Break operations of DC inductive (L=20mH) load currents up to about 5A with 14V were conducted in air with AgSnO2 contact pairs under different contact opening speeds, first up to 20mm/s and then to 200mm/s. Average break arc duration at each current level was calculated under the respective opening speeds. While break arc durations became shorter with increases in the opening speeds at larger current levels, such reduction tendencies were less significant with an increase of the contact opening speed from 20mm/s to 200mm/s, even when operated to break a load current of 5A. Both load current levels and contact opening speed levels seem to exhibit certain roles for realizing arc shortening effects.

This paper gives an outline of key technologies necessary for science-based agriculture. In order to design future agriculture, present agriculture should be redesigned based on the context of smart agriculture that indicates the overall form of agriculture including a social system while the present precision agriculture shows a technical form of agriculture only. Wireless Sensor Network (WSN) and the various type of optical sensors are assumed to be a basic technology of smart agriculture which intends the harmony with the economic development and sustainable agro-ecosystem. In this paper, the current state and development for the optical sensing for environment and plant are introduced.

A salient feature extraction algorithm is proposed to improve the recognition rate of the speech emotion. Firstly, the spectrogram of the emotional speech is calculated. Secondly, imitating the selective attention mechanism, the color, direction and brightness map of the spectrogram is computed. Each map is normalized and down-sampled to form the low resolution feature matrix. Then, each feature matrix is converted to the row vector and the principal component analysis (PCA) is used to reduce features redundancy to make the subsequent classification algorithm more practical. Finally, the speech emotion is classified with the support vector machine. Compared with the tradition features, the improved recognition rate reaches 15%.

The paper addresses a scheme of lightly supervised training of an acoustic model, which exploits a large amount of data with closed caption texts but not faithful transcripts. In the proposed scheme, a sequence of the closed caption text and that of the ASR hypothesis by the baseline system are aligned. Then, a set of dedicated classifiers is designed and trained to select the correct one among them or reject both. It is demonstrated that the classifiers can effectively filter the usable data for acoustic model training. The scheme realizes automatic training of the acoustic model with an increased amount of data. A significant improvement in the ASR accuracy is achieved from the baseline system and also in comparison with the conventional method of lightly supervised training based on simple matching.

Passive SONAR receives a mixed form of signal that is a combination of continuous and discrete line-component spectrum signals. The conventional algorithms, DEMON and LOFAR, respectively target each type of signal, but do not consider the other type of signal also present in the practical environment. Thus when features from two types of signals are presented at the same time, analysis results may cause confusion. In this paper, we propose an integrated analysis algorithm for underwater signals using the modulation spectrogram domain. The proposed domain presents the visual difference between the different types of signal, and therefore can prevent confusion that would otherwise be feasible. Moreover, the proposed algorithm is more efficient than multiband DEMON in terms of computation complexity, while in colored ambient noise environment, it has similar performance to conventional DEMON and LOFAR. We prove the validity of the proposed algorithm through the relevant experiments with synthesized signal and actual underwater recordings.

Nowadays, many individuals and organizations tend to outsource their data to a cloud storage for reducing the burden of data storage and maintenance. However, a cloud provider may be untrustworthy. The cloud thus leads to a numerous security challenges: data availability, data integrity, and data confidentiality. In this paper, we focus on data availability and data integrity because they are the prerequisites of the existence of a cloud system. The approach of this paper is the network coding-based Proof of Retrievability (POR) scheme which allows a client to check whether his/her data stored on the cloud servers are intact. Although many existing network coding-based PORs have been proposed, most of them still incur high costs in data check and data repair, and cannot prevent the small corruption attack which is a common attack in the POR scheme. This paper proposes a new network coding-based POR using the dispersal coding technique, named the ND-POR (Network coding - Dispersal coding POR) to improve the efficiency in data check and data repair and to protect against the small corruption attack.

In this letter, a simple dispersion matrix design method for generalized space-time shift keying is presented, in which the dispersion matrices are systematically constructed with cyclic identity matrix, without the need of computer search. The proposed scheme is suitable for any number of transmit antennas greater than two, and can achieve the transmit diversity order of two except two special cases. Simulation results are presented to verify our theoretical analysis and the performance of the proposed scheme.

In the case of a disaster such as an earthquake or a tsunami, the city, town, and village administration usually issues an evacuation advisory and other information through the Outdoor Public Address Speakers for the disaster reduction broadcasting system covering its area of jurisdiction. However, in areas those have previous experience of a disaster, people frequently voice the lack of audibility of the disaster reduction broadcast. In this research, we conducted a questionnaire survey on the residents in the central area of Ishinomaki City, Miyagi Prefecture, who are the victims of the Great East Japan Earthquake Disaster, on the audible quality of outdoor public address (PA) speakers of the disaster reduction broadcasting system so as to understand the current state of such broadcasts and to propose ideal methods of sending and receiving information at the time of a future disaster.

