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.

Transmit diversity gain can be obtained in cooperative communication by cooperating the multiple users with single antenna. In cooperative communication, in the first step, each mobile station (MS) transmits its own data to both the base station (BS) and the other MS. In the second step, each MS's data is transmitted from the other MS to BS. As a result, transmit diversity gain can be obtained without implementing multiple transmit antennas at MS. In the conventional relay method, if error is detected within the received packet by using cyclic redundancy check (CRC) code, MS transmits its own data to BS instead of relaying the other MS's data in the second step. As a result, transmit diversity gain cannot be obtained. In this paper, we propose a novel cooperative method. In the proposed method, if the CRC decoder detects error within the received packet, MS transmits soft decision symbol which is obtained from the decoded data in second step. As a result, the transmit diversity gain always can be obtained. From the computer simulation, we show that the proposed method can achieve the better error rate performance than the conventional one.

In order to obtain better generalization performance in supervised learning, model parameters should be determined appropriately, i.e., they should be determined so that the generalization error is minimized. However, since the generalization error is inaccessible in practice, the model parameters are usually determined so that an estimator of the generalization error is minimized. The regularized subspace information criterion (RSIC) is such a generalization error estimator for model selection. RSIC includes an additional regularization parameter and it should be determined appropriately for better model selection. A meta-criterion for determining the regularization parameter has also been proposed and shown to be useful in practice. In this paper, we show that there are several drawbacks in the existing meta-criterion and give an alternative meta-criterion that can solve the problems. Through simulations, we show that the use of the new meta-criterion further improves the model selection performance.

This paper presents 3D keyframe animation watermarking using orientation interpolators. 3D keyframe animation consists of a number of transform nodes, including a geometrical node from the initial model and several interpolator nodes that represent object movement. Therefore, the proposed algorithm randomly selects transform nodes with orientation interpolator nodes, then resamples the quaternion components to maintain a uniform key time. Thereafter, watermark bits are embedded into quaternion components with large rotation angles. Experimental results verify the robustness of the proposed algorithm to geometrical and timeline attacks, along with the subjective and objective quality of its invisibility.

In this paper, we describe a single system image (SSI) architecture for distributed systems. The SSI architecture is constructed through three components: single process space (SPS), process migration, and dynamic load balancing. These components attempt to share all available resources in the cluster among all executing processes, so that the distributed system operates like a single node with much more computing power. To this end, we first resolve broken pipe problems and bind errors on server socket in process migration. Second, we realize SPS based on block process identifier (PID) allocation. Finally, we design and implement a dynamic load balancing scheme. The dynamic load balancing scheme exploits our novel metric, effective tasks, to effectively distribute jobs to a large distributed system. The experimental results show that these three components present scalability, new functionality, and performance improvement in distributed systems.

In OFDMA systems, various subcarrier allocation (SA) algorithms have been developed and adopted to realize the low-cost implementation or the optimized usage of resources, such as bandwidth and total transmit power, at the cost of increased complexity. Regardless of SA algorithms, however, it is computationally inefficient for a user who uses only a small number of subcarriers to use a full fast Fourier transform (FFT) for multicarrier demodulation at a conventional receiver in a downlink environment. In response, this letter proposes a novel low complexity FFT scheme that demodulates a set of desired multicarriers with smaller-size FFTs is proposed for computationally efficient and/or low hardware-cost receiver in OFDMA systems. Furthermore, a decision rule for the optimum size of FFT in the known transform decomposition methods is provided.

This letter concerns the Schur stability of convex combinations of complex polynomials. For given two Schur stable complex polynomials, a sufficient condition is given such that the convex combination of polynomials is also Schur stable.

This letter proposes an integrated authentication model for NGN to access the wired/wireless infrastructure. The existing authentication model based on port-based access control is not best suited for integrated network environments. The main focus of this paper is on developing a feasible authentication model using the authenticator positioned in the control function in the NGN.

This work is concerned with the problem of robot localization using standard RFID tags as landmarks and an RFID reader as a landmark sensor. A main advantage of such an RFID-based localization system is the availability of landmark ID measurement, which trivially solves the data association problem. While the main drawback of an RFID system is its low spatial accuracy. The result in this paper is an improvement of the localization accuracy for a standard short-range RFID sensor. One of the main contributions is a proposal of a machine learning approach in which multiple classifiers are trained to distinguish RFID-signal features of each location. Another contribution is a design tool for tag arrangement by which the tag configuration needs not be manually designed by the user, but can be automatically recommended by the system. The effectiveness of the proposed technique is evaluated experimentally with a real mobile robot and an RFID system.

