Abstract: An illegal drug sensing device provides a sensor coupled to a processor which evaluates the likely presence of illegal drug consumption and/or manufacture at a particular location. Upon the processor determining that such a condition is likely, an alarm is transmitted to at least one of locally or remotely such as through wired or wireless systems which are employed to alert at least one interested party such as the authorities (i.e., the police department) or a land owner of the likely presence of illegal drug activity at the premises.

Abstract: A monitoring device and method is utilized to monitor certain individuals. A processor receives input from at least one if not a plurality of sensors. In preferred embodiments, if multiple sensors reach certain milestones relative to certain limits, an alarm condition is provided to alert a predetermined recipient of the alarm condition, such as the authorities in the case of a person going above a certain speed limit and moving at least one of his feet to correspond to braking and/or accelerating events. Another sensor such as near the wrist of an individual could be utilized to coordinate with directional headings of the individual to create a condition appearing to be steering of a car wheel.

Abstract: An apparatus and related methods using a variety of heterogeneous sensors to accurately track, in real time, the location of the tip of a surgical instrument inside the human body. The system accounts for real time changes in the surrounding environment during surgery, and when integrated with noninvasive image-guided surgery (IGS), this invention makes IGS possible and safe without tedious offline calibration. Sensors include, but are not limited to, optical, electromagnetic (EM), and sonar.

Abstract: A method and system are provided for merging data from a plurality of multiplexed measurement sources to a decision-maker. The method includes operations for receiving a corresponding plurality of measurements of the data, processing each measurement to respectively obtain local state estimates and local error covariances, determining a corresponding plurality of lag periods, offsetting each of the corresponding event times, supplying to a track fusion center the local state estimates and the local error covariances for summing the pluralities of the local state estimates as a fusion state estimate and the local error covariances as a fusion error covariance. The measurements to be fused are each acquired from its respective source and correspond to an associated sampling period within an acquisition interval. The lag periods represent a wait duration for obtaining the corresponding local state estimates and local error covariances.

Abstract: Power industry boiler tube failures are a major cause of utility forced outages in the United States, with approximately 41,000 tube failures occurring every year at a cost of $5 billion a year. Accordingly, early tube leak detection and isolation is highly desirable. Early detection allows scheduling of a repair rather than suffering a forced outage, and significantly increases the chance of preventing damage to adjacent tubes. The instant detection scheme starts with identification of boiler tube leak process variables which are divided into universal sensitive variables, local leak sensitive variables, group leak sensitive variables, and subgroup leak sensitive variables, and which may be automatically be obtained using a data driven approach and a leak sensitivity function. One embodiment uses artificial neural networks (ANN) to learn the map between appropriate leak sensitive variables and the leak behavior.

Abstract: Power industry boiler tube failures are a major cause of utility forced outages in the United States, with approximately 41,000 tube failures occurring every year at a cost of $5 billion a year. Accordingly, early tube leak detection and isolation is highly desirable. Early detection allows scheduling of a repair rather than suffering a forced outage, and significantly increases the chance of preventing damage to adjacent tubes. The instant detection scheme starts with identification of boiler tube leak process variables which are divided into universal sensitive variables, local leak sensitive variables, group leak sensitive variables, and subgroup leak sensitive variables, and which may be automatically be obtained using a data driven approach and a leak sensitivity function. One embodiment uses artificial neural networks (ANN) to learn the map between appropriate leak sensitive variables and the leak behavior.