nils-herrmann/clean_scirepeval huggingface dataset:
A channel condition dependent QoS enabling scheme for IEEE802.11 Wireless LAN and its Linux based implementation

This paper presents a distributed adaptive bandwidth allocation scheme, designed to provide quality of service support in IEEE 802.11 wireless LANs. A centralized bandwidth manager that runs at the access point performs the dynamic bandwidth allocation based on the realtime wireless channel condition and the service policy of downlink and uplink traffic. A set of distributed bandwidth agents, which are located on the mobile hosts, perform uplink traffic scheduling according to the quality of service requirement of different applications that are running at the devices. At the same time, real-time wireless channel monitors provide information regarding the condition of the wireless channel to the centralized bandwidth manager. In this paper, we discuss the design of the distributed adaptive bandwidth allocation scheme and the interactive signaling mechanism between the centralized manager and the distributed agents. Furthermore, we set up and provide results from our experimental IEEE 802.11b wireless LAN test environment, where we have implemented the designed technology. A thorough test and performance analysis are also presented in this paper.
A Fast and Configurable Pattern Matching Hardware Architecture for Intrusion Detection

The current hardware architectures of intrusion detection system have several limitations on performance and configurability. In this paper we describe the architecture design and hardware implementation of gigabits NIDS using a programmable network processor and a FPGA co-processor. We discuss the requirements of NIDS, system hardware architecture and report measurements. In particular, we demonstrate performance improved by optimized parallel pattern match processing and efficient memory access in Field Programmable Gate Array (FPGA). We show an NIDS which can exploit our approach hardware platform, and make suggestions about implementation features that can significantly improve the performance and configurability of intrusion detection systems.
Delay Estimation Using Instantaneous Frequency and Phase Difference—Simulation Study

We propose a time-domain delay estimator that takes the slope of the best fit line crossing the origin in the instantaneous frequency-phase difference plane as the delay estimate. This formulation differs from existing phase-based estimators in two respects. First, we find the instantaneous frequency at all individual sample points, including large and abrupt spikes caused by destructive interference in the coherent scattering process. This differs from Loupas which finds a smoothed-out center frequency estimate within an observation window. We show that under high signal-to-noise ratio (SNR), the information from these spikes can be properly used. Second, we show that error ought to be considered as the deviation of the phase difference from the best fit line rather than deviation from the averaged phase difference. Without considering instantaneous frequency, phase-based estimators make the following two errors: samples with phase difference far away from the center frequency need not be errors as they naturally have large phase difference when their instantaneous frequency is large; samples with phase difference close to the center frequency may in fact be errors if their instantaneous frequency is large. We derive the Gauss-Markov least-squares best fit line and then propose an iterative variant that removes samples from the line-fitting process if its deviation from the best fit line is sufficiently large. The iterative version can reduce the effect of aliasing for larger delays and also further reduce the root-mean-square error (RMSE) of the estimate. Simulation studies using various bandwidth, SNR, and delay parameters indicate that iterative phase least squares (PLS) begins to outperform correlation phase Loupas at between SNR of 30 dB (for larger bandwidths and larger delays) and 60 dB (for smaller bandwidths and smaller delays). As SNR increases, iterative PLS can reach a 30- to 50-dB increase in performance over correlation phase Loupas with respect to RMSE in the most favorable conditions.
Image Compression With Edge-Based Inpainting

In this paper, image compression utilizing visual redundancy is investigated. Inspired by recent advancements in image inpainting techniques, we propose an image compression framework towards visual quality rather than pixel-wise fidelity. In this framework, an original image is analyzed at the encoder side so that portions of the image are intentionally and automatically skipped. Instead, some information is extracted from these skipped regions and delivered to the decoder as assistant information in the compressed fashion. The delivered assistant information plays a key role in the proposed framework because it guides image inpainting to accurately restore these regions at the decoder side. Moreover, to fully take advantage of the assistant information, a compression-oriented edge-based inpainting algorithm is proposed for image restoration, integrating pixel-wise structure propagation and patch-wise texture synthesis. We also construct a practical system to verify the effectiveness of the compression approach in which edge map serves as assistant information and the edge extraction and region removal approaches are developed accordingly. Evaluations have been made in comparison with baseline JPEG and standard MPEG-4 AVC/H.264 intra-picture coding. Experimental results show that our system achieves up to 44% and 33% bits-savings, respectively, at similar visual quality levels. Our proposed framework is a promising exploration towards future image and video compression.
Cooperative magnetic induction based through-the-earth communication

