Asthma is not very easy to be correctly diagnosed by physicians where asthma is considered a common chronic inflammatory disease. Distinguishing of cough sound can be used to diagnose asthma. The use of signal processing techniques of cough sound for detecting asthma will be addressed in this paper to help the diagnosis of asthma by physicians. Since cough sounds are non-stationary and are stochastic signals inherently, time-frequency transform techniques are used to deal with such signals. Time-frequency analyses are performed to show in a comprehensive approach the characteristics of the cough sound signal. Time-frequency analysis techniques, specifically Wigner distribution in addition to wavelet transform to analyse cough signals, are used in this paper. The features extracted from the time-frequency domain of the cough sound are used as inputs to the asthma and non-asthma classifier. The results of the proposed algorithm are competitive to the best existing algorithms in the literature.
In distributed network algorithms, network flooding algorithm is considered one of the simplest and most fundamental algorithms. This research specifies the basic synchronous memory-less network flooding algorithm where nodes on the network don’t have memory, for any fixed size of network, in Linear Temporal Logic. The specification can be customized to any single network topology or class of topologies. A specification of the termination problem is formulated and used to compare different topologies for earlier termination. This research gives a worked example of one topology resulting in earlier termination than another, for which we perform a formal verification using the model checker NuSMV
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