Untrained neural network embedded Fourier phase retrieval from few measurements

Fourier phase retrieval (FPR) is a challenging task widely used in various applications. It involves recovering an unknown signal from its Fourier phaseless measurements. FPR with few measurements is important for reducing time and hardware costs, but it suffers from serious ill-posedness. Recently, untrained neural networks have offered new approaches by introducing learned priors to alleviate the ill-posedness without requiring any external data. However, they may not be ideal for reconstructing fine details in images and can be computationally expensive. This paper proposes an untrained neural network (NN) embedded algorithm based on the alternating direction method of multipliers (ADMM) framework to solve FPR with few measurements. Specifically, we use a generative network to represent the image to be recovered, which confines the image to the space defined by the network structure. To improve the ability to represent high-frequency information, total variation (TV) regularization is imposed to facilitate the recovery of local structures in the image. Furthermore, to reduce the computational cost mainly caused by the parameter updates of the untrained NN, we develop an accelerated algorithm that adaptively trades off between explicit and implicit regularization. Experimental results indicate that the proposed algorithm outperforms existing untrained NN-based algorithms with fewer computational resources and even performs competitively against trained NN-based algorithms

Phase Retrieval with Background Information: Decreased References and Efficient Methods

Fourier phase retrieval(PR) is a severely ill-posed inverse problem that arises in various applications. To guarantee a unique solution and relieve the dependence on the initialization, background information can be exploited as a structural priors. However, the requirement for the background information may be challenging when moving to the high-resolution imaging. At the same time, the previously proposed projected gradient descent(PGD) method also demands much background information. In this paper, we present an improved theoretical result about the demand for the background information, along with two Douglas Rachford(DR) based methods. Analytically, we demonstrate that the background required to ensure a unique solution can be decreased by nearly $1/2$ for the 2-D signals compared to the 1-D signals. By generalizing the results into $d$-dimension, we show that the length of the background information more than $(2^{\frac{d+1}{d}}-1)$ folds of the signal is sufficient to ensure the uniqueness. At the same time, we also analyze the stability and robustness of the model when measurements and background information are corrupted by the noise. Furthermore, two methods called Background Douglas-Rachford (BDR) and Convex Background Douglas-Rachford (CBDR) are proposed. BDR which is a kind of non-convex method is proven to have the local R-linear convergence rate under mild assumptions. Instead, CBDR method uses the techniques of convexification and can be proven to own a global convergence guarantee as long as the background information is sufficient. To support this, a new property called F-RIP is established. We test the performance of the proposed methods through simulations as well as real experimental measurements, and demonstrate that they achieve a higher recovery rate with less background information compared to the PGD method

The influence of passivation and photovoltaic properties of α-Si:H coverage on silicon nanowire array solar cells

Silicon nanowire (SiNW) arrays for radial p-n junction solar cells offer potential advantages of light trapping effects and quick charge collection. Nevertheless, lower open circuit voltages (V(oc)) lead to lower energy conversion efficiencies. In such cases, the performance of the solar cells depends critically on the quality of the SiNW interfaces. In this study, SiNW core-shell solar cells have been fabricated by growing crystalline silicon (c-Si) nanowires via the metal-assisted chemical etching method and by depositing hydrogenated amorphous silicon (α-Si:H) via the plasma-enhanced chemical vapor deposition (PECVD) method. The influence of deposition parameters on the coverage and, consequently, the passivation and photovoltaic properties of α-Si:H layers on SiNW solar cells have been analyzed

Transverse electric field–induced deformation of armchair single-walled carbon nanotube

The deformation of armchair single-walled carbon nanotube under transverse electric field has been investigated using density functional theory. The results show that the circular cross-sections of the nanotubes are deformed to elliptic ones, in which the tube diameter along the field direction is increased, whereas the diameter perpendicular to the field direction is reduced. The electronic structures of the deformed nanotubes were also studied. The ratio of the major diameter to the minor diameter of the elliptic cross-section was used to estimate the degree of the deformation. It is found that this ratio depends on the field strength and the tube diameter. However, the field direction has little role in the deformation

