This paper investigates an ATM multiplexer with a resume level, which uses a selective cell discarding strategy as a priority control, and a Markov-modulated deterministic process (MMDP) as the burst input traffic. Assuming that a system is loaded with a superposition of several independent and homogeneous On-Off bursty sources with two priority classes, we obtain the cell loss probability of each priority class of an ATM multiplexer with a resume level. The performance analysis derived here includes as special cases one without priority and one with a threshold level. From the numerical results, we compare the cell loss probability, the mean queue length, the mean queuing delay, the level crossing rate, and the queue length distribution at the embedded points for the case of a threshold level with those for the case of a resume level. By selecting an appropriate resume level, we can reduce the sensitive state change around the threshold level.

In this paper, the performances of texture classification based on pyramidal and uniform decomposition are comparatively studied with and without feature selection. This comparison using the subband variance as feature explores the dependence among features. It is shown that the main problem when employing 2-D non-separable wavelet transforms for texture classification is the determination of the suitable features that yields the best classification results. A Max-Max algorithm which is a novel evaluation function based on genetic algorithms is presented to evaluate the classification performance of each subset of selected features. It is shown that the performance with feature selection in which only about half of features are selected is comparable to that without feature selection. Moreover, the discriminatory characteristics of texture spread more in low-pass bands and the features extracted from the pyramidal decomposition are more representative than those from the uniform decomposition. Experimental results have verified the selectivity of the proposed approach and its texture capturing characteristics.

This paper describes a classification method for rotated and scaled textured images using invariant parameters based on spectral-moments. Although it is well known that rotation invariants can be derived from moments of grey-level images, the use is limited to binary images because of its computational unstableness. In order to overcome this drawback, we use power spectrum instead of the grey levels to compute moments and adjust the integral region of moment evaluation to the change of scale. Rotation and scale invariants are obtained as the ratios of the different rotation invariants on the basis of a spectral-moment property with respect to scale. The effectiveness of the approach is illustrated through experiments on natural textures from the Brodatz album. In addition, the stability of the invariants with respect to the change of scale is discussed theoretically and confirmed experimentally.

In this paper, we propose a new approach for describing image patterns. We integrate the concepts of multiscale image analysis, aura matrix (Gibbs random fields and cooccurrences related statistical model of texture analysis) to define image features, and to obtain the features having robustness with illumination variations and shading effects, we analyse images based on the Topographic Structure described by the Surface-Shape Operator, which describe gray-level image patterns in terms of 3D shapes instead of intensity values. Then, we illustrate usefulness of the proposed features with texture classifications. Results show that the proposed features extracted from multiscale images work much better than those from a single scale image, and confirm that the proposed features have robustness with illumination and shading variations. By comparisons with the MRSAR (Multiresolution Simultaneous Autoregressive) features using Mahalanobis distance and Euclidean distance, the proposed multiscale features give better performances for classifying the entire Brodatz textures: 112 categories, 2016 samples having various brightness in each category.

The TMN that appears to operate the various communication networks generally and efficiently is developed under the different platform environment such as the different hardware and the different operating system. One of the main problems is that all the agents of the TMN system must be duplicated and maintain the software and the data blocks that perform the identical function. Therefore, the standard of the Q3 interface development cannot be defined and the multi-platform cannot be supported in the development of the TMN agent. In order to overcome these problems, the Farming methodology that is based on the Farmer model has been suggested. The main concept of the Farming methodology is that the software and the data components that are duplicated and stored in each distributed object are saved in the Platform Independent Class Repository (PICR) by converting into the format of the independent componentware in the platform, so that the componentwares that are essential for the execution can be loaded and used statically or dynamically from PICR as described in the framework of each distributed object. The distributed TMN agent of the personal communication network is designed and developed by using the Farmer model.

Huynen has already provided a method to decompose a Mueller matrix in order to retrieve detailed target information in a polarimetric radar system. However, this decomposition sometimes fails in the presence of small error or noise in the elements of a Mueller matrix. This paper attempts to improve Huynen's decomposition method. First, we give the definition of stable decomposition and present an example, showing a problem of Huynen's approach. Then two methods are proposed to carry out stable decompositions, based on the nonlinear least square method and the Newton's method. Stability means the decomposition is not sensitive to noise. The proposed methods overcomes the problems on the unstable decomposition of Mueller matrix, and provides correct information of a target.

Many image-processing techniques are based on texture features or gradation features of the image. However, Landsat images are complex; they also include physical features of reflection radiation and heat radiation from land cover. In this paper, we describe a method of constructing a super-resolution image of Band 6 of the Landsat TM sensor, oriented to analysis of an agricultural area, by combining information (texture features, gradation features, physical features) from other bands. In this method, a knowledge-based hierarchical classifier is first used to identify land cover in each pixel and then the least-squares approach is applied to estimate the mean temperature of each type of land cover. By reassigning the mean temperature to each pixel, a finer spatial resolution is obtained in Band 6. Computational results show the efficiency of this method.

