Human Face Recognition and Detection

Human face detection and recognition play important roles in many applications such as video surveillance and face image database management. In our project, we have studied worked on both face recognition and detection techniques and developed algorithms for them. In face recognition the algorithm used is PCA (principal component analysis), MPCA(Multilinear Principal Component Analysis) and LDA(Linear Discriminant Analysis) in which we recognize an unknown test image by comparing it with the known training images stored in the database as well as give information regarding the person recognized. These techniques works well under robust conditions like complex background, different face positions. These algorithms give different rates of accuracy under different conditions as experimentally observed. In face detection, we have developed an algorithm that can detect human faces from an image. We have taken skin colour as a tool for detection. This technique works well for Indian faces which have a specific complexion varying under certain range. We have taken real life examples and simulated the algorithms in MATLAB successfully

Original Article

Phosphodiesterase specific for the hydrolysis of diphenylphosphate, one of aromatic phosphodiesters, was obtained from hog kidney by following procedure. To hog kidney homogenized in a Waring blendor with 3 volumes of distilled water was added solid sodium chloride to a final concentration of 1% and after adjusting of pH to 5 with ca. 5 N hydrochloric acid, it was shaken for 10 minutes with an equal volume of n-butanol, allowed to stand for one hour, and centrifuged. The aqueous extract present beneath the floating gel layer of protein and butanol mixture, was siphoned out and fractionated with ammonium sulfate. The precipitate obtained between 45 and 70 % saturation was dissolved in a small volume of distilled water, dialyzed for 48 hours against running tap water, and then, to inactivate the monoesterase, heated in water bath of 100° for 5 minutes, whereby the solution became faintly turbid. The enzyme solution, thus prepared, hydrolyzed diphenylphosphate, liberating phenol but no inorganic phosphate, while it was inactive to monophenylphosphate. Diphenylphosphate of a final concentration of 0.0005M could be hydrolyzed in one hour at the optimum pH 7 to the extent of 40%. At this pH, the enzyme activity was not influenced by addition of Mg^, Ca^, or CN^. Bis-dichloroisopropylphosphate, dibenzylphosphate, diethylphosphate, lysolecithin, RNA, and DNA were resistant to this enzyme. It is conceivable that these diesters are hydrolyzed by other enzymes different from the aromatic phosphodiesterase, presented in this paper

Dynamic Characteristics of Drop-substrate Interactions in Direct Ceramic Ink-jet Printing using High Speed Imaging System

Solid freeform fabrication has the potential to construct ceramic parts, directly from computer aided design (CAD) data, without a mould or a die by the addition of material. Direct ceramic ink-jet printing is one of the techniques used in freeform fabrication. Ceramic tiles used in space vehicles can be produced by this method wherein a porous ceramic substrate (Al2O3/SiC) can be filled with a ceramic ink and processed subsequently. The success of this process depends on the systematic preparation of ceramic inks and the deposition of the ceramic ink on the substrate. In this paper, photographic studies were made on the characteristics of ceramic ink droplets when these are deposited on a porous ceramic substrate from a burette under gravity. For this investigation, ceramic inks were prepared using different amounts (0.253.0 vol. %) of an organic dispersant (oleic acid) added to a ceramic composition containing different amounts: (a) (7.5 17.5 vol. %) of alumina and (b) (7.515.0 vol. %) of zirconia with ethyl alcohol as a carrier. From this study, the drop formation, sedimentation in the drop, spread of drop on the substrate, splashing of drop impinging a previous ceramic ink layer on the substrate, and merging of droplets after deposition, are observed. This method is useful for manufacturing of parts with ceramic fibres filled with ceramic particles and this study can provide inner details on the behaviour of ink drops.Defence Science Journal, 2009, 59(6), pp.675-682, DOI:http://dx.doi.org/10.14429/dsj.59.157

