This paper illustrates various content sharing systems that take advantage of cloud's storage and computational resources as well as their supporting conventional technologies. First, basic technology concepts supporting cloud-based systems from a client-server to cloud computing as well as their relationships and functional linkages are shown. Second, the taxonomy of cloud-based system models from the aspect of multiple clouds' interoperability is explained. Interoperability can be categorized into provider-centric and client-centric scenarios. Each can be further divided into federated clouds, hybrid clouds, multi-clouds and aggregated service by broker. Third, practical cloud-based systems related to contents sharing are reported and their characteristics are discussed. Finally, future direction of cloud-based content sharing is suggested.

Target determination based on Doppler frequency shift (DFS) measurements is a nontrivial problem because of the nonlinear relation between the position space and the measurements. The conventional methods such as numerical iterative algorithm and grid searching are used to obtain the solution, while the former requires an initial position estimate and the latter needs huge amount of calculations. In this letter, to avoid the problems appearing in those conventional methods, an effective solution is proposed, in which two best linear unbiased estimators (BULEs) are employed to obtain an explicit solution of the proximate target position. Subsequently, this obtained explicit solution is used to initialize the problem of original maximum likelihood estimation (MLE), which can provide a more accurate estimate.

A chirp spread spectrum (CSS) system uses a chirp signal which changes the instantaneous frequency according to time for spreading a transmission bandwidth. In the CSS system, the transmission performance can be simply improved by increasing the time-bandwidth product which is known as the processing gain. However, increasing the transmission bandwidth is limited because of the spectrum regulation. In this letter, we propose a correlation-based chirp rate allocation method to improve the transmission performance by analyzing the cross-correlation coefficient in the same time-bandwidth product. In order to analyze the transmission performance of the proposed method, we analytically derive the cross-correlation coefficient according to the time-bandwidth separation product and simulate the transmission performance. The simulation results show that the proposed method can analytically allocate the optimal chirp rate and improve the transmission performance.

Identifying IDC (Internet Data Center) IP addresses and analyzing the connection relationship of IDC could reflect the IDC network resource allocation and network layout which is helpful for IDC resource allocation optimization. Recent research mainly focuses on minimizing electricity consumption and optimizing network resource allocation based on IDC traffic behavior analysis. However, the lack of network-wide IP information from network operators has led to problems like management difficulties and unbalanced resource allocation of IDC, which are still unsolved today. In this paper, we propose a method for the IP identification and connection relationship analysis of IDC based on the flow connection behavior analysis. In our method, the frequent IP are extracted and aggregated in backbone communication network based on the traffic characteristics of IDC. After that, the connection graph of frequent IP (CGFIP) are built by analyzing the behavior of the users who visit the IDC servers, and IDC IP blocks are thus identified using CGFIP. Furthermore, the connection behavior characteristics of IDC are analyzed based on the connection graphs of IDC (CGIDC). Our findings show that the method can accurately identify the IDC IP addresses and is also capable of reflecting the relationships among IDCs effectively.

A modular serial pipelined sorting architecture for continuous input sequences is presented. It supports continuous sequences, whose lengths can be dynamically changed, and does so using a very simple control strategy. It consists of identical serial cascaded sorting cells, and lends itself to high frequency implementation with any number of sorting cells, because both data and control signals are pipelined. With L cascaded sorting cells, it produces a fully sorted result for sequences whose length N is equal to or less than L+1; for longer sequences, the largest L elements are sorted out. Being modularly designed, several independent smaller sorters can be dynamically configured to form a larger sorter.

In this paper, we propose a framework to allocate code rates of component codes in a Plotkin-type unequal error protection (UEP) code. We derive an equivalent noise variance for each component code using structure of the Plotkin construction and Gaussian assumption. Comparing the equivalent noise variance and Shannon limit, we can find a combination of the code rates for the component codes. We investigate three types of code rate combinations and analyse their UEP performance. We also estimate a performance crossing signal to noise ratio (SNR) of the Plotkin-type UEP code. It indicates that which code has better performance for a given SNR. We confirm that the proposed framework is appropriate to obtain a desired UEP capability.

