nils-herrmann/clean_scirepeval huggingface dataset:
Low-Pressure-Assisted Large-Area (>800 mm2) Sintered-Silver Bonding for High-Power Electronic Packaging

Nanosilver paste, as a kind of new thermal interface material (TIM) with superior thermal performance, has the potential to be a replacement for the conventional TIMs, such as greases, gels, and phase-change materials. In this paper, by combining double-print method and rapid-dry process, we achieved robust large-area bonded profile (>800 mm2) based on low-pressure-assisted sintering of silver paste. The low-temperature sintering process and sintering mechanism were discussed. The silver paste consists of nanosilver particles, microsilver flakes, and organics including dispersants, binders, and thinners. According to the thermogravimetric analysis and corresponding scanning electron microscopy observation, most organics were removed at temperature range from 70 °C to 160 °C. The nanosilver particles were instantly sintered and formed sintering necks at temperature range from 200 °C to 220 °C. The sintering of microsilver flakes needs higher temperature and longer time than the sintering of nanosilver particles. At 275 °C, microsilver flakes grew into spherical microsilver big particles at the expense of smaller nanosilver sintering necks and small microsilver flakes. Finally, robust sintered-silver-bonded plates were achieved by an optimized low-pressure-assisted sintering profile. The large-area bonded plate showed low porosity and high shear strength. Effects of a couple of sintering factors, including dry temperature, pressure on sinter quality, and fracture mode, were studied. The large-area bonded plates showed cohesive failure in sintered-silver bond line.
A 359fs Er-doped fiber laser based on topological insulator: Bi2Se3

A 359fs erbium-doped fiber laser using topological insulator Bi2Se3/Polyvinyl alcohol (PVA) composite film as a saturable absorber was demonstrated. When changing the length of the erbium-doped fiber, a mode-locking pulse ranging from 1557nm to 1600nm could be generated.
Empirical numerical study on denoising by wavelet and by second kind of fourier analysis

As the wavelet bases usually have relative small support sets, three defects of wavelet denoising for a complex time series can be deduced. The first defect is that for in large sample size, the wavelet denoising is not better than the second kind, Fourier analysis denoising. The second is that wavelet denoising has the worse reconstructive capacity for the high frequency signal. The last defect is that the wavelet denoising is not better when there are many outliers. The numerical experiments are carried out to make sure the three deductions.
A Double-talk Robust and Fast Converging Echo Canceller

In the application of network communication, echo cancellers with faster convergence and more robustness to double-talk are highly expected. The robustness can be realized by an echo canceller with two echo path models to avoid false adaptation problem under double-talk and algorithms based on PNLMS (proportionate normalized least mean square), which were reported recently, are ones to achieve more rapid convergence. In this paper a new echo canceller with two echo path models is presented, which will choose appropriate algorithm between PNLMS and NLMS to update the background filter coefficients according to the difference of two echo path models. This echo canceller can take advantage of both PNLMS and NLMS so that it converges as fast as PNLMS and shows better performance after fast initial convergence. In our experiments and applications, better performance during double-talk is obtained with proposed echo canceller with two echo path models compared with conventional one. The real-time realization of the new echo canceller is implemented on a fixed-point DSP from ADI
Remote sensing change detection based on Growing Hierarchical Self-Organization Map

Remote sensing change detection is a hot issue in recent years. However, most methods originate from statistical pattern recognition. Parameter resolution and time-consuming are main disadvantages of these methods. Hence, in this paper we propose a novel remote sensing change detection method which originates from neural network pattern recognition. The method is based on Growing Hierarchical Self Organization Map(GHSOM). GHSOM has flexible network architecture to adjust remote sensing scene complexity. Theoretically speaking, GHSOM is able to extract change areas well. In experiment, we select three pairs of remote sensing image. We compare the results with Gaussian Mixture Model result and traditional SOFM result. The experiment shows the proposed method is advantageous in efficiency and detection accuracy. It can be expected that the method will be applied in GIS data updating, land use cover surveying, and natural disaster evaluation. Keywords-remote snesing; chang detection; Growing Hierarchical Self-organization Mapping; Differential Image
Parameter optimization of wireless communication system in video surveillance network

