nils-herrmann/clean_scirepeval huggingface dataset:
A discussion on the DC and switching performance of a gallium nitride CAVET for 1.2kV application

A cascoded GaN Current Aperture Vertical Electron Transistor (CAVET), which incorporates a normally off low-voltage Si MOSFET at the input and a normally on high-voltage GaN CAVET at the output in a cascode form, has been simulated and analyzed using a device/circuit hybrid simulator implemented in Silvaco ATLAS. The hybrid simulator was achieved by integrating the Silvaco ATLAS based 2D drift-diffusion simulator with the SPICE based circuit simulator. This model provides a reliable method of evaluating GaN-based power transistor. A baseline of the hybrid simulator was first established using commercial cascoded GaN HEMT where a good agreement of simulated data and device specs as published on the datasheet was obtained. The hybrid simulator was then applied to compare SiC and GaN power devices using commercial MOSFET and JFET for SiC and a CAVET in GaN. A cascoded GaN CAVET has 2x faster switching time, 6x lower switching loss than standard commercial SiC MOSFET and JFET, suggesting its potential advantage over SiC devices for power converters. Another advantage of cascoded GaN CAVET is that it enables switches operating at frequencies of several hundreds of kilohertz with low power loss, which enable smaller converter size and high system efficiency.
A weighted fusion method based on robust error scale estimation

In view of the interference of complex electromagnetic environment and the non-robustness of traditional method in the weighted fusion of multi-source electronic measurement data, with robust statistics theory as a guide, this paper proposed a robust filter method based on real-time median estimation, and applied the filter residual error scale to the dynamic fusion weights calculation under the optimal weight assignment principle, then obtained a new distributed robust weighted fusion method. The results of the test with typical measured data showed that the methods proposed were effective and robust in practical engineering application.
A New Mechanism for the Vibration Control of Large Flexible Space Structures With Embedded Smart Devices

Large flexible space structures (LFSS), such as solar panels and synthetic aperture radar antennae, are widely employed in spacecraft. Owing to their structural peculiarities in addition with the special environment in outer space, it is easy to be aroused for the structural vibration of LFSS. This kind of vibration is very harmful to spacecraft. In this paper, we proposed a new mechanism for the vibration control of LFSS. An embedded smart device with a piezoelectric ceramic stack was built up, also. First, the nonlinear theory of structural vibration coupled with inner forces was deduced. Second, the basic principle of the new mechanism with the piezoelectric ceramic stack was expounded. Third, the smart device manufacturing and the embedded techniques were introduced. Finally, taking several solar panel structures as illustrative examples, we carried out both digital simulations and ground experiments. The research results showed us that the proposed new mechanism is quite effective to the vibration control of LFSS. This paper gives a new way to solve the vibration control problem of LFSS, especially for those with low frequencies, which is very valuable for improving the accuracy and performance of spacecraft.
Experimental verification of theoretical predictions concerning the optimum defibrillation waveform

The efficacy of electrical therapy at terminating ventricular fibrillation is highly dependent on the waveform used. We present experimental results which test one theory for defibrillation waveform dependence. Forty-four defibrillation waveforms (22 monophasic, 22 biphasic) were designed according to the theoretical construct of Fishler (2000). The waveforms were then tested on 67 male guinea pigs (46 for monophasic, 21 for biphasic waveforms) using a custom designed defibrillator and 12-mm subcutaneous disc electrodes. There was considerable agreement between the theoretical and experimental results. For example, as predicted, the ascending exponential waveform of 1 ms proved to be the most effective (86.4%) monophasic waveform, where efficacy is the number of successful shocks divided by the total number delivered. In addition, the efficacy decrease with duration increase was accurately predicted by the model for monophasic waveforms. For biphasic waveforms, as predicted by the model, when the first phase was optimized, an increase in second phase duration caused an increase in defibrillation efficacy (10 of 11 tested duration pairs). We conclude that the theoretical framework adequately explains the mechanism by which the defibrillation waveform affects efficacy for monophasic waveforms and, in at least one aspect, biphasic waveforms
A 1V 1.8GHz Low Power Differential Colpitts VCO

In this paper, the design and simulation results of a low power Colpitts VCO operating at 1.8GHz are presented, with the supply voltage as low as 1 V, using the 0.18mu CMOS process. The simulated performance characterizes in the power consumption of 2.5mW, the phase noise of -120dBc and -124.3dBc at offsets of 600KHz and 1 MHz, respectively, and a frequency tuning range of 10.3%. When it works with a Vdd of 0.8 V, the VCO consumes a power of 1.2 mW
Design of Intelligent Thyristor Converter Based on dsPIC

