Compressive sensing is attractive to distributed video coding with respect to two issues: low complexity in encoding and low data rate in transmission. In this paper, a novel compressive sensing-based distributed video coding system is presented based on a combination of predictive coding and Wyner-Ziv difference coding of compressively sampled frames. Experimental results show that the data volume in transmission in the proposed method is less than one tenth of the distributed compressive video sensing. The quality of decoded video was evaluated in terms of PSNR and structural similarity index as well as visual inspections.

This paper presents a low-latency, low-cost architecture for computing square and cube roots in the fixed-point format. The proposed architecture is designed based on a non-iterative root calculation scheme to achieve fast computations. While previous non-iterative root calculators are restricted to a square-root operation due to the limitation of their mathematical property, the root computation is generalized in this paper to apply an approximation method to the non-iterative scheme. On top of that, a recurrent method is proposed to select parameters, which enables us to reduce the table size while keeping the maximum relative error value low. Consequently, the proposed root calculator can support both square and cube roots at the expense of small delay and low area overheads. This extension can be generalized to compute the nth roots, where n is a positive integer.

The Euler number of a binary image is an important topological property for pattern recognition, image analysis, and computer vision. A famous method for computing the Euler number of a binary image is by counting certain patterns of bit-quads in the image, which has been improved by scanning three rows once to process two bit-quads simultaneously. This paper studies the bit-quad-based Euler number computing problem. We show that for a bit-quad-based Euler number computing algorithm, with the increase of the number of bit-quads being processed simultaneously, on the one hand, the average number of pixels to be checked for processing a bit-quad will decrease in theory, and on the other hand, the length of the codes for implementing the algorithm will increase, which will make the algorithm less efficient in practice. Experimental results on various types of images demonstrated that scanning five rows once and processing four bit-quads simultaneously is the optimal tradeoff, and that the optimal bit-quad-based Euler number computing algorithm is more efficient than other Euler number computing algorithms.

We propose a method of interframe prediction in depth map coding that uses pixel-wise 3D motion estimated from encoded textures and depth maps. By using the 3D motion, an approximation of the depth map frame to be encoded is generated and used as a reference frame of block-wise motion compensation.

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.

This paper presents past and recent trends of optical networks and addresses the future directions. First, we describe path networks with the historical backgrounds and trends. path networks have advanced by using various multiplexing technologies. They include time-division multiplexing (TDM), asynchronous transfer mode (ATM), and wavelength-division multiplexing (WDM). ATM was later succeeded to multi-protocol label switching (MPLS). Second, we present generalized MPLS technologies (GMPLS). In GMPLS, the label concept of MPLS is extended to other labels used in TDM, WDM, and fiber networks. GMPLS enables network operators to serve networks deployed by different technologies with a common protocol suite of GMPLS. Third, we describe multi-layer traffic engineering and a path computation element (PCE). Multi-layer traffic engineering designs and controls networks considering resource usages of more than one layer. This leads to use network resources more efficiently than the single-layer traffic engineering adopted independently for each layer. PCE is defined as a network element that computes paths, which are used for traffic engineering. Then, we address software-defined networks, which put the designed network functions into the programmable data plane by way of the management plane. We describe the evaluation from GMPLS to software defined networking (SDN) and transport SDN. Fifth, we describe the advanced devices and switches for optical networks. Finally, we address advances in networking technologies and future directions on optical networking.

This paper considers the recovery problem of distributed compressed sensing (DCS), where J (J≥2) signals all have sparse common component and sparse innovation components. The decoder attempts to jointly recover each component based on {Mj} random noisy measurements (j=1,…,J) with the prior information on the support probabilities, i.e., the probabilities that the entries in each component are nonzero. We give both the sufficient and necessary conditions on the total number of measurements $sum olimits_{j = 1}^J M_j$ that is needed to recover the support set of each component perfectly. The results show that when the number of signal J increases, the required average number of measurements $sum olimits_{j = 1}^J M_j/J$ decreases. Furthermore, we propose an extension of one existing algorithm for DCS to exploit the prior information, and simulations verify its improved performance.

