We consider a rate control algorithm for elastic services to allocate bandwidth in a network subject to throughput and fairness constraints. Our algorithm achieves a max-min fair bandwidth allocation among contending elastic connections, and has desirable properties in that it can operate in a decentralized and asynchronous manner accounting in part for heterogeneity in round trip delays. The algorithm is simple and scalable in that, 1) the network links make local measurements of capacity and calculate local 'explicit rates' which are fed back to sources without requiring knowledge of the number of on-going connections, while 2) sources adjust their transmission rates so as not to exceed the received explicit rate indication. The algorithm is designed to track a "dynamic" network environment. We discuss its stability, convergence, and feasibility issues related to fair allocation and rate-based flow control. We also consider the role of sessions with priorities to differentiate among users with elastic services. Through rigorous analysis and simulations, we have shown that it has indeed desirable characteristics for networks with elastic services as well as other service types, which are expected to be common in future network environment.

Electronic Toll Collection (ETC), an application of Dedicated Short Range Wireless Communication (DSRC), had suffered from wrong operations due to multipath problems. To solve this problem, we proposed to apply a simple configured path determination scheme for the ETC system. The system consists of a vector network analyzer, low-noise amplifier, and X-Y positioner and achieves an automatic measurement of the spatial transfer function with emphasis on accurate measurement and reproducibility. For the reliable identification of the propagating paths, 3-D Unitary ESPRIT and SAGE algorithms were employed. Having developed the system, field experiments at the toll gate of the highway was carried out. In the measurements, we could determine many propagation paths so that the dominant propagation phenomena at the toll gate was identified. They included a ground-canopy twice reflected wave, which was a potential path that caused wrong operation. Consequently, their reflection coefficients and polarization characteristics were investigated. From the results, applicability of the path determination system for short range on-site measurement was confirmed.

Voice over Internet protocol (VoIP) is to transfer voice packets over IP networks, while voice signal is processed by using digital signal processing technology before being transmitted. VoIP quality cannot be expected, because it is hard to predict the influence of delay, packet loss rate, packet error, etc. It is difficult to rebuild the voice wave form, if a large amount of voice packets are lost. This paper mainly studies on how to maintain a better voice quality over hybrid fiber/coaxial (HFC) networks, if it is inevitable to drop packets. We particularly consider the data over cable service interface specification (DOCSIS) version 1.1 with the unsolicited grant service with activity detection (UGS/AD) for VoIP services. We propose a smallest successive times first (SSTF) scheduling algorithm to schedule VoIP packets for cable modem termination system (CMTS), which can support fair transmission and long-term transmission continuity for VoIP connections. We analyze voice quality about continuity of the transmitted VoIP packets, consecutive clipping times, and VoIP packet drop rate for all connections. Performance measurement shows excellent results for the proposed algorithm by simulation experiments and objective evaluation.

The maximum a posteriori (MAP) algorithm is the optimum solution for decoding concatenated codes, such as turbo codes. Since the MAP algorithm is computationally complex, more efficient algorithms, such as the Max-Log-MAP algorithm and the soft-output Viterbi algorithm (SOVA), can be used as suboptimum solutions. Especially, the Max-Log-MAP algorithm is widely used, due to its near-optimum performance and lower complexity compared with the MAP algorithm. In this paper, we propose an efficient algorithm for decoding concatenated codes by modifying the Max-Log-MAP algorithm. The efficient implementation of the backward recursion and the log-likelihood ratio (LLR) update in the proposed algorithm improves its computational efficiency. Memory is utilized more efficiently if the sliding window algorithm is adopted. Computer simulations and analysis show that the proposed algorithm requires a considerably lower number of computations compared with the Max-Log-MAP algorithm, while providing the same overall performance.

This paper addresses the scheduling problem of a class of automated manufacturing systems with multiple resource requests. In the automated manufacturing system model, a set of jobs is to be processed and each job requires a sequence of operations. Each operation may need more than one resource type and multiple identical units with the same resource type. Upon the completion of an operation, resources needed in the next operation of the same job cannot be released and the remaining resources cannot be released until the start of the next operation. The scheduling problem is formulated by Timed Petri nets model under which the scheduling goal consists in sequencing the transition firing sequence in order to avoid the deadlock situation and to minimize the makespan. In the proposed genetic algorithm with deadlock-free constraint, Petri net transition sequence is coded and a deadlock detection method based on D-siphon technology is proposed to reschedule the sequence of transitions. The enabled transitions should be fired as early as possible and thus the quality of solutions can be improved. In the fitness computation procedure, a penalty item for the infeasible solution is involved to prevent the search process from converging to the infeasible solution. The method proposed in this paper can get a feasible scheduling strategy as well as enable the system to achieve good performance. Numerical results presented in the paper show the efficiency of the proposed algorithm.