Amplitude measurements of Faraday waves

A light reflection technique is used to measure quantitatively the surface elevation of Faraday waves. The performed measurements cover a wide parameter range of driving frequencies and sample viscosities. In the capillary wave regime the bifurcation diagrams exhibit a frequency independent scaling proportional to the wavelength. We also provide numerical simulations of the full Navier-Stokes equations, which are in quantitative agreement up to supercritical drive amplitudes of 20%. The validity of an existing perturbation analysis is found to be limited to 2.5% overcriticaly.Comment: 7 figure

Nonlinear Measures for Characterizing Rough Surface Morphologies

We develop a new approach to characterizing the morphology of rough surfaces based on the analysis of the scaling properties of contour loops, i.e. loops of constant height. Given a height profile of the surface we perform independent measurements of the fractal dimension of contour loops, and the exponent that characterizes their size distribution. Scaling formulas are derived and used to relate these two geometrical exponents to the roughness exponent of a self-affine surface, thus providing independent measurements of this important quantity. Furthermore, we define the scale dependent curvature and demonstrate that by measuring its third moment departures of the height fluctuations from Gaussian behavior can be ascertained. These nonlinear measures are used to characterize the morphology of computer generated Gaussian rough surfaces, surfaces obtained in numerical simulations of a simple growth model, and surfaces observed by scanning-tunneling-microscopes. For experimentally realized surfaces the self-affine scaling is cut off by a correlation length, and we generalize our theory of contour loops to take this into account.Comment: 39 pages and 18 figures included; comments to [email protected]

Embed systemic equity throughout industrial ecology applications: How to address machine learning unfairness and bias

\ua9 2024 The Authors. Journal of Industrial Ecology published by Wiley Periodicals LLC on behalf of International Society for Industrial Ecology. Recent calls have been made for equity tools and frameworks to be integrated throughout the research and design life cycle —from conception to implementation—with an emphasis on reducing inequity in artificial intelligence (AI) and machine learning (ML) applications. Simply stating that equity should be integrated throughout, however, leaves much to be desired as industrial ecology (IE) researchers, practitioners, and decision-makers attempt to employ equitable practices. In this forum piece, we use a critical review approach to explain how socioecological inequities emerge in ML applications across their life cycle stages by leveraging the food system. We exemplify the use of a comprehensive questionnaire to delineate unfair ML bias across data bias, algorithmic bias, and selection and deployment bias categories. Finally, we provide consolidated guidance and tailored strategies to help address AI/ML unfair bias and inequity in IE applications. Specifically, the guidance and tools help to address sensitivity, reliability, and uncertainty challenges. There is also discussion on how bias and inequity in AI/ML affect other IE research and design domains, besides the food system—such as living labs and circularity. We conclude with an explanation of the future directions IE should take to address unfair bias and inequity in AI/ML. Last, we call for systemic equity to be embedded throughout IE applications to fundamentally understand domain-specific socioecological inequities, identify potential unfairness in ML, and select mitigation strategies in a manner that translates across different research domains

Embed systemic equity throughout industrial ecology applications:How to address machine learning unfairness and bias

Recent calls have been made for equity tools and frameworks to be integrated throughout the research and design life cycle —from conception to implementation—with an emphasis on reducing inequity in artificial intelligence (AI) and machine learning (ML) applications. Simply stating that equity should be integrated throughout, however, leaves much to be desired as industrial ecology (IE) researchers, practitioners, and decision-makers attempt to employ equitable practices. In this forum piece, we use a critical review approach to explain how socioecological inequities emerge in ML applications across their life cycle stages by leveraging the food system. We exemplify the use of a comprehensive questionnaire to delineate unfair ML bias across data bias, algorithmic bias, and selection and deployment bias categories. Finally, we provide consolidated guidance and tailored strategies to help address AI/ML unfair bias and inequity in IE applications. Specifically, the guidance and tools help to address sensitivity, reliability, and uncertainty challenges. There is also discussion on how bias and inequity in AI/ML affect other IE research and design domains, besides the food system—such as living labs and circularity. We conclude with an explanation of the future directions IE should take to address unfair bias and inequity in AI/ML. Last, we call for systemic equity to be embedded throughout IE applications to fundamentally understand domain-specific socioecological inequities, identify potential unfairness in ML, and select mitigation strategies in a manner that translates across different research domains.</p