Recently, a promising packet forwarding architecture COPE was proposed to essentially improve the throughput of multihop wireless networks, where each network node can intelligently encode multiple packets together and forward them in a single transmission. However, COPE is still in its infancy and has the following limitations: (1) COPE adopts the FIFO packet scheduling and thus does not provide different priorities for different types of packets. (2) COPE simply classifies all packets destined to the same nexthop into small-size or large-size virtual queues and examines only the head packet of each virtual queue to find coding solutions. Such a queueing structure will lose some potential coding opportunities, because among packets destined to the same nexthop at most two packets (the head packets of small-size and large-size queues) will be examined in the coding process, regardless of the number of flows. (3) The coding algorithm adopted in COPE is fast but cannot always find good solutions. In order to address the above limitations, in this paper we first present a new queueing structure for COPE, which can provide more potential coding opportunities, and then propose a new packet scheduling algorithm for this queueing structure to assign different priorities to different types of packets. Finally, we propose an efficient coding algorithm to find appropriate packets for coding. Simulation results demonstrate that this new COPE architecture can further greatly improve the node transmission efficiency.

We address the issue of MIMO channel estimation with the aid of a priori temporal correlation statistics of the channel as well as the spatial correlation. The temporal correlations are incorporated to the estimation scheme by assuming the Gauss-Markov channel model. Under the MMSE criteria, the Kalman filter performs an iterative optimal estimation. To take advantage of the enhanced estimation capability, we focus on the problem of channel estimation from a partial channel measurement in the MIMO antenna selection system. We discuss the optimal training sequence design, and also the optimal antenna subset selection for channel measurement based on the statistics. In a highly correlated channel, the estimation works even when the measurements from some antenna elements are omitted at each fading block.

Lower-dimensional transformations in similar sequence matching show different performance characteristics depending on the type of time-series data. In this paper we propose a hybrid approach that exploits multiple transformations at a time in a single hybrid index. This hybrid approach has advantages of exploiting the similar effect of using multiple transformations and reducing the index maintenance overhead. For this, we first propose a new notion of hybrid lower-dimensional transformation that extracts various features using different transformations. We next define the hybrid distance to compute the distance between the hybrid transformed points. We then formally prove that the hybrid approach performs similar sequence matching correctly. We also present the index building and similar sequence matching algorithms based on the hybrid transformation and distance. Experimental results show that our hybrid approach outperforms the single transformation-based approach.

For statistical language model training, target domain matched corpora are required. However, training corpora sometimes include both target domain matched and unmatched sentences. In such a case, training set selection is effective for both reducing model size and improving model performance. In this paper, training set selection method for statistical language model training is described. The method provides two advantages for training a language model. One is its capacity to improve the language model performance, and the other is its capacity to reduce computational loads for the language model. The method has four steps. 1) Sentence clustering is applied to all available corpora. 2) Language models are trained on each cluster. 3) Perplexity on the development set is calculated using the language models. 4) For the final language model training, we use the clusters whose language models yield low perplexities. The experimental results indicate that the language model trained on the data selected by our method gives lower perplexity on an open test set than a language model trained on all available corpora.

In this paper, we propose a novel Hidden-terminal and Exposed-terminal Interference aware routing metric (HEI-ETT) for Multi-Radio and Multi-Rate wireless mesh networks in which each stationary mesh node is equipped with multi-radio interfaces that relays traffic to the extend networks by using multi-hop transmissions. We have two main design goals for HEI-ETT. First, we will characterize interferences as Hidden-terminal Interference and Exposed-terminal Interference regardless of inter- or intra-flow interference and should take into account both interference effects while computing the path metric. Second, an efficient transmission rate adaptation should be employed in HEI-ETT to enhance the network throughput. We incorporated our metric in well known Optimized Link State Routing protocol version 2 (OLSRv2) which is one of the two standard routing protocols for MANETs and evaluated the performance of our metric by simulation. The results show that our metric outperforms existing metrics such as ETX, ETT and WCETT.