This paper proposes a simple method for estimation and compensation of signal direction, to deal with relative change of sound source location caused by the movements of a microphone array and a sound source. This method introduces a delay filter that has shifted and sampled sinc functions. This paper presents a concept for the joint optimization of arrival time differences and of the coordinate system of a mobile microphone array. We use the LMS algorithm to derive this method by maintaining a certain relationship between the directions of the microphone array and the sound source directions. This method directly estimates the relative directions of the microphone array to the sound source directions by minimizing the relative differences of arrival time among the observed signals, not by estimating the time difference of arrival (TDOA) between two observed signals. This method also compensates the time delay of the observed signals simultaneously, and it has a feature to maintain that the output signals are in phase. Simulation results support effectiveness of the method.

In this paper, an adaptive bound reduced-form genetic algorithm (ABRGA) to tune the control points of B-spline neural networks is proposed. It is developed not only to search for the optimal control points but also to adaptively tune the bounds of the control points of the B-spline neural networks by enlarging the search space of the control points. To improve the searching speed of the reduced-form genetic algorithm (RGA), the ABRGA is derived, in which better bounds of control points of B-spline neural networks are determined and paralleled with the optimal control points searched. It is shown that better efficiency is obtained if the bounds of control points are adjusted properly for the RGA-based B-spline neural networks.

We introduce an inheritance concept into a specification method of a concurrent system in order to reuse and refine existing specifications. Reusability by inheritance is emphasized in this paper. We take multiple inheritance to enable to reuse several specifications at a time. An upper specification can be skillfully reused by dividing inherited parts and non-inherited ones if the specification contains unnecessary parts for a lower specification. As an application, we specify the FIPA contract net interaction protocol (IP) with the function of an agent authentication. This is accomplished by using multiple inheritance. We also specify the FIPA iterated contract net IP by reusing the FIPA contract net IP. We have been developing a validation support tool for specifications described with the proposed method.

In this paper, we propose an optimal bit rate control algorithm which is fully compatible with MPEG-4 or H.263+. The proposed algorithm is designed to identify the optimal quantizer set through Lagrangian optimization when used for optimal bit allocation. To find the optimal quantizer set, we make use of the Viterbi algorithm in order to solve the dependency between quantization parameters of each macroblock due to the unique characteristics of MPEG-4 or H.263+. We set the Lagrangian cost function as a cost function of the Viterbi algorithm. We implement the proposed algorithm in MPEG-4 coders and compare its performance to the VM8 and optimal bit rate control algorithm, using independent quantization parameters in the circumstance of a low bit rate.

This paper describes a new fast learning algorithm for Self-Organizing Map employing a "rough comparison winner-take-all" and its digital hardware architecture. In rough comparison winner-take-all algorithm, the winner unit is roughly and strictly assigned in early and later learning stage, respectively. It realizes both of high accuracy and fast learning. The digital hardware of the self-organizing map with proposed WTA algorithm is implemented using FPGA. Experimental results show that the designed hardware is superior to other hardware with respect to calculation speed.

Nonminimum phase systems are difficult to be controlled with a conventional PID-type controller because of their inherent characteristics of undershooting. A neuro-controller combined with a PID-type controller has been shown to improve the control performance of the nonminimum phase systems while maintaining stability. In this paper, we apply a multiobjective evolutionary optimization method for training the neuro-controller to reduce the undershooting of the nonminimum phase system. The computer simulation shows that the proposed multiobjective approach is very effective and suitable because it can minimize the control error as well as reduce undershooting and chattering. This method can be applied to many industrial nonminimum phase problems with ease.

When we try to solve Multiobjective Optimization Problems (MOPs) using an evolutionary algorithm, the Pareto Genetic Algorithm (Pareto GA) introduced by Goldberg in 1989 has now become a sort of standard. After the first introduction, this approach was further developed and lead to many applications. All of these approaches are based on Pareto ranking and use the fitness sharing function to maintain diversity. On the other hand in the early 50's another scheme was presented by Nash. This approach introduced the notion of Nash Equilibrium and aimed at solving optimization problems having multiobjective functions that are originated from Game Theory and Economics. Since the concept of Nash Equilibrium as a solution of these problems was introduced, game theorists have attempted to formalize aspects of the equilibrium solution. The Nash Genetic Algorithm (Nash GA), which is introduced by Sefrioui, is the idea to bring together genetic algorithms and Nash strategy. The aim of this algorithm is to find the Nash Equilibrium of MOPs through the genetic process. Another central achievement of evolutionary game theory is the introduction of a method by which agents can play optimal strategies in the absence of rationality. Not the rationality but through the process of Darwinian selection, a population of agents can evolve to an Evolutionary Stable Strategy (ESS) introduced by Maynard Smith in 1982. In this paper, we propose Game theory based Co-Evolutionary Algorithm (GCEA) and try to find the ESS as a solution of MOPs. By applying newly designed co-evolutionary algorithm to several MOPs, the first we will confirm that evolutionary game can be embodied by co-evolutionary algorithm and this co-evolutionary algorithm can find ESSs as a solutions of MOPs. The second, we show optimization performance of GCEA by applying this model to several test MOPs and comparing with the solutions of previously introduced evolutionary optimization algorithms.

