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.

The notion of multiple constant multiplication (MCM) is extensively adopted in digital signal processing (DSP) applications such as finite impulse filter (FIR) designs. A set of adders is utilized to replace regular multipliers for the multiplications between input data and constant filter coefficients. Though many algorithms have been proposed to reduce the total number of adders in an MCM block for area minimization, they do not consider the actual bitwidth of each adder, which may not estimate the hardware cost well enough. Therefore, in this article we propose a bitwidth-aware MCM optimization algorithm that focuses on minimizing the total number of adder bits rather than the adder count. It first builds a subexpression graph based on the given coefficients, derives a set of constraints for adder bitwidth minimization, and then optimally solves the problem through integer linear programming (ILP). Experimental results show that the proposed algorithm can effectively reduce the required adder bit count and outperforms the existing state-of-the-art techniques.

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.

This paper investigates a signal detection method with a RAKE combiner for the case wherein the receiving signals use the same spreading code. In the case where multiple user interference with the same spreading code (MUI-SC) occurs, blind channel estimation is difficult and as far as we know has not been investigated. To tackle the issue of MUI-SC, we propose two blind channel estimation methods based on the multimodulus algorithm (MMA), i.e., MMA-IQ and MMA-I methods. When a one dimensional modulation scheme, such as differential binary phase-shift keying (DBPSK), is used, the output of the MMA-IQ channel estimation method can, under MUI-SC, have two states. The first state is that the channel estimate corresponds to a channel response for one of the received signals, and the second state is that the channel estimate corresponds to combined channel responses for two of the received signals. This is because the MMA-IQ uses two degrees of freedom (both axes in the IQ-plane), however one DBPSK signal uses only one degree of freedom. In the case of the second state, it is possible to detect two signals/packets at once. However, in the MMA-IQ, the receiver has to recognize the state of the channel estimate before the signal detection, thus we also propose a state recognition method. In the MMA-I channel estimation method, only the I-axis is used thus the channel estimate always corresponds the case with one signal. Numerical results show that the average number of detected packets of the MMA-IQ is more than that of the MMA-I in high signal-to-noise power ratio case. In addition, several aspects of the MMA-I and MMA-IQ based RAKE signal detection methods are shown.

A soft-decision recursive decoding algorithm (RDA) for the class of the binary linear block codes recursively generated using a u|u+v-construction method is proposed. It is well known that Reed-Muller (RM) codes are in this class. A code in this class can be decomposed into left and right components. At a recursive level of the RDA, if the component is decomposable, the RDA is performed for the left component and then for the cosets generated from the left decoding result and the right component. The result of this level is obtained by concatenating the left and right decoding results. If the component is indecomposable, a proposed iterative bounded-distance decoding algorithm is performed. Computer simulations were made to evaluate the RDA for RM codes over an additive white Gaussian-noise channel using binary phase-shift keying modulation. The results show that the block error rates of the RDA are relatively close to those of the maximum-likelihood decoding for the third-order RM code of length 26 and better than those of the Chase II decoding for the third-order RM codes of length 26 and 27, and the fourth-order RM code of length 28.

Online Social Networks (OSNs) have recently been playing an important role in communication. From the audience aspect, they enable audiences to get unlimited information via the information feeding mechanism (IFM), which is an important part of the OSNs. The audience relies on the quantity and quality of the information served by it. We found that existing IFMs can result in two problems: information overload and cultural ignorance. In this paper, we propose a new type of IFM that solves these problems. The advantage of our proposed IFM is that it can filter irrelevant information with consideration of audiences' culture by using the Naïve Bayes (NB) algorithm together with features and factors. It then dynamically serves interesting and important information based on the current situation and preference of the audience. This mechanism helps the audience to reduce the time spent in finding interesting information. It can be applied to other cultures, societies and businesses. In the near future, the audience will be provided with excellent, and less annoying, communication. Through our studies, we have found that our proposed IFM is most appropriate for Thai and some groups of Japanese audiences under the consideration of audiences' culture.

With the ever-growing prevalence of web 2.0, users can access information and resources easily and ubiquitously. It becomes increasingly important to understand the characteristics of user's complex behavior for efficient network management and security monitoring. In this paper, we develop a novel method to visualize and measure user's web-communication-behavior character in large-scale networks. First, we employ the active and passive monitoring methods to collect more than 20,000 IP addresses providing web services, which are divided into 12 types according to the content they provide, e.g. News, music, movie and etc, and then the IP address library is established with elements as (servicetype, IPaddress). User's behaviors are complex as they stay in multiple service types during any specific time period, we propose the behavior spectrum to model this kind of behavior characteristics in an easily understandable way. Secondly, two kinds of user's behavior characters are analyzed: the character at particular time instants and the dynamic changing characters among continuous time points. We then employ Renyi cross entropy to classify the users into different groups with the expectation that users in the same groups have similar behavior profiles. Finally, we demonstrated the application of behavior spectrum in profiling network traffic patterns and finding illegal users. The efficiency and correctness of the proposed methods are verified by the experimental results using the actual traffic traces collected from the Northwest Regional Center of China Education and Research Network (CERNET).

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.

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.

