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.
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.
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.
8758984
The evaluation of evidence for exponentially distributed data
At present, likelihood ratios for two-level models are determined with the use of a normal kernel estimation procedure when the between-group distribution is thought to be non-normal. An extension is described for a two-level model in which the between-group distribution is very positively skewed and an exponential distribution may be thought to represent a good model. The theoretical likelihood ratio is derived. A likelihood ratio based on a biweight kernel with an adaptation at the boundary is developed. The performance of this kernel is compared alongside those of normal kernels and normal and exponential parametric models. A comparison of performance is made for simulated data where results may be compared with those of theory, using the theoretical model, as the true parameter values for the models are known. There is also a comparison for forensic data, using the concentration of aluminium in glass as an exemplar. Performance is assessed by determining the numbers of occasions on which the likelihood ratios for sets of fragments from the same group are supportive of the proposition that they are from different groups and the numbers of occasions on which the likelihood ratios for sets of fragments from different groups are supportive of the proposition that they are from the same group.
8305796
Fuzzy Interpolation and Extrapolation: A Practical Approach
Fuzzy interpolation does not only help to reduce the complexity of fuzzy models, but also makes inference in sparse rule-based systems possible. It has been successfully applied to systems control, but limited work exists for its applications to tasks like prediction and classification. Almost all fuzzy interpolation techniques in the literature make strong assumptions that there are two closest adjacent rules available to the observation, and that such rules must flank the observation for each attribute. Also, some interpolation approaches cannot handle fuzzy sets whose membership functions involve vertical slopes. To avoid such limitations and develop a more practical approach, this paper extends the work of Huang and Shen. The result enables both interpolation and extrapolation which involve multiple fuzzy rules, with each rule consisting of multiple antecedents. Two realistic applications, namely truck backer-upper control and computer activity prediction, are provided in this paper to demonstrate the utility of the extended approach. Experiment-based comparisons to the most commonly used Mamdani fuzzy reasoning mechanism, and to other existing fuzzy interpolation techniques are given to show the significance and potential of this research.
58396793
Combining qualitative simulation and fuzzy sets
17692683
Webpage Classification with ACO-Enhanced Fuzzy-Rough Feature Selection
Due to the explosive growth of electronically stored information, automatic methods must be developed to aid users in maintaining and using this abundance of information effectively. In particular, the sheer volume of redundancy present must be dealt with, leaving only the information-rich data to be processed. This paper presents an approach, based on an integrated use of fuzzy-rough sets and Ant Colony Optimization (ACO), to greatly reduce this data redundancy. The work is applied to the problem of webpage categorization, considerably reducing dimensionality with minimal loss of information.