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.

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.

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.

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.

Depth-image-based-rendering (DIBR) is a popular technique for view synthesis. The rendering process mainly introduces artifacts around edges, which leads to degraded quality. This letter proposes a DIBR-synthesized image quality metric by measuring the Statistics of both Edge Intensity and Orientation (SEIO). The Canny operator is first used to detect edges. Then the gradient maps are calculated, based on which the intensity and orientation of the edge pixels are computed for both the reference and synthesized images. The distance between the two intensity histograms and that between the two orientation histograms are computed. Finally, the two distances are pooled to obtain the overall quality score. Experimental results demonstrate the advantages of the presented method.

Many basic tasks in Wireless Sensor Networks (WSNs) rely heavily on the availability and accuracy of target locations. Since the number of targets is usually limited, localization benefits from Compressed Sensing (CS) in the sense that measurements can be greatly reduced. Though some CS-based localization schemes have been proposed, all of these solutions make an assumption that all targets are located on a pre-sampled and fixed grid, and perform poorly when some targets are located off the grid. To address this problem, we develop an adaptive dictionary algorithm where the grid is adaptively adjusted. To achieve this, we formulate localization as a joint parameter estimation and sparse signal recovery problem. Additionally, we transform the problem into a tractable convex optimization problem by using Taylor approximation. Finally, the block coordinate descent method is leveraged to iteratively optimize over the parameters and sparse signal. After iterations, the measurements can be linearly represented by a sparse signal which indicates the number and locations of targets. Extensive simulation results show that the proposed adaptive dictionary algorithm provides better performance than state-of-the-art fixed dictionary algorithms.

This paper presents a system for the automatic generation of Galois-field (GF) arithmetic circuits, named the GF Arithmetic Module Generator (GF-AMG). The proposed system employs a graph-based circuit description called the GF Arithmetic Circuit Graph (GF-ACG). First, we present an extension of the GF-ACG to handle GF(pm) (p≥3) arithmetic circuits, which can be efficiently implemented by multiple-valued logic circuits in addition to the conventional binary circuits. We then show the validity of the generation system through the experimental design of GF(pm) multipliers for different p-values. In addition, we evaluate the performance of three types of GF(2m) multipliers and typical GF(pm) multipliers (p≥3) empirically generated by our system. We confirm from the results that the proposed system can generate a variety of GF parallel multipliers, including practical multipliers over GF(pm) having extension degrees greater than 128.

Recently, numerous methods have been proposed to tackle the problem of fine-grained image classification. However, rare of them focus on the pre-processing step of image alignment. In this paper, we propose a new pre-processing method with the aim of reducing the variance of objects among the same class. As a result, the variance of objects between different classes will be more significant. The proposed approach consists of four procedures. The “parts” of the objects are firstly located. After that, the rotation angle and the bounding box could be obtained based on the spatial relationship of the “parts”. Finally, all the images are resized to similar sizes. The objects in the images possess the properties of translation, scale and rotation invariance after processed by the proposed method. Experiments on the CUB-200-2011 and CUB-200-2010 datasets have demonstrated that the proposed method could boost the recognition performance by serving as a pre-processing step of several popular classification algorithms.

In this paper, the problem of channel estimation in orthogonal frequency-division multiplexing systems over fast time-varying channel is investigated by using a Basis Expansion Model (BEM). Regarding the effects of the Gibbs phenomenon in the BEM, we propose a new method to alleviate it and reduce the modeling error. Theoretical analysis and detail comparison results show that the proposed BEM method can provide improved modeling error compared with other BEMs such as CE-BEM and GCE-BEM. In addition, instead of using the frequency-domain Kronecker delta structure, a new clustered pilot structure is proposed to enhance the estimation performance further. The new clustered pilot structure can effectively reduce the inter-carrier interference especially in the case of high Doppler spreads.

The development of multi-view video has paved the way for emerging 3D applications. In general multi-view video streaming, video frames for all viewpoints, i.e., cameras, must be transmitted to viewers because the view-switching demands of all viewers are unpredictable. However, existing transmission schemes are highly vulnerable to frame loss. Specifically, frame loss in one viewpoint can induce a collapse in decoding for other viewpoints. To improve loss-resilience, this paper proposes a multi-path based multi-view video transmission scheme. Our scheme encodes video frames into multiple versions that are independent of each other, using inter-view prediction. The scheme then transmits each version using multiple transmission paths. Our scheme makes three contributions: 1) it reduces video traffic even for a large number of cameras, 2) it prevents an increase in the number of undecoded video frames caused by single-frame loss, and 3) it conceals frame loss by taking video frames from other paths. Evaluations show that our proposed scheme improves video quality by 3 dB, as compared to existing transmission schemes in loss-prone environments.

E-mails, which vary in length, are a special form of text. The difference in the lengths of e-mails increases the difficulty of text analysis. To better analyze e-mail, our models must analyze not only long e-mails but also short e-mails. Unlike normal documents, short texts have some unique characteristics, such as data sparsity and ambiguity problems, making it difficult to obtain useful information from them. However, long text and short text cannot be analyzed in the same manner. Therefore, we have to analyze the characteristics of both. We present the Biterm Author Topic in the Sentences Model (BATS) model; it can discover relevant topics of corpus and accurately capture the relationship between the topics and authors of e-mails. The Author Topic (AT) model learns from a single word in a document, while the BATS is modeled on word co-occurrence in the entire corpus. We assume that all words in a single sentence are generated from the same topic. Accordingly, our method uses only word co-occurrence patterns at the sentence level, rather than the document or corpus level. Experiments on the Enron data set indicate that our proposed method achieves better performance on e-mails than the baseline methods. What's more, our method analyzes long texts effectively and solves the data sparsity problems of short texts.

