Time of arrival (TOA) is a widely used wireless cellular network ranging technology. How to perform accurate TOA estimation in multi-path and non-line-of-sight (NLOS) environments and then accurately calculating mobile terminal locations are two critical issues in positioning research. NLOS identification can be performed in the TOA measurement part and the position calculation part. In this paper, for the above two steps, two schemes for mitigating NLOS errors are proposed. First, a TOA ranging method based on clustering theory is proposed to solve the problem of line-of-sight (LOS) path estimation in multi-path channels. We model the TOA range as a Gaussian mixture model and illustrate how LOS and NLOS can be measured and identified based on non-parametric Bayesian methods when the wireless transmission environment is unknown. Moreover, for NLOS propagation channels, this paper proposes a user location estimator based on the maximum a posteriori criterion. Combined with the TOA estimation and user location computation scheme proposed in this paper, the terminal's positioning accuracy is improved. Experiments showed that the TOA measurement and localization algorithms presented in this paper have good robustness in complex wireless environments.

This paper describes the development of superconductor-insulator-superconductor (SIS) mixers for the Atacama Large Millimeter/submillimeter Array (ALMA) from the device point of view. During the construction phase of ALMA, the National Astronomical Observatory of Japan (NAOJ) successfully fabricated SIS mixers to meet the stringent ALMA noise temperature requirements of less than 230 K (5 times the quantum noise) for Band 10 (787-950 GHz) in collaboration with the National Institute of Information and Communications Technology. Band 10 covers the highest frequency band of ALMA and is recognized as the most difficult band in terms of superconducting technology. After the construction, the NAOJ began development studies for ALMA enhancement such as wideband and multibeam SIS mixers according to top-level science requirements, which are also presented.

To meet the increasing demand for high-speed communication in VLSI (very large-scale integration) systems, next-generation high-speed data transmission standards (e.g., IEEE 802.3bs and PCIe 6.0) will adopt four-level pulse amplitude modulation (PAM-4) for data coding. Although PAM-4 is spectrally efficient to mitigate inter-symbol interference caused by bandwidth-limited wired channels, it is more sensitive than conventional non-return-to-zero line coding. To evaluate the received signal quality when using adaptive coefficient settings for a PAM-4 equalizer during data transmission, we propose an eye-opening monitor technique based on machine learning. The proposed technique uses a Gaussian mixture model to classify the received PAM-4 symbols. Simulation and experimental results demonstrate the feasibility of adaptive equalization for PAM-4 coding.

In this paper, we propose a method which enables us to control the variance of the coefficients of the LMS-type adaptive filters. In the method, each coefficient of the adaptive filter is modeled as an random variable with a Gaussian distribution, and its value is estimated as the mean value of the distribution. Besides, at each time, we check if the updated value exists within the predefined range of distribution. The update of a coefficient will be canceled when its updated value exceeds the range. We propose an implementation method which has similar formula as the Gaussian mixture model (GMM) widely used in signal processing and machine learning. The effectiveness of the proposed method is evaluated by the computer simulations.

In this work, we introduce notions of quantum frequency arrangements consisting of quantum frequency squares, cubes, hypercubes and a notion of orthogonality between them. We also propose a notion of quantum mixed orthogonal array (QMOA). By using irredundant mixed orthogonal array proposed by Goyeneche et al. we can obtain k-uniform states of heterogeneous systems from quantum frequency arrangements and QMOAs. Furthermore, some examples are presented to illustrate our method.

In recent years, the use of big data has attracted more attention, and many techniques for data analysis have been proposed. Big data analysis is difficult, however, because such data varies greatly in its regularity. Heterogeneous mixture machine learning is one algorithm for analyzing such data efficiently. In this study, we propose online heterogeneous learning based on an online EM algorithm. Experiments show that this algorithm has higher learning accuracy than that of a conventional method and is practical. The online learning approach will make this algorithm useful in the field of data analysis.

