University of Pennsylvania

Georgia Tech Research Corporation

Other

Kleen-Tex Industries Inc.

George J Vachtsevanos

Rosana Esteller

Brian Litt

Javier Ramón Echauz

Iqbal Mahmud Dar

Javier Ram&oacute;n Echauz

Waqar Mahmood

Jayant Ratti

Javier Echauz

Muid Ur-Rehman Mufti

Emanuel Jones

Kent Davey

J Lewis Dorrity

Erin Sahinci

Kimberly E Newman

Kent R Davey

Multi-wing hovering and gliding flapping Micro Air Vehicles ('MAV') are disclosed. The MAV can have independent wing control to provide enhance energy efficiency and high maneuverability. Power to each wing can be controlled separately by varying the amplitude of the wing flapping, the frequency of the wing flapping, or both. The flapping frequency can be controlled such that it is at or near the natural frequency of the wings for improved energy efficiency. The wings can be controlled by a gear train, coil-magnet arrangement or many other actuation systems that enable variable frequency flapping, variable amplitude flapping, or a combination of both. The gear train mechanism provides gyroscopic stability during flight. The wing flapping can include a rotation, or feathering motion, for improved efficiency. The wings can be transitioned between flapping flight and fixed wing flight to enable gliding and hovering in a single configuration.

Hovering and gliding multi-wing flapping micro aerial vehicle

A method and system for assessing a quality of life index to adjust an implanted device to optimize patient-specific feature signals and treatment therapies. Accumulated energy of intracranial electroencephalogram (IEEG) signals is calculated over multiple data channels during seizures over a fixed time period. Accumulated energy of a treatment control is calculated over the multiple data channels over all times of activation of the implanted device over the fixed time period. The accumulated energy of both the IEEG signals and treatment control are weighted by seizure and treatment factors to determine a quality value for the fixed time period. A quality of life index is determined as a weighted average of current and previous quality values for a plurality of fixed time periods.

Unified probabilistic framework for predicting and detecting seizure onsets in the brain and multitherapeutic device

An adaptive method and apparatus for forecasting and controlling neurological abnormalities in humans such as seizures or other brain disturbances. The system is based on a multi-level control strategy. Using as inputs one or more types of physiological measures such as brain electrical, chemical or magnetic activity, heart rate, pupil dilation, eye movement, temperature, chemical concentration of certain substances, a feature set is selected off-line from a pre-programed feature library contained in a high level controller within a supervisory control architecture. This high level controller stores the feature library within a notebook or external PC. The supervisory control also contains a knowledge base that is continuously updated at discrete steps with the feedback information coming from an implantable device where the selected feature set (feature vector) is implemented. This high level controller also establishes the initial system settings (off-line) and subsequent settings (on-line) or tunings through an outer control loop by an intelligent procedure that incorporates knowledge as it arises. The subsequent adaptive settings for the system are determined in conjunction with a low-level controller that resides within the implantable device. The device has the capabilities of forecasting brain disturbances, controlling the disturbances, or both. Forecasting is achieved by indicating the probability of an oncoming seizure within one or more time frames, which is accomplished through an inner-loop control law and a feedback necessary to prevent or control the neurological event by either electrical, chemical, cognitive, sensory, and/or magnetic stimulation.

Adaptive method and apparatus for forecasting and controlling neurological disturbances under a multi-level control

