This paper presents an automatic synthesis method of active analog circuits that uses evolutionary search and employs some topological features of analog integrated circuits. Our system firstly generates a set of circuits at random, and then evolves their topologies and device sizing to fit an environment which is formed by the fitness function translated from the electrical specifications of the circuit. Therefore expert knowledge about circuit topologies and sizing are not needed. The capability of this method is demonstrated through experiments of automatic synthesis of CMOS operational amplifiers.

In this paper, we propose an approach to solve the power control issue in a DS-CDMA cellular system using genetic algorithms (GAs). The transmitter power control developed in this paper has been proven to be efficient to control co-channel interference, to increase bandwidth utilization and to balance the comprehensive services that are sharing among all the mobiles with attaining a common signal-to-interference ratio(SIR). Most of the previous studies have assumed that the transmitter power level is controlled in a constant domain under the assumption of uniform distribution of users in the coverage area or in a continuous domain. In this paper, the optimal centralized power control (CPC) vector is characterized and its optimal solution for CPC is presented using GAs in a large-scale DS-CDMA cellular system under the realistic context that means random allocation of active users in the entire coverage area. Emphasis is put on the balance of services and convergence rate by using GAs.

The reduction of the drain current for InGaAs/AlGaAs pseudomorphic high electron mobility transistors (PHEMTs) has been observed due to the RIE-damage induced under the gate region. However, it has been found that the drain current can be recovered after the gate-drain reverse current stress even at room temperature. The recovery rate of the drain current strongly depends on the gate-drain reverse current density. The activation energy of the recovery rate has been confirmed to decrease from 0.531 eV to 0.119 eV under the gate-drain reverse current stress. This phenomenon can be understood as a recombination enhanced defect reaction of holes generated by the avalanche breakdown. The non-radiative recombination of holes at the defect level is believed to enhance the recovery of the RIE-damage.

We propose an encoding method for one-point algebraic geometry codes that usually requires less computation than the ordinary systematic encoder.

A new algorithm for the maximum a posteriori (MAP) decoding of linear block codes is presented. The proposed algorithm can be regarded as a conventional BCJR algorithm for a section trellis diagram, where branch metrics of the trellis are computed by the recursive MAP algorithm proposed by the authors. The decoding complexity of the proposed algorithm depends on the sectionalization of the trellis. A systematic way to find the optimum sectionalization which minimizes the complexity is also presented. Since the algorithm can be regarded as a generalization of both of the BCJR and the recursive MAP algorithms, the complexity of the proposed algorithm cannot be larger than those algorithms, as far as the sectionalization is chosen appropriately.

Before we gave a fast generalized minimum distance (GMD) decoding algorithm for one-point algebraic-geometry (AG) codes. In this paper, we propose another fast GMD decoding algorithm for these codes, where the present method includes an erasure deletion procedure while the past one uses an erasure addition procedure. Both methods find a minimal polynomial set of a given syndrome array, which is a candidate for an erasure-and-error locator polynomial set constrained with an erasure locator set of each size. Although both erasure addition and deletion GMD decoding algorithms have been established for one-dimensional algebraic codes such as RS codes, nothing but the erasure addition GMD decoding algorithm for multidimensional algebraic codes such as one-point AG codes have been given. The present erasure deletion GMD decoding algorithm is based on the Berlekamp-Massey-Sakata (BMS) algorithm from the standpoint of constrained multidimensional shift register synthesis. It is expected that both our past and present methods play a joint role in decoding for one-point AG codes up to the error correction bound.

A coded modulation scheme based on a low density parity check (LDPC) code is presented. A modified sum-product algorithm suitable for the LDPC-coded modulation scheme is also devised. Several simulation results show the excellent decoding performance of the proposed coding scheme. For example, an LDPC-coded 8PSK scheme of block length 3976 symbols achieves the symbol error probability 10-5 at only 1.2 dB away from the Shannon limit of the channel.

In this paper we consider a tensor-based approach to the analytical computation of higher-order expectations of quantized trajectories generated by Piecewise Affine Markov (PWAM) maps. We formally derive closed-form expressions for expectations of trajectories generated by three families of maps, referred to as (n,t)-tailed shifts, (n,t)-broken identities and (n,t,π)-mixing permutations. These families produce expectations with asymptotic exponential decay whose detailed profile is controlled by map design. In the (n,t)-tailed shift case expectations are alternating in sign, in the (n,t)-broken identity case they are constant in sign, and the (n,t,π)-mixing permutation case they follow a dumped periodic trend.

