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Risk Management is an integral part of the project management. This is because projects always go along with various kinds of Risks. The management of risk in projects is currently one of the main topics of interest for researchers and practitioners working in the area of project management. Risk identification is one of the most important steps of risk management that analyzed in more details. Risk identification is discovering, defining, describing, documenting and communicating risks before they become problems and adversely affect a project. In this paper, Enhancing Risk Management with an efficient risk identification approach is presented. Risk identification process is analyzed at all points and A New Model for Risk Management with focus on risk identification is addressed.

A circuit for automatic detection of action potentials (spikes) in neural recording devices is presented. This circuit, designed to operate in discrete time, provides an analogue threshold level based on standard deviation of the analogue samples of the input neural signal. Functionality of the circuit was tested for normal prerecorded neural signals as the input. To examine whether the spike detection threshold level tracks low-frequency baseline variations (e.g. local field potentials), the input neural signal was superposed on both a step function and a low-frequency sine wave. Designed in a 0.18 um CMOS technology, according to simulations the circuit consumes 47 µW from a ±0.9 V supply. A prototype version of the circuit was implemented and successfully tested.