For the power amplifier used in CDMA cellular phones, the supply voltage is switched between high and low at a transmission power several decibels higher than 10 dBm using a DC-DC converter to improve operational efficiency. The longer the operation time under low supply voltage, the lower the current consumption of the cellular phone. In order to increase the output power under low supply voltage, we applied the 2nd harmonic-injection technique, which is useful for distortion compensation. With 2nd harmonic-injection, there is an inflectional power point. The distortion increases rapidly when output power goes beyond the inflectional power point. It is important to make this inflectional power point high in order to compensate for distortion in the high output-power region. We report here that the inflectional power point can be increased by connecting a 2nd harmonic short circuit to the drain terminal of the FET to which the 2nd harmonic for distortion compensation is injected. A prototype of the final stage of the power amplifier under a supply voltage of Vdd=1.5 V is presented. We report that applying a CDMA uplink signal, 1.5 dB higher output power and 12% higher drain efficiency is achieved compared when only 2nd harmonic injection is employed.

High performance delay-locked loop (DLL) is key to the high data rate chip-to-chip communication, suggesting the output jitter, due to power noise, bang-bang noise, temperature-voltage drift, etc, should be properly controlled. In this paper, high speed DRAM operation can be achieved by a dual loop DLL with various novel techniques; a new counting code with hysteretic bit-transitions can remove the large DAC glitches by preventing the binary bit-transitions in the locking states. A delay buffer, which is insensitive to the power supply fluctuations, is proposed. The voltage-temperature (VT) dependencies of the feedback path and the open clock path are balanced, minimizing the VT shift of the clock. As a result, the high-speed DRAM interface with the maximized setup/hold window can be accomplished.

This paper newly proposes a method to automatically decompose real scene images into multiple object-oriented component regions. First, histogram patterns of a specific image feature, such as intensity or hue value, are estimated from image sequence and stored up. Next, Gaussian distribution parameters which correspond to object components involved in the scene are estimated by applying the EM algorithm to the accumulated histogram. The number of the components is simultaneously estimated by evaluating the minimum value of Bayesian Information Criterion (BIC). This method can be applied to a variety of computer vision issues, for example, the color image segmentation and the recognition of scene situation transition. Experimental results applied for indoor and outdoor scenes showed the effectiveness of the proposed method.

In this paper two algorithms for the synthesis and minimization of a CA (cellular array architecture) are proposed. Starting from a completely specified single-output switching function, our methods produce rectangularly shaped arrays of cells, interconnected in chains, with an effort to minimize the number of the produced chains (cascades). This kind of cellular topology is known throughout the bibliography as Maitra cellular arrays. The significance of those algorithms is underlined by the fact that this particular type of cellular architecture can be mapped to reversible circuits and gates (generalized Toffoli gates), which are the type of logic used in quantum circuits. The proposed methodologies include use of ETDDs (EXOR ternary decision diagrams), and switching function decompositions (including new types of boolean expansions).

The linear operation of a HBT with a GaAs/InGaP composite collector structure is demonstrated. The composite collector structure allows for a thin collector design that is suitable for the linear operation of a HBT without critical degradation of the breakdown voltage. The load pull measurements under a 1.95 GHz WCDMA signal have shown that a composite-collector HBT with a 400-nm thick collector layer operates with power-added-efficiency (PAE) as high as 53% at VCE = 3.5 V as a result of improved distortion characteristics. Despite the thin collector design, collector-emitter breakdown voltage of 11 V was achieved even at current density of 10 kA/cm2. The composite-collector HBT has even greater advantage for future low voltage (< 3 V) applications where maintaining PAE and linearity becomes one of the critical issues.

Global computing system (GCS) harnesses the idle CPU resources of clients connected to Internet for solving large problems that require high volume of computing power. Since GCS scale to millions of clients, many projects usually adopt coarse-grained scheduling in order to reduce server-side contention at the expense of sacrificing the degree of parallelism and wasting CPU resources. In this paper, we propose a new type of client, i.e., a scheduling proxy that enables adaptive-grained scheduling between the server and clients. While the server allocates coarse-grained work units to scheduling proxies alone, clients download fine-grained work units from a relatively nearby scheduling proxy not from the distant server. By computation of small work units at client side, the turnaround time of work unit can be reduced and the waste of CPU time by timeout can be minimized without increasing the performance cost of contention at the server. In addition, in order not to lose results in the failure of scheduling proxies, we suggest a technique of result caching in clients.

