A Non-contact measurement technique to measure micro surface stress and obtain deformation profiles of the order of 1nm in Micro-cantilever based structures by single image Optical Diffraction method

A new method based on analysis of a single diffraction pattern is proposed to measure deflections in micro-cantilever (MC) based sensor probes, achieving typical deflection resolutions of 1nm and surface stress changes of 50 mu N/m. The proposed method employs a double MC structure where the deflection of one of the micro-cantilevers relative to the other due to surface stress changes results in a linear shift of intensity maxima of the Fraunhofer diffraction pattern of the transilluminated MC. Measurement of such shifts in the intensity maxima of a particular order along the length of the structure can be done to an accuracy of 0.01mm leading to the proposed sensitivity of deflection measurement in a typical microcantilever. This method can overcome the fundamental measurement sensitivity limit set by diffraction and pointing stability of laser beam in the widely used Optical Beam Deflection method (OBDM).

Image analysis using multifractals

The efficacy of the multifractal spectrum as a tool for characterizing images has been studied. This spectrum has been computed for digitized images of the nucleus of human cervical cancer cells and it was observed that the entire spectrum is almost fully reproduced for a normal cell while only the right half (q<0) of the spectrum is reproduced for a cancerous cell. Cells in stages in between the two extremes show a shortening of the left half of the spectrum proportional to their condition. The extent of this shortening has been found to be sufficient to permit a classification between three classes of cells at varying distances from a basal cancerous layer-the superficial cells, the intermediate cells and the parabasal cells. This technique may be used for automatic screening of the population while also indicating the stage of malignancy

Image reconstruction from incomplete projections data: A multiresolution modeling approach

Computerized tomography is an imaging technique which produces cross sectional map of an object from its line integrals. Image reconstruction algorithms require collection of line integrals covering the whole measurement range. However, in many practical situations part of projection data is inaccurately measured or not measured at all. In such incomplete projection data situations, conventional image reconstruction algorithms like the convolution back projection algorithm (CBP) and the Fourier reconstruction algorithm, assuming the projection data to be complete, produce degraded images. In this paper, a multiresolution multiscale modeling using the wavelet transform coefficients of projections is proposed for projection completion. The missing coefficients are then predicted based on these models at each scale followed by inverse wavelet transform to obtain the estimated projection data.

Lossless and lossy image compression using Boolean function minimization

A novel approach for lossless as well as lossy compression of monochrome images using Boolean minimization is proposed. The image is split into bit planes. Each bit plane is divided into windows or blocks of variable size. Each block is transformed into a Boolean switching function in cubical form, treating the pixel values as output of the function. Compression is performed by minimizing these switching functions using ESPRESSO, a cube based two level function minimizer. The minimized cubes are encoded using a code set which satisfies the prefix property. Our technique of lossless compression involves linear prediction as a preprocessing step and has compression ratio comparable to that of JPEG lossless compression technique. Our lossy compression technique involves reducing the number of bit planes as a preprocessing step which incurs minimal loss in the information of the image. The bit planes that remain after preprocessing are compressed using our lossless compression technique based on Boolean minimization. Qualitatively one cannot visually distinguish between the original image and the lossy image and the value of mean square error is kept low. For mean square error value close to that of JPEG lossy compression technique, our method gives better compression ratio. The compression scheme is relatively slower while the decompression time is comparable to that of JPEG.

Vilsmeier-Haack reaction of 1-methyl-34-dihydroisoquino lines-unexpected formation of 2,3-bisdimethylamino-5,6-dihydropyrrolo [2,1-Image]isoquinolines

Novel pyrroloisoquinolines 4 are obtained from 1-methyl-3,4-dihydroisoquinolines 1 by the action of POCl3 and DMF, along with the expected mono- and dialdehydes 2 and 3 respectively and also directly from N-acetyl-2-phenethylamines.

Naphthalenes Image microwave irradiation induced rearrangement on montmorillonite K-10

Irradiation of 4-aryl-4-alkylhex-5-en-2-ones (e.g. 1a) or 5-aryl-4-alkylhex-5-en-2-ones (e.g. 2a) adsorbed on montmorillonite K-10 in a commercial microwave oven furnishes the multialkylated naphthalenes (e.g. 3).

A Procedure for the 3D Reconstruction of Biological Organs from 2D Image Sequences

In this work, a procedure is presented for the reconstruction of biological organs from image sequences obtained through CT-scan. Although commercial software, which can accomplish this task, are readily available, the procedure presented here needs only free software. The procedure has been applied to reconstruct a liver from the scan data available in literature. 3D biological organs obtained this way can be used for the finite element analysis of biological organs and this has been demonstrated by carrying out an FE analysis on the reconstructed liver.

Spherical aberration and its correction using Lie algebraic methods

We discuss three methods to correct spherical aberration for a point to point imaging system. First, results obtained using Fermat's principle and the ray tracing method are described briefly. Next, we obtain solutions using Lie algebraic techniques. Even though one cannot always obtain analytical results using this method, it is often more powerful than the first method. The result obtained with this approach is compared and found to agree with the exact result of the first method.

Network Error Correction for Unit-Delay, Memory-Free Networks Using Convolutional Codes

A single source network is said to be memory-free if all of the internal nodes (those except the source and the sinks) do not employ memory but merely send linear combinations of the symbols received at their incoming edges on their outgoing edges. In this work, we introduce network-error correction for single source, acyclic, unit-delay, memory-free networks with coherent network coding for multicast. A convolutional code is designed at the source based on the network code in order to correct network- errors that correspond to any of a given set of error patterns, as long as consecutive errors are separated by a certain interval which depends on the convolutional code selected. Bounds on this interval and the field size required for constructing the convolutional code with the required free distance are also obtained. We illustrate the performance of convolutional network error correcting codes (CNECCs) designed for the unit-delay networks using simulations of CNECCs on an example network under a probabilistic error model.

Correction for Bias in Downscaling GCM Simulations for Hydrologic Impact Assessment

In a statistical downscaling model, it is important to remove the bias of General Circulations Model (GCM) outputs resulting from various assumptions about the geophysical processes. One conventional method for correcting such bias is standardisation, which is used prior to statistical downscaling to reduce systematic bias in the mean and variances of GCM predictors relative to the observations or National Centre for Environmental Prediction/ National Centre for Atmospheric Research (NCEP/NCAR) reanalysis data. A major drawback of standardisation is that it may reduce the bias in the mean and variance of the predictor variable but it is much harder to accommodate the bias in large-scale patterns of atmospheric circulation in GCMs (e.g. shifts in the dominant storm track relative to observed data) or unrealistic inter-variable relationships. While predicting hydrologic scenarios, such uncorrected bias should be taken care of; otherwise it will propagate in the computations for subsequent years. A statistical method based on equi-probability transformation is applied in this study after downscaling, to remove the bias from the predicted hydrologic variable relative to the observed hydrologic variable for a baseline period. The model is applied in prediction of monsoon stream flow of Mahanadi River in India, from GCM generated large scale climatological data.