Combining heterogeneous wireless networks that cross licensed and unlicensed spectra is a promising way of supporting the surge in mobile traffic. The unlicensed band is mostly used by wireless LAN (WLAN) nodes which employ carrier sense multiple access/collision avoidance (CSMA/CA). Since the number of WLAN devices and their traffic are increasing, the wireless resource of the unlicensed band is expected be more depleted in 2020s. In such a wireless environment, the throughput could be extremely low and unstable due to the hidden terminal problem and exposed terminal problem despite of the large resources of the allocated frequency band and high peak PHY rate. In this paper, we propose user equipment (UE) centric access in the unlicensed band, with support by licensed band access in the mobile network. The proposed access enables robust downlink transmission from the access point (AP) to the UEs by mitigating the hidden terminal problem. The licensed spectrum access passes information on the user data waiting at the AP to the UEs and triggers UE reception opportunity (RXOP) acquisition. Furthermore, the adaptive use of UE centric downlink access is presented by using the channel utilization measured at the AP. Computer simulations confirm that licensed access assistance enhances the robustness of the unlicensed band access against the hidden terminal problem.

We present a method for recognition of acoustic events in conversation scenarios where speech usually overlaps with other acoustic events. While speech is usually considered the most informative acoustic event in a conversation scene, it does not always contain all the information. Non-speech events, such as a door knock, steps, or a keyboard typing can reveal aspects of the scene that speakers miss or avoid to mention. Moreover, being able to robustly detect these events could further support speech enhancement and recognition systems by providing useful information cues about the surrounding scenarios and noise. In acoustic event detection, state-of-the-art techniques are typically based on derived features (e.g. MFCC, or Mel-filter-banks) which have successfully parameterized the spectrogram of speech but reduce resolution and detail when we are targeting other kinds of events. In this paper, we propose a method that learns features in an unsupervised manner from high-resolution spectrogram patches (considering a patch as a certain number of consecutive frame features stacked together), and integrates within the deep neural network framework to detect and classify acoustic events. Superiority over both previous works in the field, and similar approaches based on derived features, has been assessed by statical measures and evaluation with CHIL2007 corpus, an annotated database of seminar recordings.

A new algorithm for separating mass spectra into individual substances is proposed for explosives detection. The conventional algorithm based on probabilistic latent component analysis (PLCA) is effective in many cases because it makes use of the fact that non-negativity and sparsity hold for mass spectra in explosives detection. The algorithm, however, fails to separate mass spectra in some cases because uncertainty can not be resolved only by non-negativity and sparsity constraints. To resolve the uncertainty, an algorithm based on shift-invariant PLCA (SIPLCA) utilizing temporal correlation of mass spectra is proposed in this paper. In addition, to prevent overfitting, the temporal correlation is modeled with a function representing attenuation by focusing on the fact that the amount of a substance is attenuated continuously and slowly with time. Results of an experimental evaluation of the algorithm with data obtained in a real railway station demonstrate that the proposed algorithm outperforms the PLCA-based conventional algorithm and the simple SIPLCA-based one. The main novelty of this paper is that an evaluation of the detection performance of explosives detection is demonstrated. Results of the evaluation indicate that the proposed separation algorithm can improve the detection performance.

A new algorithm for separating mass spectra into individual substances for explosives detection is proposed. In the field of mass spectrometry, separation methods, such as principal-component analysis (PCA) and independent-component analysis (ICA), are widely used. All components, however, have no negative values, and the orthogonality condition imposed on components also does not necessarily hold in the case of mass spectra. Because these methods allow negative values and PCA imposes an orthogonality condition, they are not suitable for separation of mass spectra. The proposed algorithm is based on probabilistic latent-component analysis (PLCA). PLCA is a statistical formulation of non-negative matrix factorization (NMF) using KL divergence. Because PLCA imposes the constraint of non-negativity but not orthogonality, the algorithm is effective for separating components of mass spectra. In addition, to estimate the components more accurately, a sparsity constraint is applied to PLCA for explosives detection. The main contribution is industrial application of the algorithm into an explosives-detection system. Results of an experimental evaluation of the algorithm with data obtained in a real railway station demonstrate that the proposed algorithm outperforms PCA and ICA. Also, results of calculation time demonstrate that the algorithm can work in real time.

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%.

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.

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.

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.

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.

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 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.

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.

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.

