are very common in real applications. To deal with this, multi-dimensional spatial occlusion relation (MSO) is put forward. The foundation of MSO is MRCC which is the multi-dimensional extension of RCC. So MSO is suitable for both real and abstract objects. The conception neighborhood and composition of MSO is given. Finally MSO is extended to spatio-temporal relation by adding time feature. MSO is an appropriate frame to express spatio-temporal knowledge.
Supply Chain Coordination with Service Level Constraint

The coordination issue is investigated in a supply chain with service level constraint. The supply chain consists of one risk-neutral supplier and one risk-averse retailer. Both inventory model and coordination model are established and the supply chain performance measure is presented for coordination under the Pareto optimal criterion. Sequentially, the practicability of cost sharing contracts is examined to show that it could not achieve the complete coordination but Pareto improvement. Furthermore, the difference is demonstrated between service level constraint and downside risk constraint so that a strong/weak risk aversion definition is proposed. Since the strong risk aversion is indicated to be one of difficulties in supply chain coordination, it is suggested avoiding these scenarios by policies.
Fuzzy Clustering Approaches Based on AFS Fuzzy Logic II

This study is a sequel of part I of this paper [Fuzzy Clustering Approaches Based on AFS Logic I, submitted with this paper to WCICA'06]. In this paper, we will improve and study the fuzzy clustering index defined by X.D. Liu, W. Wang and T.Y. Chai ( Transaction on Systems, Man, Cybernetics, 2005), and design a new classifier based on the AFS fuzzy logic clustering algorithm proposed by X.D.Liu, W.Wang and T.Y.Chai. As an example, the new classifier is applied to Iris data. The stability and reclassification accuracy of the classifiers are studied through the Iris data, the examples show that the new classifiers are simple and efficient
A comparison of ultrasonic wavefront distortion and compensation in one-dimensional and two-dimensional apertures

A comparison of wavefront distortion and compensation in one-dimensional and two-dimensional apertures is made using two-dimensional transmission measurements through 14 different specimens of human abdominal wall. The measurements were employed to emulate data in one dimension by summing waveforms in the elevation direction after a geometric correction was performed using a fourth-order polynomial fit to the surface of arrival time. Distortion calculation and time-shift compensation were performed independently on waveforms in the one-dimensional and two-dimensional apertures and the waveforms were focussed using a Fourier transform method to obtain the point-spread function in the central elevation plane. The results show that distortion is smoothed increasingly by one-dimensional apertures as the elevation dimension grows and that the smoothing is substantial for elevations similar to those employed in present clinical imagers. The results also show that one-dimensional compensation becomes less effective than two-dimensional compensation as the size of the elevation increases but that one-dimensional compensation performs almost as well as two-dimensional compensation in apertures with elevations like those in current imaging systems.<<ETX>>
Fast GPU-Based Automatic Time Gain Compensation for Ultrasound Imaging

The Compute Unified Device Architecture (CUDA) is a new programming platform making use of the unified shader design of the most current Graphics Processing Units (GPUs) from NVIDIA. In this paper, we apply this revolutionary new technology to implement the automatic time gain compensation (ATGC) for medical ultrasound imaging. The parallel box filtering method and general matrix computation algorithms are also presented. This ATGC method achieves a frame rate of 125 fps for the 512×261 image, about 79 times faster than the CPU implementation. Testing results from GPU and CPU are compared in terms of visual image quality and program runtime with different image sizes.
A Mobile Micropayment Protocol Based on Chaos

By research on typical payment protocol of the PayWord, the paper points out its security problems, and presents a new mobile micropayment protocol based on chaotic Hash function. This protocol generates pay certificate and encrypts order messages transmitted in the network with symmetric encryption algorithm. It also constructs secure chaotic Hash function based on Hénon-like mapping, and creates two payment chains using chaotic Hash function in the protocol, The protocol is not only the anonymous concerning so that the user information which can ensure the confidentiality of the ordered message, but also realizes the partial fairness. Research results demonstrate that the new protocol has greater security and efficiency, and suits to those mobile micropayment transactions systems.
Characterization of terahertz detectors based on niobium nitride superconducting tunnel junctions

Terahertz direct detectors based on superconducting tunnel junctions (STJs) have the merits of short response time, large dynamic range and relatively high operating temperature, providing a good choice for terahertz detection. Compared with commonly used niobium STJs, niobium nitride (NbN) ones have a high transition temperature close to 16K. Hence they might be useful for terahertz detection operating at temperatures well above the LHe temperature of 4.2K. In this paper, we investigate the properties of NbN STJ detectors at different bath temperatures. Detailed experiment results are given.
Detection of Intima-Media Layer of Common Carotid Artery with Dynamic Programming Based Active Contour Model

