This paper presents an adaptive MLSE (Maximum Likelihood Sequence Estimator) suitable for TDMA cellular systems. The proposed MLSE has two special features such as handling wide dynamic range signals without analogue gain controls and fast channel tracking capability. In order to handle wide dynamic range signals without conventional AGCs (Automatic Gain Controller), the proposed MLSE uses envelope components of received signals obtained from a non-linear log-amplifier module which has wide log-linear gain characteristics. By using digital signal processing technique, the log-converted envelope components are normalized and converted to linear values which conventional adaptive MLSEs can handle. As a channel tracking algorithm of the channel estimator, the proposed MLSE adopts a QT-LMS (Quick-Tracking Least Mean Square) algorithm, which is obtained by modifying LMS algorithm to enable a faster tracking capability. The algorithm has a fast tracking capability with low complexity and is suitable for implementation in a fixed-point digital signal processor. The performances of the MLSE have been evaluated through experiments in TDMA cellular environments with π/4-shifted QPSK, 24.3k symbol/sec. It is shown that, under conditions of 65dB amplitude variations and 80Hz Doppler frequency, the MLSE successfully achieves less than 3% B.E.R., which is required for digital cellular systems.

When measuring the ejection fraction for the evaluation of the ventricular pumping function by means of the thermodilution technique, the slow response a conventional thermistor has caused it to be considered unsuitable, and fast thermistors have been proposed as an alternative. However, in this paper we propose improving the time-domain response of a conventional thermistor using a signal processing technique composed of a series of first-order high-pass filters which is known as the natural observation system. We considered the rise time of the thermistor in response to a step temperature change to effect correction for the measurement of the ejection fraction. The coefficients of the natural observation system were calculated by minimizing the square error between the step-response signal of the thermistor and the band-limited reference signal. In an experiment using a model ventricle, the thermodilution curve obtained from a conventional thermistor was improved using the proposed technique, thus enabling successful measurement of the ejection fraction of the ventricles.

This paper discusses the asynchronous and synchronous parallel derivation of languages based on standard formal grammars. Some of the synchronous languages defined in this paper are essentially equivalent to the languages of E0L and EIL systems. Languages with restrictions on the number of parallel derivation steps are difined so that a t-time language is the set of strings w derived in t(w) or less parallel derivatio steps, where t(n) is an integer function. the properties of asynchronous derivation are generally discussed to clarify their conditions so that the derivation results are independent of the order in which productions are applied. It is shown that: (1) Any context sensitive grammar (CSG) G can be transformed into a CSG G such that the language generated by synchronous derivation in G is equal to that generated by asynchronous derivation in G , and vice versa; (2) Any regular language is a log-time context free language (CFL); (3) The class of CFLs is incomparable with that of log-time CSLs; and (4) If there is a bounded cellular automaton recognizing any language L in time T(n), then L is an O(T(n))-time CSL.

The optical waveguides containing phthalocyanine as an optically active material were fabricated and transmission properties were investigated experimentally and numerically. The positive refractive index change was observed in the glass waveguide with a vanadyl phthalocyanine thin film as a top layer. The thermal influence on refractive index change was estimated by surface plasmon measurements.

In many applications involving the processing of noisy signals, it is desired to know the noise variance. This paper proposes a new method for estimating the noise variance from the signals of autoregressive (AR) and autoregressive moving-average (ARMA) systems corrupted by additive white noise. The method proposed here uses the low-order Yule-Walker (LOYW) equations and the lattice filter (LF) algorithm for the estimation of noise variance from the noisy output measurements of AR and ARMA systems, respectively. Two techniques are proposed here: iterative technique and recursive one. The accuracy of the methods depends on SNR levels, more specifically on the inherent accuracy of the Yule-Walker and lattice filter methods for signal plus noise system. The estimated noise variance is used for the blind indentification of AR and ARMA systems. Finally, to demonstrate the effectiveness of the method proposed here many numerical results are presented.

This paper introduces some modelling methods of time-varying stochastic process and its linear/nonlinear adaptive identification. Time-varying models are often identified by using a least square criterion. However the criterion should assume a time invariant stochastic model and infinite observed data. In order to adjust these serious different assumptions, some windowing techniques are introduced. Although the windows are usually applied to a batch processing of parameter estimates, all adaptive methods should also consider them at difference point of view. In this paper, two typical windowing techniques are explained into adaptive processing. In addition to the use of windows, time-varying stochastic ARMA models are built with these criterions and windows. By using these criterions and models, this paper explains nonlinear parameter estimation and the property of estimation convergence. On these discussions, some approaches are introduced, i.e., sophisticated stochastic modelling and multi-rate processing.

