A class E low dv/dt PWM synchronous rectifier regulating the output voltage at a fixed frequency is presented, analyzed and verified experimentally. This rectifier is derived from the class E low dv/dt rectifier by replacing the controlled switch (MOSFET with its anti-parallel diode) with the rectifier diode in class E low dv/dt rectifier, and by using the synchronized PWM signal to control the output voltage at desired value. The ZVS condition of the controlled switch can be maintained from full-loaded to open-loaded. The experimental results measured at switching frequency 1 MHz are in good agreement with the theoretical prediction.

Over recent years, "Internet-able" applications and architectures have been used to support domains where there are multiple interconnected systems that are both decentralized and autonomous. In enterprise-level data management domains, both the schema of the data repository and the individual query needs of the users evolve over time. To handle this evolution, the resulting architecture must enforce the autonomy in systems that support the client needs and constraints, in addition to maintaining the autonomy in systems that support the actual data schema and extraction mechanisms. At the MITRE Corporation, this domain has been identified in the development of a composite data repository for the Center for Advanced Aviation System Development (CAASD). In the development of such a repository, the supporting architecture includes specialized mechanisms to disseminate the data to a diverse evolving set of researchers. This paper presents the motivation and design of such an architecture to support these autonomous data extraction environments. This run-time configurable architecture is implemented using web-based technologies such as the Extensible Markup Language (XML), Java Servlets, Extensible Stylesheets (XSL), and a relational database management system (RDBMS).

We present a 5.8 GHz VCO for the 5 GHz HIPERLAN/2 and U-NII band. The VCO uses a center-tapped inductor and a substrate shield to improve phase noise. Sensitivity to supply voltage and temperature is reduced by an amplitude control block. The design is based on a distributed inductor model which allows optimization without antecedent inductor measurements. The circuit is fabricated in a 0.8 µ m 45 GHz fT low-cost SiGe-HBT technology and operates with a supply voltage of -2.0 V to -3.3 V .

This paper deals with the approximation of multi-dimensional chaotic dynamics by using the multi-stage fuzzy inference system. The number of rules included in multi-stage fuzzy inference systems is remarkably smaller compared to conventional fuzzy inference systems where the number of rules are proportional to an exponential of the number of input variables. We also propose a method to optimize the shape of membership function and the appropriate selection of input variables based upon the genetic algorithm (GA). The method is applied to the approximation of typical multi-dimensional chaotic dynamics. By dividing the inference system into multiple stages, the total number of rules is sufficiently depressed compared to the single stage system. In each stage of inference only a portion of input variables are used as the input, and output of the stage is treated as an input to the next stage. To give better performance, the shape of the membership function of the inference rules is optimized by using the GA. Each individual corresponds to an inference system, and its fitness is defined by using the prediction error. Experimental results lead us to a relevant selection of the number of input variables and the number of stages by considering the computational cost and the requirement. Besides the GA in the optimization of membership function, we use the GA to determine the input variables and the number of input. The selection of input variable to each stage, and the number of stages are also discussed. The simulation study for multi-dimensional chaotic dynamics shows that the inference system gives better prediction compared to the prediction by the neural network.

Most of the current research is focused on the row-column scanning keyboard interface for English letter and number input. At the present time, there are insufficient methods to control the computer mouse effectively. In this study, a categorized row-column scanning computer interface is developed to improve the conventional single key-in row-column scanning method. The beneficial developments include: speed enhancement by categorizing radicals of keyboard, input control of mouse, and multiple selection of input methods such as surface electromyographic (SEMG) control, breath pressure sensibility control with puff, force sensibility control, infrared sensibility control and single key-in control. Meanwhile, an enhancement software package is developed to increase the row-column scanning keyboard capabilities and to upgrade the completeness of the computer mouse for the disabled persons to control the operation of data entry and the associated implementation better.

We propose hysteresis neural network solving combinatorial optimization problems, Box Puzzling Problem. Hysteresis neural network searches solutions of the problem with nonlinear dynamics. The output vector becomes stable only when it corresponds with a solution. This system does never become stable without satisfying constraints of the problem. After estimating hardware calculating time, we obtain that numerical calculating time increases extremely comparing with hardware time as problem's scale increases. However the system has possibility of limit cycle. Though it is very hard to remove limit cycle completely, we propose some methods to remove this phenomenon.

In this paper, in order to control uncertain chaotic system, an adaptive fuzzy control (AFC) scheme is developed for the multi-input/multi-output plants represented by the Takagi-Sugeno (T-S) fuzzy models. The proposed AFC scheme provides robust tracking of a desired signal for the T-S fuzzy systems with uncertain parameters. The developed control law and adaptive law guarantee the boundedness of all signals in the closed-loop system. In addition, the chaotic state tracks the state of the stable reference model (SRM) asymptotically with time for any bounded reference input signal. The suggested AFC design technique is applied to control of a uncertain Lorenz system based on T-S fuzzy model such as stabilization, synchronization and chaotic model following control (CMFC).

