Browsing by Author "CHANG, PR"

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A COLOR CONSTANCY MODEL FOR ADVANCED TELEVISION CAMERAS
(1992-06-01)
CHANG, PR
;
HSIEH, TH
;
YEH, BF
;
交大名義發表
;
電信工程研究所
In high-fidelity color sensing, one may attempt to use recorded video image data from an electronic camcorder to generate a rendering that appears the same as the original image did at the time of acquisition. To achieve the goal, a color constancy mechanism is proposed to perceive the image color from the strengths of three RGB responses of a color camera or the like, independent of the color of the light illuminating the object. Being able to extract the color descriptors that are independent of the illumination is desirable because of the varity of the situation in which color is important but illumination conditions cannot be controlled. The first step of our method is to use a finite dimensional linear model to estimate the color signals. We have shown that any color signal can be characterized as a linear combination of four principal component basis functions. Once the color signals have been estimated, the unknown illuminant can be determined by the Tominaga and Wandell's estimation method which is based on a dichromatic model. In color constancy, the knowledge of surface reflectance helps in determining the canonical color descriptors despite the variation in the spectral power distribution of the ambient light. It is shown that the estimate of surface reflectance is derived from both the estimated color signal and illuminant straightforwardly. The model of a combination of the estimations of the surface spectral reflectance and the color signals can be employed in designing a color constancy electronic video camera which indeed improves the shortcomings of the traditional video camcorders.
A HIGH-SPEED NEURAL ANALOG CIRCUIT FOR COMPUTING THE BIT-LEVEL TRANSFORM IMAGE-CODING
(1991-08-01)
CHANG, PR
;
HWANG, KS
;
GONG, HM
;
電信工程研究所
;
Institute of Communications Engineering
This paper presents a Hopfield-type neural network approach which leads to an analog circuit for implementing the bit-level transform image. Different from the conventional digital approach to image coding, the analog coding system would operate at a much higher speed and requires less hardware than digital system. In order to utilize the concept of neural net, the computation of a two-dimensional DCT-based transform coding should be reformulated as minimizing a quadratic nonlinear programming problem subject to the corresponding 2's complement binary variables of 2-D DCT coefficients. A novel Hopfield-type neural net with a number of graded-response neurons designed to perform the quadratic nonlinear programming would lead to such a solution in a time determined by RC time constants, not by algorithmic time complexity. A fourth order Runge-Kutta simulation is conducted to verify the performance of the proposed analog circuit. Experiments show that the circuit is quite robust and independent of parameter variations and the computation time of an 8 x 8 DCT is about 1ns for RC = 10(-8). In practice, programmable hybrid digital-analog MOS circuits are required to implement the neural-based DCT optimizer. The circuit techniques are based on extremely simple and programmable analog parameterized MOS modules with such attractive features as reconfigurability, input/output compatibility, and unrestricted fan-in/fan-out capability.1
A NEURAL NETWORK APPROACH TO MVDR BEAMFORMING PROBLEM
(1992-03-01)
CHANG, PR
;
YANG, WH
;
CHAN, KK
;
交大名義發表
;
電信工程研究所
A Hopfield-type neural network approach which leads to an analog circuit for implementing the real-time adaptive antenna array is presented. An optimal pattern of the array can be steered by updating the weights across the array in order to maximize the output signal-to-noise ratio (SNR). Furthermore, it is shown that the problem of adjusting the array weights can be characterized as a constrained quadratic nonlinear programming. Practically, the adjustment of settings is required to respond to a rapid time-varying environment. Many numerical algorithms have been developed for solving such problems using digital computers. The main disadvantage of these algorithms is that they generally converge slowly. To tackle this difficulty, a neural analog circuit solution is particularly attractive in real-time applications with minimization of a cost function subject to constraints. A novel Hopfield-type neural net with a number of graded-response neurons designed to perform the constrained quadratic nonlinear programming would lead to such a solution in a time determined by RC time constants, not by algorithmic time complexity. The constrained quadratic programming neural net has associated it with an energy function which the net always seeks to minimize. A fourth-order Runge-Kutta simulation is conducted to verify the performance of the proposed analog circuit. It shows that the circuit operates at a much higher speed than conventional techniques and the computation time of solving a linear array of 10 elements is about 0.1 ns for RC = 5 x 10(-9).
