Abstract: An apparatus and method for classifying a driver's activity state use sensor data relating to at least one vehicle operating condition to determine driver activity and classify into one of at least two states. A two state classifier (200) uses inputs related to vehicle monitors (210), driving conditions (212), driver activity (214), vehicle controls (216), and driver identification (218) to set an output (204) to a status corresponding to “maneuver” and “non-maneuver.

Abstract: A method of assessing vehicle operator performance includes the steps of receiving vehicle operating data (502); monitoring an interior portion of the vehicle and receiving operator activity data from the interior portion vehicle (504); receiving vehicle environment data from the environment external to the vehicle (506); monitoring the vehicle operator and receiving operator condition data (508); and determining an operator assessment value (510). The operator assessment value is based upon the vehicle operating data, the operator activity data, the environment data and the operator condition data and is indicative of vehicle operator performance.

Abstract: A method (500) of informing a vehicle operator to improve the operator's performance includes the steps of receiving vehicle operating data (502); monitoring an interior portion of the vehicle and receiving operator activity data from the interior portion of the vehicle (504); receiving vehicle environment data (506); monitoring the vehicle operator and receiving operator condition data relating to a condition of the vehicle operator (508); estimating an operator cognitive load (510); and prioritizing vehicle information based upon the operator cognitive load for selectively informing the operator of the vehicle information (512).

Abstract: An apparatus and method for classifying a driver's activity state use sensor data relating to at least one vehicle operating condition to determine driver activity and classify into one of at least two states. A two state classifier (200) uses inputs related to vehicle monitors (210), driving conditions (212), driver activity (214), vehicle controls (216), and driver identification (218) to set an output (204) to a status corresponding to “maneuver” and “non-maneuver.

Abstract: A method (600) of synthesizing a response to an operating situation of a vehicle includes the step of generating a master condition list (602), determining the existence of an operating situation (604), determining an operator cognitive load (606) and determining a response to the operating situation based in part on the master condition list and the operator cognitive load (608). The master condition list is a fusion of sensor data gathered within and around the vehicle. The operator cognitive load is a measure of the operator's ability to handle tasks within the vehicle.

Abstract: A wireless communication device (300) having a settable service state includes a sensor fusion module (306) to provide context data to the wireless communication device (300). The wireless communication device also receives a context parameter. In a method (800) according to a preferred embodiment of the invention, the wireless communication device (300) is operable to set a service state based upon the context data and the context parameter (806).

Abstract: A method (600) of synthesizing a response to an operating situation of a vehicle includes the step of generating a master condition list (602), determining the existence of an operating situation (604), determining an operator cognitive load (606) and determining a response to the operating situation based in part on the master condition list and the operator cognitive load (608). The master condition list is a fusion of sensor data gathered within and around the vehicle. The operator cognitive load is a measure of the operator's ability to handle tasks within the vehicle.

Abstract: A method (500) of informing a vehicle operator to improve the operator's performance includes the steps of receiving vehicle operating data (502); monitoring an interior portion of the vehicle and receiving operator activity data from the interior portion of the vehicle (504); receiving vehicle environment data (506); monitoring the vehicle operator and receiving operator condition data relating to a condition of the vehicle operator (508); estimating an operator cognitive load (510); and prioritizing vehicle information based upon the operator cognitive load for selectively informing the operator of the vehicle information (512).

Abstract: A method of assessing vehicle operator performance includes the steps of receiving vehicle operating data (502); monitoring an interior portion of the vehicle and receiving operator activity data from the interior portion vehicle (504); receiving vehicle environment data from the environment external to the vehicle (506); monitoring the vehicle operator and receiving operator condition data (508); and determining an operator assessment value (510). The operator assessment value is based upon the vehicle operating data, the operator activity data, the environment data and the operator condition data and is indicative of vehicle operator performance.

