Many fatal accidents involving safety-critical reactive systems have occurred in unexpected situations that were not considered during the design and test phases of development. To prevent such accidents, reactive systems should be designed to respond appropriately to any request from an environment at any time. Verifying this property during the specification phase reduces development reworking. This property of a specification is commonly known as realizability. Realizability checking for reactive system specifications involves complex and intricate analysis. The complexity of realizability problems is 2EXPTIME-complete. To detect typical simple deficiencies in specifications efficiently, we introduce the notion of bounded strong satisfiability (a necessary condition for realizability), and present a method for checking this property. Bounded strong satisfiability is the property that, for all input patterns represented by loop structures of a given size k, there is a response that satisfies a given specification. We report a checking method based on a satisfiability solver, and show that the complexity of the bounded strong satisfiability problem is co-NEXPTIME-complete. Moreover, we report experimental results showing that our method is more efficient than existing approaches.

A maximum-likelihood code acquisition scheme is investigated for frequency-selective fading channels with an emphasis on the decision strategies. Using the maximum-likelihood estimation technique, we first derive an optimal decision rule, which is optimal in the viewpoint of probability of detection. Based on the derived optimal decision rule, a practical and simple decision rule is also developed, and its performance is assessed for both single dwell and double dwell acquisition systems. Simulation results demonstrate that the proposed acquisition scheme significantly outperforms the previously proposed schemes in frequency-selective fading channels.

Many detailed studies ranging from networking to hardware as well as standardization activities over the last few years have advanced the performance of the elastic optical network. Thanks to these intensive works, the elastic optical network has been becoming feasible. This paper reviews the recent advances in the elastic optical network from the aspects of networking technology and hardware design. For the former, we focus on the efficient elastic network design technology related to routing and spectrum assignment (RSA) of elastic optical paths including network optimization or standardization activities, and for the latter, two key enabling technologies are discussed: elastic transponders/regenerators and gridless optical switches. Making closely-dependent networking and hardware technologies work synergistically is the key factor in implementing truly effective elastic optical networks.

This paper proposes a simple and practical scheme to decide the direction of a phased array antenna beam in wireless access systems using Radio-over-Fiber (RoF) technique. The feasibility of the proposed scheme is confirmed by the optical and wireless transmission experiments using 2GHz RoF signals. In addition, two-dimensional steering operation in the millimeter-wave band is demonstrated for targeting future high-speed wireless communication systems. The required system parameters for practical use are also provided by investigating the induced transmission penalties. The proposed detection scheme is applicable to two-dimensional antenna beam steering in the millimeter-wave band by properly designing the fiber length and wavelength variable range.

Acoustic feedback is a major complaint of hearing aid users. Adaptive filters are a common method for suppressing acoustic feedback in digital hearing aids. In this letter, we propose a new variable step-size algorithm for normalized least mean square and an affine projection algorithm to combine with a variable step-size affine projection algorithm and global speech absence probability in an adaptive filter. The computer simulation used to test the proposed algorithm results in a lower misalignment error than the comparison algorithm at a similar convergence rate. Therefore, the proposed algorithm suggests an effective solution for the feedback suppression system of digital hearing aids.

This paper proposes a novel approach for integrating spectral feature extraction and acoustic modeling in hidden Markov model (HMM) based speech synthesis. The statistical modeling process of speech waveforms is typically divided into two component modules: the frame-by-frame feature extraction module and the acoustic modeling module. In the feature extraction module, the statistical mel-cepstral analysis technique has been used and the objective function is the likelihood of mel-cepstral coefficients for given speech waveforms. In the acoustic modeling module, the objective function is the likelihood of model parameters for given mel-cepstral coefficients. It is important to improve the performance of each component module for achieving higher quality synthesized speech. However, the final objective of speech synthesis systems is to generate natural speech waveforms from given texts, and the improvement of each component module does not always lead to the improvement of the quality of synthesized speech. Therefore, ideally all objective functions should be optimized based on an integrated criterion which well represents subjective speech quality of human perception. In this paper, we propose an approach to model speech waveforms directly and optimize the final objective function. Experimental results show that the proposed method outperformed the conventional methods in objective and subjective measures.