Network caches reduce network traffic as well as user response time. When implementing network caches, the object replacement problem is one of the core problems; The problem is to determine which objects should be evicted from a cache when there is insufficient space. This paper first formalizes the problem and gives a simple but sufficient condition for deterministic online algorithms to be competitive. Based on the condition, a general framework to make a non-competitive algorithm competitive is constructed. As an application of the framework, an online algorithm, called Competitive_SIZE, is proposed. Both event-driven and trace-driven simulations show that Competitive_SIZE is better than previously proposed algorithms such as LRU (Least Recently Used).

In this paper, we are concerned in obtaining multicast trees in packet-switched networks such as ATM nets, when there exist constraints on the packet (cell)-replication capabilities of the individual switching nodes. This problem can be formulated as the Steiner tree problem with degree bounds on the nodes, so we call it the Degree-Constrained Steiner Tree problem (DCST). Four heuristic algorithms are proposed: the first is a combined version of two well-known Steiner tree algorithms, heuristic Naive and the shortest path heuristic (SPH), and the second is a relaxation algorithm based on a mathematical formulation of the DCST, and the last two use a tree reconfiguration scheme based on the concept of 'logical link. ' We experimentally compare our algorithms with the previous ones in three respects; number of solved instances, objective value or tree cost, and computation time. The experimental results show that there are few instances unsolved by our algorithms, and the objective values are mostly within 5% of optimal. Computation times are also acceptable.

Web caching has become an important problem when addressing the performance issues in Web applications. The expiration time of the Web data item is useful a piece of information for performance enhancement in Web caching. In this paper, we introduce the notion of the effective reference probability that incorporates the effect of expiration time for Web caching. For a formal approach, we propose the continuous independent reference model extending the existing independent reference model. Based on this model, we define formally the effective reference probability and derive it theoretically. By simply replacing the reference probability in the existing cache replacement algorithms with the effective reference probability, we can take the effect of expiration time into account. The results of performance experiments show that the replacement algorithms using the effective reference probability always outperform existing ones. In particular, when the cache fraction is 0.05 and data update is comparatively frequent (i.e., the update frequency is more than 1/10 of the reference frequency), the performance is enhanced by more than 30% in LRU-2 and 13% in Aggarwal's method. The results show that the effective reference probability significantly enhances the performance of Web caching when the expiration time is given.

Investigations into the suitability of artificial neural network for the prediction of rain attenuation based on radio, meteorological and geographical data from ITU-R data bank are presented. First successful steps towards a prediction model of rain attenuation for radio communication based on adaptive learning from the measurement are made. Rain attenuation prediction with the model based on artificial neural network shows good conformity with the measurement. Moreover, a new evolutionary system, EPNet is used to evolve the artificial neural network rain attenuation model obtained both in architecture and weight, and an optimal rain attenuation model with simpler architecture and better prediction accuracy based on EPNet-evolved artificial neural network is obtained. Compared with the ITU-R model, the EPNet-evolved artificial neural network model of rain attenuation proposed in this paper improves the accuracy of rain attenuation prediction and creates a novel way to predict rain attenuation.

This paper deals with the approximation of multi-dimensional chaotic dynamics by using the multi-stage fuzzy inference system. The number of rules included in multi-stage fuzzy inference systems is remarkably smaller compared to conventional fuzzy inference systems where the number of rules are proportional to an exponential of the number of input variables. We also propose a method to optimize the shape of membership function and the appropriate selection of input variables based upon the genetic algorithm (GA). The method is applied to the approximation of typical multi-dimensional chaotic dynamics. By dividing the inference system into multiple stages, the total number of rules is sufficiently depressed compared to the single stage system. In each stage of inference only a portion of input variables are used as the input, and output of the stage is treated as an input to the next stage. To give better performance, the shape of the membership function of the inference rules is optimized by using the GA. Each individual corresponds to an inference system, and its fitness is defined by using the prediction error. Experimental results lead us to a relevant selection of the number of input variables and the number of stages by considering the computational cost and the requirement. Besides the GA in the optimization of membership function, we use the GA to determine the input variables and the number of input. The selection of input variable to each stage, and the number of stages are also discussed. The simulation study for multi-dimensional chaotic dynamics shows that the inference system gives better prediction compared to the prediction by the neural network.

Given a graph G=(V,E), five distinct vertices u1,u2,u3,u4,u5 V and five natural numbers n1,n2,n3,n4,n5 such that Σ5i=1ni=|V|, we wish to find a partition V1,V2,V3,V4,V5 of the vertex set V such that ui Vi, |Vi|=ni and Vi induces a connected subgraph of G for each i, 1i5. In this paper we give a simple linear-time algorithm to find such a partition if G is a 5-connected internally triangulated plane graph and u1,u2,u3,u4,u5 are located on the outer face of G. Our algorithm is based on a "5-canonical decomposition" of G, which is a generalization of an st-numbering and a "canonical ordering" known in the area of graph drawings.

