The antennas for subsurface radar are usually covered with a conducting cavity to prevent the radiation field from affecting the electromagnetic environment and to protect the received field from external noises. Furthermore, radiowave absorber is attached to the interior wall of the cavity in order to suppress the multiple reflections in the cavity. In this paper, the characteristics of the two-dimensional cavity-backed antenna having the absorber and the over-all properties of this subsurface radar due to buried objects are numerically analyzed by the Finite-Difference Time-Domain method. It is shown that the pulse propagation in the ground is confined to the narrow region due to the cavity. It is also shown that the multiple reflections in the cavity are effectively suppressed by choosing the suitable absorber, and so that the distinctive pulse echo can be obtained.

An analysis of wave guidance by surface-relief grating waveguides is presented for the case of oblique propagation. This analysis is based on the first-order differential equations expressing the coupling of the space harmonics and an improved differential method is applied to solve the equations in the grating region with arbitrary profile. The propagation constants are calculated for isotropic grating waveguids with sinusoidal profile and the calculated results indicate that the accurate solutions can be obtained by increasing the number of expansion terms and the number of segments. Moreover, this method is extended to the case of the analysis of obliquely propagating waves and it is shown that peculiar leaky waves and stop bands appear owing to the coupling between TE and TM waves.

In this paper, we show that Petri nets can be applied practically to design and analysis of concurrent, parallel and embedded mode systems such as a lift system that is familiar to our daily life. Modeling behavioral characteristics of the lift, we extend a standard Petri net by constant timed transition, faultable transition, stochastic transition and condition transition concepts. Likewise, we prsesnt some results of design and analysis of the system. This method can be applied to design and analysis of another concurrent systems.

We have achieved the room temperature cw lasing operation of GaInAsP/InP surface emitting lasers for the first time. By employing a buried heterostructure with 1.3 µm range active region and a MgO/Si heat sink mirror, cw operation was obtained up to 14 with the threshold current of 22 mA.

This paper tends to analyze the trends of the research in logic synthesis. The first part is devoted to an expertise of the efficiency of factorization methods developed during the last decade and to the proposal of dedicated methods for complex logic blocks. The second part shows the importance of Binary Decision Diagrams as representation of Boolean functions. Their use in the technology mapping phase of multiplexor-based FPGAs in an industrial tool is taken as illustration.

This paper proposes a new subscriber distribution method called FTTA (Fiber To The Area), which uses millimeter-wave radio band to connect subscribers with base station and optical fiber to connect base station with control station in order to obtain broad-band transmission. Usually two main causes of signal degradation, i.e., rainfall attenuation on radio channel and intermodulation distortion on optical channel are considered in this system. Taking into considerations of these two factors, we analyze the available capacity of FTTA system for various 22nQAM modulation levels. The analysis clarifies that there exists an optimum modulation level that can maxize the available capacity, and AGC circuit in the base station is useful to compensate the rainfall attenuation. It is shown that 18.0Gbps is available under the optimum modulation method of the 64QAM with AGC and 12.0Gbps under the 16QAM without AGC when 20 carriers are used.

A new method of multipath diversity combination is proposed for Direct Sequence Spread Spectrum (DS-SS) mobile communications. In this method, the transmitted signal from the base staion is the sum of a number of the same spread signal, each one delayed and scaled according to the delay and the strength of the multipaths of the transmission channel. As a result the received signal at the mobile unit will already be a Rake combination of the multipath signals. This new method is called Pre-Rake diversity combination because the Rake diversity combination process is performed before transmission By this method the size and complexity of the mobile unit can be minimized, and the unit is made as simple as a non-combining single path receiver. A theoretical examination of the Signal to Noise Ratio (SNR) and the Bit Error Rate (BER) results for the traditional Rake and the Pre-Rake combiners as well as computer simulations show that the performance of the Pre-Rake combiner is equivalent to that of the Rake combiner.

In this paper, we propose an adaptive RAKE receiver, which does not need to send the sounding signals and can track the fluctuations caused by fading. The channel estimation can be done by using a least squares method of the first and second equations suppressing additive noise and tracking the channel fluctuations. It is confirmed by computer simulations that the result has good agreement with theory and the performance is almost same as that of the conventional RAKE with the sounding signals.

