This paper proposes a power supply voltage control technique, and demonstrates its effectiveness for eliminating the overkills and underkills due to the power supply characteristic difference between an automatic test equipment (ATE) and a practical operating environment of the DUT. The proposed method controls the static power supply voltage on the ATE system, so that the ATE can eliminate misjudges for the Pass or Fail of the DUT. The method for calculating the power supply voltage is also described. Experimental results show that the proposed method can eliminate 89% of overkills and underkills in delay fault testing with 105 real silicon devices. Limitations of the proposed method are also discussed.

In satellite/terrestrial integrated mobile communication systems (STICSs), a user terminal directly connects both terrestrial and satellite base stations. STICS enables expansion of service areas and provides a robust communication service for large disasters. However, the cell radius of the satellite system is large (approximately 100km), and thus a capacity enhancement of the satellite subsystem for accommodating many users is needed. Therefore, in this paper, we propose an application of two methods — multiple-input multiple-output (MIMO) transmission using multi-satellites and non-orthogonal multiple access (NOMA) for STICS — to realize the performance improvement in terms of system capacity and user fairness. Through numerical simulations, we show that system capacity and user fairness are increased by the proposed scheme that applies the two methods.

Among various training concepts for speaker adaptation, Speaker Adaptive Training (SAT) has been successfully applied to a standard Hidden Markov Model (HMM) speech recognizer, whose state is associated with Gaussian Mixture Models (GMMs). On the other hand, focusing on the high discriminative power of Deep Neural Networks (DNNs), a new type of speech recognizer structure, which combines DNNs and HMMs, has been vigorously investigated in the speaker adaptation research field. Along these two lines, it is natural to conceive of further improvement to a DNN-HMM recognizer by employing the training concept of SAT. In this paper, we propose a novel speaker adaptation scheme that applies SAT to a DNN-HMM recognizer. Our SAT scheme allocates a Speaker Dependent (SD) module to one of the intermediate layers of DNN, treats its remaining layers as a Speaker Independent (SI) module, and jointly trains the SD and SI modules while switching the SD module in a speaker-by-speaker manner. We implement the scheme using a DNN-HMM recognizer, whose DNN has seven layers, and elaborate its utility over TED Talks corpus data. Our experimental results show that in the supervised adaptation scenario, our Speaker-Adapted (SA) SAT-based recognizer reduces the word error rate of the baseline SI recognizer and the lowest word error rate of the SA SI recognizer by 8.4% and 0.7%, respectively, and by 6.4% and 0.6% in the unsupervised adaptation scenario. The error reductions gained by our SA-SAT-based recognizers proved to be significant by statistical testing. The results also show that our SAT-based adaptation outperforms, regardless of the SD module layer selection, its counterpart SI-based adaptation, and that the inner layers of DNN seem more suitable for SD module allocation than the outer layers.

Ramp metering is the most effective and direct method to control a vehicle entering a freeway. This study proposes a novel density-based ramp metering method. Existing methods typically use flow data that has low reliability, and they suffer from various problems. Furthermore, when ramp metering is performed based on freeway congestion, additional congestion and over-capacity can occur in the ramp. To solve these problems faced with existing methods, the proposed method uses the density and acceleration data of vehicles on the freeway and considers the ramp status. The experimental environment was simulated using PTV Corporation's VISSIM simulator. The Traffic Information and Condition Analysis System was developed to control the VISSIM simulator. The experiment was conducted between 2:00 PM and 7:00 PM on October 5, 2014, during severe traffic congestion. The simulation results showed that total travel time was reduced by 10% compared to existing metering system during the peak time. Thus, we solved the problem of ramp congestion and over-capacity.

This paper presents a modulation scheme for impulse radio that uses the first sidelobe for transmitting a non-return-to-zero baseband signal and the implementation of a dual frequency conversion demodulator. The proposed modulation technique realizes two times higher frequency efficiency than that realized by binary phase-shift keying modulation and does not require an up-converter in the transmitter. The dual frequency conversion demodulator compensates for the spectrum distortion caused by the frequency response of the circuits and channel. The effect of frequency compensation is analytically studied. The fabricated demodulator test chip of 65 nm CMOS achieves clock and data recovery at 5.7 Gbps with a power consumption of 24 mW.

