In this paper, the stress wave propagation in a coupled pendulum system with friction force is discussed experimentally and numerically. The coupled system is analogous to the one dimensional fault dynamics model in seismicity. However, we will not intend to discuss about the geophysical feature of the system. The system has rich characteristics of the spatio-temporal stress wave propagation effected by nonlinear friction force. The relation between the wave propagation and the vibration of the pendulum is mainly discussed on the standpoint of nonlinear coupled system.

This paper presents a low-power video A/D conversion technique using features of moving pictures. Neighboring frames in typical video sequences and neighboring pixels in each video frame are highly correlated. This property is effectively used for the video A/D conversion to reduce the number of comparators and the resulting power consumption. A set of reference voltages is given to a comparator array so that the iterative A/D conversion converges in the logarithmic order of the prediction error. Simulation results using standard moving pictures showed that the average number of iterations for the A/D conversion is less than 3 for all the moving pictures tested. In the proposed 12 b A/D converter, the number of comparators can be reduced to about 1/5 compared with that of the two-step flash A/D converters, which are commonly used for video applications. The A/D converter is particularly useful for the integration to CMOS image sensors.

A nonlinear time series predictor was proposed, in which a nonlinear sub-predictor (NSP) and a linear sub-predictor (LSP) are combined in a cascade form. This model is called "hybrid predictor" here. The nonlinearity analysis method of the input time series was also proposed to estimate the network size. We have considered the nonlinear prediction problem as a pattern mapping one. A multi-layer neural network, which consists of sigmoidal hidden neurons and a single linear output neuron, has been employed as a nonlinear sub-predictor. Since the NSP includes nonlinear functions, it can predict the nonlinearity of the input time series. However, the prediction is not complete in some cases. Therefore, the NSP prediction error is further compensated for by employing a linear sub-predictor after the NSP. In this paper, the prediction mechanism and a role of the NSP and the LSP are theoretically and experimentally analyzed. The role of the NSP is to predict the nonlinear and some part of the linear property of the time series. The LSP works to predict the NSP prediction error. Furthermore, predictability of the hybrid predictor for noisy time series is investigated. The sigmoidal functions used in the NSP can suppress the noise effects by using their saturation regions. Computer simulations, using several kinds of nonlinear time series and other conventional predictor models, are demonstrated. The theoretical analysis of the predictor mechanism is confirmed through these simulations. Furthermore, predictability is improved by slightly expanding or shifting the input potential of the hidden neurons toward the saturation regions in the learning process.

Modern micro-architectures employ superscalar techniques to enhance system performance. Since the superscalar microprocessors must fetch at least one instruction cache line at a time to support high issue rate and large amount speculative executions. There are cases that multiple branches are often encountered in one cycle. And in practical implementation this would cause serious problem while there are variable number of instruction addresses that look up the Branch Target Buffer simultaneously. In this paper, we propose a Range Associative Branch Target Buffer (RABTB) that can recognize and predict multiple branches in the same instruction cache line for a wide-issue micro-architecture. Several configurations of the RABTB are simulated and compared using the SPECint95 benchmarks. We show that with a reasonable size of prediction scope, branch prediction can be improved by supporting multiple / up to 8 branch predictions in one cache line in one cycle. Our simulation results show that the optimal RABTB should be 2048 entry, 8-column range-associate and 8-entry modified ring buffer architecture using PAs prediction algorithm. It has an average 5.2 IPC_f and branch penalty per branch of 0.54 cycles. This is almost two times better than a mechanism that makes prediction only on the first encountered branch.

In this paper, we propose an adaptive encoding method of fixed codebook in CELP coders and implement an adaptive fixed code-excited linear prediction (AF-CELP) speech coder as a low-bit-rate extension to the 8 kbit/s CS-ACELP. The AF-CELP can be implemented at low bit rates as well as low complexity by exploiting the fact that the fixed codebook contribution to the speech signal is periodic, as is the adaptive codebook (or pitch filter) contribution. Listening tests show that the 6.4 kbit/s AF-CELP has a comparable quality to the 8 kbit/s CS-ACELP under real environmental test conditions.

