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Search Results: 1 - 10 of 30302 matches for " Wen-Jyi Hwang "
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Efficient VLSI Architecture for Training Radial Basis Function Networks
Zhe-Cheng Fan,Wen-Jyi Hwang
Sensors , 2013, DOI: 10.3390/s130303848
Abstract: This paper presents a novel VLSI architecture for the training of radial basis function (RBF) networks. The architecture contains the circuits for fuzzy C-means (FCM) and the recursive Least Mean Square (LMS) operations. The FCM circuit is designed for the training of centers in the hidden layer of the RBF network. The recursive LMS circuit is adopted for the training of connecting weights in the output layer. The architecture is implemented by the field programmable gate array (FPGA). It is used as a hardware accelerator in a system on programmable chip (SOPC) for real-time training and classification. Experimental results reveal that the proposed RBF architecture is an effective alternative for applications where fast and efficient RBF training is desired.
Efficient Fuzzy C-Means Architecture for Image Segmentation
Hui-Ya Li,Wen-Jyi Hwang,Chia-Yen Chang
Sensors , 2011, DOI: 10.3390/s110706697
Abstract: This paper presents a novel VLSI architecture for image segmentation. The architecture is based on the fuzzy c-means algorithm with spatial constraint for reducing the misclassification rate. In the architecture, the usual iterative operations for updating the membership matrix and cluster centroid are merged into one single updating process to evade the large storage requirement. In addition, an efficient pipelined circuit is used for the updating process for accelerating the computational speed. Experimental results show that the the proposed circuit is an effective alternative for real-time image segmentation with low area cost and low misclassification rate.
Efficient Phase Unwrapping Architecture for Digital Holographic Microscopy
Wen-Jyi Hwang,Shih-Chang Cheng,Chau-Jern Cheng
Sensors , 2011, DOI: 10.3390/s111009160
Abstract: This paper presents a novel phase unwrapping architecture for accelerating the computational speed of digital holographic microscopy (DHM). A fast Fourier transform (FFT) based phase unwrapping algorithm providing a minimum squared error solution is adopted for hardware implementation because of its simplicity and robustness to noise. The proposed architecture is realized in a pipeline fashion to maximize through put of thecomputation. Moreover, the number of hardware multipliers and dividers are minimized to reduce the hardware costs. The proposed architecture is used as a custom user logic in a system on programmable chip (SOPC) for physical performance measurement. Experimental results reveal that the proposed architecture is effective for expediting the computational speed while consuming low hardware resources for designing an embedded DHM system.
Efficient k-Winner-Take-All Competitive Learning Hardware Architecture for On-Chip Learning
Chien-Min Ou,Hui-Ya Li,Wen-Jyi Hwang
Sensors , 2012, DOI: 10.3390/s120911661
Abstract: A novel k-winners-take-all (k-WTA) competitive learning (CL) hardware architecture is presented for on-chip learning in this paper. The architecture is based on an efficient pipeline allowing k-WTA competition processes associated with different training vectors to be performed concurrently. The pipeline architecture employs a novel codeword swapping scheme so that neurons failing the competition for a training vector are immediately available for the competitions for the subsequent training vectors. The architecture is implemented by the field programmable gate array (FPGA). It is used as a hardware accelerator in a system on programmable chip (SOPC) for realtime on-chip learning. Experimental results show that the SOPC has significantly lower training time than that of other k-WTA CL counterparts operating with or without hardware support.
FPGA Implementation of Generalized Hebbian Algorithm for Texture Classification
Shiow-Jyu Lin,Wen-Jyi Hwang,Wei-Hao Lee
Sensors , 2012, DOI: 10.3390/s120506244
Abstract: This paper presents a novel hardware architecture for principal component analysis. The architecture is based on the Generalized Hebbian Algorithm (GHA) because of its simplicity and effectiveness. The architecture is separated into three portions: the weight vector updating unit, the principal computation unit and the memory unit. In the weight vector updating unit, the computation of different synaptic weight vectors shares the same circuit for reducing the area costs. To show the effectiveness of the circuit, a texture classification system based on the proposed architecture is physically implemented by Field Programmable Gate Array (FPGA). It is embedded in a System-On-Programmable-Chip (SOPC) platform for performance measurement. Experimental results show that the proposed architecture is an efficient design for attaining both high speed performance andlow area costs.
Hybrid Genetic Algorithm for Design of Robust Communication Systems
Chien-Min Ou,Jing-Jhih Chen,Wen-Jyi Hwang
Journal of Software , 2006, DOI: 10.4304/jsw.1.3.24-31
Abstract: A novel hybrid genetic algorithm (GA) for jointly optimizing source and channel codes is presented in this paper. The algorithm first uses GA for the coarse search of source and channel codes. An iterative search is then followed for the refinement of the coarse search. The hybrid GA enhances the robustness of the design of source and channel codes. The distributed GA scheme can also be used in conjunction with the proposed hybrid GA algorithm for further performance improvement.
