The ability to discriminate a reentry vehicle (RV) from booster parts and other debris is critical to theater ballistic missile defense (TBMD). As it travels along its trajectory, a threat missile separates into a reentry vehicle (RV) and clutter. The latter consists of several tanks, separation debris and fragments of hot fuel. Interception of the RV requires discrimination of the RV from the ...

Syllable or phoneme spotting if reliably achieved, provides a good solution to the spoken word andlor continuous speech recognition problem, . We previously showed tha t the Time-Delay Neural Network (TDNN) provided excellent recognition performance (98.6%) of the "BDG" consonant task. We would also like to extend the encouraging performance of TDNN to wordlcontinuous speech recognition. In thi...

In this paper we show how the Multi-State Time Delay Neural Network (MS-TDNN), which is already used successfully in continuous speech recognition tasks, can be applied both to online single character and cursive (continuous) handwriting recognition. The MS-TDNN integrates the high accuracy single character recognition capabilities of a TDNN with a non-linear time alignment procedure (dynamic t...

his article explores a time-delay neural network (TDNN) for exo-atmospheric discrimination of a missile reentry vehicle (RV) from other missile parts and thrust termination debris. The TDNN is an enhanced version of a back-propagation neural network that accounts for the features in the time domain by using the rate of change of the infrared signature over several seconds as a discriminant. We ...

The Multi-State Time Delay Neural Network (MS-TDNN) integrates a nonlinear time alignment procedure (DTW) and the highaccuracy phoneme spotting capabilities of a TDNN into a connectionist speech recognition system with word-level classification and error backpropagation. We present an MS-TDNN for recognizing continuously spelled letters, a task characterized by a small but highly confusable voc...

Time-Delay Neural Networks (TDNN) have been shown by Waibel et al. [1] to be a good method for the classification of dynamic speech sounds such as voiced stop consonants. In this paper we discuss key issues in the design and training of a TDNN, based on a Multi-Layer Perceptron (MLP), when used for classification of the sets of voiced stop consonants (/b/, /d/, and /g/) and unvoiced stop conson...

The sun spot time series is a random series generated with a complicated nonlinear system. Time delay neural networks (TDNN) are typical nonlinear systems that could be used to perform time series prediction. In this project, we applied the TDNN trained with both MSE and MEE criteria to predict the time series. Both criteria showed the effectiveness of the nonlinear systems. Furthermore, the TD...

Geometric Dilution of Precision (GDOP) is a coefficient for constellations of Global Positioning System (GPS) satellites. These satellites are organized geometrically. Traditionally, GPS GDOP computation is based on the inversion matrix with complicated measurement equations. A new strategy for calculation of GPS GDOP is construction of time series problem; it employs machine learning and artif...

An enhanced time-delay neural network (TDNN), using time series sensor response data, improved pattern recognition ability of an electronic nose (e-nose) in discriminating four different spices. TDNN was used for analysis of e-nose time series sensor data from 0 to 4 min, while two popular pattern recognition methods, discriminant function analysis (DFA) and multilayer perceptron (MLP) trained ...

The time-delay neural network (TDNN) can consider multiple frames of information simultaneously, making it particularly suitable for dialect identification. However, previous TDNN architectures have focused on only one aspect either the temporal or channel information, lacking a unified optimization both domains. We believe that extracting appropriate contextual and enhancing channels are criti...