Feasibility of a color shutter using ferroelectric liquid crystal polymer panel and a field sequential ultra high-resolution CRT with the color shutter as a color field-switching device was studied. The color shutter consists of ferroelectric liquid crystal polymer panels and color polarizers. First, evaluation indices of the color shutter, such as the color gamut, the average transmittance and the white chromaticity shift, were formulated, and the simulation of evaluation indices was examined, where the spectral transmittance characteristics of the polarizer were changed in steps. It was indicated that there was a tradeoff between the color gamut and the average transmittance of the color shutter, and the shutter configuration that provides 0.096 (63% to NTSC) color gamut and 4.3% average transmittance was selected based on the simulation results. Next, the three-line simultaneous scanning method of the monochrome CRT was improved so that the disturbance due to the raster modulation was eliminated by averaging the distribution of beam luminance apparently. To confirm results of the study, the prototype of 21-inch screen size was produced, and the following display characteristics was obtained: luminance of 71 cd/m2, contrast ratio of 146:1 and color gamut of 0.096 (63% to NTSC) under the standard room lighting environment.

Particulate media composed of very small particles were studied to determine high-density recording performance and thermal stability. Studied media included metal particulate media with mean particle length of 71, 102 and 148 nm, and Ba ferrite particulate media with mean diameter of 22, 28 and 50 nm. Using a loss-term simulation program, taking into account gap-loss, spacing-loss and particle length loss, the recording capability (D20 of 265 kFRPI for MP and 290 kFRPI for Ba ferrite media) was estimated. Thermal stability was evaluated from magnetization time decay measurements. It was found that MP media with large Ku values and 71 nm particles were satisfactorily stable, and the particle volume is still large enough in respect of thermal stability. However, 22-nm Ba ferrite media were less stable, primarily because of small Ku values and particle volume. It was also clarified that positive inter-particle interaction accelerates magnetization time decay, in the presence of a large reverse field.

This paper presents a fast inversion method for electromagnetic imaging of cylindrical dielectric objects with the optimal regularization parameter used in the Levenberg-Marquardt method. A novel procedure for choosing the optimal regularization parameter is proposed. The method of moments with pulse-basis functions and point matching is applied to discretize the equations for the scattered electric field and the total electric field inside the object. Then the inverse scattering problem is reduced to solving the matrix equation for the unknown expansion coefficients of a contrast function, which is represented as a function of the relative permittivity of the object. The matrix equation may be solved in the least-squares sense with the Levenberg-Marquardt method. Thus the contrast function can be reconstructed by the minimization of a functional, which is expressed as the sum of a standard error term on the scattered electric field and an additional regularization term. While a regularization parameter is usually chosen according to the generalized cross-validation (GCV) method, the optimal one is now determined by minimizing the absolute value of the radius of curvature of the GCV function. This scheme is quite different from the GCV method. Numerical results are presented for a circular cylinder and a stratified circular cylinder consisting of two concentric homogeneous layers. The convergence behaviors of the proposed method and the GCV method are compared with each other. It is confirmed from the numerical results that the proposed method provides successful reconstructions with the property of much faster convergence than the conventional GCV method.

This paper first gives the exact surface integral representation for PO diffracted electromagnetic fields from bounded flat plate through the deformations of the original surface by using field equivalence principle. This exact representation with the surface integral can be approximately reduced to novel line integral along the boundary of the plate by the use of Maggi-Rubinowicz transformation, which keeps a high accuracy even in near zone. Numerical results for the scattering of the electric dipole wave from the square planar plate are presented for demonstrating the accuracy.

A signal suffers from nonlinear, linear, and additive distortion when transmitted through a channel. Linear equalizers are commonly used in receivers to compensate for linear channel distortion. As an alternative, novel equalizer structures utilizing neural computation have been developed for compensating for nonlinear channel distortion. In this paper, we propose a neural detector based on self-organizing map (SOM) in a 16 QAM system. The proposed scheme uses the SOM algorithm and symbol-by-symbol detector to form a neural detector, and it adapts well to the changing channel conditions, including nonlinear distortions because of the topology-preserving property of the SOM algorithm. According to the theoretical analysis and computer simulation results, the proposed scheme is shown to have better performance than traditional linear equalizer when facing with nonlinear distortion.

A polarization diversity planar inverted-F antenna (PIFA) on portable telephone in the practical use near the operator's body is investigated at 1,800 MHz under multipath urban environment. The antenna structure comprises a center-fed square patch with one permanent short-pin and two RF-switches on three corners. The RF-switches perform as the polarization branch switches for dominantly vertical polarization (VP) or dominantly horizontal polarization (HP) modes. The radiation efficiency of the polarization diversity PIFA is 58% and 53% for VP and HP modes, respectively, which is higher than the 52% efficiency of the reference λ/4 monopole antenna under the same condition. The mean effective gain (MEG) of VP and HP modes decrease with respect to the increasing cross-polarization power ratio (XPR). The correlation coefficient of two diversity branches is between 0.66 through all the possible XPR ranging from -10 dB to +10 dB. The diversity gain is computed from the MEG and correlation coefficient to determine the diversity antenna gain (DAG). The diversity gain, based on 10-3 BER for selective combining, is 7.5 dB over non-diversity reception. The DAG is -1.2+2.8 dBi which is approximately 4 dB lower than the case without human body. In other words, the presence of the human body degrades the communication performance by a half.