A method and an apparatus for predicting and detecting epileptic seizure onsets within a unified multiresolution probabilistic framework, enabling a portion of the device to automatically deliver a progression of multiple therapies, ranging from benign to aggressive as the probabilities of seizure warrant. Based on novel computational intelligence algorithms, a realistic posterior probability function P(S|x) representing the probability of one or more seizures starting within the next T minutes, given observations x derived from IEEG or other signals, is periodically synthesized for a plurality of prediction time horizons. When coupled with optimally determined thresholds for alarm or therapy activation, probabilities defined in this manner provide anticipatory time-localization of events in a synergistic logarithmic-like array of time resolutions, thus effectively circumventing the performance vs. prediction-horizon tradeoff of single-resolution systems. The longer and shorter prediction time scales are made to correspond to benign and aggressive therapies respectively. The imminence of seizure events serves to modulate the dosage and other parameters of treatment during open-loop or feedback control of seizures once activation is triggered. Fast seizure onset detection is unified within the framework as a degenerate form of prediction at the shortest, or even negative, time horizon. The device is required to learn in order to find the probabilistic prediction and control strategies that will increase the patient's quality of life over time. A quality-of-life index (QOLI) is used as an overall guide in the optimization of patient-specific signal features, the multitherapy activation decision logic, and to document if patients are actually improving.

A method and apparatus for forecasting and controlling neurological abnormalities in humans such as seizures or other brain disturbances. The system is based on a multi-level control strategy. Using as inputs one or more types of physiological measures such as brain electrical, chemical or magnetic activity, heart rate, pupil dilation, eye movement, temperature, chemical concentration of certain substances, a feature set is selected off-line from a pre-programmed feature library contained in a high level controller within a supervisory control architecture. This high level controller stores the feature library within a notebook or external PC. The supervisory control also contains a knowledge base that is continuously updated at discrete steps with the feedback information coming from an implantable device where the selected feature set (feature vector) is implemented. This high level controller also establishes the initial system settings (off-line) and subsequent settings (on-line) or tunings through an outer control loop by an intelligent procedure that incorporates knowledge as it arises. The subsequent adaptive settings for the system are determined in conjunction with a low-level controller that resides within the implantable device. The device has the capabilities of forecasting brain disturbances, controlling the disturbances, or both. Forecasting is achieved by indicating the probability of an oncoming seizure within one or more time frames, which is accomplished through an inner-loop control law and a feedback necessary to prevent or control the neurological event by either electrical, chemical, cognitive, sensory, and/or magnetic stimulation.

A method and an apparatus for predicting and detecting epileptic seizure onsets within a unified multiresolution probabilistic framework, enabling a portion of the device to automatically deliver a progression of multiple therapies, ranging from benign to aggressive as the probabilities of seizure warrant. Based on novel computational intelligence algorithms, a realistic posterior probability function P(S &verbar;x) representing the probability of one or more seizures starting within the next T minutes, given observations x derived from IEEG or other signals, is periodically synthesized for a plurality of prediction time horizons. When coupled with optimally determined thresholds for alarm or therapy activation, probabilities defined in this manner provide anticipatory time-localization of events in a synergistic logarithmic-like array of time resolutions, thus effectively circumventing the performance vs. prediction-horizon tradeoff of single-resolution systems. The longer and shorter prediction time scales are made to correspond to benign and aggressive therapies respectively. The imminence of seizure events serves to modulate the dosage and other parameters of treatment during open-loop or feedback control of seizures once activation is triggered. Fast seizure onset detection is unified within the framework as a degenerate form of prediction at the shortest, or even negative, time horizon. The device is required to learn in order to find the probabilistic prediction and control strategies that will increase the patient's quality of life over time. A quality-of-life index (QOLI) is used as an overall guide in the optimization of patient-specific signal features, the multitherapy activation decision logic, and to document if patients are actually improving.

This invention is a method, and system for predicting the onset of a seizure prior to electrograph onset in an individual. During an &ldquo;off-line&rdquo; mode, signals representing brain activity of an individual (either stored or real time) are collected, and features are extracted from those signals. A subset of features, which comprise a feature vector, are selected by a predetermined process to most efficiently predict (and detect) a seizure in that individual. An intelligent prediction subsystem is also trained &ldquo;off-line&rdquo; based on the feature vector derived from those signals. During &ldquo;on-line&rdquo; operation, features are continuously extracted from real time brain activity signals to form a feacture vector, and the feature vector is continuously analyzed with the intelligent prediction subsystem to predict seizure onset in a patient. The system, and method are preferably implemented in an implanted device ( ) that is capable of warning externally an individual of the probability of a seizure, and/or automatically taking preventative actions to abort the seizure. In addition, methods are provided for applying intervention measures to an animal to abort or modulat a seizure by adjusting the modality of an intervention measure; and/or parameters of an intervention measure based upon a probability measure indicative of a likelihood of seizure occurrence; and/or a predicted time to seizure onset.

