nils-herrmann/clean_scirepeval huggingface dataset:
Design and implementation of an optical burst-switched network testbed

Optical burst switching has been receiving increased attention as a promising technology for building the next-generation optical Internet. This article presents the design and discusses the implementation of an overlay mode optical burst-switched network testbed. A flexible "transceiver + forwarding" edge node architecture is proposed to perform both electronic burst assembly/disassembly and optical burst forwarding. It is designed to provide class of service and wavelength selection for locally generated bursts, and transparency to cut-through bursts. The functional modules of the control plane and related key design issues are presented. In particular, we discuss a scheduling mechanism that efficiently combines two different contention resolutions in space and wavelength domains. Experimental results show the effectiveness of this scheme in reducing burst blocking probability. Furthermore, the performance of the network testbed and corresponding methods of improving it are discussed.
Building an application framework for speech and pen input integration in multimodal learning interfaces

While significant advances have been made improve speech recognition performance, and gesture and handwriting recognition, speech- and pen-based systems have still not found broad acceptance in everyday life. One reason for this is the inflexibility of each input modality when used alone. Human communication is very natural and flexible because we can take advantage of a multiplicity of communication signals working in concert to supply complementary information or increase robustness with redundancy. We present a multimodal interface capable of jointly interpreting speech, pen-based gestures, and handwriting in the context of an appointment scheduling application. The interpretation engine based on semantic frame merging correctly interprets 80% of a multimodal data set assuming perfect speech and gesture/handwriting recognition; in the presence of recognition errors the interpretation performance is in the range of 35-62%. A dialog processing scheme uses task domain knowledge to guide the user in supplying information and permits human-computer interactions to span several related multimodal input events.
A novel approach for developing interoperable services in cloud environment

Cloud computing has seen a tremendous growth in the last five years. Along with the growth, many cloud models have been marketed. They deliver hardware and software as virtualization enabled services to users. Although cloud computing offers considerable advantages such as unlimited resources, manageability and lower investment costs but there are still barriers to exploit it. One of the barriers is the difficulties, which users are being faced when developing and deploying their own services into clouds. In this paper, we present a novel approach for developing interoperable services that can be deployed in different cloud infrastructures at the same time. The approach provides an instrument with emphasis on abstraction, inheritance and code reuse. Using the approach, cloud-based services are developed easily by extending existing abstractions classes provided by the instrument or other developers. The interoperability between different clouds is solved via the basic abstraction class of the instrument and all services are inherited and benefited from this advantage.
An IDA-PBC approach for the control of 1D plasma profile in tokamaks

In this paper, a PCH model obtained from the spatial reduction of a thermo-magneto-hydrodynamics model for plasma dynamics in tokamaks is used to design an optimal IDA-PBC (Interconnection and Damping Assignment - Passivity Based Control) controller of the magnetic field profile. Due to the actuator constraints which imply to have a finite dimensional base of physically feasible current profile deposits, the controller inputs/outputs are reduced to be compatible with the system limitations. The loop voltage at the plasma boundary and the total external heating source power are considered as controller outputs. Reference on the total plasma current and the central safety factor value is given to compute the magnetic field profile reference. Bootstrap current and non-linearity of plasma resistivity or external current deposit scaling laws are taken into account. Simulation results based on Tore Supra WEST tokamak (a project of Tore Supra upgrade) are provided to illustrate the effectiveness of the method.
Coordinated reactive power control of DFIG to improve LVRT characteristics of FSIG in wind turbine generation

In almost all European countries, the penetration of wind power systems and the wind turbines size continuously increases. Therefore, the grid code requirements are more and more restrictive concerning the necessity that the wind turbine to remain connected to the grid during a fault situation. This paper analyzes the possibilities to have more accurate wind generation transient stability models for fixed speed wind turbined and variable speed wind turbines. Wind system using a fixed-speed wind power generator SCIG (FSIG) cannot stay connected to the grid during faults because they cannot deliver reactive power causing a voltage drop and even voltage instability. To respect the recent grid codes and to improve the low voltage ride through capability of fixed-speed wind power generatorthis paper proposes a new control capability of PI technique method for a variable-speed wind power doubly-fed induction generator located close of the grid. The proposed control method of both grid-side converter and rotor-side converter enables the doubly fed induction generator to regulate the reactive power consumed by the fixed-speed wind power generator. The results of the simulation confirmed the theoretical wind turbines operation.
Current ratios: a self-scaling technique for production IDDQ testing