Spring-mass systems are widely used in computer animation to model soft objects. Although the systems can be numerically solved either by explicit methods or implicit methods, it has been difficult to obtain stable results from explicit methods. This paper describes detailed discussion on stabilizing explicit methods in spring-mass simulation. The simulation procedures are modeled as a linear digital system, and system stability is mathematically defined. This allows us to develop theories of simulation stability. The application of these theories to explicit methods allows them to become as stable as implicit methods. Furthermore, a faster explicit method is proposed. Experiments confirm the theories and demonstrate the efficiency of the proposed methods.

A new hybrid method for characterizing the irregular power/ground plane pair is developed in this paper by combining the conventional eigen-mode expansion method with the new-presented inverted composition method and a simple model order reduction. By the approach, the eigen-mode expansion method can be extended to the characteristics research of the power/ground plane pair with holes. In this gridless method, ports and decoupling capacitors can be arbitrarily placed on the plane pair. The numerical example demonstrates its good validity.

Penumbral imaging is a technique which exploits the fact that spatial information can be recovered from the shadow or penumbra that an unknown source casts through a simple large circular aperture. Since the technique is based on linear deconvolution, it is sensitive to noise. In this paper, a two-step method is proposed for decoding penumbral images: first, a noise-reduction algorithm based on ICA-domain (independent component analysis-domain) shrinkage is applied to smooth the given noise; second, the conventional linear deconvolution follows. The simulation results show that the reconstructed image is dramatically improved in comparison to that without the noise-removing filters, and the proposed method is successfully applied to real experimental X-ray imaging.

A novel unified phase compiler framework for embedded VLIWs and DSPs is shown. In this compiler, a given program is represented in 3-D representation space, which enables quantitatively estimating required resources and elapsed time. Transformation of a 3-D representation graph that corresponds to a code optimization method for a specific processor architecture is also proposed. The proposal compiler and the code optimization methods are compared with an ordinary compiler in terms of their generated codes. The results demonstrate their effectiveness.

This paper provides algorithms in order to solve an interval implicit function of the Poincare map generated by a continuous piece-wise linear (CPWL) vector field, with the use of interval arithmetic. The algorithms are implemented with the use of MATLAB and INTLAB. We present an application to verification of canards in two-dimensional CPWL vector field appearing in nonlinear piecewise linear circuits frequently, and confirm that the algorithms are effective.

This paper describes techniques of scoring prosodic proficiency of English sentences spoken by Japanese. The multiple regression model predicts the prosodic proficiency using new prosodic measures based on the characteristics of Japanese novice learners of English. Prosodic measures are calculated by comparing prosodic parameters, such as F0, power and duration, of learner's and native speaker's speech. The new measures include the approximation error of the fitting line and the comparison result of prosodic parameters for a limited segment of the word boundary rather than the whole utterance. This paper reveals that the introduction of the new measures improved the correlation by 0.1 between the teachers' and automatic scores.

This paper presents a novel algorithm for spectral subtraction (SS). The method is derived from a relation between the spectrum obtained by the discrete Fourier transform (DFT) and that by a subspace decomposition method. By using the relation, it is shown that a noise reduction algorithm based on subspace decomposition is led to an SS method in which noise components in an observed signal are eliminated by subtracting variance of noise process in the frequency domain. Moreover, it is shown that the method can significantly reduce computational complexity in comparison with the method based on the standard subspace decomposition. In a similar manner to the conventional SS methods, our method also exploits the variance of noise process estimated from a preceding segment where speech is absent, whereas the noise is present. In order to more reliably detect such non-speech segments, a novel robust voice activity detector (VAD) is then proposed. The VAD utilizes the spread of eigenvalues of an autocorrelation matrix corresponding to the observed signal. Simulation results show that the proposed method yields an improved enhancement quality in comparison with the conventional SS based schemes.

We report tunable optical devices based on fiber Bragg gratings (FBGs), whose filtering characteristics are controlled by strain distributions. These devices include a widely wavelength tunable filter, a tunable group-velocity dispersion (GVD) compensator, a tunable dispersion slope (DS) compensator, and a variable-bandwidth optical add/drop multiplexer (OADM), which will play important roles for next-generation reconfigurable optical networks.

This paper presents an analysis of the applicability of Sparse Kernel Principal Component Analysis (SKPCA) for feature extraction in speech recognition, as well as, a proposed approach to make the SKPCA technique realizable for a large amount of training data, which is an usual context in speech recognition systems. Although the KPCA (Kernel Principal Component Analysis) has proved to be an efficient technique for being applied to speech recognition, it has the disadvantage of requiring training data reduction, when its amount is excessively large. This data reduction is important to avoid computational unfeasibility and/or an extremely high computational burden related to the feature representation step of the training and the test data evaluations. The standard approach to perform this data reduction is to randomly choose frames from the original data set, which does not necessarily provide a good statistical representation of the original data set. In order to solve this problem a likelihood related re-estimation procedure was applied to the KPCA framework, thus creating the SKPCA, which nevertheless is not realizable for large training databases. The proposed approach consists in clustering the training data and applying to these clusters a SKPCA like data reduction technique generating the reduced data clusters. These reduced data clusters are merged and reduced in a recursive procedure until just one cluster is obtained, making the SKPCA approach realizable for a large amount of training data. The experimental results show the efficiency of SKPCA technique with the proposed approach over the KPCA with the standard sparse solution using randomly chosen frames and the standard feature extraction techniques.