TCAD (Technology Computer Aided Design) is the simulation of semiconductor processes and devices. Despite twenty years of development, there are still many TCAD skeptics. This paper will discuss some of the problems and limitations of TCAD, present some successful examples of its use, and discuss future simulation needs from a user's perspective. A key point is that the time pressures in modern semiconductor technology development often dictate the use of simple models for approximate results.

In this paper, we propose an adaptive predictive coding method based on image segmentation for lossless compression. MAR (Multiplicative Autoregressive) predictive coding is an efficient lossless compression scheme. Predictors of the MAR model can be adapted to changes in the local image statistics due to its local image processing. However, the performance of the MAR method is reduced when applied to images whose local statistics change within the block-by-block subdivided image. Furthermore, side-information such as prediction coefficients must be transmitted to the decoder with each block. In order to enhance the compression performance, we improve the MAR coding method by using image segmentation. The proposed MAR predictor can be adapted to the local statistics of the image efficiently at each pixel. Furthermore, less side-information need be transmitted compared with the conventional MAR method.

Under a contamination of background sound noises, it seems difficult especially in a real working situation to evaluate various type statistics of only an objective sound signal fluctuation. In many cases of the noise evaluation, some signal processing method have been employed to eliminate the effect of background sound noises by first measuring emitted sound levels. In this study, a new evaluation method of sound level fluctuation is proposed in principle on the basis of the measurement of heterogeneous physical quantity other than sound pressures or sound levels to eliminate the effect of background sound noises. Though the theoretical analysis on acoustical emission caused by a mechanical vibration seems very difficult in a working situation, the sound noise fluctuation emitted only from an objective sound source can be effectively evaluated through its related vibration measurement by employing a fairly unified stochastic method proposed on the basis of a generalized regression analysis between sound and vibration. Here, the regression coefficients are determined by employing the least squares error method to minimize the mean square of estimation error to illustrate well the sound data by means of vibration data. Finally, the effectiveness of proposed method has been experimentally applied to the sound noise evaluation of a jigsaw.

This paper describes a 10 bit CMOS current-mode A/D converter with a current predictor and a modular current reference circuit. A current predictor and a modular current reference circuit are employed to reduce the number of comparator and reference current mirrors and consequently to decrease a power dissipation. The 10 bit current-mode A/D converter is fabricated by the 0.6 µm n-well double poly/triple metal CMOS technology. The measurement results show the input current range of 16 µA to 528 µA, DNL and INL of 0.5 LSB and 1.0 LSB, conversion rate of 10 Msamples, and power dissipation of 94.4 mW with a power supply of 5 V. The effective chip area excluding the pads is 1.8 mm 2.4 mm.

Noise greatly degrades the image quality and performance of image compression algorithms. This paper presents an approach for the representation and compression of noisy synthetic images. A new concept region-based prediction (RBP) model is first introduced, and then the RBP model is utilized on noisy images. In the conventional predictive coding techniques, the context for prediction is always composed of individual pixels surrounding the pixel to be processed. The RBP model uses regions instead of individual pixels as the context for prediction. An algorithm for the implementation of RBP is proposed and applied to noisy synthetic images in our experiments. Using RBP to find the residual data and encoding them, we achieve a bit rate of 1.10 bits/pixel for the noisy synthetic image. The decompressed image achieves a peak SNR of 42.59 dB. Compared with a peak SNR of 41.01 dB for the noisy synthetic image, the quality of the decompressed synthetic image is improved by 1.58 dB in the MSE sense. In contrast to our proposed compression algorithm with its improvement in image quality, conventional coding methods can compress image data only at the expense of lower image quality. At the same bit rate, the image compression standard JPEG provides a peak SNR of 33.17 dB for the noisy synthetic image, and the conventional median filter with a 33 window provides a peak SNR of 25.89 dB.

