nils-herrmann/clean_scirepeval huggingface dataset:
A complementary injection technique for performance enhancing of CML dividers

A complementary injection technique is proposed to enhance the overall performance of current mode logic (CML) dividers in this paper. With injection currents introduced by pMOSFETs, the equivalent injection efficiency of CML dividers are improved and thus CML dividers achieve a better performance. Extensive simulation results show that the proposed technique improves the division bandwidth of 24-GHz divide-by-2 CML divider by 5GHz, which is from 12GHz to 30GHz. The designed divide-by-2 CML frequency divider draws only 2-mA current from 1.2-V supply.
Comparative performance of texton based vascular tree segmentation in retinal images

This paper considers the problem of vessel segmentation in optical fundus images of the retina. We adopt an approach that uses a machine learning paradigm to identify texture features called textons and present a new filter bank (MR11) that includes bar detectors for vascular feature extraction and other kernels to detect edges and photometric variations in the image. Textons are generated by k-means clustering and texton maps representing vessels are derived by back-projecting pixel clusters onto hand labelled ground truth. A further step is implemented to ensure that the best combinations of textons are represented in the map and subsequently used to identify vessels in the test set. The experimental results on two benchmark datasets show that our proposed method performs well compared to other published work and the results of human experts. A further test of our system on an independent set of optical fundus images verified its consistent performance.
Analyzing Sequence Data Based on Conditional Random Fields with Co-training

Sequence data plays an important role in data analysis applications, such as sequence classification. One important aspect of sequence data analysis is to obtain the labeled sequence data and use a machine learning model to predict the sequence structures. Conditional Random Fields (CRF) is such a machine learning method which is popular used in sequential data analysis. This is because that CRF can effectively capture the data correlations in context with abundant training data. However, in real applications, the labeled training data is usually difficult to be collected. In order to reduce the requirement of the amount of the labeled training data, a novel model is proposed named Conditional Random Fields with Co-training (Co-CRF). The Co-CRF model can work well even on the reduced labeled training data. Empirical results show that Co-CRF can produce a more accurate analysis than the traditional CRF, especially with very limited training data.
An Acquisition Algorithm Based on FRFT for Weak GNSS Signals in A Dynamic Environment

To improve the acquisition sensitivity of the weak and dynamic global navigation satellite systems signal, the fractional Fourier transform (FRFT) is introduced to deal with the acquisition process. The acceleration can be estimated using the FRFT approach, and the gain of coherent integration as well as the detection probability could be significantly improved. Partially matched filter technique is used to compare with FRFT. The digital computation complexity and the mean acquisition time are provided. Theoretical analysis for the detection probability has been proposed, and simulations are conducted to verify the high performance of the technique.
Design and Implementation of Ultralow Current-Mode Amplifier for Biosensor Applications

Recently, along with the booming of research and production of a CMOS integrated biosensing system (IBS), the ultralow current-mode amplifier (ULCA), which serves for the preamplification of biosensor signals, has been in high demand in areas like ultralow biocurrent analysis and detection, deoxyribonucleic acid probing and surveying systems, etc. In this brief, a novel ULCA is presented and implemented in SMIC 0.18- mum CMOS technology by elaborately considering gain, bandwidth, noise, and offset issues. The experimental results show that the proposed ULCA can completely satisfy the specifications of IBS and stably provide the current gain of 19.9 dB, 3-dB bandwidth of 15 kHz, and input range of -0.4 to 0.4 mA, with only 1.5 nA of dc quiescent current. The input-referred offset and the noise current are 96.6 pA and 37.6 pArms, respectively.
Analysis and simulation tools for short circuit dynamic characteristics of photovoltaic power plants

One of the main trends in the development of solar photovoltaic (PV) grid-connected technology is the grid code compliance assessment for grid connection of PV power plant. But it is difficult to obtain the dynamic performance of the PV power plant under different operation scenarios through field test. In particular, it is costly to obtain the characteristics of the point of connection of PV power plant. The paper introduces support tools for simulation-based dynamic characteristics analysis of grid connection of PV power plant. Firstly, the function and flow chart of the developed support tools is described in detail. Then, the photovoltaic inverter detailed modelled. Finally, the support tools are developed based on the Powerfactory C++ API and Python package. The tools have many automation features that can be applied to online services which have be installed in the clean energy big data center. Test results have illustrated that the support tools can reflect dynamic performance of grid connection of PV power plant and can provide a decision support for the renewable energy priority scheduling.
An ontology-based multi-class terrain surface classification system for aerial imagery

