nils-herrmann/clean_scirepeval huggingface dataset:
Matrix-Based Channel Hopping Algorithms for Cognitive Radio Networks

Cognitive radio networks (CRNs) have emerged as a critical technique for solving spectrum shortage problems and enhancing the utilization of licensed channels. To prevent interference with the co-locate incumbent networks, before data transmission, nodes in CRNs should rendezvous on an available channel (i.e., idle licensed channel) for establishing a link or exchanging control information. However, implementing rendezvous is challenging because the availability of channels is time-varying and position-varying. For reducing rendezvous failure and increasing throughput, a node pair in CRN should be able to rendezvous on every licensed channels (i.e., maximizing rendezvous diversity) and rendezvous on an available channel as soon as possible (i.e., minimizing maximum conditional time to rendezvous (MCTTR)). Besides, to take full advantage of the frequency diversity of multi-channel medium access, rendezvous should be spread out in time and channel (i.e., minimizing channel loading). In this paper, we propose two rendezvous channel hopping algorithms, T-CH and D-CH, which can be used without time synchronization and role pre-assignment (each node has a pre-assigned role as either a sender or a receiver). D-CH requires that SUs in CRNs should have a unique ID (identifier), while T-CH does not. Both our T-CH and D-CH have maximum rendezvous diversity and minimum channel loading, and outperform in terms of MCTTR.
Motion Measurement Using Inertial Sensors, Ultrasonic Sensors, and Magnetometers With Extended Kalman Filter for Data Fusion

Inertial measurement units (IMUs) are widely used in motion measurement. However, the drift of IMUs results in significant accumulated errors for long-term position and orientation measurement. This paper reports a method to compensate the drift of inertial sensors with the assist of ultrasonic sensors and magnetometers. The magnetometer is combined with a three-axis accelerometer as a digital compass for orientation determination in static state by measuring the gravity and the earth's magnetic field vectors. A three-axis gyroscope is used to measure the orientation in dynamic state to complement the digital compass. Displacement determination is implemented using the accelerometer through double integration, and an ultrasonic sensor is employed to periodically calibrate the accumulated errors of the accelerometer. The redundant data from the multi-sensors are fused using extended Kalman filter (EKF), in which the position measured by the ultrasonic sensor and the orientation measured by the digital compass are defined as the observation values, and the position, velocity, and orientation are included in the state vector. Experimental results show that the accumulated errors of inertial sensors are reduced by ultrasonic sensors and magnetometers, and EKF improves the accuracy of orientation and position measurements.
CNT-BCB composites as a bonding interface material for 3D integration

Thermal management is one of the critical challenges in three-dimensional (3D) integration. This paper reports an approach to increasing the thermal conductivity of the bonding adhesive Benzocyclobutene (BCB) by loading carbon nanotubes (CNTs). By exploiting the aromatic property of BCB, an ultrasonication and mechanical stirring assisted non-covalent dispersion method is developed to disseminate CNTs with different concentrations ranging from 0.25% to 2% into BCB. The experimental results show a 70% improvement in the thermal conductivity by loading CNTs. The bonding strength of BCB-CNT composites were increased by CNTs.
A Novel Electronic-Throttle-Valve Controller Based on Approximate Model Method

An electronic throttle is a dc servo drive which positions the throttle plate, thus providing drive-by-wire control of engine torque. In this paper, an approximate model-based robust nonlinear control (AMRNC) strategy is proposed for electronic throttle valve. The AMRNC includes two main parts: approximate model controller and uncertainty compensation. The approximate model controller, utilized as a feedforward controller, is developed from a linearization of the input-output model of the plant using Taylor expansion technique, and it is implemented using fuzzy system modeling. Moreover, a robustness filter in the feedback structure is employed as uncertainty compensation. The robust stability is established by Lyapunov stability theorem. A simulation and an experiment are provided to verify the effectiveness of the AMRNC strategy.
Compression of color facial images using feature correction two-stage vector quantization

A feature correction two-stage vector quantization (FC2VQ) algorithm was previously developed to compress gray-scale photo identification (ID) pictures. This algorithm is extended to color images in this work. Three options are compared, which apply the FC2VQ algorithm in RGB, YCbCr, and Karhunen-Loeve transform (KLT) color spaces, respectively. The RGB-FC2VQ algorithm is found to yield better image quality than KLT-FC2VQ or YCbCr-FC2VQ at similar bit rates. With the RGB-FC2VQ algorithm, a 128 x 128 24-b color ID image (49,152 bytes) can be compressed down to about 500 bytes with satisfactory quality. When the codeword indices are further compressed losslessly using a first order Huffman coder, this size is further reduced to about 450 bytes.
Signaling Latency Analysis of Peer-to-Peer SIP Systems