The perceived age of a singing voice is the age of the singer as perceived by the listener, and is one of the notable characteristics that determines perceptions of a song. In this paper, we describe an investigation of acoustic features that have an effect on the perceived age, and a novel voice timbre control technique based on the perceived age for singing voice conversion (SVC). Singers can sing expressively by controlling prosody and voice timbre, but the varieties of voices that singers can produce are limited by physical constraints. Previous work has attempted to overcome this limitation through the use of statistical voice conversion. This technique makes it possible to convert singing voice timbre of an arbitrary source singer into those of an arbitrary target singer. However, it is still difficult to intuitively control singing voice characteristics by manipulating parameters corresponding to specific physical traits, such as gender and age. In this paper, we first perform an investigation of the factors that play a part in the listener's perception of the singer's age at first. Then, we applied a multiple-regression Gaussian mixture models (MR-GMM) to SVC for the purpose of controlling voice timbre based on the perceived age and we propose SVC based on the modified MR-GMM for manipulating the perceived age while maintaining singer's individuality. The experimental results show that 1) the perceived age of singing voices corresponds relatively well to the actual age of the singer, 2) prosodic features have a larger effect on the perceived age than spectral features, 3) the individuality of a singer is influenced more heavily by segmental features than prosodic features 4) the proposed voice timbre control method makes it possible to change the singer's perceived age while not having an adverse effect on the perceived individuality.

This paper presents a voice conversion technique using speaker-dependent Restricted Boltzmann Machines (RBM) to build high-order eigen spaces of source/target speakers, where it is easier to convert the source speech to the target speech than in the traditional cepstrum space. We build a deep conversion architecture that concatenates the two speaker-dependent RBMs with neural networks, expecting that they automatically discover abstractions to express the original input features. Under this concept, if we train the RBMs using only the speech of an individual speaker that includes various phonemes while keeping the speaker individuality unchanged, it can be considered that there are fewer phonemes and relatively more speaker individuality in the output features of the hidden layer than original acoustic features. Training the RBMs for a source speaker and a target speaker, we can then connect and convert the speaker individuality abstractions using Neural Networks (NN). The converted abstraction of the source speaker is then back-propagated into the acoustic space (e.g., MFCC) using the RBM of the target speaker. We conducted speaker-voice conversion experiments and confirmed the efficacy of our method with respect to subjective and objective criteria, comparing it with the conventional Gaussian Mixture Model-based method and an ordinary NN.

We propose a cooperative compressed spectrum sensing scheme for correlated signals in wideband cognitive radio networks. In order to design a reconstruction algorithm which accurately recover the wideband signals from the compressed samples in low SNR (Signal-to-Noise Ratio) environments, we consider the multiple measurement vector model exploiting a sequence of input signals and propose a cooperative sparse Bayesian learning algorithm which models the temporal correlation of the input signals. Simulation results show that the proposed scheme outperforms existing compressed sensing algorithms for low SNRs.

Break arcs are generated in a DC48V and 12A resistive circuit. Silver electrical contacts are separated at constant opening speed. The cathode contact surface is irradiated by a blue LED. The center wavelength of the emission of the LED is 470nm. There is no spectral line of the light emitted from the break arcs. Only the images of contact surface are observed by a high-speed camera and an optical band pass filter. Another high-speed camera observes only the images of the break arc. Time evolutions of the cathode surface morphology being eroded by the break arcs and the motion of the break arcs are observed with these cameras, simultaneously. The images of the cathode surface are investigated by the image analysis technique. The results show that the moments when the expanded regions on the cathode surface are formed during the occurrence of the break arcs. In addition, it is shown that the expanded regions are not contacted directly to the cathode roots of the break arcs.

