Cooper Tire & Rubber Company

University of Denver

Other

Rahmat A Shoureshi

Christopher Aasted

George A Alers

Stephen F Albert

Michael T Chenowith

Methods and systems for controlling a prosthesis using a brain imager that images a localized portion of the brain are provided according to one embodiment of the invention. For example, the brain imager can provide motor cortex activation data using near infrared imaging techniques and EEG techniques among others. EEG and near infrared signals can be correlated with brain activity related to limbic control and may be provided to a neural network, for example, a fuzzy neural network that maps brain activity data to limbic control data. The limbic control data may then be used to control a prosthetic limb. Other embodiments of the invention include fiber optics that provide light to and receive light from the surface of the scalp through hair.

Brain imaging system and methods for direct prosthesis control

Methods and systems for controlling a prosthesis using a brain imager that images a localized portion of the brain are provided according to one embodiment of the invention. The brain imager provides motor cortex activation data by illuminating the motor cortex with near infrared light (NIR) and detecting the spectral changes of the NIR light as passes through the brain. These spectral changes can be correlated with brain activity related to limbic control and may be provided to a neural network, for example, a fuzzy neural network that maps brain activity data to limbic control data. The limbic control data may then be used to control a prosthetic limb. Other embodiments of the invention include fiber optics that provide light to and receive light from the surface of the scalp through hair.

This invention is a temperature-based smart insole capable of continuously or intermittently measuring the foot temperature of the patient at one or more locations of the foot while the insole is worn. The device provides feedback to the patient alerting the individual of risk based on his/her plantar temperatures. Benefits of this device include: its ability to free the patient from the clinical setting and increase patient's confidence to be mobile, thus enhancing circulation while at the same time allowing the patient to self-monitor their feet.

Smart insole for diabetic patients

A self-contained apparatus for inspecting energized and de-energized conductors. The apparatus includes a housing, at least one transmitter contained within the housing for generating and impinging pulses onto an electrical conductor, such that the pulses travel through and are reflected by the electrical conductor, and at least one receiver contained within the housing for receiving reflected pulses from the electrical conductor. The apparatus further including a controller for determining a condition of the electrical conductor based on an evaluation of the reflected pulses, and at least one indicator for displaying the condition of the electrical conductor determined by the controller.

Self-contained apparatus for inspection of electric conductors

The method of the invention identifies damage to an in-service conductor associated with the delivery (transmission and distribution) of electric power. Electro-magnetic acoustic energy is generated in an in-service conductor associated with the delivery of electric power. Corresponding return electro-magnetic acoustic energy is then measured. Features are then extracted from the return electro-magnetic acoustic energy to characterize damage to the in-service conductor. The features may be extracted through a variety of signal processing techniques, such as wavelet signal processing. The extracted features may be classified using a neural network, fuzzy logic, or a combination of both.

Apparatus and method for utilizing electromagnetic acoustic transducers to non-destructively analyze in-service conductive materials

An improved active vibration control system using feedback and pseudo-feedforward sensor inputs is provided for solving the problem of random and repetitive active vibration control and noise cancellation in a system. In a first embodiment of the invention, an artificial neural network is used for learning the dynamics of a structure and for providing output signals that follow the state variables of the structure. In one implementation of the neural network, a plurality of neurons obtain biasing inputs derived from sensor inputs, as well as inputs from the other neurons in the network. Further, each neuron obtains a feedback input from itself. Each input to a neuron is weighted using a weighting function derived on-line. The neural network supplies structure parameters and state variables to an optimal controller which derives and provides a control signal to the actuators so as to counteract vibrations and/or noise sensed in the system. In a second embodiment an optimal controller utilizing a modified generalized predictive control algorithm is used to to consider the limitations on the physical characteristics of the actuator(s), on-line, in terms of the output level and the rate of change of the output in the system. Additional embodiments wherein an optimized control signal is sent to the actuator(s) to minimize vibration incident to the structure are provided.

Active vibration control method and apparatus

A generalized minimum variance type of control operates on combined optimal and self-tuning control theorems, and is applicable to the design of active, semi-active, and hybrid vibration control systems. The system operates in a multiple-input/multiple- output manner, so when both noise and vibration are important, e.g. interior of a vehicle, the resulting nulling signal will be based on diminishing both vibrations and noise. The system operates by directly nulling primary vibrations in an active mode, and/or by developing a variable bandwidth mechanical filter, in a semi-active mode, and applying nulling signals accordingly to the vibration source. Artificial intelligence is incorporated into the system to learn on-line the dynamics of the system, e.g. vehicle modal parameters, without reprogramming or tuning the system. Software resident in memory includes a first program a modal extraction of vibration and/or noise from the sensors, to perform a minimum variance calculation based on recorded past performance and on fuzzy logic compensation. Output signals generated and applied to actuators minimize and/or filter the vibration or noise.

Method and apparatus for intelligent active and semi-active vibration

A passive/adaptive vibration control system is provided wherein various elements of the system may be adapted on-line in response to sensed vibrations. A spring/mass vibration absorber is attached to a vibrating body to absorb energy generated by that body, to minimize the transmission of vibrations to a structure in mechanical communication with the vibrating body. In one embodiment of the present invention, the mass of the vibration absorber may be adjusted, on-line, to compensate for sensed vibrations. A vibration sensor is connected to either the vibrating body or the attached structure so as to sense the level of vibration above a desired level and to send a signal representative of that vibration to an electronic controller. The electronic controller is designed to instruct the actuator to adapt the mass to compensate for the sensed vibration. In additional embodiments of the system, the stiffness of the spring, rather than the mass of the weight, may be adjusted on-line to 'tune' the vibration absorber. Further, if more than one excitation frequency is present, a plurality of on-line adaptive and/or purely passive vibration absorbers may be cascaded so as to minimize vibrations due to a plurality of excitation frequencies. Depending on the number of vibration axes to be attenuated, one or a plurality of passive/adaptive vibration absorber elements may be used.

Adaptive-passive vibration control system

A generalized minimum variance type of control operates on combined optimal and self-tuning control theorems, and is applicable to the design of active, semi-active, and hybrid vibration control systems. The system operates in a multiple-input/multiple-output manner, so when both noise and vibration are important, e.g. interior of a vehicle, the resulting nulling signal will be based on diminishing both vibrations and noise. The system operates by directly nulling primary vibrations, in an active mode, and/or by developing a variable bandwidth mechanical filter, in a semi-active mode, and applying nulling signals accordingly to the vibration source. Artificial intelligence is incorporated into the system to learn on-line the dynamics of the system, e.g. vehicle modal parameters. This intelligence is used to modify decision making in the system, based on results of past performance, without reprogramming or tuning of the system. The system incorporates digital electronic circuitry to convert acceleration and/or audio signals into proper format for the software logic residing in a microprocessor chip. Synchronizing signal is not needed. Hardware used includes a state of the art high power microprocessor, capable of handling sixteen sensory input signals and generating eight output signals. Controlling noise and vibration control in a vehicle, four audio signals from the driver, passenger, and back seat areas, and accelerometer signals from different seat tracks, the steering column, and the floor pan can be input to the microprocessor. Resulting control signals can be two for adjustable front engine mounts, and six for the adjustable body (cradle) mounts. Software resident in memory includes a first program to perform a modal extraction of vibration and/or noise from the sensors, to perform a minimum variance calculation, then to perform an intelligent control calculation based on recorded past performance and on fuzzy logic compensation. Output signals generated and applied to actuators minimize and/or filter the vibration or noise.

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Golden, CO, United States of America

Rahmat A Shoureshi