Abstract: A method including providing a database with a plurality of items, each item having several attributes, and developing and displaying a cluster map (SOM) attribute plane from the items and attributes, including clusters of items having similar attributes. Selecting an attribute of interest and splitting the plurality of items into clusters of interest based on distribution of the selected attribute. Utilizing rule induction to provide a rule for each cluster of interest, drawing a line around adjacent items in the SOM attribute plane for each rule to form at least one complete fenced area in the display, and comparing fenced areas to clusters to separate statistically meaningful rules from statistically meaningless rules or coincidences.

Abstract: A neural network (10) organizes the data items into a graphically oriented format by retrieving data items from a database (68) where each data item has a plurality of attributes. The neural network is organized (102) such that data items having similar attributes are assigned to neurons located closer together. The neurons of the neural network are matched (104) with the data items from the database and stored in a cross reference table. The cross reference table is displayed (106) on a computer screen (108) in a graphical format so that user visually relates the food items and sees the similarities and differences in their attribute data by the proximity of the data items to one another. The graphic format allows easy visual interpretation of the data items. For large databases, multiple neural networks (110, 112) can be organized hierarchically.

Abstract: A computer simulator is provided for displaying the state of an artificial neural network in a simplified yet meaningful manner on a computer display terminal. The user may enter commands to select one or more areas of interest within the neural network for further information regarding its state of learning and operation. One display mode illustrates the output activity of each neuron as representatively sized and shaded boxes within the border of the neuron, while another display mode shows the connectivity as weighted synapses between a user-selected neuron and the remaining neurons of the network in a similar manner. A third display mode provides a tuning curve wherein the synapses associated with each of the neurons are represented within the borders of the same. Both grid block and line graph type characterization are supported. The methodology allows large neural networks on the order of thousands of neurons to be displayed in a meaningful manner.

Abstract: An artificial neural network is provided using a digital architecture having feedforward and feedback processors interconnected with a digital computation ring or data bus to handle complex neural feedback arrangements. The feedforward processor receives a sequence of digital input signals and multiplies each by a weight in a predetermined manner and stores the results in an accumulator. The accumulated values may be shifted around the computation ring and read from a tap point thereof, or reprocessed through the feedback processor with predetermined scaling factors and combined with the feedforward outcomes for providing various types neural network feedback computations. Alternately, the feedforward outcomes may be placed sequentially on a data bus for feedback processing through the network.

Abstract: An artificial neuron is provided using a simple distance calculation between the input signal vector and the synapse weight signals for providing an output signal. A difference signal is developed by subtracting a weight signal from an input signal. The difference signal is processed through a weighting function having a predetermined polarity and accumulated for providing the output signal of the neuron. A digital embodiment is supported with a memory circuit for storing the digital weights and a memory lookup table or possibly a multiplexer circuit for weighting of the difference signal. An analog embodiment uses a plurality of comparators responsive to the input signal vector and the weight signals for providing the output signal of the neuron as the absolute value of the difference of the input signal vectors and the weight signals.

Abstract: A neural network is provided for performing bi-directional signal transformations through a matrix of synapses by alternately sending and receiving signal vectors therethrough via switchable driver circuits. In the forward direction, the input signal is transformed according to the weighting elements of the synapses for providing an output signal. The drive direction of the switchable driver circuits may be reversed allowing the output signal to flow back through the same synapses thereby performing a reverse transformation, which may actually be an improved estimate of the original input signal. Sample and hold circuits are provided for latching the output signals of the switchable driver circuits back to the inputs thereof for repeated forward and reverse signal transformations until an acceptable transformation of the original input signal is realized, thereby achieving an improved estimate of the input signal and corresponding output transformation.

Abstract: A random access memory (RAM) circuit is provided wherein an input signal matrix forming an identifiable original pattern is learned and stored such that a distorted facsimile thereof may be applied to generate an output signal matrix forming a replication of the original pattern having improved recognizable features over the distorted facsimile. The input signal matrix is logically divided into a plurality of predetermined subsets comprising a unique element of the input signal matrix and the elements in the neighborhood thereof. Each predetermined subset is quantized into a first digital address and applied at the address inputs of a memory circuit for retrieving data stored in the addressed memory location, while one signal of the predetermined subset is digitized and weighted and combined with the data retrieved from the addressed memory location for storage in the same addressed memory location.