A location system is disclosed for commercial wireless telecommunication infrastructures. The system is an end-to-end solution having one or more location centers for outputting requested locations of commercially available handsets or mobile stations (MS) based on, e.g., CDMA, AMPS, NAMPS or TDMA communication standards, for processing both local MS location requests and more global MS location requests via, e.g., Internet communication between a distributed network of location centers. The system uses a plurality of MS locating technologies including those based on: (1) two-way TOA and TDOA; (2) pattern recognition; (3) distributed antenna provisioning; (5) GPS signals, (6) angle of arrival, (7) super resolution enhancements, and (8) supplemental information from various types of very low cost non-infrastructure base stations for communicating via a typical commercial wireless base station infrastructure or a public telephone switching network. Accordingly, the traditional MS location difficulties, such as multipath, poor location accuracy and poor coverage are alleviated via such technologies in combination with strategies for: (a) automatically adapting and calibrating system performance according to environmental and geographical changes; (b) automatically capturing location signal data for continual enhancement of a self-maintaining historical data base retaining predictive location signal data; (c) evaluating MS locations according to both heuristics and constraints related to, e.g., terrain, MS velocity and MS path extrapolation from tracking and (d) adjusting likely MS locations adaptively and statistically so that the system becomes progressively more comprehensive and accurate. Further, the system can be modularly configured for use in location signing environments ranging from urban, dense urban, suburban, rural, mountain to low traffic or isolated roadways. Accordingly, the system is useful for 911 emergency calls, tracking, routing, people and animal location including applications for confinement to and exclusion from certain areas.

Wireless location using multiple location estimators

A location system is disclosed for wireless telecommunication infrastructures. The system is an end-to-end solution having one or more location systems for outputting requested locations of hand sets or mobile stations (MS) based on, e.g., AMPS, NAMPS, CDMA or TDMA communication standards, for processing both local mobile station location requests and more global mobile station location requests via, e.g., Internet communication between a distributed network of location systems. The system uses a plurality of mobile station locating technologies including those based on: (1) two-way TOA and TDOA; (2) home base stations and (3) distributed antenna provisioning. Further, the system can be modularly configured for use in location signaling environments ranging from urban, dense urban, suburban, rural, mountain to low traffic or isolated roadways. The system is useful for 911 emergency calls, tracking, routing, people and animal location including applications for confinement to and exclusion from certain areas.

Locating a mobile station and applications therefor

A location is disclosed for wireless telecommunication infrastructures. The systems output requested locations of hand sets or mobile stations (MS) based on, e.g., AMPS, NAMPS, CDMA or TDMA communication standards. The system determines both local MS locations, and more global MS location requests via, e.g., Internet communication. The system uses a plurality of mobile station locating technologies, including those based on, e.g., (1) two-way TOA and TDOA; (2) home base stations and (3) distributed antenna provisioning. The system is useful for 911 emergency calls, tracking, routing, people and animal location including applications for confinement to and exclusion from certain areas. The system is particularly useful for estimating a location of an MS using information received from a commercial mobile radio service provider (CMRS) for which the MS is not registered for subscriber services.

Locating a mobile station using a plurality of wireless networks and applications therefor

The methods and systems of the invention utilize limited data to yield information about the validity of any given interaction with a website Once validity information is available, an operator can determine whether or not to continue offering interactions to a given user The determination could also relate to whether to report website interaction statistics based on undesired interactions, how to handle billing or payment for such undesired interactions, and what type of content to send to users who are interacting with the website in an undesirable manner

System and method to determine the validity of an interaction on a network

The invention provides improved methods and apparatus for control using field and control devices that provide a virtual machine environment and that communicate via an IP network. By way of non-limiting example, such field device can be an “intelligent” transmitter or actuator that includes a low power processor, along with a random access memory, a read-only memory, FlashRAM, and a sensor interface. The processor can execute a real-time operating system, as well as a Java virtual machine (JVM). Java byte code executes in the JVM to configure the field device to perform typical process control functions, e.g., for proportional integral derivative (PID) control and signal conditioning. Control networks can include a plurality of such field and control devices interconnected by an IP network, such as an Ethernet.

