A novel evolutionary algorithm is described for designing the topology of spanning tree-based communication networks. Two specific performance objectives are dealt with: the optimum communication spanning tree problem (OCSTP), and the quadratic minimum spanning tree problem (q-MST). Improved network performance is reliably obtained when using the proposed algorithm on accepted benchmark instances, in comparison with the previous best-known approaches. The same methodology can be applied straightforwardly to the design of communication networks with other objectives.

Anycast refers to the transmission of data from a source node to (any) one member in the group of designed recipients in a network. When the physical network and the set of anycast requests are given, the Anycast Routing and Wavelength Assignment (ARWA) problem is to find a set of light-paths, one for each source, for anycasting messages to any one of the member in the anycast destination group such that not any path using the same wavelength passes through the same link. The goal of the ARWA problem is to minimize the number of used wavelengths. In this paper, the ARWA problem is formulated and studied; since ARWA problem is NP-hard, a three-phase genetic algorithm is proposed to solve it. This algorithm is used to find the close-to-optimal solution. Simulated results show that the proposed algorithm is able to achieve good performance.

This paper presents a novel method of structure selection and identification for Hammerstein type nonlinear systems. An unknown nonlinear static part to be estimated is approximately represented by an automatic choosing function (ACF) model. The connection coefficients of the ACF and the system parameters of the linear dynamic part are estimated by the linear least-squares method. The adjusting parameters for the ACF model structure, i.e. the number and widths of the subdomains and the shape of the ACF are properly selected by using a genetic algorithm, in which the Akaike information criterion is utilized as the fitness value function. The effectiveness of the proposed method is confirmed through numerical experiments.

Though block-based motion estimation techniques are primarily designed for video coding applications, they are increasingly being used in other video analysis applications due to their simplicity and ease of implementation. The major drawback associated with these techniques is that noises, in the form of false motion vectors, cannot be avoided while capturing block motion vectors. Similar noises may further be introduced when the technique of global motion compensation is applied to obtain true object motion from video sequences where both the camera and object motions are present. This paper presents a new technique for capturing large number of true object motion vectors by eliminating the false motion vector fields, for use in the application of object motion based video indexing and retrieval applications. Experimental results prove that our proposed technique significantly increases the percentage of retained true object motion vectors while eliminating all false motion vectors for variety of standard and non-standard video sequences.

We propose a learning method combining query learning and a "genetic translator" we previously developed. Query learning is a useful technique for high-accuracy, high-speed learning and reduction of training sample size. However, it has not been applied to practical optical character readers (OCRs) because human beings cannot recognize queries as character images in the feature space used in practical OCR devices. We previously proposed a character image reconstruction method using a genetic algorithm. This method is applied as a "translator" from feature space for query learning of character recognition. The results of an experiment with hand-written numeral recognition show the possibility of training sample size reduction.

Multilayered filters with a dielectric distribution along their thickness forming a one-dimensional quasi-fractal structure are theoretically analyzed, focusing on exposing their resonant properties in order to understand a dielectric Menger's sponge resonator [4],[5]. "Quasi-fractal" refers to the triadic Cantor set with finite generation. First, a novel calculation method that has the ability to deal with filters with fine fractal structures is derived. This method takes advantage of Clifford algebra based on the theory of thin-film optics. The method is then applied to classify resonant modes and, especially, to investigate quality factors for them in terms of the following design parameters: a dielectric constant, a loss tangent, and a stage number. The latter determines fractal structure. Finally, behavior of the filters with perfect fractal structure is considered. A crucial finding is that the high quality factor of the modes is not due to the complete self-similarity, but rather to the breaking of such a fractal symmetry.

Most of analytical models proposed so far for the IEEE 802.11 distributed coordination function (DCF) focus on saturation performance. In this paper, we develop an analytic model for unsaturation performance evaluation of the IEEE 802.11 DCF with and without slow contention window decrease (SCWD). The model explicitly takes into account the carrier sensing mechanism and an additional backoff interval after successful frame transmission, both of which can be ignored under saturation conditions. Expressions are derived for throughput and delay characteristics by means of the equilibrium point analysis. The accuracy of our model is validated through computer simulation. Numerical results based on the IEEE 802.11b with CCK show that the SCWD can stably achieve approximately 20% performance gain over the normal 802.11 DCF under unsaturation conditions as well as saturation ones.

This paper present three characteristic functions which can express the luminance distribute characteristic much better. Based on these functions a region classification algorithm is presented. The algorithm can offer more information on regions' similarity and greatly improve the efficiency and performance of match seeking in fractal coding. It can be widely applied to many kinds of fractal coding algorithms. Analysis and experimental results proved that it can offer more information on luminance distribute characteristics among regions and greatly improve the decoding quality and compression ratio with holding the running speed.

