This paper proposes a new method to determine the shape of a surface by learning the mapping between three image irradiances observed under illumination from three lighting directions and the corresponding surface gradient. The method uses Phong reflectance function to describe specular reflectance. Lambertian reflectance is included as a special case. A neural network is constructed to estimate the values of reflectance parameters and the object surface gradient distribution under the assumption that the values of reflectance parameters are not known in advance. The method reconstructs the surface gradient distribution after determining the values of reflectance parameters of a test object using two step neural network which consists of one to extract two gradient parameters from three image irradiances and its inverse one. The effectiveness of this proposed neural network is confirmed by computer simulations and by experiment with a real object.

A network system is proposed for segmenting and extracting multiple moving objects in 2D images. The system uses an interconnected neural network in which grouping factors, such as edge proximity, smoothness of edge orientatio, and smoothness of velocity perpendicular to an edge, are embedded. The system groups edges so that the network energy may be minimized, i.e. edges may be organized into perceptually plausible configuration. Experimantal results are provided to indicate the performance and noise robustness of the system in extracting objects in synthetic images.

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.

Many previous works state that a multiple Sidelobe canceller (MSLC) with two auxiliary antennas is successful in suppressing two interference signals received simultaneously by sidelobes of a main antenna. In this paper, we show that the MSLC does not always guarantee such capability in three dimensional applications where the incident direction of interference signals is defined by two angles (elevation and azimuth). We show the singularity of the autocorrelation matrix for the auxiliary channel signals induces the degradation of the capability by analyzing characteristics of MSLC's in three dimensional applications from the view point of the eigenvalue problem. To overcome this singularity, we propose a novel MSLC controlling the placement of auxiliary antennas by means of switching over three antennas arranged triangularly. Some simulations are conducted to show the effectiveness of the proposed MSLC.

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 paper advocates the use of linear objective function in analytic analog placement. The role of linear and quadratic objctive functions in the behavior and results of an analog placement algorithm based on the force directed method is discussed. Experimental results for a MCNC benchmark circuit and another one from text books are shown to demonstrate the effect of a linear and a quadratic objective function on the analog constraint satisfaction and CPU time. By introducing linear objective function to the algorithm, we obtain better placements in terms of analog constraint satisfaction and computation cost than in case of conventional quadratic objective function.

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.

In this paper we analize the performance of Trellis Coded Modulation (TCM) schemes with coherent detection operating in a frequency flat, mobile Rayleigh fading environment, and with different knowledge levels on both the amplitude and phase fading processes (the latter is not assumed as usual to be ideally tracked), or Channel State Information (CSI). For example, whereas ideal CSI means that both the amplitude and phase fading characteristics are perfectly known by the receiver, other situations that are treated consider perfect knowledge of the amplitude (or phase) with complete disregard of the phase (or amplitude), as well as non concern on any of them. Since these are extreme cases, intermediate situations can be also defined to get extended bounds based on Chernoff which allow the phase errors, in either form of constant phase shifts or randomly distributed phase jitter, to be included in the upper bounds attainable by transfer function methods, and are applicable to multiphase/level signaling schemes. We found that when both fading characteristics are considered, the availability of CSI enhances significatively the performance. Furthermore, for non constant envelope schemes with non ideal CSI and for constant envelope schemes with phase errors, an asymmetry property of the pairwise error probability is identified. Theoretical and simulation results are shown in support of the analysis.

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.

Persistent data structures, introduced by Sarnak and Tarjan, have been found especially useful in designing geometric algorithms. In this paper, we present a persistent form of binary-binary search tree, and then apply this data structure to solve various geometric searching problems, such as, three dimensional ray-shooting, hidden surface removal, polygonal point enclosure searching and so on. In all applications, we are able to either improve existing bounds or establish new bounds.

This paper proposes a new method of extracting feature attentive regions in a learnt multi-layer neural network. We difine a function which calculates the degree of dependence of an output unit on an inpur unit. The value of this function can be used to investigate whether a learnt network detects the feature regions in the training patterns. Three computer simulations are presented: (1) investigation of the basic characteristic of this function; (2) application of our method to a simpie pattern classification task; (3) application of our method to a large scale pattern classfication task.