Asymmetric bilinear maps using Type-3 pairings are known to be advantageous in several points (e.g., the speed and the size of a group element) to symmetric bilinear maps using Type-1 pairings. Kremer and Mazaré introduce a symbolic model to analyze protocols based on bilinear maps, and show that the symbolic model is computationally sound. However, their model only covers symmetric bilinear maps. In this paper, we propose a new symbolic model to capture asymmetric bilinear maps. Our model allows us to analyze security of various protocols based on asymmetric bilinear maps (e.g., Joux's tripartite key exchange, and Scott's client-server ID-based key exchange). Also, we show computational soundness of our symbolic model under the decisional bilinear Diffie-Hellman assumption.

Given a graph G, a set of spanning trees of G are completely independent spanning trees (CISTs for short) if for any vertices x and y, the paths connecting them on these trees have neither vertex nor edge in common, except x and y. Hasunuma (2001, 2002) first introduced the concept of CISTs and conjectured that there are k CISTs in any 2k-connected graph. Later on, this conjecture was unfortunately disproved by Péterfalvi (2012). In this note, we show that Hasunuma's conjecture holds for graphs restricted in the class of 4-regular chordal rings CR(n,d), where both n and d are even integers.

This paper proposes a read-only memory driving circuit for RFID tags based on a-IGZO thin-film transistors. The circuit consists of a Johnson counter and monotype complementary gates. By utilizing complementary signals to drive a decoder based on monotype complementary gates, the propagation delay can be decreased and the redundant current can be reduced. The Johnson counter reduces the number of registers. The new circuit can effectively avoid glitch generation, and reduce circuit power consumption and delay.

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.

In both theoretical analysis and practical use for an antidictionary coding algorithm, an important problem is how to encode an antidictionary of an input source. This paper presents a proposal for a compact tree representation of an antidictionary built from a circular string for an input source. We use a technique for encoding a tree in the compression via substring enumeration to encode a tree representation of the antidictionary. Moreover, we propose a new two-pass universal antidictionary coding algorithm by means of the proposal tree representation. We prove that the proposed algorithm is asymptotic optimal for a stationary ergodic source.

Pseudorandom number generators have been widely used in Monte Carlo methods, communication systems, cryptography and so on. For cryptographic applications, pseudorandom number generators are required to generate sequences which have good statistical properties, long period and unpredictability. A Dickson generator is a nonlinear congruential generator whose recurrence function is the Dickson polynomial. Aly and Winterhof obtained a lower bound on the linear complexity profile of a Dickson generator. Moreover Vasiga and Shallit studied the state diagram given by the Dickson polynomial of degree two. However, they do not specify sets of initial values which generate a long period sequence. In this paper, we show conditions for parameters and initial values to generate long period sequences, and asymptotic properties for periods by numerical experiments. We specify sets of initial values which generate a long period sequence. For suitable parameters, every element of this set occurs exactly once as a component of generating sequence in one period. In order to obtain sets of initial values, we consider a logistic generator proposed by Miyazaki, Araki, Uehara and Nogami, which is obtained from a Dickson generator of degree two with a linear transformation. Moreover, we remark on the linear complexity profile of the logistic generator. The sets of initial values are described by values of the Legendre symbol. The main idea is to introduce a structure of a hyperbola to the sets of initial values. Our results ensure that generating sequences of Dickson generator of degree two have long period. As a consequence, the Dickson generator of degree two has some good properties for cryptographic applications.

Systematic research on electromagnetic compatibility (EMC) in Japan started in 1977 by the establishment of a technical committee on “environmental electromagnetic engineering” named EMCJ, which was founded both in the Institute of Electronics and Communication Engineers or the present IEICE (Institute of Electronics, Information and Communication Engineers) and in the Institute of Electrical Engineers of Japan or the IEEJ. The research activities have been continued as the basic field of interdisciplinary study to harmonize even in the electromagnetic (EM) environment where radio waves provide intolerable EM disturbances to electronic equipment and to that environment itself. The subjects and their outcomes which the EMCJ has dealt with during about 40 years from the EMCJ establishment include the evaluation of EM environment, EMC of electric and electronic equipment, and EMC of biological effects involving bioelectromagnetics and so on. In this paper, the establishment history and structure of the EMCJ are reviewed along with the change in activities, and topics of the technical reports presented at EMCJ meetings from 2006 to 2016 are surveyed. In addition, internationalization and its related campaign are presented in conjunction with the EMCJ research activities, and the status quo of the EMCJ under the IEICE is also discussed along with the prospects.