A NUMERICALLY STABLE PIPELINE NET VLSI ARCHITECTURE FOR THE ISOMORPHIC HOPFIELD MODEL
(1993-05-01)
CHANG, PR
;
YEH, BF
;
交大名義發表
;
電信工程研究所
;
National Chiao Tung University
This correspondence presents a reconfigurable pipeline net VLSI architecture for implementing Hopfield neural models. It is known that the Hopfield models involve computing the hyperbolic trigonometric functions which are hard to be realized by digital VLSI architectures. In order to tackle such difficulty, a useful isomorphic nonlinear mapping is introduced to convert those hyperbolic trigonometric nonlinear functions into the simple second-order polynomial functions. Moreover, the isomorphic formulation provides the higher ability to decompose the problem into several independent tasks which can be assigned to a number of processors. Handling the digital realizations on the Hopfield model, the previous attemps were to use the technique based on the first-order approximation called Euler's method which has poor numerical stability and large truncation error. To find a numerical solution with a prescribed accuracy, one of the promising approaches is a combination of both a single-step Runge-Kutta method and a multistep predictor-corrector method which has a larger stability interval and is particularly suitable for parallel computation. Since the mixed-type procedure requires data broadcasting, common VLSI architectures with fixed connections cannot offer such flexible connectivities. A pipeline net VLSI architecture which is a programmable two-level pipelined and dynamically reconfigurable systolic array would be adopted as the design platform. The pipelining period and block pipelining period of the proposed architecture have the computational orders of O(1) and O(n), respectively, where n is the number of neurons.
A TRIANGULAR CONNECTION HOPFIELD NEURAL-NETWORK APPROACH TO ANALOG-TO-DIGITAL CONVERSION
(1994-12-01)
CHANG, PR
;
WANG, BC
;
GONG, HM
;
電信工程研究所
;
Institute of Communications Engineering
A Hopfield-type neural network approach which leads to an analog circuit for implementing the A/D conversion is presented. The solution of the original symmetric connection Hopfield A/D converter sometimes may reach a ''spurious state'' that does not correspond to the correct digital representation of the input signal. An A/D converter based on the model of nonsymmetrical neural networks is proposed to obtain the stable and correct encoding. Due to the infeasible conventional RC-active implementation, a cost-effective switched-capacitor implementation by means of Schmitt triggers is adopted. It is capable of achieving high performance as well as a high convergence rate. Finally, a simulation using a tool called SWITCAP is conducted to verify the validity and performance of the proposed implementation.
ADAPTIVE DECISION-FEEDBACK EQUALIZATION FOR DIGITAL SATELLITE CHANNELS USING MULTILAYER NEURAL NETWORKS
(1995-02-01)
CHANG, PR
;
WANG, BC
;
交大名義發表
;
電信工程研究所
;
National Chiao Tung University
This paper introduces an adaptive decision feedback equalization using the multilayer perceptron structure of an Mary PSK signal through a TDMA satellite radio channel. The transmission is disturbed not only by intersymbol interference (ISI) and additive white Gaussian noise, but also by the nonlinearity of transmitter amplifiers. The conventional decision feedback equalizer (DFE) is not well-suited to detect the transmitted sequence, whereas the neural-based DFE is able to take into account the nonlinearities and therefore to detect the signal much better. Nevertheless, the applications of the traditional multilayer neural networks have been limited to real-valued signals. To overcome this difficulty, a neural-based DFE is proposed to deal with the complex PSK signal over the complex-valued nonlinear MPSK satellite channel without performing time consuming complex-valued back-propagation training algorithms, while maintaining almost the same computational complexity as the original real-valued training algorithm. Moreover, a modified backpropagation algorithm with better convergence properties is derived on the basis of delta-bar-delta rule. Simulation results for the equalization of QPSK satellite channels show that the neural-based DFE provides a superior bit error rate performance relative to the conventional mean square DFE, especially in poor signal-to-noise ratio conditions.
