Advanced Seal Sessions I and II

As aircraft operators continue to seek higher fuel efficiency, lower emissions, and longer on-wing performance, turbine engine designers are scrutinizing all components for areas of improvement. To achieve overall goals, turbine pressure ratios and by-pass ratios continue to climb. Also, designers are seeking to minimize parasitic and cooling flows to extract the most useful work out of the flow stream, placing a renewed interest on seal technology and secondary flow path management. In the area of future manned spacecraft, advancements are being examined for both habitat seals and re-entry thermal protection system thermal barrierseals. For long duration space craft, designers are continuing to look for savings in parasitic losses to reduce the amount of cabin re-supply air that needs to be brought along. This is placing greater demands on seal designs and materials to exhibit low leakage and be resistant to space environments. For future missions to and from distant planets, the re-entry heating will be higher than for low-earth orbit or lunar return motivating advanced thermal barrier development. This presentation will provide an overview of the seal challenges and opportunities in these diverse areas

Recognition of American Sign Language words with a sensory glove using neural networks and a probabilistic model

American Sign Language (ASL) is the primary mode of communication of the deaf people with the hearing world. This thesis presents the design of an ASL word recognition system using a sensory glove which is aimed at providing a platform for communication between the deaf community and the hearing world --Abstract, page iii

American Sign Language Word Recognition with a Sensory Glove Using Artificial Neural Networks

An American Sign Language (ASL) recognition system is being developed using artificial neutral networks (ANN) to translate the ASL words into English. The system uses a sensory glove CybergloveTM and a flock of Bird 3-D motion tracker to extract the gesture features. The finger joint angle data obtained from strain gages in the sensory glove defines the handshape while the data from the tracker describes the trajectory of hand movement. The data from these devices is processed by two neural networks, a velocity network and a word recognition network. Our goal is to continuously recognize ASL gestures using these devices in real time. We trained and tested our ANN model for 50 ASL word for different number of samples. Our test results show that the accuracy of recognition is 94%

A real-time American Sign Language word recognition system based on neural networks and a probabilistic model

The development of an American Sign Language (ASL) word recognition system based on neural networks and a probabilistic model is presented. We use a CyberGlove and a Flock of Birds motion tracker to extract the gesture data. The finger joint angle data obtained from the sensory glove defines the handshape while the data from the motion tracker describes the trajectory of the hand movement. The four gesture features, namely handshape, hand position, hand orientation, and hand movement, are recognized using different functions that include backpropagation neural networks. The sequence of these features is used to generate a specific sign or word in ASL based on a probabilistic model. The system can recognize the ASL signs in real time and update its database based interactively. The system has an accuracy of 95.4% over a vocabulary of 40 ASL words