UWB pulse radars enable us to measure a target location with high range-resolution, and so are applicable for measurement systems for robots and automobile. We have already proposed a robust and fast imaging algorithm with an envelope of circles, which is suitable for these applications. In this method, we determine time delays from received signals with the matched filter for a transmitted waveform. However, scattered waveforms are different from transmitted one depending on the target shape. Therefore, the resolution of the target edges deteriorates due to these waveform distortions. In this paper, a high-resolution imaging algorithm for convex targets is proposed by iteration of the shape and waveform estimation. We show application examples with numerical simulations and experiments, and confirm its capability to detect edges of an object.

This paper proposes a new blind adaptive MLSE equalizer for frequency selective mobile radio channels. The proposed equalizer performs channel estimation for each survivor path of the Viterbi algorithm (VA), and restricts the number of symbol candidates for the channel estimation in order to reduce prohibitive complexity. In such channel estimation, autocorrelation matrices of the symbol candidates are likely to become singular, which increases the estimation error. To cope with the singularity, the proposed equalizer employs a recursive channel estimation algorithm using the Moore-Penrose generalized inverse of the autocorrelation matrix. As another problem, the blind channel estimation can yield plural optimal estimates of a channel impulse response, and the ambiguity of the estimates degrades the BER performance. To avoid this ambiguity, the proposed equalizer is enhanced so that it can take advantage of the fractional sampling. The enhanced equalizer performs symbol-spaced channel estimation for each fractional sampling phase. This equalizer combines separate channel estimation errors, and provides the sum to the VA processor as the branch metric, which tremendously reduces the probability that a correct estimate turns into a false one. Computer simulation demonstrates the effectiveness of the proposed equalizers in the frequency selective fading channels.

An iterative reconstruction algorithm of accelerating the estimation of the complex relative permittivity of a cylindrical dielectric object based on the multigrid optimization method (MGOM) is presented. A cost functional is defined by the norm of a difference between the scattered electric fields measured and calculated for an estimated contrast function, which is expressed as a function of the complex relative permittivity of the object. Then the electromagnetic inverse scattering problem can be treated as an optimization problem where the contrast function is determined by minimizing the cost functional. We apply the conjugate gradient method (CGM) and the frequency-hopping technique (FHT) to the minimization of the cost functional, and also employ the multigrid method (MGM) with a V-cycle to accelerate the rate of convergence for getting the reconstructed profile. The reconstruction scheme is called the multigrid optimization method. Computer simulations are performed for lossy and inhomogeneous dielectric circular cylinders by using single-frequency or multifrequency scattering data. The numerical results demonstrate that the rate of convergence of the proposed metod is much faster than that of the conventional CGM for both noise-free and noisy cases.

A network based on the Inverse Function Delayed (ID) model which can recall a temporal sequence of patterns, is proposed. The classical problem that the network is forced to make long distance jumps due to strong attractors that have to be isolated from each other, is solved by the introduction of the ID neuron. The ID neuron has negative resistance in its dynamics which makes a gradual change from one attractor to another possible. It is then shown that a network structure consisting of paired conventional and ID neurons, perfectly can recall a sequence.

In this paper, the authors present a new digital circuit of neuron hardware using a field programmable gate array (FPGA). A new Inverse function Delayed (ID) neuron model is implemented. The Inverse function Delayed model, which includes the BVP model, has superior associative properties thanks to negative resistance. An associative memory based on the ID model with self-connections has possibilities of improving its basin sizes and memory capacity. In order to decrease circuit area, we employ stochastic logic. The proposed neuron circuit completes the stimulus response output, and its retrieval property with negative resistance is superior to a conventional nonlinear model in basin size of an associative memory.

