Parallel matrix inversion techniques

In this paper, we present techniques for inverting sparse, symmetric and positive definite matrices on parallel and distributed computers. We propose two algorithms, one for SIMD implementation and the other for MIMD implementation. These algorithms are modified versions of Gaussian elimination and they take into account the sparseness of the matrix. Our algorithms perform better than the general parallel Gaussian elimination algorithm. In order to demonstrate the usefulness of our technique, we implemented the snake problem using our sparse matrix algorithm. Our studies reveal that the proposed sparse matrix inversion algorithm significantly reduces the time taken for obtaining the solution of the snake problem. In this paper, we present the results of our experimental work

India’s Revenue Deficit: A Challenge Ahead

A developing country like India needs revenue surplus for the capital investment at the same time to pursue the economic development through demand expansion it needs expenditure especially in the social sectors such as health, education etc,. The recent global economic crisis also compels India to induce the expenditure for sustainability of the growth that it has achieved recently. This also needs enormous expenditure. On the other hand, current expenditure over current revenue of an economy makes revenue deficit. India’s Thirteenth Finance Commission’s one of the recommendation is that revenue deficit (as % of GDP) of the Centre needs to be progressively reduced and eliminated, followed by emergence of a revenue surplus by 2014-15 and a long term and permanent target for the Central Government should be to maintain, at the minimum, a zero revenue deficit. In the light of the above recommendation analyzing revenue deficit is imperative at this hour.India; Revenue Deficit; Revenue Receipts; Revenue Expenditure; Thirteenth Finance Commission

Thermal analysis, optimization and design of a Martian oxygen production plant

The objective is to optimally design the thermal components of a system that uses carbon dioxide (CO2) from the Martian atmosphere to produce oxygen (O2) for spacecraft propulsion and/or life-support. Carbon dioxide is thermally decomposed into carbon monoxide (CO) and O2 followed by the electrochemical separation of O2. The design of the overall system and its various individual components depends on, among other things, the fraction of the stoichiometric yield of O2 that can be realized in the system and the temperature of operation of the electrochemical separation membrane. The analysis indicates that a substantial reduction could be obtained in the mass and power requirements of the system if the unreacted CO2 were to be recycled. The concepts of an optimum temperature of the zirconia cell and impracticality of plant operation at low cell efficiencies are also discussed. The design of the thermal equipment is such that the mass and power requirements of the individual components and of the overall system are optimized

DeepFuse: A Deep Unsupervised Approach for Exposure Fusion with Extreme Exposure Image Pairs

We present a novel deep learning architecture for fusing static multi-exposure images. Current multi-exposure fusion (MEF) approaches use hand-crafted features to fuse input sequence. However, the weak hand-crafted representations are not robust to varying input conditions. Moreover, they perform poorly for extreme exposure image pairs. Thus, it is highly desirable to have a method that is robust to varying input conditions and capable of handling extreme exposure without artifacts. Deep representations have known to be robust to input conditions and have shown phenomenal performance in a supervised setting. However, the stumbling block in using deep learning for MEF was the lack of sufficient training data and an oracle to provide the ground-truth for supervision. To address the above issues, we have gathered a large dataset of multi-exposure image stacks for training and to circumvent the need for ground truth images, we propose an unsupervised deep learning framework for MEF utilizing a no-reference quality metric as loss function. The proposed approach uses a novel CNN architecture trained to learn the fusion operation without reference ground truth image. The model fuses a set of common low level features extracted from each image to generate artifact-free perceptually pleasing results. We perform extensive quantitative and qualitative evaluation and show that the proposed technique outperforms existing state-of-the-art approaches for a variety of natural images.Comment: ICCV 201