This paper presents a concept of a domain-specific framework for software analytics by enabling querying, modeling, and integration of heterogeneous software repositories. The framework adheres to a multi-layered abstraction mechanism that consists of domain-specific operators. We showcased the potential of this approach by employing a case study.

Many applications, such as image signal processing, has an inherent tolerance for insignificant inaccuracies. Multiplication is a key arithmetic function for many applications. Approximate multipliers are considered an efficient technique to trade off energy relative to performance and accuracy for the error-tolerant applications. Here, we design and analyze four approximate multipliers that demonstrate lower power consumption and shorter critical path delay than the conventional multiplier. They employ an approximate tree compressor that halves the height of the partial product tree and generates a vector to compensate accuracy. Compared with the conventional Wallace tree multiplier, one of the evaluated 8-bit approximate multipliers reduces power consumption and critical path delay by 36.9% and 38.9%, respectively. With a 0.25% normalized mean error distance, the silicon area required to implement the multiplier is reduced by 50.3%. Our multipliers outperform the previously proposed approximate multipliers relative to power consumption, critical path delay, and design area. Results from two image processing applications also demonstrate that the qualities of the images processed by our multipliers are sufficiently accurate for such error-tolerant applications.

This paper studies the performance of the quantitative RF power variation in Radio-over-Fiber beam forming system utilizing a phased array-antenna integrating photo-diodes in downlink network for next generation millimeter wave band radio access. Firstly, we described details of fabrication of an integrated photonic array-antenna (IPA), where a 60GHz patch antenna 4×2 array and high-speed photo-diodes were integrated into a substrate. We evaluated RF transmission efficiency as an IPA system for Radio-over-Fiber (RoF)-based mobile front hall architecture with remote antenna beam forming capability. We clarified the characteristics of discrete and integrated devices such as an intensity modulator (IM), an optical fiber and the IPA and calculated RF power radiated from the IPA taking account of the measured data of the devices. Based on the experimental results on RF tone signal transmission by utilizing the IPA, attainable transmission distance of wireless communication by improvement and optimization of the used devices was discussed. We deduced that the antenna could output sufficient power when we consider that the cell size of the future mobile communication systems would be around 100 meters or smaller.

A diversity strategy is efficient to reduce the fluctuation of communication quality caused by fading. In order to further maintain the communication quality and improve the communication capacity, this paper proposes a two-dimensional diversity approach by serially-concatenating spectral precoding and power normalized-differential space time block coding (PN-DSTBC). Spectral precoding is able to take benefit from a frequency diversity effect without loss in spectral efficiency. In addition, PN-DSTBC is robust against serious phase noise in an extremely high frequency (EHF) band by exploiting a spatial diversity effect. However, there is a problem that a naive concatenation degrades the performance due to the imbalance of equivalent noise variances over transmit frequencies. Thus, we examine an equalized PN-DSTBC decoder as a modified approach to uniform equivalent noise variances over frequencies. The performance evaluation using computer simulations shows that the proposed modified approach yields the performance improvement at any modulation schemes and at any number of transmit frequencies. Furthermore, in the case of 64QAM and two transmit frequencies, the performance gain of the modified approach is 4dB larger than that of PN-DSTBC only at uncoded BER=10-4.

One of essential requirements for the next generation communications is to support higher spectral efficiency (SE) and energy efficiency (EE) than the existing communication system. For increasing the SE, carrier aggregation (CA) has received great attention. In this paper, we propose an energy efficient smart crest factor reduction (E2S-CFR) method for increasing the EE while satisfying the required SE when the CA is applied. The proposed E2S-CFR exploits different weights on each carrier according to the required error vector magnitude (EVM), and efficiently reduces the peak to average power ratio (PAR). Consequently, we can reduce the bias voltage of a power amplifier, and it leads to save total consumed energy. Through performance evaluation, we demonstrate that the proposed E2S-CFR improves the EE by 11.76% compared to the existing schemes.

