This paper proposes a new self-healing scheme that differentiates the bandwidth requirement for each network service on ATM networks. First, we show the necessity of our proposed scheme. In the future network, we must satisfy two demands, rapid restoration from failure and differentiated bandwidth requirements. The conventional restoration scheme, called the self-healing scheme, realizes rapid restoration, but does not support bandwidth differentiation; the new self-healing scheme proposed herein does. We also show that the proposed scheme reduces the spare resources required for backup. The scheme can be realized as a simple extension of the conventional self-healing scheme. Finally, simulations show that the proposed scheme requires fewer spare resources while offering comparable restoration time to the conventional approach against any demand pattern.

The purpose of this paper is to deal with the problem of recovering a signal from its noisy version. One example is to restore old images degraded by noise. The recovery solution is given within the framework of series expansion and we shall show that for the general case the recovery functions have to be elements of an extended pseudo biorthogonal basis (EPBOB) in order to suppress efficiently the corruption noise. After we discuss the different situations of noise, we provide some methods to construct the optimal EPBOB in order to deal with these situations.

In this paper, we present two parallel algorithms for computing the all nearest neighbors of an n n binary image on the Bulk-Synchronous Parallel(BSP) model. The BSP model is an asynchronous parallel computing model, where its communication features are abstracted by two parameters L and g: L denotes synchronization periodicity and g denotes a reciprocal of communication bandwidth. We propose two parallel algorithms for the all nearest neighbor problems based on two distance metrics. The first algorithm is for Lp distance, and the second algorithm is for weighted distance. Both two algorithms run in O(n2/p + L) computation time and in O(g(n/p) + L) communication time using p (1 p n) processors and in O(n2/p + (d+L)(log(p/n)/log(d+1))) computation time and in O(g(n/p) + (gd+L)(log(p/n)/log(d+1))) communication time using p (n< p n2) processors on the BSP model, for any integer d(1 dp/n).

A nonlinear inverse filter is proposed for restoring signals degraded by a linear system and additive Gaussian noise. The proposed filter consists of combination of a linear high pass filter and an ε-filter, which is modified from the cascaded linear filter. The nonlinear property of the ε-filter is utilized to suppress pre-enhanced additive random noise and to restore sharp edges. It is demonstrated that the filter can be reduced to a multi-layered neural network model, and the optimal design is described by using the back propagation algorithm. The nonlinear function is approximated by a piecewise linear function, which results in simple and robust training algorithm. An application to image restoration is also presented, illustrating the effectiveness over the linear filter, especially when the amplitude of additive noise is small.

The ATM multicast Tree (AMT) is the Mbone of video/audio conferencing and other multicasting applications in ATM (Asynchronous Transfer Mode) networks. However, real problems such as temporarily moving switches, changing optic fiber connections and/or tangible/intangible failures of ATM networks will cause many service disruptions. Thus we must carefully consider the system's SQOS (Survivable QOS) when we construct the system. A point-to-point self-healing scheme utilizing a conventional pre-planned backup mechanism is proposed to protect the AMT from failure. This scheme uses point-to-point pre-planned backup Root-to-Leaf Routes (RLR) as the root-to-leaf structure of an AMT. Though AMT protection via preplanned backup RLR requires no search time, duplicate paths may cause redundant bandwidth consumption. This paper also proposes a closest-node method, which can locate the minimum-length route structure during the initial design and also rebuild the AMT in the event of a network failure. To enhance the survivability of the system, we introduce two near optimal re-routing algorithms, a most-decent search algorithm, and also a predictive-decent search algorithm in order to find the minimum lost flow requirement. These near optimal schemes use search technique to guide the local optimal lost flow to the most-decent lost flow direction. The predictive way is an especially economical technique to reduce the calculation complexity of lost flow function. For the evaluation of the feasibility and performance of the new schemes, we simulate AMT restoration and the simulation results show the closest-node scheme provides superior AMT restoration compared to a system with a preplanned point-to-point backup scheme. In addition, the predictive-decent search algorithm is faster than the most-decent search one.

In the present paper we shall examine the real-time restoration of biomedical signals under additive noises. Biomedical signals measured by instruments such as catheter manometers, ambulatory electrocardiographs and thermo-dilution sensors are susceptible to distortion and noise. Therefore, such signals must be restored to their original states. In the present study, nonstationary biomedical signals are observed and described using a mathematical model, and several restoration filters that are composed of a series of applications of this model are proposed. These filters restored band-limited approximations of the original signals in real-time. In addition, redundancy is introduced into these restoration filters in order to suppress additive noise. Finally, an optimum filter that accounts for restoration error and additive noise is proposed.

