This letter presents new methods for transforming perfect ternary sequences into perfect 8-QAM+ sequences. Firstly, based on perfect ternary sequences with even period, two mappings which can map two ternary variables to an 8-QAM+ symbol are employed for constructing new perfect 8-QAM+ sequences. In this case, the proposed construction is a generalization of the existing one. Then based on perfect ternary sequence with odd period, perfect 8-QAM sequences are generated. Compared with perfect 8-QAM+ sequences, the resultant sequences have no energy loss.

The emerging Wireless Access in Vehicular Environment (WAVE) architecture, which aims to provide critical traffic information and Internet services, has recently been standardized in the IEEE 802.11p specification. A typical WAVE network consists of one road-side-unit (RSU) and one or more on-board-units (OBUs), wherein the RSU supports one control channel (CCH) and one or more service channels (SCH) for the OBUs to access. Generally, an OBU is equipped with a single transceiver and needs to periodically switch between the CCH and one of the SCHs in order to receive emergency messages and service information from the CCH and to deliver Internet traffic over an SCH. Synchronizing all OBUs to alternatively access the CCH and SCHs is estimated to waste as much as 50% of the channel's resources. To improve efficiency, we propose an innovative scheme, namely coordinated interleaving access (CIA) scheme, which optimizes the SCH throughput by smartly grouping the OBUs to let them access the CCH and SCHs in an interleaved and parallel manner. To further the capability of CIA scheme, an enhanced version is also proposed to handle the case where OBUs with multiple transceivers. Performance analysis and evaluation indicates that the proposed CIA scheme achieves a significant improvement in resource. Thus it can be advantageous to adapt it into the IEEE 802.11p protocol for its adoption in multi-channel wireless vehicular networks.

We propose a method for downsizing line pictures to generate pixel line arts. In our method, topological properties such as connectivity of lines and segments are preserved by allowing slight distortion in the form of objects in input images. When input line pictures are painted with colors, the number of colors is preserved by our method.

A 16×16-element corporate-feed waveguide slot array antenna in the 60-GHz band is designed to achieve broadband reflection and high antenna efficiency. The sub-arrays consisting of 2×2-elements are designed to improve the reflection bandwidth by implementing lower Q and triple resonance. The designed antenna is fabricated by diffusion bonding of thin copper plates. A wide reflection bandwidth with VSWR less than 2.0 is obtained over 21.5%, 13.2GHz (54.7-67.8GHz). The measured gain is 32.6dBi and the corresponding antenna efficiency is 76.5%. The broad bandwidth of more than 31.5-dBi gain is realized over 19.2%, 11.9GHz (56.1-68.0GHz). The gain in bandwidth covers the whole of the license-free 60-GHz band (57-66GHz).

This letter investigates the relationship between antenna position and data communication performance in a magnetic resonance wireless power transfer (MRWPT) system. In MRWPT information such as the types of equipments, the required amount of electrical power, or the timing of power transfer should be exchanged. It is assumed here that power transfer coils in the MRWPT system are employed as antennas for data communication. The frequency characteristics of the antennas change due to coil displacements. The power transfer coils are modeled as a band pass filter (BPF) and the frequency characteristics of the filter are presented in this letter. The characteristics of the filter are derived through circuit simulation and resulting data communication performance is evaluated. Numerical results obtained through computer simulation show that the bit error late (BER) performance can be improved by controlling the center frequency of the communication link.

A lot of work has been conducted on time series classification and similarity search over the past decades. However, the classification of a time series with high accuracy is still insufficient in applications such as ubiquitous or sensor systems. In this paper, a novel textual approximation of a time series, called TAX, is proposed to achieve high accuracy time series classification. l-TAX, an extended version of TAX that shows promising classification accuracy over TAX and other existing methods, is also proposed. We also provide a comprehensive comparison between TAX and l-TAX, and discuss the benefits of both methods. Both TAX and l-TAX transform a time series into a textual structure using existing document retrieval methods and bioinformatics algorithms. In TAX, a time series is represented as a document like structure, whereas l-TAX used a sequence of textual symbols. This paper provides a comprehensive overview of the textual approximation and techniques used by TAX and l-TAX

This paper presents a new technique to enhance the bandwidth of a printed dipole antenna for ultra-wideband applications. The basic idea is to exploit mutual coupling between the feeding line, which is designed closed and paralleled to dipole arms, the dipole arms and other elements of the antenna. Dipole arms, feeding lines as well as other parts are investigated in order to expand antenna bandwidth while still retaining antenna compactness. Based on the proposed technique, we develop two sample printed dipole antennas for advanced wireless communications. One is an ultra-wideband antenna which is suitable for multi-band-mode ultra-wideband applications or being a sensing antenna in cognitive radio. The other is a reconfigurable antenna which would be applicable for wideband cognitive radios. Antenna characteristics such as radiation patterns, current distributions, and gains at different frequencies are also investigated for both sample antennas.