Peer-to-Peer SIP (P2PSIP) is proposed to provide fully distributed multimedia communication systems. This paper focuses on analysis of call setup delay "(CSD) of P2PSIP system to evaluate whether it could provide equivalent signaling performance to that of traditional SIP or telephone networks. Call setup delay is composed of lookup latency in DHT overlay and INVITE transaction latency, and mainly decided by Round Trip Time (RTT) experienced on hops and message exchange procedures of lookup and INVITE transactions. Simulation with active measurement RTT data showed that call setup delay ranges widely from under 100 ms to more than ten seconds, and lookup latency takes up more than 60% of call setup delay. It is concluded that P2PSIP system within limited geographic range could achieve acceptable signaling latency, and efficient latency optimization should be considered to ensure the signaling performance when P2PSIP system is used spread across the public Internet.
An effective dissolve detection approach with temporal and spatial considerations

Dissolves are the basic artificial effects of gradual transitions in video sequences, which are widely used in TV programs and movies. Through dissolve detection, interesting and important video temporal segments can be easily located and indexed for various applications. In this paper, we present an effective dissolve detection algorithm, which takes into account the dissolve properties in both temporal and spatial domains. In particular, in the temporal domain, we use frame difference to capture the dissolve characteristics. In the spatial domain, the central area is given bigger weight than the four sides. Experimental results show a good performance of our proposed algorithm.
Kernel Sparse Representation Classifier with Center Enhanced SPM for Vehicle Classification

In this paper, we proposes a visual-based vehicle classification system, in which it involves visual feature representation and classification step. In the feature representation step, we present a center enhanced spatial pyramid matching (CE-SPM) to extract the feature from images. In this work, we defined additional region in the center of each images to calculate the histograms of visual words and then pool them together with some weights to construct the feature representation vector of an image. In the classification step, kernel sparse representation classifier is used to address the problem of visual-based vehicle classification. The kernel function maps the features from original space into higher space dimension. The modified active-set algorithm for l1 non-negative least square problem is adopted to solve the optimization problem. The experimental results show the improvement of proposed method over the original SPM. The proposed method can achieve the performance of 93.7% using particular vehicle image dataset.
An efficient approach to sip design integration

System-in-Package (SiP) design methodology integrates multiple dies which come from different vendors into a package. It is more advantageous than Printed Circuit Board (PCB) and System-on-a-Chip (SoC) design methodologies from the aspects of development cost, power consumption, time-to-market, and miniaturization. Since these integrated dies can be manufactured and tested separately, the best-quality SiP design can be achieved by using these best-quality dies. However, with considering cost constraint, the best-quality dies are not always qualified due to high cost. Thus, this paper proposes an algorithm to optimize the combination of dies with different yields and test coverages in an SiP such that a minimal cost is achieved subject to a quality constraint, or a least defect level is reached under the cost constraint. The proposed method significantly prunes the solution space, and efficiently determines the combination of dies.
Assessing the visual effect of non-periodic temporal variation of quantization stepsize in compressed video

This work investigates the impact of non-periodic quantization step size variation on perceptual video quality. We composed non-periodic temporal variation patterns from periodic variation patterns. We present subjective test results and examine how does the overall quality under non-periodic variation relate to the qualities of individual periodic components, and how does it relate to the instantaneous qualities at different time points. We propose two quality models based on our observations and data analysis. Such quality assessment and modeling are essential for designing video adaptation strategy when delivering video over dynamically changing wireless links.
Research on architecture of ITS based Smart Charging Guide System

Nowadays, environmental-friendly electric vehicles (EVs) attract great attention around the world. However, the adoption of EVs will impact the power system greatly. Different from previous work which concentrated on “when to charge”, research on “where to charge” to minimize the impacts on power grid is presented based on the interaction with Intelligent Transportation System (ITS). With the transportation information from ITS, a Smart Charging Guide System (SCGS) is designed in this paper. Architecture based on interaction between TCC (Traffic Control Center) and PSCC (Power System Control Center) is presented with the following three functions. Firstly, initial routes navigating to destination are selected with necessary charging stations. Secondly, load forecasting is carried out to determine the future power demands on different charging stations. Finally, based on the forecasted load, security evaluation on power system is adopted to produce a security index (SI). The SI will be used to revise the traffic distance to an electrical distance, which involves the contribution to power system operation. Two typical simulation cases are presented to explain the ideas of SCGS and further research prospects are discussed.
A MEMS-Based Electric Field Sensor for Measurement of High-Voltage DC Synthetic Fields in Air

