This paper presents a new hole-filling method that uses extrapolated spatio-temporal background information to obtain a synthesized free-view. A new background codebook for extracting reliable temporal background information is introduced. In addition, the paper addresses estimating spatial local background to distinguish background and foreground regions so that spatial background information can be extrapolated. Background holes are filled by combining spatial and temporal background information. Finally, exemplar-based inpainting is applied to fill in the remaining holes using a new priority function. The experimental results demonstrated that satisfactory synthesized views can be obtained using the proposed algorithm.

A layered light-field display based on light-field factorization is considered. In the original work, the factorization is formulated under the assumption that the light field is captured with orthographic cameras. In this paper, we introduce a generalized framework for light-field factorization that can handle both the orthographic and perspective camera projection models. With our framework, a light field captured with perspective cameras can be displayed accurately.

With the ever-increasing number of web services registered in service communities, many users are apt to find their interested web services through various recommendation techniques, e.g., Collaborative Filtering (i.e., CF)-based recommendation. Generally, CF-based recommendation approaches can work well, when a target user has similar friends or the target services (i.e., services preferred by the target user) have similar services. However, when the available user-service rating data is very sparse, it is possible that a target user has no similar friends and the target services have no similar services; in this situation, traditional CF-based recommendation approaches fail to generate a satisfying recommendation result. In view of this challenge, we combine Social Balance Theory (abbreviated as SBT; e.g., “enemy's enemy is a friend” rule) and CF to put forward a novel data-sparsity tolerant recommendation approach Ser_RecSBT+CF. During the recommendation process, a pruning strategy is adopted to decrease the searching space and improve the recommendation efficiency. Finally, through a set of experiments deployed on a real web service quality dataset WS-DREAM, we validate the feasibility of our proposal in terms of recommendation accuracy, recall and efficiency. The experiment results show that our proposed Ser_RecSBT+CF approach outperforms other up-to-date approaches.

A bi-dimensional empirical mode decomposition (BEMD) is one of the powerful methods for decomposing non-linear and non-stationary signals without a prior function. It can be applied in many applications such as feature extraction, image compression, and image filtering. Although modified BEMDs are proposed in several approaches, computational cost and quality of their bi-dimensional intrinsic mode function (BIMF) still require an improvement. In this paper, an iteration-free computation method for bi-dimensional empirical mode decomposition, called iBEMD, is proposed. The locally partial correlation for principal component analysis (LPC-PCA) is a novel technique to extract BIMFs from an original signal without using extrema detection. This dramatically reduces the computation time. The LPC-PCA technique also enhances the quality of BIMFs by reducing artifacts. The experimental results, when compared with state-of-the-art methods, show that the proposed iBEMD method can achieve the faster computation of BIMF extraction and the higher quality of BIMF image. Furthermore, the iBEMD method can clearly remove an illumination component of nature scene images under illumination change, thereby improving the performance of text localization and recognition.

Conventional feature-rich parsers based on manually tuned features have achieved state-of-the-art performance. However, these parsers are not good at handling long-term dependencies using only the clues captured by a prepared feature template. On the other hand, recurrent neural network (RNN)-based parsers can encode unbounded history information effectively, but they perform not well for small tree structures, especially when low-frequency words are involved, and they cannot use prior linguistic knowledge. In this paper, we propose a simple but effective framework to combine the merits of feature-rich transition-based parsers and RNNs. Specifically, the proposed framework incorporates RNN-based scores into the feature template used by a feature-rich parser. On English WSJ treebank and SPMRL 2014 German treebank, our framework achieves state-of-the-art performance (91.56 F-score for English and 83.06 F-score for German), without requiring any additional unlabeled data.

In this paper, we propose a method to diagnose a bridging fault between a clock line and a gate signal line. Assuming that scan based flush tests are applied, we perform fault simulation to deduce candidate faults. By analyzing fault behavior, it is revealed that faulty clock waveforms depend on the timing of the signal transition on a gate signal line which is bridged. In the fault simulation, a backward sensitized path tracing approach is introduced to calculate the timing of signal transitions. Experimental results show that the proposed method deduces candidate faults more accurately than our previous method.