In this paper, we present a new method to enhance classification performance of a multiple classifier system by combining a boosting technique called AdaBoost.M2 and Kernel Discriminant Analysis (KDA). To reduce the dependency between classifier outputs and to speed up the learning, each classifier is trained in a different feature space, which is obtained by applying KDA to a small set of hard-to-classify training samples. The training of the system is conducted based on AdaBoost.M2, and the classifiers are implemented by Radial Basis Function networks. To perform KDA at every boosting round in a realistic time scale, a new kernel selection method based on the class separability measure is proposed. Furthermore, a new criterion of the training convergence is also proposed to acquire good classification performance with fewer boosting rounds. To evaluate the proposed method, several experiments are carried out using standard evaluation datasets. The experimental results demonstrate that the proposed method can select an optimal kernel parameter more efficiently than the conventional cross-validation method, and that the training of boosting classifiers is terminated with a fairly small number of rounds to attain good classification accuracy. For multi-class classification problems, the proposed method outperforms both Boosting Linear Discriminant Analysis (BLDA) and Radial-Basis Function Network (RBFN) with regard to the classification accuracy. On the other hand, the performance evaluation for 2-class problems shows that the advantage of the proposed BKDA against BLDA and RBFN depends on the datasets.

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.

In the current broadcasting system or Internet content distribution system, content providers distribute decoders (STB) that contain secret keys for content decryption, prior to content distribution. A content provider sends encrypted content to each user, who then decodes it with his or her STB. While users can get the services at their houses if they have an STB, it is hard for them to get the services outside their houses. A system that allowed users to carry around their secret keys would improve usability, but it would require countermeasures against secret key exposure. In this paper, we propose such an extended broadcasting system using tokens and group signature. The content providers can control the number of keys that users can use outside their houses. The system enables the broadcasters to minimize the damage caused by group signature key exposures and the user to get services outside his or her home.

To describe joint time- and frequency-selective (doubly-selective) channels in mobile broadband wireless communications, we propose to use the finite parameter model based on the same Bessel functions for each tap (Bessel model). An expression of channel estimation mean squared error (MSE) based on the finite parameter models in Orthogonal Frequency Division Multiplexing (OFDM) systems is derived. Then, our Bessel model is compared with commonly used finite parameter models in terms of the channel estimation MSE. Even if the channel taps have different channel correlations and some of the taps do not coincide with the Bessel function, the channel estimation MSE of the Bessel model is shown to be comparable or outperform existing models as validated by Monte-Carlo simulations over an ensemble of channels in typical urban and suburban environments.

In order to obtain better learning results in supervised learning, it is important to choose model parameters appropriately. Model selection is usually carried out by preparing a finite set of model candidates, estimating a generalization error for each candidate, and choosing the best one from the candidates. If the number of candidates is increased in this procedure, the optimization quality may be improved. However, this in turn increases the computational cost. In this paper, we focus on a generalization error estimator called the regularized subspace information criterion and derive an analytic form of the optimal model parameter over a set of infinitely many model candidates. This allows us to maximize the optimization quality while the computational cost is kept moderate.

This paper tries to answer the following question: What type of support should be given to an automobile driver when it is determined, via some method to monitor the driver's behavior and the traffic environment, that the driver's intent may not be appropriate to a traffic condition? With a medium fidelity, moving-base driving simulator, three conditions were compared: (a) Warning type support in which an auditory warning is given to the driver to enhance his/her situation recognition, (b) action type support in which an autonomous safety control action is executed to avoid an accident, and (c) the baseline condition in which no driver support is given. Results were as follows: (1) Either type of driver support was effective in accident prevention. (2) Acceptance of driver support functions varied context dependently. (3) Participants accepted a system-initiated automation invocation as long as no automation surprises were possible to occur.

In this paper, the cross-correlation distribution between a p-ary m-sequence s(t) and its p + 1 distinct decimated sequences s(dt + l) is derived. For an odd prime p, an even integer n, and d = pk +1 with gcd(n, k) = 1, there are p + 1 distinct decimated sequences s(dt + l), 0 ≤ l < p + 1, for a p-ary m-sequence s(t) of period pn -1 because gcd(d, pn - 1) = p + 1. The maximum magnitude of their cross-correlation values is 1 + p if l ≡ 0 mod p + 1 for n ≡ 0 mod 4 or l ≡ (p + 1)/2 mod p + 1 for n ≡ 2 mod 4 and otherwise, 1 + . Also by using s(t) and s(dt + l), a new family of p-ary sequences of period pn -1 is constructed, whose family size is pn and Cmax is 1 + p.

