nils-herrmann/clean_scirepeval huggingface dataset:
Meso spray transfer in GMAW of aluminum and its control

Meso spray transfer mode in GMAW of aluminum exists in a very narrow arc voltage, which makes it very difficult to be controlled. Traditionally, Meso spray transfer mode is applied for GMAW of aluminum with constant current. The method is not liable to be used for medium plate more than 6 mm thick. Through analyzing the characteristic of Meso spray transfer mode, an adaptive control method is developed to adjust welding current according to arc voltage. The frequency and value of the welding current will change in pulsed mode with fluctuation of the arc voltage, thus match with the wire feed rate. With the adaptive control method, the droplet transfer mode is kept in the meso transfer zone. A control circuit is designed based on the method and the welding technology experiment result shows that the control system can provide a stable process of welding with meso transfer mode. The new developed GMAW welding technology is suitable for medium plate welding.
Design and Optimization of a Microfluidic Cell Separator Based on Dielectrophoresis

This paper presents the design and optimization of a dielectrophoresis-based cell separator by numerically analyzing the effects of geometrical dimensions on the separation performance and device safety in biological use. The separator uses negative DEP force to gather cells so that all cells have the same initial condition, and employs positive DEP to separate different cells. Simulation results show that the high electric field at the electrode edges could cause cell lysis. Therefore, we propose the maximum electric field as the criterion for measuring the safety of this device. We firstly obtain the optimal ratio of the electrode width to electrode interval by making a trade-off between separation efficiency and cell integrity. This result could be used as a general rule for designing DEP-based separators. The effects of channel height to separation efficiency are also discussed
A silicon microcantilever biosensor fabricated on SOI wafers using a two-step releasing approach

This paper reports the fabrication of a piezoresistive microcantilever biosensor using a unique two-step front-side releasing method, which combines anisotropic etching and isotropic etching. The microcantilever sensors are fabricated on single crystalline SOI wafers to exploit the high piezoresistive coefficients. The anisotropic etching exposes the sidewalls beneath the microcantilevers, and the following isotropic etching undercuts the substrate and suspends the microcantilevers. Thin and uniform microcantilevers with a yield of 100% have been successfully fabricated. Significant improvement in releasing efficiency of 10 times higher than the conventional backside releasing has been achieved. Detection of Immunoglobulin G (IgG) specific binding down to 0.2 mug/ml with the microcantilever sensor demonstrates the potential applications as a biosensor for biomolecular specific binding detection.
Heterogeneous integration of MEMS sensor array and CMOS readout IC with Through Silicon Via interconnects

Through Silicon Via (TSV) forms electrical feedthrough and makes it possible to vertically stack chips with various functions which including logic, memory, analog and MEMS etc. This paper presents a TSV 3D- heterogeneous integration structure of MEMS sensor array with CMOS readout IC (ROIC) and its fabrication technology. Surface micromaching of sensor array are co-designed with TSV fabrication processes to enable TSVs for electrical signals output from backside in sensor chip, sensor chip and its corresponding ROIC chip are vertically stacked, and chip to chip interconnection is achieved by Cu/Sn-Cu microbump bonding. The stacked structure are then assembled to relized MEMS-CMOS 3D heterogeneous integration. Overall, the present work describes an approach for high density MEMS integration.
Identifying mild traumatic brain injury patients from MR images using bag of visual words

Mild traumatic brain injury (mTBI) is a growing public health problem with an estimated incidence of one million people annually in US. Neurocognitive tests are used to both assess the patient condition and to monitor the patient progress. This work aims to directly use MR images taken shortly after injury to detect whether a patient suffers from mTBI, by incorporating machine learning and computer vision techniques to learn features suitable discriminating between mTBI and normal patients. We focus on 3 regions in brain, and extract multiple patches from them, and use bag-of-visual-word technique to represent each subject as a histogram of representative patterns derived from patches from all training subjects. After extracting the features, we use greedy forward feature selection, to choose a subset of features which achieves highest accuracy. We show through experimental studies that BoW features perform better than the simple mean value features which were used previously.
Improved Otsu Target Tracking Algorithm Based on Iteration

An improved maximum between-cluster variance (Otsu) method based on iteration was proposed for the purpose of realizing target recognition and target tracking effectively. Improved Otsu method was used to segment image collected by CCD, so as to extract the target from image and finish the target localization. By calculating centroid of the target in a single frame and extracting feature point of outline constituted by centroids of the single-frame target within unit interval, a polygon can be formed, the gravity center of which was taken as the target position. Filter model was established of which the control input is nonzero based on Kalman filter Principle, which was combined with target position at this moment can be used to predict the gravity center of the target at the next moment. The experiment and simulation results indicated that improved Otsu method can solve the problem that target and background are segmented mistakenly when image thresholding. Kalman filtering was able to predict target trajectory effectively after target being recognized. The requirement of target tracking was satisfied and target tracking was realized through this improved Otsu target tracking algorithm based on iteration.
A possibilistic Petri-Nets-based service discovery

