This paper describes IP encapsulation technologies for the Mobile RSVP tunnel in next generation networks. Bandwidth is inherently a scarce network resource, and hence signaling overhead should be minimized as much as possible. However, because of duplicate RSVP messages, the existing RSVP tunnel-based mechanism suffers from bandwidth overhead and tunnel problems. The waste of network resources prevents low-cost network construction and the maximization of integrated network utility, which are the goals of next generation networks, and can lower the reliability of networks with the increase of service subscribers and resultant expansion of resource consumption. To solve these problems and to support end-to-end QoS efficiently, RSVP needs to be changed at a minimum degree. In this paper, a new IP encapsulation mechanism for saving of network resources in the Mobile RSVP tunnel (IPEnc-RSVP) is proposed. In order to compare the proposed mechanism and the existing RSVP tunnel-based mechanism in Mobile IP-based networks, we perform a comparative analysis of bandwidth consumption gain, throughput, mean packet delay, etc., and demonstrate the superiority of the proposed mechanism. In addition, we analyze several performance factors of RSVP protocols by applying the existing RSVP tunnel-based mechanism and the proposed mechanism, respectively.

In this paper, we employ time-reversal space-time block coding (TR-STBC) in single-carrier direct sequence code-division multiple access (DS-CDMA) block transmission in the presence of multiple access interference (MAI) as well as intersymbol interference (ISI), which is subject to fairly long delay spread. We introduce the transmission rate improvement by capitalizing on the assignment of additional spreading codes to each user so as to expand the cardinality of space-time code matrix with no sacrifice of diversity order. Given perfect channel state information at the receiver, a simple linear frequency-domain interference suppression scheme on a basis of symbol-by-symbol processing is developed under certain circumstances. A "turbo principle" receiver is facilitated by exploiting the serially concatenated structure at the transmitter to further enhance system performance. Simulation results justify the efficacy of our proposed system and also present performance comparisons with some existing systems in terms of bit error rate (BER).

We analyzed the correlation between three kinds of visualization of the motion blurs (colour of blurs, luminance of the blur and sharpness of object images) and physical properties in regard to image properties and panel characteristics. We also performed subjective evaluations to specify optimal or allowable level of motion blurring with various images on one CRT display and four types of LCDs with various characteristics.

Due to the ultra low power spectral desity of the ultra-wide band (UWB), narrow band interference (NBI) with high-level emission power will degrade the accuracy of UWB ranging system. We propose a novel waveform to suppress the accuracy degradation by NBI with a given frequency. In addition, we compare the ranging error ratio (RER) of the proposed scheme with the traditional one with Gaussian monocycle in this letter.

The recent progress in sensor and wireless communication technologies has enabled the design and implementation of new applications such as sensor telemetry which is the use of wireless sensors to gather fine-grained information from products, people and places. In this work, we consider a realistic telemetry application in which an area is periodically monitored by a sensor network which gathers data from equally spaced sample points. The objective is to maximize the lifetime of the network by jointly selecting the sensing nodes, the node transmission powers and the route to the base station from each sensing node. We develop an optimization-based algorithm OPT-RE and a low complexity algorithm SP-RE for this purpose and analyze their dynamics through extensive numerical studies. Our results indicate that SP-RE is a promising algorithm which has comparable performance to that of the more computationally intensive OPT-RE algorithm. The energy consumption is significantly affected by the channel access method, and in this paper, we also compare the effects of the collision free TDMA and contention based CSMA/CA methods. We propose practical enhancements to CSMA/CA so that the energy consumption due to collisions is reduced. Our simulation results indicate that with the proposed enhancements contention based channel access can provide comparable performance to that of the collision free methods.

In this paper, we propose the stream multiplexing scheme to achieve the high spectral efficiency on OFDM-MIMO system considering (a) the effects of the inter-stream interference suppression techniques, (b) the influence of the quality measurement in the receiver to select the modulation and coding scheme (MCS) and (c) the influence of the signaling overheads to the spectral efficiency. Two kinds of schemes for stream multiplexing, namely Multiple CodeWord (MCW) and Single CodeWord (SCW) are investigated and compared. In the simulations, we assume real channel estimation for SINR measurement for MCS selection and we compare the schemes with the spectral efficiency taking into account overheads. In consequence, it is presented that MCW can achieve the higher efficiency than SCW in the middle to high SINR region. Moreover, in 44 antenna configuration, a 2CW-MCW scheme where 2 codewords mapped onto 4 streams according to SINR of each streams is proposed. The scheme gives higher throughput by improving the MCS selection in the low SINR region due to increase of data length per a codeword and by reducing the signaling overhead due to the decrease of the number of codewords. As a result, the proposed scheme achieves spectral efficiency improvement in whole SINR region.

