This letter points out some flaws in the previous works on UKS (unknown key-share) attacks. We show that Blake-Wilson and Menezes' revised STS-MAC (Station-to-Station Message Authentication Code) protocol, which was proposed to prevent UKS attack, is still vulnerable to a new UKS attack. Also, Hirose and Yoshida's key agreement protocol presented at PKC'98 is shown to be insecure against public key substitution UKS attacks. Finally, we discuss countermeasures for such UKS attacks.

Cellular Digital Packet Data (CDPD) provides wireless data communication services to mobile users by sharing unused RF channels with AMPS on a non-interfering basis. To prevent interference on the voice activities, CDPD makes forced hop to a channel stream when a voice request is about to use the RF channel occupied by the channel stream. The number of forced hops is affected by the voice channel selection policy. We propose analytic models to investigate the CDPD channel holding time for the the least-idle and random voice channel selection policies. Under various system parameters and voice channel selection policies, we provide guidelines to reduce the number of forced hops.

In the food, beverage and cosmetic industry and so on, odor sensing systems instead of human sensory test are demanded. We have developed odor sensing systems using QCM (quartz crystal microbalance) sensor array and pattern recognition method. Since the properties of the sensors depend on the gas sorption characteristics of the sensing films coated on them, the optimum films according to target odors should be selected. In this study, we tried to select sensing films appropriate for discrimination of slightly different apple flavors. The examples of typical apple flavors were prepared blending 9 compounds. The sensing films were extracted from various kinds of materials such as lipid, stationary phase material of GC (gas chromatography) and cellulose. The selection method under the condition of the small number of measurements was studied. We analyzed the data of steady-state sensor responses in terms of the Euclidean distance, and the films appropriate for apple flavor discrimination were successfully selected.

An optimum representative-frames (r-frames) selection method using step-wise function approximation has been developed to provide automatic indexing for a video-query-agent (VQA) system. It uses dynamic programming to simultaneously select the r-frames and corresponding segment boundaries. Experiments showed that the approximation error of the selected r-frames was less than that of two conventional methods. Retrieval experiments using a feature-based image-search engine showed that the proposed method is more robust and effective than the two conventional methods. The proposed method was implemented in a VQA system and processing time was evaluated. The results showed that the processing time for indexing was shorter than that of the conventional method.

A broadband and flexible receiver architecture is investigated for the handheld software defined radio (SDR). The proposed SDR architecture is based on the direct conversion and low intermediate frequency (low-IF) principle with digital channel filtering, which provides the receiver with flexibility for the multi-standard application. This architecture also enables analog-to-digital converter activating essentially in baseband or low frequency so that the clock jitter, which serves as an important subject in the well-known IF sampling method, can be reduced. Basic performance of the proposed architecture has been confirmed by the experimental model.

Feature selection is an integral part of any pattern recognition system. Removal of redundant features improves the efficiency of a classifier as well as cut down the cost of future feature extraction. Recently neural network classifiers have become extremely popular compared to their counterparts from statistical theory. Some works on the use of artificial neural network as a feature selector have already been reported. In this work a simple feature selection algorithm has been proposed in which a fractal neural network, a modified version of multilayer perceptron, has been used as a feature selector. Experiments have been done with IRIS and SONAR data set by simulation. Results suggest that the algorithm with the fractal network architecture works well for removal of redundant informations as tested by classification rate. The fractal neural network takes lesser training time than the conventional multilayer perceptron for its lower connectivity while its performance is comparable to the multilayer perceptron. The ease of hardware implementation is also an attractive point in designing feature selector with fractal neural network.

In this paper, a new feature which is characterized by the extrema density of 2-D wavelet frames estimated at the output of the corresponding filter bank is proposed for texture segmentation. With and without feature selection, the discrimination ability of features based on pyramidal and tree-structured decompositions are comparatively studied using the extrema density, energy, and entropy as features, respectively. These comparisons are demonstrated with separable and non-separable wavelets. With the three-, four-, and five-category textured images from Brodatz album, it is observed that most performances with feature selection improve significantly than those without feature selection. In addition, the experimental results show that the extrema density-based measure performs best among the three types of features investigated. A Min-Min method based on genetic algorithms, which is a novel approach with the spatial separation criterion (SPC) as the evaluation function is presented to evaluate the segmentation performance of each subset of selected features. In this work, the SPC is defined as the Euclidean distance within class divided by the Euclidean distance between classes in the spatial domain. It is shown that with feature selection the tree-structured wavelet decomposition based on non-separable wavelet frames has better performances than the tree-structured wavelet decomposition based on separable wavelet frames and pyramidal decomposition based on separable and non-separable wavelet frames in the experiments. Finally, we compare to the segmentation results evaluated with the templates of the textured images and verify the effectiveness of the proposed criterion. Moreover, it is proved that the discriminatory characteristics of features do spread over all subbands from the feature selection vector.

