The prediction of machined surface parameters is an important factor in machining centre development. There is a great need to elaborate a method for on-line surface roughness estimation [1-7]. Among various measurement techniques, optical methods are considered suitable for in-process measurement of machined surface roughness. These techniques are non-contact, fast, flexible and tree-dimensional in nature.

The optical method suggested in this paper is based on the vision system created to acquire an image of the machined surface during the cutting process. The acquired image is analyzed to correlate its parameters with surface parameters. In the application of machined surface image analysis, the wavelet methods were introduced. A digital image of a machined surface was described using the one-dimensional Digital Wavelet Transform with the basic wavelet as Coiflet. The statistical description of wavelet components made it possible to develop the quality measure and correlate it with surface roughness [8-11].

For an estimation of surface roughness a neural network estimator was applied [12-16]. The estimator was built to work in a recurrent way. The current value of the Ra estimation and the measured change in surface image features were used for forecasting the surface roughness Ra parameter. The results of the analysis confirmed the usability of the application of the proposed method in systems for surface roughness monitoring.

The objective of the study was to assess the potential use of optical measuring instruments to determine the minimum chip thickness in face milling. Images of scanned surfaces were analyzed using mother wavelets. Filtration of optical signals helped identify the characteristic zones observed on the workpiece surface at the beginning of the cutting process. The measurement data were analyzed statistically. The results were then used to estimate how accurate each measuring system was to determine the minimum uncut chip thickness. Also, experimental verification was carried out for each mother wavelet to assess their suitability for analyzing surface images.

The paper presents a research program carried out to improve understanding of the fluid dynamics mechanisms that lead to rotating stall in the axial flow low speed compressor stage. The stalling behavior of this compressor stage was studied by measuring unsteady casing pressure by means of a circumferentially and axially spaced array of high frequency pressure transducers. Another probe used was a disc static pressure probe, with the pressure transducer, for in-flow and out-flow measurements along the blade span. It was expected that understanding of the fluid dynamics will facilitate at least two important tasks. The first was to accurately predict of when and how a particular compressor would stall. The second was to control, delay, or eventually suppress the rotating stall and surge. In consequence, one could extend the useful operating range of the axial compressor. Another motivation for the research was to compare the results from the three applied analysis techniques by using a single stall inception event. The first one was a simple visual inspection of the traces, which brought about a very satisfactory effect. The second one was application of spatial Fourier decomposition to the analysis of stall inception data, and the third method of analysis consisted in application of wavelet filtering in order to better understand the physical mechanisms which lead to rotating stall. It was shown that each of these techniques would provide different information about compressor stall behavior, and each method had unique advantages and limitations.

The main objective of this paper is to produce an applications-oriented review covering infrared techniques and devices. At the beginning infrared systems fundamentals are presented with emphasis on thermal emission, scene radiation and contrast, cooling techniques, and optics. Special attention is focused on night vision and thermal imaging concepts. Next section concentrates shortly on selected infrared systems and is arranged in order to increase complexity; from image intensifier systems, thermal imaging systems, to space-based systems. In this section are also described active and passive smart weapon seekers. Finally, other important infrared techniques and devices are shortly described, among them being: non-contact thermometers, radiometers, LIDAR, and infrared gas sensors.

A traditional frequency analysis is not appropriate for observation of properties of non-stationary signals. This stems from the fact that the time resolution is not defined in the Fourier spectrum. Thus, there is a need for methods implementing joint time-frequency analysis (t/f) algorithms. Practical aspects of some representative methods of time-frequency analysis, including Short Time Fourier Transform, Gabor Transform, Wigner-Ville Transform and Cone-Shaped Transform are described in this paper. Unfortunately, there is no correlation between the width of the time-frequency window and its frequency content in the t/f analysis. This property is not valid in the case of a wavelet transform. A wavelet is a wave-like oscillation, which forms its own “wavelet window”. Compression of the wavelet narrows the window, and vice versa. Individual wavelet functions are well localized in time and simultaneously in scale (the equivalent of frequency). The wavelet analysis owes its effectiveness to the pyramid algorithm described by Mallat, which enables fast decomposition of a signal into wavelet components.