Cochannel interference and multipath propagation reduce the performance of mobile communication systems. Multi-input MLSE with whitening processing can mitigate the influence of the interference and provide path diversity gain. In conventional considerations, however, the required complexity rapidly rises with the number of array elements. In this paper, we propose multi-input MLSE that whitens error signals in the signal space by using a multibeam adaptive array. This scheme can reduce the computational load of multi-input MLSE than the conventional type when using a large-element array. The results of an analysis show that the proposed type is equivalent to conventional one in the sense of the metric and provides less computational complexity.

We have constructed a parallelizing compiler for Standard ML (SML) based upon algorithmic skeletons. We present an implementation of a Parallel Molecular Dynamics (PMD) simulation in order to compare our functional approach with a traditional imperative approach. Although we present performance data, the principal benefits from our approach are in the modularity of the code and the ease of programming. Extant FORTRAN90 code for an O(N 2) algorithm is translated, firstly into imperative SML and then into purely functional SML which is then parallelized. The ease of programming and the performance of the FORTRAN90 and SML code are compared. Modest parallel performance is obtained from the parallel SML but with a much slower sequential execution time compared to the FORTRAN90. We then improve the implementation with a ring topology implementation which gives much closer performance to the FORTRAN90 implementation.

Described here is an automatic text summarization system for Japanese newspaper articles on sassho-jiken (murders and bodily harms). We extract the pieces of information from a text, inter-connect them to represent the scenes and participants involved in the sassho-jiken, and finally produce a summary by generating sentences from the information extracted. An experiment and its evaluation show that, while a limitation being imposed on the domain, our method works well in depicting important information from the newspaper articles and the summaries produced are better in adequacy and readability than those obtained by extracting sentences.

Neural networks for Vector Quantization (VQ) such as K-means, Neural-Gas (NG) network and Kohonen's Self-Organizing Map (SOM) have been proposed. K-means, which is a "hard-max" approach, converges very fast. The method, however, devotes itself to local search, and it easily falls into local minima. On the other hand, the NG and SOM methods, which are "soft-max" approaches, are good at the global search ability. Though NG and SOM exhibit better performance in coming close to the optimum than that of K-means, the methods converge slower than K-means. In order to the disadvantages that exist when K-means, NG and SOM are used individually, this paper proposes hybrid methods such as NG-K, SOM-K and SOM-NG. NG-K performs NG adaptation during short period of time early in the learning process, and then the method performs K-means adaptation in the rest of the process. SOM-K and SOM-NG are similar as NG-K. From numerical simulations including an image compression problem, NG-K and SOM-K exhibit better performance than other methods.

In this paper, a Compensatory Neuro-Fuzzy Network (CNFN) for nonlinear system control is proposed. The compensatory fuzzy reasoning method is using adaptive fuzzy operations of neural fuzzy network that can make the fuzzy logic system more adaptive and effective. An on-line learning algorithm is proposed to automatically construct the CNFN. They are created and adapted as on-line learning proceeds via simultaneous structure and parameter learning. The structure learning is based on the fuzzy similarity measure and the parameter learning is based on backpropagation algorithm. The advantages of the proposed learning algorithm are that it converges quickly and the obtained fuzzy rules are more precise. The performance of CNFN compares excellently with other various exiting model.

A fast winner search method for VQ based on 2-pixel-merging sum pyramid is proposed in order to reject a codeword at an earlier stage to reduce the computational burden. The necessary search scope of promising codewords is meanwhile narrowed by using sorted real sums. The high search efficiency is confirmed by experimental results.

A brain-computer interface using an electroencephalogram as input into an artificial neural network is investigated as a potentially general control system applicable to all subjects and time frames. Using the intent and imagination of bending the left or right elbow, the left and right desired movements are successfully distinguished using event-related desynchronization resolved by fast Fourier transformation of the electroencephalogram and analysis of the power spectrum using the artificial neural network. The influence of age was identified and eliminated through the use of a frequency distribution in the α band, and the recognition rate was further improved by confirmation based on forced excitement of the β band in the case of an error. The proposed system was effectively trained for general use by using the combined data of a cross-section of subjects.

A method for a genuinely multivariate analysis of statistical phase synchronization phenomena in empirical data is presented. It is applied to EEG data from a psychological experiment, obtaining results which indicate a possible relevance of this method in the context of cognitive science as well as in other fields.