We propose an adaptive beamforming scheme for the combination of orthogonal frequency division multiplexing (OFDM) and adaptive antenna array. The combinational scheme is characterized by the sample matrix inverse (SMI) algorithm, frequency-to-time pilot transform and pre-FFT combination. For every OFDM block containing both data and pilot symbols, we transform the frequency-domain pilot symbols to the corresponding time-domain components. One of the obvious advantages of this transform is that the time interval of the antenna weight vector update can be reduced to only one OFDM sample interval, from one OFDM block interval of the conventional beamforming scheme in which the transform is not applied. This feature can greatly accelerate the convergence of SMI beamforming. The simulation results verify that the proposed beamforming scheme is capable of improving the convergence behavior significantly.

Magnetoencephalography (MEG) is a method to measure a magnetic field generated by electrical neural activity in a brain, and it plays increasingly important role in clinical diagnoses and neurophysiological studies. However, in MEG analysis, the estimation of the brain activity, of the electric current density distribution in a brain which is represented by current dipoles, is problematic. A spatial filter and subsequent reconstruction of the current density distribution estimated by the spatial filter (spatial filtered reconstruction: SFR) are proposed. The spatial filter is designed to be used without prior or temporal information. The proposed spatial filter ensures that it concentrates the current distribution around the activated sources in the conductor. The current distribution estimated by the spatial filter is reconstructed by multiple linear regression. Redundant current dipoles are eliminated, and the current distribution is optimized in the sense of the Mallows Cp statistic. Numerical studies are demonstrated and show successful estimation by SFR in multiple-dipole cases. In single-dipole cases with SNRs of 101 and more, the location of the true dipole was successfully estimated for about 80% of the simulations. The reconstruction with multiple linear regression corrected the location of the maximum current density estimated by the proposed spatial filtering. The dipole on the correct position contributes to more than 70% of the total dipoles in the estimated current distribution in those cases. These results show that the current distribution is effectively localized by SFR. We also investigate the differences among SFR, the LCMV (linearly constrained minimum variance) beamformer and the SAM (synthetic aperture magnetometry), the representatives of spatial filters in MEG analyses. It is indicated that spatial resolution is improved by avoiding dependence on temporal information.

A noise reduction technique to reduce wideband and sinusoidal noise in a noisy speech is proposed. In an actual environment, background noise includes not only wideband noise but also sinusoidal noise, such as ventilation fan and engine noise. In this paper, we propose a new noise reduction system which uses two types of adaptive line enhancers (ALE) and a noise estimation filter (NEF). First, the two ALEs are used to estimate speech components. The first ALE is used to reduce sinusoidal noise superposed on speech and wideband noise, while the second ALE is used to reduce wideband noise superposed on speech. However, since the quality of the speech enhanced by two ALEs is not good enough due to the difficulty in estimating unvoiced sound using the two ALEs, the NEF is used to improve on noise reduction capability. The NEF accurately estimates the background noise from the signal occupied by noise components, which is obtained by subtracting the speech enhanced by two ALEs from noisy speech. The enhanced speech is obtained by subtracting the estimated noise from noisy speech. Furthermore, the noise reduction system with feedback path is proposed to improve further the quality of enhanced speech.

Montgomery algorithm has demonstrated its effectiveness in applications like cryptosystems. Most of the existing works on finding the Montgomery inverse of an element over the Galois field are based on the software implementation, which is then extended to derive the scalable hardware architecture. In this work, we consider a fundamental change at the algorithmic level and eliminate the potential problems in hardware implementation which makes the resulting modified Montgomery inverse algorithm over GF(2m) very suitable for hardware realization. Due to its structural simplicity, the modified algorithm can be easily mapped onto a high-speed and possibly low-complexity circuit. Experimental results show that our development can achieve both the area and speed advantages over the previous work when the inversion operation over GF(2m) is under consideration and the improvement becomes more significant when we increase the value of m as in the applications of cryptosystems. The salient property of our development sustains the high-speed operation as well as low hardware complexity over a wide range of m for commercial cryptographic applications and makes it suitable for both the scalable architecture and direct hardware implementation.