Automatic classification of terrain surfaces from aerial imagery is essential for military planning, unmanned ground vehicle navigation, environmental monitoring, earth resource management, etc. In this paper we present a terrain surface classification system based on classification ontology to deal with complex multi-class terrain surface identification. The system leverages both humans' a priori knowledge about the characteristics and ambiguity of different terrain classes and a powerful fuzzy decision forest technique to construct an effective, robust, and easily extensible terrain surface classification system. We have tested the developed system on a set of challenging real aerial imagery covering 4000×4000 square meters geospatial areas in California state and achieved 85.5% classification accuracies over eight major terrain classes.
Control of Active Power Exchange With Auxiliary Power Loop in a Single-Phase Cascaded Multilevel Converter-Based Energy Storage System

Cascaded multilevel converter (CMC)-based energy storage system, which consists of cascaded H-bridge converters and energy storage components, is a promising option to compensate fluctuating electric power of renewable energy. This paper proposes a novel single-phase CMC-based battery storage system, which includes an LC branch. The cascaded converter cells and the LC branch form an auxiliary power loop, which could realize active power exchange between different cells with the proposed dual-frequency phase-shifted carrier pulse width modulation (DF-PSC PWM). The principle and effectiveness of DF-PSC PWM and the power transfer mechanism are analyzed. Meanwhile, the design of the tuned filter, output filter, and the state of charge balancing control system is introduced. The operation principle of the power exchange in the proposed energy storage system has been successfully verified by simulation and experimental results.
A novel ant-based clustering algorithm with an artificial force field

This paper presents a novel ant-based clustering algorithm. In the algorithm, the objects are first preprocessed by Principal Component Analysis(PCA), then their two principal components are retained and processed as the projecting coordinates. Moreover, different from conventional ant-based clustering algorithms in which the objects are picked up or dropped by virtual ants, our proposed algorithm looks each object as an ant. After the objects are projected to the plan, an artificial force field is created. The object (ant) is attracted by the similar and repelled by the dissimilar ones in its local surrounding. The object moves to a certain place at the work of these forces. The moving direction and moving range are determined by the composite of all forces. The clusters are created by this force influence after many iterative cycles. The paper gives the detailed process of the algorithm. The performance of the algorithm is compared with other classic algorithms on synthetic and real datasets. The results are very encouraging in terms of the computation efficiency and clustering quality.
A method of optical flow computation based on LUV color space

For improving accuracy of optical flow computation, we propose a new method to compute optical flow from both gray and color information. First, the Gauss filter is used to pre-filter the color image sequence. Then optical flow recovery is based on the method of neighborhood least squares using brightness information. And the weight coefficients of local neighborhood are determined by U and V channels in LUV color space. Finally, the optical flow result is filtered by median filter. The experiment results show that the proposed method produces dense optical flow field and accuracy of the results can also be improved.
A new expression for sculling motion compensation algorithms

The error compensation is the important part of high-accuracy strapdown inertial navigation system (SINS). The incremental angular and velocity were adopted in classical compensation algorithms as inputs. The new formula bought forward utilized the angular rate and acceleration signals additionally. Based on this, the error characteristic of this algorithm under the condition of sculling motion was analyzed. The simulations of new algorithm were achieved. Compared with the classical algorithms, the new one has higher precision and stabilization.
Dc flashover performance and surface discharge resistance of the fluorinated epoxy insulator in nitrogen gas

The surface of the epoxy insulator was modified by direct fluorination in a laboratory vessel using a F2/N2 mixture with 12.5% F2 by volume at 55 °C and 0.1 MPa for 30 min. The modified surface was examined by ATR-FTIR and SEM techniques. ATR-FTIR results show substantial changes in chemical composition and structure of the surface layer by the fluorination, and SEM observations reveal the fluorinated surface layer having a thickness of 0.76 μm and a roughed surface. Dc flashover performance and surface discharge resistance of the fluorinated surface layer in nitrogen gas were simultaneously evaluated using a simple sample/finger electrode arrangement in a stainless steel chamber at room temperature. The test results indicate a definite improvement in dc flashover voltage and a great increase in tracking resistance to the flashover discharge. The former is mainly attributed to easy leakage and dispersion of the charge from the cathode triple junction on the fluorinated surface due to high conductivity of the fluorinated layer, and the latter is due to the formation of C-F bonds and the disappearance of conjugated double bonds in the surface layer by the substitution and addition of fluorine atoms.
Design of dynamic simulation of wind turbine