Ransomware becomes more and more threatening nowadays. In this paper, we propose CLDSafe, a novel and efficient file backup system against ransomware. It keeps shadow copies of files and provides secure restoration using cloud storage when a computer is infected by ransomware. After our system measures file similarities between a new file on the client and an old file on the server, the old file on the server is backed up securely when the new file is changed substantially. And then, only authenticated users can restore the backup files by using challenge-response mechanism. As a result, our proposed solution will be helpful in recovering systems from ransomware damage.

The quality of visual comfort and depth perception is a crucial requirement for virtual reality (VR) applications. This paper investigates major causes of visual discomfort and proposes a novel virtual camera controlling method using visual saliency to minimize visual discomfort. We extract the saliency of each scene and properly adjust the convergence plane to preserve realistic 3D effects. We also evaluate the effectiveness of our method on free-form architecture models. The results indicate that the proposed saliency-guided camera control is more comfortable than typical camera control and gives more realistic depth perception.

This paper looks at the history of research in the Technical Committee on Information Networks from the time of its inception to the present and provides an overview of the latest research in this area based on the topics discussed in recent meetings of the committee. It also presents possible future developments in the field of information networks.

Research efforts initiated by the EXAT Initiative are described to realize Exabit/s optical communications, utilizing the 3M technologies, i.e. multi-core fiber, multi-mode control and multi-level modulation.

As the role of wireless communication is becoming more important for realizing a future connected society for not only humans but also things, spectrum scarcity is becoming severe, because of the huge numbers of mobile terminals and many types of applications in use. In order to realize sustainable wireless connection under limited spectrum resources in a future wireless world, a new dynamic spectrum management scheme should be developed that considers the surrounding radio environment and user preferences. In this paper, we discuss a new spectrum utilization framework for a future wireless world called the “smart spectrum.” There are four main issues related to realizing the smart spectrum. First, in order to recognize the spectrum environment accurately, spectrum measurement is an important technology. Second, spectrum modeling for estimating the spectrum usage and the spectrum environment by using measurement results is required for designing wireless parameters for dynamic spectrum use in a shared spectrum environment. Third, in order to effectively gather the measurement results and provide the spectrum information to users, a measurement-based spectrum database can be used. Finally, smart spectrum management that operates in combination with a spectrum database is required for realizing efficient and organized dynamic spectrum utilization. In this paper, we discuss the concept of the smart spectrum, fundamental research studies of the smart spectrum, and the direction of development of the smart spectrum for targeting the future wireless world.

This paper describes the broadband radio access techniques for Universal Mobile Terrestrial Systems (UMTS)/Wideband Code Division Multiple Access (W-CDMA), High-Speed Downlink Packet Access (HSDPA)/High-Speed Uplink Packet Access (HSUPA), Long Term Evolution (LTE), and LTE-Advanced. Major technical pillars are almost identical regardless of the radio access systems of the respective generations. However, the key techniques that provide distinct performance improvements have changed according to the system requirements in each generation. Hence, in this paper, we focus on the key techniques associated with the system requirements. We also describe the requirements, radio access technology candidates, and challenges toward the future 5G systems.

Researchers have already attributed a certain amount of variability and “drift” in an individual's handwriting pattern to mental workload, but this phenomenon has not been explored adequately. Especially, there still lacks an automated method for accurately predicting mental workload using handwriting features. To solve the problem, we first conducted an experiment to collect handwriting data under different mental workload conditions. Then, a predictive model (called SVM-GA) on two-level handwriting features (i.e., sentence- and stroke-level) was created by combining support vector machines and genetic algorithms. The results show that (1) the SVM-GA model can differentiate three mental workload conditions with accuracy of 87.36% and 82.34% for the child and adult data sets, respectively and (2) children demonstrate different changes in handwriting features from adults when experiencing mental workload.