With the rapid increase of link speed in recent years, packet sampling has become a very attractive and scalable means in collecting flow statistics; however, it also makes inferring original flow characteristics much more difficult. In this paper, we develop techniques and schemes to identify flows with a very large number of packets (also known as heavy-hitter flows) from sampled flow statistics. Our approach follows a two-stage strategy: We first parametrically estimate the original flow length distribution from sampled flows. We then identify heavy-hitter flows with Bayes' theorem, where the flow length distribution estimated at the first stage is used as an a priori distribution. Our approach is validated and evaluated with publicly available packet traces. We show that our approach provides a very flexible framework in striking an appropriate balance between false positives and false negatives when sampling frequency is given.

In this letter, we consider a class of approximately feedback linearized systems that contain both triangular and feedforward forms. With a utilization of the transformation scaling factor, we analytically show that the considered system can be globally exponentially stabilized, globally bounded, or locally stabilized depending on the shapes of triangular and feedforward forms. Our new method broadens a class of nonlinear systems under consideration over the existing results.

It is well known that Pisarenko's frequency estimate for a single real tone can be computed easily using the sample covariance with lags 1 and 2. In this Letter, we propose to use alternative covariance expressions, which are inspired from the modified covariance (MC) frequency estimator, in Pisarenko's algorithm. Computer simulations are included to corroborate the theoretical development of the variant and to demonstrate its superiority over the MC and Pisarenko's methods.

In this paper, an efficient cyclic prefix reconstruction (CPR) technique with turbo equalization is developed for multi-antenna single-carrier frequency-domain equalization (SC-FDE) systems, which are for multi-input multi-output (MIMO), space-time block code (STBC), and space-frequency block code (SFBC) applications. The proposed method includes pre-processing estimation (PPE), weighted interblock interference cancellation (WIBIC), or residual intercarrier interference suppression (RICIS). PPE is employed to compute initial values of MIMO turbo equalization and the WIBIC is developed to cancel interblock interference (IBI) at the initial iteration of the CPR for STBC SC-FDE. RICIS is used to mitigate residual intercarrier interference (ICI) after each iteration of the CPR. By applying the proposed method to the multi-antenna SC-FDE system with insufficient cyclic prefix (CP), we can significantly improve its error performance, obtaining the benefits of spectral efficiency gain and multiplexing/diversity gain in MIMO/STBC/SFBC.

This paper describes near-zero ultra-flattened chromatic dispersion and low confinement loss that can be achieved from a decagonal photonic crystal fiber (D-PCF). The finite difference method with anisotropic perfectly matched boundary layer (PML) is used for the numerical analysis. It is demonstrated that it is possible to design a four-ring D-PCF with ultra-flattened dispersion of 0 0.69 ps/(nm-km) in a 1.30 to 1.75 µm wavelength range and 0 0.22 ps/(nm-km) in a 1.35 to 1.65 µm wavelength range with very low confinement losses of order 0.0011 dB/km. The proposed D-PCF shows promising dispersion tolerance.

This paper discusses the behavior of the second-order modes (Hankel singular values) of linear discrete-time systems under bounded-real transformations, where the transformations are given by arbitrary transfer functions with magnitude bounded by unity. Our main result reveals that the values of the second-order modes are decreased under any of the above-mentioned transformations. This result is the generalization of the theory of Mullis and Roberts, who proved that the second-order modes are invariant under any allpass transformation, i.e. any lossless bounded-real transformation. We derive our main result by describing the controllability/observability Gramians of transformed systems with the help of the discrete-time bounded-real lemma.

Machine learning techniques have long been applied in many fields and have gained a lot of success. The purpose of learning processes is generally to obtain a set of parameters based on a given data set by minimizing a certain objective function which can explain the data set in a maximum likelihood or minimum estimation error sense. However, most of the learned parameters are highly data dependent and rarely reflect the true physical mechanism that is involved in the observation data. In order to obtain the inherent knowledge involved in the observed data, it is necessary to combine physical models with learning process rather than only fitting the observations with a black box model. To reveal underlying properties of human speech production, we proposed a learning process based on a physiological articulatory model and a coarticulation model, where both of the models are derived from human mechanisms. A two-layer learning framework was designed to learn the parameters concerned with physiological level using the physiological articulatory model and the parameters in the motor planning level using the coarticulation model. The learning process was carried out on an articulatory database of human speech production. The learned parameters were evaluated by numerical experiments and listening tests. The phonetic targets obtained in the planning stage provided an evidence for understanding the virtual targets of human speech production. As a result, the model based learning process reveals the inherent mechanism of the human speech via the learned parameters with certain physical meaning.

