In this paper we study traffic engineering in Multiprotocol Label Switching (MPLS) networks. We consider off-line computation of disjoint working and restoration paths where path rerouting is used as the restoration scheme. We first compute maximum number of paths for each demand such that paths satisfy diversity requirements. Using the generated path set we study four different approaches for selecting working and restoration paths, and formulate each method as an Integer Linear Programming (ILP) problem. The first two methods treat working and restoration path design problems separately. We propose two new path design methods that jointly optimize the working and restoration paths. A traffic uncertainty model is developed in order to evaluate performances of these four approaches based on their robustness with respect to changing traffic patterns. We compare these design approaches based on the number of additional demands carried and the distribution of residual capacity over the network. It is shown through simulations that the weighted load balancing method proposed in this paper outperforms the other three methods in handling traffic demand uncertainty.

In this letter, our proposed approach exploits the use of original and simplest Cellular Neural Network (CNN) for 2D Doubly Complementary (DC) Infinite Impulse Response (IIR) filter banks design. The properties of feedback and feedforward templates are studied for this purpose. Through some examples it is shown how generalizations of these templates can be used for DC IIR filter banks design. We modify Lagrangian function which is used for optimizing a low-pass filter design considering the constraint for stability of CNN. The brief conclusions with design examples that illustrate the proposed method and an image enhancement and restoration applications of designed filter banks are presented.

A new blind restoration method applying Real-coded genetic algorithm (RcGA) will be proposed, and this method will be proven valid for the blurred image restoration with unidentified degradation in the experiments. In this restoration method, the degraded and blurred image is going to get restricted to the images possible to be expressed in the point spread function (PSF), then the restoration filter for this degraded image, which is also the 2-dimentional inverse filter, will be searched among several points applying RcGA. The method will enable to seek efficiently among vast solution space consists of numeral coefficient filters. And perceiving the essential features of the spectrum in the frequency space, an evaluation function will be proposed. Also, it will be proposed to apply the Rolling-ball transform succeeding an appropriate Gaussian degrade function against the dual degraded image with blur convoluting impulse noise. By above stated features of this restoration method, it will enable to restore the degraded image closer to the original within a practical processing time. Computer simulations verify this method for image restoration problem when the factors causing image distortions are not identified.

In interconnection networks, deadlock recovery has been studied in routing strategy. The routing strategy for the deadlock recovery is intended to optimize the routing performance when deadlocks do not occur. On the other hand, it is important to improve the routing performance by handling deadlocks if they occur. In this paper, a routing strategy for suspensive deadlock recovery called an escape-restoration routing is proposed and its performance is evaluated. In the principle of the proposed techniques, a small amount of exclusive buffer (escape-buffer) at each router is prepared for handling one of deadlocked packets. The transmission of the packet is suspended by temporarily escaping it to the escape-buffer. After the other deadlocked packets were sent, the suspended transmission resumes by restoring the escaped packet. Evaluation results show that the proposed techniques can improve the routing performance more than that of the previous recovery-based techniques in handling deadlocks.

Practical image restoration filters usually include a parameter that controls regularizability, trade-off between fidelity of a restored image and smoothness of it, and so on. Many criteria for choosing such a parameter have been proposed. However, the relation between these criteria and the squared error of a restored image, which is usually used to evaluate the restoration performance, has not been theoretically substantiated. Sugiyama and Ogawa proposed the subspace information criterion (SIC) for model selection of supervised learning problems and showed that the SIC is an unbiased estimator of the expected squared error between the unknown model function and an estimated one. They also applied it to restoration of images. However, we need an unbiased estimator of the unknown original image to construct the criterion, so it can not be used for general situations. In this paper, we present a modified version of the SIC as a new criterion for choosing a parameter of image restoration filters. Some numerical examples are also shown to verify the efficacy of the proposed criterion.

We propose a new solvable Markov random field model for Bayesian image processing and give the exact expressions of the marginal likelihood and the restored image by using the multi-dimensional Gaussian formula and the discrete Fourier transform. The proposed Markov random field model includes the conditional autoregressive model and the simultaneous autoregressive model as a special case. The estimates of hyperparameters are obtained by maximizing the marginal likelihood. We study some statistical properties of the solvable Markov random field model. In some numerical experiments for standard images, we show that the proposed Markov random field model is useful for practical applications in image restorations. The investigation of probabilistic information processing by means of a solvable probabilistic model is recently in progress not only for image processing but also for error correcting codes and so on. The solvable probabilistic model gives us some important aspects for the availability of probabilistic computational systems.

