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In this paper, we address the issue of providing quality-of-service (QoS) in an optical burst-switched network. QoS is provided by introducing prioritized contention resolution policies in the network core and a composite burst-assembly technique at the network edge. In the core, contention is resolved through prioritized burst segmentation and prioritized deflection. The burst segmentation scheme allows high-priority bursts to preempt low-priority bursts and enables full class isolation between bursts of different priorities. At the edge of the network, a composite burst-assembly technique combines packets of different classes into the same burst, placing lower class packets toward the tail of the burst. By implementing burst segmentation in the core, packets that are placed at the tail of the burst are more likely to be dropped than packets that are placed at the head of the burst. The proposed schemes are evaluated through analysis and simulation, and it is shown that significant differentiation with regard to packet loss and delay can be achieved. Index Terms-Burst assembly, burst segmentation, Internet protocol (IP), optical burst switching (OBS), quality-of-service (QoS), wavelength-division multiplexing (WDM). Vinod M. Vokkarane (S'02) received the B.Eng.

Worldwide, in 2014, more than 1.9 billion adults, 18 years and older, were overweight. Of these, over 600 million were obese. Accurately documenting dietary caloric intake is crucial to manage weight loss, but also presents challenges because most of the current methods for dietary assessment must rely on memory to recall foods eaten. The ultimate goal of our research is to develop computer-aided technical solutions to enhance and improve the accuracy of current measurements of dietary intake. Our proposed system in this paper aims to improve the accuracy of dietary assessment by analyzing the food images captured by mobile devices (e.g., smartphone). The key technique innovation in this paper is the deep learning-based food image recognition algorithms. Substantial research has demonstrated that digital imaging accurately estimates dietary intake in many environments and it has many advantages over other methods. However, how to derive the food information (e.g., food type and portion size) from food image effectively and efficiently remains a challenging and open research problem. We propose a new Convolutional Neural Network (CNN)-based food image recognition algorithm to address this problem. We applied our proposed approach to two real-world food image data sets (UEC-256 and Food-101) and achieved impressive results. To the best of our knowledge, these results outperformed all other reported work using these two data sets. Our experiments have demonstrated that the proposed approach is a promising solution for addressing the food image recognition problem. Our future work includes further improving the performance of the algorithms and integrating our system into a real-world mobile and cloud computing-based system to enhance the accuracy of current measurements of dietary intake.

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