The use of a single pass/fail threshold for IDDQ testing is unworkable as chip background currents increase to the point where they exceed many defect currents. This paper describes a method of using "current signatures" which uses only simple comparisons on the tester, and which automatically scales with process variations which give a wide range of background currents. Dynamic thresholds are used, based on the ratio of maximum to minimum current. Using a single IDDQ measurement for each die, upper and lower comparator values are set, against which IDDQ for each vector in the suite is compared. Production data is presented to verify the validity of the method.
Design and Implementation of Media Content Sharing Services in Home-Based IoT Networks

The penetration ratio of broadband networks into residential areas increases rapidly by the wide distribution of Internet service providers and networks. People are able to distribute and play various media content with many types of networked multimedia devices for home multimedia entertainment in residential environments. This paper addresses a new idea of home-based IoT networks where home-networked devices are able to communicate with others in a friendly, networked manner instead of traditional manual configurations and wired cabling operations. Accordingly, this paper proposes a novel intelligent media distribution system based on a home-based IoT network. The design of this system integrates UPnP, face recognition, intelligent human-machine interface, and family database technologies. UPnP-compatible HNDs With UPnP, networked devices can discover neighboring devices in a network. Face recognition is incorporated and so provides the UPnP networked devices with the capability of identifying the operating user in front of them. When a user moves in a home-based network, the intelligent human-machine interface allows a user to enforce any media content to be distributed to or displayed onto the UPnP-based device nearby the user. Furthermore, this paper presents a prototypical development, as well as a real demonstration with experimental UPnP-based network devices in home networks. Therefore, the study in this paper enables a ubiquitous media distribution service in home-based IoT network environments.
Bilateral filter based mixture model for image segmentation

This paper introduces a bilateral filtering based mixture model for image segmentation. The mixture model uses Markov Random Field (MRF) to incorporate spatial relationship among neighboring pixels into the Gaussian Mixture Model (GMM) in order to perform a segmentation that is robust against noise and other environmental factors. The bilateral filtering is used to smooth the posterior probability map as part of the MRF used. The advantage of the proposed model is its simplified structure so that the Expectation Maximization algorithm can be directly applied to the log-likelihood function to compute the optimum parameters of the mixture model. The method has been extensively tested on synthetic and natural images and compared with some of the state-of-the-arts algorithms currently available. The experimental results show that the proposed method is comparable to the other methods in terms of accuracy and quality and simpler in terms of implementation.
A Combination of Positive and Negative Fuzzy Rules for Image Classification Problem

In this paper, we propose a new fuzzy rule-based system for application in image classification problem. Each rule in our proposed system can represent more than one class. While traditional fuzzy systems consider positive fuzzy rules only, in this paper, we focus on combining negative fuzzy rules with traditional positive ones leading to fuzzy inference systems. This new approach has been tested on image classification problem consisting of multiple images with excellent results.
ActiveCampus: experiments in community-oriented ubiquitous computing

The continuing proliferation of handheld computing devices offers a new platform for mobile computing applications that could enrich our experience of the world around us. Yet many questions about realizing this vision remain open. We have been investigating these questions at the University of California, San Diego, through the ActiveCampus Project. ActiveCampus explores technologies that can enrich the learning community in the midst of these changes. It explores wireless context-aware computing as a means to enhance the "learning community" experience of a large urban university. We have tested two applications: ActiveClass supports classroom activities, such as asking questions during a lecture, and ActiveCampus Explorer supports context-aware activities, such as instant messaging and location-aware maps annotated with dynamic hyperlinked information.
${\hbox {Nd}}^{3+}{\hbox {:LaF}}_{3}$ as a Step-Wise Excited Scintillator for Femtosecond Ultraviolet Pulses

Vacuum ultraviolet fluorescence from Czochralski method-grown Nd3+:LaF3 excited by 290 nm femtosecond pulses from a Ti:sapphire laser is reported. Peak emission at 175 nm with 7 ns lifetime is observed. This decay time would be one of the fastest among solid-state, inorganic scintillators. Laser emission in the VUV region may also be achievable by pumping with existing ultraviolet solid-state lasers.
A dynamic programming approach for fast and robust object pose recognition from range images

Joint object recognition and pose estimation solely from range images is an important task e.g. in robotics applications and in automated manufacturing environments. The lack of color information and limitations of current commodity depth sensors make this task a challenging computer vision problem, and a standard random sampling based approach is prohibitively time-consuming. We propose to address this difficult problem by generating promising inlier sets for pose estimation by early rejection of clear outliers with the help of local belief propagation (or dynamic programming). By exploiting data-parallelism our method is fast, and we also do not rely on a computationally expensive training phase. We demonstrate state-of-the art performance on a standard dataset and illustrate our approach on challenging real sequences.
An XL-based data link layer routing protocol for wireless mesh networks

