A low power scheduling scheme with resources operating at multiplevoltages

This paper presents resource and latency constrained scheduling algorithms to minimize power/energy consumption when the resources operate at multiple voltages (5 V, 3.3 V, 2.4 V, and 1.5 V). The proposed algorithms are based on efficient distribution of slack among the nodes in the data-flow graph. The distribution procedure tries to implement the minimum energy relation derived using the Lagrange multiplier method in an iterative fashion. Two algorithms are proposed, 1) a low complexity O(n²) algorithm and 2) a high complexity O(n² log(L)) algorithm, where n is the number of nodes and L is the latency. Experiments with some HLS benchmark examples show that the proposed algorithms achieve significant power/energy reduction. For instance, when the latency constraint is 1.5 times the critical path delay, the average reduction is 39%     

The extraction of lactic acid by emulsion type of liquid membranes using alamine 336 in escaid 100

The extraction of lactic acid from aqueous solutions through an emulsion liquid membrane containing Alamine 336 as carrier was investigated. The influence of mixing speed, diluent type, surfactant concentration, extractant concentration, feed solution pH, stripping concentration, phase ratio, and feed concentration were examined. Liquid membrane consists of a diluent (n‐heptane, toluene, kerosene, Escaid 100, and Escaid 200), a surfactant (Span 80) and an extractant (Alamine 336), and Na₂CO₃ were used as a stripping solution. It is possible to extract 91% of lactic acid from aqueous solutions using Alamine 336 in Escaid 100, as an extractant and a diluent respectively.     

Variable voltage task scheduling algorithms for minimizing energy/power

In this paper, we propose variable voltage task scheduling algorithms that minimize energy or minimize peak power for the case when the task arrival times, deadline times, execution times, periods, and switching activities are given. We consider aperiodic (earliest due date, earliest deadline first), as well as periodic (rate monotonic, earliest deadline first) scheduling algorithms. We use the Lagrange multiplier method to theoretically determine the relation between the task voltages such that the energy or peak power is minimum, and then develop an iterative algorithm that satisfies the relation. The asymptotic complexity of the existing scheduling algorithms change very mildly with the application of the proposed algorithms. We show experimentally (random experiments as well as real-life cases), that the voltage assignment obtained by the proposed low-complexity algorithm is very close to that of the optimal energy (0.1% error) and optimal peak power (1% error) assignment.