In this paper 1 we propose a framework for Statistical Static Timing Analysis (SSTA) considering intra-die process variations. Given a cell library, we propose an accurate method to characterize the gate and interconnect delay as well as slew as a function of underlying parameter variations. Using these accurate delay models, we propose a method to perform SSTA based on a quadratic delay and slew model. The method is based on efficient dimensionality reduction technique used for accurate computation of the max of two delay expansions. Our results indicate less than 4% error in the variance of the delay models compared to SPICE Monte Carlo and less than 1% error in the variance of the circuit delay compared to Monte Carlo simulations.

An ECG and tri-axial accelerometer signal monitoring and analysis method for the homecare of elderly persons or patients, using wireless sensors technology was design and implemented. This paper presents a prototype of wellness monitoring system capable of recording, and analyzing continuous ECG and accelerometer data received from the human body. The system provides an application for recording activities, events and potentially important medical symptoms. The ECG features are used to detect life-threatening arrhythmias, with an emphasis on the software for analyzing the P-wave, QRS complex, and T-wave in ECG signals at server connected to base station which is receiving data from the wireless sensor on the patient body. Activity such as walking and running are detected from the body movements recorded by the accelerometer sensor. IEEE802.15.4 is used for wireless communication between sensor and base station. If any abnormality occurs at server then the alarm condition sends to the doctor' Personal Digital Assistant (PDA).

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