Recently, Kundur and Hatzinakos showed that a linear restoration filter designed by using the almost obvious a priori knowledge on the original image, such as (i) nonnegativity of the true image and (ii) the smallest rectangle encompassing the original object, can realize a remarkable performance for a blind image deconvolution problem. In this paper, we propose a new set-theoretic blind image deconvolution scheme based on a recently developed convex projection technique called Hybrid Steepest Descent Method (HSDM), where some partial information can be utilized set-theoretically by parallel projections onto convex sets while the others are incorporated in a cost function to be minimized by a steepest descent method. Numerical comparisons with the standard set-theoretic scheme based on POCS illustrate the effectiveness of the proposed scheme.

A new motion analysis method and an image restoration process for removing motion blur are proposed. Motion analysis includes the motion estimation and motion-based segmentation. Based on the analysis, we can obtain an image divided into multiple segments with different point spread functions. For removing motion blur, we propose an image degradation model for the motion with an arbitrary direction and a regularized iterative restoration method. By using the proposed degradation model and the restoration method, we can efficiently remove the space-variant motion blur.

This paper presents a new method for image interpolation based on truncated projections onto convex sets (POCS). By using the convergence property to properly defined convex sets, the proposed algorithm can restore high frequency details in the original high resolution image. In order to apply the POCS method to the interpolation procedure, we first present a two-dimensional separable image degradation model for a low resolution imaging system. According to the model, we propose a truncated POCS-based spatial interpolation algorithm for image sequences. Experimental results with synthetic and real image sequence show that the proposed algorithm gives indiscernible interpolation performance compared with the conventional POCS-base algorithm, while it significantly reduces computational complexity and is suitable for processing image sequences.

The self-healing capability against network failure is one of indispensable features for the B-ISDN infrastructure. One problem in realizing such self-healing backbone network is the inefficient utilization of the large spare capacity designed for the failure-restoration purpose since it will be used only in the failure time that does not occur frequently. "Pool-capacity" is the concept that allows some VPs (virtual paths) to efficiently utilize this spare capacity part. Although the total capacity can be saved by using the "Pool Capacity," it is paid by less reliability of VPs caused by the emerging influence of indirect-failure. Thus, this influence of indirect-failure has to be considered in the capacity designing process so that network-designers can trade off the saving of capacity with the reliability level of VPs in their self-healing networks. In this paper, Damage Rate:DR which is the index to indicate the level of the influence caused by indirect-failure is defined and the pool-capacity design scheme with DR consideration is proposed. By the proposed scheme, the self-healing network with different cost (pool-capacity) can be designed according to the reliability level of VPs.

Effects of the model order estimation error in the TLS-ESPRIT algorithm were investigated. It was found that if the model order is overestimated true signal parameters are preserved even though spurious signals of which power values are negligibly small appear, whereas if the model order is underestimated some signals degenerate to each others, resulting in the erroneous estimates.

Network survivability against various unexpected failures is one of indispensable technologies for the B-ISDN infrastructure. Self-healing algorithm is the technique to automatically restore the failed VP's (virtual paths) in the backbone ATM network. Since the B-ISDN transports various kinds of traffic with various levels of priority (Grade of Service: GoS), the effective self-healing algorithm should orderly restore the failed VP's based on the priority of their traversing traffic. This paper proposes the priority based restoring self-healing algorithm, which realizes the priority based restoring function by the two-timer mechanisms and a simple capacity reserving protocol. The simulation results show that the proposed algorithm can schedule the restoration process so that the failed VP's with higher priority are restored before the others with lower priority. In addition, the significant improvement in restoration speed for the highest priority traffic class has been achieved.

A multicarrier modulation called orthogonal frequency division multiplex (OFDM) is attracting attention as a transmission scheme which is robust against multipath propagation. A major disadvantage of OFDM is that it is sensitive to nonlinear distortion due to its wide transmission amplitude range. The scope of this study is to cope with the nonlinear problem. We propose a nonlinear distortion compensation scheme using an iterative method which has been applied to an image signal restoration.