State of the arts on guided-wave optical switch arrays are reviewed. In this paper, electro-optic Ti:LiNbO3 devices are mainly described in comparison with crosspoint switch element structures and switch array architectures. Packaging technologies and stability problems are discussed for practical system applications. Recent development on other materials such as semiconductor waveguides, thermo-optic glass/polymer waveguides are also reviewed briefly.

One of the most important problems in machine learning is to predict a binary value by observing a sequence of outcomes, up to the present time step, generated from some unknown source. Vovk and Cesa-Bianchi et al. independently proposed an on-line prediction model where prediction algorithms are assumed to be given a collection of prediction strategies called experts and hence be allowed to use the predictions they make. In this model, no assumption is made about the way the sequence of bits to be predicted is generated, and the performance of the algorithm is measured by the difference between the number of mistakes it makes on the bit sequence and the number of mistakes made by the best expert on the same sequence. In this paper we extend the model by introducing a notion of investment. That is, both the prediction algorithm and the experts are required to make bets on their predictions at each time step, and the performance of the algorithm is now measured with respect to the total money lost, rather than the number of mistakes. We analyze our algorithms in the particular situation where all the experts share the same amount of bets at each time step. In this shared bet model, we give a prediction algorithm that is in some sense optimal but impractical, and we also give an efficient prediction algorithm that turns out to be nearly optimal.

State of the arts on guided-wave optical switch arrays are reviewed. In this paper, electro-optic Ti:LiNbO3 devices are mainly described in comparison with crosspoint switch element structures and switch array architectures. Packaging technologies and stability problems are discussed for practical system applications. Recent development on other materials such as semiconductor waveguides, thermo-optic glass/polymer waveguides are also reviewed briefly.

Connector contact resistance may become unstable if fretting occurs. Such motions result in the formation of insulating oxides on the surface of base metal contacts or organic polymers on contacts made of platinum group metals. These degradations are termed fretting corrosion and frictional polymerization, respectively. Motion may be caused by external vibration or fluctuating temperature. The lower the frequency of movement, the fewer the number of cycles to contact failure. Increasing the contact normal load or reducing the amplitude of movement may stabilize the connection. Tin and palladium and many of their alloys are especially prone to fretting failure. Tin mated to gold is worse than all-tin contacts. Gold and high gold-silver alloys that are softer when mated to palladium stabilize contact resistance since these metals transfer to the palladium during fretting; but flash gold coatings on palladium and palladium nickel offer marginal improvement for the gold often quickly wears out. Dissimilar metal contact pairs show behaviors like that of the metal which predominates on the surface by transfer. Contact lubricants can often prevent fretting failures and may even restore unlubricated failed contacts to satisfactory service.

The applicability of composite materials containing laminar solid lubricants to sliding contacts was studied. Performances of several composite materials prepared by incorporating solid lubricants with the basic alloys of the Cu-Nb system and Cu-Sn system were investigated to test the suitability of the composite materials as sliding contacts. As a result, it was clarified that the composite materials based on Cu-Sn alloy were superior to those based on Cu-Nb alloy and those containing only WS2 and not MoS2 were more effective in reducing both the contact resistance and the coefficient of friction. Based on the relationship between the contact resistance and the coefficient of friction obtained in this experimental study, the author proposed a new model for electric contact of composite materials.

Simple expressions for constriction resistance of multitude conducting spots were analytically formulated by Greenwood. These expressions, however, include some approximations. Nakamura presented that the constriction resistance of one circular spot computed using the BEM is closed to Maxwell's exact value. This relative error is only e=0. 00162 [%]. In this study, the constriction resistances of two, five and ten conducting spots are computed using the boundary element method (BEM), and compared with those obtained using Greenwood's expressions. As the conducting spots move close to each other, the numerical deviations between constriction resistances computed using Greenwood's expressions and the BEM increase. As a result, mutual resistance computed by the BEM is larger than that obtained from Greenwood's expressions. The numerical deviations between the total resistances computed by Greenwood's expressions and that by the BEM are small. Hence, Greenwood's expressions are valid for the total constriction resistance calculation and can be applied to problems where only the total resistance of two contact surfaces, such as a relay and a switch, is required. However, the numerical deviations between the partial resistances computed by Greenwood's expression and that by the BEM are very large. The partial resistance calculations of multitude conducting spots are beyond the applicable range of Greenwood's expression, since Greenwood's expression for constriction resistance of two conducting spots is obtained by assuming that the conducting spots are equal size. In particular, the deviation between resistances of conducting spots, which are close to each other, is very large. In the case of partial resistances which are significant in semiconductor devices, Greenwood's expressions cannot be used with high precision.