Ultrasound measurements of the carotid artery wall in image are usually obtained by manually tracing. In this paper, we present an automatic segmentation method to detect the intima-media layer in far wall of the common carotid artery. The energy definition of active contour model is used. Different from the traditional approach applied in snake techniques, we treat the optimization problem as finding the shortest cost path in a directed graph. Dynamic programming is selected to search the shortest path. The external force and internal force in snake model are modified to be suitable for our approach. To reduce the effect of speckle noise, a new method in speckle reduction by anisotropic diffusion is adopted. At last, we compare the result of our method with other two methods. Results show that our method can detect the intimal and adventitia layers as well as other methods, the two layers will not cross with each other as traditional dynamic programming does. Moreover, our method needs less manual input than others.
$H_\infty $  Observer Design for Continuous-Time Takagi–Sugeno Fuzzy Model With Unknown Premise Variables via Nonquadratic Lyapunov Function

This paper deals with the problem of observer design for continuous-time Takagi-Sugeno fuzzy models with unmeasurable premise variables. First, in order to improve the existing results of observer design, a new method is proposed to bound the time derivatives of the membership function. Then, by applying the nonquadratic Lyapunov function and the matrix decoupling technique, the controller gains and observer gains are designed to guarantee that the error system is asymptotically stale. Furthermore, better H∞ performance can be obtained by solving an optimization problem. All of the results are presented as linear matrices inequalities and three examples are provided to demonstrate the merits of the proposed approach.
Fuzzy Neural Control of Satellite Attitude by TD Based Reinforcement Learning

With recent development of the space science and technology, higher requirements such as accuracy, robustness and disturbance rejection ability in satellite attitude control system have led to the more promising intelligent control methods. In this paper, a fuzzy neural control approach applied to the three-axis stabilized satellite is presented. In order to solve the problems of online learning and tuning of the fuzzy neural network parameters, the reinforcement learning based on temporal difference (TD) is also proposed and studied so that the training samples for the self-learning controller are not needed. Since the vibration of the solar swing cannot be ignored, a flexible mathematic model of the satellite is studied, employing quaternion and Euler-angles representations. The simulation results showed that the proposed control method with reinforcement learning architecture could not only improve the accuracy and robustness of the system, but also could deal with the uncertainties and external disturbance efficiently
Reducing measurement noise in rock bolts detecting

For nonstationary signal in rock bolts detecting, an effective denoise method based on Hilbert-Huang transform is presented, and a few key questions using HHT method are discussed. At first, by empirical mode decomposition (EMD) algorithm, a sum of intrinsic mode functions (IMF) are got, and then, the IMF coefficients in useful signal dominative layer are proceeded using soft threshold method, at last, those IMF coefficients are reconstructed. Compared with the traditional wavelet transform, the HHT method is better to remove measurement noise.
Distributed containment control of linear multi-agent systems with multiple leaders and reduced-order controllers

This paper considers the containment control problems for both continuous-time and discrete-time multi-agent systems with general linear dynamics under directed communication topologies. Distributed reduced-order observer-based containment controllers relying on the relative outputs of neighboring agents are constructed for both continuous-time and discrete-time cases, under which the states of the followers will asymptotically converge to the convex hull formed by those of the leaders if for each follower there exists at least one leader that has a directed path to that follower. Sufficient conditions on the existence of these containment controllers are given.
A new evidence model and its application for discriminating enemy operation intention

Considering the signatures captured from information about the hostile operation intention, this paper puts forward concepts of the simplest evidence support format, duality evidence support format, and same-type evidence and so on. Based on these concepts, it discusses the problem of the orthogonal sum with the duality evidence support format expressed as the simplest evidence support format, and then, drawing its conclusion to show the relation between the enemy intention and the characteristic information. Thereby, it establishes a system for operation intention discrimination to complete the final determinant. This new model brings expression and combination of evidences simplified and computation reduced. By practical application, this algorithm has been proved feasibility and effectiveness.
Blood Flow Velocity Measurement: CCF Based on I/Q signal

Blood flow velocity measurement by ultrasonic techniques has proved to be a valuable tool for clinical diagnosis. Cross-correlation technique(CCF) is a classic one among various measurement methods. For most CCF blood flow measurement related papers, RF signal is widely mentioned, without in-phase/quadrature (I/Q) signal. This paper discusses CCF based on I/Q signal instead of traditional RF echoes, for more precise blood flow velocity measurement, we apply several methods to improve it, weighted average method which assigns different weight for different space distance echoes, and absolute distance limiting method which quite improves traditional parabolic-fit interpolation bias problem. Computer simulation and testing demonstrate that our proposed methods have better performance compared with traditional ones.