Measurement of the electric fields of high-voltage direct-current (HVDC) transmission lines is essential for safety and reliability of power systems. However, air breakdown and corona discharge at the vicinity of HVDC lines create ion flows that induce a charging effect to measurement equipment, causing significant measurement errors. This paper reports an MEMS-based sensor to measure the HVDC fields coupled with ion flows. It consists of two identical chambers and two MEMS electric field sensors that are placed at different levels in the chambers. The chambers use a shielding metal to prevent ion flows from accumulating on the MEMS sensors inside, and thus the electric fields inside the chambers can be measured by the MEMS sensors. The relation between the electric fields inside and outside the chambers is established, and the HVDC fields coupled with ion flows are obtained by using the relation and the measured fields inside the chambers. The design, fabrication, assembly, and test of the MEMS-based sensor are given in detail. The measurement results show that the sensor is able to measure HVDC electric fields coupled with ion flows.
In-Situ Heat Capacity Measurement of Carbon Nanotubes Using Suspended Microstructure-Based Microcalorimetry

This paper reports a method for measuring the heat capacity of as-grown carbon nanotubes (CNTs) using a microcalorimeter. The microcalorimeter consists of a double-layer suspended silicon dioxide microstructure and two silicon resistors sandwiched in-between the silicon dioxide layers. CNTs for heat capacity measurement are locally synthesized on the surface of the microstructure using laser-assisted chemical vapor deposition. The CNTs and the microcalorimeter are heated to a high temperature with a silicon resistor, and the temperature of the microcalorimeter with CNTs is measured with using the other silicon resistor. The heat capacity of the CNTs is obtained by dividing the measured effective heating power by the rate of temperature increase. The suspended configuration and the low thermal conductivity of silicon dioxide achieve low thermal dissipation, which together with the minute thermal capacity enable significant temperature changes. Using this sensitive microcalorimeter, the heat capacity of as-grown CNTs, 6-14 nJ/K from 340 to 440 K, is measured in-situ without the need of CNT transfer and pretreatment, avoiding damage to the CNT samples. This microcalorimeter is also applicable to in-situ measurement of structure-related thermal properties of porous materials.
Interactive Dispatch Modes and Bidding Strategy of Multiple Virtual Power Plants Based on Demand Response and Game Theory

This paper examines operation models of multiple virtual power plants (multiVPPs), aiming at unified management of the multiVPP through VPP central controller, which reveals the controllability of the VPP as source and load in general. Two operation models will be introduced in this paper: 1) VPP dispatch model; and 2) the game theoretic model for multiVPP dispatch. In the VPP dispatch model, considering interactive coordination between VPP and energy consumers, a demand response model based on time-of-use pricing mechanism and interruptible loads is employed. And then the optimal results of VPP dispatch model are applied in the game theoretic model for multiVPP dispatch. During the process of market competition, the bidding strategy of each VPP is determined by its affordable power output and fuel cost. To solve the inherent variability and unpredictability of the renewable generation sources, the multitime scale rolling scheduling strategy is adopted. Finally, taking the multiVPP, which consists of various distributed generations and battery storage devices, as an example, variables including transferred load, compensation capacity, optimal bidding strategy, and profits for each VPP are obtained. From the analysis of the results, the method serves as a foundation for dispatch of multiVPP.
Modeling of Rate and Perceptual Quality of Compressed Video as Functions of Frame Rate and Quantization Stepsize and Its Applications

This paper first investigates the impact of frame rate and quantization on the bit rate and perceptual quality of compressed video. We propose a rate model and a quality model, both in terms of the quantization stepsize and frame rate. Both models are expressed as the product of separate functions of quantization stepsize and frame rate. The proposed models are analytically tractable, each requiring only a few content-dependent parameters. The rate model is validated over videos coded using both scalable and nonscalable encoders, under a variety of encoder settings. The quality model is validated only for a scalable video, although it is expected to be applicable to a single-layer video as well. We further investigate how to predict the model parameters using the content features extracted from original videos. Results show accurate bit rate and quality prediction (average Pearson correlation >;0.99) can be achieved with model parameters predicted using three features. Finally, we apply rate and quality models for rate-constrained scalable bitstream adaptation and frame rate adaptive rate control. Simulations show that our model-based solutions produce better video quality compared with conventional video adaptation and rate control.
The Conception of Logistics Service Supermarket for Enterprises

This paper proposes a new logistics service mode--Logistics Service Supermarket for Enterprises(LSSE), viz. in the enterprise gathering places or economic development zones the logistics service supermarket is established to sale, lease, customize goods or service (such as lighting equiments, communication equipments and so on. ) all day and night, which are the common goods used in all the enterprises. Then it analyses and compares the operation costs between the self-operating of enterprises and the operation of LSSE and the costs between accepting special stop service which is out of the zone and accepting LSSE service. At last, the conclusion is get that it is better accetping LSSE service than self-operating of enterprises. Furthermore, this paper analyses the benefit and risk of enterprises after accepting LSSE service and influence factors of LSSE operating.
Neural Network Adaptive L2 Robust Control for Induction Motors

