nils-herrmann/clean_scirepeval huggingface dataset:
A hybrid control method for photovoltaic grid-connected interleaved flyback micro-inverter to achieve high efficiency in wide load range

For flyback micro-inverters, Boundary Conduction Mode (BCM) and Discontinuous Conduction Mode (DCM) control strategies are widely used. Loss analysis is investigated for the interleaved flyback micro-inverter under BCM and DCM control strategies under different load condition. The BCM and DCM control strategies have different impact on the loss distribution and thus the efficiency of the flyback micro-inverter. Based on the loss analysis, a new hybrid control strategy combing the two-phase DCM and one-phase DCM control is proposed to improve the efficiency in wide load range by reducing the dominant losses depending on the load current. The experimental results verified the benefits of the proposed control.
Characteristics of sub-100nm ferroelectric field effect transistor with high-k buffer layer

The simulation work is carried out using two dimension device simulator to investigate the characteristics of sub-100 nm ferroelectric field effect transistor (FeFET) with high-k material as the buffer layer. Different configurations of gate stack are simulated and analyzed. It is shown that the structure of double-layer buffer can improve the device performance efficiently. Some important issues for FeFET scaling down are also discussed in this paper.
A Microprocessor-Based Nuclear Magnetic Resonance Spectrometer

A microprocessor-based Nuclear Magnetic Resonance spectrometer is proposed. This spectrometer, in which the microprocessor, pulse generator, RF waveform transmitter and digital receiver are integrated onto one card, can complete all tasks without the participation of the PC. Moreover, this spectrometer can be customized attributes for its variation applications by communication with a PC through USB connection.
A new adaptive bacterial foraging optimizer based on field

Bacterial foraging optimizer (BFO) is predominately used to find solutions for real-world problems. One of the major characteristics of BFO is the chemotactic movement of a virtual bacterium that models a trial solution of the problems. It is pointed out that the chemotaxis employed by classical BFO usually results in sustained oscillation, especially on flat fitness landscapes, when a bacterium cell is close to the optima. In this paper we propose a novel adaptive computational chemotaxis based on the concept of field, in order to accelerate the convergence speed of the group of bacteria near the tolerance. Firstly, a simple scheme is designed for adapting the chemotactic step size of each field which is comprised of two or three dimensional space. Then, the scheme chooses the fields which perform better to boost further the convergence speed. Empirical simulations over several numerical benchmarks demonstrate that BFO with adaptive chemotactic operators based on field has better convergence behavior, as compared against other versions of adaptive BFO.
DSP implementation of predictive control strategy for power factor correction (PFC)

A predictive algorithm for digital control PFC is presented in this paper. Based on this algorithm, all of the duty cycles required to achieve unity power factor in one half line period are calculated in advance by the DSP. A boost converter controlled by these precalculated duty cycles can achieve sinusoidal current waveform. Input voltage feed-forward compensation makes the output voltage insensitive to the input voltage variation and guarantees sinusoidal input current even if the input voltage is distorted. A prototype of boost PFC controlled by a DSP evaluation board was setup to implement the proposed predictive control strategy. Test results show that the proposed predictive strategy for PFC achieves unity power factor.
A Distributed Architecture Based on Microbank Modules With Self-Reconfiguration Control to Improve the Energy Efficiency in the Battery Energy Storage System

This paper proposes a new distributed battery energy architecture based on the microbank module (MBM) for dc microgrids. The benefits of the proposed architecture include: 1) no voltage sharing problem and no overcharge/overdischarge problem; 2) high compatibility and reliability; 3) high energy utilization efficiency; 4) reduced volume and weight of the battery management system (BMS). The proposed MBM consists of a microbidirectional dc/dc converter, a micro-BMS and a cell bank. Moreover, taking advantage of the battery recovery effect, a self-reconfiguration discharge strategy is also proposed to further enhance the battery performance and discharge efficiency of the new battery energy storage system (BESS). To optimize the proposed control, an efficiency analytical model considering the battery recovery effect is proposed using the curve fitting method. Owing to the bidirectional capability, soft switching capability and high efficiency, the dual active bridge (DAB) converters are chosen as the microbidirectional dc/dc converters. A hybrid modulation strategy with variable switching frequency combining the conventional phase-shift modulation and triangular current modulation is proposed for the DAB converter to reduce the dominant loss and improve the efficiency in wide load range based on the minimum loss model. A 1.5-kW experimental testing platform consisting of four MBMs and four 12 V/100 Ah lithium battery modules was built to verify the proposed architecture with the control and the proposed model. The experimental results show that the discharge time of the proposed distributed BESS is increased significantly under wide operation condition with the self-reconfiguration control. The discharge efficiency of the BESS is improved by 7.1% with the idling time of 5 min under the power level of 1.5 kW.
A dynamic-weight RED gateway