The indexes of the degradation of C/N, ΔT/T and I/N, which can be converted from one to another, are used to evaluate the impact of interference on the satellite link. However, it is not suitable to intuitively understand how these parameters degrade the quality of services. In this paper, we propose to evaluate the impact of interference on the performance of BSS (Broadcasting Satellite Services) in terms of the increase rate of the outage time caused by the rain attenuation. Some calculation results are given for the 12GHz band BSS in Japan.

In this paper, a measurement method for the impedance and mutual coupling of multi-antennas that we have proposed is summarized. Impedance and mutual coupling characteristics are obtained after reducing the influence of the coaxial cables by synthesizing the measured S-parameters under the condition that unbalanced currents on the outside of the coaxial cables are canceled at feed points. We apply the proposed method to two closely positioned monopole antennas mounted on a small ground plane and demonstrate the validity and effectiveness of the proposed method by simulation and experiment. The proposed method is significantly better in terms of the accuracy of the mutual coupling data. In the presented case, the errors at the resonant frequency of the antennas are only 0.5dB in amplitude and 1.8° in phase.

Kiasu-BC is a recently proposed tweakable variant of the AES-128 block cipher. The designers of Kiasu-BC claim that no more than 7-round Meet-in-the-Middle (MitM) attack can be launched against it. In this letter, we present a MitM attack, utilizing the differential enumeration technique, on the 8-round reduced cipher. The attack has time complexity of 2116 encryptions, memory complexity of 286 128-bit blocks, and data complexity of 2116 plaintext-tweak combinations.

We propose a speech analysis method based on the source-filter model using multivariate empirical mode decomposition (MEMD). The proposed method takes multiple adjacent frames of a speech signal into account by combining their log spectra into multivariate signals. The multivariate signals are then decomposed into intrinsic mode functions (IMFs). The IMFs are divided into two groups using the peak of the autocorrelation function (ACF) of an IMF. The first group characterized by a spectral fine structure is used to estimate the fundamental frequency F0 by using the ACF, whereas the second group characterized by the frequency response of the vocal-tract filter is used to estimate formant frequencies by using a peak picking technique. There are two advantages of using MEMD: (i) the variation in the number of IMFs is eliminated in contrast with single-frame based empirical mode decomposition and (ii) the common information of the adjacent frames aligns in the same order of IMFs because of the common mode alignment property of MEMD. These advantages make the analysis more accurate than with other methods. As opposed to the conventional linear prediction (LP) and cepstrum methods, which rely on the LP order and cut-off frequency, respectively, the proposed method automatically separates the glottal-source and vocal-tract filter. The results showed that the proposed method exhibits the highest accuracy of F0 estimation and correctly estimates the formant frequencies of the vocal-tract filter.

This paper proposes statistical analysis of phase-only correlation functions with phase-spectrum differences following wrapped distributions. We first assume phase-spectrum differences between two signals to be random variables following a linear distribution. Next, based on directional statistics, we convert the linear distribution into a wrapped distribution by wrapping the linear distribution around the circumference of the unit circle. Finally, we derive general expressions of the expectation and variance of the POC functions with phase-spectrum differences following wrapped distributions. We obtain exactly the same expressions between a linear distribution and its corresponding wrapped distribution.

Two new authenticated key agreement protocols in the certificateless setting are presented in this paper. Both are proved secure in the extended Canetti-Krawczyk model, under the BDH assumption. The first one is more efficient than the Lippold et al.'s (LBG) protocol, and is proved secure in the same security model. The second protocol is proved secure under the Swanson et al.'s security model, a weaker model. As far as we know, our second proposed protocol is the first one proved secure in the Swanson et al.'s security model. If no pre-computations are done, the first protocol is about 26% faster than LBG, and the second protocol is about 49% faster than LBG, and about 31% faster than the first one. If pre-computations of some operations are done, our two protocols remain faster.

