Group key establishment is an important mechanism to construct a common session key for group communications. Conventional group key establishment protocols use an on-line trusted key generation center (KGC) to transfer the group key for each participant in each session. However, this approach requires that a trusted server be set up, and it incurs communication overhead costs. In this article, we address some security problems and drawbacks associated with existing group key establishment protocols. Besides, we use the concept of secret sharing scheme to propose a secure key transfer protocol to exclude impersonators from accessing the group communication. Our protocol can resist potential attacks and also reduce the overhead of system implementation. In addition, comparisons of the security analysis and functionality of our proposed protocol with some recent protocols are included in this article.

In this paper, analysis of average bit error ratio (BER) performance of a quadriphase shift keying (QPSK) direct-sequence code-division multiple-access (DS-CDMA) system with narrow-band interference (NBI) suppression complex adaptive infinite-impulse response (IIR) notch filter is presented. QPSK DS-CDMA signal is transmitted over a Rayleigh frequency-nonselective fading channel and the NBI has a randomly-varying frequency. A closed-form expression that relates BER with complex coefficient IIR notch filter parameters, received signal-to-noise ratio (SNR), number of DS-CDMA active users and processing gain is derived. The derivation is based on the Standard Gaussian Approximation (SGA) method. Accuracy of the BER expression is confirmed by computer simulation results.

In MANET (Mobile Ad-hoc NETworks), there are two kinds of routing methods: proactive and reactive. Each has different characteristics and advantages. The latter generally employs the flooding technique to finding a routing path to the destination. However, flooding has big overheads caused by broadcasting RREQ packets to the entire network. Therefore, reducing this overhead is really needed to enable several network efficiencies. Previous studies introduced many approaches which are mainly concerned with the restriction of flooding. However, they usually configure the detailed routing path in the forward flooding procedure and ignore the factors causing the flooding overheads. In this paper, we propose the FSRS (First Search and Reverse Setting) routing protocol which is a new approach in flooding techniques and a new paradigm shift. FSRS is based on cluster topology and is composed of two main mechanisms: inter-cluster and intra-cluster flooding. Inter-cluster routing floods RREQ packets between cluster units and sets a cluster path. When the destination node receives the RREQ packet, it floods RREP packets to an intra-cluster destination which is a gateway to relay the RREP packet to a previous cluster. This is called intra-cluster routing. So to speak, a specific routing path configuration progresses in the RREP process through the reverse cluster path. Consequently, FSRS is a new kind of hybrid protocol well adapted to wireless ad-hoc networks. This suggests a basic wireless networking architecture to make a dynamic cluster topology in future work. In the simulation using NS-2, we compare it to several other protocols and verify that FSRS is a powerful protocol. In the result of the simulation, FSRS conserves energy by a maximum of 12% compared to HCR.

This paper introduces a new dynamic 3D mesh representation that provides 3D animation support of progressive display and drastically reduces the amount of storage space required for 3D animation. The primary purpose of progressive display is to allow viewers to get animation as quickly as possible, rather than having to wait until all data has been downloaded. In other words, this method allows for the simultaneous transmission and playing of 3D animation. Experiments show that coarser 3D animation could be reconstructed with as little as 150 KB of data transferred. Using the sustained transmission of refined operators, viewers feel that resolution approaches that of the original animation. The methods used in this study are based on a compression technique commonly used in 3D animation - clustered principle component analysis, using the linearly independent rules of principle components, so that animation can be stored using smaller amounts of data. This method can be coupled with streaming technology to reconstruct animation through iterative updating. Each principle component is a portion of the streaming data to be stored and transmitted after compression, as well as a refined operator during the animation update process. This paper considers errors and rate-distortion optimization, and introduces weighted progressive transmitting (WPT), using refined sequences from optimized principle components, so that each refinement yields an increase in quality. In other words, with identical data size, this method allows each principle component to reduce allowable error and provide the highest quality 3D animation.

In processing stream data, time is one of the most significant facts not only because the size of data is dramatically increased but because the characteristics of data is varying over time. To learn stream data evolving over time effectively, it is required to detect the drift of concept. We present a window adaptation function on domain value (WAV) to determine the size of windowed batch for learning algorithms of stream data and a method to detect the change of data characteristics with a criterion function utilizing correlation. When applying our adaptation function to a clustering task on a multi-stream data model, the result of learning synopsis of windowed batch determined by it shows its effectiveness. Our criterion function with correlation information of value distribution over time can be the reasonable threshold to detect the change between windowed batches.

The doubly constrained robust Capon beamformer (DCRCB), which employs a spherical uncertainty set of the steering vector together with the constant norm constraint, can provide robustness against arbitrary array imperfections. However, its performance can be greatly degraded when the uncertainty bound of the spherical set is not properly selected. In this paper, combining the DCRCB and the weight-vector-norm-constrained beamformer (WVNCB), we suggest a new robust adaptive beamforming method which allows us to overcome the performance degradation due to improper selection of the uncertainty bound. In WVNCB, its weight vector norm is limited not to be larger than a threshold. Both WVNCB and DCRCB belong to a class of diagonal loading methods. The diagonal loading range of WVNCB, which dose not consider negative loading, is extended to match that of DCRCB which can have a negative loading level as well as a positive one. In contrast to the conventional DCRCB with a fixed uncertainty bound, the bound in the proposed method varies such that the weight vector norm constraint is satisfied. Simulation results show that the proposed beamformer outperforms both DCRCB and WVNCB, being far less sensitive to the uncertainty bound than DCRCB.