Deep neural networks (DNNs) have achieved significant success in the field of automatic speech recognition. One main advantage of DNNs is automatic feature extraction without human intervention. However, adaptation under limited available data remains a major challenge for DNN-based systems because of their enormous free parameters. In this paper, we propose a filterbank-incorporated DNN that incorporates a filterbank layer that presents the filter shape/center frequency and a DNN-based acoustic model. The filterbank layer and the following networks of the proposed model are trained jointly by exploiting the advantages of the hierarchical feature extraction, while most systems use pre-defined mel-scale filterbank features as input acoustic features to DNNs. Filters in the filterbank layer are parameterized to represent speaker characteristics while minimizing a number of parameters. The optimization of one type of parameters corresponds to the Vocal Tract Length Normalization (VTLN), and another type corresponds to feature-space Maximum Linear Likelihood Regression (fMLLR) and feature-space Discriminative Linear Regression (fDLR). Since the filterbank layer consists of just a few parameters, it is advantageous in adaptation under limited available data. In the experiment, filterbank-incorporated DNNs showed effectiveness in speaker/gender adaptations under limited adaptation data. Experimental results on CSJ task demonstrate that the adaptation of proposed model showed 5.8% word error reduction ratio with 10 utterances against the un-adapted model.

The gap states of tetratetracontane (C44H90; TTC), which is a model oligomer of polyethylene, was examined by using high-sensitivity UV photoemission spectroscopy (HS-UPS). The high sensitivity enabled us to directly observe the weak gap states distributed in the HOMO-LUMO gap from the valence band top to 3.0 eV below the vacuum level. On the basis of the density-of-states derived from UPS results, the tribocharging nature of polyethylene was discussed in comparison with our previous result for nylon-6,6 film.

Spectral graph theory gives an algebraic approach to the analysis of the dynamics of a network by using the matrix that represents the network structure. However, it is not easy for social networks to apply the spectral graph theory because the matrix elements cannot be given exactly to represent the structure of a social network. The matrix element should be set on the basis of the relationship between persons, but the relationship cannot be quantified accurately from obtainable data (e.g., call history and chat history). To get around this problem, we utilize the universality of random matrices with the feature of social networks. As such a random matrix, we use the normalized Laplacian matrix for a network where link weights are randomly given. In this paper, we first clarify that the universality (i.e., the Wigner semicircle law) of the normalized Laplacian matrix appears in the eigenvalue frequency distribution regardless of the link weight distribution. Then, we analyze the information propagation speed by using the spectral graph theory and the universality of the normalized Laplacian matrix. As a result, we show that the worst-case speed of the information propagation changes up to twice if the structure (i.e., relationship among people) of a social network changes.

The Game of Life, a two-dimensional computationally universal cellular automaton, is known to exhibits 1/f noise in the evolutions starting from random configurations. In this paper we perform the spectral analysis on the computation process by a Turing machine constructed on the array of the Game of Life. As a result, the power spectrum averaged over the whole array has almost flat line at low frequencies and a lot of sharp peaks at high frequencies although some regions in which complicated behavior such as frequent memory rewriting occurs exhibit 1/f noise. This singular power spectrum is, however, easily turned into 1/f by slightly deforming the initial configuration of the Turing machine. These results emphasize the peculiarity of the computation process on the Game of Life that is never shared with the evolutions from random configurations. The Lyapunov exponents have positive values in three out of six trials and zero or negative values in other three trails. That means the computation process is essentially chaotic but it has capable of recovering a slight error in the configuration of the Turing machine.

Hydrophobic DNA (H-DNA) nano-film was formed on a thin glass plate of 50μm thick working as a slab optical waveguide. Bromothymol blue (BTB) molecules were immobilized from aqueous solution with direct contacting to the H-DNA nano-film for 20 minutes. From changes in absorption spectra observed with slab optical wave guide (SOWG) during automated solution exchange (SE) processes for 100 times, it was found that about 95% of bromothymol blue (BTB) molecules was immobilized in the H-DNA nano-film with keeping their functionality of color change responsible to pH change in the solution.

