Abstract-This paper considers the design of cross-layer opportunistic transport for stored video over wireless networks with a slow varying (average) capacity. We focus on two key ideas: (1) scheduling data transmissions when capacity is high; and (2), exploiting knowledge of future capacity variations. The latter is possible when users' mobility is known or predictable, e.g., users riding on public transportation or using navigation systems. We consider the design of cross-layer transmission schedules which minimize system utilization (and thus possibly transmit/receive energy) while avoiding, if at all possible, rebuffering/delays, in several scenarios. For the single-user anticipative case where all future capacity variations are known beforehand; we establish the optimal transmission schedule is a Generalized Piecewise Constant Thresholding (GPCT) scheme. For the single-user partially anticipative case where only a finite window of future capacity variations is known, we propose an online Greedy Fixed Horizon Control (GFHC). An upper bound on the competitive ratio of GFHC and GPCT is established showing how performance loss depends on the window size, receiver playback buffer, and capacity variability. Finally we consider the multiuser case where we can exploit both future temporal and multiuser diversity. Our simulations and evaluation based on a measured wireless capacity trace exhibit robust potential gains for our proposed transmission schemes.
Medical text classification assigns medical related text into different categories such as topics or disease types. Machine learning based techniques have been widely used to perform such tasks despite the obvious drawback in such ''black box'' approach, leaving no easy way to fine-tune the resultant model for better performance. We propose a novel constructive heuristic approach to generate a set of regular expressions that can be used as effective text classifiers. The main innovation of our approach is that we develop a novel regular expression based text classifier with both satisfactory classification performance and excellent interpretability. We evaluate our framework on real-world medical data provided by our collaborator, one of the largest online healthcare providers in the market, and observe the high performance and consistency of this approach. Experimental results show that the machine-generated regular expressions can be effectively used in conjunction with machine learning techniques to perform medical text classification tasks. The proposed methodology improves the performance of baseline methods (Naive Bayes and Support Vector Machines) by 9% in precision and 4.5% in recall. We also evaluate the performance of modified regular expressions by human experts and demonstrate the potential of practical applications using the proposed method. INDEX TERMS Regular expressions, text classification, constructive heuristic method.
For fast, easy modeling, this practical guide provides all the essential information you need to know. A wide range of topics is covered, including custom protocols, programming in C++, External Model Access (EMA) modeling and co-simulation with external systems, giving you the guidance not provided in the OPNET documentation. A set of high-level wrapper APIs is also included to simplify programming custom OPNET models, whether you are a newcomer to OPNET or an experienced user needing to model efficiently. From the basic to the advanced, you will find topics are easy to follow with theory kept to a minimum, many practical tips and answers to frequently asked questions spread throughout the book and numerous step-by-step case studies and real-world network scenarios included.
The complexity of channel scheduling in MultiRadio Multi-Channel (MR-MC) wireless networks is an open research topic. This problem asks for the set of edges that can support maximum amount of simultaneous traffic over orthogonal channels under a certain interference model. There exist two major interference models for channel scheduling, with one under the physical distance constraint, and one under the hop distance constraint. The complexity of channel scheduling under these two interference models serves as the foundation for many problems related to network throughput maximization. However, channel scheduling was proved to be NP-Hard only under the hop distance constraint for SR-SC wireless networks. In this paper, we fill the void by proving that channel scheduling is NP-Hard under both models in MR-MC wireless networks. In addition, we propose a polynomial-time approximation scheme (PTAS) framework that is applicable to channel scheduling under both interference models in MR-MC wireless networks. Furthermore, we conduct a comparison study on the two interference models and identify conditions under which these two models are equivalent for channel scheduling.
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