The online interval coloring problem has been extensively studied for many years. Kierstead and Trotter (Congressus Numerantium 33, 1981) proved that their algorithm is an optimal online algorithm for this problem. The number of colors used by the algorithm is at most 3ω(G)-2, where ω(G) is the size of the maximum clique in a given graph G. Also, they presented an instance for which the number of colors used by any online algorithm is at least 3ω(G)-2. This instance includes intervals with various lengths, which cannot be applied to the case when the lengths of the given intervals are restricted to one, i.e., the online unit interval coloring problem. In this case, the current best upper and lower bounds on the number of colors used by an online algorithm are 2ω(G)-1 and 3ω(G)/2 respectively by Epstein and Levy (ICALP2005). In this letter, we conduct a complete performance analysis of the Kierstead-Trotter algorithm for online unit interval coloring, and prove it is NOT optimal. Specifically, we provide an upper bound of 3ω(G)-3 on the number of colors used by their algorithm. Moreover, the bound is the best possible.

Recently, many wireless sensor networks (WSNs) have employed mobile sensor nodes to collect a variety of data from mobile elements such as humans, animals and cars. In this letter, we propose an efficient mobile data aggregation scheme to improve the overall performance in gathering the data of the mobile nodes. We first propose a spatial mobile data aggregation scheme to aggregate the data of the mobile node spatially, which is then extended to a two-tier mobile data aggregation by supplementing a temporal mobile data aggregation scheme to aggregate the data of multiple mobile nodes temporally. Simulation results show that our scheme significantly reduces the energy consumption and gathering delay for data collection from mobile nodes in WSNs.

This paper presents a weighted diversity combining technique for the cyclostationarity detection based spectrum sensing of orthogonal frequency division multiplexing signals in cognitive radio. In cognitive radio systems, secondary users must detect the desired signal in an extremely low signal-to-noise ratio (SNR) environment. In such an environment, multiple antenna techniques (space diversity) such as maximum ratio combining are not effective because the energy of the target signal is also extremely weak, and it is difficult to synchronize some received signals. The cyclic autocorrelation function (CAF) is used for traditional cyclostationarity detection based spectrum sensing. In the presented technique, the CAFs of the received signals are combined, while the received signals themselves are combined with general space diversity techniques. In this paper, the value of the CAF at peak and non-peak cyclic frequencies are computed, and we attempt to improve the sensing performance by using different weights for each CAF value. The results were compared with those from conventional methods and showed that the presented technique can improve the spectrum sensing performance.

The most commonly used scattering parameters (S parameters) are normalized to a real reference resistance, typically 50Ω. In some cases, the use of S parameters normalized to some complex reference impedance is essential or convenient. But there are different definitions of complex-referenced S parameters that are incompatible with each other and serve different purposes. To make matters worse, different simulators implement different ones and which ones are implemented is rarely properly documented. What are possible scenarios in which using the right one matters? This tutorial-style paper is meant as an informal and not overly technical exposition of some such confusing aspects of S parameters, for those who have a basic familiarity with the ordinary, real-referenced S parameters.

Collaborative filtering (CF) has been widely used in recommender systems to generate personalized recommendations. However, recommender systems using CF are vulnerable to shilling attacks, in which attackers inject fake profiles to manipulate recommendation results. Thus, shilling attacks pose a threat to the credibility of recommender systems. Previous studies mainly derive features from characteristics of item ratings in user profiles to detect attackers, but the methods suffer from low accuracy when attackers adopt new rating patterns. To overcome this drawback, we derive features from properties of item popularity in user profiles, which are determined by users' different selecting patterns. This feature extraction method is based on the prior knowledge that attackers select items to rate with man-made rules while normal users do this according to their inner preferences. Then, machine learning classification approaches are exploited to make use of these features to detect and remove attackers. Experiment results on the MovieLens dataset and Amazon review dataset show that our proposed method improves detection performance. In addition, the results justify the practical value of features derived from selecting patterns.

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.

In this paper, an iterative robust minimum-mean square error (MMSE) receiver for space-time block coding (STBC) is proposed to mitigate the performance degradations caused by channel state information (CSI) errors. The proposed scheme estimates an instantaneous covariance matrix of the effective noise, which includes additive white Gaussian noise and the effect of CSI errors. For this estimation, multiple solution candidate vectors are selected based on the distances between the MMSE estimate of the solution and the constellation points, and their a-posteriori probabilities are utilized to execute the estimation of the covariance matrix. To improve the estimation accuracy, the estimated covariance matrix is updated iteratively. Simulation results show that proposed robust receiver achieves substantial performance gains in terms of bit error rates as compared to conventional receiver schemes under CSI errors.