This paper presents a computational approach to the imaging of a partially immersed conducting cylinder. Both cubic-spline method and trigonometric series for shape description are used and compared. Based on the boundary condition and the recorded scattered field, a set of nonlinear integral equations is derived and the imaging problem is reformulated into an optimization problem. The genetic algorithm is employed to find out the global extreme solution of the object function. It is found that the shape described by Fourier series can be reconstructed by cubic-spline. In the opposite case, the shape described by cubic-spline and reconstructed by Fourier series expansion will fail. Even when the initial guess is far away from the exact one, the cubic-spline expansion and genetic algorithm can avoid the local extreme and converge to a global extreme solution. Numerical results are given to show that the shape description by using cubic-spline method is much better than that by the Fourier series. In addition, the effect of Gaussian noise on the reconstruction is investigated.

Target reconstruction algorithm from its monostatic radar cross section (RCS) has been proposed for polygonal cylinders with curved surfaces. This algorithm is based on our previous finding that the main contribution to the back scattering is due to edge diffracted fields excited at a facet of nearly specular reflection direction. Dimension of this constitutive facet of the target is estimated from the local maxima and its lobe width in the angular RCS variation. Half and quarter circular cylinders are used as canonical scattering objects, and their measured and numerically simulated monostatic RCS values have been studied extensively to find scattering pattern characteristic difference between flat and circularly curved surfaces. Thus estimated constitutive facets are connected in order, and this procedure will be continued until the distance between the first and the final edges would be minimized. Our algorithm has been tested for other targets, and it is found that it works well for predicting metal convex targets with flat and curved facets.

An inverse scattering problem of estimating the surface impedance for an inhomogeneous half-space is investigated. By virtue of the fact that the far field representation contains the spectral function of the scattered field, complex values of the function are estimated from a set of absolute values of the far field. An approximate function for the spectral function is reconstructed from the estimated complex values by the least-squares sense. The surface impedance is estimated through calculating the field on the surface of the half-space expressed by the inverse Fourier transform. Numerical examples are given and the accuracy of the estimation is discussed.

This paper aims at challenging Bernstein's problem called the "Degrees-of-Freedom problem," which remains unsolved from both the physiological and robotics viewpoints. More than a half century ago A.N. Bernstein observed that "dexterity" residing in human limb motion emerges from accumulated involvement of multi-joint movements in surplus DOF. It is also said in robotics that redundancy of DOFs in robot mechanisms may contribute to enhancement of dexterity and versatility. However, kinematic redundancy incurs a problem of ill-posedness of inverse kinematics from task-description space to joint space. In the history of robotics research such ill-posedness problem of inverse-kinematics has not yet been attacked directly but circumvented by introducing an artificial performance index and determining uniquely an inverse kinematics solution by minimizing it. Instead of it, this paper introduces two novel concepts named "stability on a manifold" and "transferability to a submanifold" in treating the case of human multi-joint movements of reaching and shows that a sensory feedback from task space to joint space together with a set of adequate dampings enables any solution to the overall closed-loop dynamics to converge naturally and coordinately to a lower-dimensional manifold describing a set of joint states fulfilling a given motion task. This means that, without considering any type of inverse kinematics, the reaching task can be accomplished by a sensory feedback with adequate choice of a stiffness parameter and damping coefficients. It is also shown that these novel concepts can cope with annoying characteristics called "variability" of redundant joint motions seen typically in human skilled reaching. Finally, it is pointed out that the proposed control signals can be generated in a feedforward manner in case of human limb movements by referring to mechano-chemical characteristics of activation of muscles. Based on this observation, generation of human skilled movements of reaching can be interpreted in terms of the proposed "Virtual-Spring" hypothesis instead of the traditional "Equilibrium-Point" hypothesis.

Self-connection can enlarge the memory capacity of an associative memory based on the neural network. However, the basin size of the embedded memory state shrinks. The problem of basin size is related to undesirable stable states which are spurious. If we can destabilize these spurious states, we expect to improve the basin size. The inverse function delayed (ID) model, which includes the Bonhoeffer-van der Pol (BVP) model, has negative resistance in its dynamics. The negative resistance of the ID model can destabilize the equilibrium states on certain regions of the conventional neural network. Therefore, the associative memory based on the ID model, which has self-connection in order to enlarge the memory capacity, has the possibility to improve the basin size of the network. In this paper, we examine the fundamental characteristics of an associative memory based on the ID model by numerical simulation and show the improvement of performance compared with the conventional neural network.