Software defined radio (SDR) is receiving much attention as the key technology to realize the next generation wireless communication system. This paper proposes the concept of system diversity on SDR and investigates the effectiveness of system diversity by using a concrete simulation model. System diversity allows the wireless communication system being used to be dynamically changed in addition to the signal processing algorithm or modulation/coding scheme being used. To clarify the validity of system diversity, we examine a system simulation model consisting of three wireless communication systems; algorithms are introduced to show how system diversity can be controlled using the QoS parameters of received signal level, data transmission rate, and channel capacity. The process by which system diversity switching is triggered is elucidated, and a practical example is introduced. Simulation results confirm that system diversity offers higher performance in terms of data throughput and system channel capacity than existing wireless communication systems. Finally, a comprehensive algorithm is described that protects existing single-mode traffic from being degraded by SDR switching.

A halftoning technique that uses a simple GA has proven to be very effective to generate high quality halftone images. Recently, the two major drawbacks of this conventional halftoning technique with GAs, i.e. it uses a substantial amount of computer memory and processing time, have been overcome by using an improved GA (GA-SRM) that applies genetic operators in parallel putting them in a cooperative-competitive stand with each other. The halftoning problem is a true multiobjective optimization problem. However, so far, the GA based halftoning techniques have treated the problem as a single objective optimization problem. In this work, the improved GA-SRM is extended to a multiobjective optimization GA to simultaneously generate halftone images with various combinations of gray level precision and spatial resolution. Simulation results verify that the proposed scheme can effectively generate several high quality images simultaneously in a single run reducing even further the overall processing time.

Routing security is related to the confidentiality of the route taken by the data transmitted over the network. If the route is detected by the adversary, the probability is higher that the data are lost or the data can be intercepted by the adversary. Therefore, the route must be protected. To accomplish this, we select an intermediate node secretly and transmit the data using this intermediate node, instead of sending the data to the destination node using the shortest path. Furthermore, if we use a number of secret routes from the starting node to the destination node, data security is much stronger since we can transmit partial data rather than the entire data along a secret route. In this paper, the routing algorithm for multiple secret paths on MRNS (Mixed Radix Number System) Network, which requires O(l) for the time complexity where l is the number of links on a node, is presented employing the HCLS (Hamiltonian Circuit Latin Square) and is analyzed in terms of entropy.

This paper proposes distributed evolutionary digital filters (EDFs) as an improved version of the original EDF. The EDF is an adaptive digital filter which is controlled by adaptive algorithm based on evolutionary computation. In the proposed method, a large population of the original EDF is divided into smaller subpopulations. Each sub-EDF has one subpopulation and executes the small-sized main loop of the original EDF. In addition, the distributed algorithm periodically selects promising individuals from each subpopulation. Then, they migrate to different subpopulations. Numerical examples show that the distributed EDF has a higher convergence rate and smaller steady-state value of the square error than the LMS adaptive digital filter, the adaptive digital filter based on the simple genetic algorithm and the original EDF.

The adaptive system design by 16-bit fixed point processing enables to employ an inexpensive digital signal processor (DSP). The narrow dynamic range of such 16 bits, however, does not guarantee the same performance that is confirmed beforehand by computer simulations. A cause of degrading the performance originates in the operation halving the word length doubled by multiplication. This operation rounds off small signals staying in the lower half of the doubled word length to zero. This problem can be solved by limiting the multiplier to only its sign () like the signed regressor algorithm, named 'bi-quantized-x' algorithm in this paper, for the convenience mentioned below. This paper first derives the equation describing the convergence property provided by a type of signed regressor algorithms, the bi-quantized-x normalized least mean square (NLMS) algorithm, and then formulates its convergence condition and the step size maximizing the convergence rate. This paper second presents a technique to improve the convergence property. The bi-qiantized-x NLMS algorithm quantizes the reference signal to 1 according to the sign of the reference signal, whereas the technique moreover assigns zero to the reference signal whose amplitude is less than a predetermined level. This paper explains the principle that the 'tri-qunatized-x' NLMS algorithm employing the technique can improve the convergence property, and confirms the improvement effect by computer simulations.

In this paper, a novel adaptive digital watermarking approach based upon human visual system model and fuzzy clustering technique is proposed. The human visual system model is utilized to guarantee that the watermarked image is imperceptible. The fuzzy clustering approach has been employed to obtain the different strength of watermark by the local characters of image. In our experiments, this scheme allows us to provide a more robust and transparent watermark.