During recent years, there has been a rapid growth in deployment of gossip-based protocol in many multicast applications. In a typical gossip-based protocol, each node acts as dual roles of receiver and sender, independently exchanging data with its neighbors to facilitate scalability and resilience. However, most of previous work in this literature seldom considered cheating issue of end users, which is also very important in face of the fact that the mutual cooperation inherently determines overall system performance. In this paper, we investigate the dishonest behaviors in decentralized gossip-based protocol through extensive experimental study. Our original contributions come in two-fold: In the first part of cheating study, we analytically discuss two typical cheating strategies, that is, intentionally increasing subscription requests and untruthfully calculating forwarding probability, and further evaluate their negative impacts. The results indicate that more attention should be paid to defending cheating behaviors in gossip-based protocol. In the second part of anti-cheating study, we propose a receiver-driven measurement mechanism, which evaluates individual forwarding traffic from the perspective of receivers and thus identifies cheating nodes with high incoming/outgoing ratio. Furthermore, we extend our mechanism by introducing reliable factor to further improve its accuracy. The experiments under various conditions show that it performs quite well in case of serious cheating and achieves considerable performance in other cases.

Clustering is indispensable to obtain a general view of series data from a number of data such as gene expression profiles. We propose a novel metric for clustering. The proposed metric automatically normalizes data to minimize a logarithmic scale distance between the data series.

Through fractional sampling, it is possible to separate multipath components and achieve diversity gain. However, power consumption grows as the sampling rate increases. This paper proposes a novel scheme for OFDM systems that selects the sampling rate according to the channel's frequency response. Numerical results through computer simulation show that the proposed sampling rate selection scheme reduces power consumption by reducing oversampling ratio when delay spread is small.

Based on the expectation-maximization (EM) algorithm, an iterative time-domain channel estimation approach capable of using a priori information is proposed for orthogonal frequency division multiplexing (OFDM) systems in this letter: it outperforms its noniterative counterpart in terms of estimation accuracy as well as bit error rate (BER) performance. Numerical simulations demonstrate that an SNR gain of 1 dB at BER=10-4 with only one iteration and estimation mean square error (MSE) which nearly coincides with the Cramer-Rao bound (CRB) in the low SNR region can be obtained, thanks to the efficient use of a priori information.

In inter-vehicle networks, it is required that collision warning messages be distributed over multiple hops with significantly short delay. Cut-through packet forwarding is known to be an effective method to reduce forwarding latency. However, it suffers from the reliability issue, which is especially severe in highly mobile scenario, due to multi-path fading. This paper tries to establish a reliable cut-through forwarding scheme by applying diversity combining method to collision warning messages. By exploiting the nature of the multi-hop forwarding of a broadcast packet in CDMA wireless networks, the diversity combining scheme does not incur any additional communication overhead or delay. The simulation results show that diversity combining schemes, especially Maximal Ratio Combining (MRC), can effectively reduce BER by orders of magnitude.

In this letter, we propose an adaptive transmission method for an OFDMA system supporting both band-AMC and diversity modes in a frame, simultaneously. In the proposed method, users are classified into the two groups preferring the band-AMC mode or the diversity mode based on their channel parameters. Then the BS performs resource allocation to maximize the throughput. It is observed that the proposed adaptive transmission method can reduce the feedback overhead with negligible performance loss.

We propose and demonstrate a scheme enhancing the performance of optical access networks with Manchester coded downstream and re-modulated NRZ coded upstream. It is achieved by threshold level control of a limiting amplifier at a receiver, and the minimum sensitivity of upstream is significantly improved for the re-modulation scheme with 5 Gb/s Manchester coded downstream and 2.488 Gb/s NRZ upstream data rates.

The system level performance of a superposition coded broadcast/unicast service overlay system is considered. Cellular network for unicast service only is considered as interference limited system, where increasing the transmission power does not help improve the network throughput especially when the frequency reuse factor is close to 1. In such cases, the amount of power that does not contribute to improving the throughput can be considered as "unused." This situation motivates us to use the unused power for broadcast services, which can be efficiently provided in OFDM based single frequency networks as in digital multimedia broadcast systems. In this paper, we investigate the performance of such a broadcast/unicast overlay system in which a single frequency broadcast service is superimposed over a unicast cellular service. Alternative service multiplexing using FDM/TDM is also considered for comparison.

This paper presents the processor architecture which provides much higher level dependability than the current ones. The features of it are: (1) fault tolerance and secure processing are integrated into a modern superscalar VLSI processor; (2) light-weight effective soft-error tolerant mechanisms are proposed and evaluated; (3) timing errors on random logic and registers are prevented by low-overhead mechanisms; (4) program behavior is hidden from the outer world by proposed address translation methods; (5) information leakage can be avoided by attaching policy tags for all data and monitoring them for each instruction execution; (6) injection attacks are avoided with much higher accuracy than the current systems, by providing tag trackings; (7) the overall structure of the dependable processor is proposed with a dependability manager which controls the detection of illegal conditions and recovers to the normal mode; and (8) an FPGA-based testbed system is developed where the system clock and the voltage are intentionally varied for experiment. The paper presents the fundamental scheme for the dependability, elemental technologies for dependability and the whole architecture of the ultra dependable processor. After showing them, the paper concludes with future works.