In this paper, single-input multiple-output (SIMO)-model-based blind source separation (BSS) is addressed, where unknown mixed source signals are detected at microphones, and can be separated, not into monaural source signals but into SIMO-model-based signals from independent sources as they are at the microphones. This technique is highly applicable to high-fidelity signal processing such as binaural signal processing. First, we provide an experimental comparison between two kinds of SIMO-model-based BSS methods, namely, conventional frequency-domain ICA with projection-back processing (FDICA-PB), and SIMO-ICA which was recently proposed by the authors. Secondly, we propose a new combination technique of the FDICA-PB and SIMO-ICA, which can achieve a higher separation performance than the two methods. The experimental results reveal that the accuracy of the separated SIMO signals in the simple SIMO-ICA is inferior to that of the signals obtained by FDICA-PB under low-quality initial value conditions, but the proposed combination technique can outperform both simple FDICA-PB and SIMO-ICA.

This paper describes a polarimetric feature extraction method from urban areas using the POLSAR (Polarimetric Synthetic Aperture Radar) data. The scattering characteristic of urban areas is different from that of natural distributed areas. The main point of difference is polarimetric correlation coefficient, because urban areas do not satisfy property of azimuth symmetry, Shh = Shv = 0. The decomposition technique based on azimuth symmetry can not be applied to urban areas. We propose a new model fit suitable for urban areas. The proposed model fit consists of odd-bounce, even-bounce and cross scattering models. These scattering models can represent the polarimetric backscatter from urban areas, and satisfy Shh 0 and Shv 0. In addition, the combination with the proposed model fit and the three component scattering model suited for natural distributed areas is examined. It is possible to apply the combined technique to POLSAR data which includes both urban areas and natural distributed areas. The combined technique is used for feature extraction of actual X-band POLSAR data acquired by Pi-SAR. It is shown that the proposed model fit is useful to extract polarimetric features from urban areas.

Ubiquitous computing (ubicomp) demands new security and privacy enhancing technologies for the new information and communication environments where a huge number of computers interact with each other in a distributed and ad hoc manner to access various resources and services. This paper surveys emerging security and privacy enhancing technologies, focusing on access control in ubiquitous computing environments because this is the underlying core technology to enforce security and privacy policies. We classify access control technologies into three types associated with the three access control phases of prevention, avoidance, and detection, and provide an overview of ubiquitous computing-oriented technologies and solutions, ranging from security and privacy models and policies to the enforcement of policies and system implementation.

In a ubiquitous computing environment, people are surrounded by hundreds of mobile or embedded computers each of which may be used to support one or more user applications due to limitations in their individual computational capabilities. We need an approach to coordinating heterogeneous computers that acts as a virtual computer around a mobile and ubiquitous computing environment and supports various applications beyond the capabilities of single computers. This paper presents a framework for building and aggregating distributed applications from one or more mobile components that can be dynamically deployed at mobile or stationary computers during the execution of the application. Since the approach involves mobile-transparent communications between components and component relocation semantics, it enables a federation of components to adapt its structure and deployment on multiple computers whose computational resources, such as input and output devices, can satisfy the requirement of the components in a self-organized manner. This paper also describes a prototype implementation of the approach and its application.

Energy-efficient operations are essential to prolonging the lifetime of wireless sensor networks. Clustering sensor nodes is one approach that can reduce energy consumption by aggregating data, controlling transmission power levels, and putting redundant sensor nodes to sleep. To distribute the role of a cluster head, clustering approaches should be based on efficient cluster configuration schemes. Therefore, low overhead in the cluster configuration process is one of the key constraints for energy-efficient clustering. In this paper, we present an autonomous clustering approach using a coverage estimation-based self-pruning algorithm. Our strategy for clustering is to allow the best candidate node within its own cluster range to declare itself as a cluster head and to dominate the other nodes in the range. This same self-declaration strategy is also used in the active sensor election process. As a result, the proposed scheme can minimize clustering overheads by obviating both the requirements of collecting neighbor information beforehand and the iterative negotiating steps of electing cluster heads. The proposed scheme allows any type of sensor network application, including spatial query execution or periodic environment monitoring, to operate in an energy-efficient manner.