In OFDM systems, link performance depends heavily on the estimation of symbol-timing and frequency offsets. Performance sensitivity to these estimates is a major drawback of OFDM systems. Timing errors destroy the orthogonality of OFDM signals and lead to inter-symbol interference (ISI) and inter-carrier interference (ICI). The interference due to timing errors can be exploited as a metric for symbol-timing synchronization. In this paper, we propose a novel method to extract interference components using a DFT of the upsampled OFDM signals. Mathematical analysis and formulation are given for the dependence of interference on timing errors. From a numerical analysis, the proposed interference estimation shows robustness against channel dispersion.

Massive multiple input multiple output (MIMO) communication system offers high rate transmission and/or support of a large number of users by invoking the power of a large array antenna, but one of its problem is the heavy computational burden required for the design and signal processing. Assuming the utilization of a large array in the transmitter side and much fewer users than the maximum possible value, this paper first presents a subarray based design approach of MIMO system with a low computational load taking into account efficient subarray grouping for the realization of higher performance; a large transmit array is first divided into subarrays based on channel gain or channel correlation, then block diagonalization is applied to each of them, and finally a large array weight is reconstructed by maximal ratio combining (MRC). In addition, the extension of the proposed method to two-stage design is studied in order to support a larger number of users; in the process of reconstruction to a large array, subarrays are again divided into groups, and block diagonalization is applied to those subarray groups. Through computer simulations, it is shown that the both channel gain and correlation based grouping strategies are effective under certain conditions, and that the number of supported users can be increased by two-stage design if certain level of performance degradation is acceptable.

Side match vector quantization (SMVQ) has been originally developed for image compression and is also useful for steganography. SMVQ requires to create its own state codebook for each block in both encoding and decoding phases. Since the conventional method for the state codebook generation is extremely time-consuming, this letter proposes a fast generation method. The proposed method is tens times faster than the conventional one without loss of perceptual visual quality.

The user authorization query (UAQ) problem determines whether there exists an optimum set of roles to be activated to provide a set of permissions requested by a user. It has been deemed as a key issue for efficiently handling user's access requests in role-based access control (RBAC). Unfortunately, the weight is a value attached to a permission/role representing its importance, should be introduced to UAQ, has been ignored. In this paper, we propose a comprehensive definition of the weighted UAQ (WUAQ) problem with the role-weighted-cardinality and permission-weighted-cardinality constraints. Moreover, we study the computational complexity of different subcases of WUAQ, and show that many instances in each subcase are intractable. In particular, inspired by the idea of the genetic algorithm, we propose an algorithm to approximate solve an intractable subcase of the WUAQ problem. An important observation is that this algorithm can be efficiently modified to handle the other subcases of the WUAQ problem. The experimental results show the advantage of the proposed algorithm, which is especially fit for the case that the computational overhead is even more important than the accuracy in a large-scale RBAC system.

Named Entity Translation Equivalents extraction plays a critical role in machine translation (MT) and cross language information retrieval (CLIR). Traditional methods are often based on large-scale parallel or comparable corpora. However, the applicability of these studies is constrained, mainly because of the scarcity of parallel corpora of the required scale, especially for language pairs of Chinese and Japanese. In this paper, we propose a method considering the characteristics of Chinese and Japanese to automatically extract the Chinese-Japanese Named Entity (NE) translation equivalents based on inductive learning (IL) from monolingual corpora. The method adopts the Chinese Hanzi and Japanese Kanji Mapping Table (HKMT) to calculate the similarity of the NE instances between Japanese and Chinese. Then, we use IL to obtain partial translation rules for NEs by extracting the different parts from high similarity NE instances in Chinese and Japanese. In the end, the feedback processing updates the Chinese and Japanese NE entity similarity and rule sets. Experimental results show that our simple, efficient method, which overcomes the insufficiency of the traditional methods, which are severely dependent on bilingual resource. Compared with other methods, our method combines the language features of Chinese and Japanese with IL for automatically extracting NE pairs. Our use of a weak correlation bilingual text sets and minimal additional knowledge to extract NE pairs effectively reduces the cost of building the corpus and the need for additional knowledge. Our method may help to build a large-scale Chinese-Japanese NE translation dictionary using monolingual corpora.