The congestion control mechanisms used in Internet have been the focus of numerous studies and have undergone a number of enhancements. However, even with these enhancements, TCP connections still experience alarmingly high loss rates, especially during times of congestion. The IETF has addressed this problem by advocating the deployment of active queue management mechanisms, such as RED, in the network. But RED have some problems, for example the average queue length changing slowly when congestion starting and ending. To alleviate this problem, we propose and experiments with a dynamic-weight RED which can quickly be conscious of the congestion and dynamic adjust the weight. Dynamic-weight RED can alleviate the congestion and drop when the congestion is over. The time of congestion is shorter than the RED and dynamic-weight RED is also able to reduce the queue delay.
A 64-Channel Versatile Neural Recording SoC With Activity-Dependent Data Throughput

Modern microtechnology is enabling the channel count of neural recording integrated circuits to scale exponentially. However, the raw data bandwidth of these systems is increasing proportionately, presenting major challenges in terms of power consumption and data transmission (especially for wireless systems). This paper presents a system that exploits the sparse nature of neural signals to address these challenges and provides a reconfigurable low-bandwidth event-driven output. Specifically, we present a novel 64-channel low-noise (2.1 <inline-formula> <tex-math notation="LaTeX">$\mu$</tex-math></inline-formula>V<inline-formula><tex-math notation="LaTeX">$_{\text{rms}}$ </tex-math></inline-formula>), low-power (23 <inline-formula><tex-math notation="LaTeX">$\mu$</tex-math> </inline-formula>W per analogue channel) neural recording system-on-chip (SoC). This features individually configurable channels, 10-bit analogue-to-digital conversion, digital filtering, spike detection, and an event-driven output. Each channel's gain, bandwidth, and sampling rate settings can be independently configured to extract local field potentials at a low data-rate and/or action potentials (APs) at a higher data rate. The sampled data are streamed through an SRAM buffer that supports additional on-chip processing such as digital filtering and spike detection. Real-time spike detection can achieve <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula>2 orders of magnitude data reduction, by using a dual polarity simple threshold to enable an event driven output for neural spikes (16-sample window). The SoC additionally features a latency-encoded asynchronous output that is critical if used as part of a closed-loop system. This has been specifically developed to complement a separate on-node spike sorting coprocessor to provide a real-time (low latency) output. The system has been implemented in a commercially available 0.35-<inline-formula><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula>m CMOS technology occupying a silicon area of 19.1 mm<inline-formula><tex-math notation="LaTeX">$^2$</tex-math></inline-formula> (0.3 mm <inline-formula><tex-math notation="LaTeX">$^2$</tex-math></inline-formula> gross per channel), demonstrating a low-power and efficient architecture that could be further optimized by aggressive technology and supply voltage scaling.
Adaptive discontinuous Current Source Driver to achieve switching loss reduction for MHz PFC boost converters

Adaptive control concept for the discontinuous Current Source Drivers (CSDs) is proposed for MHz Power Factor Correction (PFC) boost converters. Compared to the continuous CSDs, the discontinuous CSDs have lower circulating current and less conduction loss. Therefore, the discontinuous CSDs are able to use higher gate drive current to reduce the switching loss further. Using different pre-charge time to build Current Source (CS) inductor currents based on different load conditions, the gate drive currents can be achieved adaptively to the load currents, so that the efficiency of the PFC converter can be improved in a wide operation range. The CS inductor of the discontinuous CSD is as low as 120nH. This reduces the board space of the main MOSFET driver part. An 110V input, 380V/ 200W output, 1MHz PFC boost converter with the proposed adaptive discontinuous CSD was built to verify the advantages. With the discontinuous CSD, at 50% load, an efficiency improvement of 5.1% is achieved over the conventional voltage source driver (VSD), and at full load, an efficiency improvement of 4.8% is achieved. The measured PF values are all above 99% and complying with the industrial requirements.
An Novel Clutter Rejection Filters Applied to Wideband Blood Flow Velocity Estimation

Color flow imaging plays a major role in the diagnosis of many vascular diseases, especially cardiovascular disease. The imaging quality is highly influenced by the clutter originating from the vascular wall, stationary and slowly moving tissues and relative motion between the probe held by doctor and the blood flow. The clutter rejection filter in front of the mean frequency estimator will restrict the mean frequency estimation range because the power of the signal component induced by slowly moving blood is suppressed as well. Therefore, a novel scheme, using parameter estimation methods based on the two-dimensional correlation function model (2DCM) and the conventional down mixing, is proposed to clutter rejection. With the parameter estimation methods based on the 2DCM, both the center frequency and the mean Doppler frequency are estimated. The two dimensional information in range gate has been also sufficiently used. Simulation results have shown that this adaptive scheme has achieved superior performance in wideband blood flow velocity estimation
A Hybrid Fuzzy LMS Neural Network Model for Determining Weights of Criteria in MCDM