This paper proposes a new distributed connection establishment scheme involving several competing network providers in a multimedia telecommunications environment. This connection establishment scheme, which is based on the concept of open competitive bidding, enables mutual selection by users and network providers. By employing this proposed scheme, both network providers and users can pursue their own objectives, according to their own bidding and awarding strategies. In this paper, a simple bidding strategy for network providers is presented, and the effectiveness of this strategy is evaluated by means of computer simulation. It is shown that each network provider can improve its profit by adopting this strategy. In this paper, an example of utility functions for users is presented, and the effectiveness of the mechanism with which users can select a network provider is also evaluated by means of computer simulation. Each user can improve his/her utility by selecting an appropriate network provider based on this utility function.

Network applications such as FTP, WWW, Mirroring etc. are presently operated with little or no knowledge about the characteristics of the underlying network. These applications could operate more efficiently if the characteristics of the network are known and/or are made available to the concerned application. But network characteristics are hard to come by. The IP Performance Metrics working group (IETF-IPPM-WG) is working on developing a set of metrics that will characterize Internet data delivery services (networks). Some tools are being developed for measurements of these metrics. These generally involve active measurements or require modificationsin applications. Both techniques have their drawbacks. In this work, we show a new and more practical approach of estimating network characteristics. This involves gathering and analyzing the network's experience. The experience is in the form of traffic statistics, information distilled from management related activities and ubiquitously available logs (squid access logs, mail logs, ftp logs etc. ) of network applications. An analysis of this experience provides an estimate of the characteristics of the underlying network. To evaluate the concept we have developed and experimented with a system wherein the network characteristics are generated by analyzing the logs and traffic statistics. The network characteristics are made available to network clients and administrators by Network Performance Metric (NPM) servers. These servers are accessed using standard network management protocols. Results of the evaluation are presented and a framework for efficient operation of network operations, using the network characteristics is outlined.

In this paper, we discuss the problem of active training data selection for improving the generalization capability of a neural network. We look at the learning problem from a function approximation perspective and formalize it as an inverse problem. Based on this framework, we analytically derive a method of choosing a training data set optimized with respect to the Wiener optimization criterion. The final result uses the apriori correlation information on the original function ensemble to devise an efficient sampling scheme which, when used in conjunction with the learning scheme described here, is shown to result in optimal generalization. This result is substantiated through a simulated example and a learning problem in high dimensional function space.

Since manufacturing processes inherently fluctuate, LSI chips which are produced from the same design have different propagation delays. However, the difference in delays caused by the process fluctuation has rarely been considered in most of existing high-level synthesis systems. This paper presents a new approach to module selection in high-level synthesis, which exploits the difference in functional unit delays. First, a module library model which assumes the probabilistic nature of functional unit delays is presented. Then, we propose a module selection problem and an algorithm which minimizes the cost per faultless chip. Experimental results demonstrate that the proposed algorithm finds optimal module selections which would not have been explored without manufacturing information.

In a multi-bandwidth CDMA system, the performance of a multiple order selection combining rake receiver is analyzed according to the spreading bandwidth of the system and the delay spread of a Rayleigh fading channel. The results for various channel environments indicate a tradeoff between total received signal energy and multipath fading immunity. Increasing the occupied bandwidth of the system (wide-bandwidth spreading) gives better performance for small delay spread environments, while gathering more energy (narrow-bandwidth spreading) gives better performance for large delay spread environments. It is also shown that the performance difference between low and high order selection combining grows larger as the spreading bandwidth is increased. It is noted that performance degrades by increasing the bandwidth above a certain point and the optimum spreading bandwidth for each channel environment decreases as the delay spread of the channel increases.

Fitt's law is commonly used to model target selection. But Fitts' law deals with only one kind of selection strategy. Our question is, do changes in target size, distance and direction to a target affect the differences in performance between target selection strategies? We performed the first empirical tests on a pen-based system to evaluate differences in performance between six selection strategies for selecting a target. Three target sizes, eight pen-movement-directions and three pen-movement-distances were applied to all six strategies. The results show that differences between selection strategies are affected by variations in target size but not by the other parameters (distance and direction).

