Monotonically normal topological vector spaces are stratifiable

We prove that for any Hausdorff topological vector space E over the field R there exists A subset of E such that E is homeomorphic to a subset of A x R and A x R is homeomorphic to a subset of E. Using this fact we prove that E is monotonically normal if and only if E is stratifiable.

2.4 GHz Class-E power amplifier with transmissionline harmonic terminations

The design procedure, fabrication and measurement of a Class-E power amplifier with excellent second- and third-harmonic suppression levels are presented. A simplified design technique offering compact physical layout is proposed. With a 1.2 mm gate-width GaAs MESFET as a switching device, the amplifier is capable of delivering 19.2 dBm output power at 2.41 GHz, achieves peak PAE of 60% and drain efficiency of 69%, and exhibits 9 dB power gain when operated from a 3 V DC supply voltage. When compared to the classical Class-E two-harmonic termination amplifier, the Class-E amplifier employing three-harmonic terminations has more than 10% higher drain efficiency and 23 dB better third-harmonic suppression level. Experimental results are presented and good agreement with simulation is obtained. Further, to verify the practical implementation in communication systems, the Bluetooth-standard GFSK modulated signal is applied to both two- and three-harmonic amplifiers. The measured RMS FSK deviation error and RMS magnitude error were, for the three-harmonic case, 1.01 kHz and 0.122%, respectively, and, for the two-harmonic case, 1.09 kHz and 0.133%. © 2007 The Institution of Engineering and Technology.

Modeling energy consumption in error-prone IEEE 802.11-based wireless ad-hoc networks

In the IEEE 802.11 MAC layer protocol, there are different trade-off points between the number of nodes competing for the medium and the network capacity provided to them. There is also a trade-off between the wireless channel condition during the transmission period and the energy consumption of the nodes. Current approaches at modeling energy consumption in 802.11 based networks do not consider the influence of the channel condition on all types of frames (control and data) in the WLAN. Nor do they consider the effect on the different MAC and PHY schemes that can occur in 802.11 networks. In this paper, we investigate energy consumption corresponding to the number of competing nodes in IEEE 802.11's MAC and PHY layers in error-prone wireless channel conditions, and present a new energy consumption model. Analysis of the power consumed by each type of MAC and PHY over different bit error rates shows that the parameters in these layers play a critical role in determining the overall energy consumption of the ad-hoc network. The goal of this research is not only to compare the energy consumption using exact formulae in saturated IEEE 802.11-based DCF networks under varying numbers of competing nodes, but also, as the results show, to demonstrate that channel errors have a significant impact on the energy consumption.

Prediction- and simulation-error based perceptron training: Solution space analysis and a novel combined training scheme

Previous papers have noted the difficulty in obtaining neural models which are stable under simulation when trained using prediction-error-based methods. Here the differences between series-parallel and parallel identification structures for training neural models are investigated. The effect of the error surface shape on training convergence and simulation performance is analysed using a standard algorithm operating in both training modes. A combined series-parallel/parallel training scheme is proposed, aiming to provide a more effective means of obtaining accurate neural simulation models. Simulation examples show the combined scheme is advantageous in circumstances where the solution space is known or suspected to be complex. (c) 2006 Elsevier B.V. All rights reserved.

Support vector machine classification for large data sets via minimum enclosing ball clustering

Support vector machine (SVM) is a powerful technique for data classification. Despite of its good theoretic foundations and high classification accuracy, normal SVM is not suitable for classification of large data sets, because the training complexity of SVM is highly dependent on the size of data set. This paper presents a novel SVM classification approach for large data sets by using minimum enclosing ball clustering. After the training data are partitioned by the proposed clustering method, the centers of the clusters are used for the first time SVM classification. Then we use the clusters whose centers are support vectors or those clusters which have different classes to perform the second time SVM classification. In this stage most data are removed. Several experimental results show that the approach proposed in this paper has good classification accuracy compared with classic SVM while the training is significantly faster than several other SVM classifiers.

WASP-4b: A 12th magnitude transiting hot Jupiter in the southern hemisphere

We report the discovery of WASP-4b, a large transiting gas-giant planet with an orbital period of 1.34 days. This is the first planet to be discovered by the SuperWASP-South observatory and CORALIE collaboration and the first planet orbiting a star brighter than 16th magnitude to be discovered in the southern hemisphere. A simultaneous fit to high-quality light curves and precision radial velocity measurements leads to a planetary mass of 1.22(-0.08)(+0.09) M-Jup and a planetary radius of 1.42(-0.04)(+0.07) R-Jup. The host star is USNO-B1.0 0479-0948995, a G7 V star of visual magnitude 12.5. As a result of the short orbital period, the predicted surface temperature of the planet is 1761 K, making it an ideal candidate for detections of the secondary eclipse at infrared wavelengths.

Experimental assessment of oyster transfers as a vector for macroalgal introductions

Introduction of non-indigenous species can alter marine communities and ecosystems. In shellfish farming, transfer of livestock, especially oysters, is a common practice and potentially constitutes a pathway for non-indigenous introductions. Many species of seaweeds are believed to have been accidentally introduced in association with these transfers, but there is little direct evidence.

Hull fouling on commercial ships as a vector of macroalgal introduction

Hull fouling is thought to have been the vector of introduction for many algal species. We studied ships arriving at a Mediterranean harbour to clarify the present role of commercial cargo shipping in algal introductions. A total of 31 macroalgal taxa were identified from 22 sampled hulls. The majority of records (58%) were of species with a known cosmopolitan geographical distribution. Due to a prevalence of cosmopolitan species and a high turnover of fouling communities, species composition of assemblages did not appear to be influenced by the area of origin, length of ship or age of coating. In the light of the present results, hull fouling on standard trading commercial vessels does not seem to pose a significant risk for new macroalgal species introductions. However, a high proportion of non-cosmopolitan species found on a ship with non-toxic coating may modify this assessment, especially in the light of the increasing use of such coatings and the potential future changes in shipping routes.

Artificial Intelligence Model for Flowable Concrete Mixtures Used in Underwater Construction and Repair

This study explores using artificial neural networks to predict the rheological and mechanical properties of underwater concrete (UWC) mixtures and to evaluate the sensitivity of such properties to variations in mixture ingredients. Artificial neural networks (ANN) mimic the structure and operation of biological neurons and have the unique ability of self-learning, mapping, and functional approximation. Details of the development of the proposed neural network model, its architecture, training, and validation are presented in this study. A database incorporating 175 UWC mixtures from nine different studies was developed to train and test the ANN model. The data are arranged in a patterned format. Each pattern contains an input vector that includes quantity values of the mixture variables influencing the behavior of UWC mixtures (that is, cement, silica fume, fly ash, slag, water, coarse and fine aggregates, and chemical admixtures) and a corresponding output vector that includes the rheological or mechanical property to be modeled. Results show that the ANN model thus developed is not only capable of accurately predicting the slump, slump-flow, washout resistance, and compressive strength of underwater concrete mixtures used in the training process, but it can also effectively predict the aforementioned properties for new mixtures designed within the practical range of the input parameters used in the training process with an absolute error of 4.6, 10.6, 10.6, and 4.4%, respectively.