TCP Performance Improvement with ACK Pacing in Wireless Data Networks
Wireless data services are fundamental in ubiquitous networks. In wireless data networks, the TCP congestion mechanism can mistake a wireless channel error or blackout for congestion on the route. Several wireless TCP protocols have been proposed considering TCP performance degradation in wireless systems. However, these protocols are too complex to deploy in wireless systems. In this paper, we propose the ACK Pacing algorithm for long lasting loss periods like a hand-off or a blackout. By pacing ACK packets sending, we prevent bursty data delivery to the old path and a spurious timeout occurrence. Also, paced ACK packets trigger the route update to the new path faster. The proposed algorithm can be implemented in the wireless-node-side which is easily applicable to wireless systems. From simulation results, ACK Pacing improves TCP performance compared with many TCP variations. The ACK Pacing algorithm shows good performance during a long lasting loss period.

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: Evolutionary Learning of Modular Neural Networks with Genetic Programming
abstract: 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: 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: 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