Travel recommendation and travel diary generation applications can benefit significantly from methods that infer the durations and locations of visits from travelers' GPS data. However, conventional inference methods, which cluster GPS points on the basis of their spatial distance, are not suited to inferring visit durations. This paper presents a pace-based clustering method to infer visit locations and durations. The method contributes two novel techniques: (1) It clusters GPS points logged during visits by considering the speed and applying a probabilistic density function for each trip. Consequently, it avoids clustering GPS points that are near but unrelated to visits. (2) It also includes additional GPS points in the clusters by considering their temporal sequence. As a result, it is able to complete the clusters with GPS points that are far from the visits but are logged during the visits, caused, for example, by GPS noise indoors. The results of an experimental evaluation comparing our proposed method with three published inference methods indicate that our proposed method infers the duration of a visit with an average error rate of 8.7%, notably outperforming the other methods.

This letter studies the simple dynamic binary neural network characterized by signum activation function and ternary connection parameters. In order to control the sparsity of the connections and the stability of the stored signal, a simple evolutionary algorithm is presented. As a basic example of teacher signals, we consider a binary periodic orbit which corresponds to a control signal of ac-dc regulators. In the numerical experiment, applying the correlation-based learning, the periodic orbit can be stored. The sparsification can be effective to reinforce the stability of the periodic orbit.

In this letter, we propose a new secure and efficient certificateless aggregate signature scheme which has the advantages of both certificateless public key cryptosystem and aggregate signature. Based on the computational Diffie-Hellman problem, our scheme can be proven existentially unforgeable against adaptive chosen-message attacks. Most importantly, our scheme requires short group elements for aggregate signature and constant pairing computations for aggregate verification, which leads to high efficiency due to no relations with the number of signers.

Linear dynamical systems are basic state space models literally dealing with underlying system dynamics on the basis of linear state space equations. When the model is employed for time-series data analysis, the system identification, which detects the dimension of hidden state variables, is one of the most important tasks. Recently, it has been found that the model has singularities in the parameter space, which implies that analysis for adverse effects of the singularities is necessary for precise identification. However, the singularities in the models have not been thoroughly studied. There is a previous work, which dealt with the simplest case; the hidden state and the observation variables are both one dimensional. The present paper extends the setting to general dimensions and more rigorously reveals the structure of singularities. The results provide the asymptotic forms of the generalization error and the marginal likelihood, which are often used as criteria for the system identification.

A joint continuous phase frequency shift keying (CPFSK) modulation and physical-layer network coding (PNC), i.e., CPFSK-PNC, is proposed for two-way relay channels (TWRCs). This letter discusses the signal detection of the CPFSK-PNC scheme with emphasis on the maximum-likelihood sequence detection (MLSD) algorithm for the relay receiver. The end-to-end error performance of the proposed CPFSK-PNC scheme is evaluated through simulations.

Participatory sensing is an emerging system that allows the increasing number of smartphone users to share effectively the minute statistical information collected by themselves. This system relies on participants' active contribution including intentional input data. However, a number of privacy concerns will hinder the spread of participatory sensing applications. It is difficult for resource-constrained mobile phones to rely on complicated encryption schemes. We should prepare a privacy-preserving participatory sensing scheme with low computation complexity. Moreover, an environment that can reassure participants and encourage their participation in participatory sensing is strongly required because the quality of the statistical data is dependent on the active contribution of general users. In this article, we present MNS-RRT algorithms, which is the combination of negative surveys and randomized response techniques, for preserving privacy in participatory sensing, with high levels of data integrity. By using our method, participatory sensing applications can deal with a data having two selections in a dimension. We evaluated how this scheme can preserve the privacy while ensuring data integrity.

We determine the linear complexity of binary sequences derived from the polynomial quotient modulo p defined by $F(u)equiv rac{f(u)-f_p(u)}{p} ~(mod~ p), qquad 0 le F(u) le p-1,~uge 0,$ where fp(u)≡f(u) (mod p), for general polynomials $f(x)in mathbb{Z}[x]$. The linear complexity equals to one of the following values {p2-p,p2-p+1,p2-1,p2} if 2 is a primitive root modulo p2, depending on p≡1 or 3 modulo 4 and the number of solutions of f'(u)≡0 (mod) p, where f'(x) is the derivative of f(x). Furthermore, we extend the constructions to d-ary sequences for prime d|(p-1) and d being a primitive root modulo p2.

Demand for wireless communication is increasing significantly, but the frequency resources available for wireless communication are quite limited. Currently, various countries are prompting the use of TV white spaces (TVWS). IEEE 802.11 Working Group (WG) has started a Task Group (TG), namely IEEE 802.11af, to develop an international standard for Wireless local Area Networks (WLANs) in TVWS. In order to increase maximum throughput, a channel aggregation mechanism is introduced in the draft standard. In Japan, ISDB-T based area-one-segment broadcasting system (Area-1seg) which is a digital TV broadcast service in limited areas has been permitted to offer actual TVWS services since April 2012. The operation of the IEEE 802.11af system shall not jeopardize the Area-1seg system due to the common operating frequency band. If the Area-1seg partially overlaps with the IEEE 802.11af in some frequency, the IEEE 802.11af cannot use the channel aggregation mechanism due to a lack of channels. As a result, the throughput of the IEEE 802.11af deteriorates. In this paper, the physical layer of IEEE 802.11af D4.0 is introduced briefly, and a partial subcarrier system for IEEE 802.11af is proposed to efficiently use the TVWS spectrum. The IEEE 802.11af co-exist with the Area-1seg by using null subcarriers. Computer simulation shows up to around 70% throughput gain is achieved with the proposed mechanism.