Analog Hopfield neural networks (HNNs) have so far been used to solve many kinds of optimization problems, in particular, combinatorial problems such as the TSP, which can be described by an objective function and some equality constraints. When we solve a minimization problem with equality constraints by using HNNs, however, the constraints are satisfied only approximately. In this paper we propose a circuit which rigorously realizes the equality constraints and whose energy function corresponds to the prescribed objective function. We use the SPICE program to solve circuit equations corresponding to the above circuits. The proposed method is applied to several kinds of optimization problems and the results are very satisfactory.

A microstrip array antenna with 45-degree inclined linear polarization is proposed for automotive radars. The proposed antenna has the advantages of high aperture efficiency, low profile and ease of manufacture. The rectangular radiating elements inclined at 45 degrees to the straight microstrip line are directly connected to it at their corners in the proposed array antenna. The radiating element has a feature that radiation conductance for co-polarization is controlled widely enough to set desired amplitude distribution keeping excited mode for cross-polarization negligibly small. The feed line loss of the linear array antenna having 15 wavelengths is estimated 0.9 dB in the design taking the loss of the microstrip line into account. The performance of two types of developed antennas, for electrical and mechanical scanning radars, is presented. The fan beam subarray antenna for electrical scanning radars has an aperture efficiency of 53% with gain of 22.5 dBi at 76.5 GHz. For mechanical scanning radars, the two-stage series feeding circuit is also proposed for lower feed line loss and setting desired amplitude distribution. The pencil beam array antenna has an aperture efficiency of 39% with gain of 32.2 dBi at 76.5 GHz.

Self-organizing map is a widely used tool in high-dimensional data visualization. However, despite its benefits of plotting very high-dimensional data on a low-dimensional grid, browsing and understanding the meaning of a trained map turn to be a difficult task -- specially when number of nodes or the size of data increases. Though there are some well-known techniques to visualize SOMs, they mainly deals with cluster boundaries and they fail to consider raw information available in original data in browsing SOMs. In this paper, we propose our Factor controlled Hierarchical SOM that enables us select number of data to train and label a particular map based on a pre-defined factor and provides consistent hierarchical SOM browsing.

This paper describes a method of analyzing musical sound using a self-organizing map. To take compound factors into account, energy spectra whose frequency ranges were based on the psycho-acoustic experiments were used as input data. Results for music compact discs confirmed that our method could effectively display the positioning and relationships among musical sounds on a map.

This paper proposes a new approach to fuzzy inference system for modeling nonlinear systems based on measured input and output data. In the suggested fuzzy inference system, the number of fuzzy rules and parameter values of membership functions are automatically decided by using the extended kernel method. The extended kernel method individually performs linear transformation and kernel mapping. Linear transformation projects input space into linearly transformed input space. Kernel mapping projects linearly transformed input space into high dimensional feature space. Especially, the process of linear transformation is needed in order to solve difficulty determining the type of kernel function which presents the nonlinear mapping in according to nonlinear system. The structure of the proposed fuzzy inference system is equal to a Takagi-Sugeno fuzzy model whose input variables are weighted linear combinations of input variables. In addition, the number of fuzzy rules can be reduced under the condition of optimizing a given criterion by adjusting linear transformation matrix and parameter values of kernel functions using the gradient descent method. Once a structure is selected, coefficients in consequent part are determined by the least square method. Simulated results of the proposed technique are illustrated by examples involving benchmark nonlinear systems.

In this paper, we present an output feedback controller using a fuzzy controller and observer for nonlinear systems with unknown time-delay. Recently, Cao et al. proposed a stabilization method for the nonlinear time-delay systems using a fuzzy controller when the time-delay is known. In general, however, it is impossible to know or measure this time-varying delay. The proposed method requires only the upper bound of the derivative of the time-delay. We represent the nonlinear system with the unknown time-delay by Takagi-Sugeno (T-S) fuzzy model and design the fuzzy controller and observer for the systems using the parallel distributed compensation (PDC) scheme. In addition, we derive the sufficient condition for the asymptotic stability of the equilibrium point by applying Lyapunov-Krasovskii theorem to the closed-loop system and solve the condition in the formulation of LMI. Finally, computer simulations are included to demonstrate the effectiveness of the suggested method.

A Virtual Cellular Network (VCN) is a wireless cellular network wherein a single Mobile Station (MS) can communicate simultaneously with more than one Base Station (BS). In this paper, we analyze handoff for two kinds of VCN: a 'Distributed Resource-control VCN' (DR-VCN) and a 'Centralized Resource-control VCN' (CR-VCN). A VCN can take advantage of the fact that the same data is received by multiple base stations. The DR-VCN is a system in which every BS controls its own channels, while the CR-VCN is a system wherein a central station controls all system channels. Results from analysis and simulation show that both the new call drop rate and handoff refusal rate of the CR-VCN are much lower than those of the DR-VCN.

