arXiv subjects

Peng Xiao

Publications and source records attributed to Peng Xiao.

At least 37 records · Page 2Linked to original sources

arXiv2023

Engineering heat transport across epitaxial lattice-mismatched van der Waals heterointerfaces

Artificially engineered 2D materials offer unique physical properties for thermal management, surpassing naturally occurring materials. Here, using van der Waals epitaxy, we demonstrate the ability to engineer extremely insulating ultra-thin thermal metamaterials based on crystalline lattice-mismatched Bi2Se3/MoSe2 superlattices and graphene/PdSe2 heterostructures with exceptional thermal resistances (70-202 m^2K/GW) and ultralow cross-plane thermal conductivities (0.01-0.07 Wm^-1K^-1) at room temperature, comparable to those of amorphous materials. Experimental data obtained using frequency-domain thermoreflectance and low-frequency Raman spectroscopy, supported by tight-binding phonon calculations, reveal the impact of lattice mismatch, phonon-interface scattering, size effects, temperature and interface thermal resistance on cross-plane heat dissipation, uncovering different thermal transport regimes and the dominant role of long-wavelength phonons. Our findings provide essential insights into emerging synthesis and thermal characterization methods and valuable guidance for the development of large-area heteroepitaxial van der Waals films of dissimilar materials with tailored thermal transport characteristics.

cond-mat.mtrl-sci↗

arXiv2023

Bayesian Federated Neural Matching that Completes Full Information

Federated learning is a contemporary machine learning paradigm where locally trained models are distilled into a global model. Due to the intrinsic permutation invariance of neural networks, Probabilistic Federated Neural Matching (PFNM) employs a Bayesian nonparametric framework in the generation process of local neurons, and then creates a linear sum assignment formulation in each alternative optimization iteration. But according to our theoretical analysis, the optimization iteration in PFNM omits global information from existing. In this study, we propose a novel approach that overcomes this flaw by introducing a Kullback-Leibler divergence penalty at each iteration. The effectiveness of our approach is demonstrated by experiments on both image classification and semantic segmentation tasks.

cs.LG↗

arXiv2022

OMSN and FAROS: OCTA Microstructure Segmentation Network and Fully Annotated Retinal OCTA Segmentation Dataset

The lack of efficient segmentation methods and fully-labeled datasets limits the comprehensive assessment of optical coherence tomography angiography (OCTA) microstructures like retinal vessel network (RVN) and foveal avascular zone (FAZ), which are of great value in ophthalmic and systematic diseases evaluation. Here, we introduce an innovative OCTA microstructure segmentation network (OMSN) by combining an encoder-decoder-based architecture with multi-scale skip connections and the split-attention-based residual network ResNeSt, paying specific attention to OCTA microstructural features while facilitating better model convergence and feature representations. The proposed OMSN achieves excellent single/multi-task performances for RVN or/and FAZ segmentation. Especially, the evaluation metrics on multi-task models outperform single-task models on the same dataset. On this basis, a fully annotated retinal OCTA segmentation (FAROS) dataset is constructed semi-automatically, filling the vacancy of a pixel-level fully-labeled OCTA dataset. OMSN multi-task segmentation model retrained with FAROS further certifies its outstanding accuracy for simultaneous RVN and FAZ segmentation.

eess.IV↗

arXiv2022

Probabilistic Federated Learning of Neural Networks Incorporated with Global Posterior Information

In federated learning, models trained on local clients are distilled into a global model. Due to the permutation invariance arises in neural networks, it is necessary to match the hidden neurons first when executing federated learning with neural networks. Through the Bayesian nonparametric framework, Probabilistic Federated Neural Matching (PFNM) matches and fuses local neural networks so as to adapt to varying global model size and the heterogeneity of the data. In this paper, we propose a new method which extends the PFNM with a Kullback-Leibler (KL) divergence over neural components product, in order to make inference exploiting posterior information in both local and global levels. We also show theoretically that The additional part can be seamlessly concatenated into the match-and-fuse progress. Through a series of simulations, it indicates that our new method outperforms popular state-of-the-art federated learning methods in both single communication round and additional communication rounds situation.