In this paper an ATM call level model, where service classes with QoS guarantees (CBR/VBR) as well as elastic (best effort) services (ABR/UBR) coexist, is proposed and a number of simulations have been carried out on three different network topologies. Elastic traffic gives on the network level rise to new challenging problems since for a given elastic connection the bottleneck link determines the available bandwidth and thereby put constraints on bandwidth at other links. Thereby bandwidth allocation at call arrivals but also bandwidth reallocation at call departure becomes, together with routing, an important issue for investigation. Two series of simulations have been carried out where three different routing schemes have been evaluated together with two bandwidth allocation algorithms. The results indicate that the choice of routing algorithm is load dependent and in a large range the shortest path algorithm properly adopted to the mixed CBR/ABR environment performs very well.

Segmenting the images obtained from magnetic resonance imaging (MRI) is an important process for visualization of the human soft tissues. For the application of MR, we often have to introduce a reasonable segmentation technique. Neural networks may provide us with superior solutions for the pattern classification of medical images than the conventional methods. For image segmentation with the aid of neural networks of a reasonable size, it is important to select the most effective combination of secondary indices to be used for the classification. In this paper, we introduce a vector quantized class entropy (VQCCE) criterion to evaluate which indices are effective for pattern classification, without testing on the actual classifiers. We have exploited a newly developed neural tree classifier for accomplishing the segmentation task. This network effectively partitions the feature space into subregions and each final subregion is assigned a class label according to the data routed to it. As the tree grows on, the number of training data for each node decreases, which results in less weight update epochs and decreases the time consumption. The partitioning of the feature space at each node is done by a simple neural network; the appropriateness of which is measured by newly proposed estimation criterion, i. e. the measure for assessment of neuron (MAN). It facilitates the obtaining of a neuron with maximum correlation between a unit's value and the residual error at a given output. The application of this criterion guarantees adopting the best-fit neuron to split the feature space. The proposed neural classifier has achieved 95% correct classification rate on average for the white/gray matter segmentation problem. The performance of the proposed method is compared to that of a multilayered perceptron (MLP), the latter being widely exploited network in the field of image processing and pattern recognition. The experiments show the superiority of the introduced method in terms of less iterations and weight up dates necessary to train the neural network, i. e. lower computational complexity; as well as higher correct classification rate.

While active researches have been continuously made on the ATM switch architectures and the QoS service guarantees, most of them have been treated independently in the past. In this paper, we first explain the architectural requirement on the ATM switches to implement the mechanism of QoS guarantees in the context of ATM congestion control. Then we discuss how a vital link between two should be built, and remaining problems are pointed out.

This paper presents a new self-organization classification algorithm for remote-sensing images. Kohonen and other scholars have proposed self-organization algorithms. Kohonen's model easily converges to the local minimum by tuning the elaborate parameters. In addition to others, S. C. Amatur and Y. Takefuji have also proposed self-organization algorithm model. In their algorithm, the maximum neuron model (winner-take-all neuron model) is used where the parameter-tuning is not needed. The algorithm is able to shorten the computation time without a burden on the parameter-tuning. However, their model has a tendency to converge to the local minimum easily. To remove these obstacles produced by the two algorithms, we have proposed a new self-organization algorithm where these two algorithms are fused such that the advantages of the two algorithms are combined. The number of required neurons is the number of pixels multiplied by the number of clusters. The algorithm is composed of two stages: in the first stage we use the maximum self-organization algorithm until the state of the system converges to the local-minimum, then, the Kohonen self-organization algorithm is used in the last stage in order to improve the solution quality by escaping from the local minimum of the first stage. We have simulated a LANDSAT-TM image data with 500 pixel 100 pixel image and 8-bit gray scaled. The results justifies all our claims to the proposed algorithm.

This paper focuses on competitive learning algorithms for WTA (winner-take-all) networks which perform rotation invariant pattern classification. Although WTA networks may theoretically be possible to achieve rotation invariant pattern classification with infinite memory capacities, actual networks cannot memorize all input data. To effectively memorize input patterns or the vectors to be classified, we present two algorithms for learning vectors in classes (LVC1 and LVC2), where the cells in the network memorize not only weight vectors but also their firing numbers as statistical values of the vectors. The LVC1 algorithm uses simple and ordinary competitive learning functions, but it incorporates the firing number into a coefficient of the weight change equation. In addition to all the functions of the LVC1, the LVC2 algorithm has a function to utilize under-utilized weight vectors. From theoretical analysis, the LVC2 algorithm works to minimize the energy of all weight vectors to form an effective memory. From computer simulation with two-dimensional rotated patterns, the LVC2 is shown to be better than the LVC1 in learning and generalization abilities, and both are better than the conventional Kohonen self-organizing feature map (SOFM) and the learning vector quantization (LVQ1). Furthermore, the incorporation of the firing number into the weight change equation is shown to be efficient for both the LVC1 and the LVC2 to achieve higher learning and generalization abilities. The theoretical analysis given here is not only for rotation invariant pattern classification, but it is also applicable to other WTA networks for learning vector quantization.

