This paper presents a new model for propagation‐loss prediction in indoor mobile communications. It uses the free‐space loss model added to an empiric function (based on the Padé approximant), and a term that describes the signal randomness (typical of the RF propagation). In order to validate this model, a measurement verification was accomplished for a typical five‐floor building of a university. The loss predicted by this model had a mean error of 4.78 dB and a standard deviation of 6.05 dB in relation to the measured data. The formulation of the model does not require accurate information about the environment used in the study, such as the permittivity of the construction materials, layout of the furniture, and so forth, but only needs to be adaptable to any indoor environment. © 2005 Wiley Periodicals, Inc. Microwave Opt Technol Lett 48:255–261, 2006; Published Online in Wiley InterScience (www.interscience.wiley.com) DOI 10.1002/mop.21321

No abstract

Background:The authors provide a review of true aneurysms of the posterior communicating artery (PCoA). Three cases admitted in our hospital are presented and discussed as follows.Case Descriptions:First patient is a 51-year-old female presenting with a Fisher II, Hunt-Hess III (headache and confusion) subarachnoid hemorrhage (SAH) from a ruptured true aneurysm of the right PCoA. She underwent a successful ipsilateral pterional craniotomy for aneurysm clipping and was discharged on postoperative day 4 without neurological deficit. Second patient is a 53-year-old female with a Fisher I, Hunt-Hess III (headache, mild hemiparesis) SAH and multiple aneurisms, one from left ophthalmic carotid artery and one (true) from right PCoA. These lesions were approached and successfully treated by a single pterional craniotomy on the left side. The patient was discharged 4 days after surgery, with complete recovery of muscle strength during follow-up. Third patient is a 69-year-old male with a Fisher III, Hunt-Hess III (headache and confusion) SAH, from a true PCoA on the right. He had a left subclavian artery occlusion with flow theft from the right vertebral artery to the left vertebral artery. The patient underwent endovascular treatment with angioplasty and stent placement on the left subclavian artery that resulted in aneurysm occlusion.Conclusion:In conclusion, despite their seldom occurrence, true PCoA aneurysms can be successfully treated with different strategies.

No abstract

The amount of information available in the Internet does not allow performing manual content analysis to identify information of interest. Thus automated analyses are used to identify information of interest, and one increasingly important approach is the polarity analysis. Polarity analysis is the classification of a text document in positive, negative, and neutral, according to a certain topic. This classification of information is particularly useful in the finance domain, where news about a company can affect the performance of its stocks. Although most of the methods in financial domain consider that the whole document is associated with a particular entity, this is not always the case. In fact, it is common that authors cite several entities in a single document and these entities are cited with different polarity. Accordingly, the objective of this paper was to study strategies for polarity detection in financial documents with multiple entities. Specifically, we studied methods based on learning of multiple models, one for each observed entity, using SVM classifiers. We evaluated models based on the partition of documents into fragments according to the entities they cite. We used several heuristics to segment documents based on shallow and deep natural language processing (NLP). We found that entity-specific models created by partitioning the document collection into segments outperformed the strategy based on the use of entire documents. We also observed that more complex segmentation using anaphora resolution was not able to outperform a low-cost approach, based on simple string matching.

No abstract

Abstract. This paper proposes a strategy to determine how much a given network can affect the QoS parameters of another, by interference. In order to achieve this, a measurement campaign was carried out in two stages: firstly with a single AP and later with two APs separated by a distance less than three meters, using the same channel. After the measurement, an analysis of the results and a set of inferences were made by using Bayesian Networks, whose inputs were the experimental data, i.e. QoS metrics such as: throughput, jitter, packet loss, PMOS and physical metrics like power and distance.

This paper proposes a WLAN planning strategy through the use of computational intelligence and genetic algorithm. A measurement technique was used to collect data from a real WLAN network. Metrics like power, distance, delay, jitter, packet loss, throughput and PMOS were analysed through the use of bayesian networks. Finally, to optimize the QoS parameters a genetic algorithm was applied to find the best distance from the AP to support the QoS ITU-T recommendations.