Input-wavelength-insensitive tunable wavelength conversion was achieved in the range of 1530 to 1560 nm using cascaded semiconductor laser wavelength converters (a DFB laser and an SSG-DBR laser). The power penalty in the wavelength conversion of input signal between 1530 and 1555 nm, where the wavelength ranged between 1537 and 1557 nm, is less than 1 dB for 5 Gbit/s signals.

This paper describes a classification method for rotated and scaled textured images using invariant parameters based on spectral-moments. Although it is well known that rotation invariants can be derived from moments of grey-level images, the use is limited to binary images because of its computational unstableness. In order to overcome this drawback, we use power spectrum instead of the grey levels to compute moments and adjust the integral region of moment evaluation to the change of scale. Rotation and scale invariants are obtained as the ratios of the different rotation invariants on the basis of a spectral-moment property with respect to scale. The effectiveness of the approach is illustrated through experiments on natural textures from the Brodatz album. In addition, the stability of the invariants with respect to the change of scale is discussed theoretically and confirmed experimentally.

2-pole band-pass filters (BPFs) with tap-excitation are prepared by using high temperature superconductors (HTS). The possibility of realizing superconducting coplanar filters with attenuation poles is revealed.

The Multiwavelength Optical Networking (MONET) program consists of a consortium of industrial partners, working together with the intent to demonstrate the key capabilities needed for configurable WDM networks. This involves integrating WDM technologies with optical switching technologies to provide a managed, high capacity, national scale WDM server layer to transport optical signals transparently across multiple interworking subnetworks.

In this work, we give a true concurrency semantics for FIFO-nets, that are Petri nets in which places behave as queues, tokens take values in a finite alphabet and the firing of a transition depends on sequences on the alphabet. We introduce fn-processes to represent the concurrent behavior of a FIFO-net N during a sequence of transition firings. Fn-processes are modeled by a mapping from a simple FIFO-net without queue sharing and cycles, named FIFO-occurrence net, to N. Moreover, the relation among the firings expressed by the FIFO-occurrence net has been enriched by an ordering relation among the elements of the FIFO-occurrence net representing values entered into a same queue of N. We give a way to build fn-processes step by step in correspondance with a sequence of transition firings and the fn-processes operationally built are all those abstractly defined. The FIFO-occurrence nets of fn-processes have some interesting properties; for example, such nets are always discrete and, consequently, there is at least a transition sequence corresponding to each fn-process.

The paper obtains an algorithm to estimate the irregular sampling in wavelet subspaces. Compared to our former work on the problem, the new estimate is relaxed for some wavelet subspaces.

This paper describes the wavelength division multiplexing technology for the long-haul optical communication system, especially for the undersea cable system. At first, the present WDM technology for the undersea cable system is reviewed briefly. After that, some experiments using compensation of the dispersion slope of the transmission fiber are discussed as future technical options of undersea systems with over 100 Gbit/s capacity.

This paper presents a neural network-based control system for Adaptive Noise Control (ANC). The control system derives a secondary signal to destructively interfere with the original noise to cut down the noise power. This paper begins with an introduction to feedback ANC systems and then describes our adaptive algorithm in detail. Three types of noise signals, recorded in destroyer, F16 airplane and MR imaging room respectively, were then applied to our noise control system which was implemented by software. We obtained an average noise power attenuation of about 20 dB. It was shown that our system performed as well as traditional DSP controllers for narrow-band noise and achieved better results for nonlinear broadband noise problems. In this paper we also present a hardware implementation method for the proposed algorithm. This hardware architecture allows fast and efficient field training in new environments and makes real-time real-life applications possible.

The Asynchronous Transfer Mode (ATM) technique has been accepted as a basis for the future B-ISDN networks. In ATM networks, all information is packetized and transferred in small packets of fixed length, called cells. The packetized information transfer, without flow control between the user and the network and the use of statistical multiplexing, results in a need of a policing mechanism to control the traffic parameters of each virtual connection in order to guarantee the required quality of service (QoS). Policing of the peak cell rate is generally not complex and can be achieved by using a cell spacer or other policing mechanisms (PMs). Monitoring of the mean cell rate is more difficult, but is intended to improve the link utilization when it has to handle bursty traffic sources. Conventional PMs, such as the Leaky Bucket Mechanism (LBM) and Window Mechanisms (WMs), are not well suited to the bursty nature of the sources supported by ATM networks, therefore intelligent PMs are needed. In this paper, we propose a Fuzzy Policing Mechanism (FPM) for multimedia applications over ATM networks. We consider the case of still picture source control. The performance evaluation via simulation shows that the FPM efficiently controls the mean cell rate of the still picture source. The proposed FPM shows a good response behavior against parameter variations and the selectivity characteristics approach very close to the ideal characteristic required for a PM. The FPM has a better characteristic compared with the LBM.

This paper is concerned with a concept called universality or completeness of sets of logic devices. Universality characterizes sets of logic devices which can be used for the construction of arbitrary logic circuits. The elemental universality proposed here is the most general condition of universality which covers logic devices with/without delay time and combinational/sequential circuits. The necessary and sufficient condition of elemental universality shows that nonlinearity and nonmonotonicity are essential conditions for the realization of various digital mechanisms.

A constant modulus adaptive array algorithm is derived using analysis and synthesis filter banks to permit adaptive digital beamforming for wideband signals. The properties of the CMA adaptive array using the filter banks are investigated. This array would be used to realize adaptive digital beamforming when this is difficult by means of ordinary (that is, non-subband) processing due to the limited speed of signal processor operations. As an actual application, we present a beamspace adaptive array structure that combines the analysis and synthesis filter banks with RF-domain multibeam array antennas, such as those utilizing optical signal processing.