cs.LG↗

arXiv2022

Thermal insulation and heat guiding using nanopatterned MoS2

In the modern electronics overheating is one of the major reasons for device failure. Overheating causes irreversible damage to circuit components and can also lead to fire, explosions, and injuries. Accordingly, in the advent of 2D material-based electronics, an understanding of their thermal properties in addition to their electric ones is crucial to enable efficient transfer of excess heat away from the electronic components. In this work we propose structures based on free-standing, few-layer, nanopatterned MoS2 that insulate and guide heat in the in-plane direction. We arrive at these designs via a thorough study of the in-plane thermal conductivity as a function of thickness, porosity, and temperature in both pristine and nanopatterned MoS2 membranes. Two-laser Raman thermometry was employed to measure the thermal conductivities of a set of free-standing MoS2 flakes with diameters greater than 20 um and thicknesses from 5 to 40 nm, resulting in values from 30 to 85 W/mK, respectively. After nanopatterning a square lattice of 100-nm diameter holes with a focused ion beam we have obtained a greater than 10-fold reduction of the thermal conductivities for the period of 500 nm and values below 1 W/mK for the period of 300 nm. The results were supported by equilibrium molecular dynamic simulations for both pristine and nanopatterned MoS2. The selective patterning of certain areas results in extremely large difference in thermal conductivities within the same material. Exploitation of this effect enabled for the first time thermal insulation and heat guiding in the few-layer MoS2. The patterned regions act as high thermal resistors: we obtained a thermal resistance of 4x10-6 m2K/W with only four patterned lattice periods of 300 nm, highlighting the significant potential of MoS2 for thermal management applications.

cond-mat.mes-hall↗

arXiv2022

Excitation and detection of acoustic phonons in nanoscale systems

Phonons play a key role in the physical properties of materials, and have long been a topic of study in physics. While the effects of phonons had historically been considered to be a hindrance, modern research has shown that phonons can be exploited due to their ability to couple to other excitations and consequently affect the thermal, dielectric, and electronic properties of solid state systems, greatly motivating the engineering of phononic structures. Advances in nanofabrication have allowed for structuring and phonon confinement even down to the nanoscale, drastically changing material properties. Despite developments in fabricating such nanoscale devices, the proper manipulation and characterization of phonons continues to be challenging. However, a fundamental understanding of these processes could enable the realization of key applications in diverse fields such as topological phononics, information technologies, sensing, and quantum electrodynamics, especially when integrated with existing electronic and photonic devices. Here, we highlight seven of the available methods for the excitation and detection of acoustic phonons and vibrations in solid materials, as well as advantages, disadvantages, and additional considerations related to their application. We then provide perspectives towards open challenges in nanophononics and how the additional understanding granted by these techniques could serve to enable the next generation of phononic technological applications.

cond-mat.mtrl-sci↗

arXiv2022

Graph-based Matched Field Localization for an Underwater Source

Matched Field Processing (MFP) locates the underwater sources by matching the received data with the replica vectors, which could be regarded as a generalized beamformer. In this paper, the MFP method is combined with a recently developed framework -- Graph Signal Processing (GSP) method. Following the paradigm of GSP, a spatial adjacency matrix is constructed for the arbitrary distributed sensors based on the Green's function, then the source is located by utilizing the graph Fourier transform. The simulation results illustrate that the Graph-based MFP outperforms the the conventional MFP processors -- the Bartlett processor and the Minimum Variance processor -- for its good accuracy and robustness.

eess.SP↗

arXiv2021

Phonon dynamics and thermal conductivity of PtSe2 thin films: Impact of crystallinity and film thickness on heat dissipation

We present a comparative investigation of the influence of crystallinity and film thickness on the acoustic and thermal properties of 2D layered PtSe2 thin films of varying thickness (0.6-24 nm) by combining a set of experimental techniques, namely, frequency domain thermo-reflectance, low-frequency Raman and pump-probe coherent phonon spectroscopy. We find a 35% reduction in the cross-plane thermal conductivity of polycrystalline films with thickness larger than 12 nm compared to the crystalline films of the same thickness due to phonon grain boundary scattering. Density functional theory calculations are in good agreement with the experiments and further reveal the ballistic nature of cross-plane heat transport in PtSe2 up to a certain thickness (~20 nm). In addition, our experiments revealed strong interlayer interactions in PtSe2, short acoustic phonon lifetimes in the range of picoseconds, out-of-plane elastic constant C33=31.8 GPa and layer-dependent group velocity ranging from 1340 m/s in bilayer PtSe2 to 1873 m/s in 8 layers of PtSe2. The potential of tuning the lattice cross-plane thermal conductivity of layered 2D materials with the level of crystallinity and the real-time observation of coherent phonon dynamics, which have direct implications on the cooling and transport of electrons, open a new playground for research in 2D thermoelectric devices and provide guidelines for thermal management in 2D electronics.

cond-mat.mtrl-sci↗

arXiv2021

Optical phase modulation by natural eye movements: application to time-domain FF-OCT image retrieval