A new class of least-squares algorithms is presented for adaptive filtering. The idea is to use a fixed set of directions and perform line search with one direction at a time in a cyclic fashion. These algorithms are called Euclidean Direction Search (EDS) algorithms. The fast version of this class is called the Fast-EDS or FEDS algorithm. It is shown to have O(N) computational complexity and a convergence rate comparable to that of the RLS algorithm. Computer simulations are presented to illustrate the performance of the new algorithm.

Several methods have been developed for solving blind deconvolution problem. Recursive inverse filtering method is proposed recently and shown to have good convergence properties. This method requires accurate estimate of the region of support. In this paper, we propose to modify the original method by incorporating split, merge and grouping algorithm to find the region of support automatically.

The requirement to realize large-capacity, high-speed and guaranteed Quality of Service (QoS) communications in IP networks is a recent development. A technique to satisfy these requirements, Multi-Protocol Label Switching (MPLS) is the focus of this paper. In the future, it is expected that congestion and faults on a Label Switched Path (LSP) will seriously affect service contents because various applications are densely served in a large area. In MPLS, however, methods to solve these problems are not clear. Therefore, this study proposes a concrete traffic engineering method to avoid heavy congestion, and at the same time, endeavors to realize a fault-tolerant network by autonomous restoration, or self-healing.

Space-variant approaches subject to local image characteristics are useful in practical image restoration because many natural images are nonstationary. Motivated by the success of denoising approaches in the wavelet domain, we propose a region-adaptive restoration approach which adopts a wavelet denoising technique in flat regions after an under-regularized constrained least squares restoration. Experimental results verify that this approach not only improves image quality in mean square error but also contributes to ringing reduction.

In Chirp-Pulse Microwave Computed Tomography (CP-MCT) the images are affected by the blur which is inherent to the measurement principle and is described by a space-variant Point Spread Function (PSF). In this paper we investigate the PSF of CP-MCT including the space dependence both experimentally and computationally. The experimental evaluation is performed by measuring the projections of a target consisting of a thin low-loss dielectric rod surrounded by a saline solution and placed at various positions in the measuring region. On the other hand, the theoretical evaluation is obtained by computing the projections of the same target via a numerical solution of Maxwell's equations. Since CP-MCT uses a chirp signal, the numerical evaluation is carried out by the use of a FD-TD method. The projections of the rod could be obtained by computing the field during the sweep time of the chirp signal for each position of the receiving antenna. Since this procedure is extremely time consuming, we compute the impulse response function of the system by exciting the transmitting antenna with a wide-band Gaussian pulse. Then the signal transmitted in CP-MCT is obtained by computing the convolution product in time domain of the input chirp pulse with the impulse response function of the system. We find a good agreement between measured and computed PSF. The rationality of the computed PSF is verified by three distinct ways and the usefulness of this function is shown by a remarkable effect in the restoration of CP-MCT images. Knowledge on the space-variant PSF will be utilized for more accurate image deblurring in CP-MCT.

Most of the image restoration filters proposed so far include parameters that control the restoration properties. For bringing out the optimal restoration performance, these parameters should be determined so as to minimize a certain error measure such as the mean squared error (MSE) between the restored image and original image. However, this is not generally possible since the unknown original image itself is required for evaluating MSE. In this paper, we derive an estimator of MSE called the subspace information criterion (SIC), and propose determining the parameter values so that SIC is minimized. For any linear filter, SIC gives an unbiased estimate of the expected MSE over the noise. Therefore, the proposed method is valid for any linear filter. Computer simulations with the moving-average filter demonstrate that SIC gives a very accurate estimate of MSE in various situations, and the proposed procedure actually gives the optimal parameter values that minimize MSE.

We report on the demonstration of a fast restorable all-optical WDM network. This network consisted of four 44 optical cross-connects (OXC's) and four in-line optical amplifiers. These OXC's monitored not only the status of various network elements and quality of optical signals but also the optical path of each channel continuously. Thus, this network could automatically identify the causes of most network failures. For the fast restoration, we implemented these OXC's by using thermo-optic polymer switches (switching time: < 1.5 ms) and used hardware interrupt when LOS was detected. In addition, we used a pre-planned routing table made by using a simple heuristic routing and wavelength assignment algorithm. The results show that this network could be restored from any single link failure within 6 ms even when the restoration path was 400 km.

We report on the demonstration of a fast restorable all-optical WDM network. This network consisted of four 44 optical cross-connects (OXC's) and four in-line optical amplifiers. These OXC's monitored not only the status of various network elements and quality of optical signals but also the optical path of each channel continuously. Thus, this network could automatically identify the causes of most network failures. For the fast restoration, we implemented these OXC's by using thermo-optic polymer switches (switching time: < 1.5 ms) and used hardware interrupt when LOS was detected. In addition, we used a pre-planned routing table made by using a simple heuristic routing and wavelength assignment algorithm. The results show that this network could be restored from any single link failure within 6 ms even when the restoration path was 400 km.

