RFRA : Random Forests Rate Adaptation for vehicular networks

Rate adaptation in vehicular networks is known to be more challenging than in WLANs due to the high mobility of stations. Nevertheless, vehicular networks are subject to certain recurring patterns particularly if stations communicate to roadside units. This has lead to the proposal of learning-based rate adaptation schemes which are trained for a certain propagation environment. In general, these schemes outperform other approaches at the price of being specific for a particular environment. In this paper we present RFRA, a novel rate adaptation scheme for vehicular networks. It is based on the machine-learning algorithm Random Forests which is known to be superior to most other learning approaches. Firstly, we show that RFRA outperforms other learning-based methods significantly. We also study the question how sensitive RFRA is to changes of the learned environment, especially with respect to the propagation characteristics. We show that, although this reduces the gain of our scheme, RFRA still provides a much higher performance than state-of-the-art rate adaptation schemes.QC 20131030</p

Distribution of hydantoinase activity in bacterial isolates from geographically distinct environmental sources

Hydantoin cleaving bacterial isolates were recovered from terrestrial soil samples originating from different geographic sources (Antarctica, South Africa and China) using culture-based screening methods (selective agar plates and shake flask cultures supplemented with hydantoins). Thirty-two bacterial isolates possessing the capability to transform the model substrates benzylhydantoin and dihydrouracil to the corresponding N-carbamoyl-amino acids were successfully cultured. Amplification and sequencing of the 16S rDNA revealed that the isolates belonged to the genera Arthrobacter, Burkholderia, Bacillus, Delftia, Enterobacter, Flavobacterium, Ochrobactrum, Pseudomonas and Stenotrophomonas, with one isolate assigned to the family Microbacteriacae. We have shown that microorganisms with hydantoinase activity are: (i) distributed in various geographically distinct environmental habitats, (ii) distributed worldwide and (iii) found in certain bacterial genera. Furthermore, we have demonstrated the presence of hydantoinase activity in genera in which hydantoinase activity has not previously been reported

Cooperative Speed Estimation of an RF Jammer in Wireless Vehicular Networks

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.In this paper, we are concerned with the problem of estimating the speed of an RF jammer that moves towards a group/platoon of moving wireless communicating nodes. In our system model, the group of nodes receives an information signal from a master node, that they want to decode, while the Radio Frequency (RF) jammer desires to disrupt this communication as it approaches them. For this system model, we propose first a transmission scheme where the master node remains silent for a time period while it transmits in a subsequent slot. Second, we develop a joint data and jamming estimation algorithm that uses Linear Minimum Mean Square Error (LMMSE) estimation. We develop analytical closed-form expressions that characterize the Mean Square Error (MSE) of the data and jamming signal estimates. Third, we propose a cooperative jammer speed estimation algorithm based on the jamming signal estimates at each node of the network. Our numerical and simulation results for different system configurations prove the ability of our overall system to estimate with high accuracy and the RF jamming signals and the speed of the jammer