While conventional studies on real-time systems have mostly considered the real-time constraint of real-time systems only, recent research initiatives are trying to incorporate a security constraint into real-time scheduling due to the recognition that the violation of either of two constrains can cause catastrophic losses for humans, the system, and even environment. The focus of most studies, however, is the single-criticality systems, while the security of mixed-criticality systems has received scant attention, even though security is also a critical issue for the design of mixed-criticality systems. In this paper, we address the problem of the information leakage that arises from the shared resources that are used by tasks with different security-levels of mixed-criticality systems. We define a new concept of the security constraint employing a pre-flushing mechanism to cleanse the state of shared resources whenever there is a possibility of the information leakage regarding it. Then, we propose a new non-preemptive real-time scheduling algorithm and a schedulability analysis, which incorporate the security constraint for mixed-criticality systems. Our evaluation demonstrated that a large number of real-time tasks can be scheduled without a significant performance loss under a new security constraint.

This paper presents a technique to reduce the quantum cost by making temporary changes to the functionality of a given Boolean function. This technique is one of the very few known methods based on manipulating Exclusive-or Sum-Of-Products (ESOP) expressions to reduce the quantum cost of the corresponding circuit. The idea involves adding Mixed Polarity Multiple-Control Toffoli (MPMCT) gates to temporarily change the functionality of the given function, so that the modified function has a smaller quantum cost. To compensate for the temporary change, additional gates are inserted into the circuit. The proposed method finds a small ESOP expression for the given function, and then finds a good pair of product terms in the ESOP expression so that the quantum cost can be reduced by applying the transformation. The proposed approach is likely to produce a better quantum cost reduction than the existing methods, and indeed experimental results confirm this expectation.

The rapid prototyping of a mixed-signal system-on-chip (SoC) has been enabled by reusing predesigned intellectual properties (IPs) and by integrating newly designed IP into the top design of SoC. The IPs have been designed on various hardware description levels, which leads to challenges in simulations that evaluate the prototyping. One traditional solution is to convert these heterogeneous IP models into equivalent models, that are described in a single description language. This conversion approach often requires manual rewriting of existing IPs, and this results in description loss during the model projection due to the absence of automatic conversion tools. The other solutions are co-simulation/emulation approaches that are based on the coupling of multiple simulators/emulators through connection modules. The conventional methods do not have formal theoretical backgrounds and an explicit interface for integrating the simulator into their solutions. In this paper, we propose a general co-simulation approach based on the high-level architecture (HLA) and a newly-defined programming language interface for interoperation (PLI-I) between heterogeneous IPs as a formal simulator interface. Based on the proposed PLI-I and HLA, we introduce formal procedures of integration and interoperation. To reduce integration costs, we split these procedures into two parts: a reusable common library and an additional model-dependent signal-to-event (SE) converter to handle differently abstracted in/out signals between the coupled IPs. During the interoperation, to resolve the different time-advance mechanisms and increase computation concurrency between digital and analog simulators, the proposed co-simulation approach performs an advanced HLA-based synchronization using the pre-simulation concepts. The case study shows the validation of interoperation behaviors between the heterogeneous IPs in mixed-signal SoC design, the reduced design effort in integrating, and the synchronization speedup using the proposed approach.

This report presents experimental measurements of mixed-mode oscillations (MMOs) generated by a weakly driven four-segment piecewise linear Bonhoeffer-van der Pol (BVP) oscillator. Such a roughly approximated simple piecewise linear circuit can generate MMOs and mixed-mode oscillation-incrementing bifurcations (MMOIBs). The laboratory experiments well agree with numerical results. We experimentally and numerically observe time series and Lorenz plots of MMOs generated by successive and nonsuccessive MMOIBs.

In general, semi-supervised clustering can outperform unsupervised clustering. Since 2001, pairwise constraints for semi-supervised clustering have been an important paradigm in this field. In this paper, we show that pairwise constraints (ECs) can affect the performance of clustering in certain situations and analyze the reasons for this in detail. To overcome these disadvantages, we first outline some exemplars constraints. Based on these constraints, we then describe a semi-supervised clustering framework, and design an exemplars constraints expectation-maximization algorithm. Finally, standard datasets are selected for experiments, and experimental results are presented, which show that the exemplars constraints outperform the corresponding unsupervised clustering and semi-supervised algorithms based on pairwise constraints.