The Generalized Feistel Structure (GFS) is one of the structures used in designs of blockciphers and hash functions. There are several types of GFSs, and we focus on Type 1 and Type 2 GFSs. The security of these structures are well studied and they are adopted in various practical blockciphers and hash functions. The round function used in GFSs consists of two layers. The first layer uses the nonlinear function. Type 1 GFS uses one nonlinear function in this layer, while Type 2 GFS uses a half of the number of sub-blocks. The second layer is a sub-block-wise permutation, and the cyclic shift is generally used in this layer. In this paper, we formalize Type 1.x GFS, which is the natural extension of Type 1 and Type 2 GFSs with respect to the number of nonlinear functions in one round. Next, for Type 1.x GFS using two nonlinear functions in one round, we propose a permutation which has a good diffusion property. We demonstrate that Type 1.x GFS with this permutation has a better diffusion property than other Type 1.x GFS with the sub-block-wise cyclic shift. We also present experimental results of evaluating the diffusion property and the security against the saturation attack, impossible differential attack, differential attack, and linear attack of Type 1.x GFSs with various permutations.

Introducing inter-satellite ranging and communication links in a Global Navigation Satellite System (GNSS) can improve its performance. In view of the highly dynamic characteristics and the rapid but reliable acquisition requirement of inter-satellite link (ISL) signal of navigation constellation, we utilize navigation data, which is the special resource of navigation satellites, to assist signal acquisition. In this paper, we introduce a method that uses the navigation data for signal acquisition from three aspects: search space, search algorithm, and detector structure. First, an iteration method to calculate the search space is presented. Then the most efficient algorithm is selected by comparing the computation complexity of different search algorithms. Finally, with the navigation data, we also propose a method to guarantee the detecting probability constant by adjusting the non-coherent times. An analysis shows that with the assistance of navigation data, we can reduce the computing cost of ISL signal acquisition significantly, as well effectively enhancing acquisition speed and stabling the detection probability.

This paper proposes a visualization system for supporting the task of monitoring bug update information. Recent growth of the Web has brought us various kinds of text stream data, such as bulletin board systems (BBS), blogs, and social networking services (SNS). Bug update information managed by bug tracking systems (BTS) is also a kind of text stream data. As such text stream data continuously generates new data, it is difficult for users to watch it all the time. Therefore, the task of monitoring text stream data inevitably involves breaks of monitoring, which would cause users to lose the context of monitoring. In order to support such a monitoring task involving breaks, the proposed system employs several visualization techniques. The dynamic relationship between bugs is visualized with animation, and a function of highlighting updated bugs as well as that of replaying a part of the last monitoring time is also proposed in order to help a user grasping the context of monitoring. The result of experiment with test participants shows that highlighting and replay functions can reduce frequency of checking data and monitoring time.

Mobility load balancing (MLB) is a key technology for self-organization networks (SONs). In this paper, we explore the mobility load balancing problem and propose a unified cell specific offset adjusting algorithm (UCSOA) which more accurately adjusts the largely uneven load between neighboring cells and is easily implemented in practice with low computing complexity and signal overhead. Moreover, we evaluate the UCSOA algorithm in two different traffic conditions and prove that the UCSOA algorithm can get the lower call blocking rates and handover failure rates. Furthermore, the interdependency of the proposed UCSOA algorithm's performance and that of the inter-cell interference coordination (ICIC) algorithm is explored. A self-organization soft frequency reuse scheme is proposed. It demonstrates UCSOA algorithm and ICIC algorithm can obtain a positive effect for each other and improve the network performance in LTE system.

Squared-loss mutual information (SMI) is a robust measure of the statistical dependence between random variables. The sample-based SMI approximator called least-squares mutual information (LSMI) was demonstrated to be useful in performing various machine learning tasks such as dimension reduction, clustering, and causal inference. The original LSMI approximates the pointwise mutual information by using the kernel model, which is a linear combination of kernel basis functions located on paired data samples. Although LSMI was proved to achieve the optimal approximation accuracy asymptotically, its approximation capability is limited when the sample size is small due to an insufficient number of kernel basis functions. Increasing the number of kernel basis functions can mitigate this weakness, but a naive implementation of this idea significantly increases the computation costs. In this article, we show that the computational complexity of LSMI with the multiplicative kernel model, which locates kernel basis functions on unpaired data samples and thus the number of kernel basis functions is the sample size squared, is the same as that for the plain kernel model. We experimentally demonstrate that LSMI with the multiplicative kernel model is more accurate than that with plain kernel models in small sample cases, with only mild increase in computation time.