Voltage and Temperature Aware Statistical Leakage Analysis Framework Using Artificial Neural Networks

Artificial neural networks (ANNs) have shown great promise in modeling circuit parameters for computer aided design applications. Leakage currents, which depend on process parameters, supply voltage and temperature can be modeled accurately with ANNs. However, the complex nature of the ANN model, with the standard sigmoidal activation functions, does not allow analytical expressions for its mean and variance. We propose the use of a new activation function that allows us to derive an analytical expression for the mean and a semi-analytical expression for the variance of the ANN-based leakage model. To the best of our knowledge this is the first result in this direction. Our neural network model also includes the voltage and temperature as input parameters, thereby enabling voltage and temperature aware statistical leakage analysis (SLA). All existing SLA frameworks are closely tied to the exponential polynomial leakage model and hence fail to work with sophisticated ANN models. In this paper, we also set up an SLA framework that can efficiently work with these ANN models. Results show that the cumulative distribution function of leakage current of ISCAS'85 circuits can be predicted accurately with the error in mean and standard deviation, compared to Monte Carlo-based simulations, being less than 1% and 2% respectively across a range of voltage and temperature values.

Artificial commutation for inversion into a weak AC system in a multiterminal HVDC system

This paper deals with the application of artificial commutation for a normally rated inverter connecting a weak AC system in a multiterminal HVDC (MTDC) system. Artificial commutation is achieved using series capacitors. A modular digital simulation technique is developed to study the dynamic performance of the system. It is shown that by a proper selection of the value of the capacitor it is possible to limit the valve stresses and the DC harmonics to acceptable levels and achieve an improved performance during severe transient conditions. The determination of the value of the series capacitor is based on a parametric study.

Artificial neural network application to power system voltage stability improvement

As power systems grow in their size and interconnections, their complexity increases. Rising costs due to inflation and increased environmental concerns has made transmission, as well as generation systems be operated closer to design limits. Hence power system voltage stability and voltage control are emerging as major problems in the day-to-day operation of stressed power systems. For secure operation and control of power systems under normal and contingency conditions it is essential to provide solutions in real time to the operator in energy control center (ECC). Artificial neural networks (ANN) are emerging as an artificial intelligence tool, which give fast, though approximate, but acceptable solutions in real time as they mostly use the parallel processing technique for computation. The solutions thus obtained can be used as a guide by the operator in ECC for power system control. This paper deals with development of an ANN architecture, which provide solutions for monitoring, and control of voltage stability in the day-to-day operation of power systems.

Evaluation of chitosan nanoformulations as potent anti-HIV therapeutic systems

Background: Antiretroviral Therapy (ART) is currently the major therapeutic intervention in the treatment of AIDS. ART, however, is severely limited due to poor availability, high cytotoxicity, and enhanced metabolism and clearance of the drug molecules by the renal system. The use of nanocarriers encapsulating the antiretroviral drugs may provide a solution to the aforementioned problems. Importantly, the application of mildly immunogenic polymeric carrier confers the advantage of making the nanoparticles more visible to the immune system leading to their efficient uptake by the phagocytes. Methods: The saquinavir-loaded chitosan nanopartides were characterized by transmission electron microscopy and differential scanning calorimetry and analyzed for the encapsulation efficiency, swelling characteristics, particle size properties, and the zeta potential. Furthermore, cellular uptake of the chitosan nanocarriers was evaluated using confocal microscopy and Flow cytometry. The antiviral efficacy was quantified using viral infection of the target cells. Results: Using novel chitosan carriers loaded with saquinavir, a protease inhibitor, we demonstrate a drug encapsulation efficiency of 75% and cell targeting efficiency greater than 92%. As compared to the soluble drug control, the saquinavir-loaded chitosan carriers caused superior control of the viral proliferation as measured by using two different viral strains, NL4-3 and Indie-C1, and two different target T-cells, Jurkat and CEM-CCR5. Conclusion: Chitosan nanoparticles loaded with saquinavir were characterized and they demonstrated superior drug loading potential with greater cell targeting efficiency leading to efficient control of the viral proliferation in target T-cells. General significance: Our data ascertain the potential of chitosan nanocarriers as novel vehicles for HIV-1 therapeutics. (C) 2013 Elsevier B.V. All rights reserved.