In this paper, a Wiener filtering method in wavelet domain is proposed for restoring an image corrupted by additive white noise. The proposed method utilizes the multiscale characteristics of wavelet transform and the local statistics of each subband. The size of a filter window for estimating the local statistics in each subband varies with each scale. The local statistics for every pixel in each wavelet subband are estimated by using only the pixels which have a similar statistical property. Experimental results show that the proposed method has better performance over the Lee filter with a window of fixed size.

In this study, we consider a filtering method for image sequence degraded by additive Gaussian noise and/or impulse noise (i.e., mixed noise). For removing the mixed noise from the 1D/2D signal, weighted median filters are well known as a proper choice. We have also proposed a filtering tool based on the weighted median filter with a data-dependent method. We call this data-dependent weighted median (DDWM) filters. Nevertheless, the DDWM filter, its weights are controlled by some local information, is not enough performance to restore the image sequence degraded by the noise. The reason is that the DDWM filter is not able to obtain good filtering performance both in the still and moving regions of an image sequence. To overcome above drawback, we add motion information as a motion detector to the local information that controls the weights of the filters. This new filter is proposed as a Video-Data Dependent Weighted Median (Video-DDWM) filter. Through some simulations, the Video-DDWM filter is shown to give effective restoration results than that given by the DDWM filtering and the conventional filtering method with a motion-conpensation (MC).

This paper proposes a new restoration concept for ATM networks. It realizes the rapid and multiple reliability/cost level restoration required to support many different network services. First, the necessity in realizing rapid and multiple-reliability-level restoration in the future network is shown. The self-healing schemes that is based on distributed restoration mechanism satisfies the rapidity in restoration, but does not satisfy multiple reliability levels. Thus a new self-healing scheme that satisfies them is presented and a Multiple Reliability Level Virtual Path network concept is proposed based on the new self-healing scheme. Next, how to realize the new self-healing scheme is explained as an extension of the existing self-healing scheme with two new simple functions. Finally, evaluations confirm the effectiveness of the proposed scheme. These results show that the proposed new scheme realizes a network that fulfills the rapidity and multiple reliability requirements that are strongly required.

QoS restoration, a new approach to keep QoS of end-to-end ATM connections for failures is proposed. In a network with QoS restoration, each end-to-end connection's customer pre-defines the minimum QoS requirements such as minimum throughput. When a failure occurs, resources such as bandwidth of working connections are reallocated for restoration if they are dispensable to keep the minimum requirements along with the pre-assigned spare resources. This resource reallocation is done in a distributed manner and the result of the modification of a connection is notified to the customer of the connection to help him adjust the way of using it. The effect of the reallocation is mathematically evaluated. It is shown that the reallocation enables to achieve high restoration ratio with insufficient pre-assigned spare resources, such as to restore double-link failures with spare resources prepared for single-link failures, or even to restore single-link failures with no spare resources. It is also shown that pre-assigned spare resources can be reduced if the reallocation is considered in network design phase. The performance of the proposed distributed algorithm is evaluated with an event-driven simulator. The result shows that regardless of whether or not pre-assigned spare resources exist, a restoration ratio which is close to the theoretical maximum can be achieved. A proof-of-concept experimental system is developed by controlling commercial ATM switches via SNMP. The system shows it can effectively manage failures in WAN environment.

This paper proposes an optimal design scheme for photonic transport networks that interconnect multiple wavelength division multiplexing (WDM) self-healing ring systems by using optical cross connects (OXCs). To calculate the number of OXCs required in each hub to interconnect these ring systems, a virtual mesh network is generated, on which the route of each optical path (OP) going through multiple adjacent rings ("ring" is defined as circle in network topology) is determined based on a list of hubs. An integer-programming-based design problem is then formulated that minimizes the overall cost of facilities including OXCs as well as ring systems to accommodate a given demand. By solving this problem, we can simultaneously optimize required number of ring systems in each ring, wavelength assignment within each individual bidirectional ring system, required number of OXCs in each hub, and capacity to be allocated to each OP. Numerical examples show that the ring-based network is more cost-effective than the mesh restorable network when the cost of an OADM is lower than that of an OXC, and the OXC-to-fiber cost-coefficient ratio is sufficiently large.

The purpose of this letter is to modify the correlation least mean squares algorithm using a sum of the lagged squared errors as the cost function and extend the modified CLMS algorithm to two-dimensional domain. The effectiveness of the proposed algorithm is shown by the computer simulation.