Abstract: A method, computer program product, and system identifying a probability of a medical condition in a patient. The method includes a processor obtaining data set(s) related to a patient population diagnosed with a medical condition and based on a frequency of features in the data set(s), identifying common features. The processor generates pattern(s) including a portion of the common features to generate a machine learning algorithm(s). The processor compiles a training set of data to use to tune the machine learning algorithm(s). The processor dynamically adjusts common features in the pattern(s) such that the machine learning algorithm(s) can distinguish patient data indicating the medical condition from patient data not indicating the medical condition. The processor applies the machine learning algorithm(s) to data related to the undiagnosed patient, to determine the probability.

Abstract: A method, computer program product, and system identifying a probability of a medical condition in a patient. The method includes a processor obtaining data set(s) related to a patient population diagnosed with a medical condition and based on a frequency of features in the data set(s), identifying common features. The processor generates pattern(s) including a portion of the common features to generate a machine learning algorithm(s). The processor compiles a training set of data to use to tune the machine learning algorithm(s). The processor dynamically adjusts common features in the pattern(s) such that the machine learning algorithm(s) can distinguish patient data indicating the medical condition from patient data not indicating the medical condition. The processor applies the machine learning algorithm(s) to data related to the undiagnosed patient, to determine the probability.

Abstract: A communication node operable to estimate faults in a mobile ad hoc network and method of performing the same. In one embodiment, the communication node includes a processor and memory including computer program code configured to, with the processor, cause the communication node to monitor a set of observable parameters at the communication node, produce a fault estimate at the communication node from the set of observable parameters, and provide the fault estimate to a global fault detector at another communication node.

Abstract: A communication node operable to estimate faults in a mobile ad hoc network and method of performing the same. In one embodiment, the communication node includes a processor and memory including computer program code configured to, with the processor, cause the communication node to monitor a set of observable parameters at the communication node, produce a fault estimate at the communication node from the set of observable parameters, and provide the fault estimate to a global fault detector at another communication node.

Abstract: A virtual ad hoc network testbed provides the capability to instrument a testbed in order to support the execution of network-aware applications “as is.” Network aware applications are a special class of applications that interact with a network not only by using the network for communication purposes, but also configure or read the status of network devices. Local stack management provides the means to automatically construct standard APIs for accessing the information residing in a simulated or emulated network, and instantiate these APIs. The testbed is designed to bridge a standard management module (such as SNMP) and a simulation or emulation model, starting from a MIB module. The testbed uses CORBA as a communication means. The process is divided into two parts, agent side and model side.

Abstract: A solution to the problem of automated policy generation for mobile ad hoc networks includes an optimization-based, utility-driven approach aimed at generating optimal policies with respect to the given network objectives. The combination of optimization heuristics and network simulation is used to solve the problem. Specifically, the problem of automated generation of network management policies based on available network plans and related information is solved by converting the policy generation into the following optimization problem: given network information and objectives as input, generate optimal policies as output. The optimization process is guided by a utility function based on performance evaluation criteria reflecting the network objectives.

Abstract: A solution to the problem of automated policy generation for mobile ad hoc networks includes an optimization-based, utility-driven approach aimed at generating optimal policies with respect to the given network objectives. The combination of optimization heuristics and network simulation is used to solve the problem. Specifically, the problem of automated generation of network management policies based on available network plans and related information is solved by converting the policy generation into the following optimization problem: given network information and objectives as input, generate optimal policies as output. The optimization process is guided by a utility function based on performance evaluation criteria reflecting the network objectives.

Abstract: A virtual ad hoc network testbed provides the capability to instrument a testbed in order to support the execution of network-aware applications “as is.” Network aware applications are a special class of applications that interact with a network not only by using the network for communication purposes, but also configure or read the status of network devices. Local stack management provides the means to automatically construct standard APIs for accessing the information residing in a simulated or emulated network, and instantiate these APIs. The testbed is designed to bridge a standard management module (such as SNMP) and a simulation or emulation model, starting from a MIB module. The testbed uses CORBA as a communication means. The process is divided into two parts, agent side and model side.

Abstract: Systems and methods for managing network congestion through detecting the closeness to network congestion. The network includes a plurality of network nodes, where each node has at least one neighboring node and each node has a buffer for a queue of packets from other nodes. The system measures queue length at a node and the node's neighboring nodes, processes the measured queue lengths to obtain patterns of fluctuations for the measured queue length. The system determines if one or more of the measured nodes are in a transition-onset status toward a phase transition point based on the obtained patterns of fluctuation and generates congestion control signals based on the determination to route network traffic away. The phase transition point corresponds to a change from a non-congestive phase of the measured nodes to a congestive phase of the measured nodes.

Abstract: A solution to the problem of automated policy generation for mobile ad hoc networks includes an optimization-based, utility-driven approach aimed at generating optimal policies with respect to the given network objectives. The combination of optimization heuristics and network simulation is used to solve the problem. Specifically, the problem of automated generation of network management policies based on available network plans and related information is solved by converting the policy generation into the following optimization problem: given network information and objectives as input, generate optimal policies as output. The optimization process is guided by a utility function based on performance evaluation criteria reflecting the network objectives.

Abstract: A solution to the problem of automated policy generation for mobile ad hoc networks includes an optimization-based, utility-driven approach aimed at generating optimal policies with respect to the given network objectives. The combination of optimization heuristics and network simulation is used to solve the problem. Specifically, the problem of automated generation of network management policies based on available network plans and related information is solved by converting the policy generation into the following optimization problem: given network information and objectives as input, generate optimal policies as output. The optimization process is guided by a utility function based on performance evaluation criteria reflecting the network objectives.