We propose a speech analysis-synthesis and deep neural network (DNN)-based text-to-speech (TTS) synthesis framework using Gaussian mixture model (GMM)-based approximation of full-band spectral envelopes. GMMs have excellent properties as acoustic features in statistic parametric speech synthesis. Each Gaussian function of a GMM fits the local resonance of the spectrum. The GMM retains the fine spectral envelope and achieve high controllability of the structure. However, since conventional speech analysis methods (i.e., GMM parameter estimation) have been formulated for a narrow-band speech, they degrade the quality of synthetic speech. Moreover, a DNN-based TTS synthesis method using GMM-based approximation has not been formulated in spite of its excellent expressive ability. Therefore, we employ peak-picking-based initialization for full-band speech analysis to provide better initialization for iterative estimation of the GMM parameters. We introduce not only prediction error of GMM parameters but also reconstruction error of the spectral envelopes as objective criteria for training DNN. Furthermore, we propose a method for multi-task learning based on minimizing these errors simultaneously. We also propose a post-filter based on variance scaling of the GMM for our framework to enhance synthetic speech. Experimental results from evaluating our framework indicated that 1) the initialization method of our framework outperformed the conventional one in the quality of analysis-synthesized speech; 2) introducing the reconstruction error in DNN training significantly improved the synthetic speech; 3) our variance-scaling-based post-filter further improved the synthetic speech.

The test of homogeneity for normal mixtures has been used in various fields, but its theoretical understanding is limited because the parameter set for the null hypothesis corresponds to singular points in the parameter space. In this paper, we shed a light on this issue from a new perspective, variational Bayes, and offer a theory for testing homogeneity based on it. Conventional theory has not reveal the stochastic behavior of the variational free energy, which is necessary for constructing a hypothesis test, has remained unknown. We clarify it for the first time and construct a new test base on it. Numerical experiments show the validity of our results.

A high degree of freedom in spectral domain allows us to accommodate additional optical signal processing for wavelength division multiplexing in photonic analog-to-digital conversion. We experimentally verified a spectral compression to save a necessary bandwidth for soliton self-frequency shift based optical quantization through the cascade of the four-wave mixing based and the sum-frequency generation based spectral compression. This approach can realize 0.03 nm individual bandwidth correspond to save up to more than 85 percent of bandwidth for 7-bit optical quantization in C-band.

Wavelength division multiplexing (WDM) scheme is used widely in photonic metro-core networks. In a WDM network, wavelength continuity constraint is employed to simply construct relay nodes. This constraint reduces the wavelength usage efficiency of each link. To improve the same, an all-optical wavelength converter (AO-WC) has been attracting attention in recent years. In particular, an AO-WC is a key device because it enables simultaneous conversion of multiple wavelengths of signal lights to other wavelengths, independent of the modulation format. However, each AO-WC requires installation of multiple laser sources with narrow bandwidth because the lights emitted by the laser sources are used as pump lights when the wavelengths of the signal lights are converted by the four-wave mixing (FWM) process. To reduce the number of laser sources, we propose a remote pumped AO-WC, in which the laser sources of the pump lights are aggregated into several relay nodes. When the request for the wavelength conversion from the relay node without the laser source is conveyed, the relay node with the laser source transmits the pump light through the optical link. The proposed scheme enables reduction in the number of laser sources of the pump lights. Herein we analyze the distortion of the pump light by propagating it through the optical link We also evaluate the effect of the noise in optical amplifiers and nonlinearities in optical fibers using numerical simulations employing the representative parameters for a practical WDM network.

The machine-to-machine (M2M) service network platform that accommodates and controls various types of Internet of Things devices has been presented. This paper investigates program file placement strategies for the M2M service network platform that achieve low blocking ratios of new task requests and accommodate as many tasks as possible in the dynamic scenario. We present four strategies for determining program file placement, which differ in the computation method and whether the relocation of program files being used by existing tasks is allowed or not. Simulation results show that a strategy based on solving a mixed-integer linear programming model achieves the lowest blocking ratio, but a heuristic algorithm-based strategy can be an attractive option by allowing recomputation of the placement when the placement cannot be obtained at the timing of new task request arrival.