Methods and apparatus for control using control devices that provide a virtual machine environment and that communicate via an ip network

A continuous emission monitoring system for manufacturing plant (10) includes a control system (16) which has associated therewith a virtual sensor network (18). The network (18) is a predictive network that receives as inputs both control values to the plant (10) and also sensor values. The network (18) is then operable to map the inputs through a stored representation of the plant (10) to output a predicted pollutant sensor level. This predicted pollutant sensor level is essentially the prediction of an actual pollutant sensor level that can be measured by a pollutant sensor (14). The network (18) therefore is a substitute for the pollutant sensor (14), thus providing a virtual sensor. The sensor values from the plant (10) are first processed through a sensor validation system (22). The sensor validation system (22) is operable to determine which of the sensors falls outside of acceptable limits and then substitutes therefor a predicted output, such that the inputs to the virtual sensor network (18) are within acceptable limits.

Virtual continuous emission monitoring system with sensor validation

An internal combustion engine (360) is provided with a plurality of sensors to monitor the operation thereof with respect to various temperature measurements, pressure measurements, etc. A predictive model processor (322) is provided that utilizes model parameters stored in the memory (324) to predict from the sensor inputs a predicted emissions output. The model is trained with inputs provided by the sensor and an actual emissions sensor output. During operation, this predicted output on line (326) can be utilized to provide an alarm or to be stored in a history database in a memory (328). Additionally, the internal combustion engine (260) can have the predicted emissions output thereof periodically checked to determine the accuracy of the model. This is effected by connecting the output of the engine to an external emissions sensor (310) and taking the difference between the actual output and the predicted output to provide an error. This is compared to a threshold and, if the error exceeds the threshold, the predictive model processor (322) can be placed in a training operation wherein new model parameters are generated. Additionally, retraining could take place external to the predictive model processor (322). During runtime, a control network (350) can be utilized to predict control parameters for minimizing the emissions output.

Virtual emissions monitor for automobile and associated control system

A plant (72) is operable to receive control inputs c(t) and provide an output y(t). The plant (72) has associated therewith state variables s(t) that are not variable. A control network (74) is provided that accurately models the plant (72). The output of the control network (74) provides a predicted output which is combined with a desired output to generate an error. This error is back propagated through an inverse control network (76), which is the inverse of the control network (74) to generate a control error signal that is input to a distributed control system (73) to vary the control inputs to the plant (72) in order to change the output y(t) to meet the desired output. The control network (74) is comprised of a first network NET 1 that is operable to store a representation of the dependency of the control variables on the state variables. The predicted result is subtracted from the actual state variable input and stored as a residual in a residual layer (102). The output of the residual layer (102) is input to a hidden layer (108) which also receives the control inputs to generate a predicted output in an output layer (106). During back propagation of error, the residual values in the residual layer (102) are latched and only the control inputs allowed to vary.

Residual activation neural network

A continuous emission monitoring system for a manufacturing plant (10) includes a control system (16) which has associated therewith a virtual sensor network (18). The network (18) is a predictive network that receives as inputs both control values to the plant (10) and also sensor values. The network (18) is then operable to map the inputs through a stored representation of the plant (10) to output a predicted pollutant sensor level. This predicted pollutant sensor level is essentially the prediction of an actual pollutant sensor level that can be measured by a pollutant sensor (14). The network (18) therefore is a substitute for the pollutant sensor (14), thus providing a virtual sensor. The sensor values from the plant (10) are first processed through a sensor validation system (22). The sensor validation system (22) is operable to determine which of the sensors falls outside of acceptable limits and then substitutes therefor a predicted output, such that the inputs to the virtual sensor network (18) are within acceptable limits.

Virtual continuous emission monitoring system

A neural network system is provided that models the system in a system model (12) with the output thereof providing a predicted output. This predicted output is modified or controlled by an output control (14). Input data is processed in a data preprocess step (10) to reconcile the data for input to the system model (12). Additionally, the error resulted from the reconciliation is input to an uncertainty model to predict the uncertainty in the predicted output. This is input to a decision processor (20) which is utilized to control the output control (14). The output control (14) is controlled to either vary the predicted output or to inhibit the predicted output whenever the output of the uncertainty model (18) exceeds a predetermined decision threshold, input by a decision threshold block (22). Additionally, a validity model (16) is also provided which represents the reliability or validity of the output as a function of the number of data points in a given data region during training of the system model (12). This predicts the confidence in the predicted output which is also input to the decision processor (20). The decision processor (20) therefore bases its decision on the predicted confidence and the predicted uncertainty. Additionally, the uncertainty output by the data preprocess block (10) can be utilized to train the system model (12).

Ralph Bruce Ferguson

Papers

Virtual continuous emission monitoring system with sensor validation

Virtual emissions monitor for automobile and associated control system

Residual activation neural network

Virtual continuous emission monitoring system

Method for operating a neural network with missing and/or incomplete data