Adaptive fuzzy power control for mobile radio systems
(1995)
CHANG, PR
;
WANG, BC
;
電信工程研究所
;
Institute of Communications Engineering
ADAPTIVE PACKET EQUALIZATION FOR INDOOR RADIO CHANNEL USING MULTILAYER NEURAL NETWORKS
(1994-08-01)
CHANG, PR
;
YEH, BF
;
CHANG, CC
;
交大名義發表
;
電信工程研究所
This paper investigates the application of the multilayer perceptron structure to the packet-wise adaptive decision feedback equalization of a M-ary QAM signal through a TDMA indoor radio channel in the presence of intersymbol interference (ISI) and additive Gaussian noise. Since the multilayer neural networks are capable of producing complex decision regions with arbitrarily nonlinear boundaries, this would greatly improve the performance of conventional decision feedback equalizer (DFE) where the decision boundaries of conventional DFE are linear. However, the applications of the traditional multilayer neural networks have been limited to real-valued signals. To tackle this difficulty, a neural-based DFE is proposed to deal with the complex QAM signal over the complex-valued fading multipath radio channel without performing time-consuming complex-valued back-propagation training algorithms, while maintaining almost the same computational complexity as the original real-valued training algorithm. Moreover, this neural-based DFE trained by packet-wise backpropagation algorithm would approach an ideal equalizer after receiving a sufficient number of packets. In this paper, another fast packet-wise training algorithm with better convergence properties is derived on the basis of a recursive least-squares (RLS) routine. Results show that the neural-based DFE trained by both algorithms provides a superior bit-error-rate performance relative to the conventional least mean square (LMS) DFE, especially in poor signal to noise ratio conditions.
COLOR CORRECTION FOR SCANNER AND PRINTER USING B-SPLINE CMAC NEURAL NETWORKS
(1994)
CHANG, PR
;
CHANG, CC
;
電信工程研究所
;
Institute of Communications Engineering
COMMENTS ON A NEURAL-NETWORK APPROACH TO MVDR BEAMFORMING PROBLEM - REPLY
(1993-09-01)
CHANG, PR
;
YANG, WH
;
CHAN, KK
;
交大名義發表
;
電信工程研究所
COMPLEX-VALUED NEURAL-NETWORK FOR DIRECTION-OF-ARRIVAL ESTIMATION
(1994-03-31)
YANG, WH
;
CHAN, KK
;
CHANG, PR
;
交大名義發表
;
電信工程研究所
A complex-valued neural network for the implementation of a direction of arrival (DoA) estimator is presented. The network is based on the linear prediction method, with data processed fully in the complex domain. Simulation results are given to demonstrate the effectiveness of the network performances.
COMPUTER-AIDED-DESIGN TOOL FOR SIMPLIFICATION OF ROBOT DYNAMIC-MODEL BASED ON MANIPULATOR SYSTEM PERFORMANCE
(1991-05-01)
CHANG, PR
;
交大名義發表
;
電信工程研究所
;
National Chiao Tung University
;
Institute of Communications Engineering
The dynamic performance of a robot manipulator is directly dependent on the efficiency of the controller and the dynamic model of the robot. This paper addresses the fundamental issue of how much manipulator dynamics information should be included in the manipulator dynamic model for control such that the manipulator will achieve the desired system performance under a proportional-plus-derivative control scheme. An efficient minimax simplification scheme has been developed which automatically generates simplified closed-form manipulator motion equations in symbolic form while maintaining the desired manipulator system performance under a proportional-plus-derivative controller. The scheme involves the identification and selection of basis functions that represent the dynamic coefficients in the dynamic model. These basis functions consist of a linear combination of the product terms of sinusoidal and polynomial functions of the generalized coordinates and form a Chebyshev set on the workspace of the manipulator. A multi-layered decision scheme is developed for selecting significant basis terms in each layer for each dynamic coefficient. The linear combination of these significant basis terms is then utilized to construct each simplified dynamic coefficient based on the minimax approximation technique. A verification of the proposed scheme on a Stanford robot arm is included for discussion.
