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Patterning a surface by scratching at micro-nano scale requires precise instrumentation strategies for generation of accurate patterns which are used in variety of surfaces ranging from bacteria resistant surfaces to water repellent ones. The scratching action leads to temperature rise of tool tip causing expansion of tool and work surface. This results in positioning inaccuracies and surface defects of the machined work. In this work a fiber Bragg grating sensor (FBG) is used to record the temperature rise at the tool tip under different scratching/cutting speeds. The surface integrity of the patterned work was evaluated after machining using a profilometer. The optimized value of cutting speed for minimum inaccuracy and minimum burr was found using Lagrangian multiplier based technique. This work thus ensures real time control of scratching speed based on tool tip temperature variations only, which finally results in better surface integrity of patterned profile. [ABSTRACT FROM AUTHOR]

For a battery powered safety critical system the safe duration of power for executing a specific task is extremely important. It is necessary to avoid unacceptable consequences due to unwanted battery power failure. An early stage estimation of this duration reduces the overall risk through optimization of current consumption by switching off noncritical load ahead of delivery of power to a critical load. In order to address this issue, an online battery state of charge estimator on chip is conceived and implemented using Kalman filter. The Kalman filter estimates the true values of measurements by predicting a value, considering the estimated uncertainty of the predicted value, and then computing a weighted average of the predicted value and the measured value. The basic idea is more accurate state prediction is possible when the state predicted value is fused with sensor prediction under any uncertain disturbance. The state estimator is developed in the form of an algorithm and stored into a single chip microcontroller. It is finally used to generate an early stage warning signal against battery failure. The paper presents a methodology for creating energy aware system that would avoid sudden system failure due to power outage. The authors used a generalized state space model of the battery to estimate the effect of unobserved battery parameters for duration estimation. An experiment was conducted in this regard through discharging the battery under constant load. Subsequently the internal parameters of battery were calculated. The model was simulated through MATLAB/simulink R2008a software and efficiency was tested. The program for prediction was finally emulated in a microcontroller and found satisfactory result.(Abstract) [ABSTRACT FROM PUBLISHER]