Truncated sum (TSUM for short) is useful for MV-PLA's realization. This paper introduces a new class of multiple-valued logic functions that are expressed by truncated sum, differential product (DPRODUCT for short), NOT and variables, where TSUM (x, y)min (xy, p1) and DPRODUCT (x, y)max (xy(p1), 0) is newly defined as the product that is derived by applying De Morgan's laws to TSUM. We call the functions T-functios. First, this paper clarifies that a set of T-functions is not a lattice. It clarifies that Lukasiewicz implication can be expressed by TSUM and NOT. It guarantees that a set of p-valued T-functios is not complete but complete with constants. Next, the speculations of the number of T-functions for less than ten radixes are derived. For eleven or more radix p, a speculation of the number of p-valued T-functions is shown. Moreover, it compares the T-functions with B-functions. The B-functions have been defined as the functions expressed by MAX, MIN, NOT and variables. As a result, it shows that a set of T-functions includes a set of B-functions. Finally, an inclusion relation among these functional sets and normality condition is shown.

Wave digital filters are a class of digital filters. They are equivalent to commensurate transmission line circuits synthesized with uniform, lossless, and commensurated transmission lines. In order to extend their applications to physical wave phenomena including quantum electronics, it is necessary to consider a generalized distributed line whose velocity of energy flow has frequency characteristics. This paper discusses a generalized distributed circuit, and we obtain two types of lines, lossless and cut-off. In order to analyze these lines, we discuss signal flow graphs of steady state voltage and current. The reflection factors we obtain here are the same as that for an active power or a diagonal element of a scattering matrix, which is zero in conjugate matching. By using this reflection factor, we obtain band-pass filters synthesized with the cut-off lines. We also describe an analysis method for nonuniform line related to Riccati differential equation.

New detection method of passivation defect was studied. The method was the Cu decoration method without bias (bias-free Cu decoration). As the result of comparison with conventional method, it was found that a bias-free Cu decoration method was effective, sensitive and simple. In this method, the difference of humidity resistance induced by poor passivation coverage could be evaluated.

Precise matching of the SEM (secondary electron microscope) image of the DUT (device under test) interconnection pattern with the CAD layout is required in the CAD-linked electron beam test system. We propose the point pattern matching method that utilizes a corner pattern in the CAD layout. In the method, a corner pattern which consists of a small number of pixels is derived by taking into account the design rules of VLSIs. By using the corner pattern as a template, the matching points of the template are sought in both the SEM image and CAD layout. Then, the point image obtained from the SEM image of DUT is matched with that from the CAD layout. Even if the number of points obtained in the DUT pattern is different from that in the CAD layout due to the influence of noise present in the SEM image of the DUT pattern, the point matching method would be successful. The method is applied to nonpassivated and passivated LSIs. Even for the passivated LSI where the contrast in the SEM image is mainly determined by voltage contrast, matching is successful. The computing time of the proposed method is found to be shortened by a factor of 4 to 10 compared with that in a conventional correlation coefficient method.

This letter extends the Yule-Walker method to the estimation of ARMA parameters from output measurements corrupted by noise. In the proposed method it is assumed that the noise variance and the input are unknown. An algorithm for the estimation of noise variance is, therefore, given. The use of the variance estimation method proposed here together with the Yule-Walker equations allow the estimation of the parameters of a minimum phase ARMA model based only on noisy measurements of its output. Moreover, using this method it is not necessary to slove a set of nonlinear equations for MA parameter estimation as required in the conventional correlation based methods.

This paper uses both network analysis and experiments to confirm that the neural network learning algorithm that minimizes output variation (BPV) provides much more robustness than back-propagation (BP) or BP with noise-modified training samples (BPN). Network analysis clarifies the relationship between sample displacement and what and how the network learns. Sample displacement generates variation in the output of the output units in the output layer. The output variation model introduces two types of deformation error, both of which modify the mean square error. We propose a new error which combines the two types of deformation error. The network analysis using this new error considers that BPV learns two types of training samples where the modification is either towards or away from the category mean, which is defined as the center of sample distribution. The magnitude of modification depends on the position of the training sample in the sample distribution and the degree of leaning completion. The conclusions is that BPV learns samples modified towards to the category mean more stronger than those modified away from the category mean, namely it achieves nonuniform learning. Another conclusion is that BPN learns from uniformly modified samples. The conjecture that BPV is much more robust than the other two algorithms is made. Experiments that evaluate robustness are performed from two kinds of viewpoints: overall robustness and specific robustness. Benchmark studies using distorted handprinted Kanji character patterns examine overall robustness and two specifically modified samples (noise-modified samples and directionally-modified samples) examine specific robustness. Both sets of studies confirm the superiority of BPV and the accuracy of the conjecture.

In this letter, we present a distinct alternative of cross talk formulation of associative memory based on the outer product algorithm extended to the higher order and a performance evaluation in terms of the probability of exact data recall by using this formulation. The significant feature of these formulations is that both cross talk and the probability formulated are explicitly represented as the functional forms of Hamming distance between the memorized keys and the applied input key, and the degree of higher order correlation. Simulation results show that exact data retrieval ability of the associative memory using randomly generated data and keys is in well agreement with our theoretical estimation.