CONSTRAINED NONLINEAR OPTIMIZATION APPROACHES TO COLOR-SIGNAL SEPARATION
(1995-01-01)
CHANG, PR
;
HSIEH, TH
;
交大名義發表
;
電信工程研究所
;
National Chiao Tung University
The process of separating a color signal into illumination and surface reflectance components is a fundamental issue in color reproduction and constancy. This color-signal separation can be carried out by minimizing the error in the least squares fit of the product of the illumination and the surface spectral reflectance to the actual color signal. Moreover, when taking in account the physical realizability constraints on both the surface reflectance and illumination, the feasible solutions to the nonlinear least-squares problem should satisfy a number of linear inequalities. Four distinct novel optimization algorithms are presented to employ these constraints to minimize the nonlinear least squares fitting error. The first approach, which is based on Ritter's superlinear convergent method, provides a computationally superior algorithm to find the minimum solution to the nonlinear least-squares error problem subject to linear inequality constraints. Unfortunately, this gradient-like algorithm may sometimes be trapped at a local minimum or become unstable when the parameters involved in the algorithm are not tuned properly. The remaining three methods are based on the stable and promising global minimizer called simulated annealing. The annealing algorithm can always find the global minimum solution with probability one, but its convergence is extremely slow. To tackle this difficulty, a cost-effective variable-separable formulation based on the concept of Golub and Pereyra is adopted to reduce the nonlinear least-squares problem to be a small-scale nonlinear least-squares problem whose solution state space is less than that of the original space. It will be shown that the computational burden is reduced by an order of magnitude. The computational efficiency can be further improved when the original Boltzman generating distribution of the classical annealing is replaced by the Cauchy distribution. Finally, a number of test samples are conducted to verify the effectiveness of the proposed methods.
FORMULATION AND OPTIMIZATION OF CUBIC POLYNOMIAL JOINT TRAJECTORIES FOR INDUSTRIAL ROBOTS
(1983-01-01)
LIN, CS
;
CHANG, PR
;
LUH, JYS
;
電信工程研究所
;
Institute of Communications Engineering
FORMULATION AND OPTIMIZATION OF CUBIC POLYNOMIAL JOINT TRAJECTORIES FOR INDUSTRIAL ROBOTS
(1983-01-01)
LIN, CS
;
CHANG, PR
;
LUH, JYS
;
電信工程研究所
;
Institute of Communications Engineering
INVERSE FILTERING OF A LOUDSPEAKER AND ROOM ACOUSTICS USING TIME-DELAY NEURAL NETWORKS
(1994-06-01)
CHANG, PR
;
LIN, CG
;
YEH, BF
;
交大名義發表
;
電信工程研究所
This paper presents the design of a neural-based acoustic control used for the equalization of the response of a sound reproduction system. The system usually can be modeled as a composite system of a loudspeaker and an acoustic signal-transmission channel. Generally, an acoustic signal radiated inside a room is linearly distorted by wall reflections. However, in a loudspeaker, the nonlinearity in the suspension system produces a significant distortion at low frequencies and the inhomogeneity in the flux density causes a nonlinear distortion at large output signals. Both the linear and nonlinear distortions should be reduced so that high fidelity sound can be reproduced However, the traditional adaptive equalizer which is only capable of dealing with linear systems or specific nonlinear systems cannot compensate these nonlinear distortions. The time-delay feedforward neural network (TDNN) which has the capability to learn arbitrary nonlinearity and process the temporal audio patterns are particularly recognized as the best nonlinear inverse filter of the composite system. The performance of a TDNN-based acoustic controller is verified by some simulation results.
JOINT TRAJECTORIES OF MECHANICAL MANIPULATORS FOR CARTESIAN PATH APPROXIMATION
(1983-01-01)
LIN, CS
;
CHANG, PR
;
電信工程研究所
;
Institute of Communications Engineering
JOINT TRAJECTORIES OF MECHANICAL MANIPULATORS FOR CARTESIAN PATH APPROXIMATION
(1983-01-01)
LIN, CS
;
CHANG, PR
;
電信工程研究所
;
Institute of Communications Engineering
MODEL-REFERENCE COLOR REPRODUCTION FOR VIDEO SYSTEM BASED ON BACKPROPAGATION NETWORKS
(1994)
CHANG, PR
;
TAI, CC
;
YEH, BF
;
電信工程研究所
;
Institute of Communications Engineering
NEURAL-BASED PACKET EQUALIZATION FOR INDOOR RADIO CHANNEL BY FAST BACK-PROPAGATION ALGORITHM
(1994)
CHANG, PR
;
YEH, BF
;
TSENG, CM
;
電信工程研究所
;
Institute of Communications Engineering