A neural network adaptive L2 robust control method is proposed for induction motors. The proposed controllers are combined with a rotor flux observer. The disturbances caused by the uncertainties of stator and rotor resistances and load torque are compensated using radial basis function neural networks (RBFNNs). Based on backstepping, RBFNN, and HJI (Hamilton-Jaccobi-Issacs) inequality, the neural network adaptive L2 robust controllers are designed and the control system is proved to have L2-gain less than or equal to a specified positive constant gamma without solving the HJI inequality directly. The simulation results indicate that the proposed method is robust to the considered uncertainties of the induction motor parameters and has high dynamic performance.
Channel coordination through discount pricing policies when demand is price and effort dependent

In this paper, we investigate the performance of discount pricing policies as coordination mechanisms in decentralized channels where demand is sensitive to both price and sales effort. Most related studies have shown that volume discount is an effective channel coordination device. However, our paper shows that, when demand is affected by both price and sales effort, regular volume discount not only is unable to coordinate the channel effectively, but some times it performs even worse than the simple price-only contract. Fortunately, our results show that a continuous volume discount schedule can perfectly coordinate such an effort-sensitive channel.
Converting 2D Video to 3D: An Efficient Path to a 3D Experience

The 3D experience is poised to be the future of entertainment. The path to that future, however, is bumpy. Despite widely available 3D display devices, a lack of 3D content impedes the 3D industry's rapid growth. Here the authors discuss some common 3D content-creation processes and the key elements that affect quality, and present an approach that converts 2D content to 3D content as an efficient path to a 3D multimedia experience.
Power-rate-quality optimized scalable video adaptation

In this paper, we investigate the power-rate constrained scalable video adaptation for popular wireless video streaming application, where the wireless access network bandwidth and mobile remaining battery capacity are usually limited. Towards this goal, we have developed a scalable video decoding complexity model with the focus on the joint temporal and amplitude scalability, which can be translated to the power consumption model easily for mobile processor. Overall, there are three parameters for our proposed decoding complexity model. Currently, we propose to embed these parameters in the header field. We have validated our complexity model using various videos with different contents, resolutions, and bit rates. Results show that our proposed model can estimate the scalable video decoding complexity accurately with small root mean square error (RMSE) and high Pearson correlation (PC). Together with our rate and perceptual quality models for scalable video, we have made the power-rate constrained scalable video adaptation analytically tractable without requiring exhaustive search.
Power Allocation Based on Duality in a Cognitive System

In a cognitive radio network based on underlay, the secondary users need to ensure that the interference to the primary users does not exceed the maximum interference tolerance. In this paper, a new algorithm of power allocation is proposed by solving optimal problem according to the theory of duality. The algorithm proposed can satisfy both the limitation of interference tolerance and the requirement of maximum utility, while the requirement of rate can also be considered. Numerical results indicate that the proposed algorithm can limit the secondary users' interference under a predefined threshold while maintaining a maximum utility of the system.
Dynamic scheduling optimization model for virtual power plant connecting with wind-photovoltaic-energy storage system

In order to promote wind power and solar power as the representative of the development of the distributed energy grid, this paper based on the wind power, photovoltaic power generation, gas turbine, the energy storage system and incentive model to build a virtual power plant (vpp) demand response, introducing the theory of conditional value at risk (cvar) and confidence method to describe the vpp operating uncertainty, and operating income maximization as the objective function. Vpp routine scheduling optimization model is established. Then this paper further to determine the threshold value of vpp operating earnings. Using cvar theory to describe the vpp operating uncertainty factors in the objective function, confidence method is applied to convert the constraint conditions including uncertainty variables, which run the risk of vpp stochastic scheduling optimization model is established. Improved ieee30 node system is chosen as the finally, the simulation system, the simulation results show that the price type of demand response can gently electricity load curve, energy storage system and incentive mode demand response can increase the vpp operating income. This means that the cvar theory and confidence method can be used to describe confidence vpp operating risks, thus by setting the threshold value and the confidence to reflect the decision makers for risk attitude, thus helping the risk control.
BDD-based synthesis of reconfigurable single-electron transistor arrays

Single-electron transistor (SET) is an ultra-low power device, which has been demonstrated as a promising alternative for CMOS devices in reducing power consumption. A suitable structure for realizing logic function using SET is a binary decision diagram (BDD)-based SET array. Previous works proposed product term-based automated synthesis methods to map a given logic function onto an SET array. In this work, we propose a novel BDD-based synthesis method that exploits the structure similarity between an SET array and a BDD. Our method transforms a BDD of a Boolean function into a planar graph and further maps the graph onto an SET array. Experiment results showed that compared to the state-of-the-art synthesis method, our method saves 51% in area on average and is more than 16 times faster.