A fast and accurate method for Generalized Harmonic Analysis is proposed. The proposed method estimates the parameters of a sinusoid and subtracts it from a target signal one by one. The frequency of the sinusoid is estimated around a peak of Fourier spectrum using binary search. The binary search can control the trade-off between the frequency accuracy and the computation time. The amplitude and the phase are estimated to minimize the squared sum of the residue after extraction of estimated sinusoids from the target signal. Sinusoid parameters are recalculated to reduce errors introduced by the peak detection using windowed Discrete-Time Fourier Transform. Audio signals are analyzed by the proposed method, which confirms the accuracy compared to existing methods. The proposed algorithm has high degree of concurrency and is suitable to be implemented on Graphical Processing Unit (GPU). The computational throughput can be made higher than the input audio signal rate.

In this letter, a closed form approximation for the average sum rate of random beamforming is derived. It provides a near-exact approximation for arbitrary numbers of beams, users, and received SNR. The approximation is also applied to an average-sense multimode random beamforming scheme which optimizes the number of random beams without any type of instantaneous channel information. The proposed scheme shows better sum rate performance than random beamforming as well as an existing dual mode random beamforming scheme based on instantaneous channel information, while the number of feedback bits for beam index is reduced compared to random beamforming.

Two discrete-time Wirtinger-type inequalities relating the power of a finite-length signal to that of its circularly-convolved version are developed. The usual boundary conditions that accompany the existing Wirtinger-type inequalities are relaxed in the proposed inequalities and the equalizing sinusoidal signal is free to have an arbitrary phase angle. A measure of this sinusoidal signal's power, when corrupted with additive noise, is proposed. The application of the proposed measure, calculated as a ratio, in the evaluation of the power of a sinusoid of arbitrary phase with the angular frequency π/N, where N is the signal length, is thoroughly studied and analyzed under additive noise of arbitrary statistical characteristic. The ratio can be used to gauge the power of sinusoids of frequency π/N with a small amount of computation by referring to a ratio-versus-SNR curve and using it to make an estimation of the noise-corrupted sinusoid's SNR. The case of additive white noise is also analyzed. A sample permutation scheme followed by sign modulation is proposed for enlarging the class of target sinusoids to those with frequencies M π/N, where M and N are mutually prime positive integers. Tandem application of the proposed scheme and ratio offers a simple method to gauge the power of sinusoids buried in noise. The generalization of the inequalities to convolution kernels of higher orders as well as the simplification of the proposed inequalities have also been studied.

In multi-hop wireless networks, communication quality depends on the selection of a path between source and destination nodes from several candidate paths. Exploring how path selection affects communication quality is important to characterize the best path. To do this, in [1], we used expected transmission count (ETX) as a metric of communication quality and theoretically characterized minimum route ETX, which is the ETX of the best path, in a static one-dimensional random multi-hop network. In this paper, we characterize minimum route ETX in static two-dimensional multi-hop networks. We give the exact formula of minimum route ETX in a two-dimensional network, assuming that nodes are located with lattice structure and that the ETX function satisfies three conditions for simplifying analysis. This formula can be used as an upper bound of minimum route ETX without two of the three conditions. We show that this upper bound is close to minimum route ETX by comparing it with simulation results. Before deriving the formula, we also give the formula for a one-dimensional network where nodes are located at constant intervals. We also show that minimum route ETX in the lattice network is close to that in a two-dimensional random network if the node density is large, based on a comparison between the numerical and simulation results.

Similar to orthogonal frequency-division multiplexing (OFDM) systems, orthogonal frequency-division multiple access (OFDMA) is vulnerable to carrier frequency offset (CFO). Since the CFO of each user is different, CFO compensation in OFDMA uplink is much more involved than that in OFDM systems. It has been shown that the zero-forcing (ZF) compensation method is a simple yet effective remedy; however, it requires the inversion of a large matrix and the computational complexity can be very high. Recently, we have developed a low-complexity iterative method to alleviate this problem. In this paper, we consider the theoretical aspect of the algorithm. We specifically analyze the output signal-to-interference-plus-noise-ratio (SINR) of the algorithm. Two approaches are used for the analysis; one is simple but approximated, and the other is complicated but exact. The convergence problem is also discussed. In addition to the analysis, we propose a pre-compensation (PC) method enhancing the performance of the algorithm. Simulations show that our analysis is accurate and the PC method is effective.

