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This work addresses a new problem of dynamic voltage scaling (DVS) in multicore platforms. We solve the multicore DVS problem, i.e., simultaneously scheduling execution of tasks assigned to cores and determining dynamically-varying voltage levels, with the objective of minimizing total energy consumption of the cores and voltage regulators (VRs) in the reconfigurable VR-to-core power distribution network (PDN) of platform while meeting the arrival/deadline constraint of tasks. Here, the key factors to be exploited for energy saving are (1) available voltage levels, (2) power conversion efficiency curve of VRs, and (3) turning on/off VRs. Specifically, we formulate the problem of task scheduling with the relation between factors 1, 2, and 3 into a linear programming problem and solve optimally in polynomial time.

Spontaneous emission from InGaAs/GaAs quantum dots was investigated above the lasing threshold through windows structure on a laser diode. We found a clear evidence of slow carrier relaxation from the excited state to the ground state in a lasing condition.

Over the last two decades, it has been widely accepted that dynamic voltage/frequency scaling (DVS) is one of the most effective techniques of minimizing the energy consumption of the embedded systems. One common assumption of almost all of the existing DVS algorithms is that the value of power consumption monotonically increases as the level of applied voltage to the system increases, and the power is a convex function of the voltage. Theoretically, under that assumption, previous works have shown that the DVS problem for a set of tasks with continuous or discrete convex power function is energy-optimally solvable in polynomial time. However, recently it is observed that some of the DC-DC converters do not follow the convexity. Thus, the power function of the whole system including a DC-DC converter will not be convex any more. In this context, we want to answer the following question: is there an energy-optimal polynomial-time algorithm that is able to solve the DVS problem with any non-convex power function? The work answers ‘yes’, and proposes an energy-optimal polynomial-time algorithm for the new problem.

Accurate information regarding the maximum available pulse current can help to determine the power capability of the battery and allow the battery to be operated within the safe operating voltage range. However, although estimation methods of the maximum available pulse current have been developed, they have the drawback of variation in the resistance inside the batteries. In this paper, the state of charge (SOC) and resistance of the battery are estimated from the dual extended Kalman filter (dual EKF). Furthermore, the lumped resistance, which represents the impedance inside the battery during the predefined time, is calculated using an easy numerical calculation based on the estimated values.

We present architecture for implementing independent, extensible, and interoperable clinical decision support service in perspective of EHR. In this architecture, components for implementing independent knowledge service and interface mechanism with EHR service or existing hospital information system are identified. In our architecture knowledge engine is critical component for implementing knowledge service. In this paper, we suggested interoperable CDSS(clinical decision support system) architecture and knowledge engine architecture.

The open circuit voltage (OCV) is widely used to estimate the state of charge (SOC) in many SOC estimation algorithms. But the relationship between the OCV and SOC can not be exactly same for all batteries. Because the conventional OCV-SOC differs between batteries, there is a problem in that the OCV-SOC data should be measured to accurately estimate the SOC in different batteries. Therefore, the conventional OCV- SOC should be modified. In this paper, a new OCV-SOC that is independent of the battery conditions is proposed. Thus, problems resulting from the defects of the EKF can be avoided by preventing the OCV-SOC data from varying. In this paper, the SOC and battery capacity are estimated using the dual EKF with the proposed OCV-SOC.