Parallel Inference for Latent Dirichlet Allocation on Graphics Processing Units

The recent emergence of Graphics Processing Units (GPUs) as general-purpose parallel computing devices provides us with new opportunities to develop scalable learning methods for massive data. In this work, we consider the problem of parallelizing two inference methods on GPUs for latent Dirichlet Allocation (LDA) models, collapsed Gibbs sampling (CGS) and collapsed variational Bayesian (CVB). To address limited memory constraints on GPUs, we propose a novel data partitioning scheme that effectively reduces the memory cost. This partitioning scheme also balances the computational cost on each multiprocessor and enables us to easily avoid memory access conflicts. We use data streaming to handle extremely large datasets. Extensive experiments showed that our parallel inference methods consistently produced LDA models with the same predictive power as sequential training methods did but with 26x speedup for CGS and 196x speedup for CVB on a GPU with 30 multiprocessors. The proposed partitioning scheme and data streaming make our approach scalable with more multiprocessors. Furthermore, they can be used as general techniques to parallelize other machine learning models.

Thermocapillary migration mechanism of molten silicon droplets on horizontal solid surfaces

Abstract Effective lubrication under extreme conditions such as high temperature is of considerable importance to ensure the reliability of a mechanical system. New lubricants that can endure high temperatures should be studied and employed as alternatives to traditional oil-based lubricant. In this paper, a thermocapillary model of a silicone-oil droplet is developed by solving the Navier–Stokes and energy equations to obtain the flow, pressure, and temperature fields. This is accomplished using a conservative microfluidic two-phase flow level set method designed to track the interface between two immiscible fluids. The numerical simulation accuracy is examined by comparing the numerical results with experimental results obtained for a silicone-oil droplet. Hence, the movement and deformation of molten silicon droplets on graphite and corundum are numerically simulated. The results show that a temperature gradient causes a tension gradient on the droplet surface, which in turn creates a thermocapillary vortex. As the vortex develops, the droplet migrates to the low-temperature zone. In the initial stage, the molten silicon droplet on the corundum substrate forms two opposite vortex cells, whereas two pairs of opposite vortices are formed in the silicone fluid on the graphite substrate. Multiple vortex cells gradually develop into a single vortex cell, and the migration velocity tends to be stable. The greater the basal temperature gradient, the stronger the internal thermocapillary convection of the molten silicon droplet has, which yields higher speeds

Electrophoretic Deposited Quartz Powder-Assisted Growth of Multicrystalline Silicon

Ingot multicrystalline silicon (Mc-Si) needs to be improved in quality and reduced in cost compared with Czochralski monocrystalline silicon. A uniform and dense quartz nucleation layer was obtained by the electrophoretic deposition of quartz powder on the surface of the silicon wafer. The deposited silicon wafer was annealed at 600 °C for 1 h, and one side of the silicon wafer with the quartz layer was glued to the crucible. During the growth of Mc-Si crystal, the dense quartz powder can play a nucleation role. The results show that the average lifetime of the minority carriers a of quartz-assisted silicon ingot is 7.4 μs. The overall dislocation density of an electrophoretic deposition quartz-assisted silica ingot is low, and the defect density in the middle of the silica ingot is 1.5%, which is significantly lower than that of spray quartz (3.1%) and silicon particle (4.2%). Moreover, electrophoretic deposited quartz-assisted mc-Si can obtain oriented grains, which offers a potential to apply alkaline texturing on mc-Si wafers

Electrophoretic Deposited Quartz Powder-Assisted Growth of Multicrystalline Silicon

Ingot multicrystalline silicon (Mc-Si) needs to be improved in quality and reduced in cost compared with Czochralski monocrystalline silicon. A uniform and dense quartz nucleation layer was obtained by the electrophoretic deposition of quartz powder on the surface of the silicon wafer. The deposited silicon wafer was annealed at 600 °C for 1 h, and one side of the silicon wafer with the quartz layer was glued to the crucible. During the growth of Mc-Si crystal, the dense quartz powder can play a nucleation role. The results show that the average lifetime of the minority carriers a of quartz-assisted silicon ingot is 7.4 μs. The overall dislocation density of an electrophoretic deposition quartz-assisted silica ingot is low, and the defect density in the middle of the silica ingot is 1.5%, which is significantly lower than that of spray quartz (3.1%) and silicon particle (4.2%). Moreover, electrophoretic deposited quartz-assisted mc-Si can obtain oriented grains, which offers a potential to apply alkaline texturing on mc-Si wafers