In 2006, Tanaka has proposed an efficient variant of Maurer-Yacobi's identity-based non-interactive key sharing scheme. In Tanaka's scheme, the computational complexity to generate each user's secret information is much smaller than that of Maurer-Yacobi's scheme. Tanaka's original key sharing scheme does not provide completeness, and so Tanaka has corrected the original scheme to provide completeness. In this paper, we show that Tanaka's corrected key sharing scheme is not secure against collusion attacks. That is, two users can collaborate to factorize a system modulus with their secret information and thus break the key sharing scheme.

This work presents a full-chip RLC crosstalk budgeting routing flow to generate a high-quality routing design under stringent crosstalk constraints. Based on the cost function addressing the sensitive nets in visited global cells for each net, global routing can lower routing congestion as well as coupling effect. Crosstalk-driven track routing minimizes capacitive coupling effects and decreases inductive coupling effects by avoiding placing sensitive nets on adjacent tracks. To achieve inductive crosstalk budgeting optimization, the shield insertion problem can be solved with a minimum column covering algorithm which is undertaken following track routing to process nets with an excess of inductive crosstalk. The proposed routing flow method can identify the required number of shields more accurately, and process more complex routing problems than the linear programming (LP) methods. Results of this study demonstrate that the proposed approach can effectively and quickly lower inductive crosstalk by up to one-third.

In this letter, we propose a multi-cell cooperation method for broadcast packet transmission in the OFDM-based cellular system with multiple transmit antennas. In the proposed method, to transmit two streams of spatially demultiplexed or transmit diversity coded symbols, we divide a coded packet into subparts to each of which different cell group and antenna pairs are assigned. It is shown that the proposed method reduces the outage probability with only negligible increase in channel estimation.

With quick topology changes due to mobile node movement or signal fading in MANET environments, conventional routing restoration processes are costly to implement and may incur high flooding of network traffic overhead and long routing path latency. Adopting the traditional shortest path tree (SPT) recomputation and restoration schemes used in Internet routing protocols will not work effectively for MANET. An object of the next generation SPT restoration system is to provide a cost-effective solution using an adaptive learning control system, wherein the SPT restoration engine is able to skip over the heavy SPT computation. We proposed a novel SPT restoration scheme, called Cognitive Shortest Path Tree Restoration (CSPTR). CSPTR is designed based on the Network Simplex Method (NSM) and Sensitivity Analysis (SA) techniques to provide a comprehensive and low-cost link failure healing process. NSM is used to derive the shortest paths for each node, while the use of SA can greatly reduce the effort of unnecessary recomputation of the SPT when network topology changes. In practice, a SA range table is used to enable the learning capability of CSPTR. The performance of computing and communication overheads are compared with other two well-known schemes, such as Dijstra's algorithm and incremental OSPF. Results show that CSPTR can greatly eliminate unnecessary SPT recomputation and reduce large amounts of the flooding overheads.

In laparoscopic surgery, the lack of tactile sensation and 3D visual feedback make it difficult to identify the position of a blood vessel intraoperatively. An unintentional partial tear or complete rupture of a blood vessel may result in a serious complication; moreover, if the surgeon cannot manage this situation, open surgery will be necessary. Differentiation of arteries from veins and other structures and the ability to independently detect them has a variety of applications in surgical procedures involving the head, neck, lung, heart, abdomen, and extremities. We have used the artery's pulsatile movement to detect and differentiate arteries from veins. The algorithm for change detection in this study uses edge detection for unsupervised image registration. Changed regions are identified by subtracting the systolic and diastolic images. As a post-processing step, region properties, including color average, area, major and minor axis lengths, perimeter, and solidity, are used as inputs of the LVQ (Learning Vector Quantization) network. The output results in two object classes: arteries and non-artery regions. After post-processing, arteries can be detected in the laparoscopic field. The registration method used here is evaluated in comparison with other linear and nonlinear elastic methods. The performance of this method is evaluated for the detection of arteries in several laparoscopic surgeries on an animal model and on eleven human patients. The performance evaluation criteria are based on false negative and false positive rates. This algorithm is able to detect artery regions, even in cases where the arteries are obscured by other tissues.

