Spray visualisation and acoustic analysis in a gas-turbine type burner

This paper presents the characterisation of self-excited oscillations in a kerosene burner. The combustion instability exhibits two different modes and frequencies depending on the air flow rate. Experimental results reveal the influence of the spray to shift between these two modes. Pressure and heat release fluctuations have been measured simultaneously and the flame transfer function has been calculated from these measurements. The Mie scattering technique has been used to record spray fluctuations in reacting conditions with a high speed camera. Innovative image processing has enabled us to obtain fluctuations of the Mie scattered light from the spray as a temporal signal acquired simultaneously with pressure fluctuations. This has been used to determine a transfer function relating the image intensity and hence the spray fluctuations to changes in air velocity. This function has identified the different role the spray plays in the two modes of instability. At low air flow rates, the spray responds to an unsteady air flow rate and the time varying spray characteristics lead to unsteady combustion. At higher air flow rates, effective evaporation means that the spray dynamics are less important, leading to a different flame transfer function and frequency of self-excited oscillation. In conclusion, the combustion instabilities observed are closely related with the fluctuations of the spray motion and evaporation.

On-line topographic analysis in the SEM

A novel method for on-line topographic analysis of rough surfaces in the SEM has been investigated. It utilises a digital minicomputer configured to act as a programmable scan generator and automatic focusing unit. The computer is coupled to the microscope through digital-to-analogue converters which enable it to generate ramp waveforms allowing the beam to be scanned over a small sub-region of the field under program control. A further digital-to-analogue converter regulates the current supply to the objective lens of the microscope. The video signal is sampled by means of an analogue-to-digital converter and the resultant binary code stored in the computer's memory as an array of numbers describing relative image intensity. Computations based on the intensity gradient of the image allow the objective lens current to be found for the in-focus condition, which may be related to the working distance through a previous calibration experiment. The sensitivity of the method for detecting small height changes is theoretically of the order of 1 μm. In practice the operator specifies features of interest by means of a mobile spot cursor injected into the SEM display screen, or he may scan the specimen at sub-regions corresponding to pre-determined points on a regular grid defined by him. The operation then proceeds under program control. | A novel method for on-line topographic analysis of rough surfaces in the SEM has been investigated. It utilizes a digital minicomputer configured to act as a programmable scan generator and automatic focusing unit. A further digital-to-analog converter regulates the current supply to the objective lens of the microscope. The video signal is sampled by means of an analog-to-digital converter and the resultant binary code stored in the computer's memory as an array of numbers describing relative image intensity. The sensitivity of the method for detecting small height changes is theroretically of the order of 1 mu m.

Topographical analysis in the SEM using an automatic focusing technique

A novel technique for automated topographical analysis in the SEM has been investigated. It utilizes a 16-bit minicomputer arranged to act as an automatic focusing unit. The computer is coupled to the objective lens of the microscope, by means of a digital to analogue converter, and may regulate the excitation of the lens under program control. Further digital-to-analogue converters allow the computer to act as a programmable scan generator by applying ramp waveforms to the scan amplifiers, permitting the beam to be swept over a small sub-region of the field of interest. The video signal is sampled and applied to an analogue-to-digital converter; the resultant binary numbers are stored in computer memory as an array of values representing relative image intensities within a subregion. A differencing algorithm applied to the collected data allows the level of objective lens excitation to be found at which the sharpness of the image is optimized, and the excitation may be related to the working distance for that subregion through a previous calibration experiment. The sensitivity of the method for detecting small height changes is theoretically of the order of 1 μm.

Incremental Linear Discriminant Analysis Using Sufficient Spanning Sets and Its Applications

This paper presents an incremental learning solution for Linear Discriminant Analysis (LDA) and its applications to object recognition problems. We apply the sufficient spanning set approximation in three steps i.e. update for the total scatter matrix, between-class scatter matrix and the projected data matrix, which leads an online solution which closely agrees with the batch solution in accuracy while significantly reducing the computational complexity. The algorithm yields an efficient solution to incremental LDA even when the number of classes as well as the set size is large. The incremental LDA method has been also shown useful for semi-supervised online learning. Label propagation is done by integrating the incremental LDA into an EM framework. The method has been demonstrated in the task of merging large datasets which were collected during MPEG standardization for face image retrieval, face authentication using the BANCA dataset, and object categorisation using the Caltech101 dataset. © 2010 Springer Science+Business Media, LLC.