This paper presents an electrolaryngeal (EL) speech enhancement method capable of significantly improving naturalness of EL speech while causing no degradation in its intelligibility. An electrolarynx is an external device that artificially generates excitation sounds to enable laryngectomees to produce EL speech. Although proficient laryngectomees can produce quite intelligible EL speech, it sounds very unnatural due to the mechanical excitation produced by the device. Moreover, the excitation sounds produced by the device often leak outside, adding to EL speech as noise. To address these issues, there are mainly two conventional approached to EL speech enhancement through either noise reduction or statistical voice conversion (VC). The former approach usually causes no degradation in intelligibility but yields only small improvements in naturalness as the mechanical excitation sounds remain essentially unchanged. On the other hand, the latter approach significantly improves naturalness of EL speech using spectral and excitation parameters of natural voices converted from acoustic parameters of EL speech, but it usually causes degradation in intelligibility owing to errors in conversion. We propose a hybrid approach using a noise reduction method for enhancing spectral parameters and statistical voice conversion method for predicting excitation parameters. Moreover, we further modify the prediction process of the excitation parameters to improve its prediction accuracy and reduce adverse effects caused by unvoiced/voiced prediction errors. The experimental results demonstrate the proposed method yields significant improvements in naturalness compared with EL speech while keeping intelligibility high enough.

In this paper, an automatic and unsupervised method using context-dependent hidden Markov models (CD-HMMs) is proposed for the prosodic labeling of speech synthesis databases. This method consists of three main steps, i.e., initialization, model training and prosodic labeling. The initial prosodic labels are obtained by unsupervised clustering using the acoustic features designed according to the characteristics of the prosodic descriptor to be labeled. Then, CD-HMMs of the spectral parameters, F0s and phone durations are estimated by a means similar to the HMM-based parametric speech synthesis using the initial prosodic labels. These labels are further updated by Viterbi decoding under the maximum likelihood criterion given the acoustic feature sequences and the trained CD-HMMs. The model training and prosodic labeling procedures are conducted iteratively until convergence. The performance of the proposed method is evaluated on Mandarin speech synthesis databases and two prosodic descriptors are investigated, i.e., the prosodic phrase boundary and the emphasis expression. In our implementation, the prosodic phrase boundary labels are initialized by clustering the durations of the pauses between every two consecutive prosodic words, and the emphasis expression labels are initialized by examining the differences between the original and the synthetic F0 trajectories. Experimental results show that the proposed method is able to label the prosodic phrase boundary positions much more accurately than the text-analysis-based method without requiring any manually labeled training data. The unit selection speech synthesis system constructed using the prosodic phrase boundary labels generated by our proposed method achieves similar performance to that using the manual labels. Furthermore, the unit selection speech synthesis system constructed using the emphasis expression labels generated by our proposed method can convey the emphasis information effectively while maintaining the naturalness of synthetic speech.

There have been steady demands for a speech segmentation method to handle various speech applications. Conventional segmentation algorithms show reliable performance but they require a sufficient training database. This letter proposes a manner class segmentation method based on the acoustic event and landmark detection used in the knowledge-based speech recognition system. Measurements of sub-band abruptness and additional parameters are used to detect the acoustic events. Candidates of manner classes are segmented from the acoustic events and determined based on the knowledge of acoustic phonetics and acoustic parameters. Manners of vowel/glide, nasal, fricative, stop burst, stop closure, and silence are segmented in this system. In total, 71% of manner classes are correctly segmented with 20-ms error boundaries.

Existing fault localization approaches usually do not provide a context for developers to understand the problem. Thus, this paper proposes a novel approach using the dynamic backward slicing technique to enrich contexts for existing approaches. Our empirical results show that our approach significantly outperforms five state-of-the-art fault localization techniques.

This paper introduces an ontology-based method for checking requirements specification. Requirements ontology is a knowledge structure that contains functional requirements (FR), attributes of FR and relations among FR. Requirements specification is compared with functional nodes in the requirements ontology, then rules are used to find errors in requirements. On the basis of the results, requirements team can ask questions to customers and correctly and efficiently revise requirements. To support this method, an ontology-based checking tool for verification of requirements has been developed. Finally, the requirements checking method is evaluated through an experiment.

