Zhong Chen

Bio: Zhong Chen is an academic researcher from Nanyang Technological University. The author has contributed to research in topics: Medicine & Chemistry. The author has an hindex of 80, co-authored 1000 publications receiving 28171 citations. Previous affiliations of Zhong Chen include Institute of High Performance Computing Singapore & National Institute of Education.

On a relationship among optical power, current density, and junction temperature for InGaN-based light-emitting diodes

Abstract: Theories of spontaneous emission rates and carrier recombination mechanisms for multiple-quantum-well InGaN-based blue light-emitting diodes (LEDs) have been carefully studied. A relationship among the optical power, the current density, and the temperature (heat-sink temperature or p-n junction temperature) is identified, and an optical-electrical-thermal model (OETM) is proposed. Thereafter, spectral measurements have been carried out to confirm the validity of this OETM. Results show that measured optical powers under various current densities and heat-sink temperatures agree satisfactorily with those determined by the OETM. Furthermore, the traditional forward-voltage method (FVM) has also been carried out for comparison. Junction temperatures determined by this OETM is in accordance with those measured by the FVM. Therefore, this model can serve as an alternative tool for fast estimating junction temperatures after relevant fitting coefficients having been determined.

Magnetic resonance image reconstruction using similarities learnt from multi-modal images

Abstract: Compressed sensing has shown great potential to speed up magnetic resonance imaging (MRI) assuming the image is sparse and compressible in a transform domain. Conventional methods typically use a pre-defined sparsifying transform such as wavelets or finite difference, which sometimes does not lead to a sufficient sparse representation. In this paper, we design a patch-based nonlocal operator (PANO) to model the sparsity between image patches. The linearity of PANO allows us to establish a general formulation to reconstruct magnetic resonance image from undersampled data and provides feasibility to incorporate prior information learnt from guide images. To demonstrate the feasibility and performance of PANO, learning similarities from multi-modal images are presented to significantly improve the reconstructed images over conventional redundant wavelets in terms of visual quality and reconstruction errors.

In vivo spatially localized high resolution 1H MRS via intermolecular single‐quantum coherence of rat brain at 7 T

Abstract: Purpose: To compare the conventional localized point-resolved spectroscopy (PRESS) with localized 2D intermolecular single-quantum coherence (iSQC) magnetic resonance spectroscopy (MRS) and obtain in vivo MRS spectrum of rat brain using the latter technique. Materials and Methods: A brain phantom, an intact pig brain tissue, and mature Sprague–Dawley rat were studied by PRESS, Nano magic-angle spinning spectroscopy, and iSQC MRS. Results: Using PRESS, high-resolution MRS can be obtained from the brain phantom and pig brain tissue with a small voxel in a relatively homogeneous field. When a large voxel is selected, the field homogeneity is distinctly reduced. No useful information is obtained from the PRESS spectra. However, using the iSQC MRS, high-resolution spectra can be obtained from the two samples with a relatively large voxel. In the same way, an iSQC MRS spectrum can be obtained from a relatively large voxel of in vivo rat brain with a comparable resolution to the PRESS spectrum with a small voxel. Conclusion: Compared to PRESS, the iSQC MRS may be more feasible and promising for detection of strongly structured tissues with relatively large voxels. J. Magn. Reson. Imaging 2013;37:359–364. © 2012 Wiley Periodicals, Inc.

Neural Network Method for Diffusion-Ordered NMR Spectroscopy.

Abstract: Diffusion-ordered NMR spectroscopy (DOSY) presents an essential tool for the analysis of compound mixtures by revealing intrinsic diffusion behaviors of the mixed components. For the interpretation of the diffusion information, intrinsically designed algorithms for a DOSY spectrum reconstruction are required. The estimated diffusion coefficients are desired to have consistency for all the spectral signals from the same molecule and good separation of signals from different molecules. For this purpose, we propose a novel method that adopts a coordinated multiexponential fitting to ensure the consistency of diffusion coefficients and apply a sparse constraint to enhance the robustness. A lightweight neural network is applied as an optimizer to solve this highly nonlinear and nonconvex optimization problem. The proposed method provides estimated diffusion coefficients with excellent distinguishment between species and outperforms the state-of-the-art reconstruction algorithms, such as the Laplacian inversion and the multivariate fitting methods.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Fast parallel algorithms for short-range molecular dynamics

Abstract: Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

Principles of Neural Science

Abstract: I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Graphitic Carbon Nitride (g-C3N4)-Based Photocatalysts for Artificial Photosynthesis and Environmental Remediation: Are We a Step Closer To Achieving Sustainability?

Abstract: As a fascinating conjugated polymer, graphitic carbon nitride (g-C3N4) has become a new research hotspot and drawn broad interdisciplinary attention as a metal-free and visible-light-responsive photocatalyst in the arena of solar energy conversion and environmental remediation. This is due to its appealing electronic band structure, high physicochemical stability, and “earth-abundant” nature. This critical review summarizes a panorama of the latest progress related to the design and construction of pristine g-C3N4 and g-C3N4-based nanocomposites, including (1) nanoarchitecture design of bare g-C3N4, such as hard and soft templating approaches, supramolecular preorganization assembly, exfoliation, and template-free synthesis routes, (2) functionalization of g-C3N4 at an atomic level (elemental doping) and molecular level (copolymerization), and (3) modification of g-C3N4 with well-matched energy levels of another semiconductor or a metal as a cocatalyst to form heterojunction nanostructures. The constructi...