Resonant slots are widely used for conventional slotted waveguide array. Reflection from each slot causes a standing wave in the waveguide and beam tilting technique is essential to suppress the reflection at the antenna input port. But the slot reflection narrows the overall frequency bandwidth and the design taking it into account is complicated. This paper proposes a reflection cancelling slot pair as an array element, which consists of two slots spaced by 1/4λg. Round trip path-length difference between them is 1/2λg and reflection waves from a pair disappear and traveling-wave excitation in the waveguide is realized. The full wave analysis reveals that mutual coupling between paired slots is large and seriously reduces the radiation from a pair. Offset arrangement of slots in a pair is recommended to decrease the mutual coupling and to realize strong coupling. In practical array design, the mutual couplings from other pairs were simulated by imposing periodic boundary conditions above the aperture. To clarify the advantages of the slot pair over a conventional resonant slot, the predicted characteristics are compared. Reflection characteristics of the array using the slot pair is excellent and a boresite beam array can be realized. In addition, a slot pair can realize stronger coupling than the conventional resonant slot, while the bandwidth of the former in terms of the aperture field phase illumination is narrower than that of the latter. These suggests that the slot pair array is much more suitable for a small array than conventional one. Finally, the predicted characteristics are confirmed by experiments.

A novel pulse neural network model for sound localization has been proposed. Our model is based on the physiological auditory nervous system. Human beings can perceive the sound direction using inter-aural time difference (ILD) and inter-aural level difference (ILD) of two sounds. The model extracts these features using only pulse train information. The model is divided roughly into three sections: preprocessing for input signals; transforming continuous signals to pulse trains; and extracting features. The last section consists of two parts: ITD extractor and ILD extractor. Both extractors are implemented using a pulse neuron model. They have the same network structure, differing only in terms of parameters and arrangements of the pulse neuron model. The pulse neuron model receives pulse trains and outputs a pulse train. Because the pulses have only simple informations, their data structures are very simple and clear. Thus, a strict design is not required for the implementation of the model. These advantages are profitable for realizing this model by hardware. A computer simulation has demonstrated that time and level differences between two signals have been successfully extracted by the model.

We propose a large capacity broadband packet switch architecture using multiple optical star couplers and tunable devices whose tuning range is restricted. The proposed switch has the conventional three-stage switch structure. With the use of the generalized knockout principle and tunable lasers arranged in an appropriate manner, the switch becomes an output queueing system that yields the best possible delay/throughput performance. This switch requires minimal hardware at the cost of the increased number of wavelengths.

Recent developments and case studies regarding VLSI device chip failure analysis are reviewed. The key failure analysis techniques reviewed include EMMS (emission microscopy), OBIC (optical beam induced current), LCM (liquid crystal method), EBP (electron beam probing), and FIB (focused ion beam method). Further, future possibilities in failure analysis, and some promising new tools are introduced.

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.

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.

The mechanism for data retention failure of EPROM has been investigated by the Optical Beam Induced Current(OBIC) technique. It was found that the data of failure cells were changed from '1' to '0' during read-mode by laser irradiation by OBIC. The data in good cells was not changed. This result suggests the effective barrier height between Si and SiO2 is being lowered. In addition, the cross section technique revealed that gate electrode and gate oxide were exposed due to lack of dielectric layers. This defect seemed to be the cause of the barrier height lowering. The OBIC technique not only gives the failure location but a detailed information of the failure mechanism. We found that OBIC technique is a very powerful tool for the analysis of EPROM failure mechanisms. The usefulness of the Emission Micro Scope (EMS) technique is also discussed.

An analysis of the circuit for dead angle compensation in the dc-to-dc converter controlled by a magnetic amplifier is presented. This circuit suppresses the dead angle so that the core loss may be reduced without spoiling the current surge suppression characteristics of the magnetic amplifier. The analysis is given by modeling the magnetization characteristics of the core containing the saturation inductance and the reverse recovery of the diode. As a result, the control characteristics of the converter with the compensation circuit are expressed analytically and a limit of compensation is derived theoretically.