A generalized radial basis function network consisting of (1 + cosh x)-1 as the basis function of the same class as Gaussian functions is investigated in terms of the feasibility of analog-hardware implementation. A simple way of hardware-implementing (1 + cosh x)-1 is proposed to generate the exact input-output response curve on an analog circuit constructed with bipolar transistors. To demonstrate that networks consisting of the basis function proposed actually work, the networks are applied to numerical experiments of forecasting chaotic time series contaminated with observational random noise. Stochastic gradient descent is used as learning rule. The networks are capable of learning and making short-term forecasts about the dynamic behavior of the time series with comparable performance to Gaussian radial basis function networks.

The performance of AC plasma displays has been improved in the area of brightness and contrast, while significant advances in image quality are still required for the HDTV quality. In particular, in full color motion video, motion artifacts and lack of color depth are still visible in some situations. These motional artifacts are mitigated as the number of the subfields increases, usually at the cost of losing brightness or increasing driving circuitry. Therefore, it is still one of our great concerns to find out the optimized subfield configuration through weighting and order of each subfield, and their coding of combination. For evaluation and improvement of motion picture disturbance, we have established a procedure that fully simulates the image quality of displays which utilize the subfield driving scheme. The simulation features virtually located sensor pixels on human retina, eye-tracking sensor windows, and a built-in spatial low pass filter. The model pixelizes the observers retina like a sensor chip in a CCD camera. An eye-tracking sensor window is assigned to every light emission from the display, to calculate the emissions from one to four adjoining pixels along the trajectory of motion. Through this model, a scene from original motion picture without disturbance is transformed into the still image with simulated disturbance. The integration of the light emission from adjoining pixels through the window, also functions as a built-in spatial low pass filter to secure the robust output, considering the MTF of the human eye. Both simulation and actual 42-in-diagonal PDPs showed close results under various conditions, showing that the model is simple, but reasonable. Through the simulation, general properties of the subfield driving scheme for gray scale have been elucidated. For example, a PWM-like coding offers a better performance than an MSB-split coding in many cases. The simulation also exemplifies the motion picture disturbance as a non-linear filter process caused by the dislocation of bit weightings, suggesting that tradeoffs between disturbance and resolution in motion area are mandatory.

The new technique for reducing the load latency is presented. This technique, named tunneling-load, utilizes the register specifier buffer in order to reduce the load latency without fetching the data cache speculatively, and thus eliminates the drawback of any load address prediction techniques. As a consequence of the trend toward increasing clock frequency, the internal cache is no longer able to fill the speed gap between the processor and the external memory, and the data cache latency degrades the processor performance. In order to hide this latency, several techniques predicting the load address have been proposed. These techniques carry out the speculative data cache fetching, which causes the explosion of the memory traffic and the pollution of the data cache. The tunneling-load solves these problems. We have evaluated the effects of the tunneling-load, and found that in an in-order-issue superscalar platform the instruction level parallelism is increased by approximately 10%.

To improve speech coding quality, in particular, the long-term dependency prediction characteristics, we propose a new nonlinear predictor, i. e. , a fully connected recurrent neural network (FCRNN) where the hidden units have feedbacks not only from themselves but also from the output unit. The comparison of the capabilities of the FCRNN with conventional predictors shows that the former has less prediction error than the latter. We apply this FCRNN instead of the previously proposed recurrent neural networks in the code-excited predictive speech coding system (i. e. , CELP) and shows that our system (FCRNN) requires less bit rate/frame and improves the performance for speech coding.