Networking technologies have recently been evolving and network applications are now expected to support flexible composition of upper-layer network services, such as security, QoS, or personal firewall. We propose a multi-platform framework called FreeNA* that extends existing applications by incorporating the services based on user definitions. This extension does not require users to modify their systems at all. Therefore, FreeNA is valuable for experimental system usage. We implemented FreeNA on both Linux and Microsoft Windows operating systems, and evaluated their functionality and performance. In this paper, we describe the design and implementation of FreeNA including details on how to insert network services into existing applications and how to create services in a multi-platform environment. We also give an example implementation of a service with SSL, a functionality comparison with relevant systems, and our performance evaluation results. The results show that FreeNA offers finer configurability, composability, and usability than other similar systems. We also show that the throughput degradation of transparent service insertion is 2% at most compared with a method of directly inserting such services into applications.

IEEE 802.15.4 is a new standard, uniquely designed for low rate wireless personal area networks (LR-WPANs). It targets ultra-low complexity, cost, and power, for low-data-rate wireless connectivity. However, one of the main problems of this new standard is its insufficient, and inefficient, media access control (MAC) for priority data. This paper introduces an extended contention access period (XCAP) concept for priority packets, also an traffic adaptive contention differentiation utilizing the XCAP (TACDX). The TACDX determines appropriate transmission policy alternatively according to the traffic conditions and type of packet. TACDX achieves not only enhanced transmission for priority packets but it also has a high energy efficiency for the overall network. The proposed TACDX is verified with simulations to measure the performances.

In the near future, wireless local area networks (WLANs) will overlap to provide continuous coverage over a wide area. In such ubiquitous WLANs, a mobile node (MN) moving freely between multiple access points (APs) requires not only permanent access to the Internet but also continuous communication quality during handover. In order to satisfy these requirements, an MN needs to (1) select an AP with better performance and (2) execute a handover seamlessly. To satisfy requirement (2), we proposed a seamless handover method in a previous study. Moreover, in order to achieve (1), the Received Signal Strength Indicator (RSSI) is usually employed to measure wireless link quality in a WLAN system. However, in a real environment, especially if APs are densely situated, it is difficult to always select an AP with better performance based on only the RSSI. This is because the RSSI alone cannot detect the degradation of communication quality due to radio interference. Moreover, it is important that AP selection is completed only on an MN, because we can assume that, in ubiquitous WLANs, various organizations or operators will manage APs. Hence, we cannot modify the APs for AP selection. To overcome these difficulties, in the present paper, we propose and implement a proactive AP selection method considering wireless link condition based on the number of frame retransmissions in addition to the RSSI. In the evaluation, we show that the proposed AP selection method can appropriately select an AP with good wireless link quality, i.e., high RSSI and low radio interference.

This letter investigates an ADILC (Iterative Learning Control with Advanced Output Data) scheme for nonminimum phase systems using a partially known impulse response. ADILC has a simple learning structure that can be applied to both minimum phase and nonminimum phase systems. However, in the latter case, the overall control time horizon must be considered in the input update law, which makes the dimension of the matrices in the convergence condition very large. Also, this makes it difficult to find a proper learning gain matrix. In this letter, a new sufficient condition is derived from the convergence condition, which can be used to find the learning gain matrix for nonminimum phase systems if we know the first part of the impulse response up to a sufficient order. Based on this, an iterative learning control scheme is proposed using the estimation of the first part of the impulse response for nonminimum phase systems.

In this paper, a new video frame interpolation method based on image morphing for frame rate up-conversion is proposed. In this method, image features are extracted by Scale-Invariant Feature Transform in each frame, and their correspondence in two contiguous frames is then computed separately in foreground and background regions. By using the above two functions, the proposed method accurately generates interpolation frames and thus achieves frame rate up-conversion.

Orthogonal frequency division multiplexing (OFDM) is a critical technology in 3G evolution systems, which can effectively avoid intra-cell interference, but may bring with serious inter-cell interference. Inter-cell interference cancellation is one of effective schemes taken in mitigating inter-cell interference, but for many existing schemes in inter-cell interference cancellation, various generalized spatial diversities are taken, which always bring with extra interference and blind spots, or even need to acquire extra information on source and channel. In this paper, a novel inter-cell interference mitigation method is proposed for 3G evolution systems. This method is based on independent component analysis in blind source separation, and the input signal to interference plus noise ratio (SINR) is set as objective function. By generalized eigenvalue decomposition and algorithm iterations, maximum signal noise ratio (SNR) can be obtained in output. On the other hand, this method can be worked with no precise knowledge of source signal and channel information. Performance evaluation shows that such method can mitigate inter-cell interference in a semi-blind state, and effectively improve output SNR with the condition that lower input SINR, higher input SNR and longer lengths of the processing frame.

