Pattern recognition using invariants defined from higher order spectra : 2-D image inputs

A new algorithm for extracting features from images for object recognition is described. The algorithm uses higher order spectra to provide desirable invariance properties, to provide noise immunity, and to incorporate nonlinearity into the feature extraction procedure thereby allowing the use of simple classifiers. An image can be reduced to a set of 1D functions via the Radon transform, or alternatively, the Fourier transform of each 1D projection can be obtained from a radial slice of the 2D Fourier transform of the image according to the Fourier slice theorem. A triple product of Fourier coefficients, referred to as the deterministic bispectrum, is computed for each 1D function and is integrated along radial lines in bifrequency space. Phases of the integrated bispectra are shown to be translation- and scale-invariant. Rotation invariance is achieved by a regrouping of these invariants at a constant radius followed by a second stage of invariant extraction. Rotation invariance is thus converted to translation invariance in the second step. Results using synthetic and actual images show that isolated, compact clusters are formed in feature space. These clusters are linearly separable, indicating that the nonlinearity required in the mapping from the input space to the classification space is incorporated well into the feature extraction stage. The use of higher order spectra results in good noise immunity, as verified with synthetic and real images. Classification of images using the higher order spectra-based algorithm compares favorably to classification using the method of moment invariants

Characterizing the signal pattern of a four-cylinder diesel engine using acoustic emission and vibration analysis

Condition monitoring of diesel engines can prevent unpredicted engine failures and the associated consequence. This paper presents an experimental study of the signal characteristics of a 4-cylinder diesel engine under various loading conditions. Acoustic emission, vibration and in-cylinder pressure signals were employed to study the effectiveness of these techniques for condition monitoring and identifying symptoms of incipient failures. An event driven synchronous averaging technique was employed to average the quasi-periodic diesel engine signal in the time domain to eliminate or minimize the effect of engine speed and amplitude variations on the analysis of condition monitoring signal. It was shown that acoustic emission (AE) is a better technique than vibration method for condition monitor of diesel engines due to its ability to produce high quality signals (i.e., excellent signal to noise ratio) in a noisy diesel engine environment. It was found that the peak amplitude of AE RMS signals correlating to the impact-like combustion related events decreases in general due to a more stable mechanical process of the engine as the loading increases. A small shift in the exhaust valve closing time was observed as the engine load increases which indicates a prolong combustion process in the cylinder (to produce more power). On the contrary, peak amplitudes of the AE RMS attributing to fuel injection increase as the loading increases. This can be explained by the increase fuel friction caused by the increase volume flow rate during the injection. Multiple AE pulses during the combustion process were identified in the study, which were generated by the piston rocking motion and the interaction between the piston and the cylinder wall. The piston rocking motion is caused by the non-uniform pressure distribution acting on the piston head as a result of the non-linear combustion process of the engine. The rocking motion ceased when the pressure in the cylinder chamber stabilized.

An epistemic community as influencer and implementer in local government accounting in Australia

This paper adopts an epistemic community framework to explicate the dual role of epistemic communities as influencers of accounting policy within regulatory space and as implementers who effect change within the domain of accounting. The context is the adoption and implementation of fair value accounting within local government in New South Wales (NSW). The roles and functions of Australian local government are extensive, and include the development and maintenance of infrastructure, provision of recreational facilities, certain health and community services, buildings, cultural facilities, and in some cases, water and sewerage (Australian Local Government Association, 2009). The NSW state Department of Local Government (DLG) is responsible for legislation and policy development to ensure that local councils are able to deliver ‘quality services to their communities in a sustainable manner’ (DLG, 2008c). These local councils receive revenue from various sources including property rates, government grants and user-pays service provision. In July 2006 the DLG issued Circular 06-453 to councils (DLG, 2006c), mandating the staged adoption of fair value measurement of infrastructure assets. This directive followed the policy of NSW State Treasury (NSW Treasury, 2007),4 and an independent inquiry into the financial sustainability of local councils (LGSA, 2006). It was an attempt to resolve the inconsistency in public sector asset valuation in NSW Local Governments, and to provide greater usefulness and comparability of financial statements.5 The focus of this study is the mobilization of accounting change by the DLG within this wider political context. When a regulatory problem arises, those with political power seek advice from professionals with relevant skill and expertise (Potter, 2005). This paper explores the way in which professionals diffuse accounting ‘problems’ and the associated accounting solutions ‘across time and space’ (Potter, 2005, p. 277). The DLG’s fair value accounting policy emanated from a ‘regulatory space’ (Hancher and Moran, 1989)6 as a result of negotiations between many parties, including accounting and finance professionals. Operating within the local government sector, these professionals were identified by the DLG as being capable of providing helpful input. They were also responsible for the implementation of the new olicy within local councils. Accordingly they have been dentified as an pistemic community with the ability to ranslate regulatory power by changing he domain of ccounting (Potter, 2005, p. 278).7 The paper is organised as follows. The background to the LG’s decision to require the introduction of fair value accounting for infrastructure assets is explored. Following this, the method of the study is described, and the epistemic community framework outlined. In the next sections, evidence of the influencing and implementing roles of epistemic groups is provided. Finally, conclusions are drawn about the significance of these groups both within regulatory space in developing accounting regulation, and in embedding change within the domain of accounting.

Pattern recognition using invariants defined from higher order spectra - one-dimensional inputs

A new approach to pattern recognition using invariant parameters based on higher order spectra is presented. In particular, invariant parameters derived from the bispectrum are used to classify one-dimensional shapes. The bispectrum, which is translation invariant, is integrated along straight lines passing through the origin in bifrequency space. The phase of the integrated bispectrum is shown to be scale and amplification invariant, as well. A minimal set of these invariants is selected as the feature vector for pattern classification, and a minimum distance classifier using a statistical distance measure is used to classify test patterns. The classification technique is shown to distinguish two similar, but different bolts given their one-dimensional profiles. Pattern recognition using higher order spectral invariants is fast, suited for parallel implementation, and has high immunity to additive Gaussian noise. Simulation results show very high classification accuracy, even for low signal-to-noise ratios.