We propose a one-step error detection and correction interface for a voice word processor. This correction interface performs analysis region detection, user intention understanding and error correction utterance recognition, all from a single user utterance input. We evaluate the performance of each component first, and then compare the effectiveness of our interface to two previous interfaces. Our evaluation demonstrates that each component is technically superior to the baselines and that our one-step error detection and correction method yields an error correction interface that is more convenient and natural than the two previous interfaces.

We propose a method of spread spectrum digital watermarking with quantization index modulation (QIM) and evaluate the method on the basis of IHC evaluation criteria. The spread spectrum technique can make watermarks robust by using spread codes. Since watermarks can have redundancy, messages can be decoded from a degraded stego-image. Under IHC evaluation criteria, it is necessary to decode the messages without the original image. To do so, we propose a method in which watermarks are generated by using the spread spectrum technique and are embedded by QIM. QIM is an embedding method that can decode without an original image. The IHC evaluation criteria include JPEG compression and cropping as attacks. JPEG compression is lossy compression. Therefore, errors occur in watermarks. Since watermarks in stego-images are out of synchronization due to cropping, the position of embedded watermarks may be unclear. Detecting this position is needed while decoding. Therefore, both error correction and synchronization are required for digital watermarking methods. As countermeasures against cropping, the original image is divided into segments to embed watermarks. Moreover, each segment is divided into 8×8 pixel blocks. A watermark is embedded into a DCT coefficient in a block by QIM. To synchronize in decoding, the proposed method uses the correlation between watermarks and spread codes. After synchronization, watermarks are extracted by QIM, and then, messages are estimated from the watermarks. The proposed method was evaluated on the basis of the IHC evaluation criteria. The PSNR had to be higher than 30 dB. Ten 1920×1080 rectangular regions were cropped from each stego-image, and 200-bit messages were decoded from these regions. Their BERs were calculated to assess the tolerance. As a result, the BERs were less than 1.0%, and the average PSNR was 46.70 dB. Therefore, our method achieved a high image quality when using the IHC evaluation criteria. In addition, the proposed method was also evaluated by using StirMark 4.0. As a result, we found that our method has robustness for not only JPEG compression and cropping but also additional noise and Gaussian filtering. Moreover, the method has an advantage in that detection time is small since the synchronization is processed in 8×8 pixel blocks.

With the rapid increase of various uses of wireless communications in modern life, the high microwave and millimeter wave frequency bands are attracting much attention. However, the existing databases on above 6GHz radio-frequency (RF) electromagnetic (EM) field exposure of biological bodies are obviously insufficient. An in-vivo research project on local and whole-body exposure of rats to RF-EM fields above 6GHz was started in Japan in 2013. This study aims to perform a dosimetric design for the whole-body-average specific absorption rates (WBA-SARs) of unconstrained rats exposed to 6GHz RF-EM fields in a reverberation chamber (RC). The required input power into the RC is clarified using a two-step evaluation method in order to achieve a target exposure level in rats. The two-step method, which incorporates the finite-difference time-domain (FDTD) numerical solutions with electric field measurements in an RC exposure system, is used as an evaluation method to determine the whole-body exposure level in the rats. In order to verify the validity of the two-step method, we use S-parameter measurements inside the RC to experimentally derive the WBA-SARs with rat-equivalent phantoms and then compare those with the FDTD-calculated ones. It was shown that the difference between the two-step method and the S-parameter measurements is within 1.63dB, which reveals the validity and usefulness of the two-step technique.

This paper describes an evaluation of a temporally stable spectral envelope estimator proposed in our past research. The past research demonstrated that the proposed algorithm can synthesize speech that is as natural as the input speech. This paper focuses on an objective comparison, in which the proposed algorithm is compared with two modern estimation algorithms in terms of estimation performance and temporal stability. The results show that the proposed algorithm is superior to the others in both aspects.

In traditional speech emotion recognition systems, when the training and testing utterances are obtained from different corpora, the recognition rates will decrease dramatically. To tackle this problem, in this letter, inspired from the recent developments of sparse coding and transfer learning, a novel sparse transfer learning method is presented for speech emotion recognition. Firstly, a sparse coding algorithm is employed to learn a robust sparse representation of emotional features. Then, a novel sparse transfer learning approach is presented, where the distance between the feature distributions of source and target datasets is considered and used to regularize the objective function of sparse coding. The experimental results demonstrate that, compared with the automatic recognition approach, the proposed method achieves promising improvements on recognition rates and significantly outperforms the classic dimension reduction based transfer learning approach.

