Palladium‐nanoparticle intercalated vermiculite for selective hydrogenation of α,β‐unsaturated aldehydes

Palladium nanoparticles were generated in the interlamellar region of swelling 2:1 type vermiculite clay using an adsorption excess isotherm. An adsorption excess isotherm was constructed for vermiculite after exchanging the exchangeable Na⁺ ions in the interlamellar region using a binary liquid mixture (ethanol:toluene). Based on adsorption excess isotherm, 1% w/w palladium was intercalated into the interlamellar space of vermiculite. The Pd‐intercalated vermiculite was characterized by X‐ray diffraction, transmission electron microscopy and atomic absorption spectroscopy. The 1% w/w Pd‐intercalated vermiculite was tested for its catalytic activity towards selective hydrogenation reactions involving some α,β‐unsaturated carbonyl compounds using a pulse reactor and its catalytic activity was compared with commercial 1% Pd/C. The intercalated catalyst was found to be very selective towards the desired product of unsaturated alcohols. Copyright © 2007 Society of Chemical Industry     

Experimental investigation with optimal prediction of emission in diesel engine using combined NSR–SCR catalyzer

Clean Technologies and Environmental Policy - The utilization of catalytic converters is one of the well-known strategies to clean the exhaust. The catalytic converters oxidize the destructive...     

Rendering localized spatial audio in a virtual auditory space

High-quality virtual audio scene rendering is required for emerging virtual and augmented reality applications, perceptual user interfaces, and sonification of data. We describe algorithms for creation of virtual auditory spaces by rendering cues that arise from anatomical scattering, environmental scattering, and dynamical effects. We use a novel way of personalizing the head related transfer functions (HRTFs) from a database, based on anatomical measurements. Details of algorithms for HRTF interpolation, room impulse response creation, HRTF selection from a database, and audio scene presentation are presented. Our system runs in real time on an office PC without specialized DSP hardware.     

A fast algorithm for learning a ranking function from large-scale data sets

We consider the problem of learning the ranking function that maximizes a generalization of the Wilcoxon-Mann-Whitney statistic on the training data. Relying on an $\epsilon$-accurate approximation for the error-function, we reduce the computational complexity of each iteration of a conjugate gradient algorithm for learning ranking functions from O(m2) to O(m2), where m is the number of training samples. Experiments on public benchmarks for ordinal regression and collaborative filtering indicate that the proposed algorithm is as accurate as the best available methods in terms of ranking accuracy, when the algorithms are trained on the same data. However, since it is several orders of magnitude faster than the current state-of-the-art approaches, it is able to leverage much larger training datasets.     

Vapor‐phase selective hydrogenation of citral over Pd/bentonite: effect of reduction method

BACKGROUND: Well‐dispersed nanoparticles of palladium were synthesized by wet impregnation technique over bentonite followed by three different reduction methods (H₂ or NaBH₄ or ethanol) and characterized by transmission electron microscopy, X‐ray diffraction, temperature‐programmed reduction and atomic absorption spectroscopy. Hydrogenation of citral over Pd‐supported bentonite catalysts was studied in vapor phase using a micro‐reactor. The effect of reduction method and metal loading on the conversion of citral and selectivity towards nerol and geraniol were examined. RESULTS: Among the catalysts evaluated in the vapor phase, Pd/bentonite reduced by ethanol was found to give the highest conversion and Pd/bentonite reduced by NaBH₄ was found to give the highest selectivity towards nerol and geraniol. This may be attributed to the smallest particle size of Pd in the former catalyst and presence of boron species on the latter catalyst, respectively. CONCLUSION: The presence of boron in proximity to palladium particles polarized C?O bond and helped C?O adsorption, thereby yielding nerol and geraniol (the unsaturated alcohols). Copyright © 2008 Society of Chemical Industry     