In conventional spread spectrum (SS) watermarking schemes, random sequences are used for the modulation of watermark information. However, because of the mutual interference among those sequences, it requires complicated removal operation to improve the performance. In this paper, we propose an efficient spread spectrum watermarking scheme by introducing the orthogonal frequency divisiion multiplexing (OFDM) technique at the modulation of watermark information. The SS sequences in the proposed method are the DCT basic vectors modulated by a pseudo-random number (PN) sequence. We investigate the SS-based method considering the host interference at the blind detection scenario and analyze the noise caused by attacks. Because every operation is invertible, the quantization index modulation (QIM)-based method is applicable for the OFDM modulated signals. We also consider the property of watermark extracting operation in SS-based and QIM-based method and formalize their models of noisy channel in order to employ an error correcting code. The performance of their methods with error correcting code is numerically evaluated under the constraints of same distortion level in watermarked content. The experimental results indicated a criteria for the selection of SS-based and QIM-based methods for given content, which is determined by the amount of host interference. In case that the host interference is 0.8 times smaller than a watermark signal, the SS-based method is suitable. When it is 1.0 times larger, the QIM-based method should be selected.

Code division multiple access (CDMA) based on direct sequence (DS) spread spectrum modulation using spreading codes is one of standard technologies for multiple access communications. In asynchronous DS/CDMA communications, spreading codes with appropriate negative auto-correlation can reduce bit error rate (BER) compared with uncorrelated sequences. In this letter, we design new binary functions for generating chaotic binary sequences with negative auto-correlation using Bernoulli chaotic map. Such binary functions can be applied to the generation of spreading codes with negative auto-correlation based on existing spreading codes (e.g., shift register sequences).

This paper investigates open-loop Stackelberg games for a class of stochastic systems with multiple players. First, the necessary conditions for the existence of an open-loop Stackelberg strategy set are established using the stochastic maximum principle. Such conditions can be represented as solvability conditions for cross-coupled forward-backward stochastic differential equations (CFBSDEs). Second, in order to obtain the open-loop strategy set, a computational algorithm based on a four-step scheme is developed. A numerical example is then demonstrated to show the validity of the proposed method.

A high-image-rejection wireless receiver with an N-phase active RC complex filter is proposed and analyzed. Signal analysis shows that the double-conversion receiver with (N+N2) mixers corrects the gain and phase mismatches of the adjacent image. Monte Carlo simulations evaluate the relation between image-rejection performances and the dispersions of device parameters for the double-conversion wireless receiver. The Monte Carlo simulations show that the image rejection ratio of the adjacent image depends almost only on R and C mismatches in the complex filter.

Using a matrix approach based on a Markov process, we investigate the reliability of a circular connected-(1,2)-or-(2,1)-out-of-(m,n):F lattice system for the i.i.d. case. We develop a modified linear lattice system that is equivalent to this circular system, and propose a methodology that allows the systematic calculation of the reliability. It is based on ideas presented by Fu and Hu [6]. A partial transition probability matrix is used to reduce the computational complexity of the calculations, and closed formulas are derived for special cases.

As the development of sensor and machine learning technologies has progressed, it has become increasingly important to detect patterns from probabilistic data streams. In this paper, we focus on complex event processing based on pattern matching. When we apply pattern matching to probabilistic data streams, numerous matches may be detected at the same time interval because of the uncertainty of data. Although existing methods distinguish between such matches, they may derive inappropriate results when some of the matches correspond to the real-world event that has occurred during the time interval. Thus, we propose two grouping methods for matches. Our methods output groups that indicate the occurrence of complex events during the given time intervals. In this paper, first we describe the definition of groups based on temporal overlap, and propose two grouping algorithms, introducing the notions of complete overlap and single overlap. Then, we propose an efficient approach for calculating the occurrence probabilities of groups by using deterministic finite automata that are generated from the query patterns. Finally, we empirically evaluate the effectiveness of our methods by applying them to real and synthetic datasets.

