Abstract: The present invention is related to a learning-based surrogate model capable of predicting operating condition-dependent time and/or frequency domain waveforms at the IC pins. The model of the present invention consists of a set of discrete features that can be extracted from a time-domain waveform and can be used to reconstruct the continuous time-domain waveform accurately and consequently reconstruct the frequency-domain characteristics of the time-domain waveform. The model of the present invention has a machine-learning framework e.g. an Artificial Neural Network, which can be trained to predict all the features extracted as a function of parameters characterizing the operating conditions.

Abstract: A method for assigning an event to a device on a network is described by using a topology editor as user-controlled interface. The event is represented as a labeled graphic indication. Other devices on a data network, also represented graphically, are to be assigned to the event by using the user-controlled interface. A rule is then defined and associated with the event. When a condition, as defined by the rule is met by an application or device on the network, all of the devices assigned to the event are notified of the condition met by the rule in accordance with such graphically defined relationships.