Conventional approaches to neural network training do not consider possibility of selecting training samples dynamically during the learning phase. Neural network is simply presented with the complete training set at each iteration of the learning. The learning can then become very costly for large data sets. Huge redundancy of data samples may lead to the ill-conditioned training problem. Ill-conditioning during the training causes rank-deficiencies of error and Jacobean matrices, which results in slower convergence speed, or in the worst case, the failure of the algorithm to progress. Rank-deficiencies of essential matrices can be avoided by an appropriate selection of training exemplars at each iteration of training. This article presents underlying theoretical grounds for dynamic sample selection (DSS), that is mechanism enabling to select a subset of training set at each iteration. Theoretical material is first presented for general objective functions, and then for the objective functions satisfying the Lipschitz continuity condition. Furthermore, implementation specifics of DSS to first order line search techniques are theoretically described.

Computational expensiveness of the training techniques, due to the extensiveness of the data set, is among the most important factors in machine learning and neural networks. Oversized data set may cause rank-deficiencies of Jacobean matrix which plays essential role in training techniques. Then the training becomes not only computationally expensive but also ineffective. In [1] the authors introduced the theoretical grounds for dynamic sample selection having a potential of eliminating rank-deficiencies. This study addresses the implementation issues of the dynamic sample selection based on the theoretical material presented in [1]. The authors propose a sample selection algorithm implementable into an arbitrary optimization technique. An ability of the algorithm to select a proper set of samples at each iteration of the training has been observed to be very beneficial as indicated by several experiments. Recently proposed approaches to sample selection work reasonably well if pattern-weight ratio is close to 1. Small improvements can be detected also at the values of the pattern-weight ratio equal to 2 or 3. The dynamic sample selection approach, presented in this article, can increase the convergence speed of first order optimization techniques, used for training MLP networks, even at the value of the pattern-weight ratio (E-FP) as high as 15 and possibly even more.

An experiment is reported comparing six pen input strategies for selecting a small target using five diffenent sized targets (1, 3, 5, 7 and 9 dot diameter circles respectively, 0. 36 mm per dot). The results showed that the best strategy, in terms of error rate, selection time and subjective preferences, was the "land-on2" strategy where the target is selected when the pen-tip touches the target for the first time after landing on the screen surface. Moreover, "the smallest maximum size" was determined to be 5 dots (1. 8 mm). This was the largest size among the targets which had a significant main effect on error rate in the six strategies. These results are important for both researchers and designers of pen-based systems.

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.

In this paper, we propose a feature selection method to extract functional structures embedded in multidimensional data. In our approach, we do not approximate functional structures directly. Instead, we focus on the seemingly trivial property that functional structures are geometrically thin in an informative subspace. Using this property, we can exclude irrelevant features to describe functional structures. As a result, we can use conventional identification methods, which use only informative features, to accurately identify functional structures. In this paper, we define Geometrical Thickness (GT) in the Cartesian System Model (CSM), a mathematical model that can manipulate symbolic data. Additionally, we define Total Geometrical Thickness (TGT) which expresses geometrical structures in data. Using TGT, we investigate a new feature selection method and show its capabilities by applying it to two sets of artificial and one set of real data.

In the future, more and more network providers will be established through the introduction of an open telecommunications market. At this time, it is necessary to guarantee the fair competition between these network providers. In this paper, a negotiation protocol for connection establishment is proposed. This negotiation protocol is based on the concept of open, competitive bidding and can guarantee fair competition between the network providers. In this negotiation protocol, each network providers objective is to maximize its profit. Conversely, each users objective is to select a network provider which will supply as much capacity as required. Employing this negotiation protocol, the users and the network providers can select each other based on their objectives. In this paper, adaptation strategies which network providers and users can adopt under the proposed negotiation protocol framework are also discussed. A network provider which adopts this strategy can obtain enough profit even when the number of connection requests is small relative to the idle bandwidth capacity. Moreover, a user who adopts this strategy can be sure to obtain bandwidth even when the idle bandwidth capacity is small relative to the number of connection requests.

A training data selection method is proposed for multilayer neural networks (MLNNs). This method selects a small number of the training data, which guarantee both generalization and fast training of the MLNNs applied to pattern classification. The generalization will be satisfied using the data locate close to the boundary of the pattern classes. However, if these data are only used in the training, convergence is slow. This phenomenon is analyzed in this paper. Therefore, in the proposed method, the MLNN is first trained using some number of the data, which are randomly selected (Step 1). The data, for which the output error is relatively large, are selected. Furthermore, they are paired with the nearest data belong to the different class. The newly selected data are further paired with the nearest data. Finally, pairs of the data, which locate close to the boundary, can be found. Using these pairs of the data, the MLNNs are further trained (Step 2). Since, there are some variations to combine Steps 1 and 2, the proposed method can be applied to both off-line and on-line training. The proposed method can reduce the number of the training data, at the same time, can hasten the training. Usefulness is confirmed through computer simulation.