This paper discusses key technologies specific for fifth generation (5G) cellular systems which are expected to connect internet of things (IoT) based vertical sectors. Because services for 5G will be expanded drastically, from information transfer services to mission critical and massive connection IoT connection services for vertical sectors, and requirement for cellular systems becomes quite different compared to that of fourth generation (4G) systems, after explanation for the service and technical trends for 5G, key wireless access technologies will be discussed, especially, from the view point of what is new and how import. In addition to the introduction of new technologies for wireless access, flexibility of networking is also discussed because it can cope with QoS support services, especially to cope with end-to-end latency constraint conditions. Therefore, this paper also discuss flexible network configuration using mobile edge computing (MEC) based on software defined network (SDN) and network slicing.

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.

Hybrid analog/digital precoding has attracted growing attention for millimeter wave (mmWave) communications, since it can support multi-stream data transmission with limited hardware cost. A main challenge in implementing hybrid precoding is that the channels will exhibit frequency-selective fading due to the large bandwidth. To this end, we propose a practical hybrid precoding scheme with finite-resolution phase shifters by leveraging the correlation among the subchannels. Furthermore, we utilize the sparse feature of the mmWave channels to design a low-complexity algorithm to realize the proposed hybrid precoding, which can avoid the complication of the high-dimensionality eigenvalue decomposition. Simulation results show that the proposed hybrid precoding can approach the performance of unconstrained fully-digital precoding but with low hardware cost and computational complexity.

The compressive sensing (CS) theory has been recognized as a promising technique to achieve the target localization in wireless sensor networks. However, most of the existing works require the prior knowledge of transmitting powers of targets, which is not conformed to the case that the information of targets is completely unknown. To address such a problem, in this paper, we propose a novel CS-based approach for multiple target localization and power estimation. It is achieved by formulating the locations and transmitting powers of targets as a sparse vector in the discrete spatial domain and the received signal strengths (RSSs) of targets are taken to recover the sparse vector. The key point of CS-based localization is the sensing matrix, which is constructed by collecting RSSs from RF emitters in our approach, avoiding the disadvantage of using the radio propagation model. Moreover, since the collection of RSSs to construct the sensing matrix is tedious and time-consuming, we propose a CS-based method for reconstructing the sensing matrix from only a small number of RSS measurements. It is achieved by exploiting the CS theory and designing an difference matrix to reveal the sparsity of the sensing matrix. Finally, simulation results demonstrate the effectiveness and robustness of our localization and power estimation approach.

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.

This paper presents FIR digital filters based on stochastic/binary hybrid computation with reduced hardware complexity and high computational accuracy. Recently, some attempts have been made to apply stochastic computation to realization of digital filters. Such realization methods lead to significant reduction of hardware complexity over the conventional filter realizations based on binary computation. However, the stochastic digital filters suffer from lower computational accuracy than the digital filters based on binary computation because of the random error fluctuations that are generated in stochastic bit streams, stochastic multipliers, and stochastic adders. This becomes a serious problem in the case of FIR filter realizations compared with the IIR counterparts because FIR filters usually require larger number of multiplications and additions than IIR filters. To improve the computational accuracy, this paper presents a stochastic/binary hybrid realization, where multipliers are realized using stochastic computation but adders are realized using binary computation. In addition, a coefficient-scaling technique is proposed to further improve the computational accuracy of stochastic FIR filters. Furthermore, the transposed structure is applied to the FIR filter realization, leading to reduction of hardware complexity. Evaluation results demonstrate that our method achieves at most 40dB improvement in minimum stopband attenuation compared with the conventional pure stochastic design.