Cost-effective cloud storage services are attracting users with their convenience, but there is a trade-off between service availability and usage cost. We develop two cloud provider selection models for cloud storage services to minimize the total cost of usage. The models select multiple cloud providers to meet the user requirements while considering unavailability. The first model, called a user-copy (UC) model, allows the selection of multiple cloud providers, where the user copies its data to multiple providers. In addition to the user copy function of the UC model, the second model, which is called a user and cloud-provider copy (UCC) model, allows cloud providers to make copies of the data to deliver them to other cloud providers. The cloud service is available if at least one cloud provider is available. We formulate both models as integer linear programming (ILP) problems. Our performance evaluation observes that both models reduce the total cost of usage, compared to the single cloud provider selection approach. As the cost of bandwidth usage between a user and a cloud provider increases, the UCC model becomes more beneficial than the UC model. We implement the prototype for cloud storage services, and demonstrate our models via Science Information Network 5.

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.

Biomimetics is a new research field that creates innovation through the collaboration of different existing research fields. However, the collaboration, i.e., the exchange of deep knowledge between different research fields, is difficult for several reasons such as differences in technical terms used in different fields. In order to overcome this problem, we have developed a new retrieval platform, “Biomimetics image retrieval platform,” using a visualization-based image retrieval technique. A biological database contains a large volume of image data, and by taking advantage of these image data, we are able to overcome limitations of text-only information retrieval. By realizing such a retrieval platform that does not depend on technical terms, individual biological databases of various species can be integrated. This will allow not only the use of data for the study of various species by researchers in different biological fields but also access for a wide range of researchers in fields ranging from materials science, mechanical engineering and manufacturing. Therefore, our platform provides a new path bridging different fields and will contribute to the development of biomimetics since it can overcome the limitation of the traditional retrieval platform.

This paper proposes a fountain coding system which has lower decoding erasure rate and lower space complexity of the decoding algorithm than the Raptor coding systems. A main idea of the proposed fountain code is employing shift and exclusive OR to generate the output packets. This technique is known as the zigzag decodable code, which is efficiently decoded by the zigzag decoder. In other words, we propose a fountain code based on the zigzag decodable code in this paper. Moreover, we analyze the overhead, decoding erasure rate, decoding complexity, and asymptotic overhead of the proposed fountain code. As a result, we show that the proposed fountain code outperforms the Raptor codes in terms of the overhead and decoding erasure rate. Simulation results show that the proposed fountain coding system outperforms Raptor coding system in terms of the overhead and the space complexity of decoding.

The present paper proposes a dynamic spectrum access policy for multi-hop cognitive radio networks (CRNs), where the transmission in each hop suffers a delay waiting for the communication channel to become available. Recognizing the energy constraints, we assume that each secondary user (SU) in the network is powered by a battery with finite initial energy. We develop an energy-efficient policy for CRNs using the Markov decision process, which searches for spectrum opportunities without a common communication channel and assigns each sensor's decision to every time slot. We first consider a single-sensor scenario. Due to the intermittent activation of the sensor, achieving the optimal sensing schedule requires excessive complexity and is computationally intractable, owing to the fact that the state space of the Markov decision process evolves exponentially with time variance. In order to overcome this difficulty, we propose a state-reduced suboptimal policy by relaxing the constrained state space, i.e., assuming that the electrical energy of a node is infinite, because this state-reduced suboptimal approach can substantially reduce the complexity of decision-making for CRNs. We then analyze the performance of the proposed policy and compare it with the optimal solution. Furthermore, we verify the performance of this spectrum access policy under real conditions in which the electrical energy of a node is finite. The proposed spectrum access policy uses the dynamic information of each channel. We prove that this schedule is a good approximation for the true optimal schedule, which is impractical to obtain. According to our theoretical analysis, the proposed policy has less complexity but comparable performance. It is proved that when the operating time of the CRN is sufficiently long, the data reception rate on the sink node side will converge to the optimal rate with probability 1. Based on the results for the single-sensor scenario, the proposed schedule is extended to a multi-hop CRN. The proposed schedule can achieve synchronization between transmitter and receiver without relying on a common control channel, and also has near-optimal performance. The performance of the proposed spectrum access policy is confirmed through simulation.