Aiming at the more and more serious traffic problem in large cities, this paper proposes a new architecture of urban traffic surveillance system based on the concept of sensor network. Since the speed and quality of communication system has a critical effect on the performance of the overall system, the optimal combination of duty cycle, one of the most important parameters, is introduced to optimize the system performance. This paper puts forward a duty cycle optimization model based on calculating n-times reachable matrix. Now that the parameter optimization model can be boiled down to a NP-hard problem, an improved genetic algorithm is introduced to solve the problem. The computational procedure and efficiency are discussed, and simulation study based on a practical road network is given to illustrate the validity of the proposed method.
An Interactive Video Annotation Frameowrk with Multiple Modalities

Active learning and semi-supervised learning methods are frequently applied in multimedia annotation tasks in order to reduce human labeling effort. However, in most of these methods only single modality is applied. This paper presents an interactive video annotation framework, which is based on semi-supervised learning and active learning with multiple multimodalities. In the proposed framework, unlabeled samples are iteratively selected to be annotated manually according to certain strategy which has taken the potentials of different modalities into account, and then a graph-based semi-supervised learning algorithm is conducted on each modality. This process repeats for several rounds, and the results obtained from multiple modalities are then fused to generate final output. The proposed framework is computationally efficient, and the experimental results on TRECVID 2005 benchmark show that the proposed framework considerably outperforms previous approaches.
Worst-Case Identification of Errors-in-Variables Models in Closed Loop

A worst-case identification method in frequency domain is proposed to cope with the identification of errors-in-variables models (EIVMs) in closed loop. With a priori bound for the disturbing noises of an EIVM in closed loop, a frequency-domain normalized coprime factor model (NCFM) with perturbation is derived and thus the identification of the EIVM becomes that of the NCFM. By employing the v-gap metric as an optimization criterion, the worst-case error for an identified nominal NCFM is easily quantified and the parameter optimization can be effectively solved by linear matrix inequalities (LMIs). During the parameter optimization, the derivative of the nominal NCFM is constrained to some degree to reduce the effect of overfitting phenomenon. Different from other EIVM identification methods, we use v-gap metric to characterize the disturbing noises and quantify the worst-case error for the nominal NCFM. As a result, the identification result is not a deterministic model but a model set. Moreover, this model set can be perfectly combined with the robust controller design. Finally, a numerical simulation is presented to verify the proposed method.
Video Annotation by Active Learning and Semi-Supervised Ensembling

Supervised and semi-supervised learning are frequently applied methods to annotate videos by mapping low-level features into semantic concepts. Due to the large semantic gap, the main constraint of these methods is that the information contained in a limited-size labeled dataset can hardly represent the distributions of the semantic concepts. In this paper, we propose a novel semi-automatic video annotation framework, active learning with semi-supervised ensembling, which tries to tackle the disadvantages of current video annotation solutions. Firstly the initial training set is constructed based on distribution analysis of the entire video dataset and then an active learning scheme is combined into a semi-supervised ensembling framework, which selects the samples to maximize the margin of the ensemble classifier based on both labeled and unlabeled data. Experimental results show that the proposed method performs superior to general semi-supervised learning algorithms and typical active learning algorithms in terms of annotation accuracy and stability
A new band-notched UWB antenna based on EBG structure

In this letter, a new band-notched ultra wideband (UWB) antenna based on electromagnetic band gap (EBG) technology has been designed and analyzed. The EBG structure is applied in a UWB antenna to create a stop band and it is located between the microstrip feedline and the ground plane. EBG characteristics, such as the reflection phase and the transmission coefficient S21, are studied. At the end of this paper, physical model and measured results are presented and measured results are well matching with simulated ones. And several main parameters of proposed antenna are analyzed and discussed in detail.
Detecting expressway traffic incident by traffic flow and robust statistics