Magnetic Induction (MI) is a through-the-earth (TTE) communication technique which can accomplish reliable wireless propagation with long distance. When used in underground mines, MI-based transmit distance is limited by several factors: high path loss, limited size for transmitter antenna due to the narrow underground space, and limited transmitting power due to anti-explosion regulations. In this paper, a method called cooperative magnetic induction (CMI) is presented to increase the TTE propagation range. Specifically, we propose two types of CMI according to different installation of the antenna coil: fixed-angle cooperative magnetic induction and optimal-angle cooperative magnetic (OCMI). Compared to MI, the latter can bring >20% increase in the transmitting distance.
Applying LTE-D2D to Support V2V Communication Using Local Geographic Knowledge

This paper applies device-to-device technique to vehicular-to-vehicular communications (D2D-V) in an underlay fashion. Different from conventional D2D systems focusing on smart phones, whose positions are usually assumed to be statical, the D2D-V system suffers more mobility and uncertainties. These dynamic features indeed complicate the design of D2D-V system, but also, give more potential to accomplish some geographic based algorithms. Armed with the geographic knowledge of highway and the dynamic GPS information in each vehicle, we propose a geographic based reuse cellular user selection scheme and two different distributed power control schemes to serve various applications in vehicular networks. We model the problem firstly, then formulate two metrics: the sum rate and the minimum-achievable rate to evaluate the performance of the proposed schemes. Simulations are proposed to validate our schemes and analysis. Influences of different system settings are also discussed here.
Intelligent Pan-Tilt Control System of Two Cameras Based on Gray-Scale Image Matching

Owning to the disadvantages of the current surveillance system of single camera in the intelligence, a set of project controlling pan-tilt of two cameras intelligently is presented in this paper. It realized monitoring the whole scene while recognizing the moving object automatically. A method using RGB-scale thresholds is improved, so the integrity of the object increased, and the noise reduced greatly. Improved gray-scale image matching algorithm satisfies the control precision for closed-loop pan-tilt control system.
MALRU: Miss-penalty aware LRU-based cache replacement for hybrid memory systems

Current DRAM based memory systems face the scalability challenges in terms of storage density, power, and cost. Hybrid memory architecture composed of emerging Non-Volatile Memory (NVM) and DRAM is a promising approach to large-capacity and energy-efficient main memory. However, hybrid memory systems pose a new challenge to on-chip cache management due to the asymmetrical penalty of memory access to DRAM and NVM in case of cache misses. Cache hit rate is no longer an effective metric for evaluating memory access performance in hybrid memory systems. Current cache replacement policies that aim to improve cache hit rate are not efficient either. In this paper, we take into account the asymmetry of cache miss penalty on DRAM and NVM, and advocate a more general metric, Average Memory Access Time (AMAT), to evaluate the performance of hybrid memories. We propose a miss penalty-aware LRU-based (MALRU) cache replacement policy for hybrid memory systems. MALRU is aware of the source (DRAM or NVM) of missing blocks and prevents high-latency NVM blocks as well as low-latency DRAM blocks with good temporal locality from being evicted. Experimental results show that MALRU improves system performance against LRU and the state-of-the-art HAP policy by up to 20.4% and 11.7% (11.1% and 5.7% on average), respectively.
Energy Efficiency of Downlink Networks With Caching at Base Stations

Caching popular contents at base stations (BSs) can reduce the backhaul cost and improve the network throughput. Yet whether locally caching at the BSs can improve the energy efficiency (EE), a major goal for fifth generation cellular networks, remains unclear. Due to the entangled impact of various factors on EE such as interference level, backhaul capacity, BS density, power consumption parameters, BS sleeping, content popularity, and cache capacity, another important question is what are the key factors that contribute more to the EE gain from caching. In this paper, we attempt to explore the potential of EE of the cache-enabled wireless access networks and identify the key factors. By deriving closed-form expression of the approximated EE, we provide the condition when the EE can benefit from caching, find the optimal cache capacity that maximizes the network EE, and analyze the maximal EE gain brought by caching. We show that caching at the BSs can improve the network EE when power efficient cache hardware is used. When local caching has EE gain over not caching, caching more contents at the BSs may not provide higher EE. Numerical and simulation results show that the caching EE gain is large when the backhaul capacity is stringent, interference level is low, content popularity is skewed, and when caching at pico BSs instead of macro BSs.
Provisioning core link bandwidths to support streaming media

Synthesizing recent researches of Internet traffic measurements and analyses, a model for packet switched core networks of packet-level and a model for circuit switched core networks of flow-level are founded respectively, and the methods of figuring the core link bandwidth are presented according the sufficient condition for the network to guarantee the media synchronization as the demand of performance metrics for streaming media. Numerical calculating and analyzing on the statistical data from actual networks shows that packet switched core networks and circuit switched core networks can provide similar service for streaming media in delay and delay jitter performance.
Research of a novel flexible load for electric vehicle test bench

