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This work presents theoretical analysis, numerical simulation, fabrication and test of a micromixer chip for mixing fluids in microchannel. A threedimensional analytical model is developed using a different mathematical approach to study passive laminar mixing phenomena and predict concentration distribution in a microchannel. The analytical model is validated by comparing with experimental and simulation results. The process of mixing fluids in a microchannel is simulated by solving the continuity, momentum and mass diffusion equations. The simulation results are validated and then parametric studies are performed to investigate the effects of channel aspect ratio, Reynolds number and diffusion coefficient on the mixing performance. The micromixer chip is fabricated with patterned SU-8 photoresist as the microchannel layer on a PMMA substrate using a combination of photolithography and micro-milling. Experiments are performed with different mixing fluids and the results were compared with that obtained from the theoretical model and simulation results.

With the growing smart grid concept it becomes important to monitor health of the power system at regular intervals for its secure and reliable operation. Phasor Measurement Units (PMUs) may play a vital role in this regard. This paper presents voltage stability monitoring in real time framework using synchrophasor measurements obtained by PMUs. Proposed approach estimates real power loading margin as well as reactive power loading margin of most critical bus using PMU data. As system operating conditions keep on changing, loading margin as well as critical bus information is updated at regular intervals using fresh PMU measurements. Simulations have been carried out using Power System Analysis Toolbox (PSAT) software. Accuracy of proposed Wide Area Monitoring System (WAMS) based estimation of voltage stability margin has been tested by comparing results with loading margin obtained by continuation power flow method (an offline approach for accurate estimation of voltage stability margin) under same set of operating conditions. Case studies performed on IEEE 14-bus system, New England 39-bus system and a practical 246-bus Indian power system validate effectiveness of proposed approach of online monitoring of loading margin.

Under rapidly changing environmental conditions, the model reference adaptive control (MRAC) based MPPT schemes need high adaptation gain to achieve fast convergence and guaranteed transient performance. The high adaptation gain causes high-frequency oscillations in the control signals resulting in numerical instability and inefficient operation. This paper proposes a novel high-frequency learning-based adjustable gain MRAC (HFLAG-MRAC) for a 2-level MPPT control architecture in photovoltaic (PV) systems to ensure maximum power delivery to the load under rapidly changing environmental conditions. In the proposed 2-level MPPT control architecture, the first level is the conventional ripple correlation control (RCC) that yields a steady-state ripple-free optimum duty cycle. The duty cycle obtained from the first level serves as an input to the proposed HFLAG-MRAC in the second level. In the proposed adaptive law, the adaptation gain varies as a function of the high-frequency ripple content of the tracking error. These high-frequency contents are the difference between the tracking error and its low-pass filtered version representing the fluctuations in output due to rapid changes in the environmental conditions. Thus, adjusting the adaptation gain by high-frequency content of the tracking error ensures fast convergence, guaranteed transient performance, and overall system stability without needing high adaptation gain. The adaptive law of the proposed HFLAG-MRAC is derived using the Lyapunov theory. Simulation studies, experimental analysis, and performance comparison with recent similar work validate the effectiveness of the proposed work.

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In this paper, a detailed survey study on single image super-resolution (SR) has been presented, which aims at recovering a high-resolution (HR) image from a given low-resolution (LR) one. It is always the research emphasis because of the requirement of higher definition video displaying, such as the new generation of Ultra High Definition (UHD) TVs. Super-resolution (SR) is a popular topic of image processing that focuses on the enhancement of image resolution. In general, SR takes one or several low resolution (LR) images as input and maps output images with high resolution (HR), which has been widely applied in remote sensing, medical imaging, biometric identification.