Genetic Algorithms (GA) provide an attractive approach to solving the challenging problem of dynamic routing and wavelength assignment (RWA) in optical Wavelength Division Multiplexing (WDM) networks, because they usually achieve a significantly low blocking probability. Available GA-based dynamic RWA algorithms were designed mainly for WDM networks with a wavelength continuity constraint, and they cannot be applied directly to WDM networks with wavelength conversion capability. Furthermore, the available GA-based dynamic RWA algorithms suffer from the problem of requiring a very time consuming process to generate the first population of routes for a request, which may results in a significantly large delay in path setup. In this paper, we study the dynamic RWA problem in WDM networks with sparse wavelength conversion and propose a novel hybrid algorithm for it based on the combination of mobile agents technique and GA. By keeping a suitable number of mobile agents in the network to cooperatively explore the network states and continuously update the routing tables, the new hybrid algorithm can promptly determine the first population of routes for a new request based on the routing table of its source node, without requiring the time consuming process associated with current GA-based dynamic RWA algorithms. To achieve a good load balance in WDM networks with sparse wavelength conversion, we adopt in our hybrid algorithm a new reproduction scheme and a new fitness function that simultaneously takes into account the path length, number of free wavelengths, and wavelength conversion capability in route selection. Our new hybrid algorithm achieves a better load balance and results in a significantly lower blocking probability than does the Fixed-Alternate routing algorithm, both for optical networks with sparse and full-range wavelength converters and for optical networks with sparse and limited-range wavelength converters. This was verified by an extensive simulation study on the ns-2 network simulator and two typical network topologies. The ability to guarantee both a low blocking probability and a small setup delay makes the new hybrid dynamic RWA algorithm very attractive for current optical circuit switching networks and also for the next generation optical burst switching networks.

The explicit construction of a universal source code for correlated sources is presented. The construction is based on a technique of simulated random coding algorithms [5]. The proposed algorithm simulates the random generation of linear codes. For every pair of correlated sources whose achievable rate region includes a given pair of encoding rates, the decoding error rate of the proposed algorithm goes to zero almost surely as the block length goes to infinity.

We propose a novel routing algorithm for reverse proxy servers, called load balancing content address hashing (LB-CAH), and evaluate the performance of the proposed routing algorithm compared with that of the content address hashing (CAH) and the hash and slide (HAS) routing algorithms. The proposed LB-CAH routing algorithm calculates the popularity of pages in the load balancer using an LFU caching technique and periodically makes a popularity list. Using this popularity list, the proposed routing algorithm selects a reverse proxy server as follows. When the requested page appears in the popularity list, the request is routed according to the round robin method; otherwise, it is routed according to the content address hashing method. We evaluate and compare the LB-CAH, CAH and HAS routing algorithms by simulation experiments from the viewpoints of load balancing, consumed cache space and cache hit rate. Simulation experiments show that the proposed LB-CAH routing algorithm achieves almost the same degree of load balancing as the HAS algorithm and the same cache hit rate as the CAH algorithm for reverse proxy servers in various web site environments.

This paper introduces the concept of measuring double directional channels in ultra wideband (UWB) systems. Antenna-independent channel data were derived by doing the measurements in a wooden Japanese house. The data were useful for investigating the impact of UWB antennas and analyzing waveform distortion. Up to 100 ray paths were extracted using the SAGE algorithm and they were regarded as being dominant. The paths were then identified in a real environment, in which clusterization analyses were done using the directional information on both sides of the radio link. Propagating power was found to be concentrated around the specular directions of reflection and diffraction. This led to the observation that the spatio-temporal characteristics of extracted paths greatly reflected the structure and size of the environment. The power in the clusters indicated that the estimated 100 paths contained 73% of the total received power, while the rest existed as diffuse scattering, i.e., the accumulation of weaker paths. The practical limits of path extraction with SAGE were also discussed. Finally, we derived the scattering loss and intra-cluster properties for each reflection order, which were crucial for channel reconstrucion based on the deterministic approach.

In this letter, we propose an efficient transmission slot selection scheme for Band Division Multi-Carrier-CDMA (BD-MC-CDMA) systems under the constraints of packet loss and delay bound for each individual session. By utilizing channel dynamics together with the delay deadline and loss history, one can determine whether to transmit or not during each time slot, based on the prediction of future channel variations. To validate the efficiency of the proposed algorithm, we model each sub-band as a discrete time Markov Chain using a finite state Markov channel (FSMC) and derive the criteria required for transmission decision. Simulation results show that our proposed scheme can satisfy quality of service (QoS) requirements for real-time traffic with minimum use of resources, while increasing throughput of non-real-time traffic with the resources saved from real-time traffic.

