Abstract: Devices, systems and methods are provided to assist with the monitoring or management of a patient's medical condition, which have one or more sensors sensing individual patient data on or near the patient. This individual patient data corresponds to at least one physiological parameter of the patient and includes a sensor that does not require the patient to apply it or activate it. The data is then transmitted to a processor for computing a risk or status signal that is based on comparison from a baseline related to a patient or related population and an alert or alarm can be generated based on the result of the signal.

Abstract: Devices, systems and methods are provided to assist with the monitoring or management of a patient's medical condition, which have one or more sensors sensing individual patient data on or near the patient. This individual patient data corresponds to at least one physiological parameter of the patient and includes a sensor that does not require the patient to apply it or activate it. The data is then transmitted to a processor for computing a risk or status signal that is based on comparison from a baseline related to a patient or related population and an alert or alarm can be generated based on the result of the signal.

Abstract: An advanced data structure allows lookup based upon the most significant 16 bits and the following variable number of K bits of the IP destination address. This 16/K scheme requires less than 2 MB memory to store the whole routing tables of present day backbone routers. A 16/Kc version utilizes bitmaps to compress the table to less than 0.5 MB. For the 16/K data structure each route lookup requires at most 2 memory accesses while the 16/Kc requires at most 3 memory accesses. By configuring the processor properly and developing a few customized instructions to accelerate route lookup, one can achieve 85 million lookups per second (MLPS) in the typical case with the processor running at 200 MHz. Further, the lookup method can be implemented using pipelining techniques to perform three lookups for three incoming packets simultaneously. Using such techniques, 100 MLPS performance can be achieved.

Abstract: A novel solution for fast packet classification includes a novel data structure to store classifier rules which enables fast packet classification, which structure employs bitmaps for each field of the incoming packet for which classification is desired. A fast packet classification algorithm using the novel data structure allows the matching rule with the highest priority to be quickly obtained. A novel rule update algorithm allows new classifier rules to be added into the data structure incrementally. In one practical implementation of a classification engine employing the structures and algorithms of the present invention, a configurable processor with customized instructions is used to accelerate packet classification.

Abstract: An advanced data structure allows lookup based upon the most significant 16 bits and the following variable number of K bits of the IP destination address. This 16/K scheme requires less than 2 MB memory to store the whole routing tables of present day backbone routers. A 16/Kc version utilizes bitmaps to compress the table to less than 0.5 MB. For the 16/K data structure each route lookup requires at most 2 memory accesses while the 16/Kc requires at most 3 memory accesses. By configuring the processor properly and developing a few customized instructions to accelerate route lookup, one can achieve 85 million lookups per second (MLPS) in the typical case with the processor running at 200 MHz. Further, the lookup method can be implemented using pipelining techniques to perform three lookups for three incoming packets simultaneously. Using such techniques, 100 MLPS performance can be achieved.