A BiCMOS circuit consists of the CMOS part which performs the logic function, and the bipolar part which drives output load. In BiCMOS circuits, transistor stuck-open faults exhibit delay faults in addition to sequential beavior. Also, stuck-on faults enhanced IDDQ (quiscent power supply current) at steady state. In this paper, a method is proposed which efficiently generates test patterns to detect stuck-open faults and stuck-on faults in BiCMOS circuits. The proposed method divides the BiCMOS circuit into pull-up part and pull-down part, and generates test patterns detect faults occured in each part by structural property of the BiCMOS circuit.

In this paper, we analyze the thermal deformation of mask frame assembly using finite element method(FEM) and predict the beam landing shift during tube operation. For realistic analysis, the apparent thermal conductivity and the apparent elastic modulus are calculated and the shadow mask is modeled as shell without aperatures. Also, all parts inside the tube are modeled and the each radiative effect is considered. Then the finite element analysis is performed for transient thermo-elastic deformation of the mask frame assembly and the beam landing shift is calculated. Experiments are eprformed for 17" cathode ray tube (CRT) to validate the FEM analysis. The temperatures of all parts inside the tube and beam landing shift on the panel are measured and the results are discussed in comparison with the results of the FEM analysis.ysis.

We have proposed a new structure of the multibit ovesampling sigma-delta modulator. To solve the problkem of requring the accurate precision of the analog components, the novel digital correction scheme with a ROM-table has been employed to enhance the SNR for the proposed system. This architecture has good features compared with the 1-bit approach, including significantly lower quantisation noise for a given oversampling ratio, as well as improve dstability characteristics. Then we hve shown the validity of the proposed system by use of the software developed for the performance evaluation and by realizing the system with SCFs(switched capacitor filters).

This paper describes a parallel implementation of a neurla network ensemble developed for object recognition on the connection machine CM-2. The implementation ensures that multiple networks are implemented simultaneously starting from different initial weights and all training samples are applied to each network by one sample per a copy of each network. When compared with a sequential implementation, this accelerates the computation speed by O(N.m.n) where N, m, and n are the network, respectively. The speedup in the computation time and the convergence characteristics of sthe modified backpropagation learning precedure were evaluated by two-dimensional object recognition problem.

Vector quantization has shown to be useful for compressing data related with a wide rnage of applications such as image processing, speech processing, and weather satellite. Neural networks of images this paper propses a efficient neural network learning algorithm, called learning count control algorithm based on the frquency sensitive learning algorithm. This algorithm can train a results more codewords can be assigned to the sensitive region of the human visual system and the quality of the reconstructed imate can be improved. We use a human visual systrem model that is a cascade of a nonlinear intensity mapping function and a modulation transfer function with a bandpass characteristic.

In passive sonar, the frequency spectrum of a sound radiated by underwater moving targets is composed of a broadband nonuniform background noise and narrowband discrete tonals. To detect the tonals, the background noise is estimated and removed. Using the existing algorithms that estimate the background noise, a week tonals are not detected. Because a freuqency line that is formed by tonals which are being extracted continuously is a feture of the target, we are nessesory to efficiently detect the tonals that compose the frequncy line. In this paper, we propose an efficient neural network that can remove automatically the background and detect the even errl tonals, and we extract the frequency line feature on the spectrogram by the proposed algorithm. The experimental results for a ship's radiated sound show a better performance in comparison with the existing TPM algorithm.

By using a selective learnable self-organizing feature map(SOFM) a more practical and generalized mehtod is proposed in which the effective nonlinear shape restoration is possible regardless of the existence of the distortion modelss. Nonlinear mapping relation is extracted from the distorted imate by using the proposed selective learning SOFGM which has the special property of effectively creating spatially organized internal representations and nonlinear relations of various input signals. For the exact extraction of the mapping relations between the distorted image and the original one, we define a disparity index as a proximal nmeasure of the present state to the final idealy trained state of the SOFM, and we used this index to adjust the training of the mapping relations form the weights of the SOFM. Simulations are conducted on various kinds of distorted images with or without distortion models, and the results show that the proposed method is very efficeint very efficient and practical in nonlinear shape restorations.