Abstract: A bioactive formulation including ethanolic concentrates of Andrographolide extract in range of 90% by wt-99% by wt and aqueous 2 hydroxypropyl beta cyclodextrin in the range of 2% by wt-10% by wt. A method for preparation of the bioactive formulation including the following steps: mixing Andrographolide extract and ethanol to prepare ethanolic concentrates of Andrographolide (solvent A), mixing hydroxypropyl beta cyclodextrin and ethanol to obtain an ethanolic concentrate 2 hydroxypropyl beta cyclodextrin (Solvent B), mixing the solvent A and solvent B to obtain a complex concentrate solution, recovering ethanol solvent from the concentrate solution through distillation to obtain a thick paste of the concentrate, drying the paste at a temperature in the range of 55° C.-65° C. and pressure in the range of ?650 to 700 mm Hg to obtain a dried complex concentrate followed by converting into an amorphous powder to obtain a bioactive formulation.

Abstract: Techniques are provided for compressive sensing (CS) in a sensor network, for improved power efficiency. A methodology implementing the techniques according to an embodiment includes determining a state of a sensor network, based on a calculated statistic of sampled data values generated by one or more sensors in the network, and on anomaly indications generated by the one or more sensors. The method further includes calculating a CS sampling schedule based on the determined state and further based on a sparse signal recovery algorithm. The method further includes broadcasting the CS schedule to the one or more sensors. The CS schedule includes a sensor identification, sampling frequency, and sampling time offset for each sensor to be sampled. The method further includes updating the state of the sensor network and the CS schedule, based on updated data values generated by the one or more sensors in accordance with the sampling schedule.

Abstract: Techniques are provided for compressive sensing (CS) in a sensor network, for improved power efficiency. A methodology implementing the techniques according to an embodiment includes determining a state of a sensor network, based on a calculated statistic of sampled data values generated by one or more sensors in the network, and on anomaly indications generated by the one or more sensors. The method further includes calculating a CS sampling schedule based on the determined state and further based on a sparse signal recovery algorithm. The method further includes broadcasting the CS schedule to the one or more sensors. The CS schedule includes a sensor identification, sampling frequency, and sampling time offset for each sensor to be sampled. The method further includes updating the state of the sensor network and the CS schedule, based on updated data values generated by the one or more sensors in accordance with the sampling schedule.