In this paper, it is proven that the following three decision problems on validation of protocols with bounded capacity channels are NP-complete. (1) Given a protocol with the channel capacity being 1, determine whether or not there exist deadlocks in the protocol. (2) Given a protocol with the channel capacity being 1, determine whether or not there exist unspecified receptions in the protocol. (3) Given a protocol with the channel capacity being 2, determine whether or not there exist overflows such that the channel capacity is not bounded by 1 in the protocol. These results suggest that, even when all channeles in a protocol are bounded by 1 or 2, protocol validation should be in general interactable. It also clarifies the boundary of computational complexity of protocol validation problems because the channel capacity 0 does not allow protocols to transmit and recieve messages.

This paper proposes an approach for approximately realizing nonlinear mappings of interval vectors by interval neural networks. Interval neural networks in this paper are characterized by interval weights and interval biases. This means that the weights and biases are given by intervals instead of real numbers. First, an architecture of interval neural networks is proposed for dealing with interval input vectors. Interval neural networks with the proposed architecture map interval input vectors to interval output vectors by interval arithmetic. Some characteristic features of the nonlinear mappings realized by the interval neural networks are described. Next, a learning algorithm is derived. In the derived learning algorithm, training data are the pairs of interval input vectors and interval target vectors. Last, using a numerical example, the proposed approach is illustrated and compared with other approaches based on the standard back-propagation neural networks with real number weights.

In this paper, we develop a unified synthesizing approach for the cloning templates of Cellular Neural Networks (CNNs). In particular, we shall consider the case when the signal processing problem is complex, and a multilayered CNN with time-variant templates is necessary. The method originates from the existence of correspondence between the cloning templates of Cellular Neural Network and its discrete counterpart, Discrete-Time Cellular Neural Network (DTCNN), in solving a prescribed image processing problem when time-variant templates are involved. Thus, one can start with calculating the cloning templates from DTCNN, and then translating the cloning templates to those for CNN operations. As a result, the mathematical tools being used in the synthesis of Discrete-time Cellular Neural Network can also be applied to the analog type Cellular Neural Network. This inevitably helps to simplify the design problem of CNN for signal processing. Examples akin to contour drawing and parallel thinning are shown to illustrate the merits of our proposed method.

A method to improve the efficiency of dynamic fault imaging (DFI) by fully utilizing the CAD data in the CAD-linked electron beam test system is proposed. In the method, in order to shorten the long acquisition time of the stroboscopic voltage contrast images over the whole area of the chip during the entire test cycle, only the area and phase (time) required for fault tracing are selected by utilizing the CAD data. Furthermore, image processing techniques are combined with the method to improve the efficiency of the DFI. In particular, the signal averaging technique is used in order to improve the signal-to-noise ratio in the stroboscopic images where all voltage information data on the equipotential electrode recognized by the CAD layout data are averaged. This enables us to reduce the acquisition time of images. Moreover, the experimental system is set up so that the image processing can be performed in parallel with the acquisition of the stroboscopic images. The proposed method is applied to part of a 2k-transistor block of a nonpassivated CMOS LSI where a marginal fault is detected. The result shows that the method is an efficient approach to the fully automatic fault diagnosis in the CAD-linked electron beam test system. The proposed method could improve the efficiency of the conventional DFI by a factor of more than 1000.

A new multiple-valued mask-ROM cell and a technique suitable for data detection are proposed. The information is programmed in each of the memory cells as both the threshold voltage and the channel length of the memory cell transistor, and the stored data are detected by selecting the bias condition of both the word-line and the data-line. The datum stored in the channel length is read-out using punch-through effect at the high drain voltage. The feasibility of this mask-ROM's is studied with device simulation and circuit simulation. With this design, it would be possible to get the high-density mask-ROM's, which might be faster in access speed and easier in fabrication process than the conventional ones. Therefore, this design is expected to be one of the most practical multiple-valued mask-ROM's.

The origin of and a method of enhancing the Optical Beam Induced Resistance Change (OBIRCH) signal for defect observation in VLSI metal interconnections is discussed based on a numerical analysis of three-dimensional thermal conduction and experimental results. The numerical analysis shows that the OBIRCH signal originates from a slight increase in the resistance of the metal line caused by laser beam heating and that its effect is influenced by the temperature of the metal layer. Both simulations and experimental results suggest that cooling the sample is preferable to detect the OBIRCH signal. The decrease in the total resistance of the metal line without any change in the amount of the resistance increase under laser illumination is found to be the main cause of the OBIRCH signal enhancement under low temperature measurement.

Following a discussion of various testing methods used in the electron beam (EB) test system, new waveform-based and image-based approaches in the CAD-linked electron beam (EB) test system are proposed. A waveform-based automatic tracing algorithm of the transistor-level performance faults is first discussed. Then, the method to improve the efficiency of an image-based method called dynamic fault imaging (DFI) by fully utilizing the CAD data is described. Third, the VLSI development cost is analyzed by using the fault models that make possible to take into consideration the effect of new testing technologies such as EB testing and focused ion beam (FIB) microfabrication. Finally, the future prospects are discussed.