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A description of modifications in the variable energy cyclotron (VEC) Chandigarh along with the installation of Proton Induced X-ray Emission (PIXE) setup is presented. A new main magnet power supply of 400A/125V out put with ±10 ppm stability and a new stabilized solid state power supply for RF oscillator has improved the beam characteristics substantially. A new chamber has been designed to cater for Proton Induced Gamma Emission (PIGE) and Rutherford Back Scattering (RBS) along with PIXE measurements. The HPGe x-ray detector, the Ge(Li) γ-ray detector and a silicon surface barrier (SSB) detector can be mounted simultaneously in the chamber for this purpose. A Turbo-Molecular vacuum pump is provided to produce a clean vacuum of the order of 10^-8 mbar in the PIXE chamber. A remotely controlled stepper motor is provided to move the wheel having 12/24-position target holder. Beam size optimization along with the minimization of background has been done with the help of graphite collimators, thus making the setup suitable for practical applications. Preliminary experiments for the PIXE analysis of aerosol, gunshot residues and kidney stone samples are presented.

The Minimum Description Length (MDL) criterion is used to fit a facet model of a car to an image. The best fit is achieved when the difference image between the car and the background has the greatest compression. MDL overcomes the overfitting and parameter precision problems which hamper the more usual maximum likelihood method of model fitting. Some preliminary results are shown.

DNA copy number (DCN) is the number of copies of DNA at a region of a genome. The alterations of DCN are highly associated with the development of different tumors. Recently, microarray technologies are being employed to detect DCN changes at many loci at the same time in tumor samples. The resulting DCN data are often very noisy, and the tumor sample is often contaminated by normal cells. The goal of computational analysis of array-based DCN data is to infer the underlying DCNs from raw DCN data. Previous methods for this task do not model the tumor/normal cell mixture ratio explicitly and they cannot output segments with DCN annotations.

We developed a novel model-based method using the minimum description length (MDL) principle for DCN data segmentation. Our new method can output underlying DCN for each chromosomal segment, and at the same time, infer the underlying tumor proportion in the test samples. Empirical results show that our method achieves better accuracies on average as compared to three previous methods, namely Circular Binary Segmentation, Hidden Markov Model and Ultrasome.

The Minimum Description Length (MDL) criterion is used to fit a facet model of a car to an image. The best fit is achieved when the difference image between the car and the background has the greatest compression. MDL overcomes the overfitting and parameter precision problems which hamper the more usual maximum likelihood method of model fitting. Some preliminary results are shown.