Energy and environment problems have been becoming more and more serious. Either electric power corporations or traditional automobile enterprises have taken part in novel electric vehicle development. Whatever, Variable and dynamic loads are required in order to satisfying variable working conditions in the electric vehicle (EV) experiments of the traffic. The scheme of power electronic impendence converter which adopts buck topology is brought forward to reach this goal. Impendence converting can be implemented by controlling converter's duty cycle. Then, the load torque is simulated by changing the variable load's value. At last, the experiments show that the scheme is simple, feasible and cost-efficiency.
Services for Context Aware Knowledge Enhancement and Its Application in the Chinese Enterprise Management Tank (CEMT)

In the era of knowledge economy, knowledge resources have become the most valuable assets for enterprises. To better understand and reuse knowledge, it is necessary to relate it with the context in which the knowledge is generated and used. This is a process that usually occurs in an experienced knowledge-worker's mind and without efficient supporting tools. This paper proposes an approach for the acquisition and utilization of context for the enhancement of knowledge and with a particular focus on methods to enable context extraction from industrial settings. The approach adopts a knowledge context ontology, to correlate knowledge and its context in the high-level activities of a knowledge worker. Knowledge context are extracted by utilizing a combination of methods including context identification, context reasoning, and context similarity measurement. Based on the proposed approach, a set of services for context aware knowledge enhancement are developed and applied in The Chinese Enterprise Management Tank (CEMT), a knowledge sharing and reusing platform for business management knowledge workers in all around China.
Noise and Bandwidth of 0.5-THz Twin Vertically Stacked SIS Junctions

Twin SIS (superconductor-insulator-superconductor) junctions, with a simple structure, yet large bandwidth and low noise temperature, have been widely used in millimeter- and submillimeter-wave heterodyne mixers. With increasing frequency, however, the length of the tuning inductance connecting the two individual SIS junctions becomes short while the junction area remains fixed. With a relatively short tuning inductance, the effect of the junction's spreading inductance becomes non-negligible, and device fabrication becomes more difficult. By adopting vertically stacked SIS junctions (VSJs), which have an equivalent geometric capacitance inversely proportional to the junction number, it becomes feasible to increase the junction tuning inductance. In this paper, we report on the design, fabrication, and characterization of twin Nb/Al-AlOx/Nb/Al-AlOx/Nb VSJs for the 500-GHz frequency band.
MR-OA: An Effective Framework for NCW Study Based on Multi-agent Simulation

The Network-Centric warfare (NCW) has been the hot research spot in military area since its potential capacity and big advantages. Since it is a complex adaptive system (CAS) with many autonomous entities, the multi-Agent modeling and simulation (MABMS) technology has been the most popular method to study it. But the fact that the calculation load increases with the Agent Number accords with exponential relationship will decrease its performance seriously and has restricted its application to a great extent. Aimed at this main problem, this paper proposed a new multi-Agent framework combining the multi-resolution technology with the OA (observe and action) circle theory named as MR-OA in short to cope with it effectively. After presenting its modeling process and mechanism at detail from three aspects, we took some simulation experiments to prove its validity, high efficiency and feasibility. The result indicates the large significance of the MR-OA framework and it may solve the high modeling complexity and low efficiency problem meeting the study of NCW based on MABMS thoroughly.
Neural network-based arithmetic coding of intra prediction modes in HEVC

In both H.264 and HEVC, context-adaptive binary arithmetic coding (CABAC) is adopted as the entropy coding method. CABAC relies on manually designed binarization processes as well as handcrafted context models, which may restrict the compression efficiency. In this paper, we propose an arithmetic coding strategy by training neural networks, and make preliminary studies on coding of the intra prediction modes in HEVC. Instead of binarization, we propose to directly estimate the probability distribution of the 35 intra prediction modes with the adoption of a multi-level arithmetic codec. Instead of handcrafted context models, we utilize convolutional neural network (CNN) to perform the probability estimation. Simulation results show that our proposed arithmetic coding leads to as high as 9.9% bits saving compared with CABAC.
Periodically Swapping Modulation (PSM) Strategy for Three-Level (TL) DC/DC Converters With Balanced Switch Currents

The asymmetrical modulation strategy is widely used in various types of three-level (TL) dc/dc converters, while the current imbalance among the power switches is one of the important issues. In this paper, a novel periodically swapping modulation (PSM) strategy is proposed for balancing the power switches’ currents in various types of TL dc/dc converters. In the proposed PSM strategy, the driving signals of the switch pairs are swapped periodically, which guarantees that the currents through the power switches are kept balanced in every two switching periods. Therefore, the proposed PSM strategy can effectively improve the reliability of the converter by balancing the power losses and thermal stresses among the power switches. The operation principle and performances of the proposed PSM strategy are analyzed in detail. Finally, the simulation and experimental results are presented to verify the proposed modulation strategy.
Pressure Measurement and Characteristic Analysis on a 252-kV Puffer-Type SF$_{6}$  Circuit Breaker