In this paper, we propose a novel gate delay time mismatch tolerant time-mode signal accumulator whose input and output are represented by a time difference of two digital signal transitions. Within the proposed accumulator, the accumulated value is stored as the time difference between the two pulses running around the same ring of a delay line, so that there is no mismatch between the periods of the two pulses, thus the output drift of the accumulator is suppressed in principle without calibrating mismatch of two rings, which is used to store the accumulated value in the conventional one. A prototype of the proposed accumulator was fabricated in 180nm CMOS. The accumulating operation is confirmed by both time and frequency domain experiments. The standard deviation of the error of the accumulating operation is 9.8ps, and compared with the previous work, the peak error over full-scale is reduced by 46% without calibrating the output drift.

With rapid growth of producing high-resolution digital contents such as Full HD, 4K, and 8K movies, the demand for low cost and high throughput sharing of content files is increasing at digital content productions. In order to meet this demand, we have proposed DRIP (Distributed chunks Retrieval and Integration Procedure), a storage and retrieval mechanism for large file sharing using forward error correction (FEC) and global dispersed storage. DRIP was confirmed that it contributes to low cost and high throughput sharing. This paper describes the design and implementation of Content Espresso, a distributed large file sharing system for digital content productions using DRIP, and presents performance evaluations. We set up experimental environment using 79 physical machines including 72 inexpensive storage servers, and evaluate file metadata access performance, file storage/retrieval performance, FEC block size, and system availability by emulating global environments. The results confirm that Content Espresso has capability to deal with 15,000 requests per second, achieves 1 Gbps for file storage, and achieves more than 3 Gbps for file retrieval. File storage and retrieval performance are not significantly affected by the network conditions. Thus, we conclude that Content Espresso is capable of a global scale file sharing system for digital content productions.

In this letter, we consider a cognitive radio network where multiple secondary users (SUs) share the spectrum bands with multiple primary users (PUs) who are facing security threats from multiple eavesdroppers. By adopting the PU secrecy outage constraint to protect the PUs, we optimize the joint user and power allocation for the SUs to maximize the SU ergodic transmission rate. Simulation results are presented to verify the effectiveness of the proposed algorithm. It is shown that the proposed algorithm outperforms the existing scheme, especially for a large number of PUs and a small number of SUs. It is also shown that the number of eavesdroppers has negligible impact on the performance improvement of the proposed algorithm compared to the existing scheme. In addition, it is shown that increasing the number of eavesdroppers has insignificant impact on the SU performance if the number of eavesdroppers is already large.

Array antenna technology for wireless systems is highly integrated for demands such as multi-functionality and high-performance. This paper details recent technologies in Japan in design techniques based on computational electromagnetics, antenna hardware techniques in the millimeter-wave band, array signal processing to add adaptive functions, and measurement methods to support design techniques, for array antennas for future wireless systems. Prospects of these four technologies are also described.

This paper presents outdoor field experimental results to clarify the 4-by-4 multiple-input multiple-output (MIMO) throughput performance when applying joint transmission (JT) and distributed MIMO to the 15-GHz frequency band in the downlink of a 5G cellular radio access system. Experimental results for JT in a 100m × 70m large-cell scenario show that throughput improvement of up to 10% is achieved in most of the area and the peak data rate is improved from 2.8Gbps to 3.7Gbps. Based on analysis of the reference signal received power (RSRP) and channel correlation, we find that the RSRP is improved in lower RSRP areas, and that the channel correlation is improved in higher RSRP areas. These improvements contribute to higher throughput performance. The advantage of distributed MIMO and JT are compared in a 20m × 20m small-cell scenario. The throughput improvement of 70% and throughput exceeding 5 Gbps were achieved when applying distributed MIMO due to the improvement in the channel correlation. When applying JT, the RSRP is improved; however the channel correlation is not. As a result, there is no improvement in the throughput performance in the area. Finally, the relationship between the transmission point (TP) allocation and the direction of user equipment (UE) antenna arrangement is investigated. Two TP positions at 90 and 180deg. from each other are shown to be advantageous in terms of the throughput performance with different direction of UE antenna arrangement. Thus, we conclude that JT and distributed MIMO are promising technologies for the 5G radio access system that can compensate for the propagation loss and channel correlation in high frequency bands.