Abstract:We report the design criteria and performance of Si ring resonators for passive athermal applications in wavelength division multiplexing (WDM). The waveguide design rules address i) positivenegative thermo-optic (TO) composite structures, ii) resonant wavelength dependent geometry to achieve constant confinement factor ( Γ ), and iii) observation of small residual second order effects. We develop exact design requirements for a temperature dependent resonant wavelength shift (TDWS) of 0 pm/K and present prototype TDWS performance of 0.5pm/K. We evaluate the materials selection tradeoffs between high-index contrast (HIC) and low-index contrast (LIC) systems and show, remarkably, that FSR and footprint become comparable under the constraint of athermal design.
The IEEE 802.16 standard, also known as WiMax, provides a mechanism for deploying high-speed wireless mesh network in metropolitan areas. In this paper, we propose an algorithm for the data sub-channel allocation, i.e., transmission scheduling, of WiMax based mesh networks. The goal is to increase spatial reuse, achieve high system throughput, and provide fair access for the subscriber stations. In contrast to the previous "hard" fairness definitions, we introduce a new fairness notion that is imposed contingent on the actual traffic demands, in such a way that a higher capacity region can be achieved. We formulate a scheduling problem whose objective is to maximize the system throughput under our fairness model. We also develop an efficient algorithm to find the optimal schedule and the accompanying resource allocation. The performance of the scheduling algorithm is evaluated through simulations.
We present a user modeling system that serves as the foundation of a personal assistant. The system ingests web search history for signed-in users, and identifies coherent contexts that correspond to tasks, interests, and habits. Unlike past work which focused on either in-session tasks or tasks over a few days, we look at several months of history in order to identify not just short-term tasks, but also long-term interests and habits. The features we use for identifying coherent contexts yield substantially higher precision and recall than past work. We also present an algorithm for identifying contexts that is 8 to 30 times faster than previous algorithms. The user modeling system has been deployed in production. It runs over hundreds of millions of users, and updates the models with a 10-minute latency. The contexts identified by the system serve as the foundation for generating recommendations in Google Now.
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