Efficient Architecture for Spike Sorting in Reconfigurable Hardware
Wen-Jyi Hwang,Wei-Hao Lee,Shiow-Jyu Lin,Sheng-Ying Lai
Sensors , 2013, DOI: 10.3390/s131114860
Abstract: This paper presents a novel hardware architecture for fast spike sorting. The architecture is able to perform both the feature extraction and clustering in hardware. The generalized Hebbian algorithm (GHA) and fuzzy C-means (FCM) algorithm are used for feature extraction and clustering, respectively. The employment of GHA allows efficient computation of principal components for subsequent clustering operations. The FCM is able to achieve near optimal clustering for spike sorting. Its performance is insensitive to the selection of initial cluster centers. The hardware implementations of GHA and FCM feature low area costs and high throughput. In the GHA architecture, the computation of different weight vectors share the same circuit for lowering the area costs. Moreover, in the FCM hardware implementation, the usual iterative operations for updating the membership matrix and cluster centroid are merged into one single updating process to evade the large storage requirement. To show the effectiveness of the circuit, the proposed architecture is physically implemented by field programmable gate array (FPGA). It is embedded in a System-on-Chip (SOC) platform for performance measurement. Experimental results show that the proposed architecture is an efficient spike sorting design for attaining high classification correct rate and high speed computation.
Kinetics of T Helper Subsets and Associated Cytokines Correlate Well with the Clinical Activity of Graft-Versus-Host Disease
Su-Peng Yeh, Yu-Min Liao, Wen-Jyi Lo, Chiao-Lin Lin, Li-Yuan Bai, Chen-Yuan Lin, Ching-Yun Hsieh, Yu-Chien Chang, Yu-Ting Huang, Chang-Fang Chiu
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0044416
Abstract: Background CD4+interferon (IFN)-γ+ T cell (Th1) and CD4+interleukin (IL)-4+ T cell (Th2) polarizations are crucial in the pathogenesis of graft-versus-host disease (GVHD). However, this hypothesis is largely based on animal experiments of Parent-into-F1 GVHD model. The causal relationship between kinetics of Th1, Th2 and associated cytokines and the clinical activity of GVHD in a real world situation remains unknown. Methodology Peripheral blood was collected every week prospectively from Day 0 to Day 210 (patients without GVHD) or Day 300 (patients with chronic GVHD) after allogeneic peripheral blood stem cell transplantation in consecutive 27 patients. The frequencies of Th1 and Th2 within CD4+ T cells were determined by flow cytometry and pplasma IFN-γ, IL-12, IL-4, and IL-10 were determined by ELISA. Principal Findings Kinetics of Th1, Th2 frequency, and the plasma IL-10 and IFN-γ more commonly coincided with, rather than predicted, the activity of GVHD. These markers are significantly higher when acute or chronic GVHD developed. The kinetics of IL-10 is especially correlated well with the activity of GVHD during clinical course of immunosuppressive treatment. For patients with hepatic GVHD, there is a positive correlation between plasma IL-10 levels and the severity of hepatic injury. The frequency of Th2 is also significant higher in acute GVHD and tends to be higher in chronic GVHD. Interestingly, there is a very good positive correlation between the frequency of Th1 and Th2 (r = 0.951, p<0.001). The plasma level of IL-4 and IL-12 are not associated with the activity of GVHD. Conclusions The frequency of Th1, Th2 within CD4+ T cells and plasma IL-10 and IFN-γ are good biomarkers of GVHD. Plasma IL-10 can also be used to monitor the therapeutic responsiveness. Furthermore, both Th1 and Th2 likely contribute to the pathogenesis of GVHD.
Accelerating diffusions
Chii-Ruey Hwang,Shu-Yin Hwang-Ma,Shuenn-Jyi Sheu
Mathematics , 2005, DOI: 10.1214/105051605000000025
Abstract: Let U be a given function defined on R^d and \pi(x) be a density function proportional to \exp -U(x). The following diffusion X(t) is often used to sample from \pi(x), dX(t)=-\nabla U(X(t)) dt+\sqrt2 dW(t),\qquad X(0)=x_0. To accelerate the convergence, a family of diffusions with \pi(x) as their common equilibrium is considered, dX(t)=\bigl(-\nabla U(X(t))+C(X(t))\bigr) dt+\sqrt2 dW(t),\qquad X(0)=x_0. Let L_C be the corresponding infinitesimal generator. The spectral gap of L_C in L^2(\pi) (\lambda (C)), and the convergence exponent of X(t) to \pi in variational norm (\rho(C)), are used to describe the convergence rate, where \lambda(C)= Sup{real part of \mu\dvtx\mu is in the spectrum of L_C, \mu is not zero}, {-2.8cm}\rho(C) = Inf\biggl{\rho\dvtx\int | p(t,x,y) -\pi(y)| dy \le g(x) e^{\rho t}\biggr}.Roughly speaking, L_C is a perturbation of the self-adjoint L_0 by an antisymmetric operator C\cdot\nabla, where C is weighted divergence free. We prove that \lambda (C)\le \lambda (0) and equality holds only in some rare situations. Furthermore, \rho(C)\le \lambda (C) and equality holds for C=0. In other words, adding an extra drift, C(x), accelerates convergence. Related problems are also discussed.
Study of quark distribution amplitudes of 1S and 2S heavy quarkonium states
Hwang, Chien-Wen
High Energy Physics - Phenomenology , 2008, DOI: 10.1140/epjc/s10052-009-1046-7
Abstract: In this paper, the quark distribution amplitudes of 1S and 2S heavy quarkonium states are studied in terms of Gaussian-type wave functions. The transverse momenta $p_\perp$ integrals of the formulae for the decay constant are performed analytically. Then the quark distribution amplitudes are obtained. In addition, the $\xi$-moments are also calculated. After fixing the relevant parameters appearing in the quark distribution amplitude, the curves of the quark distribution amplitude for 1S and 2S heavy quarkonium states are plotted. Finally, the numerical results of this approach are compared with the other theoretical predictions.
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