A YBCO/CeO2/Au MIS structure (YBCO:YBa2Cu3O7y) is fabricated on a MgO(100) substrate with the help of the all-in-situ electron-beam and heater coevaperation system. The current-voltage (I-V) characteristics of the deposited YBCO film under various gate voltages are examined. Small modulation of the I-V characteristics by gate voltages can be observed. Meanwhile, the surface morphology is also studied by means of an atomic force microscope (AFM). The relation between the field effect and the surface morphology of a thin YBCO film is discussed.

In this paper we consider multiuser detection using a neural network in a synchronous code-division multiple-access channel. In a code-division multiple-access channel, a matched filter is widely used as a receiver. However, when the relative powers of the interfering signals are large, i.e. the near-far problem, the performances of the matched filter receiver degrade. Although the optimum receiver for multiuser detection is superior to the matched filter receiver in such situations, the optimum receiver is too complex to be implemented. A simple technique to implement the optimum multiuser detection is required. Recurrent neural networks which consist of a number of simple processing units can rapidly provide a collectively-computed solution. Moreover, the network can seek out a minimum in the energy function. On the other hand, the optimum multiuser detection in a synchronous channel is carried out by the maximization of a likelihood function. In this paper, it is shown that the energy function of the neural network is identical to the likelihood function of the optimum multiuser detection and the neural network can be used to implement the optimum multiuser detection. Performance comparisons among the optimum receiver, the matched filter one and the neural network one are carried out by computer simulations. It is shown that the neural network receiver has a capability to achieve near-optimum performance in several situations and local minimum problems are few serious.

The bit error performance of a Direct Sequence Spread Spectrum Communication system in actual land mobile satellite channel is evaluated with experiments. Field test results with the ETS-V satellite in urban and suburban environments at L-band frequency show that this land mobile satellite channel of 3MHz bandwidth can be seen as a non-frequency selective Rician fading channel as well as shadowing channel. The bit error performance can be estimated from signal power measurement as in the case of narrow band modulation signals.

A time division duplex (TDD) direct sequence spread spectrum communication (DS-SS) system is proposed for operation in channels with Rayleigh fading characteristics. It is shown that using the TDD method is advantageous because the devices can be designed more simply, the method is more frequency efficient and as a result the systems will be less costly and less power consuming. It is also shown that an efficient power control method can be implemented for the TDD systems. In contrast to the traditional access techniques such as frequency division multiple access (FDMA) and time division multiple access (TDMA) that are mainly frequency limited, the code division multiple access (CDMA) method which uses the DS-SS technique is interference limited. This means that an efficient power control method can increase the capacity of the DS-SS communications system. Computer simulations are used to evaluate the performance of the TDD power control method. Performance improvement of order of 12 to 17dB at bit error rate (BER) of 10-3 can be obtained for different methods of power control. The advantages of the TDD technique for the future DS-SS systems operating in the Industrial, Scientific and Medical (ISM) band are explained in an appendix to this paper.

In this paper we present a new tracking scheme using two tracking modes which are based on the concept of Delay Lock Loop (DLL). Under the multipath fading channels, a conventional DLL has problems of jitter performance degradation, lock-off and delay offset. It is necessary to solve these problems, because mobile communications have increased drastically. We propose the combination of a coarse tracking mode and a fine tracking mode. The former mode is employed for reducing the possibility of losing lock, the latter mode is used for suppressing the jitter of delay error and the delay offset in the presence of multipath fading. The both modes utilize the power of delay paths shown in the auto-correlation function of the received signal at the DLL. Computer simulation results show that our proposed scheme is extremely useful comparing with a conventional scheme over the multipath fading channels.