The focus of this paper is to propose a service-oriented multi-agent framework to better integrate Web services and agents. In the making of this framework, there are four main components: (1) a possibilistic Petri-Nets (PPN) based service matchmaking mechanism for service discovery, (2) a service description language based on PPN for describing agent services and Web services, (3) a set of ontologies to facilitate the semantic matchmaking, and (4) an extension of tModels to bridge the Web services registered in UDDI to the PPN matchmaker.
Segmentation based linear predictive coding of multispectral images

This paper presents a segmentation based linear predictive coding (SLPC) method for multispectral images. Given a set of multispectral images, the SLPC method first segments it into statistically distinct regions. It then finds a suitable linear prediction model for each region. Finally, it quantizes the prediction error in each class using a vector quantizer. The original image set is described by the segmentation map, the model parameters for each class, and the quantized prediction errors. The SLPC method can produce very high compression gains, because the specification of the segmentation map and model parameters requires significantly fewer bits than that for the original intensity values. This method has been applied to magnetic resonance head images with three spectral bands (one T1 weighted and two T2 weighted, 256/spl times/256/spl times/12 bits/image). Images compressed by a factor of more than 22 have been regarded as indistinguishable from the originals, by several radiologists.<<ETX>>
Robust video transmission for H.264 scalable video coding using unequal error protection

In this work, we develop a novel robust scheme of two-dimensional unequal error protection (2D-UEP) for the H.264 scalable video coding (H.264/SVC) with a combined temporal and quality (SNR) scalability over packet-lossy networks. The proposed scheme combines error resilience techniques and importance measures in video coding. To avoid the waste of bits and obtain the best rate allocation, we develop a threshold-based UEP (TH-UEP) algorithm. The TH-UEP designs a predefined threshold according to the length of packet and the error correcting ability of RS code to achieve the best allocation. In addition, the proposed scheme also derives a simple mathematical model to reduce computational load of the best rate allocation. Experimental results demonstrate that the proposed H.264/SVC video transmission scheme using UEP can provide strong robustness and video quality improvement when compared to other 2-D UEP schemes.
Reader Design for Bit Patterned Media Recording at 10 Tb/in$^{2}$  Density

A reader design for reading back at very high density is proposed for bit patterned media recording (BPMR). The idea is a rotated sense head, so that the shields are aligned down-track, combined with oversampled signal processing to regain the lost down-track resolution. Simulation results show that the proposed reader has more than 20 dB gain compared with a normally oriented head array for reading back at 10 Tbit/in2. The tradeoff between oversampling and increased target length is examined. Island jitters are found to be non-Gaussian distributed. The performance of the new design is investigated for various bit patterns, island jitter and head noise.
A control configuration of wind farm for load-following and frequency support by considering the inertia issue

As penetration of wind energy goes high, many relevant issues have been dealt with. Among them, a novel active power control strategy, which we call load-following, has been investigated recently. While the conventional way is to extract energy from wind as much as possible, the load-following mode allows wind turbine generator to work away from the maximum power extraction. This is meaningful in some islanded micro-grid case where the energy should be limited to meet the local load demand. This paper continues to investigate a possible method to realize the load-following feature of wind turbine generator (WTG). The problems it encounters with WTG inertia are analyzed and solved with the speed up/down averaging (SUDA) method. A configuration for wind farm with improved load-following and frequency support features is then proposed.
Long-term prediction of μECOG signals with a spatio-temporal pyramid of adversarial convolutional networks

Video prediction into sufficiently long future has many potential applications. Modeling long-term dynamics for times series is challenging with convolution neural network structure, which is usually good for capturing short-term dependencies. In this work, we propose to embed the convolutional neural network within a spatial-temporal pyramid structure, to exploit both long-term and short-term temporal dependency and capture both macro-scale and micro-scale spatial structures. The prediction at a given scale is conditioned on the features extracted from a lower scale and past observations from the current scale. In order to overcome the blurry issue caused by the mean square error loss, we add a critic model with Wasserstein distance based adversarial loss to complement MSE. We compare our spatio-temporal pyramid model against a single scale convolution network as well as a model with multiple spatial scales only, and demonstrate that our pyramid structure performs better for predicting up to 24 future frames.
Improved BER Performance With Rotated Head Array and 2-D Detector in Two-Dimensional Magnetic Recording

