Xiang Zhang

Bio: Xiang Zhang is an academic researcher from Baylor College of Medicine. The author has contributed to research in topics: Medicine & Computer science. The author has an hindex of 154, co-authored 1733 publications receiving 117576 citations. Previous affiliations of Xiang Zhang include University of California, Berkeley & University of Texas MD Anderson Cancer Center.

Microstereolithography of lead zirconate titanate thick film on silicon substrate

Abstract: The microstereolithography mSL of lead zirconate titanate PZT thick films on platinum-buffered silicon substrates is reported for . the first time in this paper. Crack-free PZT thick films 80-130 mm thick have been fabricated by laser direct-write UV polymerization from the HDDA-based UV curable PZT suspensions. The characterization of the fired films shows dielectric permittivities of 120-200, 2 . tangent loss of 0.92-2.5% and remnant polarization of 0.9-1.7 mCrcm . The field-induced longitudinal piezoelectric coefficient d of 33 . y3 an 84-mm thick film is 100 pCrN and the piezoelectric voltage coefficient g is about 59.5 = 10 V mrN. These results 33 demonstrated the potential for mSL of advanced piezoelectric microsensors and microactuators. q 2000 Elsevier Science B.V. All rights reserved.

Review of mechanisms and deformation behaviors in 4D printing

Abstract: Although 3D printing was invented in 1984, it was not until recent years that it captured the imagination of everyone from industry experts to at-home hobbyists. Three-dimensional printing, also known as additive manufacturing or rapid prototyping, constructs an object by accumulating materials layer by layer. In recent years, 3D printing technology has been dramatically developed with respect to materials, printer, and process, which laid a foundation for 4D printing. Four-dimensional printing is the targeted evolution of the 3D-printed structure, concerning shape, property, and functionality. The object is produced by 3D printing firstly. Then, the object can self-deform, self-assemble, self-disassemble, self-repair, and change property or functionality over time when the external stimuli are imposed on it. This review mainly introduces the stimulus, types of shape-shifting behaviors, mechanisms of deformation, and applications of 4D printing.

Surface Plasmon Amplification in Planar Metal Films

Abstract: The propagation and amplification of surface plasmon polaritons (SPPs) is studied at the interfaces between metals and active media. A permittivity renormalization technique is proposed and developed to obtain an explicit analytic expression for the critical gain required to achieve infinite SPP propagation length. A specific multiple quantum-well (MQW) system is identified as a prospective medium for demonstrating efficient SPP amplification at telecommunication frequencies. The proposed system may have a strong impact on a variety of photonic devices ranging from plasmonic nanocircuits, subwavelength transmission lines and plasmonic cavities to nanosized transducers.

Metamaterial of rod pairs standing on gold plate and its negative refraction property in the far-infrared frequency regime.

Abstract: A new kind of metamaterial, an array of periodic gold rod pairs standing on gold substrate, is introduced in this paper. A commercial electromagnetic mode solver, the High-Frequency Structure Simulator, is employed to explore the propagation property of electromagnetic waves in this system. When an $S$-polarized electromagnetic (EM) wave propagates along the substrate surface, strong magnetic resonance is produced in the far-infrared regime. Based on the simulated $S$ parameters, effective refraction index is retrieved and negative value is obtained over the wavelength range from $49.2\phantom{\rule{0.3em}{0ex}}\ensuremath{\mu}\mathrm{m}$ to $66.7\phantom{\rule{0.3em}{0ex}}\ensuremath{\mu}\mathrm{m}$. A wedge made of this metamaterial with an inclined angle 26.6\ifmmode^\circ\else\textdegree\fi{} is designed. An observable negative refraction behavior of EM wave is attained in this structure at wavelength $61.2\phantom{\rule{0.3em}{0ex}}\ensuremath{\mu}\mathrm{m}$. The refractive index is calculated by Snell's law and it is consistent with the retrieved results quite well. This provides direct evidence for the negative refraction property.

Deep Residual Learning for Image Recognition

Abstract: Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers—8× deeper than VGG nets [40] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.

Very Deep Convolutional Networks for Large-Scale Image Recognition

Abstract: In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3x3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively. We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision.

Deep Residual Learning for Image Recognition

Abstract: Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers---8x deeper than VGG nets but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.

Going deeper with convolutions

Abstract: We propose a deep convolutional neural network architecture codenamed Inception that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14). The main hallmark of this architecture is the improved utilization of the computing resources inside the network. By a carefully crafted design, we increased the depth and width of the network while keeping the computational budget constant. To optimize quality, the architectural decisions were based on the Hebbian principle and the intuition of multi-scale processing. One particular incarnation used in our submission for ILSVRC14 is called GoogLeNet, a 22 layers deep network, the quality of which is assessed in the context of classification and detection.