Despite the prevalence of Java workloads across a variety of processor architectures, there is very little published data on the impact of the various processor design decisions on Java performance. We attribute the lack of data to the large design space resulting from the complexity of the modern superscalar processor and the additional complexities associated with executing Java bytecode using a virtual machine. To address this shortcoming, we use a statistically rigorous methodology to systematically quantify the the impact of the various processor microarchitecture parameters on Java execution performance. The adopted methodology enables efficient screening of significant factor effects in a large design space consisting of 35 factors (32-billion potential configurations) using merely 72 observations per benchmark application. We quantify and tabulate the significance of each of the 35 factors for 13 benchmark applications. While these tables provide various insights into Java performance, they consistently highlight the performance significance of the instruction delivery mechanism, especially the instruction cache and the ITLB design parameters. Furthermore, these tables enable the architect to identify processor bottlenecks for Java workloads by providing an estimate of the relative impact of various design decisions.

This paper proposes a novel reversible image authentication method that does not memorize the parameters for extracting embedded authentication data from an image. The proposed method once distorts an image to hide data for authentication into the image, it recovers the original image from the distorted image unless tamper is applied to the image, i.e., reversible. By comparing extracted data and data generated from the restored image, this method detects image tampering and further localizes tampered regions by the unit of block. The proposed method extracts hidden data without memorization of parameters used in its algorithm. This feature makes the proposed method practical. Whereas any method memorizing parameters faces severe problems with storage and management of parameters, according to the increase in the number of memorized parameters that is caused by serving accurate tamper localization and/or by applying itself to a huge number of image collection, e.g., video sequences. Simulation results show the effectiveness of the proposed method.

Let us introduce n ( ≥ 2) mappings fi (i=1,,n ≡ 0) defined on reflexive real Banach spaces Xi-1 and let fi:Xi-1 → Yi be completely continuous on bounded convex closed subsets Xi-1(0) ⊂ Xi-1. Moreover, let us introduce n set-valued mappings Fi : Xi-1 Yi → Fc(Xi) (the family of all non-empty compact subsets of Xi), (i=1,,n ≡ 0). Here, we have a fixed point theorem in weak topology on the successively recurrent system of set-valued mapping equations:xi ∈ Fi(xi-1, fi(xi-1)), (i=1,,n ≡ 0). This theorem can be applied immediately to analysis of the availability of system of circular networks of channels undergone by uncertain fluctuations and to evaluation of the tolerability of behaviors of those systems.

In this paper, we study low complexity transceiver for double space time transmit diversity (DSTTD) and orthogonal frequency division multiplexing (OFDM) system with antenna shuffling. Firstly, we propose a novel antenna shuffling method based on the criterion of minimizing the condition number of channel correlation matrix. The condition number is an indicator about the quality of the channel. By selecting the minimum of condition number which has better channel quality, consequently, a linear detector with respect to this new channel may achieve better performance results. A low complexity variant of the condition number calculation is also proposed, and it is shown that this criterion can be reduced to the minimum mean square error (MMSE) based criterion. Furthermore, the weighted soft decision Viterbi decoding is applied to mitigate noise enhancement inherent to zero forcing (ZF) and MMSE linear receivers and improve error rate performance. Next, we propose an algorithm to reduce the amount of feedback by exploiting the fact that the channel frequency responses across OFDM subcarriers are correlated. In the proposed algorithm, subcarriers are clustered in blocks, which are allocated the same shuffling pattern with the largest number of the shuffling patterns in the cluster. This way, the signaling overhead can be reduced in comparison with each subcarrier based feedback. Extensive simulations show that the proposed techniques for DSTTD-OFDM system outperform other existing techniques under both uncorrelated and highly spatial correlated frequency selective MIMO fading channels.

In deep submicron designs, predicting gate delays with interconnect load is a noteworthy work for Static Timing Analysis (STA). The effective capacitance Ceff concept and the Thevenin model that replaces the gate with a linear resistor and a voltage source are usually used to calculate the delay of gate with interconnect load. In conventional methods, it is not considered that the charges transferred into interconnect load and Ceff in the Thevenin model are not equal. The charge difference between interconnect load and Ceff has the large influence to the accuracy of computing Ceff. In this paper, an advanced effective capacitance model is proposed to consider the above problem in the Thevenin model, where the influence of the charge difference is modeled as one part of the effective capacitance to compute the gate delay. Experimental results show a significant improvement in accuracy when the charge difference between interconnect load and Ceff is considered.

An approach to image thresholding based on the plausibility of object and background regions by adopting a co-occurrence matrix and category utility is presented. The effectiveness of the proposed method is shown through the experimental results tested on several images and compared with conventional methods.

An ICI (Inter-Cell Interference) mitigation algorithm for exploiting macroscopic diversity for an up-link OFDMA (Orthogonal Frequency Division Multiple Access) system is proposed. To reduce the influence of carrier collision, the order of resource allocation is coordinated based on the location of each MS (Mobile Station) and the associated carrier group. This consideration significantly reduces ICI and enhances throughput at the boundary region.