Aimed at long-term monitoring of daily office conversations without recording the conversational content, a system is presented for estimating acoustic nonverbal information such as utterance duration, utterance frequency, and turn-taking. The system combines a sound localization technique based on the sound energy distribution with 16 beam-forming microphone-array modules mounted in the ceiling for reducing the influence of multiple sound reflection. Furthermore, human detection using a wide field of view camera is integrated to the system for more robust speaker estimation. The system estimates the speaker for each utterance and calculates nonverbal information based on it. An evaluation analyzing data collected over ten 12-hour workdays in an office with three assigned workers showed that the system had 72% speech segmentation detection accuracy and 86% speaker identification accuracy when utterances were correctly detected. Even with false voice detection and incorrect speaker identification and even in cases where the participants frequently made noise or where seven participants had gathered together for a discussion, the order of the amount of calculated acoustic nonverbal information uttered by the participants coincided with that based on human-coded acoustic nonverbal information. Continuous analysis of communication dynamics such as dominance and conversation participation roles through nonverbal information will reveal the dynamics of a group. The main contribution of this study is to demonstrate the feasibility of unconstrained long-term monitoring of daily office activity through acoustic nonverbal information.

This paper proposes a speaker-phonetic i-vector modeling method for text-dependent speaker verification with random digit strings, in which enrollment and test utterances are not of the same phrase. The core of the proposed method is making use of digit alignment information in i-vector framework. By utilizing force alignment information, verification scores of the testing trials can be computed in the fixed-phrase situation, in which the compared speech segments between the enrollment and test utterances are of the same phonetic content. Specifically, utterances are segmented into digits, then a unique phonetically-constrained i-vector extractor is applied to obtain speaker and channel variability representation for every digit segment. Probabilistic linear discriminant analysis (PLDA) and s-norm are subsequently used for channel compensation and score normalization respectively. The final score is obtained by combing the digit scores, which are computed by scoring individual digit segments of the test utterance against the corresponding ones of the enrollment. Experimental results on the Part 3 of Robust Speaker Recognition (RSR2015) database demonstrate that the proposed approach significantly outperforms GMM-UBM by 52.3% and 53.5% relative in equal error rate (EER) for male and female respectively.

The long short-term memory recurrent neural network (LSTM) has achieved tremendous success for automatic speech recognition (ASR). However, the complicated gating mechanism of LSTM introduces a massive computational cost and limits the application of LSTM in some scenarios. In this paper, we describe our work on accelerating the decoding speed and improving the decoding accuracy. First, we propose an architecture, which is called Projected Gated Recurrent Unit (PGRU), for ASR tasks, and show that the PGRU can consistently outperform the standard GRU. Second, to improve the PGRU generalization, particularly on large-scale ASR tasks, we propose the Output-gate PGRU (OPGRU). In addition, the time delay neural network (TDNN) and normalization methods are found beneficial for OPGRU. In this paper, we apply the OPGRU for both the acoustic model and recurrent neural network language model (RNN-LM). Finally, we evaluate the PGRU on the total Eval2000 / RT03 test sets, and the proposed OPGRU single ASR system achieves 0.9% / 0.9% absolute (8.2% / 8.6% relative) reduction in word error rate (WER) compared to our previous best LSTM single ASR system. Furthermore, the OPGRU ASR system achieves significant speed-up on both acoustic model and language model rescoring.

Conventional target recognition methods usually suffer from information-loss and target-aspect sensitivity when applied to radar high resolution range profile (HRRP) recognition. Thus, Effective establishment of robust and discriminatory feature representation has a significant performance improvement of practical radar applications. In this work, we present a novel feature extraction method, based on modified collaborative auto-encoder, for millimeter-wave radar HRRP recognition. The latent frame-specific weight vector is trained for samples in a frame, which contributes to retaining local information for different targets. Experimental results demonstrate that the proposed algorithm obtains higher target recognition accuracy than conventional target recognition algorithms.

In stepped FM radar, the transmitter intermittently transmits narrowband pulse trains of frequencies that are incremented in steps, and the receiver performs phase detection on each pulse and applies the inverse discrete Fourier transform (IDFT) to create ultra-short pulses in the time domain. Furthermore, since the transmitted signal consists of a narrowband pulse train of different frequencies, the transmitter can avoid arbitrary frequency bands while sending the pulse train (spectrum holes), allowing these systems to coexist with other narrowband wireless systems. However, spectrum holes cause degradation in the distance resolution and range sidelobe characteristics of wireless systems. In this paper, we propose a spectrum hole compensation method for stepped FM radars using Khatri-Rao product extended-phase processing to overcome the problem of spectrum holes and investigate the effectiveness of this method through experiments. Additionally, we demonstrate that the proposed method dramatically improves the range sidelobe and distance resolution characteristics.