Synchronization has gained attention in science recently. In electrical engineering, a power network requires generators and loads to be in synchronization. The increase in distributed dc power sources without sophisticated controls has made synchronization more difficult. This paper proposes a synchronous inverter designed for linking distributed power sources within a power network. The linkage inverters should have high confidential characteristics to keep the network stable. The frequency and phase synchronization between synchronous generators in power network has been acheived through power transmission systems. The synchronous inverters contribute in the development of the sophisticated power networks by providing distributed power sources that maintain synchronous operation.

In recent years, algorithms based on Computer Algebra ([1]-[3]) have been introduced into a range of control design problems because of the capacity to handle unknown parameters as indeterminates. This feature of algorithms in Computer Algebra reduces the costs of computer simulation and the trial and error process involved, enabling us to design and analyze systems more theoretically with the behavior of given parameters. In this paper, we apply Computer Algebra algorithms to H∞ control theory, representing one of the most successful achievements in post-modern control theory. More specifically, we consider the H∞ norm minimization problem using a state feedback controller. This problem can be formulated as follows: Suppose that we are given a plant described by the linear differential equation = Ax + B1w + B2u, z = Cx + Du, where A,B1,B2,C,D are matrices whose entries are polynomial in an unknown parameter k. We apply a state feedback controller u = -F x to the plant, where F is a design parameter, and obtain the system = (A - B2F)x + B1w, z =(C - DF)x. Our task is to compute the minimum H∞ norm of the transfer function G(s)(=(C - DF)(sI - A + B2F)-1B1) from w to z achieved using a static feedback controller u = -Fx, where F is a constant matrix. In the H∞ control theory, it is only possible to check if there is a controller such that ||G(s)||∞ < γ is satisfied for a given number γ, where ||G(s)||∞ denotes the H∞ norm of the transfer function G(s). Thus, a typical procedure to solve the H∞ optimal problem would involve a bisection method, which cannot be applied to plants with parameters. In this paper, we present a new method of solving the H∞ norm minimization problem that can be applied to plants with parameters. This method utilizes QE (Quantifier Elimination) and a variable elimination technique in Computer Algebra, and expresses the minimum of the H∞ norm as a root of a bivariate polynomial. We also present a numerical example to illustrate each step of the algorithm.

The power reduction of display devices has become an important issue for extending battery life and running time when they are used in digital multimedia broadcasting (DMB) mobile phones. DMB mobile phones generally use 16-bit data per pixel to reduce power consumption even though a liquid crystal display (LCD) graphic controller can support 16-, 18-, and 24-bit data per pixel. Also, the total transmission time of 16-bit data per pixel is only half that for 18- and 24-bit data per pixel. Decoded 24-bit image data in the frame memory of a DMB decoder are asymmetrically truncated to 16-bit image data. This results in a lack of smoothness such as blocking effects and/or pseudo edge artifacts. To solve these problems, the author proposes and implements a new asymmetric pixel data truncation error compensation algorithm using 1-bit least significant bit (LSB) data expansion with correlated color information for the purpose of ensuring smoothness. In the experimental results, the proposed algorithm is able to correct various artifacts.

Wireless mesh networks are attracting more and more attention as a promising technology for the next generation access infrastructure. QoS support is a unavoidable task given the rising popularity of multimedia applications, and also a challenging task for multi-hop wireless mesh networks. Among the numerous QoS factors, end-to-end delay is one of the most critical and important issues, especially for the real time applications. Over multi-hop wireless mesh networks, end-to-end delay of a flow is highly dependant on the number of hops as well as congestion condition of the hop nodes that the flow traverses through. In this paper, we propose QoS priority control schemes based on the end-to-end QoS delay metrics in order to increase traffic accommodation, i.e., the numbers of real-time flows which satisfy the requirements of end-to-end delay and packet delivery ratio over multi-hop wireless mesh networks. The first scheme enables source and forwarding nodes to perform priority control based on the number of hops of routes. The second scheme enables nodes to perform priority control based on the congestion condition of the hop nodes, where the flow traverses through. The effectiveness of the proposed schemes is investigated with NS-2 network simulator for voice and video traffics over multi-hop wireless mesh networks. Simulation results show that the scheme greatly improves the traffic accommodation for voice and video applications in multi-hop wireless mesh networks.