Over the past few years, a considerable number of studies have been conducted on modeling the bargaining game using artificial agents on within-model interaction. However, very few attempts have been made at study on the interaction and co-evolutionary process among heterogeneous artificial agents. Therefore, we present two kinds of artificial agents, based on genetic algorithm (GA) and reinforcement learning (RL), which play a game on between-model interaction. We investigate their co-evolutionary processes and analyze their parameters using the analysis of variance.

This paper deals with minimum spanning tree problems where each edge weight is a fuzzy random variable. In order to consider the imprecise nature of the decision maker's judgment, a fuzzy goal for the objective function is introduced. A novel decision making model is constructed based on possibility theory and on a stochastic programming model. It is shown that the problem including both randomness and fuzziness is reduced to a deterministic equivalent problem. Finally, a polynomial-time algorithm is provided to solve the problem.

This paper proposes an adaptive noise canceller (ANC) with low signal-distortion for human-robot communication. The proposed ANC has two sets of adaptive filters for noise and crosstalk; namely, main filters (MFs) and subfilters (SFs) connected in parallel thereto. To reduce signal-distortion in the output, the stepsizes for coefficient adaptation in the MFs are controlled according to estimated signal-to-noise ratios (SNRs) of the input signals. This SNR estimation is carried out using SF output signals. The stepsizes in the SFs are determined based on the ratio of the primary and the reference input signals to cope with a wider range of SNRs. This ratio is used as a rough estimate of the input signal SNR at the primary input. Computer simulation results using TV sound and human voice recorded in a carpeted room show that the proposed ANC reduces both residual noise and signal-distortion by as much as 20 dB compared to the conventional ANC. Evaluation in speech recognition with this ANC reveals that with a realistic TV sound level, as good recognition rate as in the noise-free condition is achieved.

An estimation method of source location of undesired electromagnetic wave from electronic devices by using the MUSIC algorithm is proposed. The MUSIC algorithm can estimate the direction of arrival accurately, however, the estimation error is large in the case of short range multiple coherent sources. In order to overcome this problem, a method to improve the estimation accuracy is presented. Experimental results show that the proposed method can reduce the maximum estimation error from 7 cm of the conventional method to 2 cm.

In recent years, there has been an increased focus on the mechanics of information transmission in spiking neural networks. Especially the Noise Shaping properties of these networks and their similarity to Delta-Sigma Modulators has received a lot of attention. However, very little of the research done in this area has focused on the effect the weights in these networks have on the Noise Shaping properties and on post-processing of the network output signal. This paper concerns itself with the various modes of network operation and beneficial as well as detrimental effects which the systematic generation of network weights can effect. Also, a method for post-processing of the spiking output signal is introduced, bringing the output signal more in line with conventional Delta-Sigma Modulators. Relevancy of this research to industrial application of neural nets as building blocks of oversampled A/D converters is shown. Also, further points of contention are listed, which must be thoroughly researched to add to the above mentioned applicability of spiking neural nets.

This paper proposes resource management in Layer 1 Virtual Private Networks (VPNs). We have been proposing Layer 1 VPNs that provide layer 1 services to multiple customers over the single optical network with per VPN control and management capabilities. We have proposed two resource management models for Layer 1 VPNs, which constitute different class of services. One is the shared model, where resources are shared among VPNs. The other is the dedicated model, where resources are explicitly pre-assigned to each VPN. In this paper, after introducing an overview of Layer 1 VPNs, we evaluate several path computation algorithms for these two models focusing on the multi layer network scenario. In the shared model, there are several existing studies for non-VPN cases, but considerations for VPN cases are not investigated. This paper evaluates algorithms originally proposed for non-VPN cases for use in VPN cases. Simulation results show that the path computation algorithm that works as saving layer 1 resources achieves better resource sharing effect. In the dedicated model, the problem is identical to non-VPN cases. There is one conventional algorithm, but amount of available resources is not well considered. We propose a novel path computation algorithm. Simulation results show effectiveness of our proposed algorithm against the conventional algorithm. Furthermore, resource usage efficiency of two resource management models is compared. We analyze and propose applicability of resource management models.

In spatial multiplexing systems using multiple antennas, the error-rate performance is heavily dependent on the residual channel estimation error. In this letter, we propose a design method that uses the genetic algorithms to optimize training sequences for accurate channel estimation.