A hybrid fuzzy LMS neural network model, with fuzzy numbers as inputs, is set up to determine the weights of each criterion of alternatives. The model can determine the weights of each criterion automatically, according to the time-series data of market, so that they are more objectively and accurately distributed. The model also has a strong self-learning ability so that calculations are greatly reduced and simplified. Further, decision maker's specific preferences for uncertainty also are considered in the model. Hence, this method can give revised results while taking into decision maker's subjective intensions for uncertainty preference. A numerical example is given to illustrate the method.
DWT-Based Audio Watermarking Resistant to Desynchronization

A robust and blind audio watermarking algorithm based on the discrete wavelet transform(DWT)is proposed in this paper as an effective way to resist the malicious de-synchronization attacks. The algorithm embeds the watermark information into the absolute average of audio low frequency coefficients in the DWT and error- correcting coding is applied in order to lower the bit error rate of watermark in extraction. Experimental results show the performance of the method is ideal against various common signal processing attacks, especially, robustness to de-synchronization attacks.
An approach on improving MODIS albedo product by using the information from MODIS LAI product

Surface albedo is an important parameter in modeling climate processes as it determines the energy budget of the earth's surface. Operational surface albedo products are available from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. However, due to the factors associated with weather, sensors and algorithms, albedo products from satellite observations often have many gaps. In this paper, we reformed the semi-empirical kernel-driven model to express the relation between the bidirectional reflectance distribution function (BRDF) and the Leaf Area Index (LAI). Within a small region, the soil properties under plant canopies are similar, therefore the reflectance is unanimous. When the land cover types are identical and the LAI values are same, the canopy top reflectance should be same. Thus, within a small region, the pixels of same properties have the same reflectance. For one pixel, if the MODIS albedo product has no retrieval but MODIS LAI product has high quality retrieval, the LAI information can be used to filled the MODIS albedo data gaps. A comparison indicates that the filled data conform well with the in-situ measurements albedo.
3D Monte Carlo simulation of Gate-All-Around Germanium nMOSFET

Gate-All-Around mosfets have been investigated as promising new device structures, and Germanium is used for its high carrier mobility. In this paper, a 3D parallel Monte Carlo simulation of GAA Ge Nanowire nMOSFET with Effective Potential Method is implemented. Compared the simulation results with classical results, we can see that the quantum effects have an affect on the distribution of density, velocity and energy, and they make a decrease on the drain current as well.
A novel improved sequential quadratic programming algorithm to solve DG dispatch in distribution system

Optimal power flow (OPF) problem for distribution networks is investigated with the integration of distributed generation (DG) in this paper. By considering the objectives of minimal line loss, minimal voltage deviation and maximum DG active power output, the proposed OPF formulation is a multi-object optimization problem. Through normalization of each objective function, the multi-objective optimization is transformed to single objective optimization. To solve such a nonconvex problem, the trust region sequential quadratic programming (TRSQP) method is proposed which iteratively approximates the OPF by a quadratic programming with the trust region guidance. The TRSQP utilizes the sensitivity analysis to approximate all the constraints with linear ones, which will reduce the optimization scale. Active set method is utilized in TRSQP to solve quadratic programming sub-problem. Numerical tests on IEEE 33-, PG&E 69- and actual 292-bus systems show the applicability of the proposed method, and comparisons with the primal-dual interior point method (PDIPM) and sequential linear programming method (SLPM) are provided, which indicates that the proposed algorithm for distributed generation control optimization in distribution system is feasible and effective.
A Horizontal-Winding Multipermeability LTCC Inductor for a Low-profile Hybrid DC/DC Converter

Distributed air-gap inductors have the advantage of reducing winding loss in high switching frequency dc/dc converters. However, they also have the disadvantage of uneven distribution of flux density, which inevitably leads to uncompleted utilization of magnetic material. This paper proposes a horizontal-winding multipermeability low-temperature cofired ceramic (LTCC) inductor to increase inductance and improve efficiency without the necessity of increasing inductor volume. For the purpose of simplicity, a two-permeability LTCC inductor is taken as an example for analysis and comparison. Design of such an inductor is demonstrated with the aid of 2-D finite elementary analysis simulation. A two-permeability LTCC inductor together with a single-permeability LTCC inductor is fabricated for measuring and testing. The measured results show the two-permeability inductor has higher inductance than the single-permeability inductor. Both inductors are tested in a 5-V input, 3.3-V output dc/dc converter to test their performances. The testing results show the two-permeability LTCC inductor could further improve the efficiency of high-frequency dc/dc converters compared with the single-permeability LTCC inductor.
A Physics-Based Compact Model of Metal-Oxide-Based RRAM DC and AC Operations