Dielectric rod arrays in a metallic waveguide alter the propagation modes and group velocities of electromagnetic waves. We have focused on TE30-to-TE10 mode converters and investigated how their behavior varies with frequency. A mode converter is proposed that passes the TE10 mode at frequencies lower than 2fc, and converts the TE30 mode into the TE10 mode for frequencies higher than 3fc.

Support vector clustering (SVC), a recently developed unsupervised learning algorithm, has been successfully applied to solving many real-life data clustering problems. However, its effectiveness and advantages deteriorate when it is applied to solving complex real-world problems, e.g., those with large proportion of noise data points and with connecting clusters. This paper proposes a support vector and K-Means based hybrid algorithm to improve the performance of SVC. A new SVC training method is developed based on analysis of a Gaussian kernel radius function. An empirical study is conducted to guide better selection of the standard deviation of the Gaussian kernel. In the proposed algorithm, firstly, the outliers which increase problem complexity are identified and removed by training a global SVC. The refined data set is then clustered by a kernel-based K-Means algorithm. Finally, several local SVCs are trained for the clusters and then each removed data point is labeled according to the distance from it to the local SVCs. Since it exploits the advantages of both SVC and K-Means, the proposed algorithm is capable of clustering compact and arbitrary organized data sets and of increasing robustness to outliers and connecting clusters. Experiments are conducted on 2-D data sets generated by mixture models and benchmark data sets taken from the UCI machine learning repository. The cluster error rate is lower than 3.0% for all the selected data sets. The results demonstrate that the proposed algorithm compared favorably with existing SVC algorithms.

This paper studies the 2-adic complexity of the self-shrinking sequence under the relationship between 2-adic integers and binary sequences. Based on the linear complexity and the number of the sequences which have the same connection integer, we conclude that the 2-adic complexity of the self-shrinking sequence constructed by a binary m-sequence of order n has a lower bound 2n-2-1. Furthermore, it is shown that its 2-adic complexity has a bigger lower bound under some circumstances.

We have succeeded in developing high-performance p-type of organic semiconductors with phenylethynyl groups, which have high filed-effect mobilities (>3 cm2V-1s-1) by improving molecular planarity. A single crystal of the organic semiconductors has a herringbone structure. It plays an important role for carrier transport. In addition, we found that they had lower contact resistances to Au electrodes as well. Then, we used the materials for the carrier injection layer deposited onto another organic semiconductor we developed recently, which achieved a high field-effect mobility, and a low threshold voltage (Vth).

In this paper, a rat-race hybrid coupler based on an open complementary split ring resonator (OCSRR) is presented. By embedding the OCSRR in the microstrip transmission line, slow-wave effect is introduced to achieve size reduction. The proposed rat-race coupler size is 37% smaller than the conventional rat-race coupler. Besides, the proposed coupler provides better third harmonic suppression up to 35 dB. The simulated results are compared with the measured data and good agreement is reported.

Due to an increase in multimedia content and the acceleration of digital convergence, demand for next-generation IPTV service is rapidly growing. IPTV service seamlessly provides both real-time broadcasting and content sharing services on diverse terminals through complex networks. In this paper, a secure and scalable content sharing framework is proposed for next-generation IPTV service. The proposed framework has an advantage over conventional content protection techniques in producing scalable content with transcodable, adjustable, and perceptual security features. Moreover, it ensures end-to-end security over the entire service range based on a single security mechanism. The suitability of the proposed approach is demonstrated experimentally using a practical service scenario with real-world environments. The experiments show that the proposed approach can provide several different levels of content security, from a perceptual level to an almost unintelligible level, while keeping the additional time overhead low. Consequently, it is expected that use of this security technology alone can have a practical contribution in creating new business opportunities for IPTV services.

In this paper, we propose a two dimensional (2D) non-separable adaptive directional lifting (ADL) structure for discrete wavelet transform (DWT) and its image coding application. Although a 2D non-separable lifting structure of 9/7 DWT has been proposed by interchanging some lifting, we generalize a polyphase representation of 2D non-separable lifting structure of DWT. Furthermore, by introducing the adaptive directional filteringingto the generalized structure, the 2D non-separable ADL structure is realized and applied into image coding. Our proposed method is simpler than the 1D ADL, and can select the different transforming direction with 1D ADL. Through the simulations, the proposed method is shown to be efficient for the lossy and lossless image coding performance.