With the availability of virtualization extension in mobile processors, e.g. ARM Cortex A-15, multiple virtual execution domains are efficiently supported in a mobile platform. Each execution domain requires high-performance graphics services for full-featured user interfaces such as smooth scrolling, background image blurring, and 3D images. However, graphics service is hard to be virtualized because multiple service components (e.g. ION and Fence) are involved. Moreover, the complexity of Graphical Processing Unit (GPU) device driver also makes harder virtualizing graphics service. In this paper, we propose a technique to virtualize the graphics architecture of Android mobile platform in KVM/ARM environment. The Android graphics architecture relies on underlying Linux kernel services such as the frame buffer memory allocator ION, the buffer synchronization service Fence, GPU device driver, and the display synchronization service VSync. These kernel services are provided as device files in Linux kernel. Our approach is to para-virtualize these device files based on a split device driver model. A major challenge is to translate guest-view of information into host-view of information, e.g. memory address translation, file descriptor management, and GPU Memory Management Unit (MMU) manipulation. The experimental results show that the proposed graphics virtualization technique achieved almost 84%-100% performance of native applications.

To use millimeter wave bands in future cellular and outdoor wireless networks, understanding the multipath cluster characteristics such as delay and angular spread for different polarization is very important besides knowing the path loss and other large scale propagation parameters. This paper presents result from analysis of wide-band full polarimetric double directional channel measurement at the millimeter wave band in a typical urban pico-cell environment. Only limited number of multipath clusters with gains ranging from -8dB to -26.8dB below the free space path loss and mainly due to single reflection, double reflection and diffraction, under both line of sight (LOS) and obstructed LOS conditions are seen. The cluster gain and scattering intensity showed strong dependence on polarization. The scattering intensities for ϑ-ϑ polarization were seen to be stronger compared to ϕ-ϕ polarization and on average 6.1dB, 5.6dB and 4.5dB higher for clusters due to single reflection, double reflection and scattering respectively. In each cluster, the paths are highly concentrated in the delay domain with delay spread comparable to the delay resolution of 2.5ns irrespective of polarization. Unlike the scattering intensity, the angular spread of paths in each cluster did not show dependence on polarization. On the base station side, average angular spread in azimuth and in elevation were almost similar with ≤3.3° spread in azimuth and ≤3.2° spread in elevation for ϑ-ϑ polarization. These spreads were slightly smaller than those observed for ϕ-ϕ polarization. On the mobile station side the angular spread in azimuth was much higher compared to the base station side. On average, azimuth angular spread of ≤11.4° and elevation angular spread of ≤5° are observed for ϑ-ϑ polarization. These spreads were slightly larger than in ϕ-ϕ polarization. Knowing these characteristics will be vital for more accurate modeling of the channel, and in system and antenna design.

This paper presents the design and experimental evaluation of 60GHz small cell radio access based on IEEE 802.11ad/WiGig. The access point (AP) prototype used combines three RF modules with beamforming technology to provide 360° area coverage. In order to compensate for limited communication distance, multiple APs are employed to achieve wide area coverage. A handover algorithm suitable for IEEE 802.11ad/WiGig is employed to achieve flexible control of the cell coverage of each AP. As a proof of concept, a prototype system is set up at Narita International Airport and the capability of multiuser Gb/s wireless access is successfully demonstrated. In addition, the system behavior under stringent conditions is evaluated by load testing and throughput degradation due to co-channel and inter-channel interference is investigated.

