Robust Instant Angle Speed Measurement for Internal Combustion Engines-A Novel Sensing Suite and Methodology.

This paper presents the development and implementation of a novel robust sensing and measurement system that achieves fine granularity and permits new insights into operation of rotational machinery. Instant angle speed measurements offer a wealth of useful information for complex machines in which the motion is the result of multidimensional, internal, and external interactions. The implementation of the proposed system was conducted on an internal combustion engine. The internal combustion engine crankshaft's angular velocity is the result of the integration of all variables of motor and resisting forces. The crankshaft angular velocity variation also reflects the interaction between the internal thermodynamic cycle of the engine and the plant it powers. To minimise the number of variables, we used for our experiments an aero piston engine for small air-vehicles-a well-made and reliable powerplant-connected to a propeller. This paper presents the need for a better sensing and measurement system. Then, we show the development of the system, the measurement protocol and process, recording and analysis of the data, and results of some experiments. We then demonstrate the possibilities this sensing suite can achieve-a deeper insight into the operation of the machine-by performing high-quality analyses of engine cycles, well beyond capabilities in the state of the art. This system can be generalised for other rotational machines and equipment.

Transformation of a high-dimensional color space for material classification.

Images in red-green-blue (RGB) color space need to be transformed to other color spaces for image processing or analysis. For example, the well-known hue-saturation-intensity (HSI) color space, which separates hue from saturation and intensity and is similar to the color perception of humans, can aid many computer vision applications. For high-dimensional images, such as multispectral or hyperspectral images, transformation images to a color space that can separate hue from saturation and intensity would be useful; however, the related works are limited. Some methods could interpret a set of high-dimensional images to hue, saturation, and intensity, but these methods need to reduce the dimension of original images to three images and then map them to the trichromatic color space of RGB. Generally, dimension reduction could cause loss or distortion of original data, and, therefore, the transformed color spaces could not be suitable for material classification in critical conditions. This paper describes a method that can transform high-dimensional images to a color space called hyper-hue-saturation-intensity (HHSI), which is analogous to HSI in high dimensions. The transformation does not need dimension reduction, and, therefore, it can preserve the original information. Experimental results indicate that the hyper-hue is independent of saturation and intensity and it is more suitable for material classification of proximal or remote sensing images captured in a natural environment where illumination usually cannot be controlled.