There is a growing emphasis to find alternative non-traditional ways to manage patients to ease the burden on health care services largely fuelled by a growing demand from sections of population that is ageing. In-hom...
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Backscatter communication networks receive much attention recently due to the small size and low power of backscatter nodes. As backscatter communication is often influenced by the dynamic wireless channel quality, ra...
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ISBN:
(纸本)9781467399548
Backscatter communication networks receive much attention recently due to the small size and low power of backscatter nodes. As backscatter communication is often influenced by the dynamic wireless channel quality, rate adaptation becomes necessary. Most existing approaches share a common drawback: they do not distinguish channel qualities from different nodes or sub-channels. Consequently, the transmission rate may be improperly selected, resulting in low network throughput. Through extensive experimental studies, we observe that channel diversity plays a significant role in rate selection. Therefore, there are opportunities of exploiting channel diversity for better rate adaptation, improving network throughput. In this paper, we propose a Channel-Aware Rate Adaptation framework (CARA) for backscatter communication networks. By employing a lightweight channel probing scheme, we are able to obtain fine-grained channel information that enables accurate channel estimation. We further design a novel channel selection algorithm, benefiting as many backscatter nodes as possible. On each selected channel, CARA chooses data rate with respect to the node that has the best channel condition. We implement CARA on commercial readers and the experiment results show that CARA achieves up to 4× goodput gain compared with state-of-the-art rate adaptation scheme.
A characteristic feature of differential-algebraic equations is that one needs to find derivatives of some of their equations with respect to time, as part of so called index reduction or regularisation, to prepare th...
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The standard software development life cycle heavily depends on requirements elicited from stakeholders. Based on those requirements, software development is planned and managed from its inception phase to closure. Du...
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The standard software development life cycle heavily depends on requirements elicited from stakeholders. Based on those requirements, software development is planned and managed from its inception phase to closure. Due to time and resource constraints, it is imperative to identify the high-priority requirements that need to be considered first during the software development process. Moreover, existing prioritization frameworks lack a store of historical data useful for selecting the most suitable prioritization technique of any similar project domain. In this paper, we propose a framework for prioritization of software requirements, called Re Pizer, to be used in conjunction with a selected prioritization technique to rank software requirements based on defined criteria such as implementation cost. ReP izer assists requirements engineers in a decision-making process by retrieving historical data from a requirements repository. Re Pizer also provides a panoramic view of the entire project to ensure the judicious use of software development resources. We compared the performance of Re Pizer in terms of expected accuracy and ease of use while separately adopting two different prioritization techniques, planning game(PG) and analytical hierarchy process(AHP). The results showed that Re Pizer performed better when used in conjunction with the PG technique.
As software architecture methods and tools become increasingly model-driven, evaluating architecture artifacts must adjust correspondingly. Model-driven evaluation of architecture quality has advantages over tradition...
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As software architecture methods and tools become increasingly model-driven, evaluating architecture artifacts must adjust correspondingly. Model-driven evaluation of architecture quality has advantages over traditional evaluation techniques, especially when applied in a model-driven context. One approach we found successful in performing model-driven analysis involves using model clone detection, whereby we detect subsystems that are similar to example systems that are positive and negative quality indicators. In this paper we present our ideas on applying model clone detection to realize model-driven evaluation of software architectures, which contain many high-level systems and interactions. We propose having model-based representations of architectural patterns and styles, and employing model clone detection to identify positive and negative architectural aspects for evaluation, including reliability and security. We provide our insights on how this research can be applied to popular architectural paradigms, relation to previous work, and present discussion points on how it will impact software architecture quality evaluation.
Document is unavailable: This DOI was registered to an article that was not presented by the author(s) at this conference. As per section 8.2.1.B.13 of IEEE's "Publication Services and Products Board Operatio...
Document is unavailable: This DOI was registered to an article that was not presented by the author(s) at this conference. As per section 8.2.1.B.13 of IEEE's "Publication Services and Products Board Operations Manual," IEEE has chosen to exclude this article from distribution. We regret any inconvenience.
Membrane computing, a recent branch of natural computing, has been gaining momentum attention in the last few decades due to its massive parallelism and efficient computation. Many researchers in the field of membrane...
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This paper investigates the multi-agent cooperation problems in Web services domain. For Pareto-optimal Nash equilibrium, reinforcement learning algorithms are used to solve the coordination problem in cooperative env...
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This paper investigates the multi-agent cooperation problems in Web services domain. For Pareto-optimal Nash equilibrium, reinforcement learning algorithms are used to solve the coordination problem in cooperative environments. Most previous works study the deterministic gain of a state. However, in practical service environments, the gain may be nondeterministic due to unstable Quality of Service (QoS). To avoid local optimal solution, we use an improved update function. The experimental results show that proposed reinforcement learning algorithm outperforms other learning methods.
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