Aspect-oriented software development (AOSD) helps to solve the problem of low scalability and high maintenance costs of legacy systems caused by code scattering and tangling by extracting cross-cutting concerns and inserting them into aspects. Identifying the cross-cutting concerns of legacy systems is the key to reconstructing such systems using the approach of AOSD. However, current dynamic approaches to the identification of cross-cutting concerns simply check the methods' execution sequence, but do not consider their calling context, which may cause low precision. In this paper, we propose an improved comprehensive approach to the identification of candidate cross-cutting concerns of legacy systems based on the combination of the analysis of recurring execution relations and fan-ins. We first analyse the execution trace with a given test case and identify four types of execution relations for neighbouring methods: exit-entry, entry-exit, entry-entry and exit-exit. Afterwards, we measure the methods' left cross-cutting degrees and right cross-cutting degrees. The former ensures that the candidate recurs in a similar running context, whereas the latter indicates how many times the candidate cross-cuts different methods. The final candidates are then obtained from those high fan-in methods, which not only cross-cut others more times than a predefined threshold, but are always entered or left under the same running context. The experiment conducted on three open source systems shows that our approach improves the precision of identifying cross-cutting concerns compared with tradition ones.

We proposes a new adaptive spectral masking method of algebraic vector quantization (AVQ) for non-sparse signals in the modified discreet cosine transform (MDCT) domain. This paper also proposes switching the adaptive spectral masking on and off depending on whether or not the target signal is non-sparse. The switching decision is based on the results of MDCT-domain sparseness analysis. When the target signal is categorized as non-sparse, the masking level of the target MDCT coefficients is adaptively controlled using spectral envelope information. The performance of the proposed method, as a part of ITU-T G.711.1 Annex D, is evaluated in comparison with conventional AVQ. Subjective listening test results showed that the proposed method improves sound quality by more than 0.1 points on a five-point scale on average for speech, music, and mixed content, which indicates significant improvement.

Pseudo-random sequences with high linear complexity play important roles in many domains. We give linear complexity of generalized cyclotomic quaternary sequences with period pq over Z4 via the weights of its Fourier spectral sequence. The results show that such sequences have high linear complexity.

Dynamic spectrum access is the key approach in cognitive wireless regional area networks, and it is adopted by secondary users to access the licensed radio spectrum opportunistically. In order to realize real-time secondary spectrum usage, a dynamic spectrum access model based on stochastic differential games is proposed to realize dynamic spectrum allocation; a Nash equilibrium solution to the model is given and analyzed in this paper. From an overall perspective, the relationships between available spectrum percentage and the spectrum access rate are studied. Changes in the available spectrum percentage of the cognitive wireless regional area networks involve a deterministic component and a stochastic component which depends upon an r-dimensional Wiener process. The Wiener process represents an accumulation of random influences over the interval, and it reflects stochastic and time-varying properties of the available spectrum percentage. Simulation results show that the dynamic spectrum access model is efficient, and it reflects the time-varying radio frequency environment. Differential games are useful tools for the spectrum access and management in the time-varying radio environment.

This paper proposes out-of-band emission reduction schemes for IEEE 802.11af based Wireless Local Area Network (WLAN) systems operating in TV White Spaces (TVWS). IEEE 802.11af adopts Orthogonal Frequency Division Multiplexing (OFDM) to exploit the TVWS spectrum effectively. The combination of the OFDM and TVWS may be able to solve the problem of frequency depletion. However the TVWS transmitter must satisfy a strict transmission spectrum mask and reduce out-of-band emission to protect the primary users. The digital convolution filter is one way of reducing the out-of-band emission. Unfortunately, implementing a strict mask needs a large number of filter taps, which causes high implementation complexity. Time-domain windowing is another effective approach. This scheme reduces out-of-band emission with low complexity but at the price of shortening the effective guard interval. This paper proposes a mechanism that jointly uses these two schemes for out-of-band emission reduction. Moreover, the appropriate windowing duration design is proposed in terms of both the out-of-band emission suppression and throughput performance for all mandatory mode of IEEE 802.11af system. The proposed time-domain windowing design reduces the number of multiplier by 96.5%.