We propose a microphone array speech enhancement method that integrates spatial-cue-based source power spectral density (PSD) estimation and statistical speech model-based PSD estimation. The goal of this research was to clearly pick up target speech even in noisy environments such as crowded places, factories, and cars running at high speed. Beamforming with post-Wiener filtering is commonly used in many conventional studies on microphone-array noise reduction. For calculating a Wiener filter, speech/noise PSDs are essential, and they are estimated using spatial cues obtained from microphone observations. Assuming that the sound sources are sparse in the temporal-spatial domain, speech/noise PSDs may be estimated accurately. However, PSD estimation errors increase under circumstances beyond this assumption. In this study, we integrated speech models and PSD-estimation-in-beamspace method to correct speech/noise PSD estimation errors. The roughly estimated noise PSD was obtained frame-by-frame by analyzing spatial cues from array observations. By combining noise PSD with the statistical model of clean-speech, the relationships between the PSD of the observed signal and that of the target speech, hereafter called the observation model, could be described without pre-training. By exploiting Bayes' theorem, a Wiener filter is statistically generated from observation models. Experiments conducted to evaluate the proposed method showed that the signal-to-noise ratio and naturalness of the output speech signal were significantly better than that with conventional methods.

Variance analysis is an important research topic to assess the quality of estimators. In this paper, we analyze the performance of the least ℓp-norm estimator in the presence of mixture of generalized Gaussian (MGG) noise. In the case of known density parameters, the variance expression of the ℓp-norm minimizer is first derived, for the general complex-valued signal model. Since the formula is a function of p, the optimal value of p corresponding to the minimum variance is then investigated. Simulation results show the correctness of our study and the near-optimality of the ℓp-norm minimizer compared with Cramér-Rao lower bound.

This letter presents a method for solving several linear equations in max-plus algebra. The essential part of these equations is reduced to constraint satisfaction problems compatible with mixed integer programming. This method is flexible, compared with optimization methods, and suitable for scheduling of certain discrete event systems.

The mixer is a crucial circuit block in a WiMax system receiver. The performance of a mixer depends on three specifications: conversion gain, linearity and noise figure. Many mixers have been recently proposed for UWB and wideband systems; however, they either cannot achieve the high conversion gain required for a WiMAX system or they are prone to high power consumption. In this paper, a folded mixer with a high conversion gain is designed for a 2-11GHz WiMAX system and it can achieve a 20MHz IF output signal. From the simulation results, the proposed folded mixer achieves a conversion gain of 18.9 to 21.5dB for the full bandwidth. With a 0.2 to 4.4dBm IIP3, the NF is 13.5 to 17.6dB. The folded mixer is designed using TSMC 0.18µm CMOS technology. The core power consumption of the mixer is 11.8mW.

In statistical methods, such as statistical static timing analysis, Gaussian mixture model (GMM) is a useful tool for representing a non-Gaussian distribution and handling correlation easily. In order to repeat various statistical operations such as summation and maximum for GMMs efficiently, the number of components should be restricted around two. In this paper, we propose a method for reducing the number of components of a given GMM to two (2-GMM). Moreover, since the distribution of each component is represented often by a linear combination of some explanatory variables, we propose a method to compute the covariance between each explanatory variable and the obtained 2-GMM, that is, the sensitivity of 2-GMM to each explanatory variable. In order to evaluate the performance of the proposed methods, we show some experimental results. The proposed methods minimize the normalized integral square error of probability density function of 2-GMM by the sacrifice of the accuracy of sensitivities of 2-GMM.

This paper provides a mobile agent based distributed variational Bayesian (MABDVB) algorithm for density estimation in sensor networks. It has been assumed that sensor measurements can be statistically modeled by a common Gaussian mixture model. In the proposed algorithm, mobile agents move through the routes of the network and compute the local sufficient statistics using local measurements. Afterwards, the global sufficient statistics will be updated using these local sufficient statistics. This procedure will be repeated until convergence is reached. Consequently, using this global sufficient statistics the parameters of the density function will be approximated. Convergence of the proposed method will be also analytically studied, and it will be shown that the estimated parameters will eventually converge to their true values. Finally, the proposed algorithm will be applied to one-dimensional and two dimensional data sets to show its promising performance.

This paper proposes an on-line rigid object tracking framework via discriminative object appearance modeling and learning. Strong classifiers are combined with 2D scale-rotation invariant local features to treat tracking as a keypoint matching problem. For on-line boosting, we correspond a Gaussian mixture model (GMM) to each weak classifier and propose a GMM-based classifying mechanism. Meanwhile, self-organizing theory is applied to perform automatic clustering for sequential updating. Benefiting from the invariance of the SURF feature and the proposed on-line classifying technique, we can easily find reliable matching pairs and thus perform accurate and stable tracking. Experiments show that the proposed method achieves better performance than previously reported trackers.

Multi-task learning in deep neural networks has been proven to be effective for acoustic modeling in speech recognition. In the paper, this technique is applied to Mandarin-English code-mixing recognition. For the primary task of the senone classification, three schemes of the auxiliary tasks are proposed to introduce the language information to networks and improve the prediction of language switching. On the real-world Mandarin-English test corpus in mobile voice search, the proposed schemes enhanced the recognition on both languages and reduced the relative overall error rates by 3.5%, 3.8% and 5.8% respectively.