Eye movements are commonly seen as an obstacle to high-resolution ophthalmic imaging. In this context we study the natural axial movements of the in vivo human eye and show that they can be used to modulate the optical phase and retrieve tomographic images via time-domain full-field optical coherence tomography (TD-FF-OCT). This approach opens a path to a simplified ophthalmic TD-FF-OCT device, operating without the usual piezo motor-camera synchronization. The device demonstrates in vivo human corneal images under the different image retrieval schemes (2-phase and 4-phase) and different exposure times (3.5 ms, 10 ms, 20 ms). Data on eye movements, acquired with a 100 kHz spectral-domain OCT with axial eye tracking, are used to study the influence of ocular motion on the probability of capturing high-signal tomographic images without phase washout. The optimal combinations of camera acquisition speed and amplitude of piezo modulation are proposed and discussed.

physics.med-ph↗

arXiv2021

Potential Advantages of Peak Picking Multi-Voltage Threshold Digitizer in Energy Determination in Radiation Measurement

The Multi-voltage Threshold (MVT) method, which samples the signal by certain reference voltages, has been well developed as being adopted in pre-clinical and clinical digital positron emission tomography(PET) system. To improve its energy measurement performance, we propose a Peak Picking MVT(PP-MVT) Digitizer in this paper. Firstly, a sampled Peak Point(the highest point in pulse signal), which carries the values of amplitude feature voltage and amplitude arriving time, is added to traditional MVT with a simple peak sampling circuit. Secondly, an amplitude deviation statistical analysis, which compares the energy deviation of various reconstruction models, is used to select adaptive reconstruction models for signal pulses with different amplitudes. After processing 30,000 randomly-chosen pulses sampled by the oscilloscope with a 22Na point source, our method achieves an energy resolution of 17.50% within a 450-650 KeV energy window, which is 2.44% better than the result of traditional MVT with same thresholds; and we get a count number at 15225 in the same energy window while the result of MVT is at 14678. When the PP-MVT involves less thresholds than traditional MVT, the advantages of better energy resolution and larger count number can still be maintained, which shows the robustness and the flexibility of PP-MVT Digitizer. This improved method indicates that adding feature peak information could improve the performance on signal sampling and reconstruction, which canbe proved by the better performance in energy determination in radiation measurement.

eess.SP↗

arXiv2020

An automated and multi-parametric algorithm for objective analysis of meibography images

Meibography is a non-contact imaging technique used by ophthalmologists to assist in the evaluation and diagnosis of meibomian gland dysfunction (MGD). While artificial qualitative analysis of meibography images could lead to low repeatability and efficiency and multi-parametric analysis is demanding to offer more comprehensive information in discovering subtle changes of meibomian glands during MGD progression, we developed an automated and multi-parametric algorithm for objective and quantitative analysis of meibography images. The full architecture of the algorithm can be divided into three steps: (1) segmentation of the tarsal conjunctiva area as the region of interest (ROI); (2) segmentation and identification of glands within the ROI; and (3) quantitative multi-parametric analysis including newly defined gland diameter deformation index (DI), gland tortuosity index (TI), and glands signal index (SI). To evaluate the performance of the automated algorithm, the similarity index (k) and the segmentation error including the false positive rate (r_P) and the false negative rate (r_N) are calculated between the manually defined ground truth and the automatic segmentations of both the ROI and meibomian glands of 15 typical meibography images. The feasibility of the algorithm is demonstrated in analyzing typical meibograhy images.

eess.IV↗

arXiv2020

Ocean Reverberation Suppression via Matrix Completion with Sensor Failure

In the ocean environment, reverberation is a common and strong interference which significantly degrades the performance of target bearing estimation. Meanwhile, sensor failure is inevitable in actual sonar deployment as the underwater scene is complicated. Therefore, it is a challenge for target detection in the ocean reverberation with sensor failure. To address this issue, we propose an improved approach based on the principle of low rank characteristics of matrix in this paper. Firstly, we utilize Hankel structured matrix to counteract the problem of sensor failure. Then, the algorithm of matrix completion (MC) based on l1-norm and l2-norm are exploited to recover the true signal matrix from the corrupted received matrix. Numerical results demonstrate that compared with other related methods, the l1-norm provides better capability in the probability of recovery and shows the best robustness of bearing estimation with the narrow beam width and low sidelobe. Moreover, the proposed approach verifies the performances of reverberation suppression and achieves high resolution of target detection.

eess.SP↗

arXiv2020

Real-time, non-contact, cellular imaging and angiography of human cornea and limbus with common-path Full-field/SDOCT

