Light field image processing: an overview

Light field imaging has emerged as a technology allowing to capture richer visual information from our world. As opposed to traditional photography, which captures a 2D projection of the light in the scene integrating the angular domain, light fields collect radiance from rays in all directions, demultiplexing the angular information lost in conventional photography. On the one hand, this higher dimensional representation of visual data offers powerful capabilities for scene understanding, and substantially improves the performance of traditional computer vision problems such as depth sensing, post-capture refocusing, segmentation, video stabilization, material classification, etc. On the other hand, the high-dimensionality of light fields also brings up new challenges in terms of data capture, data compression, content editing, and display. Taking these two elements together, research in light field image processing has become increasingly popular in the computer vision, computer graphics, and signal processing communities. In this paper, we present a comprehensive overview and discussion of research in this field over the past 20 years. We focus on all aspects of light field image processing, including basic light field representation and theory, acquisition, super-resolution, depth estimation, compression, editing, processing algorithms for light field display, and computer vision applications of light field data

RESET Current Reduction for Phase Change Memory Based on Standard 0.13-μm CMOS Technology

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Pharmacokinetics, Bioavailability, and Tissue Distribution Study of Angoroside C and Its Metabolite Ferulic Acid in Rat Using UPLC-MS/MS

Angoroside C is a phenylpropanoid glycoside compound isolated from the dried root of Scrophularia ningpoensis Hemsl., which possesses the effects of preventing ventricular remodeling, reducing pulmonary oedema, and reducing blood pressure, as well as having the properties of anti-platelet aggregation, hepatoprotection and anti-nephritis, etc. However, few investigations have been conducted on the absorption, distribution, metabolism, and excretion (ADME) study of angoroside C. Thus, a fast ultra-high performance liquid chromatography-tandem quadrupole mass spectrometry (UPLC-MS/MS) method was established for the determination of angoroside C and its metabolite ferulic acid in rat plasma and tissue homogenate. The two analytes were extracted from the biosamples using a simple protein precipitation with acetonitrile. The developed method was validated and successfully applied to the pharmacokinetics, bioavailability and tissue distribution study after the intragastric administration of angoroside C (100 mg/kg) or the intravenous administration of angoroside C (5 mg/kg), respectively. The results showed that angoroside C can be absorbed extremely quickly (Tmax = 15 min), can be eliminated very rapidly (t1/2 = 1.26 h), and its oral bioavailability is only about 2.1%. Furthermore, angoroside C was extensively distributed in all main organs (liver, heart, spleen, lung, kidney, and brain), and the highest concentration was detected in the lung 15 min after oral administration. This paper also indicated that angoroside C could be converted to the active metabolite ferulic acid in vivo. The maximum concentrations of ferulic acid in the kidney occurred at 6 h after oral administration. In summary, this study explored some of the pharmacokinetic characteristics of angoroside C in vivo, and the data produced could provide a basis for the further investigation of angoroside C

Neural-Network-Based Nonlinear Model Predictive Control of Multiscale Crystallization Process

The purpose of this study was to develop an integrated control strategy for multiscale crystallization processes. An image analysis method using a deep learning neural network is used to measure the fine-scale information of the crystallization process, and the mathematical statistical method is adopted to obtain the mean size of the crystal population. A feedforward neural network is subsequently trained and employed in a nonlinear model predictive control formulation to obtain the optimal profile of the manipulated variable. The effectiveness of the proposed nonlinear model predictive control method is evaluated using alum cooling crystallization experiments. Experimental results demonstrate benefits of the proposed combination of feedforward neural network and nonlinear model predictive control method for the multiscale crystallization process

Chemical Mechanical Polishing Slurry for Amorphous Ge2Sb2Te5

AbstractAs flash technologies face scaling issues at 32nm and beyond, phase change memory (PCM) emerges as the best performing candidate for scaled non-volatile memories of next generation. To fabricate PCM device with high volume and low power consumption, the structure of PCM has evolved from planar structure to confined cells, which pose challenges for chemical mechanical polishing (CMP) of the phase change material Ge2Sb2Te5 (GST).In this study, we discuss the properties, polishing mechanisms and slurry formulations for amorphous GST. Static etching characteristics and zeta potentials of GST powders are first explored to evaluate the chemical properties of GST. Then the polishing mechanisms of GST at pH 2 and pH 11 are discussed by means of static etching experiments, polishing performance, GST film hardness measurement, GST solubility and open circuit potential test. Finally, our efforts for GST slurry development are described. By a combination effect of inhibitors, in which one can form adsorption layer on GST by carbonyl O and the other one can further protect GST surface by forming hydrogen bond through multiple OH groups, we have overcome the corrosion issue of GST. Meanwhile, by the adsorption of the inhibitor layers on GST surface and extremely low removal rate of oxide film (<0.05nm/min), we have achieved a self-stop GST CMP process with over-polish dishing <8nm and oxide loss <6nm for 60nm GST vias. We have also resolved the issue of particle residue by using our self-formulated post-cleaning solution

Feedback Control of Crystal Size Distribution for Cooling Batch Crystallization Using Deep Learning-Based Image Analysis

The shape of the crystal size distribution directly determines the quality of crystal products. It is often assumed that distributional properties of crystal size conform to the Gaussian distribution or the log normal distribution. The mean and variance or relative crystal number are widely adopted to describe the crystal size distribution and taken as the control objectives. Therefore, the resulting control methods have difficulties in controlling the crystal size distribution with a general shape. In this article, a novel feedback control system of crystal size distribution based on image analysis is designed for the effective control of crystal size distribution with a general shape. First, a deep learning network-based image analysis method is adopted and implemented to extract the crystal size distribution. Second, the crystal size distribution is approximated by a radial basis function neural network. Consequently, a feedback controller is designed and the tracking control of the target crystal size distribution is finally realized. The results of crystallization experiments demonstrate the effectiveness of the proposed method