Rising prevalence increases the burden of personal disease management, financial expenditures and workload, both on sides of patients and healthcare systems. Firstly, the medical background of asthma is given. Pathology and symptoms are presented., IN machine learning projects for mtech ,the short-time Fourier transform (STFT), or alternatively short-term Fourier transform, is a Fourier-related transform used to determine the sinusoidal frequency and phase content of local sections of a signal as it changes over time. the problem of persistent asthma management is introduced with a short overview of traditional disease management techniques. A review on approaches to asthma tele monitoring is made.Here I proposes IEEE based Projects for CSE in Python the simple method consisting to envelope detection is performed on the time-domain respiratory sound recorded on trachea, which produces quasi-periodic function with crests representing inspiration and autocorrelation is performed on envelope by which beginnings and the ends of the inspiration are determined. Performance of the algorithm both in term of accuracy and performance remain to be tested as it seems that method has been applied only in Matlab.

In practice, the procedure for computing STFTs is to divide a longer time signal into shorter segments of equal length and then compute the Fourier transform separately on each shorter segment. This reveals the Fourier spectrum on each shorter segment. One then usually plots the changing spectra as a function of time.Simply, in the continuous-time case, the function to be transformed is multiplied by a window function which is nonzero for only a short period of time. The Fourier transform (a one-dimensional function) of the resulting signal is taken as the window is slid along the time axis, resulting in a two-dimensional representation of the signal. Mathematically, this is written as:Image Processing Projects Using Python

where w(t) is the window function, commonly a Hann window or Gaussian window centered around zero, and x(t) is the signal to be transformed. (Note the difference between w and ω.) X(τ,ω) is essentially the Fourier Transform of x(t)w(t-τ), a complex function representing the phase and magnitude of the signal over time and frequency. Often phase unwrapping is employed along either or both the time axis, τ, and frequency axis, ω, to suppress any jump discontinuity of the phase result of the STFT. The time index τ is normally considered to be "slow" time and usually not expressed in as high resolution as time t. The wavelet based approach is motivated by first reviewing the corresponding Fourier based approach. In IEEE projects on machine learning ,the basic idea, which involves filtering in a second frequency domain. Acquisition of respiratory sounds and signal processing focused on detection of wheezes and phases of respiratory cycle are the main features. First frequency estimators for wheeze detection designed for operation on a energy constrained sensor node second background noise cancellation third node level and network level power management.