We present a simple link-state routing protocol called DLL-XL that provides packet routing at the link layer for ad-hoc 802.11s-based networks. DLL-XL can be considered as a deployment of the Approximate Link state algorithm XL proposed by Levchenko et al. By building upon the XL algorithm, the DLL-XL routing protocol achieves routing efficiency by suppressing flooding of link-cost changes when appropriate to tradeoff between message flooding overhead and path costs. The correctness of DLL-XL is inherited from the formal correctness of the XL algorithm. We evaluate the performance of DLL-XL by simulation using the ns-2 network simulator.
A Central Limit Theorem for an Omnibus Embedding of Multiple Random Dot Product Graphs

Performing statistical inference on collections of graphs is of import to many disciplines. Graph embedding, in which the vertices of a graph are mapped to vectors in a low-dimensional Euclidean space, has gained traction as a basic tool for graph analysis. Here we describe an omnibus embedding in which multiple graphs on the same vertex set are jointly embedded into a single space with a distinct representation for each graph. We prove a central limit theorem for this omnibus embedding and show that simultaneous embedding into a common space allows comparison of graphs without the need to perform pairwise alignments of graph embeddings. Experimental results demonstrate that the omnibus embedding improves upon existing methods.
Bidirectional wavelength reconfigurable optical network using remote pump amplifier and tunable fiber Bragg gratings

A bidirectional wavelength reconfigurable optical network is proposed. A remote pumped Erbium doped fiber amplifier and high-reflectivity tunable fiber Bragg gratings (TFBGs) are used in this wavelength reconfigurable fiber module.  Gb/s per channel over 20 km fiber span is used to verify the metro-network-range system performance. The power penalty is less than 1 dB when compares to the back to back transmission, and the signal to noise ratio (SNR) and interband crosstalk are larger than 30 dB.
Design of a frequency-tunable gyrotron for DNP-enhanced NMR spectroscopy

We report on the design of a modular low-power (10–50W) high-frequency gyrotron (265–530GHz) for DNP enhanced Solid-State NMR spectroscopy. With the view of covering a wide range of frequencies, a 9.7T helium-free superconducting magnet (SCM) is planned for the gyrotron operation on either the fundamental or second harmonic of the electron cyclotron frequency. The gyrotron design is based on a triode electron gun (Vk=15kV, Ib=100mA, Va= 6–8kV) which is very flexible for adapting the electron beam properties to a wide variety of cavities operating at the fundamental or at the second harmonic. The gyrotron is designed for a lateral output with an internal Vlasov-type converter. The reference parameters for application of DNP-enhanced NMR spectroscopy on a 400MHz (1H) spectrometer are optimized with a RF frequency tunability corresponding to twice the proton NMR frequency. The modularity of the construction of the gyrotron allows for the possibility of changing only some elements like the cavity-uptaper system in order to adapt to the wide range of NMR spectrometers existing at EPFL.
Denoising MR Spectroscopic Imaging Data With Low-Rank Approximations

This paper addresses the denoising problem associated with magnetic resonance spectroscopic imaging (MRSI), where signal-to-noise ratio (SNR) has been a critical problem. A new scheme is proposed, which exploits two low-rank structures that exist in MRSI data, one due to partial separability and the other due to linear predictability. Denoising is performed by arranging the measured data in appropriate matrix forms (i.e., Casorati and Hankel) and applying low-rank approximations by singular value decomposition (SVD). The proposed method has been validated using simulated and experimental data, producing encouraging results. Specifically, the method can effectively denoise MRSI data in a wide range of SNR values while preserving spatial-spectral features. The method could prove useful for denoising MRSI data and other spatial-spectral and spatial-temporal imaging data as well.
An FPGA-based implementation for repeated square-and-multiply polynomials

This paper presents a novel FPGA based method to implement a repeated squared-and-multiply algorithm in polynomial rings. The repeated square-and-multiply algorithm for exponentiation is discussed and constructed for a general function f(x). From that, an algorithm to apply for f(x)=xn+1 is also constructed and described in this paper. Simulations and implementation results using an FPGA are provided and discussed.
A distributed optimization-based approach for hierarchical MPC of large-scale systems with coupled dynamics and constraints

We present a hierarchical MPC approach for large-scale systems based on dual decomposition. The proposed scheme allows coupling in both dynamics and constraints between the subsystems and generates a primal feasible solution within a finite number of iterations, using primal averaging and a constraint tightening approach. The primal update is performed in a distributed way and does not require exact solutions, while the dual problem uses an approximate subgradient method. Stability of the scheme is established using bounded suboptimality.
A Group Dynamic Source Routing Protocol for Mobile Ad Hoc Networks