Both the Hopfield network and the genetic algorithm are powerful tools for object recognition tasks, e.g., subgraph matching problems. Unfortunately, they both have serious drawbacks. The Hopfield network is very sensitive to its initial state, and it stops at a local minimum if the initial state is not properly given. The genetic algorithm, on the other hand, usually only finds a near-global solution, and it is time-consuming for large-scale problems. In this paper, we propose an integrated scheme of these two methods, while eliminating their drawbacks and keeping their advantages, to solve object recognition problems under affine transformations. Some arrangements and programming strategies are required. First, we use some specialized 2-D genetic algorithm operators to accelerate the convergence. Second, we extract the "seeds" of the solution of the genetic algorithm to serve as the initial state of the Hopfield network. This procedure further improves the efficiency of the system. In addition, we also include several pertinent post matching algorithms for refining the accuracy and robustness. In the examples, the proposed scheme is used to solve some subgraph matching problems with occlusions under affine transformations. As shown by the results, this integrated scheme does outperform many counterpart algorithms in accuracy, efficiency, and stability.

The hybrid fiber coax (HFC) technology enables the conventional cable-television (CATV) network to provide subscribers with Internet access services. In this paper, we propose a new preemptive priority scheme (PPS) for IEEE 802.14 hybrid fiber coax (HFC) networks with the intelligent nodes (INs). The INs are placed between the headend controller and stations. By using INs, that stand for downstream subscribers to contend for the demand resources, the collision probability, and the collision resolving period can be reduced. In this paper, we further extend such network architecture to support multi-priority access. In each IN or individual station, the proposed PPS will prevent a higher priority request from colliding with requests of lower priority. Moreover, in PPS, the granted bandwidth for lower priority requests can be preempted by the waiting request with higher priority. This will speedup the channel capture by priority data. The efficiency of PPS is investigated by simulations. Simulation results show that by adopting INs with PPS to be an agent for subscribers can not only shorten the collision resolving period but also minimize the average request delay of priority data.

Let G be an undirected graph and let Γ be its drawing on a plane. Each vertex in G has a label with a specified size. In this paper, we consider the problem of placing the maximum number of vertex labels in Γ in such a way that they do not overlap any vertices, edges or other labels. By refining several portions of the Kakoulis-Tollis algorithm for labeling graphical features, we present a heuristic algorithm for this problem. Experimental results show that our algorithm can place more labels than previous algorithms.

This paper proposes and theoretically evaluates two different schemes of code acquisition for pulse-position modulation (PPM) and overlapping PPM (OPPM) fiber-optic code-division multiple-access (CDMA) systems, namely threshold-based and demodulator-based code acquisition. Single-dwell detector and serial-search algorithm are employed for both schemes. Theoretical analysis is carried out for shot-noise-limited photon-counting receiver. Discussions upon effects of various parameter settings on the performance of code acquisition for PPM/OPPM fiber-optic CDMA systems, such as index of overlap, PPM/OPPM multiplicity, average photon counts per information nat, and darkcurrents, are presented. It is shown that when the threshold is properly selected, the threshold-based code acquisition system offers better performance, in terms of mean number of training frames, than the demodulator-based one.

PID control schemes have been widely used for many industrial processes, which can be represented by nonlinear systems. In this paper a new scheme for neural-net based PID controllers is presented. The connection weights and some parameters of the sigmoidal functions of the neural network are adjusted using a real-coded genetic algorithm. The effectiveness of the newly proposed control scheme for nonlinear systems is numerically evaluated using a simulation example.

In this paper we propose an inter-block evaluation method to further reduce evaluation numbers in GA-based image halftoning technique. We design the algorithm to avoid noise in the fitness function by evolving all image blocks concurrently, exploiting the inter-block correlation, and sharing information between neighbor image blocks. The effectiveness of the method when the population and image block size are reduced, and the configuration of selection and genetic operators are investigated in detail. Simulation results show that the proposed method can remarkably reduce the entire evaluation numbers to generate high quality bi-level halftone images by suppressing noise around block boundaries.

An empirical formula of resonant frequency of bow-tie microstrip antennas is presented, which is based on the cavity model of microstrip patch antennas. A procedure to design a bow-tie antenna using genetic algorithm (GA) in which we take the formula as a fitness function is also given. An optimized bow-tie antenna by genetic algorithm was constructed and measured. Numerical and experimental results are used to validate the formula and GA. The results are in good agreement.

This paper deals with an on-line identification method based on a radial basis function (RBF) network model for continuous-time nonlinear systems. The nonlinear term of the objective system is represented by the RBF network. In order to track the time-varying system parameters and nonlinear term, the recursive least-squares (RLS) method is combined in a bootstrap manner with the genetic algorithm (GA). The centers of the RBF are coded into binary bit strings and searched by the GA, while the system parameters of the linear terms and the weighting parameters of the RBF are updated by the RLS method. Numerical experiments are carried out to demonstrate the effectiveness of the proposed method.