In this paper, the new residue number system (RNS) moduli sets {22n, 2n -1, 2n+1 -1} and {22n, 2n -1, 2n-1 -1} are introduced. These moduli sets have 4n-bit dynamic range and well-formed moduli which can result in high-performance residue to binary converters as well as efficient RNS arithmetic unit. Next, efficient residue to binary converters for the proposed moduli sets based on mixed-radix conversion (MRC) algorithm are presented. The converters are ROM-free and they are realized using carry-save adders and modulo adders. Comparison with the other residue to binary converters for 4n-bit dynamic range moduli sets shown that the presented designs based on new moduli sets {22n, 2n -1, 2n+1 -1} and {22n, 2n -1, 2n-1 -1} are improved the conversion delay and result in hardware savings. Also, the proposed moduli sets can lead to efficient binary to residue converters, and they can speed-up internal RNS arithmetic processing, compared with the other 4n-bit dynamic range moduli sets.

In this letter, we propose an efficient method to improve the performance of voiced/unvoiced (V/UV) sounds decision for the selectable mode vocoder (SMV) of 3GPP2 using the Gaussian mixture model (GMM). We first present an effective analysis of the features and the classification method adopted in the SMV. And feature vectors which are applied to the GMM are then selected from relevant parameters of the SMV for the efficient V/UV classification. The performance of the proposed algorithm are evaluated under various conditions and yield better results compared to the conventional method of the SMV.

In this letter, an acoustic environment classification algorithm based on the 3GPP2 selectable mode vocoder (SMV) is proposed for context-aware mobile phones. Classification of the acoustic environment is performed based on a Gaussian mixture model (GMM) using coding parameters of the SMV extracted directly from the encoding process of the acoustic input data in the mobile phone. Experimental results show that the proposed environment classification algorithm provides superior performance over a conventional method in various acoustic environments.

This paper proposes a method of low complexity speech mixing with speech codecs based on predictive coding for multimedia conferences. The proposed method applies a filter state management (FSM) technique to a partial mixing method in order to avoid inconsistency of the filter states of encoders. The inconsistency is created by switching of the encoders when the speakers to be mixed are switched. The results of subjective evaluations of speech quality show that the proposed method avoids the inconsistency, and achieves significantly higher speech quality than the conventional partial mixing method without the FSM and almost the same speech quality as the full mixing method. The complexity evaluation results show that the proposed method achieves much lower complexity than the full mixing method.

Particle filter has attracted increasing attention from researchers of object tracking due to its promising property of handling nonlinear and non-Gaussian systems. In this paper, we mainly explore the problem of precisely estimating observation likelihoods of particles in the joint feature-spatial space. For this purpose, a mixture Gaussian kernel function based similarity is presented to evaluate the discrepancy between the target region and the particle region. Such a similarity can be interpreted as the expectation of the spatial weighted feature distribution over the target region. To adapt outburst of object motion, we also present a method to appropriately adjust state transition model by utilizing the priors of motion speed and object size. In comparison with the standard particle filter tracker, our tracking algorithm shows the better performance on challenging video sequences.

An integral equation approach with a new solution procedure using moment method (MoM) is applied for the computation of coupled currents on the surface of a printed dipole antenna and inside its high-permittivity three-dimensional dielectric substrate. The main purpose of this study is to validate the accuracy and reliability of the previously proposed MoM procedure by authors for the solution of a coupled volume-surface integral equations system. In continuation of the recent works of authors, a mixed-domain MoM expansion using Legendre polynomial basis function and cubic geometric modeling are adopted to solve the tensor-volume integral equation. In mixed-domain MoM, a combination of entire-domain and sub-domain basis functions, including three-dimensional Legnedre polynomial basis functions with different degrees is utilized for field expansion inside dielectric substrate. In addition, the conventional Rao-Wilton-Glisson (RWG) basis function is employed for electric current expansion over the printed structure. The accuracy of the proposed approach is verified through a comparison with the MoM solutions based on the spectral domain Green's function for infinitely large substrate and the results of FDTD method.

