Browsing by Author "Lin, Jia-Wei"

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A finite element based fast eigensolver for three dimensional anisotropic photonic crystals
(2019-06-01)
Chou, So-Hsiang
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Huang, Tsung-Ming
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Li, Tiexiang
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Lin, Jia-Wei
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Lin, Wen-Wei
The standard Yee's scheme for the Maxwell eigenvalue problem places the discrete electric field variable at the midpoints of the edges of the grid cells. It performs well when the permittivity is a scalar field. However, when the permittivity is a Hermitian full tensor field it would generate un-physical complex eigenvalues or frequencies. In this paper, we propose a finite element method which can be interpreted as a modified Yee's scheme to overcome this difficulty. This interpretation enables us to create a fast FFT eigensolver that can compute very effectively the band structure of the anisotropic photonic crystal with SC and FCC lattices. Furthermore, we overcome the usual large null space associated with the Maxwell eigenvalue problem by deriving a null-space free discrete eigenvalue problem which involves a crucial Hermitian positive definite linear system to be solved in each of the iteration steps. It is demonstrated that the CG method without preconditioning converges in 37 iterations even when the dimension of a matrix is as large as 5, 184, 000. (C) 2019 Elsevier Inc. All rights reserved.
Deep Learning-Constructed Joint Transmission-Recognition for Internet of Things
(2019-01-01)
Lee, Chia-Han
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Lin, Jia-Wei
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Chen, Po-Hao
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Chang, Yu-Chieh
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電信工程研究所
The widely deployed Internet-of-Things (IoT) devices provide intelligent services with its cognition capability. Since the IoT data are usually transmitted to the server for recognition (e.g., image classification) due to low computational capability and limited power supply, achieving recognition accuracy under limited bandwidth and noisy channel of wireless networks is a crucial but challenging task. In this paper, we propose a deep learning-constructed joint transmission-recognition scheme for the IoT devices to effectively transmit data wirelessly to the server for recognition, jointly considering transmission bandwidth, transmission reliability, complexity, and recognition accuracy. Compared with other schemes that may be deployed on the IoT devices, i.e., a scheme based on JPEG compression and two compressed sensing-based schemes, the proposed deep neural network-based scheme has much higher recognition accuracy under various transmission scenarios at all signal-to-noise ratios (SNRs). In particular, the proposed scheme maintains good performance at the very low SNR. Moreover, the complexity of the proposed scheme is low, making it suitable for IoT applications. Finally, a transfer learning-based training method is proposed to effectively mitigate the computing burden and reduce the overhead of online training.
NV-BNN: An Accurate Deep Convolutional Neural Network Based on Binary STT-MRAM for Adaptive AI Edge
(2019-01-01)
Chang, Chih-Chcng
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Wu, Ming-Hung
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Lin, Jia-Wei
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Li, Chun-Hsien
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Parmar, Vivek
Binary STT-MRAM is a highly anticipated embedded non-volatile memory technology in advanced logic nodes <28 nm. How to enable its in-memory computing (IMC) capability is critical for enhancing AI Edge. Based on the soon-available STT-MRAM, we report the first binary deep convolutional neural network (NV-BNN) capable of both local and remote learning. Exploiting intrinsic cumulative switching probability, accurate online training of CIFAR-10 color images (similar to 90%) is realized using a relaxed endurance spec (switching <= 20 times) and hybrid digital/IMC design. For offline training, the accuracy loss due to imprecise weight placement can be mitigated using a rapid non-iterative training-with-noise and fine-tuning scheme.
Structure-Preserving Methods for Computing Complex Band Structures of Three Dimensional Photonic Crystals
(2020-05-06)
Huang, Tsung-Ming
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Li, Tiexiang
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Lin, Jia-Wei
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Lin, Wen-Wei
;
Tian, Heng
This work is devoted to the numerical computation of complex band structure k=k(omega)is an element of C3, with omega being positive frequencies, of three dimensional isotropic dispersive or non-dispersive photonic crystals from the perspective of structured quadratic eigenvalue problems (QEPs). Our basic strategy is to fix two degrees of freedom in k and to view the remaining one as the eigenvalue of a complex gyroscopic QEP which stems from Maxwell's equations discretized by Yee's scheme. We reformulate this gyroscopic QEP into a T-palindromic QEP, which is further transformed into a structured generalized eigenvalue problem for which we have established a structure-preserving shift-and-invert Arnoldi algorithm. Moreover, to accelerate the inner iterations of the shift-and-invert Arnoldi algorithm, we propose an efficient preconditioner which makes most of the fast Fourier transforms. The advantage of our method is discussed in detail and corroborated by several numerical results.
Synthesis of novel dithienothiophene- and 2,7-carbazole-based conjugated polymers and H-bonded effects on electrochromic and photovoltaic properties
(2012-12-15)
Fang, Hsiao-Ping
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Lin, Jia-Wei
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Chiang, I-Hung
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Chu, Chih-Wei
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Wei, Kung-Hwa
Three kinds of dithienothiophene/carbazole-based conjugated polymers (P1P3), which bear acid-protected and benzoic acid pendants in P2 and P3, respectively, were synthesized via Suzuki coupling reaction. Interestingly, P1P3 exhibited reversible electrochromism during the oxidation processes of cyclic voltammogram studies, and P3 (with H-bonds) revealed the best electrochromic property with the most noticeable color change. According to powder X-ray diffraction (XRD) analysis, these polymers exhibited obvious diffraction features indicating bilayered packings between polymer backbones and p-p stacking between layers in the solid state. Compared with the XRD data of P2 (without H-bands), H-bonds of P3 induced a higher crystallinity in the small-angle region (corresponding to a higher ordered bilayered packings between polymer backbones), but with a similar crystallinity in the wide angle region indicating a comparable p-p stacking distance between layers. Moreover, based on the preliminary photovoltaic properties of PSC devices (P1P3 blended individually with PCBM acceptor in the weight ratio of 1:1), P3 (with H-bonds) possessed the highest power conversion efficiency of 0.61% (with Jsc = 2.26 mA/cm2, FF = 29.8%, and Voc = 0.9 V). In contrast to P2 (without H-bands), the thermal stability, crystallinity, and electrochromic along with photovoltaic properties of P3 were generally enhanced due to its H-bonded effects. (C) 2012 Wiley Periodicals, Inc. J Polym Sci Part A: Polym Chem, 2012