An attempt to apply neural networks to the acoustic diagnosis for the reciprocating compressor is described. The proposed neural network, Hybrid Neural Network (HNN), is composed of two multi-layered neural networks, an Acoustic Feature Extraction Network (AFEN) and a Fault Discrimination Network (FDN). The AFEN has multi-layers and the number of units in the middle hidden layer is smaller than the others. The input patterns of the AFEN are the logarithmic power spectra. In the AFEN, the error back propagation method is applied as the learning algorithm and the target patterns for the output layer are the same as the input patterns. After the learning, the hidden layer acquires the compressed input information. The architecture of the AFEN appropriate for the acoustic diagnosis is examined. This includes the determination of the form of the activation function in the output layer, the number of hidden layers and the numbers of units in the hidden layers. The FDN is composed of three layers and the learning algorithm is the same as the AFEN. The appropriate number of units in the hidden layer of the FDN is examined. The input patterns of the FDN are fed from the output of the hidden layer in the learned AFEN. The task of the HNN is to discriminate the types of faults in the compressor's two elements, the valve plate and the valve spring. The performance of the FDN are compared between the different inputs; the output of the hidden layer in the AFEN, the conventional cepstral coefficients and the filterbank's outputs. Furthermore, the FDN itself is compared to the conventional pattern recognition technique based on the feature vector distance, the Euclid distance measure, where the input is taken from the AFEN. The obtained results show that the discrimination accuracy with the HNN is better than that with the other combination of the discrimination method and its input. The output criteria of network for practical use is also discussed. The discrimination accuracy with this criteria is 85.4% and there is no case which mistakes the fault condition for the normal condition. These results suggest that the proposed decision network is effective for the acoustic diagnosis.

In this paper, we present an efficient method for the fault simulation of the reconvergent fan-out stem. Our method minimizes the fault propagating region by analyzing the topology of the circuit, whose region is smaller than that of Tulip's. The efficiency of our method is illustrated by experimental results for a set of benchmark circuits.

This paper describes a new method for the concurrent detection of faults in instruction level parallel (ILP) processors. This method uses the No OPeration (NOP) instruction slots that under branches, resource conflicts and some kind of data dependencies fill some of the pipelines (stages) in an ILP processor. NOPs are replaced by the copy of an effective instruction running in another pipeline. This allows the checking of the pipelines running the original instruction and its copy (ies), by the comparison of the outputs of their stages during the execution of the replicated instruction. We show some figures obtained for the application of this method to a two-pipeline superscalar processor.

We propose a method of diagnosing any logical fault in combinational circuits through a repetition of the single fault-net location procedure with the aid of probing, called SIFLAP-G. The basic idea of the method has been obtained through an observation that a single error generated on a fault-net often propagates to primary outputs under an individual test even though multiple fault-nets exist in the circuit under test. Therefore, candidates for each fault-net are first deduced by the erroneous path tracing under the single fault-net assumption and then the fault-net is found out of those candidates by probing. Probing internal nets is done only for some of the candidates, so that it is possible to greatly decrease the number of nets to be probed. Experimental results show that the number seems nearly proportional to the number of fault-nets (about 35 internal nets per fault-net), but almost independent of the type of faults and the circuit size.

In this paper we propose a method of formal verfication of fault-tolerance of sequential machines using regular temporal logic. In this method, fault-tolerant properties are described in the form of input-output sequences in regular temporal logic formulas and they are formally verified by checking if they hold for all possible input-output sequences of the machine. We concretely illustrate the method of its application for formal verification of fail-safeness with an example of a comparator for redundant system. The result of verification shows effectiveness of the proposed method.

This paper presents a new redundant fault identification algorithm, REDUCT. This algorithm handles the redundant fault identification problem by transforming a given circuit into another circuit. It also reduces the complexity of the transformed circuit, which is caused by a large number of reconvergences and head lines, using five circuit reduction techniques. Further, it proves redundancies and generates test patterns for hard faults more efficiently than conventional test pattern generation algorithms. We obtained 100% fault coverage for all ISCAS85 benchmark circuits using REDUCT following the execution of the test pattern generation algorithm N2-V.

A new neural network system for object recognition is proposed which is invariant to translation, scaling and rotation. The system consists of two parts. The first is a preprocessor which obtains projection from the input image plane such that the projection features are translation and scale invariant, and then adopts the Rapid Transform which makes the transformed outputs rotation invariant. The second part is a neural net classifier which receives the outputs of preprocessing part as the input signals. The most attractive feature of this system is that, by using only a simple shift invariant transformation (Rapid transformation) in conjunction with the projection of the input image plane, invariancy is achieved and the system is of reasonably small size. Experiments with six geometrical objects with different degrees of scaling and rotation shows that the proposed system performs excellent when the neural net classifier is trained by the Cascade-correlation learning algorithm proposed by Fahlman and Lebiere.