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Full-Text Articles in Physical Sciences and Mathematics
The Effect Of Microscopic Texture On The Direct Plasma Surface Passivation Of Si Solar Cells, S Mehrabian, S Xu, A A. Qaemi, B Shokri, C Chan, K Ostrikov
The Effect Of Microscopic Texture On The Direct Plasma Surface Passivation Of Si Solar Cells, S Mehrabian, S Xu, A A. Qaemi, B Shokri, C Chan, K Ostrikov
Australian Institute for Innovative Materials - Papers
Textured silicon surfaces are widely used in manufacturing of solar cells due to increasing the light absorption probability and also the antireflection properties. However, these Si surfaces have a high density of surface defects that need to be passivated. In this study, the effect of the microscopic surface texture on the plasma surface passivation of solar cells is investigated. The movement of 105Hþ ions in the texture-modified plasma sheath is studied by Monte Carlo numerical simulation. The hydrogen ions are driven by the combined electric field of the plasma sheath and the textured surface. The ion dynamics is simulated, and …
On The Combination Of Local Texture And Global Structure For Food Classification, Zhimin Zong, Duc Thanh Nguyen, Philip O. Ogunbona, Wanqing Li
On The Combination Of Local Texture And Global Structure For Food Classification, Zhimin Zong, Duc Thanh Nguyen, Philip O. Ogunbona, Wanqing Li
Faculty of Informatics - Papers (Archive)
This paper proposes a food image classification method using local textural patterns and their global structure to describe the food image. In this paper, a visual codebook of local textural patterns is created by employing Scale Invariant Feature Transformation (SIFT) interest point detector with the Local Binary Pattern (LBP) feature. In addition to describing the food image using local texture, the global structure of the food object is represented as the spatial distribution of the local textural structures and encoded using shape context. We evaluated the proposed method on the Pittsburgh Fast-Food Image (PFI) dataset. Experimental results showed that the …
Electronic Texture Of The Thermoelectric Oxide Na0.75coo2, M -H Julien, C De Vaulx, H Mayaffre, C Berthier, M Horvatic, V Simonet, J Wooldridge, G Balakrishnan, M R. Lees, Dapeng Chen, C T. Lin, P Lejay
Electronic Texture Of The Thermoelectric Oxide Na0.75coo2, M -H Julien, C De Vaulx, H Mayaffre, C Berthier, M Horvatic, V Simonet, J Wooldridge, G Balakrishnan, M R. Lees, Dapeng Chen, C T. Lin, P Lejay
Australian Institute for Innovative Materials - Papers
From 59Co and 23Na NMR, we demonstrate the impact of the Na+ vacancy ordering on the cobalt electronic states in Na0.75CoO2: at long time scales, there is neither a disproportionation into 75 % Co3+ and 25 % Co4+ states, nor a mixed-valence metal with a uniform Co3.25+ state. Instead, the system adopts an intermediate configuration in which 30 % of the lattice sites form an ordered pattern of localized Co3+ states. Above 180 K, an anomalous mobility of specific Na+ sites is found to coexist with this electronic texture, suggesting that the formation of the latter may contribute to stabilizing …
Wavelet-Based Feature-Adaptive Adaptive Resonance Theory Neural Network For Texture Identification, Jiazhao Wang, Golshah Naghdy, Philip Ogunbona
Wavelet-Based Feature-Adaptive Adaptive Resonance Theory Neural Network For Texture Identification, Jiazhao Wang, Golshah Naghdy, Philip Ogunbona
Faculty of Informatics - Papers (Archive)
A new method of texture classification comprising two processing stages, namely a low-level evolutionary feature extraction based on Gabor wavelets and a high-level neural network based pattern recognition, is proposed. The design of these stages is motivated by the processes involved in the human visual system: low-level receptors responsible for early vision processing and the high-level cognition. Gabor wavelets are used as extractors of ‘‘lowlevel’’ features that feed the feature-adaptive adaptive resonance theory (ART) neural network acting as a high-level ‘‘cognitive system.’’ The novelty of the model developed in this paper lies in the use of a self-organizing input layer …
New Wavelet Based Art Network For Texture Classification, Jiazhao Wang, Golshah Naghdy, Philip O. Ogunbona
New Wavelet Based Art Network For Texture Classification, Jiazhao Wang, Golshah Naghdy, Philip O. Ogunbona
Faculty of Informatics - Papers (Archive)
A new method for texture classification is proposed. It is composed of two processing stages, namely, a low level evolutionary feature extraction based on Gabor wavelets and a high level neural network based pattern recognition. This resembles the process involved in the human visual system. Gabor wavelets are exploited as the feature extractor. A neural network, Fuzzy Adaptive Resonance Theory (Fuzzy ART), acts as the high level decision making and recognition system. Some modifications to the Fuzzy ART make it capable of simulating the post-natal and evolutionary development of the human visual system. The proposed system has been evaluated using …
Texture Analysis Using Gabor Wavelets, Golshah Naghdy, Jianli Wang, Philip Ogunbona
Texture Analysis Using Gabor Wavelets, Golshah Naghdy, Jianli Wang, Philip Ogunbona
Faculty of Informatics - Papers (Archive)
Receptive field profiles of simple cells in the visual cortex have been shown to resemble even- symmetric or odd-symmetric Gabor filters. Computational models employed in the analysis of textures have been motivated by two-dimensional Gabor functions arranged in a multi-channel architecture. More recently wavelets have emerged as a powerful tool for non-stationary signal analysis capable of encoding scale-space information efficiently. A multi-resolution implementation in the form of a dyadic decomposition of the signal of interest has been popularized by many researchers. In this paper, Gabor wavelet configured in a 'rosette' fashion is used as a multi-channel filter-bank feature extractor for …
Texture Analysis Using Partially Ordered Markov Models, Jennifer Davidson, Ashit Talukder, Noel A. Cressie
Texture Analysis Using Partially Ordered Markov Models, Jennifer Davidson, Ashit Talukder, Noel A. Cressie
Faculty of Informatics - Papers (Archive)
Texture is a phenomenon in image data that continues to receive wide-spread interest due to its broad range of applications. The paper focuses on but one of several ways to model textures, namely, the class of stochastic texture models. the authors introduce a new spatial stochastic model called partially ordered Markov models, or POMMs. They show how POMMs are a generalization of a class of models called Markov mesh models, or MMMs, that allow an explicit closed form of the joint probability, just as do MMMs. While POMMs are a type of Markov random field model (MRF), the general MRFs …