Autonomous Decentralized Community System (ADCS) makes its basis on offering customized and dynamic services to group of end-users having common preferences at specified time and location. Owing to extreme dynamism in the system caused by rapidly varying user's demands, and severe mobility of users, it is quite difficult to assure timely service provision to all community members. This paper presents autonomous decentralized community system construction by autonomous division and integration technologies to procure service assurance under dynamic situations, without involving significant communication overhead. By adopting the concept of size threshold, the proposed technique continuously maintains the appropriate size of community in constantly and rapidly changing operating environment, to deliver optimal quality of service in terms of response time. The effectiveness of proposed technology has been shown through simulation, which reveals remarkable improvement (up to 29%) in response time.

In this letter, a genetic programming method is used to synthesize filters. In order to improve the group delay characteristics, we propose a novel two-stage fitness function reflecting not only the frequency response but also the group delay characteristics of the evolved filters. We also deal with two practical design considerations, i.e., the filters include parasitic effects and are composed of elements with discrete values. The proposed method is applied to low-pass filter design cases. The experimental results show the method can effectively generate filters satisfying the design considerations and possessing improved group delay characteristics when compared with traditional filters.

Hybrid FEC/ARQ, which is a mixture of forward error correction (FEC) and automatic repeat request (ARQ), is a well-known technique aiming for packet-loss recovery to guarantee quality of service (QoS) for real-time communications. In this paper, focusing on layered video transmission over wireless network environment, we propose a proactive retransmission scheme for hybrid FEC/ARQ. In the proposed scheme, a receiver host periodically sends probe packets to a sender host in order to check wireless channel state. If the sender host does not receive any probe packet during a pre-specified interval, it regards the wireless channel as being in burst loss state, and it proactively retransmits packets expected to be lost during the burst loss period. The buffer management associated with layered video coding is also taken into consideration. The performance of the proposed scheme is investigated by simulation. Numerical examples show that the proposed scheme transmits packets of the base layer more successfully than the conventional FEC/ARQ.

Sensor node localization is an important issue in wireless sensor networks (WSNs) due to the dynamic nature of sensor deployment. Generally, in wireless sensor network localization, the absolute positions of certain anchor nodes are required based on the use of global positioning systems, then all the other nodes are approximately localized using various algorithms based on a coordinate system of the anchors. This paper proposes a neighbor position-based localization algorithm (NPLA) that can greatly enhance the positioning accuracy when compared with current overlapping connectivity localization algorithms that attempt to use the observation of connectivity to a set of anchors to determine a node's position. The proposed method localizes the sensor nodes using both the anchors' positions and neighbor node information. However, unlike existing overlapping-based methods, the proposed NPLA does not assume the same radio transmission range. A simulation study is used to demonstrate the positioning accuracy of the proposed method with different anchor and sensor node densities.

The multi-process execution in dynamically reconfigurable processors is a technique to enhance throughput by trying to exploit more inherent parallelism of applications. Basically, a total process for an application is divided into small processes, assigned into limited areas of a reconfigurable array, and concurrently executed in a pipelined manner. In order to improve the efficiency of the multi-process execution, a systematic method for mapping processes onto a reconfigurable array consisting of multiple hardware execution units is essential. This paper proposes and investigates a systematic method for mapping an application modeled as a Kahn Process Network onto a dynamically reconfigurable processing array. In order to execute streaming applications in a pipelined manner, the size of Tiles, which is a unit area of dynamically reconfigurable array, and the grouping of processes are adjusted. Using real applications such as DCT, JPEG encoder and Turbo encoder, the impact of different versions mapped onto the NEC Dynamically Reconfigurable Processor on performance is evaluated. Evaluation results show that our proposed mapping algorithm achieves the best performance in terms of the throughput and the execution time.

A Lyapunov-based recurrent neural networks unified power flow controller (UPFC) is developed for improving transient stability of power systems. First, a simple UPFC dynamical model, composed of a controllable shunt susceptance on the shunt side and an ideal complex transformer on the series side, is utilized to analyze UPFC dynamical characteristics. Secondly, we study the control configuration of the UPFC with two major blocks: the primary control, and the supplementary control. The primary control is implemented by standard PI techniques when the power system is operated in a normal condition. The supplementary control will be effective only when the power system is subjected by large disturbances. We propose a new Lyapunov-based UPFC controller of the classical single-machine-infinite-bus system for damping enhancement. In order to consider more complicated detailed generator models, we also propose a Lyapunov-based adaptive recurrent neural network controller to deal with such model uncertainties. This controller can be treated as neural network approximations of Lyapunov control actions. In addition, this controller also provides online learning ability to adjust the corresponding weights with the back propagation algorithm built in the hidden layer. The proposed control scheme has been tested on two simple power systems. Simulation results demonstrate that the proposed control strategy is very effective for suppressing power swing even under severe system conditions.