Two-dimensional magnetic recording is a promising candidate to further extend the areal density above 1 Tb/in2 density while using a conventional writer and media. During the writing process, a shingled writer is usually used to write narrow tracks by overlapping previous tracks, which brings severe intertrack interference (ITI), fewer grains per channel bit and corresponding lower signal-to-noise ratio (SNR). As a consequence, for the current shingled magnetic recording system, a normally oriented head array (NHA) is usually implemented to detect a single track by using 2-D signal processing to mitigate the ITI and media noise. Then, a rotated head array (RHA) has been found to effectively avoid the ITI and regain the lost down-track resolution using signal processing. Correspondingly, in this paper, the RHA is investigated to simultaneously detect three tracks with 1-D and joint pattern-dependent noise-predictive (PDNP) Bahl-Cocke-Jelinek-Raviv (BCJR) detectors. Simulation indicates that, for the perfect writing at the 6 nm Voronoi grains, if the 1-D PDNP BCJR detector is implemented, the RHA combined with a designed 2-D equalizer producing multiple equalized waveforms can provide 16% density gains compared with the NHA with a 2-D equalizer and 1-D target at the target bit error rate (BER) of 10-2. If the joint PDNP BCJR detector is implemented, the RHA can provide 25% density gain compared with that for the NHA with the same detection algorithm at the target BER of 10-2. With respect to error correction, a longer codeword length of binary low density parity check code can be used for decoding of the multi-track detection compared with that for the single-track detection, which provides an extra SNR gain.
Text extraction from texture images using masked signal decomposition

Text extraction is an important problem in image processing with applications from optical character recognition to autonomous driving. Most of the traditional text segmentation algorithms consider separating text from a simple background (which usually has a different color from texts). In this work we consider separating texts from a textured background, that has similar color to texts. We look at this problem from a signal decomposition perspective, and consider a more realistic scenario where signal components are overlaid on top of each other (instead of adding together). When the signals are overlaid, to separate signal components, we need to find a binary mask which shows the support of each component. Because directly solving the binary mask is intractable, we relax this problem to the approximated continuous problem, and solve it by alternating optimization method. We show that the proposed algorithm achieves significantly better results than other recent works on several challenging images.
Scheduling optimization mode of multi-microgrid considering demand response

The paper examines operation modes of the multi-microgrid, aiming at unified management of the multi-microgrid through the multi-microgrid control systems (mmcs), which reveal the controllability of the microgrid as source and load in general. The paper puts forward three operation modes including the single microgrid island operation, the multi-microgrid island operation and the grid-connected operation respectively, establishing an economical dispatch model from parts to whole. Furthermore, on account of interactive coordination between microgrids and dg users, a demand response model is employed. What's more, taking a microgrid which consists of various distributed generation (dg) and battery storage (bs) as an example, the paper carries out simulation analysis in detail. The results show that in the proposed operation mode, interaction performs well between microgrids and power grids, microgrids and users, microgrids themselves; The problem of intermittent dg output is solved, which in all show favorable economical efficiency, interactivity and flexibility. Finally, reasonable proposals are suggested for further research of the interactive mode between microgrids and power grids.
An Intelligent Seamless Handover Mechanism Based on IEEE 802.21

Integration of heterogeneous networks is one of the important characters of next-generation networks. As different access technologies may offer different attributes, it becomes a very important and challenging issue to ensure users access the most suitable network at any time, and make seamless handover while moving between different heterogeneous networks. In this paper propose an intelligent seamless handover mechanism based on MIHF (Media Independent Handover) which make full use of the information from different TCP/IP layers. MIHF only takes into consideration the link-layer information which is not sufficient to implement efficient handover. To solve this issue, we present an improved network architecture based on hierarchical mobile IP, with a new network entity NISE added to improve the MIH protocol. As an efficient handover solution should apply to different scenarios, we synthesize motion velocity, motion direction and relative location in the network of MN to identify different handover scenarios, and select different handover strategy to deal with each scenario.
Rate-distortion optimized video coding considering frameskip

The general problem of optimized video encoding has received a great deal of attention in recent years. This paper focuses on the optimization of video coding with frameskip. We propose models that estimate the distortion for coded frames as well as non-coded frames. Using these models in conjunction with well-know models that estimate the rate allows us to formulate a rate control problem that trades-off spatial and temporal quality. Simulation results indicate moderate improvements for low motion test sequences.
A low-voltage high-swing colpitts VCO with Inherent tapped capacitors based dynamic body bias technique