A method for measuring similarity between two variables is presented. Our approach considers the case where available observations are arbitrarily filtered versions of the variables. In order to measure the similarity between the original variables from the observations, we propose an error-minimizing filter (EMF). The EMF is designed so that an error between outputs of the EMF is minimized. In this paper, the EMF is constructed by a finite impulse response (FIR) filter, and the error between the outputs is evaluated by the mean square error (EMF). We show that minimization of the MSE results in an eigenvalue problem, and the optimal solution is given in a closed form. We also reveal that the minimal MSE by the EMF is efficient in the measurement of the similarity from the viewpoint of a correlation coefficient between the originals.

Slotted wireless ad hoc networks are drawing more and more attention because of their advantage of QoS (Quality of Service) support for multimedia applications owing to their collision-free packet transmission. Time slot assignment is an unavoidable and important problem in such networks. The existing time slot assignment methods have in general a drawback of limited available bandwidth due to their local assignment optimization without the consideration of directions of the radio wave transmission of wireless links along the routes in such networks. A new time slot assignment is proposed in this paper in order to overcome this drawback. The proposed assignment is different from the existing methods in the following aspects: a) consideration of link directions during time slot assignment; b) largest bandwidth to be achieved; c) feasibility in resource limited ad hoc networks because of its fast assignment. Moreover, the effectiveness of the proposal is confirmed by some simulation results.

Although software-attack detection via dynamic taint analysis (DTA) supports high coverage of program execution, it prohibitively degrades the performance of the monitored program. This letter explores the possibility of collaborative dynamic taint analysis among members of an application community (AC): instead of full monitoring for every request at every instance of the AC, each member uses DTA for some fraction of the incoming requests, thereby loosening the burden of heavyweight monitoring. Our experimental results using a test AC based on the Apache web server show that speedy detection of worm outbreaks is feasible with application communities of medium size (i.e., 250-500).

An algorithm for blind signal separation (BSS) of convolutive mixtures is presented. In this algorithm, the BSS problem is treated as multidimensional independent component analysis (ICA) by introducing an extended signal vector which is composed of current and previous samples of signals. It is empirically known that a number of conventional ICA algorithms solve the multidimensional ICA problem up to permutation and scaling of signals. In this paper, we give theoretical justification for using any conventional ICA algorithm. Then, we discuss the remaining problems, i.e., permutation and scaling of signals. To solve the permutation problem, we propose a simple algorithm which classifies the signals obtained by a conventional ICA algorithm into mutually independent subsets by utilizing temporal structure of the signals. For the scaling problem, we prove that the method proposed by Koldovský and Tichavský is theoretically proper in respect of estimating filtered versions of source signals which are observed at sensors.

In this paper, we consider a dual-hop wireless cooperative network with amplify-and-forward (AF) relaying. The output signal-to-noise ratio (SNR) at the destination of the AF cooperative networks is in the form of the sum of harmonic mean of the source-relay channel SNR and the relay-destination channel SNR. Instead of deriving the exact probability density function (PDF) of the output SNR, we study the series expansion of this PDF around zero. This result is then applied to evaluate the performance of the AF cooperative systems over Nakagami-m fading channels, and closed-form high-SNR approximations of the average symbol error rate (SER) and the outage probability are derived. Next, we investigate the optimal power allocation (OPA) among the source node and the relays to minimize the approximate SER as well as the outage probability. It is shown that the optimal power allocation depends on the channel m parameters and the ratio of the source-relay channel gain to the relay-destination gain. In addition to the optimal power allocation, we also propose a low complexity sub-optimal power allocation (SubOPA) scheme. The performance improvement with optimal and sub-optimal power allocation is analyzed and validated by numeric results. It is shown that equal power allocation is near optimal when the relays are close to the source, while significant performance improvement is observed by both the optimal and sub-optimal power allocation schemes when the relays are close to the destination.