In order to detect expressway traffic incidents veritably, the probabilistic identifying results are suggested to demonstrate the traffic jam/congestion detection by traffic flow. First, the noise series in a traffic flow is decomposed by wavelet denoising and by second kind Fourier analysis denoising respectively. Second, hypothesis testing by chi-squared distribution is employed to sort out the robust variance (scale) estimators. Last, assuming that the detected flow point with possible congestion follows a normal distribution with the mean of the decomposed signal and the variance estimated from the decomposed noise series previously, the confidence levels of congestions are generated in a probabilistic form. Numerical experiments show that the probabilistic identifying results by this three steps method are reliable and reasonable.
A 10-bit 200-kS/s 1.76-μW SAR ADC with Hybrid CAP-MOS DAC for Energy-Limited Applications

This paper presents a low-power and area efficient 10-bit SAR ADC with hybrid capacitive-MOS consisting of a 7-bit MSB capacitive DAC (CDAC) and a 3-bit LSB MOS DAC, which consumes less power and much smaller chip area than a pure CDAC. Instead of using a string of 8 MOS transistors to control one unit capacitor, the 3-bit LSB MOS DAC is realized by a MOS string with 4 native MOS transistors to control 2 unit capacitors, which allows higher voltage drop and more reliable operation for each unit MOS. The overall energy consumption of the proposed CAP-MOS DAC is reduced by 56.2% compared with a Vcm-based 10-bit pure CDAC. Under a 200-kS/s conversion rate, the prototype 10-bit SAR ADC is implemented in a 0.18-μm CMOS technology, showing an SNDR/ SFDR of 56.91 dB/68.56 dB at 99-kHz input under a 0.6-V power-supply, while consuming 1.76 μW at 200 kS/s for a FoM of 15.38 fJ/step. The peak DNL and INL are +0.27/−0.21 LSB and +0.43/−0.45 LSB, respectively. The ADC occupies a small active area of 0.097 mm2.
A spectral based visual matching method for image classification

Visual matching algorithms can be described in terms of visual content representation and similarity measure. With local feature based representations, visual matching can be restated as: (1) how to obtain visual similarity from the local kernel matrix, and (2) how to calculate the local kernel matrix effectively and efficiently. Existing methods mostly focus on the former, and use Euclidean distance to calculate the local kernel under Gaussian noise assumption. However, this assumption may not be optimal for gradient based local features. In this paper, we propose a Local Coding based Spectral Analysis (LCSA) method to exploit the low dimensional manifold structure in feature space. Specifically, we select a set of anchor points, and represent each feature as a linear combination of anchor points with locality constraint. The spectral analysis can then be efficiently processed according to this representation. Following the derivation of Efficient Match Kernel (EMK) , a compact lower-dimensional set-level image representation is obtained for visual similarity measure. Experimental results on several benchmark image classification datasets, i.e. 15-scenes and Caltech101/256, show superior performance compared with the existing state-of-the-art techniques with SIFT feature.
A Statistical Approach to Semantic Analysis for Chinese Terms

We propose a statistical semantic analysis method for Chinese terms. We use words, part-of-speech (POS) tags, word distances, word contexts and the first sememe of a word in HowNet as features to train a Support Vector Machine (SVM) model for analyzing term semantics. The model is used to identify dependencies embedded inside a term. A Conditional Random Field (CRF) model is used afterwards to incorporate the dependencies and experimental results showed the effectiveness and validity of our approach.
Research on Digital Highway System and Its Architecture

Digital highway is an important component of digital traffic. The research of digital highway and the building of the intellectualized digital highway system platform are reliable technology guarantee of traffic informatization. In this paper, by the researches of the digital highway information constitution, the characteristics, the intelligent client computing model and the data warehouse architecture used subject-oriented and so on, the complete establishing program of the digital highway system were proposed, and it is benefit for to eliminate the problem of information islands and to provide an effective decision analysis and technical support in the field of traffic information.
Traffic signal control using fuzzy logic and MOGA

