Evolutionary Learning of Modular Neural Networks with Genetic Programming
Evolutionary design of neural networks has shown a great potential as a powerful optimization tool. However, most evolutionary neural networks have not taken advantage of the fact that they can evolve from modules. This paper presents a hybrid method of modular neural networks and genetic programming as a promising model for evolutionary learning. This paper describes the concepts and methodologies for the evolvable model of modular neural networks, which might not only develop new functionality spontaneously, but also grow and evolve its own structure autonomously. We show the potential of the method by applying an evolved modular network to a visual categorization task with handwritten digits. Sophisticated network architectures as well as functional subsystems emerge from an initial set of randomly-connected networks. Moreover, the evolved neural network has reproduced some of the characteristics of natural visual system, such as the organization of coarse and fine processing of stimuli in separate pathways.

title: Modular neural networks evolved by genetic programming
abstract: We present an evolvable model of modular neural networks which are rich in autonomy and creativity. In order to build an artificial neural network which is rich in autonomy and creativity, we have adopted the ideas and methodologies of artificial life. The paper describes the concepts and methodologies for the evolvable model of modular neural networks, which will be able not only to develop new functionality spontaneously but also to grow and evolve its own structure autonomously. Although the ultimate goal of this model is to design the control system for such behavior based robots as Khepera, we have attempted to apply the mechanism to a visual categorization task with handwritten digits. The evolutionary mechanism has shown a strong possibility to generate useful network architectures from an initial set of randomly connected networks.

title: EIMD: A New Congestion Control for Fast Long-Distance Networks
abstract: In fast long-distance networks, TCP fails to fully utilize the bandwidth due to its congestion control mechanism A plethora of congestion control schemes that may enhance the performance of the transport protocol in fast long-distance networks have been proposed The proposed schemes aim to satisfy three requirements of congestion control schemes: bandwidth scalability, TCP friendliness, and RTT fairness However, due to the trade-off among these requirements, it is difficult to satisfy all the requirements simultaneously In this paper, we propose a new window-based congestion control scheme called EIMD (Exponential Increase/Multiplicative Decrease) that increases congestion window size exponentially to quickly grap the unutilized bandwidth We evaluate the performance of EIMD via computer simulations The simulation results show that EIMD satisfies the three requirements In addition, EIMD converges fast to the fair-share points.

title: A Unified Route Optimization Scheme for Network Mobility
abstract: Recently Network Mobility(NEMO) is being concerned as new mobility issue. Lots of NEMO issues are already being touched in IETF NEMO WG but the solution is still premature especially to the Route Optimization (RO). NEMO has several problem spaces that need RO such as nested tunnels problem. Unfortunately, there is no solution that can be universally applied as one for all that results in supporting the coherent network mobility. In this paper, we propose a unified route optimization scheme that can solve several types of RO problem by using Path Control Header (PCH). In our scheme, Home Agent (HA) does piggyback the PCH on the packet which is reversely forwarded from Mobile Router (MR). That enables any PCH-aware routing facility on the route to make a RO tunnel with MR using the Care-of address of MR contained in the PCH. By applying to some already known NEMO RO problems, we show that our scheme can incrementally optimize the routes via default HA-MR tunnel through the simple PCH interpretation.

title: Decompositions of T-generalized transformation semigroups
abstract: We introduce several decompositions of T-generalized transformation semigroups and investigate some of algebraic properties of them.

title: QoS oriented bandwidth management scheme for stable multimedia services on the IMT-2000 networks
abstract: We propose a bandwidth management scheme to efficiently guarantee the QoS of various services on IMT-2000 networks. The proposed bandwidth management scheme consists of the call admission control based on the guard channel and the time slot scheduling in wireless links. For the call admission control the preemption and deprivation method are used to overcome the problems in the previous guard channel scheme. The proposed time slot scheduling scheme allocates time slots based on the QoS requirements of each user. The time slots are dynamically scheduled by considering the current traffic conditions with the DQRUMA protocol. The simulation results show that the proposed bandwidth scheme has better performance than the previous works in terms of the handoff failure probability and the packet delay time.

title: A Data-Driven Statistical Approach to Analyzing Process Variation in 65nm SOI Technology
abstract: This paper presents a simple yet effective method to analyze process variations using statistics on manufacturing in-line data without assuming any explicit underlying model for process variations. Our method is based on a variant of principal component analysis and is able to reveal systematic variation patterns existing on a die-to-die and wafer-to-wafer level individually. The separation of die variation from wafer variation can enhance the understanding of a nature of the process uncertainty. Our case study based on the proposed decomposition method shows that the dominating die-to-die variation and wafer-to-wafer variation represent 31% and 25% of the total variance of a large set of in-line parameters in 65nm SOI CMOS technology