This Project posits elementary analogies of existing Probabilistic and Machine Learning models that have been used to find solutions to the problem of the Structural Segmentation of Musical audio. I have tried to use the idea that the chord of a given beat or frame of a song is an analogous representation of the states generated by trained Hidden Markov Models in generating feature vectors for the aforementioned problem; and that the knowledge of the temporal boundaries within which, a group of frames lie, can be used as constraints in creating the feature vectors that are eventually clustered to identify the pattern in which the various segments of a song repeat.
This report provides insight into the magnetic phenomenon of Hysteresis. Hysteresis is defined as a retardation effect where the magnetisation of a magnetic material lags behind the magnetizing force. Here we will explore the hysteresis loop for a silver steel ferromagnet and use this to discover it’s magnetic properties. The method used will be to place a ferromagnet inside a solenoid with an alternating voltage which will continually reverse the magnetic field and magnetism direction. The relation between these two quantities will be used to produce a hysteresis loop from which magnetic properties can be deduced. The results obtained were: saturation magnetisation = (8.4±0.5)(105)Am-1; remnant magnetisation = (5.9±0.5)(105)Am-1; coercive field: (4.3±0.5)(104)Am-1; energy expended per cycle per unit volume of material: (1.55±0.05)(103)Jm-3s-1; energy product: (8.7±3.0)(104)Jm-3.