7724198
Admission Control for Providing QoS in Wireless Mesh Networks
An admission control algorithm should be properly designed to guarantee the quality of service (QoS) in wireless mesh networks (WMNs). Based on channel business ratio, an admission control algorithm (ACA) is proposed to provide QoS for realtime and non-realtime traffic. For realtime traffic, all the nodes on a route make the admission control decision based on the estimation of available bandwidth. For non-realtime traffic, a rate adaption algorithm is proposed to adjust the sending rates of the source nodes to prevent a network from entering a saturated status. Finally, we demonstrate the effectiveness by simulations in NS-2.
45281264
Optimum Power Assignment for Maximum Throughput in CDMA Personal Communication Systems with Integrated Voice/Data Traffic
A CDMA personal communication system with integrated voice/data traffic is considered, in which the link error performance is controlled according to the voice error rate requirement, and the acceptable data traffic error rate is ensured by ARQ. Optimum power assignment (or allocation) between voice and data users is investigated to maximize the total system throughput. A graphical method (the tangent method) is described to obtain this optimum power allocation. The maximum throughput is expressed as a function of other system parameters. The tangent method is further used to measure the impact of transmission quality on the maximum data throughput. Numerical results and a design example are given for a power controlled wideband IS-95 type wireless personal communication system.
4966601
Power controlled network protocols for Multi-Rate ad hoc networks
In this paper, we propose for multi-rate ad hoc networks a cross-layer design using power control, called MRPC. MRPC consists of two parts. First, we propose a multi-rate power controlled MAC protocol, called MRPC-MAC. By carefully controlling the transmission power, it can enable concurrent transmissions, which is otherwise impossible for the IEEE 802.11 standard. Second, we propose a multi-rate power controlled routing protocol, called MRPC-Routing. Different from traditional routing protocols, MRPC-Routing is not intended to find end-to-end paths, rather, it determines the next hop right before transmitting packets at the MAC layer. In this protocol, it uses the effective transport capacity as the routing metric such that short links with high bandwidth are preferred and more concurrent transmissions can be enabled. Having these coupled power controlled MAC protocol and routing protocol, MRPC can greatly improve the spatial reuse and the network throughput. Simulation results also show MRPC-MAC, MRPC-routing, and especially MRPC, can improve the network throughput significantly.
8088080
Data-driven fuzzy rule generation and its application for student academic performance evaluation
Several approaches using fuzzy techniques have been proposed to provide a practical method for evaluating student academic performance. However, these approaches are largely based on expert opinions and are difficult to explore and utilize valuable information embedded in collected data. This paper proposes a new method for evaluating student academic performance based on data-driven fuzzy rule induction. A suitable fuzzy inference mechanism and associated Rule Induction Algorithm is given. The new method has been applied to perform Criterion-Referenced Evaluation (CRE) and comparisons are made with typical existing methods, revealing significant advantages of the present work. The new method has also been applied to perform Norm-Referenced Evaluation (NRE), demonstrating its potential as an extended method of evaluation that can produce new and informative scores based on information gathered from data.
14165514
Speeding up the learning of equivalence classes of bayesian network structures
R. Daly, Q. Shen and S. Aitken. Speeding up the learning of equivalence classes of Bayesian network structures. Proceedings of the 10th International Conference on Artificial Intelligence and Soft Computing, pages 34-39.
6529692
Probabilistic abductive computation of evidence collection strategies in crime investigation
This paper presents a methodology for integrating two approaches to building decision support systems (DSS) for crime investigation: symbolic crime scenario abduction [16] and Bayesian forensic evidence evaluation [5]. This is achieved by means of a novel compositional modelling technique that allows for automatically generating a space of models describing plausible crime scenarios from given evidence and formally represented domain knowledge. The main benefit of this integration is that the resulting DSS is capable to formulate effective evidence collection strategies useful for differentiating competing crime scenarios. A running example is used to demonstrate the theoretical developments.
14492081
Linguistic Bayesian Networks for reasoning with subjective probabilities in forensic statistics
Recent work in forensic statistics has shown how Bayesian Networks (BNs) can be used to infer the probability of defence and prosecution statements based on forensic evidence. This is an important development as it helps to quantify the meaning of forensic expert testimony during court proceedings, for example, that there is "strong support" for the defence or prosecution position. Due to the lack of experimental data, inferred probabilities often rely on subjective probabilities provided by experts. Because these are based on informed guesses, it is very difficult to express them accurately with precise numbers. Yet, conventional BNs can only employ probabilities expressed as real numbers. To address this issue, this paper presents a novel extension of probability theory. This allow the expression of subjective probabilities as fuzzy numbers, which more faithfully reflect expert opinion. By means of practical a example, it will be shown that the accurate representation of this lack of precision in reasoning with subjective probabilities has important implications for the overall result.
27533432
Towards temporal linguistic variables
Although fuzzy logic has been successfully applied in many different problem domains, fuzzy solutions typically structure the problem task as one of static categorisation. This may not be appropriate for dynamic systems, especially where higher-order information is unavailable. The paper proposes an approach to reasoning about dynamic systems drawing upon the first-order approach to temporal logic. A brief overview of the classical theory is provided and extended to a fuzzy theory modelling the inherent vagueness of linguistic terms such as "recently". The notion of a temporal qualification is developed as a domain independent method for augmenting base domains and term set of a linguistic variable. Such extended variables are termed temporal linguistic variables. The technique is then applied to a simple dynamic system highlighting its advantage over traditional approaches. Since temporal linguistic variables are a special case of linguistic variable they are immediately applicable in fuzzy knowledge based system and machine learning contexts.