Dual-rotor machine (DRM) is a multiple input and output electromechanical device with two electrical and two mechanical ports which make it an optimal transmission system for hybrid electric vehicles. In attempt to boost its performance and efficiency, this work presents a dual-rotor permanent magnet (DR-PM) machine system used for continuously variable transmission (CVT) in HEVs. The proposed DR-PM machine is analyzed, and modeled in consideration of vehicle driving requirements. Considering energy conversion modes and torque transfer modes, operation conditions of the DR-PM machine system used for CVT are illustrated in detail. Integrated control model of the system is carried out, besides, intelligent speed ratio control strategy is designed by analyzing the dynamic coupling modes upon the integrated models to satisfy the performance requirements, reasonable energy-split between machine and engine, and optimal fuel economy. Experimental results confirm the validity of the mathematical model of the DR-PM machine system in the application of CVT, and the effectiveness of the intelligent speed ratio control strategy.

Generation of secure signatures suitable for spread-spectrum video watermarking is proposed. The method embeds a message, which is a two-dimensional binary pattern, into a three-dimensional volume, such as video, by addition of a signature. The message can be a mark or a logo indicating the copyright information. The signature is generated by shuffling or permuting random matrices along the third or time axis so that the message is extracted when they are accumulated after demodulation by the correct key. In this way, a message is hidden in the signature having equal probability of decoding any variation of the message, where the key is used to determine which one to extract. Security of the proposed method, stemming from the permutation, is evaluated as resistance to blind estimation of secret information. The matrix-based permutation allows the message to survive the spatial down-sampling without sacrificing the security. The downside of the proposed method is that it needs more data or frames to decode a reliable information compared to the conventional spread-spectrum modulation. However this is minimized by segmenting the matrices and applying permutation to sub-matrices independently. Message detectability is theoretically analyzed. Superiority of our method in terms of robustness to blind message estimation and down-sampling is verified experimentally.

For capacity expansion of the Quasi-Zenith Satellite System (QZSS) safety confirmation system, frame slotted ALOHA with flag method has previously been proposed as an access control scheme. While it is always able to communicate in an optimum state, its maximum channel efficiency is only 36.8%. In this paper, we propose adding a reservation channel (R-Ch) to the frame slotted ALOHA with flag method to increase the upper limit of the channel efficiency. With an R-Ch, collision due to random channel selection is decreased by selecting channels in multiple steps, and the channel efficiency is improved up to 84.0%. The time required for accommodating 3 million mobile terminals, each sending one message, when using the flag method only and the flag method with an R-Ch are compared. It is shown that the accommodating time can be reduced to less than half by adding an R-Ch to the flag method.

It is a hot issue that speeding up the network layers and decreasing the network parameters in convolutional neural networks (CNNs). In this paper, we propose a novel method, namely, symmetric decomposition of convolution kernels (SDKs). It symmetrically separates k×k convolution kernels into (k×1 and 1×k) or (1×k and k×1) kernels. We conduct the comparison experiments of the network models designed by SDKs on MNIST and CIFAR-10 datasets. Compared with the corresponding CNNs, we obtain good recognition performance, with 1.1×-1.5× speedup and more than 30% reduction of network parameters. The experimental results indicate our method is useful and effective for CNNs in practice, in terms of speedup performance and reduction of parameters.

We normally hold a lot of confidential information in hard disk drives and solid-state drives. When we want to erase such information to prevent the leakage, we have to overwrite the sequence of information with a sequence of symbols independent of the information. The overwriting is needed only at places where overwritten symbols are different from original symbols. Then, the cost of overwrites such as the number of overwritten symbols to erase information is important. In this paper, we clarify the minimum cost such as the minimum number of overwrites to erase information under weak and strong independence criteria. The former (resp. the latter) criterion represents that the mutual information between the original sequence and the overwritten sequence normalized (resp. not normalized) by the length of the sequences is less than a given desired value.