This paper considers a method for constructing good high-rate punctured convolutional codes through dual codes. A low-rate R=1/n convolutional code has a dual code identical to a punctured convolutional code with rate R=(n-1)/n. This implies that a low-rate R=1/n convolutional code encoder can help the search of punctured convolutional code encoders. This paper provides the procedures that obtain all the useful dual code encoders to a given CC with rate R=1/n easily, and the best PCC encoder with rate R=(n-1)/n among the encoders we derive from all the obtained dual code encoders. This paper also shows an example of the PCC the procedures obtain from some CC.

With the development and diverse use of wireless radio terminals, it is necessary to estimate the specific absorption rate (SAR) of the human body from such devices under various exposure situations. In particular, tablet computers may be used for a long time while placed near the abdomen. There has been insufficient evaluation of the SAR for the human body from tablet computers. Therefore, we investigated the SAR of various configurations of a commercial tablet computer using a numerical model with the anatomical structures of Japanese males and females, respectively. We find that the 10-g-averaged SAR of the tablet computer is strongly altered by the tablet's orientation, i.e., from -7.3dB to -22.6dB. When the tablet computer is moved parallel to the height direction, the relative standard deviations of the 10-g averaged SAR for the male and female models are within 40%. In addition, those for the different tilts of the computer are within 20%. The fluctuations of the 10-g-averaged SAR for the seated human models are within ±1.5dB in all cases.

Delayed correlation has been used to detect orthogonal frequency division multiplexing symbols with cyclic prefix in spectrum sensing. Because of the frequency offset, the outputs of the delayed correlation do not lie only on the real axis of a complex plane. Therefore, the absolute value of the outputs of the delayed correlation is employed. Furthermore, with the use of a filter bank, the number of the outputs of the delayed correlators increases and the averaging over the outputs decreases the noise variance. This paper proposes a new delayed correlation scheme that uses a filter bank and employs the absolute of the outputs of delayed correlation. The proposed scheme improves the probability of detection as the number of the branches of the delayed correlators increases. In the case of 6 branches, the proposed scheme reduces the required sample energy by 1dB the probability of detection of 0.9.

Using traditional single-layer dictionary learning methods, it is difficult to reveal the complex structures hidden in the hyperspectral images. Motivated by deep learning technique, a deep dictionary learning approach is proposed for hyperspectral image denoising, which consists of hierarchical dictionary learning, feature denoising and fine-tuning. Hierarchical dictionary learning is helpful for uncovering the hidden factors in the spectral dimension, and fine-tuning is beneficial for preserving the spectral structure. Experiments demonstrate the effectiveness of the proposed approach.

It is unclear whether Hidden Markov Model (HMM) or Dynamic Time Warping (DTW) mapping is more appropriate for visual speech recognition when only small data samples are available. In this letter, the two approaches are compared in terms of sensitivity to the amount of training samples and computing time with the objective of determining the tipping point. The limited training data problem is addressed by exploiting a straightforward template matching via weighted-DTW. The proposed framework is a refined DTW by adjusting the warping paths with judicially injected weights to ensure a smooth diagonal path for accurate alignment without added computational load. The proposed WDTW is evaluated on three databases (two in the public domain and one developed in-house) for visual recognition performance. Subsequent experiments indicate that the proposed WDTW significantly enhances the recognition rate compared to the DTW and HMM based algorithms, especially under limited data samples.

This paper proposes an updating state dependent disturbance observer (USDDOB) to reject position dependent disturbances when parameters vary slowly, and input and output are time-delayed. To reject the effects of resultant slowly-varying position dependent disturbances, the USDDOB uses the control method of the state dependent disturbance observer (SDDOB) and time-invariance approximation. The USDDOB and a main proportional integral (PI) controller constitute a robust controller. Simulations and experiments using a 1-degree-of-freedom (1-DOF) tilted planar robot show the effectiveness of the proposed method.

Verification of a design with respect to its requirement specification is important to prevent errors before constructing an actual implementation. The existing works focus on verifications where the specifications are described using temporal logics or using the same languages as that used to describe the designs. Our work considers cases where the specifications and the designs are described using different languages. To verify such cases, we propose a framework to check if a design conforms to its specification based on their simulation relation. Specifically, we define the semantics of the specifications and the designs commonly as labelled transition systems (LTSs). We appreciate LTSs since they could interpret information about the system and actions that the system may perform as well as the effect of these actions. Then, we check whether a design conforms to its specification based on the simulation relation of their LTS. In this paper, we present our framework for the verification of reactive systems, and we present the case where the specifications and the designs are described in Event-B and Promela/Spin, respectively. We also present two case studies with the results of several experiments to illustrate the applicability of our framework on practical systems.