With the development of large scale integrated circuit technology and computer technology, that using a microprocessor as the core microcomputer of excitation thyristor converter will become the main development of thyristor converters in the future. This paper introduces the features of dsPIC30F6011, and focuses on the structure and function of the intelligent detection and control modules in the intelligent thyristor converter, the intelligent detection and control modules are embedded in the intelligent thyristor converter. The experimental result has been given. The system integration rate of this intelligent thyristor converter is high, the hardware is simple, the operation is reliable, and this kind of intelligent thyristor converter has a certain economic value and practical significance.
Improving the multimedia traffic performance over wireless LAN

In order to grapple with the continuously increasing demand on the multimedia traffic over high speed wireless LAN, we need a QoS-oriented MAC for wireless multimedia system. In this thesis, we propose a efficient centralized scheduled access MAC approach in IEEE 802.11 to satisfy multimedia traffic. There are two main algorithms to achieve the goal, queue management algorithm for downlink and uplink to satisfy video traffic's high bandwidth requirement, polling list management method enhance the efficiency of the polling scheme for transmitting voice traffic. The result shows that the centralized scheduled access MAC approach we propose has excellent efficient and compelling performance.
Marine Images Segmentation Using Adaptive Fuzzy c-Means Algorithm Based on Spatial Neighborhood

In the marine environment, the underwater images have low SNR and the detail is fuzzy due to scattering and absorption of a variety of suspended matter in it and the water itself. If traditional methods are used to dispose underwater images directly, it is unlikely to obtain satisfactory results. Through the further research on traditional fuzzy c-means clustering algorithm, an adaptive FCM segmentation algorithm based on spatial information is proposed. The new method is based on the I1I2I3 color space, and the characteristic distance in the target function is defined as the weighted sum between the distance of two pixels and spatial neighborhood distance, and an adaptive weight is given to the taking value method. By this new algorithm, the image spatial information is well taken into the FCM algorithm, and a new clustering objective function including neighborhood information is established, and thus effective marine image segmentation is achieved. Experimental results show that the new algorithm can achieve good quality of clustering and segmentation results, and has a strong ability to suppress noise.
A novel object tracking algorithm based on enhanced perception hash and online template matching

Object tracking task faces serious challenges when a desired target is in complex circumstances such as obstacle occlusion, pose variation, illumination change and motion blur. Despite lots of excellent tracking algorithms have been proposed, many issues remain to be addressed. In this paper, we propose a novel object tracking algorithm to combine both Enhanced Perception Hash and Coarse-to-fine Sliding Window search strategy. First, we calculate the feature template of target by using the perception hash approach integrating Fast Fourier Transform (FFT). We make a FFT on the target area in a frame and only retain low-frequency part to save storage. Moreover, the new template is generated by fusing the templates from the current and previous frames, thus it is robust to the severe deformation and drastic variation problem. Second, we propose a coarse-to-fine sliding window search strategy to provide potential target candidates efficiently. Based on this strategy, our tracking algorithm can implement an effective online detection for target. Extensive tests on thirteen videos are conducted and demonstrate that our approach achieves promising performance as compared to the state-of-the-art methods.
MLSP2011 competition: Face recognition with integrating multiple cues

Automatic face recognition is a challenge task, especially working in practical uncontrolled environment. Over the past two decades, numerous innovative ideas and effective processing approaches have been proposed and developed, e.g. different normalization techniques, intrinsic feature extractions and representation schemes, machine learning methods and recognition mechanisms etc. Processing with integrating multiple cues is an effective approach for upgrading recognition performance. A multiple stage with integrating Harr, gradient and cuvelete features for locating eye center approach is presented. The integration based approach achieved high location accuracy and outperforms other state of the art methods. A face recognition classifier using integrating Gabor feature representation and curvelet feature representation is also presented. The experiment results show that the fusion processing can reduce error rate about 30%, compared with using Gabor feature or curvelet feature alone.
Laplacian Embedded Regression for Scalable Manifold Regularization