In the future broadband networks, multicast services such as video conferencing and distance learning will become increasingly important. To support these multimedia services, one solution is to form an AMT(ATM Multicast Tree)to connect all the conferencing members. In this paper, based on AMT survivability requirements, we investigate the self-healing of an AMT. Self-healing on AMT is a new challenge of survivability of multimedia services. The pre-assign way is a method we usually considered on protection. If we construct a disjoint backup tree, the low building probability and complicated loading on constructing is the first problem. Second, if only one link or node failed on an AMT, we need to reroute links and reserve bandwidth on whole backup tree. Moreover, since the AMT usually transmits video images, the restoration rate will be decreased because even only one branch of backup tree does not endure the required bandwidth. These enhance us to restore the AMT by dynamic restoration scheme. Two proposed dynamic restoration schemes are developed to provide prioritized restoration from a link or node failure. In the first scheme, we apply a link-based restoration scheme on the AMT. The restoration is based on the failed links of network and does not take whole AMT into account. In the second scheme, without changing the multicast services to the members, we allow reconfiguration of the AMT during the restoration phase. Reconfiguration of the AMT is based on a tree-based restoration concept. By computer simulations, we verify the characteristics of the proposed schemes and the results show that the second scheme outperforms the first.

Image restoration using estimated parameters of image model and noise statistics is presented. The image is modeled as the output of a 2-D noncausal autoregressive (NCAR) model. The parameter estimation process is done by using the autocorrelation function and a biased term to a conventional least-squares (LS) method for the noncausal modeling. It is shown that the proposed method gives better results than the other parameter estimation methods which ignore the presence of the noise in the observation data. An appropriate image model selection process is also presented. A genetic algorithm (GA) for solving a multiobjective function with single constraint is discussed.

A function approximation scheme for image restoration is presented to resolve conflicting demands for smoothing within each object and differentiation between objects. Images are defined by probability distributions in the augmented functional space composed of image values and image planes. According to the fuzzy Hough transform, the probability distribution is assumed to take a robust form and its local maxima are extracted to yield restored images. This statistical scheme is implemented by a feedforward neural network composed of radial basis function neurons and a local winner-takes-all subnetwork.

Antidiffusion is a process running the diffusion equation reversely in the time domain. Though extremely important for image restoration of the Gaussian blur, it is a horribly ill-posed problem, since minor noise leads to very erroneous results. To solve this ill-posed problem stably, in this paper we first apply a multiscale method to decompose images into various scale components using the Gaussian and Laplacian of Gaussian (LOG) filters. We then show that the restored images can be reconstructed from the components using shrunk Gaussian and LOG filters. Our algorithm has a closed form solution, and is robust to noise because it is performed by the integration computation (convolution), contrasting with the differential computation required by direct discretization of the antidiffusion equation. The antidiffusion algorithm is also computationally efficient since the convolution is row and column separable. Finally, a comparison between the algorithm and the well-known Wiener filter is conducted. Experiments show that our algorithm is really stable and images can be restored satisfactorily.

Almost all broadcasting systems and their equipment would be digitalized in the near future. In Japan, investigation of digital broadcasting has been going on for a long time, aiming at a realization of improvement of picture quality, new services, system flexibility, etc. Japanese digital broadcasting systems under development have a lot of technical merits, for example, a high transmission capacity and a hierarchical transmission scheme for satellite, and mobile reception for terrestrial digital broadcasting systems, compared to conventional digital systems.

In B-ISDN, network reliability is a very significant theme. This paper proposes the Failure-Resistant Virtual Path (FRVP) scheme that prevents any information loss even with network failure, to realize a high-end reliability service in B-ISDN. The FRVP scheme is based on simple parallel transmission established using the superior characteristics of ATM. In the FRVP scheme, the transmitter duplicates user cells and transmits them across several VPs simultaneously. The receiver chooses the perfect cells and sends them to the user. As a result, the cell stream output by the receiver is not affected by VP failure if at least one VP remains active. I develop a prototype FRVP system and conduct field trials using NTT's nationwide ATM testbed network. The FRVP scheme is shown to achieve extremely-reliable ATM networks and services.

A deterministic pushdown automaton (dpda) having just one stack symbol is called a deterministic restricted one-counter automaton (droca). A deterministic one-counter automaton (doca) is a dpda having only one stack symbol, with the exception of a bottom-of-stack marker. The class of languages accepted by droca's which accept by final state is a proper subclass of the class of languages accepted by doca's. Valiant has proved the decidability of the equivalence problem for doca's and the undecidability of the inclusion problem for doca's. Thus the decidability of the equivalence problem for droca's is obvious. In this paper, we evaluate the upper bound of the length of the shortest input string (shortest witness) that disproves the inclusion for a pair of real-time droca's which accept by accept mode, and present a direct branching algorithm for checking the inclusion for a pair of languages accepted by these droca's. Then we show that the worst-case time complexity of our algorithm is polynomial in the size of these droca's.