A Deep Learning Approach to Structured Signal Recovery

In this paper, we develop a new framework for sensing and recovering structured signals. In contrast to compressive sensing (CS) systems that employ linear measurements, sparse representations, and computationally complex convex/greedy algorithms, we introduce a deep learning framework that supports both linear and mildly nonlinear measurements, that learns a structured representation from training data, and that efficiently computes a signal estimate. In particular, we apply a stacked denoising autoencoder (SDA), as an unsupervised feature learner. SDA enables us to capture statistical dependencies between the different elements of certain signals and improve signal recovery performance as compared to the CS approach

Three-dimensional phase-field study of crack-seal microstructures - insights from innovative post-processing techniques

Numerical simulations of vein evolution contribute to a better understanding of processes involved in their formation and possess the potential to provide invaluable insights into the rock deformation history and fluid flow pathways. The primary aim of the present article is to investigate the influence of a “realistic” boundary condition, i.e. an algorithmically generated “fractal” surface, on the vein evolution in 3-D using a thermodynamically consistent approach, while explaining the benefits of accounting for an extra dimensionality. The 3-D simulation results are supplemented by innovative numerical post-processing and advanced visualization techniques. The new methodologies to measure the tracking efficiency demonstrate the importance of accounting the temporal evolution; no such information is usually accessible in field studies and notoriously difficult to obtain from laboratory experiments as well. The grain growth statistics obtained by numerically post-processing the 3-D computational microstructures explain the pinning mechanism which leads to arrest of grain boundaries/multi-junctions by crack peaks, thereby, enhancing the tracking behavior

Devlopment and validation of RP-HPLC Method for estimation of Metronidazole and Norfloxacin in suspension form

A simple reversed- phase high-performance liquid chromatographic method (RP-HPLC) has been developed and validated for estimation of Development and Validation of Analytical method for Estimation of Metronidazole and Norfloxacin in pharmaceutical dosage form . Chromatographic separation was carried out on YL 9100 equipped with PDA detector using C 18 (250mm 4.6mm, 5) as stationary phase and mobile phase Triethylamine: 0.02M Potassium Dihydrogen phosphate (pH 3.5): Methanol (0.01:70:30) at flow rate of 1ml/min. Wavelength for UV detection was 292nm. The retention time for Metronidazole and Norfloxacin was found to be 6.10 and 3.50 min. The method was validated as per ICH guideline and and can be applied for estimation of Metronidazole and Norfloxacin in suspension. The linearity was found over concentration range of 12.5-37.5g/ml for Metronidazole and Norfloxacin

Utilisation of Renewable Energy Source for Domestic Purpose Applications by Using Wind Mill

The Earth's winds comprise immense amounts of energy. Peoples use that energy for various purposes like sail ships, grind grain and pump water. In recent trend conversion of wind energy into electrical energy is mostly like one. In that by using some mechanical and electrical arrangement we able to produce electrical energy for domestic purpose as well as industrial purpose. The burning of fossil fuel produces 21.3 billion tons of CO2 per year and that are very hazardous for human existence on earth. By using renewable energy sources, we are able to overcome that problem. Due to absence of risk and disasters Renewable energy is always consult as reliable source. In this regard, various policies were prepared to encourage citizens for use of renewable source. Citizens were able to installed small wind mill for domestic purpose. It has one-time capital investment after that we can consume that energy or can connect to grid also. In this paper, we discuss about energy generation for home appliances from small windmill. This is contributing for reducing dependency on fossil fuels. [1]. Today, new wind turbines offer an efficient amount of electrical energy by catching more and more wind energy. Wind turbine converts the kinetic energy of wind into rotational kinetic energy and then converts into electrical energy by using electrical converters [2]. Wind turbines can be utilized for individual homes and also jointly to generate electricity for utility grids. Generated electricity is totally depends upon the size of rotating blades and speed of wind