In this paper, we propose a new on-line adaptive relaxation algorithm for an inverse filter in a multichannel sound reproduction system. The fluctuation of room transfer functions degrades reproduced sound in conventional sound reproduction systems in which the coefficients of the inverse filter are fixed. In order to resolve this problem, an iterative relaxation algorithm for an inverse filter performed by truncated singular value decomposition (adaptive TSVD) has been proposed. However, it is difficult to apply this method within the time duration of the sound of speech or music in the original signals. Therefore, we extend adaptive TSVD to an on-line-type algorithm based on the observed signal at only one control point, normalizing the observed signal with the original sound. The result of the simulation using real environmental data reveals that the proposed method can always carry out the relaxation process against acoustic fluctuation, for any time duration. Also, subjective evaluation in the real acoustic environment indicates that the sound quality improves without degrading the localization.

An electromagnetic (EM) inverse scattering problem that involves the reconstruction of microwave images for dielectric objects is considered in this paper. This ill-posed and nonlinear problem is treated as a global optimization problem, and is solved by the application of micro-genetic algorithm (m-GA). The reconstructed results obtained by m-GA have shown that it is an effective technique for microwave imaging and satisfactory performance is achieved when compared with the conventional genetic algorithms.

We propose an efficient method for updating the inverse of the signature waveform cross-correlations (SWC) matrix when the number of users in the synchronous direct-sequence code-division multiple-access (DS/CDMA) system changes. It is shown that the computational complexity of the proposed method is O(n2) in which n represents the number of active users in the system.

Bandpass sampling algorithm is effectively adopted to obtain the digital signal with significantly reduced sampling rate for a single radio frequency(RF) signal. In order to apply the concept to multiple RF signals, we propose bandpass sampling algorithms with the normal and the inverse placements since we are interested in uniform order of the spectrum in digital domain after bandpass sampling. In addition, we verify the propose algorithms with generalized equation forms for the multiple RF signals.

An inverse-RZ modulation scheme for dense WDM systems is proposed. Inverse-RZ signals have tolerances to chromatic dispersion and optical bandwidth limitation. The strongly pre-filtered inverse-RZ signals can be adapted to ultra-dense WDM systems, in which the spectral efficiencies are over 1.0 b/s/Hz. We have confirmed the error-free transmission of pre-filtered and co-polarized 40-Gb/s inverse-RZ signals where the channel intervals were 37.5 GHz.

It is an important problem to estimate component reliabilities. For a series system due to cost and time constraints associated with failure analysis, all components cannot be investigated and the cause of failure is narrowed to a subset of components in some cases. When such a case occurs, we say that the cause of failure is masked. It is also necessary in some cases to take account of the influence of an environmental stress on all components. In this paper, we consider 2 and 3-component series systems when the component lifelengths are exponentially distributed and an environmental stress follows either a gamma or an inverse Gaussian distribution. We show that the lifelength of the system and the cause of failure are independent of each other. By comparison between the hazard functions in both models, we see that quite short and long lifelengths are more likely to occur in a gamma model than in an inverse Gaussian one. Assuming that the masking probabilities do not depend on which component actually fails, we show that the likelihood function can be factorized into three parts by a reparametrization. For some special cases, some estimators are given in closed-form. We use the computer failure data to see that our model is useful to analyze the real masked data. As compared with the Kaplan-Meier estimator, our models fit this computer data better than no environmental stress model. Further, we determine a suitable model using AIC. We see that the gamma model is fitted to the data better than the inverse Gaussian one. From a limited simulation study for a 3-component series system, we see that the relative errors of some estimators are inversely proportional to the square root of the expected number of systems whose cause of failure is identified.