This paper describes a novel approach to flexible control of speaker characteristics using tensor representation of multiple Gaussian mixture models (GMM). In voice conversion studies, realization of conversion from/to an arbitrary speaker's voice is one of the important objectives. For this purpose, eigenvoice conversion (EVC) based on an eigenvoice GMM (EV-GMM) was proposed. In the EVC, a speaker space is constructed based on GMM supervectors which are high-dimensional vectors derived by concatenating the mean vectors of each of the speaker GMMs. In the speaker space, each speaker is represented by a small number of weight parameters of eigen-supervectors. In this paper, we revisit construction of the speaker space by introducing the tensor factor analysis of training data set. In our approach, each speaker is represented as a matrix of which the row and the column respectively correspond to the dimension of the mean vector and the Gaussian component. The speaker space is derived by the tensor factor analysis of the set of the matrices. Our approach can solve an inherent problem of supervector representation, and it improves the performance of voice conversion. In addition, in this paper, effects of speaker adaptive training before factorization are also investigated. Experimental results of one-to-many voice conversion demonstrate the effectiveness of the proposed approach.

This paper considers a velocity control problem for merging and splitting maneuvers of vehicle platoons. In this paper, an external device sends velocity commands to some vehicles in the platoon, and the others adjust their velocities autonomously. The former is pinning control, and the latter is consensus control in multi-agent control. We propose a switched pinning control algorithm. Our algorithm consists of three sub-methods. The first is an optimal switching method of pinning agents based on an MLD (Mixed Logical Dynamical) system model and MPC (Model Predictive Control). The second is a representation method for dynamical platoon formation with merging and splitting maneuver. The platoon formation follows the positional relation between vehicles or the formation demand from the external device. The third is a switching reduction method by setting a cost function that penalizes the switching of the pinning agents in the steady-state. Our proposed algorithm enables us to improve the consensus speed. Moreover, our algorithm can regroup the platoons to the arbitrary platoons and control the velocities of the multiple vehicle platoons to each target value.

The surveillance problem is to find optimal trajectories of agents that patrol a given area as evenly as possible. In this paper, we consider multiple agents with fuel constraints. The surveillance area is given by a weighted directed graph, where the weight assigned to each arc corresponds to the fuel consumption/supply. For each node, the penalty to evaluate the unattended time is introduced. Penalties, agents, and fuels are modeled by a mixed logical dynamical system model. Then, the surveillance problem is reduced to a mixed integer linear programming (MILP) problem. Based on the policy of model predictive control, the MILP problem is solved at each discrete time. In this paper, the feasibility condition for the MILP problem is derived. Finally, the proposed method is demonstrated by a numerical example.

One goal in stack-queue mixed layouts of a graph subdivision is to obtain a layout with minimum number of subdivision vertices per edge when the number of stacks and queues are given. Dujmović and Wood showed that for every integer s, q>0, every graph G has an s-stack q-queue subdivision layout with 4⌈log(s+q)q sn(G)⌉ (resp. 2+4⌈log(s+q)q qn(G)⌉) division vertices per edge, where sn(G) (resp. qn(G)) is the stack number (resp. queue number) of G. This paper improves these results by showing that for every integer s, q>0, every graph G has an s-stack q-queue subdivision layout with at most 2⌈logs+q-1sn(G)⌉ (resp. at most 2⌈logs+q-1qn(G)⌉ +4) division vertices per edge. That is, this paper improves previous results more, for graphs with larger stack number sn(G) or queue number qn(G) than given integers s and q. Also, the larger the given integer s is, the more this paper improves previous results.

A novel superpixel segmentation approach driven by uniform mixture model with spatially constrained (UMMS) is proposed. Under this algorithm, each observation, i.e. pixel is first represented as a five-dimensional vector which consists of colour in CLELAB space and position information. And then, we define a new uniform distribution through adding pixel position, so that this distribution can describe each pixel in input image. Applied weighted 1-Norm to difference between pixels and mean to control the compactness of superpixel. In addition, an effective parameter estimation scheme is introduced to reduce computational complexity. Specifically, the invariant prior probability and parameter range restrict the locality of superpixels, and the robust mean optimization technique ensures the accuracy of superpixel boundaries. Finally, each defined uniform distribution is associated with a superpixel and the proposed UMMS successfully implements superpixel segmentation. The experiments on BSDS500 dataset verify that UMMS outperforms most of the state-of-the-art approaches in terms of segmentation accuracy, regularity, and rapidity.