We often see reflection phenomenon in our life. For example, through window glass, we can see real objects, but reflection causes virtual objects to appear in front of the glass. Thus, it is sometimes difficult to recognize the real objects. Some works have been proposed to separate these real and virtual objects using an optical property called polarization. However, they have a restriction on one assumption: the angle of incidence. In this paper, we overcome this difficulty using independent component analysis (ICA). We show the efficiency of the proposed method, by experimental results.

This paper has a dual purpose. First, it proposes a virtual path management model for ATM networks. The model unifies VPC overlay network configuration, VCC routing, and capacity allocation in a single framework. It accommodates multiple end-to-end offered traffics of various QoS requirements and traffic types. Especially, it also supports point-to-multipoint as well as point-to-point connections in a resource-efficient manner. The objective is to minimize the overall network resource cost. To do so, it pursues an optimal trade-off among the gains offered by ATM technology. The application of the proposed model is naturally extended to the multiprotocol label switching framework. Second, it proposes an efficient algorithm to solve the model. The mathematical formulation of such a unifying model typically involves a very large-scale intractable optimization which, treated by a straightforward method, requires excessive computational efforts. In this paper, we show how the computational structure of formulation can be exploited to tailor a solution method providing good solutions in dramatically reduced computational efforts.

In this paper we consider a tensor-based approach to the analytical computation of higher-order expectations of quantized trajectories generated by Piecewise Affine Markov (PWAM) maps. We formally derive closed-form expressions for expectations of trajectories generated by three families of maps, referred to as (n,t)-tailed shifts, (n,t)-broken identities and (n,t,π)-mixing permutations. These families produce expectations with asymptotic exponential decay whose detailed profile is controlled by map design. In the (n,t)-tailed shift case expectations are alternating in sign, in the (n,t)-broken identity case they are constant in sign, and the (n,t,π)-mixing permutation case they follow a dumped periodic trend.

Feature subset selection basically depends on the design of a criterion function to measure the effectiveness of a particular feature or a feature subset and the selection of a search strategy to find out the best feature subset. Lots of techniques have been developed so far which are mainly categorized into classifier independent filter approaches and classifier dependant wrapper approaches. Wrapper approaches produce good results but are computationally unattractive specially when nonlinear neural classifiers with complex learning algorithms are used. The present work proposes a hybrid two step approach for finding out the best feature subset from a large feature set in which a fuzzy set theoretic measure for assessing the goodness of a feature is used in conjunction with a multilayer perceptron (MLP) or fractal neural network (FNN) classifier to take advantage of both the approaches. Though the process does not guarantee absolute optimality, the selected feature subset produces near optimal results for practical purposes. The process is less time consuming and computationally light compared to any neural network classifier based sequential feature subset selection technique. The proposed algorithm has been simulated with two different data sets to justify its effectiveness.

We have experimentally observed chaotic oscillation of outputs in a diode-pumped Nd:YAG microchip laser array with an external Talbot mirror. The oscillation of chaotic output is observed at frequencies of sub MHz corresponding to the relaxation oscillation frequencies when the Talbot mirror is slightly tilted from the perfect alignment position with the internal cavity. Chaotic intermittent bursts also appear at frequencies of sub kHz due to longitudinal mode hopping. Synchronization of chaos is observed at these two different time scales. The generation of chaotic oscillations at sub MHz is confirmed by using numerical simulations. It is found that synchronized chaotic oscillations can be observed in the vicinity of the boundary of the injection locking range.

We consider a network consisting of phase locked loops coupled one another through frequency dividers. When the network structure is rotationally symmetric, spatially periodic simple patterns in terms of the phase of the PLLs are formed. The patterns determine the lock-in frequency of the network. The stability of the pattern is determined by the spatially distributed simple coupling weight patterns. Therefore, a signal with which the network synchronizes is indirectly selected by the weight patterns when several signals are simultaneously applied to the network. The selectivity plays an important role in an intelligent network model.

In this paper, we consider the problem of active learning, and give a necessary and sufficient condition of sample points for the optimal generalization capability. By utilizing the properties of pseudo orthogonal bases, we clarify the mechanism of achieving the optimal generalization capability. We also show that the condition does not only provide the optimal generalization capability but also reduces the computational complexity and memory required to calculate learning result functions. Based on the optimality condition, we give design methods of optimal sample points for trigonometric polynomial models. Finally, the effectiveness of the proposed active learning method is demonstrated through computer simulations.