In this paper we compare various parallel preconditioners such as Point-SSOR (Symmetric Successive OverRelaxation), ILU(0) (Incomplete LU) in the Wavefront ordering, ILU(0) in the Multi-color ordering, Multi-Color Block SOR (Successive OverRelaxation), SPAI (SParse Approximate Inverse) and pARMS (Parallel Algebraic Recursive Multilevel Solver) for solving large sparse linear systems arising from two-dimensional PDE (Partial Differential Equation)s on structured grids. Point-SSOR is well-known, and ILU(0) is one of the most popular preconditioner, but it is inherently serial. ILU(0) in the Wavefront ordering maximizes the parallelism in the natural order, but the lengths of the wavefronts are often nonuniform. ILU(0) in the Multi-color ordering is a simple way of achieving a parallelism of the order N, where N is the order of the matrix, but its convergence rate often deteriorates as compared to that of natural ordering. We have chosen the Multi-Color Block SOR preconditioner combined with direct sparse matrix solver, since for the Laplacian matrix the SOR method is known to have a nondeteriorating rate of convergence when used with the Multi-Color ordering. By using block version we expect to minimize the interprocessor communications. SPAI computes the sparse approximate inverse directly by least squares method. Finally, ARMS is a preconditioner recursively exploiting the concept of independent sets and pARMS is the parallel version of ARMS. Experiments were conducted for the Finite Difference and Finite Element discretizations of five two-dimensional PDEs with large meshsizes up to a million on an IBM p595 machine with distributed memory. Our matrices are real positive, i.e., their real parts of the eigenvalues are positive. We have used GMRES(m) as our outer iterative method, so that the convergence of GMRES(m) for our test matrices are mathematically guaranteed. Interprocessor communications were done using MPI (Message Passing Interface) primitives. The results show that in general ILU(0) in the Multi-Color ordering and ILU(0) in the Wavefront ordering outperform the other methods but for symmetric and nearly symmetric 5-point matrices Multi-Color Block SOR gives the best performance, except for a few cases with a small number of processors.

This paper describes the matrix order reduction method by the nodal analysis formulation and the application of relaxation-based simulation technique to interconnect and plane networks. First, the characteristics of the power/ground plane networks are considered. Next, the formulation of the plane network by nodal analysis (NA) method is suggested. Furthermore, application and estimation results of the relaxation-based numerical analyses are shown. Finally, it is confirmed that the relaxation-based methods improved by the suggested formulation are much more efficient than the conventional direct-based methods.

This letter proposes two efficient decision-feedback (DF) detection schemes for space-time block code (STBC) over time-selective fading channels. The existing DF detection causes error propagation when the first symbol is not detected correctly. However, the proposed detection schemes provide two candidates according to a channel gain or an average log-likelihood ratio (LLR) based selection rule and choose a better candidate for the first symbol. Simulation results show that the proposed detection schemes reduce error propagation and yield significant signal-to-noise ratio (SNR) gain with moderate complexity, compared to the existing DF detection scheme.

A fine-grain reconfigurable VLSI for various applications including arithmetic operations is developed. In the fine-grain architecture, it is important to define a cell function which leads to high utilization of a logic block and reduction of a switch block. From the point of view, a universal-literal-based multiple-valued cell suitable for bit-serial reconfigurable computation is proposed. A series-gating differential-pair circuit is effectively employed for implementing a full-adder circuit of Sum and a universal literal circuit. Therefore, a simple logic block can be constructed using the circuit technology. Moreover, interconnection complexity can be reduced by utilizing multiple-valued signaling, where superposition of serial data bits and a start signal which indicates heading of one-word is introduced. Differential-pair circuits are also effectively employed for current-output replication, which leads to high-speed signaling to adjacent cells The evaluation is done based on 90 nm CMOS design rule, and it is made clear that the area of the proposed cell can be reduced to 78% in comparison with that of the CMOS implementatiuon. Moreover, its area-time product becomes 92% while the delay time is increased by 18%.

Using query-by-sketch we propose an application to efficiently create collages with some user interaction. Using rough color strokes that represent the target collage, images are automatically retrieved and segmented to create a seamless collage. The database is indexed using simple geometrical and color features for each region, and histograms that represent these features for each image. The image collection is then queried by means of a simple paint tool. The individual segments retrieved are added to the collage using Poisson image editing or alpha matting. The user is able to modify the default segmentations interactively, as well as the position, scale, and blending options for each object. The resulting collage can then be used as an input query to find other relevant images from the database.