A MANEMO node is an IP-based mobile node that has interface attachments to both a mobile network, using Network Mobility (NEMO), and a Mobile Ad Hoc Network (MANET). While communicating with a correspondent node in the Internet, the MANEMO node should use the best possible path. Therefore, as conditions change, a handover between NEMO and MANET is desirable. This paper describes the operation of a MANEMO handover when IEEE 802.11 is used. An analytical model illustrates that packet loss during a MANEMO handover may severely affect data and real-time applications. We therefore propose using buffering during the handover, by making use of the Power Save Mode in IEEE 802.11. In the proposed algorithm, a MANEMO node may rapidly switch between the two interfaces, eventually receiving packets delivered via the old network interface while initiating the Mobile IP/NEMO handover on the new interface. Performance results show that packet loss can be significantly reduced, with small and acceptable increases in signalling overhead and end-to-end delay.

This paper presents the design and implementation of a quadrature voltage-controlled ring oscillator with the improved figure of merit (FOM) using the four single-ended inverter topology. Furthermore, a new architecture to prevent the latch-up in even number of stages composed of single-ended ring inverters is proposed. The design is implemented in 0.18 µm CMOS technology and the measurement results show a FOM of -163.8 dBc/Hz with the phase noise of -125.8 dBc/Hz at 4 MHz offset from the carrier frequency of 3.4 GHz. It exhibits a frequency tuning range from 1.23 GHz to 4.17 GHz with coarse and fine frequency tuning sensitivity of 1.08 MHz/mV and 120 kHz/mV, respectively.

We propose a robust and efficient algorithm called ROCKET for clustering large-scale transaction databases. ROCKET is a divisive hierarchical algorithm that makes the most of recent hardware architecture. ROCKET handles the cases with the small and the large number of similar transaction pairs separately and efficiently. Through experiments, we show that ROCKET achieves high-quality clustering with a dramatic performance improvement.

Rapid developments in mobile technology have transformed mobile phones into mobile multimedia devices. Due to these advancements, user created mobile content is on the increase, both in terms of quality and quantity. To keep pace with such movements, the new networking technology named content centric networking (CCN), which is optimized for content sharing, has appeared. However, it virtually ignores mobile devices. So, this letter proposes a smooth mobile content migration scheme for CCN to provide lower communication overhead and shorter download time.

We present a scalable approach to automatically discovering particular objects (as opposed to object categories) from a set of images. The basic idea is to search for local image features that consistently appear in the same images under the assumption that such co-occurring features underlie the same object. We first represent each image in the set as a set of visual words (vector quantized local image features) and construct an inverted file to memorize the set of images in which each visual word appears. Then, our object discovery method proceeds by searching the inverted file and extracting visual word sets whose elements tend to appear in the same images; such visual word sets are called co-occurring word sets. Because of unstable and polysemous visual words, a co-occurring word set typically represents only a part of an object. We observe that co-occurring word sets associated with the same object often share many visual words with one another. Hence, to obtain the object models, we further cluster highly overlapping co-occurring word sets in an agglomerative manner. Remarkably, we accelerate both extraction and clustering of co-occurring word sets by Min-Hashing. We show that the models generated by our method can effectively discriminate particular objects. We demonstrate our method on the Oxford buildings dataset. In a quantitative evaluation using a set of ground truth landmarks, our method achieved higher scores than the state-of-the-art methods.

We propose two types of autonomic and distributed cooperative behaviors of peers for peer-to-peer (P2P) file-sharing networks. Cooperative behaviors of peers are mediated by query trails, and allows the exploration of better trade-off points between file search and storage load balancing performance. Query trails represent previous successful search paths and indicate which peers contributed to previous file searches and were at the same time exposed to the storage load. The first type of cooperative behavior is to determine the locations of replicas of files through the medium of query trails. Placement of replicas of files on strong query trails contributes to improvement of search performance, but a heavy load is generated due to writing files in storage to peers on the strong query trails. Therefore, we attempt to achieve storage load balancing between peers, while avoiding significant degradation of the search performance by creating replicas of files in peers adjacent to peers on strong query trails. The second type of cooperative behavior is to determine whether peers provide requested files through the medium of query trails. Provision of files by peers holding requested files on strong query trails contributes to better search performance, but such provision of files generates a heavy load for reading files from storage to peers on the strong query trails. Therefore, we attempt to achieve storage load balancing while making only small sacrifices in search performance by having peers on strong query trails refuse to provide files. Simulation results show that the first type of cooperative behavior provides equal or improved ability to explore trade-off points between storage load balancing and search performance in a static and nearly homogeneous P2P environment, without the need for fine tuning parameter values, compared to replication methods that require fine tuning of their parameters values. In addition, the combination of the second type and the first type of cooperative behavior yields better storage load balancing performance with little degradation of search performance. Moreover, even in a dynamic and heterogeneous P2P environment, the two types of cooperative behaviors yield good ability to explore trade-off points between storage load balancing and search performance.

Main bottleneck of photomosaic algorithm is a search for a best matched image. Unlike several techniques which use fast approximation search for increasing the speed, we propose a parallel framework for fast photomosaic using a programmable GPU. This paper suggests a design of vertex structure for a best match searching on each cell of photomosaic grid and shows a texture representation of image database. The shader programs which are used for searching a best match and rendering image tiles into a display are presented. In addition, a simple duplicate reduction and color correction methods are proposed. Our algorithm not only offers dramatic enhancement of speed, but also always guarantees the 'exact' result.