Adaptive and flexible network control technology is considered essential for efficient network resource utilization. Moreover, such technology is becoming a key to cost-effectively meet diverse service requirements and accommodate heavier traffic with limited network resources; demands that conventional static operation cannot satisfy. To address this issue, we previously studied dynamic network control technology for large-capacity network services including on-demand broad bandwidth provisioning services and layer-one VPN. Our previous study introduced a simple weighting function for achieving fairness in terms of path length and proposed two dynamic Make Before Break Routing algorithms for reducing blocking probability. These algorithms enhance network utilization by rerouting existing paths to alternative routes while completely avoiding disruption for highly reliable services. However, the impact of this avoidance of service disruption on blocking probability has not been clarified. In this paper, we propose modified versions of the algorithms that enhance network utilization while slightly increasing disruption by rerouting, which enable us to elucidate the effectiveness of hitless rerouting. We also provide extensive evaluations including a comparison of original and modified algorithms. Numerical examples demonstrate that they achieve not only a high degree of fairness but also low service blocking probability. Hitless rerouting is achieved with a small increase in blocking probability.

Recently, mobile networks employing high-speed high-capacity communications have been investigated extensively to satisfy the demand for faster and higher-capacity data communications. In one approach, frequencies between 6 and 100GHz are candidates to utilize relatively wide frequency bandwidths. Accordingly, radio propagation loss in these frequency bands must be characterized. Ray-tracing (RT) is the most common modeling approach to predict propagation loss in site-specific scenarios. The accuracy of RT simulations has been investigated in urban street cell environments based on comparison to measurement results and we observed that the difference between RT simulation and measurement results tends to increase as the frequency increases. In this paper, we focus on the shape of building corners at an intersection because it is this shape that is a dominant contributing factor in the region away from the intersection. In order to correct the error in the conventional RT method, we propose an alternative model that considers the detailed shape of the building corner and surface roughness. The performance of the RT simulation using the proposed method is then investigated based on comparison of two different sets of measurement results. Finally, we extract the optimal size and roughness for the proposed modeling method. Consequently, we confirm that using the proposed method with optimized parameters significantly enhances the accuracy compared to the conventional method.

When synthesizing speech from Japanese text, correct assignment of accent nuclei for input text with arbitrary contents is indispensable in obtaining naturally-sounding synthetic speech. A phenomenon called accent sandhi occurs in utterances of Japanese; when a word is uttered in a sentence, its accent nucleus may change depending on the contexts of preceding/succeeding words. This paper describes a statistical method for automatically predicting the accent nucleus changes due to accent sandhi. First, as the basis of the research, a database of Japanese text was constructed with labels of accent phrase boundaries and accent nucleus positions when uttered in sentences. A single native speaker of Tokyo dialect Japanese annotated all the labels for 6,344 Japanese sentences. Then, using this database, a conditional-random-field-based method was developed using this database to predict accent phrase boundaries and accent nuclei. The proposed method predicted accent nucleus positions for accent phrases with 94.66% accuracy, clearly surpassing the 87.48% accuracy obtained using our rule-based method. A listening experiment was also conducted on synthetic speech obtained using the proposed method and that obtained using the rule-based method. The results show that our method significantly improved the naturalness of synthetic speech.

This paper develops an online and freely available framework to aid teaching and learning the prosodic control of Tokyo Japanese: how to generate its adequate word accent and phrase intonation. This framework is called OJAD (Online Japanese Accent Dictionary) [1] and it provides three features. 1) Visual, auditory, systematic, and comprehensive illustration of patterns of accent change (accent sandhi) of verbs and adjectives. Here only the changes caused by twelve fundamental conjugations are focused upon. 2) Visual illustration of the accent pattern of a given verbal expression, which is a combination of a verb and its postpositional auxiliary words. 3) Visual illustration of the pitch pattern of any given sentence and the expected positions of accent nuclei in the sentence. The third feature is technically implemented by using an accent change prediction module that we developed for Japanese Text-To-Speech (TTS) synthesis [2],[3]. Experiments show that accent nucleus assignment to given texts by the proposed framework is much more accurate than that by native speakers. Subjective assessment and objective assessment done by teachers and learners show extremely high pedagogical effectiveness of the developed framework.

