• Journal of KIISE:Information Networking
• /
• v.34 no.6
• /
• pp.435-445
• /
• 2007

Snoop protocol is one of the efficient schemes to compensate TCP packet loss and enhance TCP throughput in wired-cum-wireless networks. However, Snoop protocol has a problem: it cannot perform local retransmission efficiently under the bursty-error prone wireless link. To solve this problem, SACK-Aware-Snoop and SNACK mechanism have been proposed. These approaches improve the performance by using SACK option field between base station and mobile host. However in the wireless channel with high packet loss rate, SACK-Aware-Snoop and SNACK mechanism do not work well because of two reason: (a) end-to-end performance is degraded because duplicate ACKs themself can be lost in the presence of bursty error, (b) energy of mobile device and bandwidth utilization in the wireless link are wasted unnecessarily because of SACK option field in the wireless link. In this paper, we propose a new local retransmission scheme based on Cross-layer approach, called Cross-layer Snoop(C-Snoop) protocol, to solve the limitation of previous localized link layer schemes. C-Snoop protocol includes caching lost TCP data and performing local retransmission based on a few policies dealing with MAC-layer's timeout and local retransmission timeout. From the simulation result, we could see more improved TCP throughput and energy efficiency than previous mechanisms.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.53 no.6
• /
• pp.110-121
• /
• 2016

The primary aim of the color constancy algorithm is to estimate illuminant chromaticity. There are various statistical-based, learning-based and combinational-based color constancy algorithms already exist. However, the statistical-based algorithms can only perform well on images that satisfy certain assumptions, learning-based methods are complex methods that require proper preprocessing and training data, and combinational-based methods depend on either pre-determined or dynamically varying weights, which are difficult to determine and prone to error. Therefore, this paper presents a new optimization based illuminant estimation method which is free from complex preprocessing and can estimate the illuminant under different environmental conditions. A strong color cast always has an odd standard deviation value in one of the RGB channels. Based on this observation, a cost function called the degree of illuminant tinge(DIT) is proposed to determine the quality of illuminant color-calibrated images. This DIT is formulated in such a way that the image scene under standard illuminant (d65) has lower DIT value compared to the same scene under different illuminant. Here, a swarm intelligence based particle swarm optimizer(PSO) is used to find the optimum illuminant of the given image that minimizes the degree of illuminant tinge. The proposed method is evaluated using real-world datasets and the experimental results validate the effectiveness of the proposed method.