Method and Device for Biometric Verification and Identification

Cardiac muscle is myogenic and is capable of generating an action potential and depolarizing and repolarizing signals from within the muscle itself. An intrinsic conduction system (ICS), a group of specialized cardiac cells, passes an electrical signal throughout the heart. This technology is a method and associated system to identify a person based on the use of statistical parameters, peak amplitudes and/or time interval lengths and/or depolarization-repolarization vector angles and/or depolarization-repolarization vector lengths for PQRST electrical signals associated with heart waves. The statistical parameters, estimated to be at least 192, serve as biometric indicia to authenticate or to decline to authenticate an asserted identity of a candidate person. There are three on-line modes of operation enrollment, verification, and identification as well as two off-line modes statistics and settings. In enrollment the raw electrocardiography (ECG) signal is processed and the results in the form of parameters are serialized and saved. Verification and Identification procedures use the feature parameters for recognition (classification) of subjects based on the same kind of parameters (features) of heartbeats extracted from the ECG signal of a person to be verified or identified.

Although biofeedback is an effective treatment for various physiological problems and can be used to optimize physiological functioning in many ways, the benefits can only be attained through a number of training sessions, and such gains can only be maintained over time through regular practice. However, adherence to regular training has been a problem that has plagued the field of physiological self-regulation limiting its utility. As with any exercise, incorporating biofeedback training with another activity encourages participation and enhances its usefulness.

Microphones and stethoscopes are regularly used by physicians to detect sounds when monitoring physiological conditions. These monitors are coupled directly to a person's body and measure in certain bandwidths either by listening or by recording the signals. The physiological processes such as respiration and cardiac activity are reflected in a different frequency bandwidth from 0.01 Hz to 500 Hz. This technology can monitor physiological conditions in the entire bandwidth range. Signals can also be wirelessly transmitted, using Bluetooth, to other recording devices at any other location.

The current Star Tracker software package is comprised of a Lumenera LW230 monochrome machine-vision camera and a FUJINON HF35SA-1 35mm lens. The star tracker cameras are all connected to and powered by the PC/104 stack via USB 2.0 ports. The software code is written in C++ and is can easily be adapted to other camera and lensing platforms by setting new variables in the software for new focal conditions. In order to identify stars in images, the software contains a star database derived from the 118,218-star Hipparcos catalog [1]. The database contains a list of every star pair within the camera field of view and the angular distance between those pairs. It also contains the inertial position information for each individual star directly from the Hipparcos catalog. In order to keep the star database size small, only stars of magnitude 6.5 or brighter were included. The star tracking process begins when image data is retrieved by the software from the data buffers in the camera. The image is translated into a binary image via a threshold brightness value so that on (bright) pixels are represented by 1s and off (dark) pixels are represented by 0s. The binary image is then searched for blobs, which are just connected groups of on pixels. These blobs represent unidentified stars or other objects such as planets, deep sky objects, other satellites, or noise. The centroids of the blob locations are computed, and a unique pattern recognition algorithm is applied to identify which, if any, stars are represented. During this process, false stars are effectively removed and only repeatedly and uniquely identifiable stars are stored. After stars are identified, another algorithm is applied on their position information to determine the attitude of the satellite. The attitude is computed as a set of Euler angles: right ascension (RA), declination (Dec), and roll. The first two Euler angles are computed by using a linear system that is derived from vector algebra and the information of two identified stars in the image. The roll angle is computed using an iterative method that relies on the information of a single star and the first two Euler angles. [1] ESA, 1997, The Hipparcos and Tycho Catalogues, ESA SP-1200

Acoustical studies of atmospheric events like convective storms, shear-induced turbulence, acoustic gravity waves, microbursts, hurricanes, and clear air turbulence over the last forty-five years have established that these events are strong emitters of infrasound (sound at frequencies below 20 Hz). Over the years, NASA Langley has designed and developed a portable infrasonic detection system which can be used to make useful infrasound measurements at a location where it was not possible previously. The system comprises an electret condenser microphone, and a small, compact windscreen. The system has been modified to be used in the air, underground, as well as underwater (to determine man-made and precursor to tsunami). The system also features a data acquisition system that permits real-time detection, bearing, and signature of a low frequency source. However, to determine bearing of the received signals, the microphones are to be arranged as an equilateral triangle with a certain microphone spacing. The spacing depends upon location of the microphone array. For a ground-based array, the microphone spacing of 100 feet (30.48m) is desired to determine time delay for signals arriving at each microphone location. The microphone spacing depends upon speed of sound through the array medium. For underwater array, the spacing between microphones would be around 1500 feet. The data acquisition system provides data output in the infrasonic bandwidth which is then analyzed using an adaptive algorithm (least-mean-squares time-delay-estimation) using modern computational power to locate source by plotting source location hyperbolas on-line. A smaller array size reduces the time resolution resulting in strong signal coherence. The innovation approach is to exploit modern signal processing methods, i.e. adaptive filtering, where computer is trained on-line to recognize features of the event to be detected. Modern computational capability permits the adaptive algorithm (least-mean-squares time-delay estimation or LMSTDE) which is vastly more powerful algorithm. This system has better resolution able to determine direction with arrived signals within five-degree accuracy.