This paper proposes a walking route recommender system aiming at continuously supporting a user to take a walk as means for health promotion. In recent years, taking a walk becomes popular with not only the elderly, but also those from all ages as one of the easiest ways for health promotion. From the viewpoint of health promotion, it is desirable to take a walk as daily exercise. However, walking is very simple activity, which makes it difficult for people to maintain their motivation. Although using an activity monitor is expected to improve the motivation for taking a walk as daily exercise, it forces users to manage their activities by themselves. The proposed system solves such a problem by recommending a walking route that can consume target calories. When a system is to be used for long period of time for supporting user's daily exercise, it should consider the case when a user will not follow the recommended route. It would cause a gap between consumed and target calories. We think this problem becomes serious when a user gradually gets bored with taking a walk during a long period of time. In order to solve the problem, the proposed method implicitly manages calories on monthly basis and recommends walking routes that could keep a user from getting bored. The effectiveness of the recommendation algorithm is evaluated with agent simulation. As another important factor for walking support, this paper also proposes a navigation interface that presents direction to the next visiting point without using a map. As users do not have to continuously focus on the interface, it is not only useful for their safety, but also gives them room to enjoy the landscape. The interface is evaluated by an experiment with test participants.

In tutoring systems, students are more likely to utilize hints to assist their decisions about difficult or confusing problems. In the meanwhile, students with weaker knowledge mastery tend to choose more hints than others with stronger knowledge mastery. Hints are important assistances to help students deal with questions. Students can learn from hints and enhance their knowledge about questions. In this paper we firstly use hints alone to build a model named Hints-Model to predict student performance. In addition, matrix factorization (MF) has been prevalent in educational fields to predict student performance, which is derived from their success in collaborative filtering (CF) for recommender systems (RS). While there is another factorization method named non-negative matrix factorization (NMF) which has been developed over one decade, and has additional non-negative constrains on the factorization matrices. Considering the sparseness of the original matrix and the efficiency, we can utilize an element-based matrix factorization called regularized single-element-based NMF (RSNMF). We compared the results of different factorization methods to their combination with Hints-Model. From the experiment results on two datasets, we can find the combination of RSNMF with Hints-Model has achieved significant improvement and obtains the best result. We have also compared the Hints-Model with the pioneer approach performance factor analysis (PFA), and the outcomes show that the former method exceeds the later one.

Conventional image retargeting methods fail to avoid distortion in the case where visually important regions are distributed all over the image. To reduce distortions, this paper proposes a novel image retargeting method that incorporates letterboxing into an image warping framework. Letterboxing has the advantage of producing results without distortion or content loss although being unable to use the entire display area. Therefore, it is preferable to combine a retargeting method with a letterboxing operator when displaying images in full screen. Experimental results show that the proposed method is superior to conventional methods in terms of visual quality measured by an objective metric.

Precise estimation of coronary arteries from computed tomography angiography (CTA) data is one of the challenging problems. This study focuses on automatic delineation of coronary arteries from 3D CTA data that may assess the clinicians in identifying the coronary pathologies. In this work, we present a technique that effectively segments the complete coronary arterial tree under the guidance of initial vesselness response without relying on heavily manual operations. The proposed method isolates the coronary arteries with accuracy by using localized statistical energy model in two directions provided with an automated seed which ensures an optimal segmentation of the coronaries. The detection of seed is carried out by analyzing the shape information of the coronary arteries in three successive cross-sections. To demonstrate the efficiency of the proposed algorithm, the obtained results are compared with the reference data provided by Rotterdam framework for lumen segmentation and the level-set active contour based method proposed by Lankton et al. Results reveal that the proposed method performs better in terms of leakages and accuracy in completeness of the coronary arterial tree.