Abstract: In this study, an optimum design of hairpin resonator is being proposed. Design has been optimized by changing dimensions of a basic hairpin resonator in a manner so as to increase its sensitivity. Simulation in HFSS software was done to study effects on resonant frequency and quality factor with varying resonator dimensions. Three different polar liquids i.e distilled water, methanol and dimethyl sulfoxide, are used for sensitivity analysis. Introduction of these liquids into resonator gap results in perturbation of electric field. Shift in resonant parameters and their sensitivities is observed for these resonators with these liquids. The design in which maximum shift is obtained in these parameters was considered as optimized design. Optimized design resembled a cuboid with a split. This novel design is referred as split cuboid resonator. Results of both resonators are compared for validation of concept.

• Publication Date: 24-Apr-2019
• DOI: 10.15224/978-1-63248-169-6-08
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Abstract: Resource management and job scheduling are two problems that go hand-in-hand and the solutions to which are primarily dependent on the nature of workload. With increasing demand to automate the entire process from allocating resources to scheduling jobs efficiently, deep reinforcement learning techniques have been brought into the picture which adapt to the environment and learn from experience. In this paper, we present SchedQRM which classifies burst time of jobs based on their signature and employs Deep Q-Network algorithm to find an optimal solution for any arbitrary job set. We also evaluate our proposed work against state-of-the-art heuristics to show the efficacy of our approach.

• Publication Date: 24-Apr-2019
• DOI: 10.15224/978-1-63248-169-6-09
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