A reactive sputtering method using an Electron-Cyclotron-Resonance (ECR) microwave plasma was used to deposit Ni-Zn ferrite thin-films for a soft magnetic backlayer of Co-containing spinel ferrite thin-film perpendicular magnetic recording (PMR) media. The Ni-Zn spinel ferrite thin-films with a preferential orientation of (100) and a relatively low coercivity of 15 Oe were obtained at a high deposition rate of 14 nm/min and at a temperature below 200 degrees C. Although post-annealing treatment in air at 200 degrees C was effective to decrease the coercivity of the Ni-Zn ferrite thin-films, the saturation magnetization and initial permeability decreased and the surface smoothness was deteriorated simultaneously. The Ni-Zn ferrite thin-films prepared by ECR sputtering are promising as the backlayer of the perpendicular magnetic recording medium, but further improvement is required in terms of the soft magnetic properties, the grain size and the surface roughness.

The cardio-music synchrogram, which is a visualization tool that helps us observe the phase synchronization between heartbeats and beats of music, is originated in cardio-respiratory synchrogram. In this paper, we proposed a novel statistical method for detecting synchronization periods in cardio-music synchrogram (CMS), and reported the synchronization periods of the cardiac activity for 8 subjects in a listening experiments. Our approach focused on the difference between the statistical distributions of the correlation coefficient calculated for the CMS in a control experiment and in the CMS in the listening experiment.

Let us introduce n ( 2) nonlinear mappings fi (i = 1,2,,n) defined on complete linear metric spaces (Xi-1,ρ) (i = 1,2,,n), respectively, and let fi: Xi-1 Xi be completely continuous on bounded convex closed subsets Xi-1,(i = 1,2,,n 0), such that fi() . Moreover, let us introduce n fuzzy-set-valued nonlinear mappings Fi: Xi-1 Xi {a family of all non-empty closed compact fuzzy subsets of Xi}. Here, we have a fixed point theorem on the recurrent system of β-level fuzzy-set-valued mapping equations: xi Fiβ(xi-1,fi(xi-1)), (i = 1,2,,n 0), where the fuzzy set Fi is characterized by a membership function µFi(xi): Xi [0,1], and the β-level set Fiβ of the fuzzy set Fi is defined as Fiβ {ξi Xi | µFi(ξi) β}, for any constant β (0,1]. This theorem can be applied immediately to discussion for characteristics of ring nonlinear network systems disturbed by undesirable uncertain fluctuations and to fine estimation of available behaviors of those disturbed systems. In this paper, its mathematical situation and proof are discussed, in detail.

This study describes the feasibility of using the penalty-function nonlinear programming neural network method to find the optimal power generating output which minimizes both the costs of generating power and power transmission losses. This method depends on neural network technology in acquiring exterior penalty function. Employing nonlinear function in equality and inequality constraints, the model is established using a neural network and additional objective functions; these additional objective functions expand cost function by using an appropriate penalty function. In this study, a 26-busbar including six generators was used to test the penalty function nonlinear programming neural network method. A comparison with the sequential unconstrained minimization technique (SUMT) demonstrates the reliability and precision of the optimal solution obtained using the new method.

A low complexity multi-user receiver with blind channel estimation and multiple access interference (MAI) suppression is proposed for a CDMA system under multipath fading and frequency offset. The design of the receiver involves the following procedure. First, a method of joint MAI suppression and channel estimation is developed based on the generalized sidelobe canceller (GSC) technique. In particular, channel estimates are obtained blindly in the form of the effective composite signature vectors (CSV) of the users. Second, a low-complexity partially adaptive (PA) realization of the receiver is proposed which incorporates reduced-rank processing based on the information of multi-user CSV's. By a judiciously designed decorrelating procedure, a new PA receiver is obtained with a much lower complexity. Finally, pilot symbols assisted frequency offset estimation and channel gain compensation give the estimate of users' symbols. Further performance enhancement is achieved by a decision aided scheme in which the signal is reconstructed and subtracted from the receiver input data, leading to significantly faster convergence. The proposed receiver is shown to be robust to multipath fading and frequency offset, and achieves nearly the same performance of the optimal maximum SINR and MMSE receivers with a much lower overhead for pilot symbols.