This paper presents a system architecture for sensor signal digitization utilizing a band-pass sigma-delta modulator (BP ΣΔM). The first version of the proposed system architecture was implemented in 5 V 0.7 µm CMOS technology. The proposed system architecture is useful for our capacitive pressure sensor measurement. The paper describes the possibilities of using the proposed enhanced system architecture in impedance spectroscopy and in capacitive pressure sensor measurement. The BP ΣΔM is well suited for wireless applications. This paper shows another way how to use its advantages.

An integral multi-disciplinary chain optimization based on a high-level cascaded Shannon-based channel modeling is proposed. It is argued that the analog part of the front-end (FE) will become a bottleneck in the overall chain. This requires a FE-centric design approach, aiming for maximizing the effective data capacity, and for an optimal exploitation of this capacity for given power dissipation. At high level, this asks for a new view on the so-called client-server relations in the chain. To substantiate this vision, some examples of research projects in our group are addressed. These include FE-driven transmission schemes, duty-cycled operation with wake-up radio, programmable FEs, smart antenna-FE combinations, smart and flexible converters, and smart pre and post correction.

In this paper, the problem of density estimation and clustering in sensor networks is considered. It is assumed that measurements of the sensors can be statistically modeled by a common Gaussian mixture model. This paper develops a distributed variational Bayesian algorithm (DVBA) to estimate the parameters of this model. This algorithm produces an estimate of the density of the sensor data without requiring the data to be transmitted to and processed at a central location. Alternatively, DVBA can be viewed as a distributed processing approach for clustering the sensor data into components corresponding to predominant environmental features sensed by the network. The convergence of the proposed DVBA is then investigated. Finally, to verify the performance of DVBA, we perform several simulations of sensor networks. Simulation results are very promising.

In this letter, we propose a new approach to SVM based speaker recognition, which utilizes a kind of novel phonotactic information as the feature for SVM modeling. Gaussian mixture models (GMMs) have been proven extremely successful for text-independent speaker recognition. The GMM universal background model (UBM) is a speaker-independent model, each component of which can be considered as modeling some underlying phonetic sound classes. We assume that the utterances from different speakers should get different average posterior probabilities on the same Gaussian component of the UBM, and the supervector composed of the average posterior probabilities on all components of the UBM for each utterance should be discriminative. We use these supervectors as the features for SVM based speaker recognition. Experiment results on a NIST SRE 2006 task show that the proposed approach demonstrates comparable performance with the commonly used systems. Fusion results are also presented.

The end-to-end round trip time (RTT) is one of the most important communication characteristics for Internet applications. From the viewpoint of network operators, RTT may also become one of the important metrics to understand the network conditions. Given this background, we should know how a factor such as a network incident influences RTTs. It is obvious that two or more factors may interfere in the observed delay characteristics, because packet transmission delays in the Internet are strongly dependent on the time-variant condition of the network. In this paper, we propose a modeling method by using mixed distribution which enables us to express delay characteristic more accurately where two or more factors exist together. And, we also propose an inferring method of network behavior by decomposition of the mixed distribution based on modeling results. Furthermore, in experiments we investigate the influence caused by each network impact factor independently. Our proposed method can presume the events that occur in a network from the measurements of RTTs by using the decomposition of the mixed distribution.

It is essential, as bandwidths of wireless communications get wider, to evaluate the imbalances among quadrature mixer ports, in terms of carrier phase offset, IQ gain imbalance, and IQ skew. Because it is time consuming to separate skew, gain imbalance and carrier phase offset evaluation during test is often performed using a composite value, without separation of the imbalance factors. This paper describes an algorithm for enabling separation among quadrature mixer gain imbalance, carrier phase offset, and skew. Since the test time is reduced by the proposed method, it can be applied during high volume production testing.

An algorithm for blind signal separation (BSS) of convolutive mixtures is presented. In this algorithm, the BSS problem is treated as multidimensional independent component analysis (ICA) by introducing an extended signal vector which is composed of current and previous samples of signals. It is empirically known that a number of conventional ICA algorithms solve the multidimensional ICA problem up to permutation and scaling of signals. In this paper, we give theoretical justification for using any conventional ICA algorithm. Then, we discuss the remaining problems, i.e., permutation and scaling of signals. To solve the permutation problem, we propose a simple algorithm which classifies the signals obtained by a conventional ICA algorithm into mutually independent subsets by utilizing temporal structure of the signals. For the scaling problem, we prove that the method proposed by Koldovský and Tichavský is theoretically proper in respect of estimating filtered versions of source signals which are observed at sensors.