This paper presents a fuzzy logic adaptive traffic signal controller for an isolated four-approaches intersection with through and lest-turning movements. The controller has the ability to make adjustments to signal timing in response to observed changes. The "urgency degree" term, which can describe the different user's demand for green time is used in fuzzy logic decision-making in our algorithm. Using three levels model of fuzzy controller, we can determine whether to extend or terminate the current signal phase and select the sequences of phases. This paper describes a flexible form of fuzzy logic signal control whose performance can be tuned off-line using a set of parameters which define the fuzzy set membership functions for the input variables. Our work also demonstrates the feasibility of using a multiobjective genetic algorithm (MOGA) to find a set of optimal parameters for a fuzzy controller with a range of possibly conflicting performance measures. Simulation results show that our proposed controller produces lower vehicle delays and percentage of stopped vehicles than the traffic-actuated controller.
Bounded consensus in multi-agent systems of asymmetrically coupled nonidentical agents

This paper investigates consensus problems of multiagent systems with asymmetrically coupled nonidentical agents in the sense of boundedness. By employing a Lyapunov function associated with the left eigenvector of the Laplacian matrix corresponding to eigenvalue zero and some graph theory, we derive a sufficient condition of global bounded consensus in form of several scalar inequalities. A distributed consensus protocol is then designed by solving a few of lower dimensional linear matrix inequalities. The presented framework for designing protocols is quite simple and of small conservation, without assuming the condition of node balance or calculating the eigenvalues of Laplacian matrix, which can be effectively used to design consensus protocols of various weighted and directed networks.
An intelligent approach for engine fault diagnosis based on wavelet pre-processing neural network model

Based on the sound intensity analysis, discrete wavelet transform (WT) and the neural network (NN) technique, a combined intelligent method for engine fault diagnosis (EFD), the so-called wavelet pre-processing neural network (WT-NN), is presented in this paper. Based on the measured multi-condition engine noise signals, a wavelet-based 21-point model for feature extraction of engine noise is established, as is a multi-layered NN model for fault pattern identification. To verify the proposed intelligent method, as an example, the WT-NN models are built and performed for recognizing eight common faults of the 2VQS type of EFI engine. The results suggest that the WT-NN models are effective and feasible for engine fault diagnosis. Due to its outstanding time-frequency characteristics, the WT-NN model can be used to deal with stationary, nonstationary and transient signals. The WT-NN technique is suggested not only to detect the engine faults, shorten maintenance time, but also to apply to other sound-related detection fields in engineering.
A kind of adaptive filter based on a new sparsity measure function

We propose a new sparsity measure function which effectively reflects the vector sparsity. Using the correspondingly developed Iterative Shrinkage/Thresholding Algorithms (ISTA) in filter's adaptation process, our algorithm reduces the impact of measurement noise on the filter performance and converges accurately to the sparse solution. We also apply the Barzilai-Borwein (BB) method, which is developed for determinate environments, to calculate the step-size of adaptive filters with random input. The validity of BB method in adaptive filters is verified by its fast convergence rate in our tests. The idea of our method can actually be applied to general adaptive filters in sparse environments with performance improvements. Numerical simulations prove the effectiveness of the method: sparsity based adaptive algorithms achieve lower mean square error than original algorithms without sacrificing convergence rate.
Robust $H_{2}/H_\infty$ Model Predictive Control for Linear Systems With Polytopic Uncertainties Under Weighted MEF-TOD Protocol