Method and apparatus for predicting the onset of seizures based on features derived from signals indicative of brain activity

A hybrid surface mount component inspection system which includes both vision and infrared inspection techniques to determine the presence of surface mount components on a printed wiring board, and the quality of solder joints of surface mount components on printed wiring boards by using data level sensor fusion to combine data from two infrared sensors to obtain emissivity independent thermal signatures of solder joints, and using feature level sensor fusion with active perception to assemble and process inspection information from any number of sensors to determine characteristic feature sets of different defect classes to classify solder defects.

Inspection system and method for bond detection and validation of surface mount devices using sensor fusion and active perception

Inspection system and method for bond detection and validation of

An automated system and associated method of counting pillings in textile fabrics. Images of consecutive sections of a fabric sample are captured by a CCD camera. A system processor then processes the images to enhance image quality. Pillings on the processed, captured fabric sample images are counted, and the resulting data is fuzzified to determine the membership of the data in one or more of a plurality of fabric classes. The present invention provides an objective rating system and method with repeatable accuracy for fabric samples that eliminates the inherent subjectivity associated with conventional manual visual fabric inspection methods.

Automated apparatus for counting pillings in textile fabrics

The Invention includes a method and apparatus which analyzes an image of an object to detect and identify defects in the object. The present invention utilizes a scanning technique which converts a 2-D image of the object into a 1-D image, a transformation technique which extracts relevant features from the image, and a fuzzy inferencing technique which utilizes the features generated by the transformation technique to detect and identify defects. The present invention preferably includes an off-line learning process which selects the optimum transform coefficients for a given set of defects and stores the corresponding features in a rulebase. Preferably, the wavelet transform is used as the transformation technique to provide an analysis of the image which is localized in the frequency and time domains. The present invention may also include an on-line learning process when the present invention is incorporated into a manufacturing process for real time inspection of the object being manufactured. The on-line learning process attempts to nullify the effects of noise which may be associated with the image sensors being used to read the image by using a similarity function to maintain a predetermined level of fuzziness of the inference engine of the present invention.

Method and apparatus for analyzing an image to detect and identify

Information is encoded on tags preferably in a form emulating conventional bar coding, in which ferromagnetic strips correspond to bars and nonferromagnetic spaces correspond to the absence of bars. The tags thus can be embedded within an article or otherwise concealed from visual scanning or inspection. The tags are read by scanning with a magnetic reader including an excitation coil and a pickup coil. Relative movement between the tags and the scanner induces a signal in the pickup coil only when a bar is scanned, so that the phase and timing of the induced signals contain information corresponding to the relative placement and width of the ferromagnetic bars and strips. Those signals are processed to provide an output signal emulating the output from a conventional optical bar code scanner.

Article identification apparatus and method using a ferromagnetic tag

A spherical motor particularly adapted for robotic control. The motor comprises a stator housing defining a semispherical socket having an opening of generally polar cap form with a spherical rotor rotatably received in the socket for rotation therein about three axes intersecting at the center of the rotor with one of the axes projecting through the opening and the other two axes being orthogonal thereto. A motion transmitter is carried by the rotor and aligned generally along one axis to project through the opening, and windings are carried by the stator housing in encompassing and in opposed relation to the rotor for rotating the rotor about the axis. A polyphase control selectively controls relative phase and energization of the windings to rotate the spherical rotor a substantially unlimited amount about one axis and in limited amounts about the other axes so as to maintain the motion transmitting member within the confines of the opening.

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Marietta, GA, United States of America

George J Vachtsevanos