This paper proposes a novel blind adaptive array scheme with subcarrier transmission power assignment (STPA) for spectrum superposing in cognitive radio networks. The Eigenvector Beamspace Adaptive Array (EBAA) is known to be one of the blind adaptive array algorithms that can suppress inter-system interference without any channel state information (CSI). However, EBAA has difficulty in suppressing interference signals whose Signal to Interference power Ratio (SIR) values at the receiver are around 0dB. With the proposed scheme, the ST intentionally provides a level difference between subcarriers. At the receiver side, the 1st eigenvector of EBAA is applied to the received signals of the subcarrier assigned higher power and the 2nd eigenvector is applied to those assigned lower power. In order to improve interference suppression performance, we incorporate Beamspace Constant Modulus Algorithm (BSCMA) into EBAA (E-BSCMA). Additionally, STPA is effective in reducing the interference experienced by the primary system. Computer simulation results show that the proposed scheme can suppress interference signals received with SIR values of around 0dB while improving operational SIR for the primary system. It can enhance the co-existing region of 2 systems that share a spectrum.

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.

In cognitive radio (CR), superposition cooperative spectrum sensing (SPCSS) is able to offer a much improved sensing reliability compared to individual sensing. Because of the differences in sensing channel condition, the reporting order for each cognitive radio user (CU) will highly affect the sensing performance of the network. In this paper, we propose an algorithm to assign the best reporting order to each CU in order to maximize sensing performance under SPCSS. The numerical results show that the proposed scheme can obtain the same performance as the optimal scheme.

Nitrogen adsorption on thermally cleaned Si(100) surfaces by pure and plasma excited NH$_{3}$ is investigated by extit{in situ} IR absorption spectroscopy and ex-situ X-ray photoelectron spectroscopy with various temperatures from RT (25$^{circ}$C) to 800$^{circ}$C and with a treatment time of 5,min. The nitrogen coverage after the treatment varies according to the treatment temperature for both pure and plasma excited NH$_{3}$. In case of the pure NH$_{3}$, the nitrogen coverage is saturated as low as 0.13--0.25 mono layer (ML) while the growth of the nitride film commenced at 550$^{circ}$C. For the plasma excited NH$_{3}$, the saturation coverage was measured at 0.54,ML at RT and it remained unincreased from RT to 550$^{circ}$C. This indicates that the plasma excited NH$_{3}$ enhances the nitrogen adsorption near at RT. It is found that main species of N is Si$_{2}=$ NH in case of the plasma excited NH$_{3}$ at RT while the pure NH$_{3}$ treatment gives rise to the Si--NH$_{2}$ passivation with Si--H at RT. We discuss the mechanism of the nitrogen adsorption on Si(100) surfaces with the plasma excited NH$_{3}$ in comparison with the study on the pure NH$_{3}$ treatment.

Gallium oxide is expected as a channel material for thin film transistors. In the conventional technologies, gallium oxide has been tried to be fabricated by atomic layer deposition (ALD) at high temperatures from 100--450$^{circ}$C, although the room-temperature (RT) growth has not been developed. In this work, we developed the RT ALD of gallium oxide by using a remote plasma technique. We studied trimethylgallium (TMG) adsorption and its oxidization on gallium oxide surfaces at RT by infrared absorption spectroscopy (IRAS). Based on the adsorption and oxidization characteristics, we designed the room temperature ALD of Ga$_{2}$O$_{3}$. The IRAS indicated that TMG adsorbs on the gallium oxide surface by consuming the adsorption sites of surface hydroxyl groups even at RT and the remote plasma-excited water and oxygen vapor is effective in oxidizing the TMG adsorbed surface and regeneration of the adsorption sites for TMG. We successfully prepared Ga$_{2}$O$_{3}$ films on Si substrates at RT with a growth per cycle of 0.055,nm/cycle.

Function speculation design with error recovery mechanisms is quite promising due to its high performance and low area overhead. Previous work has focused on two-stage function speculation and thus lacks a systematic way to address the challenge of the multistage function speculation approach. This paper proposes a multistage function speculation with adaptive predictors and applies it in a novel adder. We deduced the analytical performance and area models for the design and validated them in our experiments. Based on those models, a general methodology is presented to guide design optimization. Both analytical proofs and experimental results on the fabricated chips show that the proposed adder's delay and area have a logarithmic and linear relationship with its bit number, respectively. Compared with the DesignWare IP, the proposed adder provides the same performance with 6-17% area reduction under different bit lengths.