A physics-based compact model of metal-oxide-based resistive-switching random access memory (RRAM) cell under dc and ac operation modes is presented. In this model, the conductive filament evolution corresponding to the resistive switching process is modeled by considering the transport behaviors of oxygen vacancies and oxygen ions together with the temperature effect. Both the metallic-like and electron hopping conduction transports are considered to model the conduction of RRAM. The model can reproduce both the typical I-V characteristics of RRAM in high-/low-resistance state (LRS) and the nonlinear characteristics in LRS. Moreover, to accurately model ac operation mode, the effects of parasitic capacitance and resistance are included in our model. The developed compact model is verified and calibrated by measured data in different HfOx-based RRAM devices under dc and ac operation modes. The excellent agreement between the model predictions and experimental results shows a promising prospect of the future implementation of this compact model in large-scale circuit simulation to optimize the design of RRAM.
A real-time control model for intelligent virtual human

Intelligent virtual human is widely required in educational, entertainment, and simulation software. Providing real-time motion control architecture is very important in design this kind of digital actor. This paper presents a three-level control strategy, with which virtual human can independently navigate in virtual world and quickly make response to their environment. This model improves the subsumption architecture by adding the ability of global planning. Collision avoidance and path planning are realized in the 0-level layer and the 1-level layer, respectively. Then a simple expert system in the 2-level layer takes the responsibility for task planning to realize more intelligent behavior. Finally, we developed a real-time human animation system-smart human system, in which this control model is applied for performing intelligent behavior of the digital actors.
An event-driven SoC for neural recording

This paper presents a novel 64-channel ultra-low power/low noise neural recording System-on-Chip (SoC) featuring a highly reconfigurable Analogue Front-End (AFE) and block-selectable data-driven output. This allows a tunable bandwidth/sampling rate for extracting Local Field Potentials (LFPs) and/or Extracellular Action Potentials (EAPs). Realtime spike detection utilises a dual polarity simple threshold to enable an event driven output for neural spikes (16-sample window). The 64-channels are organised into 16 sets of 4-channel recording blocks, with each block having a dedicated 10-bit SAR ADC that is time division multiplexed among the 4 channels. Each channel can be individually powered down and configured for bandwidth, gain and detection threshold. The output can thus combine continuous-streaming and event-driven data packets with the system configured as SPI slave. The SoC is implemented in a commercially-available 0.35 μm CMOS technology occupying a silicon area of 19.1 mm2 (0.3 mm2 gross per channel) and requiring 32 μW/channel power consumption (AFE only).
A 100dB-SNR mixed CT/DT audio-band sigma delta ADC

Although audio-band sigma delta ADC has largely been implemented as discrete time circuits, a continuous time approach offers significant advantages for high accuracy low-power applications. A continuous time design allows for relaxed amplifier unity-gain bandwidth and power requirements. It also provides better noise immunity due to their inherent anti-aliasing properties. This paper introduces a mixed continuous-time/discrete- time, single-loop, fourth-order, 4-bit audio-band sigma delta ADC that combines the benefits of continuous time and discrete time circuits, while mitigating the challenges associated with continuous time design. Measurement results show that the peak SNR of this ADC reaches 100dB and the total power consumption is less than 30mW.
Comparison and analysis of two fractal dimension computing algorithms for upscaling remote sensing Leaf Area Index map

The Leaf Area Index (LAI) values derived from remote sensing images with different resolutions can be different, because of the heterogeneity of land surface. To validate the coarse resolution LAI product, the in situ LAI measurements and the high resolution LAI map need to be upscaled to the coarse resolution(such as 1 km). As the principle of fractal geometry has been applied to describe the irregular distribution of vegetation, the measured LAI can be upscaled using Fractal Dimension (FD) which is the key parameter of fractal geometry. In this paper two FD computing algorithms, Triangular Prism Surface Area method (TPSA) and Differential Box Counting algorithm (DBC), were compared in two aspects. One aspect was the ability of describing heterogeneity of LAI in different landscapes; another was the spatial distribution of FD and upscaled LAI. The log-log curves of computing algorithms showed that TPSA performs better to capture the difference between different landscapes than DBC algorithm does. Also TPSA can detect the scale inflexion of LAI spatial distribution but the DBC can't. The pattern of FD and upscaled LAI map using TPSA is similar with the real pattern of vegetation, while the FD and upscaled LAI map using DBC algorithm cannot capture the real pattern of vegetation.