In today's clinics, a cellular-resolution view of the cornea can be achieved only with an in vivo confocal microscope (IVCM) in contact with the eye. Here, we present a common-path Full-field/Spectral-domain OCT microscope (FF/SD OCT), which, for the first time, enables cell-detail imaging of the entire ocular surface (central and peripheral cornea, limbus, sclera, tear film) without contact and in real time. The device, which has been successfully tested in human subjects, is now ready for direct implementation in clinical research. Real-time performance is achieved through rapid axial eye tracking and simultaneous defocusing correction. Images, extracted from real-time videos, contain cells and nerves, which can be quantified over a millimetric field-of-view, beyond the capability of IVCM and conventional OCT. In the limbus, Palisades of Vogt, vessels and blood flow can be resolved with high contrast without contrast agent injection. The fast imaging speed of 275 frames/s (0.6 billion pixels/s) allowed direct monitoring of blood flow dynamics, enabling creation of high-resolution velocity maps for the first time. Tear flow velocity and evaporation time could be measured without fluorescein administration.

physics.med-ph↗

arXiv2019

DeepFPC: Deep Unfolding of a Fixed-Point Continuation Algorithm for Sparse Signal Recovery from Quantized Measurements

We present DeepFPC, a novel deep neural network designed by unfolding the iterations of the fixed-point continuation algorithm with one-sided l1-norm (FPC-l1), which has been proposed for solving the 1-bit compressed sensing problem. The network architecture resembles that of deep residual learning and incorporates prior knowledge about the signal structure (i.e., sparsity), thereby offering interpretability by design. Once DeepFPC is properly trained, a sparse signal can be recovered fast and accurately from quantized measurements. The proposed model is evaluated in the task of direction-of-arrival (DOA) estimation and is shown to outperform state-of-the-art algorithms, namely, the iterative FPC-l1 algorithm and the 1-bit MUSIC method.

cs.LG↗

arXiv2019

Neural Network for NILM Based on Operational State Change Classification

Energy disaggregation in a non-intrusive way estimates appliance level electricity consumption from a single meter that measures the whole house electricity demand. Recently, with the ongoing increment of energy data, there are many data-driven deep learning architectures being applied to solve the non-intrusive energy disaggregation problem. However, most proposed methods try to estimate the on-off state or the power consumption of appliance, which need not only large amount of parameters, but also hyper-parameter optimization prior to training and even preprocessing of energy data for a specified appliance. In this paper, instead of estimating on-off state or power consumption, we adapt a neural network to estimate the operational state change of appliance. Our proposed solution is more feasible across various appliances and lower complexity comparing to previous methods. The simulated experiments in the low sample rate dataset REDD show the competitive performance of the designed method, with respect to other two benchmark methods, Hidden Markov Model-based and Graph Signal processing-based approaches.

cs.LG↗

arXiv2018

The largest signless Laplacian spectral radius of uniform supertrees with diameter and pendent edges (vertices)

Let $S_{1}(m, d, k)$ be the $k$-uniform supertree obtained from a loose path $P:v_{1}, e_{1}, v_{2}, \ldots,v_{d}, e_{d}, v_{d+1}$ with length $d$ by attaching $m-d$ edges at vertex $v_{\lfloor\frac{d}{2}\rfloor+1}.$ Let $\mathbb{S}(m,d,k)$ be the set of $k$-uniform supertrees with $m$ edges and diameter $d$ and $q(G)$ be the signless Laplacian spectral radius of a $k$-uniform hypergraph $G$. In this paper, we mainly determine $S_{1}(m,d,k)$ with the largest signless Laplacian spectral radius among all supertrees in $\mathbb{S}(m,d,k)$ for $3\leq d\leq m-1$. Furthermore, we determine the unique uniform supertree with the maximum signless Laplacian spectral radius among all the uniform supertrees with $n$ vertices and pendent edges (vertices).

math.CO↗

arXiv2018

In vivo high resolution human retinal imaging with wavefront correctionless full-field OCT

As the lateral resolution of FFOCT with spatially incoherent illumination has been shown to be insensitive to aberrations, we demonstrate high resolution en face full-field OCT (FFOCT) retinal imaging without wavefront correction in the human eye in vivo for the first time. A combination of FFOCT with spectral-domain OCT (SDOCT) is applied for real-time matching of the optical path lengths (OPL) of FFOCT. Through the real-time cross-sectional SDOCT images, the OPL of the FFOCT reference arm is matched with different retinal layers in the FFOCT sample arm. Thus, diffraction limited FFOCT images of multiple retinal layers are acquired at both the near periphery and the fovea. The en face FFOCT retinal images reveal information about various structures such as the nerve fiber orientation, the blood vessel distribution, and the photoreceptor mosaic.

physics.med-ph↗

arXiv2017

Ordering of bicyclic graphs by matching energy

Let $G$ be a simple graph of order $n$ and $μ_{1},μ_{2},\ldots,μ_{n}$ be the roots of its matching polynomial. The matching energy is defined as the sum $\sum^{n}_{i=1}|μ_{i}|$, which was introduced by Gutman and Wagner in 2012. In this paper, the graphs with the first five smallest matching energies among all bicyclic graphs for order $n>5$ are determined.

math.CO↗