This paper is concerned with the robust <inline-formula> <tex-math notation="LaTeX">${H_{2}/H_\infty }$ </tex-math></inline-formula> model predictive control problem for a class of linear systems with polytopic uncertainties under weighted maximum-error-first and try-once-discard (MEF-TOD) protocol. To prevent data from collision, the weighted MEF-TOD protocol is employed during the data transmission from sensors to controller, where only one sensor is allowed to transmit the measurement at every time instant. A switched linear system is established with respect to the switching signals generated by the adopted weighted MEF-TOD protocol. By taking the influence of the exogenous disturbance and the weighted MEF-TOD protocol into consideration, an online optimization problem with nonconvex conditions is formulated. Then, to deal with the couplings of unknown variables, singular value decomposition technique is utilized to transform the nonconvex conditions into solvable ones. Subsequently, a set of dynamic output-feedback controllers is designed to guarantee the stability of the closed-loop system with guaranteed robust <inline-formula> <tex-math notation="LaTeX">${H_{2}/H_\infty }$ </tex-math></inline-formula> performance. Finally, a direct current motor example is used to illustrate the validity and effectiveness of the proposed methods.
Suppost vector machine regression applied to MODIS data for PM10 concentaration analysis

The density of absorbable particulate matter less than 10um termed as PM10 is one the most important contamination index for air quality monitoring. This article presented a new PM10 concentration analysis approach based on a quick atomospher correction (QUAC) model and support vector machines reggression (SVR). The deriviation of six MODIS bands before and after QUAC model is calculated as indicating features to atomospher matters. Several regression models including liner, logarithmic, quadratic, power and SVR are compared in term of the statistical correlation between the derivation values and groud measured concentration of PM10. The experimental result shows SVR outperforms than the other regression models.
A comparative study of three shape normalization algorithms

Shape is one of the fundamental visual features in pattern recognition and target tracking. And shape normalization is a very important pre-processing step in image understanding. In general, there are four basic forms of planar shape distortions caused by changes in viewer's location: translation, rotation, scaling and skewing. A good shape descriptor should be invariant to these distortions. In this paper, we study and compare three shape normalization algorithms: J.G. Leu's shape compacting algorithm and its two modified versions: Wang's image ellipse algorithm and Liang J.J.'s principal axis algorithm. Experiments on a set of images show that all of these algorithms have some drawbacks and we give some advices for modification.
Influence of Implantation Damages and Intrinsic Dislocations on Phosphorus Diffusion in Ge

Influence of the implant-induced damages and the threading dislocations during germanium (Ge) epitaxy was investigated on the phosphorus diffusion in Ge. An adequate n-type junction was formed by 650°C rapid thermal annealing of the implanted bulk Ge. It is observed that for epitaxial Ge on Si substrate, there was an enhanced local phosphorus diffusion approaching the Ge/Si interface. A diffusion model considering the enhancement by defects is proposed to sufficiently explain the phosphorus diffusion profiles in Ge. The intrinsic dislocation-enhanced phosphorus diffusivity found at least three orders of magnitude larger than that in the absence of dislocations.
Achieving Short-Cut Nitrification Using FA Inhibition and Real-Time Control Strategy in a Combined System Removing Nitrogen from Real Mature Landfill Leachate

A combined system consisting of a up-flow anaerobic sludge blanket (UASB), an anoxic/aerobic (A/O)reactor and a sequencing batch reactor (SBR), was introduced to treat a mature landfill leach ate with a high ammonia concentration and low ratio of COD/TN. After 110 days of stable operation, the results indicated that denitrification and methanogenesis took place simultaneously in the UASB and then the denitrification of nitrite and nitrate in the returned sludge by using the residual COD was accomplished in the A/Oreactor. The residual ammonia was removed in SBR as well as nitrite and nitrate produced by nitrification. when the total nitrogen (TN) concentration of influent leach ate was about2080 mg/L and the ammonia nitrogen concentration was about1970 mg/L, Nitrification and nitrogen removal were usually both higher than 99% and the TN and ammonia nitrogen concentrations of final effulent were about 12 mg/L and 6 mg/L, respectively. The shortcut biological nitrogen removal was achieved stably both in A/O reactor with FA control and in SBR with real-time control strategy with 94% and 93% nitrite accumulation, respectively, and microbial analysis data strongly supported that AOB and NOB competition could be manipulated through long-term application of both real-time control strategy and FA control strategy.