In order to improve the performance of the existing statistical timing analysis, slew distributions must be taken into account and a mechanism to propagate them together with delay distributions along signal paths is necessary. This paper introduces Gaussian mixture models to represent the slew and delay distributions, and proposes a novel algorithm for statistical timing analysis. The algorithm propagates a pair of delay and slew in a given circuit graph, and changes the delay distributions of circuit elements dynamically by propagated slews. The proposed model and algorithm are evaluated by comparing with Monte Carlo simulation. The experimental results show that the accuracy improvement in µ+3σ value of maximum delay is up to 4.5 points from the current statistical timing analysis using Gaussian distributions.

When we watch television or computer monitor under a certain viewing condition, we partially adapt to the display and partially to the ambient light. As an illumination level and chromaticity change, the eye's subjective white point changes between the display's white point and the ambient light's white point. In this paper, we propose a model that could predict the white point under a mixed adaptation condition including display and illuminant. Finally we verify this model by experimental results.

This paper describes a multiresolutional Gaussian mixture model (GMM) for precise and stable foreground segmentation. A multiple block sizes GMM and a computationally efficient fine-to-coarse strategy, which are carried out in the Walsh transform (WT) domain, are newly introduced to the GMM scheme. By using a set of variable size block-based GMMs, a precise and stable processing is realized. Our fine-to-coarse strategy comes from the WT spectral nature, which drastically reduces the computational steps. In addition, the total computation amount of the proposed approach requires only less than 10% of the original pixel-based GMM approach. Experimental results show that our approach gives stable performance in many conditions, including dark foreground objects against light, global lighting changes, and scenery in heavy snow.

In this paper, we have obtained the integral representation for the ground wave propagation over land-to-sea mixed-paths which uses the equivalent current source on an aperture plane. By extending the integral to the complex plane and deforming the integration path into the steepest descent path, we have derived a simple integral representation for the mixed-path ground wave propagation. We have also derived the hybrid numerical and asymptotic representation for an efficient calculation of the ground wave and for easy understanding of the diffraction phenomena. By using the method of the stationary phase applicable uniformly as the stationary phase point approaches the endpoint, we have derived the high-frequency asymptotic solution for the ground wave propagation over the mixed-path. We have confirmed the validity of the various representations by comparing both with the conventional mixed-path theory and with the experimental results performed in Kanto areas including the sea near Tokyo bay. By examining the asymptotic solution in detail, we have found out the cause or the mechanism of the recovery effect occurring on the portion of the sea over the land-to-sea mixed-path.

In this paper, a novel content addressable memory (CAM) structure is proposed to improve the performance of a static divided word matching (SDWM) CAM. In the SDWM CAM, a small pmos has to be used to keep a noise margin, but it degrades performance significantly. To resolve this problem, a conditional driver is introduced in the proposed serial-parallel CAM. Performance is improved by 28.0% without additional power consumption at a cost of about 5.6% increased area when the total bit number is 32 with four series bits and 30% of VDD is allowed as noise.

In the context of multiple constant multiplication (MCM) design, we propose a novel common sub-expression elimination (CSE) algorithm that models the optimal synthesis of coefficients into a 0-1 mixed-integer linear programming (MILP) problem with a user-defined generic logic depth constraint. We also propose an efficient solution space, which combines all minimal signed digit (MSD) representations and the shifted sum (difference) of coefficients. In the examples we demonstrate, the combination of the proposed algorithm and solution space gives a better solution comparing to existing algorithms.

The mixture modeling framework is widely used in many applications. In this paper, we propose a component reduction technique, that collapses a Gaussian mixture model into a Gaussian mixture with fewer components. The EM (Expectation-Maximization) algorithm is usually used to fit a mixture model to data. Our algorithm is derived by extending mixture model learning using the EM-algorithm. In this extension, a difficulty arises from the fact that some crucial quantities cannot be evaluated analytically. We overcome this difficulty by introducing an effective approximation. The effectiveness of our algorithm is demonstrated by applying it to a simple synthetic component reduction task and a phoneme clustering problem.