The transient pressure process in an interrupter, which determines the gas blasting characteristic of an SF6 circuit breaker (CB), is of interest to designers and developers. In this investigation, piezoresistive pressure transducers and thermally and electrically insulating tubes, which damped the reflection of the pressure waves, were employed to measure the transient gas pressure in a 252-kV puffer-type SF6 CB during no-load operation and fault current interrupting. The pressures of the puffer cylinder, throat, upstream, and downstream of the nozzle have been measured. The insulating tube can protect the pressure transducer from the high voltage and high temperature present during fault current interrupting. It is found that the reflection of the pressure wave inside the insulating tube will cause serious high-frequency oscillations, which superimpose upon the actual pressure waveforms during fault current interrupting. Filling the insulating tube completely with small polytetrafluorethylene (PTFE) particles can effectively damp the reflections and greatly suppress the oscillations. The pressure characteristics of the interrupter under 20-50 kA rms current have been analyzed through the method established in this investigation.
Simulation test of suppressing VFT in GIS by ferrite rings

Low voltage simulation tests were performed to investigate the possibility of suppressing Very Fast Transients (VFT) in Gas Insulated Substations (GIS) by ferrite rings. It is shown in the tests that putting ferrite rings around a conductor can change its wave impedance and damp the travelling waves propagating through. This suppression effect depends on the characteristics of the ferrite materials, including high frequency response, magnetic conductivity, loss and saturation.
Multistage Inventory Hybrid Intelligent Optimization Under Grey Fuzzy Uncertainty

The customer demand and replenishment lead-time can be considered as grey fuzzy variables combining grey and fuzzy twofold uncertain factors. The multistage inventory model under periodical review policy was presented based on the chance measure of grey fuzzy variable. The inventory would be replenished to certain level when the inventory level drops to the re-order point. The optimal re-order point and the inventory replenishment level of every stage can be obtained by minimizing the multistage inventory cost. The grey fuzzy simulation technology can generate input-output data for the uncertain functions. The neural network trained from the input-output data can approximate the uncertain functions. The particle swarm optimization (PSO) algorithm was improved with the differential evolution algorithm. The improved PSO algorithm was combined with neural network to optimize the inventory model. A numerical example was given to illustrate the feasibility of the model and algorithm
Snapshot Hyperspectral Light Field Imaging

This paper presents the first snapshot hyperspectral light field imager in practice. Specifically, we design a novel hybrid camera system to obtain two complementary measurements that sample the angular and spectral dimensions respectively. To recover the full 5D hyperspectral light field from the severely undersampled measurements, we then propose an efficient computational reconstruction algorithm by exploiting the large correlations across the angular and spectral dimensions through self-learned dictionaries. Simulation on an elaborate hyperspectral light field dataset validates the effectiveness of the proposed approach. Hardware experimental results demonstrate that, for the first time to our knowledge, a 5D hyperspectral light field containing 9x9 angular views and 27 spectral bands can be acquired in a single shot.
Adaptive Pooling in Multi-instance Learning for Web Video Annotation

Web videos are usually weakly annotated, i.e., a tag is associated to a video once the corresponding concept appears in a frame of this video without indicating when and where it occurs. These weakly annotated tags pose big troubles to many Web video applications, e.g. search and recommendation. In this paper, we present a new Web video annotation approach based on multi-instance learning (MIL) with a learnable pooling function. By formulating the Web video annotation as a MIL problem, we present an end-to-end deep network framework to solve this problem in which the frame (instance) level annotation is estimated from tags given at the video (bag of instances) level via a convolutional neural network (CNN). A learnable pooling function is proposed to adaptively fuse the outputs of the CNN to determine tags at the video level. We further propose a new loss function that consists of both bag-level and instance-level losses, which enables the penalty term to be aware of the internal state of network rather than only an overall loss, thus makes the pooling function learned better and faster. Experimental results demonstrate that our proposed framework is able to not only enhance the accuracy of Web video annotation by outperforming the state-of-the-art Web video annotation methods on the large-scale video dataset FCVID, but also help to infer the most relevant frames in Web videos.
Design and modification of EAST new-type ICRF antenna

A single new-type two-strap Ion Cyclotron Resonance Frequency (ICRF) antenna with output RF power of 1.5MW, has been designed and fabricated for the EAST device. it can be operated in the frequency range from 30MHz to 110 MHz. The design of new-type ICRF antenna including the current straps, faraday shield, and support structure and drive system are detailedly presented in this paper. The assembly procedure for the new-type of ICRF antenna was considered elaborately due to the complex structure. With the aim of increase the performance for new-type ICRF antenna, the modification for current strap and the development for vacuum feedthrough have been carried out successfully. The prototype of new-type of ICRF antenna was involved in the physics experiment in Autumn,2010 and have achieved good results of 6.4 H-model, 1MA plasma discharge operation, which indicated the new-type ICRF antenna have fulfilled the design requirements.