Semi-supervised learning (SSL), as a powerful tool to learn from a limited number of labeled data and a large number of unlabeled data, has been attracting increasing attention in the machine learning community. In particular, the manifold regularization framework has laid solid theoretical foundations for a large family of SSL algorithms, such as Laplacian support vector machine (LapSVM) and Laplacian regularized least squares (LapRLS). However, most of these algorithms are limited to small scale problems due to the high computational cost of the matrix inversion operation involved in the optimization problem. In this paper, we propose a novel framework called Laplacian embedded regression by introducing an intermediate decision variable into the manifold regularization framework. By using ∈-insensitive loss, we obtain the Laplacian embedded support vector regression (LapESVR) algorithm, which inherits the sparse solution from SVR. Also, we derive Laplacian embedded RLS (LapERLS) corresponding to RLS under the proposed framework. Both LapESVR and LapERLS posses a simpler form of a transformed kernel, which is the summation of the original kernel and a graph kernel that captures the manifold structure. The benefits of the transformed kernel are two-fold: (1) we can deal with the original kernel matrix and the graph Laplacian matrix in the graph kernel separately and (2) if the graph Laplacian matrix is sparse, we only need to perform the inverse operation for a sparse matrix, which is much more efficient when compared with that for a dense one. Inspired by kernel principal component analysis, we further propose to project the introduced decision variable into a subspace spanned by a few eigenvectors of the graph Laplacian matrix in order to better reflect the data manifold, as well as accelerate the calculation of the graph kernel, allowing our methods to efficiently and effectively cope with large scale SSL problems. Extensive experiments on both toy and real world data sets show the effectiveness and scalability of the proposed framework.
Comparing WebRTC video conferencing with Skype in synchronous groupware applications

A study was carried out to compare an integrated HTML5-based real-time video conferencing solution against a separately installed video conferencing solution in the same web-based meeting support application. To carry out this comparison, WebRTC and Skype have been integrated into an existing groupware application. Our study shows that comparing to a separately installed, best of breed video conferencing solution; the web browser-based solution would be an adequate and more convenient one. Our work indicates that the cost to implement WebRTC is not large in comparison to the number of benefits that can be gained. The quality of video conferencing has been continuously improving, and the implementation cost is getting lower as better WebRTC libraries emerge and evolve.
Adaptive Color-Space Transform for HEVC Screen Content Coding

This paper presents an in-loop adaptive color-space transform for the HEVC Screen Content Coding extension. In the proposed method, the prediction residual is adaptively converted into a different color space to reduce the cross-component redundancy. After the ACT, the signal is coded following the existing HEVC framework. To keep the complexity as low as possible, fixed color-space transforms that are easily implemented with shift and add operations are utilized. Significant coding gains are achieved by this method in the current HEVC Screen Content Coding reference software with no increase of decoding runtime. The proposed method has been adopted to the HEVC Screen Content Coding extension.
Applying Expert Experience to Interpretable Fuzzy Classification System Using Genetic Algorithms

Accuracy and interpretability are two important objectives in the design of fuzzy classification system. In many real-world applications, expert experiences usually have good interpretability, but their accuracy is not always high. Applying expert experiences to fuzzy classification system can obtain better accuracy and preserve interpretability. In this paper, we present a method to translate expert experiences into fuzzy sets by similarity measure. Meanwhile reasonable experiences are integrated into a fuzzy genetic-based learning mechanism. Finally, experimental results with performance evaluation on benchmark classification problems demonstrate that the learning mechanism is able to achieve accurate performance for interpretable fuzzy classification systems.
Embedded control system of automatic platelet aggregometer for multi-mode and fast test

Platelet is an important factor of thrombosis diseases such as stroke, myocardial infarction and so forth. Disorders of platelet function affect normal blood clotting and may cause thrombosis or bleeding. Therefore, high-speed and accurate platelet function analyzer is of urgent requirement. In this paper, design and implementation of a new embedded multi-mode automatic platelet analyzer based on Sequential Platelet Counting Method (SPCM) is presented. The device is an enhancement and upgrade design to improve current analyzer in accuracy and efficiency, and to expand its functions. The analyzer is controlled by a two-tier system of embedded processor + DSP controller, and with CPLD for parallel processing. Software structure for automatic multi-mode and fast test is proposed, and a signal modulation for detecting and measuring weak pulse signals of platelet is described as well as software filter. Special mode for fast test and special strategic design for measuring accuracy are given in detail respectively. Experiments validate the efficiency, accuracy and stability of the system.
Highly Parallel Line-Based Image Coding for Many Cores

Computers are developing along with a new trend from the dual-core and quad-core processors to ones with tens or even hundreds of cores. Multimedia, as one of the most important applications in computers, has an urgent need to design parallel coding algorithms for compression. Taking intraframe/image coding as a start point, this paper proposes a pure line-by-line coding scheme (LBLC) to meet the need. In LBLC, an input image is processed line by line sequentially, and each line is divided into small fixed-length segments. The compression of all segments from prediction to entropy coding is completely independent and concurrent at many cores. Results on a general-purpose computer show that our scheme can get a 13.9 times speedup with 15 cores at the encoder and a 10.3 times speedup at the decoder. Ideally, such near-linear speeding relation with the number of cores can be kept for more than 100 cores. In addition to the high parallelism, the proposed scheme can perform comparatively or even better than the H.264 high profile above middle bit rates. At near-lossless coding, it outperforms H.264 more than 10 dB. At lossless coding, up to 14% bit-rate reduction is observed compared with H.264 lossless coding at the high 4:4:4 profile.
Evolution of consumer trust in B2C e-business: A longitudinal exploration