A low-voltage high-swing voltage-biased Colpitts voltage-controlled oscillator (VCO) is proposed for wireless applications. A small capacitive voltage divide factor is chosen to enhance the output swing and improve the phase noise performance. To further enhance the negative resistance, and thus decrease the start-up time, the bulk terminals of the gm-boosting transistors are dynamic-biased by the low-swing nodes provided by the inherent tapped capacitors in Colpitts VCOs. Due to the switching operation mode of the gm-boosting transistors and the absence of common-mode nodes for the switching transistors, the flicker noises up-conversion is largely suppressed. Designed in a 0.18-μm CMOS process, the postlayout simulation results show that it provides a phase noise of −127.66 dBc/Hz at 1 MHz offset centered at 3.76 GHz and a figure-of-merit (FoM) of 192.4 dBc/Hz while dissipating 4.71 mW from a 0.6 V supply. The tuning range is 17% (from 3.28 GHz to 3.89 GHz) and the 1/f 3 phase noise corner is only 60 kHz.
Field emission characteristics of different surface morphologies of ZnO nanostructures formed on carbon nanotubes

The zinc oxide (ZnO) nanostructure grown onto the carbon nanotube (CNT) surface could enhance the CNT FE characteristics. However, the ZnO nanostructure morphology is an important parameter to decide the FE enhancement. This study used thermal chemical vapor deposition (TCVD) to form ZnO nanostructure onto CNT bundle surface, and adjust the distance (D) between the CNT bundle and the zinc acetate (zinc source) to control the surface morphology of ZnO nanostructures. Transmission electron microscopy (TEM) images showed that the ZnO nanostructures were grown onto the CNT bundle surface uniformly. The FE results show that the optimal D was 32 cm with a tapered shape and the thickness of ZnO nanostructures was about 50 nm, which achieved the lowest threshold electric field. In addition, the ZnO-coated CNT bundles exhibited a good FE uniformity and stable current density.
On generation of the minimum pattern set for data path elements in SoC design verification based on port order fault model

Embedded cores are being increasingly used in the design of large System-on-a-Chip. Because of the high complexity of SoC, the design verification is a challenge for system integrator To reduce the verification complexity, the port order fault model proposed by Tung and Jou (1998) has been used for verifying core-based designs and the corresponding verification pattern generation have been developed. Adders and multipliers are the most often used data path elements in core-based designs. Due to their regularity, the development of the verification pattern sets can be achieved in a systematic method. In this paper, we present the algorithms of generating the minimum verification pattern sets for adders and multipliers and these pattern sets are much smaller than that obtained from the automatic verification pattern generation proposed by Wang, Tung and Jou (2001).
Research on the active defense security system based on cloud computing of wisdom campus network

With the popularization of office automation in campus network, the campus network has been threatened more and more. The traditional passive defense system has been difficult to adapt to the current large-scale, complex and covert attack behavior, active defense came into being. Based on WPDRRC model, this paper proposes a new active defense model based on cloud computing. Combining with the existing security technology, in view of wisdom campus network security problems the topology of the active defense system is designed and implemented. Through the assessment, the model has the active defense function, can effectively reduce the system vulnerabilities and hacker attacks.
A Finite-Element-Based Domain Decomposition Method for Efficient Simulation of Nonlinear Electromechanical Problems

The dual-primal finite-element tearing and interconnecting (FETI-DP) method is combined with the Newton-Raphson method to expand the capability and improve the efficiency of 3-D finite-element analysis (FEA) of nonlinear electromechanical problems. Despite its modeling capability and high degree of accuracy, FEA has high computational complexity, especially for nonlinear analysis. The FETI-DP method is a robust domain decomposition method, which has been enhanced and applied to solve electromechanical problems involving linear materials. In this paper, the FETI-DP method is extended with the Newton-Raphson method to address problems involving nonlinearity and saturation. Using parallel computing techniques, the total computation time is reduced significantly. Linear and nonlinear regions are separated using the FETI-DP method. This further improves simulation efficiency and flexibility. Cubic splines and relaxation techniques are adopted to ensure stable and fast convergence of the Newton-Raphson method. The performance of the proposed method is compared with infolytica's MagNet, a commercial 3-D FEA solver.
Action recognition using three dimension convolution and long short term memory

The convolutional neural network (CNN) is more and more popular in computer vision and widely used in acoustic signal processing, image classification, and image segmentation. In this work, an architecture which is a combination of the 3-D convolutional neural network and the long short term memory (LSTM) was proposed for action recognition. It stacks the consecutive video frames, extracts spatial and time features, and trains the input dataset to achieve good recognition performance. Moreover, the LSTM model based on the relations among the frames in different time is adopted to consider the information of past frames. Simulations show that the proposed algorithm outperforms other neural network based methods and has even better performance for action recognition.