Detecting signals in a very large multiple-input multiple-output (MIMO) system requires high complexy of implementation. Thus, belief propagation based detection has been studied recently because of its low complexity. When the transmitted signal sequence is encoded using a channel code decodable by a factor-graph-based algorithm, MIMO signal detection and channel decoding can be combined in a single factor graph. In this paper, a low density parity check (LDPC) coded MIMO system is considered, and two types of factor graphs: bipartite and tripartite graphs are compared. The former updates the log-likelihood-ratio (LLR) values at MIMO detection and parity checking simultaneously. On the other hand, the latter performs the updates alternatively. Simulation results show that the tripartite graph achieves faster convergence and slightly better bit error rate performance. In addition, it is confirmed that the LLR damping in LDPC decoding is important for a stable convergence.

With the huge influx of various data nowadays, extracting knowledge from them has become an interesting but tedious task among data scientists, particularly when the data come in heterogeneous form and have missing information. Many data completion techniques had been introduced, especially in the advent of kernel methods — a way in which one can represent heterogeneous data sets into a single form: as kernel matrices. However, among the many data completion techniques available in the literature, studies about mutually completing several incomplete kernel matrices have not been given much attention yet. In this paper, we present a new method, called Mutual Kernel Matrix Completion (MKMC) algorithm, that tackles this problem of mutually inferring the missing entries of multiple kernel matrices by combining the notions of data fusion and kernel matrix completion, applied on biological data sets to be used for classification task. We first introduced an objective function that will be minimized by exploiting the EM algorithm, which in turn results to an estimate of the missing entries of the kernel matrices involved. The completed kernel matrices are then combined to produce a model matrix that can be used to further improve the obtained estimates. An interesting result of our study is that the E-step and the M-step are given in closed form, which makes our algorithm efficient in terms of time and memory. After completion, the (completed) kernel matrices are then used to train an SVM classifier to test how well the relationships among the entries are preserved. Our empirical results show that the proposed algorithm bested the traditional completion techniques in preserving the relationships among the data points, and in accurately recovering the missing kernel matrix entries. By far, MKMC offers a promising solution to the problem of mutual estimation of a number of relevant incomplete kernel matrices.

We focus on the construction of the digital signature scheme for local broadcast, which allows the devices with limited resources to securely transmit broadcast message. A multi-group authentication scheme that enables a node to authenticate its membership in multi verifiers by the sum of the secret keys has been proposed for limited resources. This paper presents a transformation which converts a multi-group authentication into a multi-group signature scheme. We show that the multi-group signature scheme converted by our transformation is existentially unforgeable against chosen message attacks (EUF-CMA secure) in the random oracle model if the multi-group authentication scheme is secure against impersonation under passive attacks (IMP-PA secure). In the multi-group signature scheme, a sender can sign a message by the secret keys which multiple certification authorities issue and the signature can validate the authenticity and integrity of the message to multiple verifiers. As a specific configuration example, we show the example in which the multi-group signature scheme by converting an error correcting code-based multi-group authentication scheme.

In this study, we address the problem of online RGB-D tracking which confronted with various challenges caused by deformation, occlusion, background clutter, and abrupt motion. Various trackers have different strengths and weaknesses, and thus a single tracker can merely perform well in specific scenarios. We propose a 3D tracker-level fusion algorithm (TLF3D) which enhances the strengths of different trackers and suppresses their weaknesses to achieve robust tracking performance in various scenarios. The fusion result is generated from outputs of base trackers by optimizing an energy function considering both the 3D cube attraction and 3D trajectory smoothness. In addition, three complementary base RGB-D trackers with intrinsically different tracking components are proposed for the fusion algorithm. We perform extensive experiments on a large-scale RGB-D benchmark dataset. The evaluation results demonstrate the effectiveness of the proposed fusion algorithm and the superior performance of the proposed TLF3D tracker against state-of-the-art RGB-D trackers.

IEEE 802.11ah is an emerging wireless LAN standard in the sub-1-GHz license-exempt bands for cost-effective and range-extended communication. One of the most challenging issues that need to be overcome in relation to IEEE 802.11ah is to ensure that thousands of stations are able to associate efficiently with a single access point. During network initialization, several thousand stations try to associate with the access point, leading to heavy channel contention and long association delay. Therefore, IEEE 802.11ah has introduced an authentication control mechanism that classifies stations into groups and only a small number of stations in a group are allowed to access the medium in a beacon interval. This grouping strategy provides fair channel access to a large number of stations. However, the approach to grouping the stations and determining the best group size is undefined in the draft of IEEE 802.11ah. In this paper, we first model the authentication/association in IEEE 802.11ah. Our analysis shows that there exists the best group size that results in minimal association delay. Consequently, the analytical model is extended to determine the best group size. Finally, an enhanced authentication control algorithm, which utilizes the best group size to provide the minimum association delay, is proposed. The numerical and the simulation results we obtained with the proposed method demonstrate that our method succeeds in minimizing the association delay.

In this paper, the recent advances in cooperative distributed antenna transmission (CDAT) are introduced for spatial diversity and multi-user spatial multiplexing in 5G mobile communications network. CDAT is an advanced version of the coordinated multi-point (CoMP) transmission. Space-time block coded transmit diversity (STBC-TD) for spatial diversity and minimum mean square error filtering combined with singular value decomposition (MMSE-SVD) for multi-user spatial multiplexing are described under the presence of co-channel interference from adjacent macro-cells. Blind selected mapping (blind SLM) which requires no side information transmission is introduced in order to suppress the increased peak-to-average signal power ratio (PAPR) of the transmit signals when CDAT is applied. Some computer simulation results are presented to confirm the effectiveness of CDAT techniques.