This study proposes a framework for understanding the process by which consumer trust in B2C E-business evolves over time. In particular, the framework draws together two distinct approaches: (1) static analysis that shows how antecedents affect trusting beliefs, which result in trusting intention, and (2) dynamic analysis that bridges trusting intention, satisfaction and e-loyalty and examines the process of transition from one period to the next. In doing so, we present a static model and a dynamic model in order to examine trust-related variables change over time as well as the dynamics of trusting belief revision. Based on plenty of extant literatures, we propose that the importance of the same antecedent of trusting belief changes at different stages of pre-purchase, post-purchase and long-term relationship commitment, and that, most important, the structure of trusting beliefs dynamically changes over time.
Improved Field Emission Properties of Multidimensional Graphene-Nanotube Emitters Decorated With Au Nanoparticles

Improved field emission properties are demonstrated based on the multidimensional graphene-carbon nanotube (G-CNT) emitters decorated with gold nanoparticles (AuNPs). Chemical vapor deposition and screen printing are adopted to prepare the complex structures of G-CNT emitters. The AuNPs decoration of the emitters is implemented by the thermal evaporation and rapid thermal annealing of the Au film. Experimental results reveal that the proposed field emitters can supply a peak current of >17 mA with the current density of 556.05 mA/cm2. G-CNTs retain the turn-ON electric field that is <;0.998 V/μm, and the emission has been proved to be very stable.
A Design of Embedded Video Server Based on H.264

In order to design a low cost, easy to maintain and code upgrade, good applicability, and can be widely used embedded video server, the design has take fully into account the excellent performance of latest generation video coding standard-H.264 and the processing power of DM642. First, optimize the encoder-H.264 at the DM642-platform, then apply it to the embedded network video server. Result shows: the embedded video server based on the DM642 can reflect a good performance in image quality and real-time of image, the system has high compression rate, lower requirement of channel bandwidth and so on.
Field Test of Measurement-Device-Independent Quantum Key Distribution

The main type of obstacles of practical applications of quantum key distribution (QKD) network are various attacks on detection. Measurement-device-independent QKD (MDIQKD) protocol is immune to all these attacks, and thus, a strong candidate for network security. Recently, several proof-of-principle demonstrations of MDIQKD have been performed. Although novel, those experiments are implemented in the laboratory with secure key rates less than 0.1 b/s. Besides, they need manual calibration frequently to maintain the system performance. These aspects render these demonstrations far from practicability. Thus, justification is extremely crucial for practical deployment into the field environment. Here, by developing an automatic feedback MDIQKD system operated at a high clock rate, we perform a field test via deployed fiber network of 30 km total length achieving a 16.9 b/s secure key rate. The result lays the foundation for a global quantum network, which can shield from all the detection-side attacks.
Design of an expert system based on neural network ensembles for missile fault diagnosis

According to the specialty and complexity of the missile fault diagnosis, a novel expert system design method based on the neural network ensembles is proposed in this paper. With large amounts of typical missile fault samples and raw measurable parametric data available, the missile fault diagnosis system based on neural network ensembles can be created applying general construction techniques of the neural network fault diagnosis system, including signal preprocessing, fault feature extraction/selection, and network training. Combining the fault diagnosis system based on neural network ensembles, the framework of the missile fault diagnosis expert system is constructed with more flexibility and effectiveness in missile fault diagnosis. It's proved that through diagnosis of the missile from several different sides by use of different parameters or combined parameters the designed system tends to give more reliable results.
Meta-activity recognition: A wearable approach for logic cognition-based activity sensing

Activity sensing has become a key technology for many ubiquitous applications, such as exercise monitoring and elder care. Most traditional approaches track the human motions and perform activity recognition based on the waveform matching schemes in the raw data representation level. In regard to the complex activities with relatively large moving range, they usually fail to accurately recognize these activities, due to the inherent variations in human activities. In this paper, we propose a wearable approach for logic cognition-based activity sensing scheme in the logical representation level, by leveraging the meta-activity recognition. Our solution extracts the angle profiles from the raw inertial measurements, to depict the angle variation of limb movement in regard to the consistent body coordinate system. It further extracts the meta-activity profiles to depict the sequence of small-range activity units in the complex activity. By leveraging the least edit distance-based matching scheme, our solution is able to accurately perform the activity sensing. Based on the logic cognition-based activity sensing, our solution achieves lightweight-training recognition, which requires a small quantity of training samples to build the templates, and user-independent recognition, which requires no training from the specific user. The experiment results in real settings shows that our meta-activity recognition achieves an average accuracy of 92% for user-independent activity sensing.