Cognitive radio (CR) systems aim for more efficient spectrum utilization by having so called secondary users (SUs) transmit on a frequency band reserved for licensed primary users (PUs). The secondary transmissions are allowed provided that no harmful interference will be caused to the PUs. SU terminals with multiple antennas can employ transmit power control with transmit precoding in order to control the interference levels. In most of the existing works, perfect channel state information (CSI) is assumed to be available for the SUs. However, in practical systems where perfect CSI is not available, the SUs are not able to guarantee that the interference constraints are sufficiently satisfied. In this paper, we investigate the problem of spectrum sharing for multiantenna CR systems using estimated CSI. Due to the random nature of the estimation error, we set a probabilistic interference constraint and, in order to satisfy it, provide a density function for the interference power. In addition, we present a power control framework for the SU to meet the probabilistic interference constraint.

New media processing applications such as image recognition and AR (Augment Reality) have become into practical on embedded systems for automotive, digital-consumer and mobile products. Many-core processors have been proposed to realize much higher performance than multi-core processors. We have developed a low-power many-core SoC for multimedia applications in 40nm CMOS technology. Within a 210mm2 die, two 32-core clusters are integrated with dynamically reconfigurable processors, hardware accelerators, 2-channel DDR3 I/Fs, and other peripherals. Processor cores in the cluster share a 2MB L2 cache connected through a tree-based Network-on-Chip (NoC). Its total peak performance exceeds 1.5TOPS (Tera Operations Per Second). The high scalability and low power consumption are accomplished by parallelized software for multimedia applications. In case of face detection, the performance scales up to 64 cores and the SoC consumes only 2.21W. Moreover, it can execute the 1080p 48fps H.264 decoding about 520mW by 28 cores and the 4K2K 15fps super resolution about 770mW by 32 cores in one cluster. Exploiting parallelism by low power processor cores, the many-core SoC provides several tens of times better energy efficiency than that of a high performance desk-top quad-core processor.

Let m ≥ 3 be an odd positive integer, n=2m and p be an odd prime. For the decimation factor $d=rac{(p^{m}+1)(p^{m}+p-1)}{p+1}$, the cross-correlation between the p-ary m-sequence {tr1n(αt)} and its all decimated sequences {tr1n(αdt+l)} is investigated, where 0 ≤ l < gcd(d,pn-1) and α is a primitive element of Fpn. It is shown that the cross-correlation function takes values in {-1,-1±ipm|i=1,2,…p}. The result presented in this paper settles a conjecture proposed by Kim et al. in the 2012 IEEE International Symposium on Information Theory Proceedings paper (pp.1014-1018), and also improves their result.

This letter proposes a novel intrusion tolerant system consisting of several virtual machines (VMs) that refresh the target system periodically and by live migration, which monitors the many features of the VMs to identify and replace exhausted VMs. The proposed scheme provides adequate performance and dependability against denial of service (DoS) attacks. To show its efficiency and security, we conduct experiments on the CSIM20 simulator, which showed 22% improvement in a normal situation and approximately 77.83% improvement in heavy traffic in terms of the response time compared to that reported in the literature. We measure and compare the response time. The result show that the proposed scheme has shorter response time and maintains than other systems and supports services during the heavy traffic.

A number of studies have been conducted on topic modeling for various types of data, including text and image data. We focus particularly on the burstiness of the local features in modeling topics within video data in this paper. Burstiness is a phenomenon that is often discussed for text data. The idea is that if a word is used once in a document, it is more likely to be used again within the document. It is also observed in video data; for example, an object or visual word in video data is more likely to appear repeatedly within the same video data. Based on the idea mentioned above, we propose a new topic model, the Correspondence Dirichlet Compound Multinomial LDA (Corr-DCMLDA), which takes into account the burstiness of the local features in video data. The unknown parameters and latent variables in the model are estimated by conducting a collapsed Gibbs sampling and the hyperparameters are estimated by focusing on the fixed-point iterations. We demonstrate through experimentation on the genre classification of social video data that our model works more effectively than several baselines.

In this paper, we focus on the multi-way relaying channel where K users wish to exchange information with each other within two phases. Precoding at each user and the relay is carefully constructed to ensure that the signals from the same user pair are grouped together and cross-pair interference can be cancelled. Reliable detection is challenging at the relay since the observation constellation is no longer one of the regular ones, due to the fact that a relay observation is the superposition of the messages from one of the $rac{1}{2}K(K-1)$ user pairs. When the trellis coded modulation is used at each node, a simple constellation mapping function and a reduced-states decoding scheme can be applied at the relay, which result in much lower complexity. Furthermore, a modified version of the decoding method is also developed which is called the re-encoding-avoidance scheme at the relay. Monte-Carlo simulation results are provided to demonstrate the performance of the proposed scheme.

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).

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.

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.

In this paper, we propose a message passing decoding algorithm which lowers decoding error rates in the error floor regions for non-binary low-density parity-check (LDPC) codes transmitted over the binary erasure channel (BEC) and the memoryless binary-input output-symmetric (MBIOS) channels. In the case for the BEC, this decoding algorithm is a combination with belief propagation (BP) decoding and maximum a posteriori (MAP) decoding on zigzag cycles, which cause decoding errors in the error floor region. We show that MAP decoding on the zigzag cycles is realized by means of a message passing algorithm. Moreover, we extend this decoding algorithm to the MBIOS channels. Simulation results demonstrate that the decoding error rates in the error floor regions by the proposed decoding algorithm are lower than those by the BP decoder.