In this paper, we have proposed a new group dynamic source routing protocol, G-DSR, using a clustering technique for highly dynamic and large ad hoc networks. We focus on pursuing routing stability, making a fast recovery of link failure, and reducing packet overhead caused by the increase of packet header size in traditional source routing protocols. The entire nodes in a network are divided into a disjoint set of clusters, each of them being assigned a unique cluster label. A routing path is represented by a sequence of cluster labels and the nodes having an identical cluster label are responsible for delivering packets cooperatively to the cluster whose cluster label is the next one in the routing path. Because of a high probability of the existence of multiple paths between adjacent clusters, most of link failures are fixed rapidly. The protocol, we believe, is attractive for dealing with real-time traffic in military or commercial wireless networks that consist of a large number of fast moving wireless stations. We compared the protocol by resorting to simulation with some existing protocols. The result is proven to be highly dependable.
Cloud-Based Smart Home Environment (CoSHE) for home healthcare

The continued increase of senior population worldwide is causing challenges to existing healthcare and support systems in terms of infrastructure and trained professionals. Recently, smart home environments are being utilized for ubiquitous health monitoring, allowing patients to stay at the comfort of their homes. In this paper we presented a Cloud-Based Smart Home Environment (CoSHE) for home healthcare. Our system collects physiological signals through non-invasive wearable sensors and provides contextual information in terms of daily activities and location in the home. The body fluid intake level is also provided. This enables healthcare professionals to study daily activities, behavioral changes and monitor rehabilitation and recovery processes. A smart home environment is set up with ambient sensors to provide contextual information. The sensor data is processed on a smart home gateway and is sent to a private cloud server. The cloud infrastructure is developed to provide real time data access for remote care givers. Our case study shows that we can successfully integrate contextual information to health data and this comprehensive information can help understand caretaker's health status.
Characterization and control of the distillation column with heat pump

This works presents the authors' research on heat pumps together with a thorough investigation of their usage within distillation columns. The full spectrum of heat pumps is analyzed pointing out for each the pros and cons. A special emphasis is given to the analysis of the control structures that are associated to both classical distillation processes and heat pumps. A set of conclusions is drawn regarding the problem of an automatic control of a distillation column that is equipped with a heat pump.
An Extension of the Standard Mixture Model for Image Segmentation

Standard Gaussian mixture modeling (GMM) is a well-known method for image segmentation. However, the pixels themselves are considered independent of each other, making the segmentation result sensitive to noise. To reduce the sensitivity of the segmented result with respect to noise, Markov random field (MRF) models provide a powerful way to account for spatial dependences between image pixels. However, their main drawback is that they are computationally expensive to implement, and require large numbers of parameters. Based on these considerations, we propose an extension of the standard GMM for image segmentation, which utilizes a novel approach to incorporate the spatial relationships between neighboring pixels into the standard GMM. The proposed model is easy to implement and compared with MRF models, requires lesser number of parameters. We also propose a new method to estimate the model parameters in order to minimize the higher bound on the data negative log-likelihood, based on the gradient method. Experimental results obtained on noisy synthetic and real world grayscale images demonstrate the robustness, accuracy and effectiveness of the proposed model in image segmentation, as compared to other methods based on standard GMM and MRF models.
A probability-based adaption method for server-pushed streaming over HTTP 2.0

Nowadays, HTTP protocol contributes significantly in media streaming. Recently, the deployment of HTTP 2.0 has introduced several novel instruments for developers to control and optimize the streaming process. In this paper, we propose a streaming solution for live streaming services with small buffer sizes, focusing on pushed function of HTTP 2.0. By adjusting the number of pushed-segments in each request from client, our method provides an option to achieve a balance between the number of requests and the stability of the service. The experimental results show that our proposed method surpasses conventional streaming strategy in terms of buffer level and overall network overhead.
Control system DC motor with speed estimator by neural networks

This paper introduces the new ability of artificial neural networks (ANNs) in estimating speed and controlling the separately excited DC motor. The neural control scheme consists of two parts. One is the neural estimator which is used to estimate the motor speed. The other is the neural controller which is used to generate a control signal for a converter. These two neurals are training by Levenberg-Marquardt back-propagation algorithm. ANNs are the standard three layers feedforward neural network with sigmoid activation functions in the input and hidden layers and purelin in the output layer. Simulation result are presented to demonstrate the effectiveness of this neural and advantage of the control system DC motor with ANNs in comparison with the conventional scheme without ANNs
A new artificial neural network controller for direct control method for matrix converters

This work presents a new artificial neural network (ANN) Controller for implementing the Direct control method (DCM) for Matrix converters (MC) to decrease the time of calculation of the conventional DSP control system. To avoid the difficult calculation of ANN-DCM, the design uses the individual training strategy with the fixed weight and the supervised models. A computer simulation program is developed using Matlab/Simulink together with the Neural Network Toolbox. The simulated results demonstrate the good quality and the robustness of the proposed ANN-DCM-Controller for MC.