In this paper, we present a lower bound for the dimension of subfield subcodes of residue Goppa codes on the curve Cab, which exceeds the lower bound given by Stichtenoth when the number of check symbols is not small. We also give an illustrative example which shows that the proposed bound for the dimension of certain residue Goppa code exceeds the true dimension of a BCH code with the same code length and designed distance.

This paper presents a method for land cover classification using the SIR-C/X-SAR imagery based on the maximum likelihood method and the polarimetric filtering. The main feature is to use polarimetric enhanced image information in the pre-processing stage for the classification of SAR imagery. First, polarimetric filtered images are created where a specific target is enhanced versus another, then the image data are incorporated into the feature vector which is essential for the maximum likelihood classification. Specific target classes within the SAR image are categorized according to the maximum likelihood method using the wavelet transform. Addition of polarimetric enhanced image in the preprocessing stage contributes to the increase of classification accuracy. It is shown that the use of polarimetric enhanced images serves efficient classifications of land cover.

Field Programmable Gate Arrays (FPGA's) are important devices for rapid system prototyping. Roth-Karp decomposition is one of the most popular decomposition techniques for Look-Up Table (LUT) -based FPGA technology mapping. In this paper, we propose a novel algorithm based on Binary Decision Diagrams (BDD's) for selecting good lambda set variables in Roth-Karp decomposition to minimize the number of consumed configurable logic blocks (CLB's) in FPGA's. The experimental results on a set of benchmarks show that our algorithm can produce much better results than the similar works of the previous approaches.

This paper presents a new multi-clustering network for the purpose of intelligent data classification. In this network, the first layer is a self-organized clustering layer and the second layer is a restricted clustering layer with a neighborhood mechanism. A new clustering algorithm is developed in this system for the efficiently use of parallel processors. This parallel algorithm enables the nodes of this network to be independently processed in order to minimize data communication load among processors. Using the parallel processors, the quite low calculation cost can be realized among the conventional networks. For example, a 4-processor parallel computing system has shown its ability to reduce the time taken for data classification to 26.75% of a single processor system without declining its performance.

This paper provides an architectural study of optical multiple-ring trunk-transmission networks using high-speed Time Division Multiplexing (TDM), and proposes two algorithms for distributed control environments. We propose a path-setup algorithm that uses Token protocol over Section Overhead (SOH) bytes, by which network-nodes communicate with each other to reserve bandwidth. A classified path restoration algorithm is also proposed that offers 3 path classes in terms of restoration performance. Class A paths, the most reliable, never lose any bit even against unpredictable disasters. They are realized by path-duplication at the source node, route diversity,and hitless switching at the destination node. Class B paths are restored by re-routing, where the original path-setup algorithm is reused. Class C paths are the most economical because a failed path is restored by maintenance action.

This paper compares signal classification performance of multilayer neural networks (MLNNs) and linear filters (LFs). The MLNNs are useful for arbitrary waveform signal classification. On the other hand, LFS are useful for the signals, which are specified with frequency components. In this paper, both methods are compared based on frequency selective performance. The signals to be classified contain several frequency components. Furthermore, effects of the number of the signal samples are investigated. In this case, the frequency information may be lost to some extent. This makes the classification problems difficult. From practical viewpoint, computational complexity is also limited to the same level in both methods.IIR and FIR filters are compared. FIR filters with a direct form can save computations, which is independent of the filter order. IIR filters, on the other hand, cannot provide good signal classification deu to their phase distortion, and require a large amount of computations due to their recursive structure. When the number of the input samples is strictly limited, the signal vectors are widely distributed in the multi-dimensional signal space. In this case, signal classification by the LF method cannot provide a good performance. Because, they are designed to extract the frequency components. On the other hand, the MLNN method can form class regions in the signal vector space with high degree of freedom.

Microwave oven interference much degrades the performance of digital radio communication systems, and, in order to obtain a good error performance under microwave oven interference environment, the digital radio communication systems should be newly designed for microwave oven interference environment. In this paper, using the Middleton's canonical class-A impulsive noise model, we propose a statistical model of microwave oven interference and discuss the performance improvement achieved by an optimum reception based on this statistical model. As the results, although the first order statistic of microwave oven interference can be modeled by class-A impulsive noise, because of the burst high level interference, the performance of optimum receiver designed for class-A noise cannot achieve a good error performance under microwave oven interference environment. In order to eliminate the effect of burst high level interference, we introduce sample interleave scheme and show that the performance of optimum receiver can be much improved by using sample interleave scheme.

The millimeter wave (MMW) radar has good compromise characteristics of both microwave radar and optical sensors. It has better angular and range resolving abilities than microwave radar, and a longer penetrating range than optical sensors. We used the MMW radar to detect targets located in the sea and among sea ice clutter based on fractals, wavelets, and neural networks. The wavelets were used as feature extractors to decompose the MMW radar images and to extract the feature vectors from approximation signals at different resolution levels. Unsupervised neural classifiers with parallel computational architecture were used to classify sea ice, sea water and targets based on the competitive learning algorithm. The fractal dimensions could provide a quantitative description of the roughness of the radar image. Using these techniques, we can detect targets quickly and clearly discriminate between sea ice, sea water, and targets.