In 1999, Gennaro, Halevi and Rabin proposed a signature which achieves provable security without assuming the random oracles, and it is the first RSA-type signature whose security is proved in the standard model. Since that time, several signatures have been proposed to achieve better efficiency or useful property along with the provable security in the standard model. In this paper, we construct a trapdoor hash function, and design an efficient online/offline signature by using the trapdoor hash function. Our signature scheme requires only one non-modular multiplication of two small integers for online signing, and it provides the fastest online signing among all online/offline signatures that achieve provable security in the standard model.

In conjunction with a first-order Taylor series approximation of the spatial scanning vector, this letter presents an iterative multiple signal classification (MUSIC) direction-of-arrival (DOA) estimation for code-division multiple access signals. This approach leads to a simple one-dimensional optimization problem to find each iterative optimal search grid. It can not only accurately estimate DOA, but also speed up the estimating process. Computer results demonstrate the effectiveness of the proposed algorithm.

We propose a learning strategy for acceleration in learning speed of genetic programming (GP), named hierarchical structure GP (HSGP). The HSGP exploits multiple learning nodes (LNs) which are connected in a hierarchical structure, e.g., a binary tree. Each LN runs conventional evolutionary process to evolve its own population, and sends the evolved population into the connected higher-level LN. The lower-level LN evolves the population with a smaller subset of training data. The higher-level LN then integrates the evolved population from the connected lower-level LNs together, and evolves the integrated population further by using a larger subset of training data. In HSGP, evolutionary processes are sequentially executed from the bottom-level LNs to the top-level LN which evolves with the entire training data. In the experiments, we adopt conventional GPs and the HSGPs to evolve image recognition programs for given training images. The results show that the use of hierarchical structure learning can significantly improve learning speed of GPs. To achieve the same performance, the HSGPs need only 30-40% of the computation cost needed by conventional GPs.

We propose an iterative channel decoding scheme for two or more multiple correlated sources. The correlated sources are separately turbo encoded without knowledge of the correlation and transmitted over noisy channels. The proposed decoder exploits the correlation of the multiple sources in an iterative soft decision decoding manner for joint detection of each of the transmitted data. Simulation results show that achieved performance for the more than two sources is also close to the Shannon and Slepian-Wolf limit and large additional SNR gain is obtained in comparison with the case of two sources. We also verify through simulation that no significant penalty results from the estimation of the source correlation in the decoding process and the code with a low error floor achieves good performance for a large number of the correlated sources.

Access to information is taken for granted in urban areas covered by a robust communication infrastructure. Nevertheless most of the areas in the world, are not covered by such infrastructures. We propose a DTN publish and subscribe system called Hikari, which uses nodes' mobility in order to distribute messages without using a robust infrastructure. The area of Disruption/Delay Tolerant Networks (DTN) focuses on providing connectivity to locations separated by networks with disruptions and delays. The Hikari system does not use node identifiers for message forwarding thus eliminating the complexity of routing associated with many forwarding schemes in DTN. Hikari uses nodes paths' information, advertised by special nodes in the system or predicted by the system itself, for optimizing the message dissemination process. We have used the Paris subway system, due to it's complexity, to validate Hikari and to analyze it's performance. We have shown that Hikari achieves a superior deliver rate while keeping redundant messages in the system low, which is ideal when using devices with limited resources for message dissemination.

How to minimize the number of mirroring resources under a QoS constraint (resource minimization problem) is an important issue in content delivery networks. This paper proposes a novel approach that takes advantage of the parallelism of dynamically reconfigurable processors (DRPs) to solve the resource minimization problem, which is NP-hard. Our proposal obtains the optimal solution by running an exhaustive search algorithm suitable for DRP. Greedy algorithms, which have been widely studied for tackling the resource minimization problem, cannot always obtain the optimal solution. The proposed method is implemented on an actual DRP and in experiments reduces the execution time by a factor of 40 compared to the conventional exhaustive search algorithm on a Pentium 4 (2.8 GHz).

This paper proposes a parallelized DVC framework that treats each bitplane independently to reduce the decoding time. Unfortunately, simple parallelization generates inaccurate bit probabilities because additional side information is not available for the decoding of subsequent bitplanes, which degrades encoding efficiency. Our solution is an effective estimation method that can calculate the bit probability as accurately as possible by index assignment without recourse to side information. Moreover, we improve the coding performance of Rate-Adaptive LDPC (RA-LDPC), which is used in the parallelized DVC framework. This proposal selects a fitting sparse matrix for each bitplane according to the syndrome rate estimation results at the encoder side. Simulations show that our parallelization method reduces the decoding time by up to 35[%] and achieves a bit rate reduction of about 10[%].