arXiv subjects

Xiaoli Tang

Publications and source records attributed to Xiaoli Tang.

25 records · Page 2Linked to original sources

arXiv2019

MuFA (Multi-type Fourier Analyzer): A tool for batch generation of MuMax3 input scripts and multi-type Fourier analysis from micromagnetic simulation output data

We present a tool for batch generation of input scripts and multi-type Fourier analysis from simulation results for the micromagnetic software MuMax3. The introduction of graphical user interface and parameter-sweeping functionality strongly speed up the input scripts creation and accelerate model optimization processes consequently. Three types of important Fourier analysis methods are provided for the acquisition of the quantitative frequency compositions, the spin-wave dispersion curve and the spatial distribution of spin-wave powers at different frequencies, respectively. Since the Fourier analysis is accelerated by parallel computations, the time cost is reduced to an acceptable level even in the presentation of tens of gigabytes data. With the MuMax3 and our proposal, a complete micromagnetic simulating tool chain from scripts generation to post analysis has been developed.

eess.SP↗

arXiv2019

Towards Real-Time Respiratory Motion Prediction based on Long Short-Term Memory Neural Networks

Radiation therapy of thoracic and abdominal tumors requires incorporating the respiratory motion into treatments. To precisely account for the patient respiratory motions and predict the respiratory signals, a generalized model for predictions of different types of respiratory motions is desired. The aim of this study is to explore the feasibility of developing a Long Short-Term Memory (LSTM)-based model for the respiratory signal prediction. To achieve that, 1703 sets of Real-Time Position Management data were collected from retrospective studies across three clinical institutions. These datasets were separated as the training, internal validity and external validity groups. 1187 datasets were used for model development and the remaining 516 datasets were used to test the generality power of the model. Furthermore, an exhaustive grid search was implemented to find the optimal hyper-parameters of the LSTM model. The hyper-parameters are the number of LSTM layers, the number of hidden units, the optimizer, the learning rate, the number of epochs, and the length of time lags. Our model achieved superior accuracy over conventional artificial neural network models: with a prediction window of 500ms, the LSTM model achieved an average relative Mean Absolute Error (MAE) of 0.037, an average Root Mean Square Error (RMSE) of 0.048, and a Maximum Error (ME) of 1.687 in the internal validity data, and an average relative MAE of 0.112, an average RMSE of 0.139 and an ME of 1.811 in the external validity data. Compared to the LSTM model trained with default hyper-parameters, the MAE of the optimized model results decreased by 20%, indicating the importance of tuning the hyper-parameters of LSTM models to obtain superior accuracy. This study demonstrates the potential of deep LSTM models for the respiratory signal prediction and illustrates the impacts of major hyper-parameters in LSTM models.

physics.med-ph↗

arXiv2017

Magnonic waveguide based on exchange-spring magnetic structure

We propose to use a soft/hard exchange-spring coupling bilayer magnetic structure to introduce a narrow channel for spin-wave propagation. We show by micromagnetic simulations that broad-band Damon-Eshbach geometry spin waves can be strongly localized into the channel and propagate effectively with a proper high group velocity. The beamwidth of the bound mode spin waves is almost independent from the frequency and is smaller than 24nm. For a low-frequency excitation, we further investigate the appearance of two other spin beams in the lateral of the channel. In contrast to a domain wall, the channel formed by exchange-spring coupling can be easier to realize in experimental scenarios and holds stronger immunity to surroundings. This work is expected to open new possibilities for energy-efficient spin-wave guiding as well as to help shape the field of beam magnonics.

physics.app-ph↗

arXiv2017

Tunable Metamaterials Fabricated by Fiber Drawing

We demonstrate a practical scalable approach to the fabrication of tunable metamaterials. Designed for THz wavelengths, the metamaterial is comprised of polyurethane filled with an array of indium wires using the well-established fiber drawing technique. Modification of the dimensions of the metamaterial provides tunability: by compressing the metamaterial we demonstrated a 50% plasma frequency shift using THz time domain spectroscopy. Releasing the compression allowed the metamaterial to return to its original dimensions and plasma frequency, demonstrating dynamic reversible tunability.

physics.optics↗

arXiv2015

The Effects of Research Level and Article Type on the Differences between Citation Metrics and F1000 Recommendations

F1000 recommendations have been validated as a potential data source for research evaluation, but reasons for differences between F1000 Article Factor (FFa scores) and citations remain to be explored. By linking 28254 publications in F1000 to citations in Scopus, we investigated the effect of research level and article type on the internal consistency of assessments based on citations and FFa scores. It turns out that research level has little impact, while article type has big effect on the differences. These two measures are significantly different for two groups: non-primary research or evidence-based research publications are more highly cited rather than highly recommended, however, translational research or transformative research publications are more highly recommended by faculty members but gather relatively lower citations. This can be expected because citation activities are usually practiced by academic authors while the potential for scientific revolutions and the suitability for clinical practice of an article should be investigated from the practitioners' points of view. We conclude with a policy relevant recommendation that the application of bibliometric approaches in research evaluation procedures should include the proportion of three types of publications: evidence-based research, transformative research, and translational research. The latter two types are more suitable to be assessed through peer review.

cs.DL↗

arXiv2014

Dynamic control of spin wave spectra using spin-polarized currents

We describe a method of controlling the spin wave spectra dynamically in a uniform nanostripe waveguide through spin-polarized currents. A stable periodic magnetization structure is observed when the current flows vertically through the center of nanostripe waveguide. After being excited, the spin wave is transmitted at the sides of the waveguide. Numerical simulations of spin-wave transmission and dispersion curves reveal a single, pronounced band gap. Moreover, the periodic magnetization structure can be turned on and off by the spin-polarized current. The switching process from full rejection to full transmission takes place within less than 3ns. Thus, this type magnonic waveguide can be utilized for low-dissipation spin wave based filters.

cond-mat.mtrl-sci↗

arXiv2009

A feasibility study of treatment verification using EPID cine images for hypofractionated lung radiotherapy

We propose a novel approach for potential on-line treatment verification using cine EPID (Electronic Portal Imaging Device) images for hypofractionated lung radiotherapy based on a machine learning algorithm. Hypofractionated radiotherapy requires high precision. It is essential to effectively monitor the target to ensure that the tumor is within the beam aperture. We modeled the treatment verification problem as a two-class classification problem and applied an Artificial Neural Network (ANN) to classify the cine EPID images acquired during the treatment into corresponding classes with the tumor inside or outside of the beam aperture. Training samples were generated for the ANN using digitally reconstructed radiographs (DRRs) with artificially added shifts in tumor location to simulate cine EPID images with different tumor locations. Principal Component Analysis (PCA) was used to reduce the dimensionality of the training samples and cine EPID images acquired during the treatment. The proposed treatment verification algorithm was tested on five hypofractionated lung patients in a retrospective fashion. On average, our proposed algorithm achieved a 98.0% classification accuracy, a 97.6% recall rate, and a 99.7% precision rate.

physics.med-ph↗