In this paper, we present a method to automatically acquire a large-scale vocabulary of evaluative expressions from a large corpus of blogs. For the purpose, this paper presents a semi-supervised method for classifying evaluative expressions, that is, tuples of subjects, their attributes, and evaluative words, that indicate either favorable or unfavorable opinions towards a specific subject. Due to its characteristics, our semi-supervised method can classify evaluative expressions in a corpus by their polarities, starting from a very small set of seed training examples and using contextual information in the sentences the expressions belong to. Our experimental results with real Weblog data as our corpus show that this bootstrapping approach can improve the accuracy of methods for classifying favorable and unfavorable opinions. We also show that a reasonable amount of evaluative expressions can be really acquired.

Expansion of imagination is crucial for lively creativity. However, such expansion is sometimes rather difficult and an environment which supports creativity is required. Because people can attain higher creativity by using words with a thematic relation rather than words with a taxonomical relation, we tried to extract word lists having thematic relations among words. We first extracted word lists from domain specific documents by utilizing inclusive relations between words based on a modifiee/modifier relationship in documents. Next, from the extracted word lists, we removed the word lists having taxonomical relations so as to obtain only word lists having thematic relations. Finally, based on the assumption what kind of knowledge a person can associate when he/she looks at a set of words correlates with how the word set is effective in creativity support, we examined whether the word lists direct us to informative pages on the Web for verifying the availability of our extracted word lists.

Dividing a lecture speech into segments and providing those segments as learning objects are quite general and convenient way to construct e-learning resources. However it is difficult to assign an appropriate title to each object that reflects its content. Since there are various aspects of analyzing discourse segments, it is inevitable that researchers will face the diversity when describing the "meanings" of discourse segments. In this paper, we propose the assignment of discourse segment titles from the representation of their "meanings." In this assigning procedure, we focus on the speaker's evaluation for the event or the speech object. To verify the effectiveness of our idea, we examined identification of the segment boundaries from the titles that were described in our procedure. We confirmed that the result of the identification was more accurate than that of intuitive identification.

An application profile specifies a set of terms, drawn from one or more standard namespaces, for annotation of data, and constrains their usage and interpretations in a particular local application. An approach to representation of and reasoning with application profiles based on the OWL and OWL/XDD languages is proposed. The former is a standard Web ontology language, while the latter is a definite-clause-style rule language that employs XML expressions as its underlying data structure. Semantic constraints are defined in terms of rules, which are represented as XDD clauses. Application of the approach to defining application profiles with fine-grained semantic constraints, involving implicit properties of metadata elements, is illustrated. A prototype application profile development environment equipped with metadata validation features has been implemented based on the proposed framework.

From lessons learned in medical data mining projects we show that integration of advanced computation techniques and human inspection is indispensable in medical data mining. We proposed an integrated approach that merges data mining and text mining methods plus visualization support for expert evaluation. We also appropriately developed temporal abstraction and text mining methods to exploit the collected data. Furthermore, our visual discovery system D2MS allowed to actively and effectively working with physicians. Significant findings in hepatitis study were obtained by the integrated approach.

Several approaches have been studied to cope with the exceptional features of non-segmented languages. When there is no explicit information about the boundary of a word, segmenting an input text is a formidable task in language processing. Not only the contemporary word list, but also usages of the words have to be maintained to cover the use in the current texts. The accuracy and efficiency in higher processing do heavily rely on this word boundary identification task. In this paper, we introduce some statistical based approaches to tackle the problem due to the ambiguity in word segmentation. The word boundary identification problem is then defined as a part of others for performing the unified language processing in total. To exhibit the ability in conducting the unified language processing, we selectively study the tasks of language identification, word extraction, and dictionary-less search engine.

For the closed loop multiple-input-multiple-output (MIMO) systems, Fisher's adaptive bit loading algorithm gives the best error performance by jointly optimizing the transmit powers, rates, and number of streams. However, its good performance comes at the cost of high and variable computational complexity for the joint optimization. In this letter, we propose an efficient multi-mode precoding algorithm using a simplified mode table. Numerical results show that the proposed algorithm provides almost the same performance as Fischer's with much less computational complexity.