Knowledge graph (KG) embedding aims at learning the latent semantic representations for entities and relations. However, most existing approaches can only be applied to KG completion, so cannot identify relations including unseen entities (or Out-of-KG entities). In this paper, motivated by the zero-shot learning, we propose a novel model, namely JointE, jointly learning KG and entity descriptions embedding, to extend KG by adding new relations with Out-of-KG entities. The JointE model is evaluated on entity prediction for zero-shot embedding. Empirical comparisons on benchmark datasets show that the proposed JointE model outperforms state-of-the-art approaches. The source code of JointE is available at https://github.com/yzur/JointE.

Deploying low power nodes (LPNs), which reuse the spectrum licensed to a macrocell network, is considered to be a promising way to significantly boost network capacity. Due to the spectrum-sharing, the deployment of LPNs could trigger the severe problem of interference including intra-tier interference among dense LPNs and inter-tier interference between LPNs and the macro base station (MBS), which influences the system performance strongly. In this paper, we investigate a spectrum-sharing approach in the downlink for two-tier networks, which consists of small cells (SCs) with several LPNs and a macrocell with a MBS, aiming to mitigate the interference and improve the capacity of SCs. The spectrum-sharing approach is described as a multi-objective optimization problem. The problem is solved by the nondominated sorting genetic algorithm version II (NSGA-II), and the simulations show that the proposed spectrum-sharing approach is superior to the existing one.

Bayesian networks are a powerful approach for representation and reasoning under conditions of uncertainty. Of the many good algorithms for learning Bayesian networks from data, the bio-inspired search algorithm is one of the most effective. In this paper, we propose a hybrid mutual information-modified binary particle swarm optimization (MI-MBPSO) algorithm. This technique first constructs a network based on MI to improve the quality of the initial population, and then uses the decomposability of the scoring function to modify the BPSO algorithm. Experimental results show that, the proposed hybrid algorithm outperforms various other state-of-the-art structure learning algorithms.

Collaborative filtering with only implicit feedbacks has become a quite common scenario (e.g. purchase history, click-through log, and page visitation). This kind of feedback data only has a small portion of positive instances reflecting the user's interaction. Such characteristics pose great challenges to dealing with implicit recommendation problems. In this letter, we take full advantage of matrix factorization and relative preference to make the recommendation model more scalable and flexible. In addition, we propose to take into consideration the concept of covisitation which captures the underlying relationships between items or users. To this end, we propose the algorithm Integrated Collaborative Filtering for Implicit Feedback incorporating Covisitation (ICFIF-C) to integrate matrix factorization and collaborative ranking incorporating the covisitation of users and items simultaneously to model recommendation with implicit feedback. The experimental results show that the proposed model outperforms state-of-the-art algorithms on three standard datasets.

This letter proposes a class of polyphase zero correlation zone (ZCZ) sequence sets with low inter-set cross-correlation property. The proposed ZCZ sequence sets are constructed from DFT matrices and r-coincidence sequences. Each ZCZ sequence set is optimal, and the absolute value of the cross-correlation function of sequences from different sets is less than or equal to $rsqrt{N}$, where N denotes the length of each sequence. These ZCZ sequence sets are suitable for multiuser environments.

This paper proposes approximated log likelihood ratios (LLRs) for single carrier millimeter-wave (mmW) transmission systems in the presence of phase noise. In mmW systems, phase noise on carrier wave signals in very high frequency bands causes severe performance degradation. In order to mitigate the impairments of phase noise, forward error correction (FEC) techniques, such as low density parity check (LDPC) code, are effective. However, if the probabilistic model does not capture the exact behavior of the random process present in the received signal, FEC performance is severely degraded, especially in higher order modulation or high coding rate cases. To address this issue, we carefully examine the probabilistic model of minimum mean square error (MMSE) equalizer output including phase noise component. Based on the derived probabilistic model, approximated LLR computation methods with low computational burden are proposed. Computer simulations confirm that the approximated LLR computations on the basis of the derived probabilistic model are capable of improving bit error rate (BER) performance without sacrificing computational simplicity in the presence of phase noise.

This letter presents a test suite CF3 designed to find bugs in arithmetic optimizers of C compilers. It consists of 13,720 test programs containing all the expression patterns covering all the permutations of 3 operators from 14 operators. CF3 detected more than 70 errors in GCC 4.2-4.5 within 2 hours.