This paper describes a novel statistical bandwidth extension (BWE) technique based on a Gaussian mixture model (GMM) and a sub-band basis spectrum model (SBM), in which each dimensional component represents a specific acoustic space in the frequency domain. The proposed method can achieve the BWE from speech data with an arbitrary frequency bandwidth whereas the conventional methods perform the conversion from fixed narrow-band data. In the proposed method, we train a GMM with SBM parameters extracted from full-band spectra in advance. According to the bandwidth of input signal, the trained GMM is reconstructed to the GMM of the joint probability density between low-band SBM and high-band SBM components. Then high-band SBM components are estimated from low-band SBM components of the input signal based on the reconstructed GMM. Finally, BWE is achieved by adding the spectra decoded from estimated high-band SBM components to the ones of the input signal. To construct the full-band signal from the narrow-band one, we apply this method to log-amplitude spectra and aperiodic components. Objective and subjective evaluation results show that the proposed method extends the bandwidth of speech data robustly for the log-amplitude spectra. Experimental results also indicate that the aperiodic component extracted from the upsampled narrow-band signal realizes the same performance as the restored and the full-band aperiodic components in the proposed method.

For phase stabilization of two-tone coherent millimeter-wave/microwave carrier generation, two types of phase detection schemes were devised based on lightwave interferometric technique, the Mach-Zehnder interferometric method and the pseudo Mach-Zehnder interferometric method. The former system showed clear eye patterns at both OOK and PSK modulations of 1 Gbit/s on the 12.5-GHz carrier. The latter system demonstrated the error-free transmission at OOK modulation of 11 Gbit/s on the 100-GHz carrier.

This paper presents an efficient secure auction protocol for M+1st price auction. In our proposed protocol, a bidding price of a player is represented as a binary expression, while in the previous protocol it is represented as an integer. Thus, when the number of players is m and the bidding price is an integer up to p, compared to the complexity of the previous protocol which is a polynomial of m and p, the complexity of our protocol is a polynomial of m and log p. We apply the Boneh-Goh-Nissim encryption to the mix-and-match protocol to reduce the computation costs.

The orthogonal array is an important object in combinatorial design theory, and it is applied to many fields, such as computer science, coding theory and cryptography etc. This paper mainly studies the existence of the mixed orthogonal arrays of strength two with seven factors and presents some new constructions. Consequently, a few new mixed orthogonal arrays are obtained.

We experimentally investigate the performance of a distributed Raman amplifier (DRA)-based pulse compressor for a phase modulated signal. A 10 Gb/s return-to-zero (RZ)-differential phase shift keying (DPSK) signal is compressed to picosecond range after transmission. Pulsewidth is continuously compressed in a wide range from 20 to 3.2 ps by changing the pump power of the DRA while the compressed waveforms are well-matched with sech2 function. Error-free operations at bit-error-rate (BER) of 10-9 are achieved for the compressed signals of various pulsewidths with low power penalties within 2.3 dB compared to the back-to-back. After the compression, the 10 Gb/s signal is used to generate a 40 Gb/s RZ-DPSK optical time division multiplexing (OTDM) signal. This 40 Gb/s OTDM signal is then successfully demultiplexed to 10 Gb/s DPSK signal by using an optical gate based on four-wave mixing (FWM) in a highly nonlinear fiber (HNLF).

Conversion between multi-level modulation formats is one of key processing functions for flexible networking aimed at high spectral efficiency (SE) in optical fiber transmission. The authors previously proposed an all-optical format conversion system from binary phase-shift keying (BPSK) to quadrature PSK (QPSK) and reported an experimental demonstration. In this paper, we consider its reversed conversion, that is, from QPSK to BPSK. The proposed system consists of a highly nonlinear fiber used to generate complex conjugate signal, and a 3-dB directional coupler used to produce converted signals by interfering the incident signal with the complex conjugate signal. The incident QPSK stream is converted into two BPSK tributaries without any loss of transmitting data. We show the system performances such as bit-error-rate and optical signal-to-noise ratio penalty evaluated by numerical simulation.

A wide-bandwidth fundamental mixer operating at a frequency above 110GHz for precise spectrum analysis was developed using the InP HEMT technology. A single-ended resistive mixer was adopted for the mixer circuit. An IF amplifier and LO buffer amplifier were also developed and integrated into the mixer chip. As for packaging into a metal block module, a flip-chip bonding technique was introduced. Compared to face-up mounting with wire connections, flip-chip bonding exhibited good frequency flatness in signal loss. The mixer module with a built-in IF amplifier achieved a conversion gain of 5dB at an RF frequency of 135GHz and a 3-dB bandwidth of 35GHz. The mixer module with an LO buffer amplifier operated well even at an LO power of -20dBm.