We describe 2.5Gb/s 4 channel WDM transmission over 1060km using 18 EDFAs. Gain bandwidth narrowing in concatenated EDFAs has been successfully suppressed using unsaturated EDFAs and a 1.53µm ASE rejection filter.

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.

Analysis of satellite images requires classificatio of image objects. Since different categories may have almost the same brightness or feature in high dimensional remote sensing data, many object categories overlap with each other. How to segment the object categories accurately is still an open question. It is widely recognized that the assumptions required by many classification methods (maximum likelihood estimation, etc.) are suspect for textural features based on image pixel brightness. We propose an image feature based neural network approach for the segmentation of AVHRR images. The learning algoriothm is a modified backpropagation with gain and weight decay, since feedforward networks using the backpropagation algorithm have been generally successful and enjoy wide popularity. Destructive algorithms that adapt the neural architecture during the training have been developed. The classification accuracy of 100% is reached for a validation data set. Classification result is compared with that of Kohonen's LVQ and basic backpropagation algorithm based pixel-by-pixel method. Visual investigation of the result images shows that our method can not only distinguish the categories with similar signatures very well, but also is robustic to noise.

We propose a high-level synthesis method that uses data path information given by a designer. The main purpose of this method is to generate a control unit, one of the most difficult aspects of hardware design. In general, designers can specify data paths easily. Therefore, we believe that basing a method on specified data path information is the best way to synthesize hardware that more closely satisfies the designer's requirements. Moreover, a datapath-constrained scheduling algorithm can perform both "scheduling" and "resource allocation" at the same time. In particular, the resource allocation explicitly decides used paths as well as functional modules in each execution state. This cannot be done with previously reported algorithms.

The generation mechanism for excess intensity noise due to optical feedback is analyzed theoretically and experimentally. Modal rate equations under the weakly coupled condition with external feedback are derived to include the mode competition phenomena in DFB and Fabry-Perot lasers. We found that the sensitivity of the external feedback strongly depends on design parameters of structure, such as the coupling constant of the corrugation, the facet reflection and the phase relation between the corrugation and the facet. A DFB laser whose oscillating wavelength is well adjusted to Bragg wavelength through insertion of a phase adjustment region becomes less sensitive to external optical feedback than a Fabry-Perot laser, but other types of DFB lasers revealing a stop band are more sensitive than the Fabry-Perot laser.

Optical soliton transmissions at 10 and 20Gbit/s over 1000km with the use of erbium-doped fiber amplifiers are described in detail. For the 10Gbit/s experiment, a bit error rate (BER) of below 110-13 was obtained with 220-1 pseudorandom patterns and the power penalty was less than 0.1dB. In the 20Gbit/s experiment optical multiplexing and demultiplexing techniques were used and a BER of below 110-12 was obtained with 223-1 pseudorandom patterns under a penalty-free condition. A new technique for sending soliton pulses over ultralong distances is presented which incorporates synchronous shaping and retiming using a high speed optical modulator. Some experimental results over 1 million km at 7.210Gbit/s are described. This technique enables us to overcome the Gordon-Haus limit, the accumulation of amplified spontaneous emission (ASE), and the effect of interaction forces between adjacent solitons. It is also shown by computer runs and a simple analysis that a one hundred million km soliton transmission is possible by means of soliton transmission controls in the time and frequency domains. This means that limit-free transmission is possible.

An adaptive fuzzy network (AFN) is described that can be used to implement most of fuzzy logic functions. We introduce a learning algorithm largely borrowed from backpropagation algorithm and train the AFN system for several typical fuzzy problems. Simulations show that an adaptive fuzzy network can be implemented with the proposed network and algorithm, which would be impractical for a conventional fuzzy system.

A detailed numerical solution of the design criteria of in-phase lateral and single-longitudinal-mode operation GaInAsP/InP DFB laser arrays is presented. The analysis, including broad-area pumped and stripe-geometry pumped index-guided arrays, was carried out on the basis of the eigenvalue equation method. It is shown that there exists a cut-off array pitch co, at which all of the higher-order array modes are cut off. For the pitch larger than the cut-off pitch co, the modal discrimination is evaluated by the threshold gain difference between the in-phase lateral and higher-order array modes. As a result, the modal discrimination was found to decrease with the increase of the number of elements and the array pitch which is limited to be smaller than twice the cut-off pitch co to attain a stable in-phase lateral- and single-longitudinal-mode operation.

The inherent simplicity of the LMS (Least Mean Square) Algorithm has lead to its wide usage. However, it is well known that high speed convergence and low final misadjustment cannot be realized simultaneously by the conventional LMS method. To overcome this trade-off problem, a new adaptive algorithm using Multiple ADF's (Adaptive Digital Filters) is proposed. The proposed algorithm modifies coefficients using multiple gradient vectors of the squared error, which are computed at different points on the performance surface. First, the proposed algorithm using 2 ADF's is discussed. Simulation results show that both high speed convergence and low final misadjustment can be realized. The computation time of this proposed algorithm is nearly as much as that of LMS if parallel processing techniques are used. Moreover, the proposed algorithm using more than 2 ADF's is discussed. It is understood that if more than 2 ADF's are used, further improvement in the convergence speed in not realized, but a reduction of the final misadjustment and an improvement in the stability are realized. Finally, a method which can improve the convergence property in the presence of correlated input is discussed. It is indicated that using priori knowledge and matrix transformation, the convergence property is quite improved even when a strongly correlated signal input is applied.

We introduce a novel neural network called the T-Model and investigates the learning ability of the T-Model neural network. A learning algorithm based on the least mean square (LMS) algorithm is used to train the T-Model and produces a very good result for the T-Model network. We present simulation results on several practical problems to illustrate the efficiency of the learning techniques. As a result, the T-Model network learns successfully, but the Hopfield model fails to and the T-Model learns much more effectively and more quickly than a multi-layer network.