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The objective of this paper is to propose pole modeling-based features for audio classification in order to achieve a high classification performance. This paper investigates the suitable pole modeling computation method and evaluates the proposed audio features in an audio database with 40 human speech samples, and 40 non human audio signals including aircraft, helicopter, drum, flutes, and piano...
Transforming database schemas into an ontology language opens the door wide to the many advantages offered by the Semantic Web. Industries in particular can benefit from intelligent systems (e.g., decision-support systems) arising from such transformations. In this paper, we propose a semi-automated algorithm to transform data to the ontology language, OWL, while taking advantage of the actual data...
In real time applications or portable devices, software implementation is not enough by itself to evaluate a signal feature analysis technique and a hardware implementation needs to be considered. The selection of the right signal feature analysis technique for an application depends on the algorithmic (software) performance, and also on the hardware efficiency of that technique. However, there are...
This paper presents a prototype biometric system that performs real time human identification using the ECG signal. Cardiac signals have been lately suggested for biometric deployment, since they carry subject specific information and at the same time allow for inherent liveness detection. The HeartID is the first system to continuously authenticate individuals who are conveniently wearing a portable,...
Simulations that incorporate both physical (hard) as well as psycho-social human behaviours (soft) are expected to improve situational awareness during critical events, yet are not readily available. Because of the complex nature of emergency response and, in particular, because of the potential for unforeseen interdependencies in the physical environment, it is difficult to objectively assess the...
A series of carefully chosen decisions by an Emergency Responder during a disaster are vital in mitigating the loss of human lives and the recovery of critical infrastructures. In this paper we propose to assist a human Emergency Responder by modeling and simulating an intelligent agent using Reinforcement Learning. The goal of the agent will be to maximize the number of patients discharged from hospitals...
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