In order to improve the noise robustness of automatic speaker recognition, many techniques on speech/feature enhancement have been explored by using deep neural networks (DNN). In this work, a DNN multi-level enhancement (DNN-ME), which consists of the stages of signal enhancement, cepstrum enhancement and i-vector enhancement, is proposed for text-independent speaker recognition. Given the fact that these enhancement methods are applied in different stages of the speaker recognition pipeline, it is worth exploring the complementary role of these methods, which benefits the understanding of the pros and cons of the enhancements of different stages. In order to use the capabilities of DNN-ME as much as possible, two kinds of methods called Cascaded DNN-ME and joint input of DNNs are studied. Weighted Gaussian mixture models (WGMMs) proposed in our previous work is also applied to further improve the model's performance. Experiments conducted on the Speakers in the Wild (SITW) database have shown that DNN-ME demonstrated significant superiority over the systems with only a single enhancement for noise robust speaker recognition. Compared with the i-vector baseline, the equal error rate (EER) was reduced from 5.75 to 4.01.

An HDR (High Dynamic Range) image synthesis is a method which is to photograph scenes with wide luminance range and to reproduce images close to real visual scenes on an LDR (Low Dynamic Range) display. In general, HDR images are reproduced by taking images with various camera exposures and using the tone synthesis of several images. In this paper, we propose an HDR image tone mapping method based on a visual brightness function using dual exposed images and a synthesis algorithm based on local surround. The proposed algorithm has improved boundary errors and color balance compared with existing methods. Also, it improves blurring and noise amplification due to image mixing.

The sparse unmixing of hyperspectral data has attracted much attention in recent years because it does not need to estimate the number of endmembers nor consider the lack of pure pixels in a given hyperspectral scene. However, the high mutual coherence of spectral libraries strongly affects the practicality of sparse unmixing. The collaborative sparse unmixing via variable splitting and augmented Lagrangian (CLSUnSAL) algorithm is a classic sparse unmixing algorithm that performs better than other sparse unmixing methods. In this paper, we propose a CLSUnSAL-based hyperspectral unmixing method based on dictionary pruning and reweighted sparse regression. First, the algorithm identifies a subset of the original library elements using a dictionary pruning strategy. Second, we present a weighted sparse regression algorithm based on CLSUnSAL to further enhance the sparsity of endmember spectra in a given library. Third, we apply the weighted sparse regression algorithm on the pruned spectral library. The effectiveness of the proposed algorithm is demonstrated on both simulated and real hyperspectral datasets. For simulated data cubes (DC1, DC2 and DC3), the number of the pruned spectral library elements is reduced by at least 94% and the runtime of the proposed algorithm is less than 10% of that of CLSUnSAL. For simulated DC4 and DC5, the runtime of the proposed algorithm is less than 15% of that of CLSUnSAL. For the real hyperspectral datasets, the pruned spectral library successfully reduces the original dictionary size by 76% and the runtime of the proposed algorithm is 11.21% of that of CLSUnSAL. These experimental results show that our proposed algorithm not only substantially improves the accuracy of unmixing solutions but is also much faster than some other state-of-the-art sparse unmixing algorithms.

Estimation of the gradient of the logarithm of a probability density function is a versatile tool in statistical data analysis. A recent method for model-seeking clustering called the least-squares log-density gradient clustering (LSLDGC) [Sasaki et al., 2014] employs a sophisticated gradient estimator, which directly estimates the log-density gradients without going through density estimation. However, the typical implementation of LSLDGC is based on a spherical Gaussian function, which may not work well when the probability density function for data has highly correlated local structures. To cope with this problem, we propose a new gradient estimator for log-density gradients with Gaussian mixture models (GMMs). Covariance matrices in GMMs enable the new estimator to capture the highly correlated structures. Through the application of the new gradient estimator to mode-seeking clustering and hierarchical clustering, we experimentally demonstrate the usefulness of our clustering methods over existing methods.