A video-based framework for the analysis of presentations/posters

Detection and recognition of textual information in an image or video sequence is important for many applications. The increased resolution and capabilities of digital cameras and faster mobile processing allow for the development of interesting systems. We present an application based on the capture of information presented at a slide-show presentation or at a poster session. We describe the development of a system to process the textual and graphical information in such presentations. The application integrates video and image processing, document layout understanding, optical character recognition (OCR), and pattern recognition. The digital imaging device captures slides/poster images, and the computing module preprocesses and annotates the content. Various problems related to metric rectification, key-frame extraction, text detection, enhancement, and system integration are addressed. The results are promising for applications such as a mobile text reader for the visually impaired. By using powerful text-processing algorithms, we can extend this framework to other applications, e.g., document and conference archiving, camera-based semantics extraction, and ontology creation.Received: 18 December 2003, Revised: 1 November 2004, Published online: 2 February 2005     

Fast Evaluation of the Room Transfer Function Using Multipole Expansion

Reverberation in rooms is often simulated with the image method due to Allen and Berkley (1979). This method has an asymptotic complexity that is cubic in terms of the simulated reverberation length. When employed in the frequency domain, it is relatively computationally expensive if there are many receivers in the room or if the source or receiver positions are changing with time. The computational complexity of the image method is due to the repeated summation of the fields generated by a large number of image sources. In this paper, a fast method to perform such summations is presented. The method is based on multipole expansion of the monopole source potential. For offline computation of the room transfer function for N image sources and M receiver points, use of the Allen-Berkley algorithm requires O(NM) operations, whereas use of the proposed method requires only O(N+M) operations, resulting in significantly faster computation of reverberant sound fields. The proposed method also has a considerable speed advantage in situations where the room transfer function must be rapidly updated online in response to source/receiver location changes. Simulation results are presented, and algorithm accuracy, speed, and implementation details are discussed. For problems that require frequency-domain computations, the algorithm is found to generate sound fields identical to the ones obtained with the frequency-domain version of the Allen-Berkley algorithm at a fraction of computational cost     

3D Structure Recovery and Unwarping of Surfaces Applicable to Planes

The deformation of applicable surfaces such as sheets of paper satisfies the differential geometric constraints of isometry (lengths and areas are conserved) and vanishing Gaussian curvature. We show that these constraints lead to a closed set of equations that allow recovery of the full geometric structure from a single image of the surface and knowledge of its undeformed shape. We show that these partial differential equations can be reduced to the Hopf equation that arises in non-linear wave propagation, and deformations of the paper can be interpreted in terms of the characteristics of this equation. A new exact integration of these equations is developed that relates the 3-D structure of the applicable surface to an image. The solution is tested by comparison with particular exact solutions. We present results for both the forward and the inverse 3D structure recovery problem.     

Background and foreground modeling using nonparametric kernel density estimation for visual surveillance

Automatic understanding of events happening at a site is the ultimate goal for many visual surveillance systems. Higher level understanding of events requires that certain lower level computer vision tasks be performed. These may include detection of unusual motion, tracking targets, labeling body parts, and understanding the interactions between people. To achieve many of these tasks, it is necessary to build representations of the appearance of objects in the scene. This paper focuses on two issues related to this problem. First, we construct a statistical representation of the scene background that supports sensitive detection of moving objects in the scene, but is robust to clutter arising out of natural scene variations. Second, we build statistical representations of the foreground regions (moving objects) that support their tracking and support occlusion reasoning. The probability density functions (pdfs) associated with the background and foreground are likely to vary from image to image and will not in general have a known parametric form. We accordingly utilize general nonparametric kernel density estimation techniques for building these statistical representations of the background and the foreground. These techniques estimate the pdf directly from the data without any assumptions about the underlying distributions. Example results from applications are presented.     

Efficient kernel density estimation using the fast gauss transform with applications to color modeling and tracking

Many vision algorithms depend on the estimation of a probability density function from observations. Kernel density estimation techniques are quite general and powerful methods for this problem, but have a significant disadvantage in that they are computationally intensive. In this paper, we explore the use of kernel density estimation with the fast Gauss transform (FGT) for problems in vision. The FGT allows the summation of a mixture of ill Gaussians at N evaluation points in O(M+N) time, as opposed to O(MN) time for a naive evaluation and can be used to considerably speed up kernel density estimation. We present applications of the technique to problems from image segmentation and tracking and show that the algorithm allows application of advanced statistical techniques to solve practical vision problems in real-time with today's computers.