An Ordered Binary Decision Diagram (OBDD) is a directed acyclic graph representing a Boolean function. The size of OBDDs largely depends on the variable ordering. In this paper, we show the size of the OBDD representing the i-th bit of the output of n-bit/n-bit integer division is Ω ( 2(n-i)/8 ) for any variable ordering. We also show that -OBDDs, -OBDDs and -OBDDs representing integer division has the same lower bounds on the size. We develop new methods for proving lower bounds on the size of -OBDDs, -OBDDs and -OBDDs.

The Heart Rate Variability (HRV) analysis has become vigorous these days. One reason for this is that the HRV analysis investigates the dynamics of the autonomic nervous system activities which control the HRV. The Integral Pulse Frequency Modulation (IPFM) model is a pulse generating mechanism model in the nervous system, that is one of the models which connects the HRV to the autonomic nervous system activities. The IPFM model is a single frequency component model; however, the real HRV has multiple frequency components. Moreover, there are refractory periods after generating action potentials are initiated. Nevertheless, the IPFM model does not consider refractory periods. In order to make sure of the accuracy and the effectiveness of the integral function (IF) method applied to the real data, we consider the absolute refractory periods and two frequency components. In this investigation, the simulated HRV was made with a single and double frequency component using the IPFM model with and without absolute refractory periods. The original generating function of the IPFM model was demodulated by using the instantaneous heart rate tachogram. The power of the instantaneous pulse rate per minute was analyzed by the direct FFT method, the IF FFT method without the absolute refractory periods, and the IF FFT method with the absolute refractory periods. It was concluded that the IF FFT method can demodulate the original generating function accurately.

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%.

Evaluating analytically computer architecture performance is mostly cheap and quick. However, existing analytical performance evaluation techniques usually have a difficult and time-consuming modeling process. Moreover, existing techniques do not support well the capability for finding the bottleneck and its cause of a target system being evaluated. To address the above problems and to enhance analytical performance evaluation technology, in this paper we propose a software tool that accepts system models described in a specification language, generating an executable program that performs the actual performance evaluation. The whole approach is built on a subsystem-oriented performance evaluation tool, which is, in turn, based on a formal subsystem-oriented performance evaluation technique and a subsystem specification language.

In this paper, dependence of storage capacity of an analogue associative memory model using nonmonotonic neurons on static synaptic noise and static threshold noise is shown. This dependence is analytically calculated by means of the self-consistent signal-to-noise analysis (SCSNA) proposed by Shiino and Fukai. It is known that the storage capacity of an associative memory model can be improved markedly by replacing the usual sigmoid neurons with nonmonotonic ones, and the Hopfield model has theoretically been shown to be fairly robust against introducing the static synaptic noise. In this paper, it is shown that when the monotonicity of neuron is high, the storage capacity decreases rapidly according to an increase of the static synaptic noise. It is also shown that the reduction of the storage capacity is more sensitive to an increase in the static threshold noise than to the increase in the static synaptic noise.

Recent progress in neural network research has demonstrated the usefulness of neural networks in a variety of areas. In this work, its application in the spatial resolution improvement of a remotely sensed low resolution thermal infrared image using high spatial resolution of visible and near-infrared images from Landsat TM sensor is described. The same work is done by an algebraic method. The tests developed are explained and examples of the results obtained in each test are shown and compared with each other. The error analysis is also carried out. Future improvements of these methods are evaluated.

This paper proposes a new approach to recover the sign of local surface curvature of object from three shading images using neural network. The RBF (Radial Basis Function) neural network is used to learn the mapping of three image irradiances to the position on a sphere. Then, the learned neural network maps the image irradiances at the neighbor pixels of the test object taken from three illuminating directions of light sources onto the sphere images taken under the same illuminating condition. Using the property that basic six kinds of surface curvature has the different relative locations of the local five points mapped on the sphere, not only the Gaussian curvature but also the kind of curvature is directly recovered locally from the relation of the locations on the mapped points on the sphere without knowing the values of surface gradient for each point. Further, two step neural networks which combines the forward mapping and its inverse mapping one can be used to get the local confidence estimate for the obtained results. The entire approach is non-parametric, empirical in that no explicit assumptions are made about light source directions or surface reflectance. Results are demonstrated by the experiments for real images.

A novel approach was proposed to recognize the non-rigid 3D objects from their corresponding 2D images by combining the benefits of the principal component analysis and the geometric hashing. For all of the object models to be recognized, we calculated the statistical point features of the training shapes using principal component analysis. The results of the analysis were a vector of eigenvalues and a matrix of eigenvectors. We calculated invariants of the new shapes that undergone a similarity transformation. Then added these invariants and the label of the model to the model database. To recognize objects, we calculated the necessary invariants from an unknown image and used them as the indexing keys to retrieve any possible matches with the model features from the model database. We hypothesized the existence of an instance of the model in the scene if the model's features scored enough hits on the vote count. This approach allowed us to store the rigid and the non-rigid object models in a model database and utilized them to recognize an instance of model from an unknown image.

It becomes essential in practice to improve a processing rate and to divide an image into small segments adjusting a limited memory, because image filing systems handle large images up to A1 size. This paper proposes a new method of an automatic skew normalization, comprising a high-speed skew detection and a distortion-free dividing rotation. We have evaluated the proposed method from the viewpoints of the processing rate and the accuracy for typed documents. As results, the processing rate is 2. 9 times faster than that of a conventional method. A practical processing rate for A1 size documents can be achieved under the condition that the accuracy of a normalized angle is controlled within 0. 3 degrees. Especially, the rotation with dividing can have no error angle, even when the A1 size documents is divided into 200 segments, whereas the conventional method cause the error angle of 1. 68 degrees.