Abstract: 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.

Abstract: 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.

Abstract: 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(ST|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.

Abstract: 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(ST|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.

Abstract: 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-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.

Abstract: Disclosed are product inspection systems and methods for monitoring, inspecting and controlling baking and cooking processes, and the like. Products are processed in a predetermined manner using a product processor, such as by baking the products, for example. A controller is used to control operation of the product processor. A conveyor moves or conveys processed products past inspection apparatus. The inspection apparatus comprises an image acquisition system for generating images of products conveyed by the conveyor past the inspection apparatus. One or more illumination devices illuminate products on the conveyor. An inspection processor processes images generated by the image acquisition system to inspect the conveyed products. The inspection processor determines locations of conveyed products by processing image data corresponding to the image frames, and classifies each of the located products to indicate if the products are acceptable or are defective.

Abstract: This invention is a method, and system for predicting the onset of a seizure prior to electrograph onset in an individual. During an “off-line” 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 “off-line” based on the feature vector derived from those signals. During “on-line” 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.

Abstract: A sphygmomanometer having an inflatable occluding cuff adapted to be applied to a patient. A pressure transducer is in fluid communication with the cuff so as to produce an electrical analog signal proportional to the actual pressure in the cuff and to superpose electrical pulses on the analog signal in response to pulsations in the patient's blood pressure during cardiac cycles. An analog-to-digital converter is coupled to the pressure transducer to produce a digital representation of the actual pressure then obtaining in the cuff. A pulse detector is coupled to the pressure transducer to detect the superposed electrical pulses. The digital representation produced by the analog-to-digital converter is displayed in response to the first-detected pulse; and the digital representation subsequently produced by the converter is displayed in response to the finally detected pulse, thereby resulting in the display of the patient's systolic and diastolic pressures, respectively.