As one kind of regenerative energy, as well as a part of smart grid, wind power has become a hot topic in many countries all over the world. Due to lack of wind farm circumstance, it is difficult to carry out an actual experiment on wind turbine in the laboratory. It is necessary to build a simulative system of wind turbine in the lab without any real wind farm surroundings so that we can not only improve our research conveniently, but also reduce the cost. In this paper, the wind turbine aerodynamic characteristic and the theory of wind energy absorption, containing the mathematical model are analyzed. Then three kinds of electric motors: DC motor, induction motor and permanent magnet synchronous motor, are presented to simulate the wind turbine. Each one's theory, as well as mathematical model is introduced and the control system is analyzed.
A fault-tolerant scheme for SINS in-motion alignment based on federal structure

In this paper, a novel scheme for SINS in-motion alignment based on federal structure is proposed to accomplish the alignment against faults. To ensure the validity of alignment if the fault occurs, this paper takes advantage of three auxiliary alignment systems referring to Global Positioning System (GPS), odometer (OD) and BeiDou-2 (BD2) to build the federal model of alignment. Besides, the extension of double states chi-square detection is beneficial to establish the fault detection algorithm to detect the abrupt and slow-varying faults of each subsystem exactly. With the fault detection value, the information factor could be distributed so that the federal fault-tolerant scheme is optimized to decrease the effect on the subsystem with the faults and reset the normal subsystem effectively. The simulation on the moving base demonstrates that the proposed scheme is effective on the condition of one or more subsystems suffering from the fault.
Cycle spinning dual-domain SAR image denoising

SAR image denoising has been an active research topic. Some image denoising methods have been implemented in both spatial and transform domains. Although state-of-the-art denoising methods are numerically impressive, they produce Gibbs-like phenomenon. To solve this problem, we proposed a SAR image denoising algorithm, which is based on dual-domain image denoising (DDID) and cycle-spinning algorithm. The proposed approach tries to change the relative position of singularity point in an image, to find the average spinning within a specific range and to smooth the artifacts due to the Gibbs-like phenomenon. Compared with DDID and wavelet method, the denoised SAR images of the proposed algorithm are smoother and have much fewer man-made textures.
Block Data Transfer protocol for communication over high-speed long-distance networks

Conventional acknowledgment based retransmission protocol such as TCP (Transmission Control Protocol) has several problems when used over high-speed long-distance networks. In this paper, a new data transfer protocol named BDT (Block Data Transfer) is proposed. It is built on the top of UDP (User Datagram Protocol) with reliability control and congestion control mechanism. In BDT, SLT (Shifted Luby Transfer) codes are used for masking packet loss. Thus the performance of communication is increased. In order to identify the occasional packet loss from packet loss caused by congestion, an evaluation model based on feedback is proposed. We describe the design and implementation of BDT and experimental results show that BDT achieves high bandwidth utilization compared with TCP and UDT.
Detecting Suicidal Ideation in Chinese Microblogs with Psychological Lexicons

Suicide is among the leading causes of death in China. However, technical approaches toward preventing suicide are challenging and remaining under development. Recently, several actual suicidal cases were preceded by users who posted micro blogs with suicidal ideation to Sina Weibo, a Chinese social media network akin to Twitter. It would therefore be desirable to detect suicidal ideations from micro blogs in real-time, and immediately alert appropriate support groups, which may lead to successful prevention. In this paper, we propose a real-time suicidal ideation detection system deployed over Weibo, using machine learning and known psychological techniques. Currently, we have identified 53 known suicidal cases who posted suicide notes on Weibo prior to their deaths. We explore linguistic features of these known cases using a psychological lexicon dictionary, and train an effective suicidal Weibo post detection model. 6714 tagged posts and several classifiers are used to verify the model. By combining both machine learning and psychological knowledge, SVM classifier has the best performance of different classifiers, yielding an F-measure of 68:3%, a Precision of 78:9%, and a Recall of 60:3%.
A multi-objective strategy based on frontier-based approach and Fisher Information Matrix for autonomous exploration

This paper presents a multi-objective exploration strategy for autonomous exploration of unknown indoor environments. The strategy mainly consists of two parts. First, it evaluates and determines the best frontier by considering the sensor information, localizability and navigation distance simultaneously. Second, a motion planning method considering the robot uncertainty is used to generate trajectories towards selected frontier. Compared to other exploration algorithms, we pay much attention to how to ensure accurate localization during the exploration and motion planning, which means the strategy should select frontiers and generate trajectories that provide sufficient information to keep the robot well-localized. Simulation experiments are presented to show the feasibility of the proposed strategy.
AHP-based Master Device Selection Scheme for Pervasive Personal Network

