When performing measurements in an outdoor field environment, various interference factors occur. So, many studies have been performed to increase the accuracy of the localization. This paper presents a novel probability-based approach to estimating position based on Apollonius circles. The proposed algorithm is a modified method of existing trilateration techniques. This method does not need to know the exact transmission power of the source and does not require a calibration procedure. The proposed algorithm is verified in several typical environments, and simulation results show that the proposed method outperforms existing algorithms.

The non-homogeneous Poisson process (NHPP) has been applied successfully to model nonstationary counting phenomena for a large class of problems. In software reliability engineering, the NHPP-based software reliability models (SRMs) are of a very important class. Since NHPP is characterized by its rate (intensity) function, which is known as the software failure rate of NHPP-based SRM, it is of great interest to estimate accurately the rate function from observed software failure data. In the existing work the same authors introduced a Haar-wavelet-based technique for this problem and found that the Haar wavelet transform provided a very powerful performance in estimating software failure rate. In this paper, we consider the application potentiality of a Daubechies wavelet estimator in the estimation of software failure rate, given the software failure time data. We give practical solutions by overcoming technical difficulties in applying the Daubechies wavelet estimator to the real software failure time data.

Let (X,Y) be a Rd R-valued random vector. In regression analysis one wants to estimate the regression function m(x):=E(Y|X=x) from a data set. In this paper we consider the convergence rate of the error for the k nearest neighbor estimators in case that m is (p,C)-smooth. It is known that the minimax rate is unachievable by any k nearest neighbor estimator for p > 1.5 and d=1. We generalize this result to any d ≥ 1. Throughout this paper, we assume that the data is independent and identically distributed and as an error criterion we use the expected L2 error.

We propose a method of controlling the view divergence of data freshness when copies of sites in a replicated database are updated asynchronously. The view divergence of the replicated data freshness is the difference in the recentness of the updates reflected in the data acquired by clients. Our method accesses multiple sites and provides a client with data that reflects all the updates received by the sites. First, we define the probabilistic recentness of updates reflected in acquired data as read data freshness (RDF). The degree of RDF of data acquired by clients is the range of view divergence. Second, we propose a way to select sites in a replicated database by using the probability distribution of the update delays so that the data acquired by a client satisfies its required RDF. This way calculates the minimum number of sites in order to reduce the overhead of read transactions. Our method continues to adaptively and reliably provide data that meets the client's requirements in an environment where the delay of update propagation varies and applications' requirements change depending on the situation. Finally, we evaluate by simulation the view divergence we can control using our method. The simulation showed that our method can control the view divergence to about 1/4 that of a normal read transaction for 100 replicas. In addition, the increase in the overhead of a read transaction imposed by our method is not as much as the increase in the total number of replicas.

Radars utilizing ultra-wide-band (UWB) pulses are attractive as an environment measurement method for various applications including household robots. Suitable filtering is essential for accurate ranging, which requires an accurate waveform estimation. This paper presents a high-resolution algorithm of estimating target location and scattered waveforms, whose accuracies are interdependent. The technique relies on iterative improvements of estimated waveforms. Description of the algorithm is followed by statistical simulation examples. The performance of the algorithm is contrasted with conventional ones and statistical bounds. Results indicate that our proposed algorithm has a remarkable performance, which is close to the theoretical limit. Next, we clarify the problem of applying HCT to multiple targets. HCT for multiple targets can not be used as an estimated waveform because of interference waves from other targets. We propose an interference suppression algorithm based on a neural network, and show an application example of the algorithm.

Environment measurement is an important issue for various applications including household robots. Pulse radars are promising candidates in a near future. Estimating target shapes using waveform data, which we obtain by scanning an omni-directional antenna, is known as one of ill-posed inverse problems. Parametric methods such as Model-fitting method have problems concerning calculation time and stability. We propose a non-parametric algorithm for high-resolution estimation of target shapes in order to solve the problems of parametric algorithms.

Software rejuvenation is a proactive fault management technique that has been extensively studied in the recent literature. In this paper, we focus on an example for a telecommunication billing application considered in Huang et al. (1995) and develop the discrete-time stochastic models to estimate the optimal software rejuvenation schedule. More precisely, two software availability models with rejuvenation are formulated via the discrete semi-Markov processes, and the optimal software rejuvenation schedules which maximize the steady-state availabilities are derived analytically. Further, we develop statistically non-parametric algorithms to estimate the optimal software rejuvenation schedules, provided that the complete sample data of failure times are given. Then, a new statistical device, called the discrete total time on test statistics, is introduced. Finally, we examine asymptotic properties for the statistical estimation algorithms proposed in this paper through a simulation experiment.

In this paper, we consider the similar software cost models with periodic rejuvenation to Garg, Puliafito, Telek and Trivedi (1995) under the cost effectiveness criteria. First, an alternative model as well as the original one are analyzed by Markov regenerative processes. We derive analytically the optimal periodic software rejuvenation policies which maximize the cost-effectiveness in the steady state for two models. Further, we develop statistical non-parametric algorithms to estimate the optimal software rejuvenation policies, provided that the sample data to characterize the system failure times are given. Then, the total time on test (TTT) concept is used. In numerical examples, we compare the periodic software rejuvenation policy with the non-periodic one, and investigate the asymptotic properties of the non-parametric estimators for the optimal software rejuvenation policies through a simulation experiment.

Since the detection of optical flow (two-dimensional motion field on an image) from image sequences is essentially an ill-posed problem, most of the conventional methods use a smoothness constraint for optical flow heuristically and detect reasonable optical flow. However, little discussion exists regarding the degree of smoothness. Furthermore, to recover the relative three-dimensional motion and depth between a camera and a rigid object, in general at first, the optical flow is detected without a rigid motion constraint, and next, the motion and depth are estimated using the detected optical flow. Rigorously speaking, the optical flow should be detected with such a constraint, and consequently three-dimensional motion and depth should be determined. To solve these problems, in this paper, we apply a parametric model to an optical flow, and construct an estimation algorithm based on this model.

This paper presents a new algorithm that can solve the problem of selecting appropriate update step size in the LMS algorithm. The proposed algorithm, called a Complementary Pair LMS (CP-LMS) algorithm, consists of two adaptive filters with different update step sizes operating in parallel, one filter re-initializing the other with the better coefficient estimates whenever possible. This new algorithm provides the faster convergence speed and the smaller steady-state error than those of a single filter with a fixed or variable step size.