The advancement of mobile communications and small computers bring pervasive personal network closer to reality. To reduce user intervention in the pervasive environment, we proposed a framework to compose a virtual terminal over distributed devices among the pervasive personal network, called UST Since Master Device performs the framework functionality in UST, selecting proper master device plays a vital role in ensuring quality of service especially. In this article we develop a master device selection scheme for UST system based on the Analytic Hierarchy Process (AHP). It also describes the state transitions and message sequences for the scheme. Experimental results show that the proposed scheme can effectively choose the optimum master device under diverse pervasive personal network conditions.
Clustering Methods for Multi-sensor Data Fusion

In the network-centric warfare, the complete battlefield situation information and attack information for weapons can be collected and calculated by multiple sensor platforms, and the results can be distributed to the command and control systems and weapon platforms to complete target attack tasks. Track initiation is the primary problem in multi-source multi-target track processing, correct track initiation can not only reduce the calculation burden of post-processing in multi-target tracking, but also be meaningful to the discovery of interfered interesting target. Centralized algorithm first fuses the raw sensor data, and then starts the track tracing with fused points, weight the clustered sample points to fuse sample points. Clustering could be generalized from the active radar side and the passive radar side. Further more, effects caused by obstacle constraints are also discussed.
CASH: A Context-Aware Scheduling for Hypervisor Based on KSM

Cache locality, or cache hit rate, is closely related with the system performance. Meanwhile the trade off between performance and fairness is also needed to be considered by OS at the system level. Hence it is a complex problem for hyper visor scheduler to make use of cache locality effectively. In fact, cloud does bring new opportunities to this problem due to its new application characteristics. In this paper, we propose a context-aware scheduling for hyper visor, based on Kernel Same page Merging (KSM) and the assistance of modern hardware, to improve system performance, making use of context similarity among VMs with similar function. To the best of our knowledge, CASH is the first scheduler to find context similarity based on KSM, and has little to no impact on the scheduling flexibility.
A reference signal based beamforming scheme for relay networks with local channel state information

Based on the minimizing mean square error (MSE) criterion, a reference signal based distributed beamforming scheme with associated adaptive algorithms is proposed. A key advantage with this scheme is that each relay node can work effectively based on only its own channel state information (CSI) and achieve a globally optimum solution without any approximation. An unconstrained adaptive algorithm is first derived and then the case with signal transmission power constraint for each individual relay node is considered. The effectiveness of the proposed scheme is verified by extensive computer simulations.
An Energy Efficient Wireless Sensor MAC Protocol with Global Sleeping Schedule

Wireless sensor networks (WSNs) aim to fulfill the need for reliable and fault-tolerant sensing services. This has made wireless sensor networks a very active research area. SMAC is an energy efficient sensor MAC protocol. An S-MAC problem is that border nodes have to adopt multiple sleeping schedules, which speed up the energy depletion of border nodes. In this paper, S-MACL is proposed to overcome this problem. In S-MACL, a global sleeping schedule is used. The simulations result shows that S-MACL achieves a greater level of energy efficiency than S-MAC.
Configurable Neural Phase Shifter With Spike-Timing-Dependent Plasticity

A configurable neural phase shifter (NPS) is fabricated for spike neuromorphic circuits by integrating an ionic/Si hybrid synaptic transistor with a conventional MOSFET. NPS can mimic a neural network to process temporal-code pulses by shifting their temporal phases by t ranged between 9.0 and 24.5 ms, which falls in the range observed in biological neural networks. t can also be configured by following the synaptic learning rule of spike-timing-dependent plasticity. The device can significantly simplify the structures and reduce the power consumption of neuromorphic circuits to emulate neural networks for spike signal processing, memory, and learning.
A hybrid filter based on an adaptive neuro-fuzzy inference system for efficient removal of impulse noise from corrupted digital images

A new impulse noise detector based on an adaptive neuro-fuzzy inference system (ANFIS) is presented. The proposed operator is a hybrid filter obtained by appropriately combining a median filtering, a wiener filtering and the ANFIS. The noise is exactly estimated through the proposed operator. The internal parameters of the ANFIS are adaptively optimized by training. The training is easily accomplished by using simple artificial images that can be generated in a computer. The distinctive feature of the proposed operator is that it offers well line, edge, detail and texture preservation performance while, at the same time, effectively removing noise from the input image. Simulation experiments show that the proposed operator may be used for efficient restoration of digital images corrupted by impulse noise without distorting the useful information in the image.